DSA Global Conference Benefits
Knowledge & Professional Development: Gain new skills & insights from global leaders
Cutting-Edge Education: Acquire new expertise and stay current with the latest trends and technologies in data science, AI, and quantum computing.
Hands-On Workshops: Master practical techniques and tools through interactive sessions led by industry experts.
Ethical Discussions: Engage in critical conversations on the responsible and unbiased application of data, AI and quantum computing.
Cutting-Edge Education: Acquire cutting-edge skills & knowledge from leaders in data science, AI, & quantum computing
Immersive Workshops: Dive deep into practical applications and hands-on exercises to master new technologies and methodologies.
Expert-Led Seminars: Hear directly from industry pioneers and academic leaders about the latest research, trends, and future directions.
Certification Pathways: Follow structured learning paths designed to build specific skills and prepare for professional certifications.
Global Networking: Form valuable relationships that foster career growth & professional support
Peer-to-Peer Connections: Connect with professionals from around the world to share experiences and build a supportive network.
Mentorship & Collaboration: Find opportunities to be mentored by seasoned experts or collaborate on innovative projects.
Industry Leader Access: Engage directly with influential figures and thought leaders to gain unique perspectives and advice.
Career Advancement: Access exclusive networking opportunities & advice to propel your professional journey
Professional Mentorship: Connect with seasoned experts who can provide personalized guidance and support to help you achieve your career goals.
Leadership Opportunities: Gain experience and visibility by taking on leadership roles within the association's committees or local chapters.
Career Resources: Access a curated job board, resume workshops, and interview coaching to give you a competitive edge in the job market.
Publish & Present: Gain visibility by publishing your paper or giving a live presentation to a global audience
Peer-Reviewed Publications: Submit your research to be published in a DSA-supported journal and contribute to the body of knowledge in the field.
Live Speaking Opportunities: Share your expertise by presenting your work on stage at global conferences and virtual events.
Content Creation: Amplify your professional brand by writing articles, case studies, or white papers for the DSA's online platforms.
Build Your Professional Brand: Elevate your reputation & expertise within the data ecosystem
Thought Leadership: Establish your expertise by participating in panel discussions or leading workshops and webinars.
Community Recognition: Enhance your reputation by being recognized for your contributions and achievements through awards and professional distinctions.
Portfolio Development: Showcase your skills and projects by creating a professional profile and portfolio on the DSA platform, gaining visibility among peers and potential employers.
Tax-Deductible: Your attendance & travel may be tax-deductible (consult with a tax professional)
Professional Development Expense: Your conference fees are often deductible as a business expense, supporting your continued professional education and career growth.
Travel & Lodging: The costs of travel, including airfare and hotel stays, are typically deductible when the primary purpose of the trip is professional development.
Meal & Entertainment Deductions: You may be able to deduct a portion of your meal expenses incurred while attending the conference.
Exclusive Access: Be the first to see new AI and quantum computing tech, products, & services from partners
Partner Showcases: Get an exclusive first look at cutting-edge tools and platforms from our industry partners before they are released to the public.
Product Demos: Participate in live demonstrations and Q&A sessions to understand how the latest AI and quantum computing products and services can be applied to your work.
Beta Testing Opportunities: Be invited to join private beta programs, allowing you to provide feedback and influence the development of new technologies.
Travel Discounts & Financial Benefits: Take advantage of special pricing on travel & accommodations
Exclusive Travel Deals: Access special rates on flights, rental cars, and hotels through partnerships with leading travel providers.
Conference Fee Discounts: Enjoy reduced registration fees for members, making attendance at professional events more affordable.
Financial Resources: Find information and guides on maximizing your tax deductions and managing expenses related to professional development.
Harnessing Data Science & AI for Financial Success
The Data Science Association (DSA) invites you to join us in the heart of New York City for our annual conference, "Harnessing Data Science & AI for Financial Success." This event brings together leading experts in data science, AI and finance to explore the transformative power of data science and AI in reshaping the financial industry. Through thought-provoking discussions, interactive workshops, and networking opportunities, we aim to foster a collaborative environment where innovative ideas can flourish and drive financial success.
Whether you're a seasoned data scientist, executive, investor, or a finance professional seeking to leverage data-driven insights, this conference offers an unparalleled opportunity to expand your knowledge, network with industry leaders, and contribute to the advancement of data science, AI and investing.
Embark on a transformative journey into the heart of data science, AI, and financial investing. This exclusive gathering will unite renowned experts, global leaders, and data scientists to explore the profound intersection of data-driven insights, AI, scenario planning, central bank policy, mature markets, emerging markets, BRICS, US dollar scenarios, blockchain technology, and global financial risks.
Located in the heart of New York City, the conference will offer a unique blend of intellectual stimulation and entertainment, cultural, and culinary adventure. Attendees will have the opportunity to:
Network with industry peers: Connect with like-minded professionals from around the world and build lasting relationships.
Learn from industry leaders: Gain valuable insights from renowned experts in data science, AI, investing, finance, and central banking.
Discover cutting-edge research: Explore the latest advancements in data science and their applications to global finance.
Engage in thought-provoking discussions: Participate in interactive sessions and debates on critical topics shaping the future of the field.
Experience the excitement of New York City: Enjoy world-class entertainment, culture, culinary and other activities.
Whether you're a seasoned data scientist, executive, investor, or a finance professional seeking to leverage data-driven insights, this conference offers an unparalleled opportunity to expand your knowledge, network with industry leaders, and contribute to the advancement of data science, AI and investing.
Embark on a transformative journey into the heart of data science, AI, and financial investing. This exclusive gathering will unite renowned experts, global leaders, and data scientists to explore the profound intersection of data-driven insights, AI, scenario planning, central bank policy, mature markets, emerging markets, BRICS, US dollar scenarios, blockchain technology, and global financial risks.
Located in the heart of New York City, the conference will offer a unique blend of intellectual stimulation and entertainment, cultural, and culinary adventure. Attendees will have the opportunity to:
Network with industry peers: Connect with like-minded professionals from around the world and build lasting relationships.
Learn from industry leaders: Gain valuable insights from renowned experts in data science, AI, investing, finance, and central banking.
Discover cutting-edge research: Explore the latest advancements in data science and their applications to global finance.
Engage in thought-provoking discussions: Participate in interactive sessions and debates on critical topics shaping the future of the field.
Experience the excitement of New York City: Enjoy world-class entertainment, culture, culinary and other activities.
We are in the process of finalizing dates and details.
Sign up for updates - dates & details coming soon - secure your spot at this exclusive event!
Sign up for updates - dates & details coming soon - secure your spot at this exclusive event!
Investors: Individuals and institutions looking to improve decision-making using data science techniques and data-driven financial technologies.
Traders: Professionals involved in buying and selling financial instruments, who can benefit from AI-powered trading strategies and tools.
AI and Data Science Professionals:
Data Scientists
AI/Machine Learning Engineers
AI Researchers
Software Engineers
Product Managers
UX Designers
Financial professionals interested in AI and data-driven insights: Anyone working in the financial industry who wants to stay up-to-date on the latest trends and innovations.
Government Officials and Regulators Involved in Financial Technology Policy: Policymakers and regulators shaping the regulatory landscape for AI-driven financial services.
Financial Regulators: Policymakers responsible for overseeing the financial industry can benefit from understanding the implications of AI and data science on financial stability, consumer protection, and market integrity.
Central Bankers: Monetary authorities can leverage AI and data science to improve forecasting, risk management, and monetary policy implementation.
Entrepreneurs and investors in the FinTech space: Individuals involved in starting and funding new financial technology companies.
Academics and researchers studying the intersection of finance and technology: Scholars and researchers exploring the theoretical and practical aspects of AI in finance.
Economists: Researchers and analysts can explore the economic implications of AI-driven financial innovation, including its impact on growth, employment, and inequality.
Actuaries: Professionals involved in risk assessment and pricing can use AI and data science to improve their models and enhance risk management practices.
Insurance Professionals: Insurance companies can benefit from AI and data science to improve underwriting, claims processing, and fraud detection.
Accountants: Accountants can leverage AI and data science to streamline processes, enhance audit quality, and gain deeper insights into financial data.
Traders: Professionals involved in buying and selling financial instruments, who can benefit from AI-powered trading strategies and tools.
AI and Data Science Professionals:
Data Scientists
AI/Machine Learning Engineers
AI Researchers
Software Engineers
Product Managers
UX Designers
Financial professionals interested in AI and data-driven insights: Anyone working in the financial industry who wants to stay up-to-date on the latest trends and innovations.
Government Officials and Regulators Involved in Financial Technology Policy: Policymakers and regulators shaping the regulatory landscape for AI-driven financial services.
Financial Regulators: Policymakers responsible for overseeing the financial industry can benefit from understanding the implications of AI and data science on financial stability, consumer protection, and market integrity.
Central Bankers: Monetary authorities can leverage AI and data science to improve forecasting, risk management, and monetary policy implementation.
Entrepreneurs and investors in the FinTech space: Individuals involved in starting and funding new financial technology companies.
Academics and researchers studying the intersection of finance and technology: Scholars and researchers exploring the theoretical and practical aspects of AI in finance.
Economists: Researchers and analysts can explore the economic implications of AI-driven financial innovation, including its impact on growth, employment, and inequality.
Actuaries: Professionals involved in risk assessment and pricing can use AI and data science to improve their models and enhance risk management practices.
Insurance Professionals: Insurance companies can benefit from AI and data science to improve underwriting, claims processing, and fraud detection.
Accountants: Accountants can leverage AI and data science to streamline processes, enhance audit quality, and gain deeper insights into financial data.
Financial Scenario Planning, Modeling and Prediction
Keynote: A renowned financial expert discussing the transformative impact of data science and AI on scenario planning, financial modeling and predictive analytics.
Panel Discussion: Industry leaders debating the ethical implications of AI-driven financial predictions.
Workshops: Hands-on sessions on time series analysis, risk analysis and modeling, and sentiment analysis.
Lightning Talks: Short presentations on innovative research and applications in this area.
Algorithmic Trading and Investment Strategies
Case Studies: Real-world examples of successful AI-powered trading strategies.
Algorithm Investment & Trading: How to develop innovative investment and trading algorithms.
Roundtable Discussions: Exploring the challenges and opportunities of high-frequency trading.
Fireside Chat: A conversation with a prominent hedge fund manager on the future of AI in finance.
Financial Technology (FinTech) and Innovation
Startup Showcase: Presentations from promising FinTech startups.
Investor Panel: Discussions on funding trends and investment opportunities in FinTech.
Regulatory Roundtable: Exploring the regulatory landscape for AI-driven financial services.
Future Trends Session: Experts sharing their vision for the future of FinTech and its impact on society.
Optional Conference Topics
Data Science & AI in Global Finance & Investing: Explore the transformative power of data science and artificial intelligence in revolutionizing global finance and investing. Delve into cutting-edge applications such as data science and AI-powered investment decision making, algorithmic trading, risk assessment, fraud detection, and portfolio optimization.
Central Banks Policy: Gain insights into the evolving landscape of central bank policies worldwide. Analyze the impact of monetary policies, quantitative easing, and interest rate adjustments on global financial markets and economic stability.
Emerging Markets Investing: Discover the opportunities and challenges presented by emerging markets. Understand the factors driving growth, assess investment risks, and explore strategies for navigating these dynamic economies.
BRICS Policy: Examine the collective influence of the BRICS nations (Brazil, Russia, India, China, and South Africa) on the global financial landscape. Analyze their economic policies, trade relations, and geopolitical dynamics.
US Dollar Scenarios: Evaluate the future trajectory of the US dollar as the world's reserve currency. Consider potential scenarios, including its appreciation, depreciation, or a shift towards a multipolar currency system.
Blockchain Technology: Explore the disruptive potential of blockchain technology in finance. Discuss its applications in areas such as cryptocurrency, smart contracts, decentralized finance (DeFi), and supply chain management.
Global Financial Risk: Identify and assess emerging global financial risks, including geopolitical tensions, climate change, cyber threats, and systemic failures. Develop strategies for mitigating these risks and building resilience.
Additional Conference Features
Networking Events: Social gatherings and receptions to facilitate connections among attendees.
Workshops and Tutorials: Hands-on training sessions for attendees of varying skill levels.
Keynote: A renowned financial expert discussing the transformative impact of data science and AI on scenario planning, financial modeling and predictive analytics.
Panel Discussion: Industry leaders debating the ethical implications of AI-driven financial predictions.
Workshops: Hands-on sessions on time series analysis, risk analysis and modeling, and sentiment analysis.
Lightning Talks: Short presentations on innovative research and applications in this area.
Algorithmic Trading and Investment Strategies
Case Studies: Real-world examples of successful AI-powered trading strategies.
Algorithm Investment & Trading: How to develop innovative investment and trading algorithms.
Roundtable Discussions: Exploring the challenges and opportunities of high-frequency trading.
Fireside Chat: A conversation with a prominent hedge fund manager on the future of AI in finance.
Financial Technology (FinTech) and Innovation
Startup Showcase: Presentations from promising FinTech startups.
Investor Panel: Discussions on funding trends and investment opportunities in FinTech.
Regulatory Roundtable: Exploring the regulatory landscape for AI-driven financial services.
Future Trends Session: Experts sharing their vision for the future of FinTech and its impact on society.
Optional Conference Topics
Data Science & AI in Global Finance & Investing: Explore the transformative power of data science and artificial intelligence in revolutionizing global finance and investing. Delve into cutting-edge applications such as data science and AI-powered investment decision making, algorithmic trading, risk assessment, fraud detection, and portfolio optimization.
Central Banks Policy: Gain insights into the evolving landscape of central bank policies worldwide. Analyze the impact of monetary policies, quantitative easing, and interest rate adjustments on global financial markets and economic stability.
Emerging Markets Investing: Discover the opportunities and challenges presented by emerging markets. Understand the factors driving growth, assess investment risks, and explore strategies for navigating these dynamic economies.
BRICS Policy: Examine the collective influence of the BRICS nations (Brazil, Russia, India, China, and South Africa) on the global financial landscape. Analyze their economic policies, trade relations, and geopolitical dynamics.
US Dollar Scenarios: Evaluate the future trajectory of the US dollar as the world's reserve currency. Consider potential scenarios, including its appreciation, depreciation, or a shift towards a multipolar currency system.
Blockchain Technology: Explore the disruptive potential of blockchain technology in finance. Discuss its applications in areas such as cryptocurrency, smart contracts, decentralized finance (DeFi), and supply chain management.
Global Financial Risk: Identify and assess emerging global financial risks, including geopolitical tensions, climate change, cyber threats, and systemic failures. Develop strategies for mitigating these risks and building resilience.
Additional Conference Features
Networking Events: Social gatherings and receptions to facilitate connections among attendees.
Workshops and Tutorials: Hands-on training sessions for attendees of varying skill levels.
When staying in the heart of New York City, you'll be surrounded by countless entertainment, cultural, and culinary experiences. Here are some top adventures to add to your itinerary:
Broadway Magic: Immerse yourself in the world of theater by catching a Broadway show. From musical hits like "&Juliet" to the fantastic "Hadestown", there's something for everyone.
Iconic Landmarks: Explore the city's most famous landmarks, including the Statue of Liberty, Central Park, and the Empire State Building. Enjoy stunning views and learn about the city's history.
Museum Marvels: Discover the rich history and culture of New York City through its world-class museums. Visit the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), or the American Museum of Natural History.
Culinary Delights: Indulge in New York City's diverse culinary scene. From street food to Michelin-starred restaurants, there's a taste for every palate. Don't miss trying iconic dishes like pizza, bagels, and hot dogs.
Nightlife Exploration: Experience the city's vibrant nightlife by visiting trendy bars, clubs, and live music venues.
Explore neighborhoods like Times Square, SoHo, or the West Village for a memorable night out.
Shopping: Explore world-class shopping destinations like Fifth Avenue and SoHo. Fifth Avenue is synonymous with luxury shopping. Home to flagship stores of renowned brands like Tiffany & Co., Saks Fifth Avenue, Bergdorf Goodman, and Gucci, this avenue offers a dazzling array of high-end fashion, jewelry, and accessories. SoHo is a trendy neighborhood known for its bohemian atmosphere and boutique shops. Discover unique fashion designers, vintage clothing stores, and eclectic home decor shops. SoHo is also a hub for art galleries, offering a glimpse into the city's vibrant art scene.
Parks and Gardens: Enjoy the outdoors in Central Park, Bryant Park, or the High Line.
By staying in the heart of New York City, you'll have easy access to these incredible experiences and more. Make the most of your visit by exploring the city's vibrant neighborhoods and immersing yourself in its rich culture.
Broadway Magic: Immerse yourself in the world of theater by catching a Broadway show. From musical hits like "&Juliet" to the fantastic "Hadestown", there's something for everyone.
Iconic Landmarks: Explore the city's most famous landmarks, including the Statue of Liberty, Central Park, and the Empire State Building. Enjoy stunning views and learn about the city's history.
Museum Marvels: Discover the rich history and culture of New York City through its world-class museums. Visit the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), or the American Museum of Natural History.
Culinary Delights: Indulge in New York City's diverse culinary scene. From street food to Michelin-starred restaurants, there's a taste for every palate. Don't miss trying iconic dishes like pizza, bagels, and hot dogs.
Nightlife Exploration: Experience the city's vibrant nightlife by visiting trendy bars, clubs, and live music venues.
Explore neighborhoods like Times Square, SoHo, or the West Village for a memorable night out.
Shopping: Explore world-class shopping destinations like Fifth Avenue and SoHo. Fifth Avenue is synonymous with luxury shopping. Home to flagship stores of renowned brands like Tiffany & Co., Saks Fifth Avenue, Bergdorf Goodman, and Gucci, this avenue offers a dazzling array of high-end fashion, jewelry, and accessories. SoHo is a trendy neighborhood known for its bohemian atmosphere and boutique shops. Discover unique fashion designers, vintage clothing stores, and eclectic home decor shops. SoHo is also a hub for art galleries, offering a glimpse into the city's vibrant art scene.
Parks and Gardens: Enjoy the outdoors in Central Park, Bryant Park, or the High Line.
By staying in the heart of New York City, you'll have easy access to these incredible experiences and more. Make the most of your visit by exploring the city's vibrant neighborhoods and immersing yourself in its rich culture.
This conference will bring together financial professionals, data scientists, AI engineers, investors, traders and policymakers to explore the transformative impact of Artificial Intelligence and Data Science on the financial sector.
We invite submissions for presentations, papers, workshops, and lightning talks across a range of compelling topics. We encourage submissions that present original research, innovative applications, case studies, and insightful analyses.
Main Conference Subject Areas
Financial Scenario Planning, Modeling and Prediction
This track will delve into the critical role of AI and data science in enhancing financial foresight. We seek contributions on:
Keynote: Submissions are invited for a keynote address by a renowned financial expert discussing the transformative impact of data science and AI on scenario planning, financial modeling, and predictive analytics.
Panel Discussion: Proposals for participation in a panel debate among industry leaders on the ethical implications of AI-driven financial predictions.
Workshops: Hands-on sessions focusing on advanced techniques in time series analysis, robust risk analysis and modeling, and sophisticated sentiment analysis for financial markets.
Lightning Talks: Short, impactful presentations on innovative research and novel applications within financial scenario planning, modeling, and prediction.
Algorithmic Trading and Investment Strategies
This track will explore the cutting-edge of automated trading and investment methodologies. We are particularly interested in:
Case Studies: Real-world examples demonstrating successful AI-powered trading strategies and their measurable outcomes.
Algorithm Investment & Trading: Presentations detailing the development of innovative investment and trading algorithms, including methodologies, backtesting results, and performance analysis.
Roundtable Discussions: Proposals for discussions exploring the challenges and opportunities presented by high-frequency trading, including regulatory considerations and market impact.
Fireside Chat: Submissions for a conversation with a prominent hedge fund manager on the future of AI in finance and its long-term implications for investment management.
Financial Technology (FinTech) and Innovation
This track will showcase the latest advancements and disruptive forces in financial technology. We welcome submissions related to:
Startup Showcase: Presentations from promising FinTech startups highlighting their innovative solutions and business models.
Investor Panel: Discussions on current funding trends, emerging investment opportunities, and challenges within the FinTech ecosystem.
Regulatory Roundtable: Exploring the evolving regulatory landscape for AI-driven financial services, including data privacy, compliance, and consumer protection.
Future Trends Session: Experts sharing their vision for the future of FinTech, its potential societal impact, and emerging technological paradigms.
Optional Conference Topics
In addition to the main subject areas, we also welcome submissions on the following optional, yet highly relevant, topics:
Data Science & AI in Global Finance & Investing: Explore the transformative power of data science and artificial intelligence in revolutionizing global finance and investing. Delve into cutting-edge applications such as data science and AI-powered investment decision-making, algorithmic trading, risk assessment, fraud detection, and portfolio optimization.
Central Banks Policy: Gain insights into the evolving landscape of central bank policies worldwide. Analyze the impact of monetary policies, quantitative easing, and interest rate adjustments on global financial markets and economic stability.
Emerging Markets Investing: Discover the opportunities and challenges presented by emerging markets. Understand the factors driving growth, assess investment risks, and explore strategies for navigating these dynamic economies.
BRICS Policy: Examine the collective influence of the BRICS nations (Brazil, Russia, India, China, and South Africa) on the global financial landscape. Analyze their economic policies, trade relations, and geopolitical dynamics.
US Dollar Scenarios: Evaluate the future trajectory of the US dollar as the world's reserve currency. Consider potential scenarios, including its appreciation, depreciation, or a shift towards a multipolar currency system.
Blockchain Technology: Explore the disruptive potential of blockchain technology in finance. Discuss its applications in areas such as cryptocurrency, smart contracts, decentralized finance (DeFi), and supply chain management.
Global Financial Risk: Identify and assess emerging global financial risks, including geopolitical tensions, climate change, cyber threats, and systemic failures. Develop strategies for mitigating these risks and building resilience.
Submission Types
We invite submissions across several categories to accommodate diverse contributions:
Oral Presentations (20 minutes): Share your research findings, innovative applications, case studies, or best practices.
Technical Papers (Full Length, 8-12 pages, IEEE format): Submit original, unpublished research that undergoes a rigorous peer-review process. Accepted papers will be published in the conference proceedings.
Experience & Insight Papers (4-6 pages, formatted for readability): This category is designed for practitioners, industry leaders, and innovators to share valuable lessons learned, practical case studies, real-world implementations, and insightful perspectives. These papers should focus on practical applications, best practices, and actionable insights that can benefit the broader community, even if they do not present novel research methodologies or extensive theoretical contributions. Submissions will undergo a peer-review process focused on clarity, relevance, and the practical value of the shared experiences.
Poster Presentations: Showcase preliminary results, ongoing research, or innovative concepts visually. Includes a dedicated poster session for discussion.
Panel Proposals (60 minutes): Suggest and moderate a discussion among 3-5 experts on a controversial, emerging, or complex topic in finance.
Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies.
Submission Guidelines
Abstract: All submissions (except workshop proposals) must include a concise abstract (maximum 300 words) summarizing the problem, approach, key findings/insights, and conclusions.
Author Information: Include full names, affiliations, and a brief professional biography (max 100 words per author).
Keywords: Provide 3-5 relevant keywords.
Originality: Submissions must represent original work that has not been previously published or is not under review elsewhere.
Audience Consideration: Presenters should be prepared to convey complex technical or business concepts clearly to a diverse audience, including both technical and non-technical attendees.
Formatting: Specific formatting guidelines for full papers will be provided upon submission portal opening.
Review Process
All submissions will undergo a rigorous peer-review process by the Program Committee, comprising leading experts in AI, data science, and related business domains. Submissions will be evaluated based on:
Relevance to conference themes
Originality and novelty of contributions
Technical merit and soundness (for technical papers)
Clarity, organization, and presentation quality
Potential impact and practical applicability
Further details on submission deadlines and the submission portal will be provided on the conference website. We encourage early submissions.
We look forward to your valuable contributions!
We invite submissions for presentations, papers, workshops, and lightning talks across a range of compelling topics. We encourage submissions that present original research, innovative applications, case studies, and insightful analyses.
Main Conference Subject Areas
Financial Scenario Planning, Modeling and Prediction
This track will delve into the critical role of AI and data science in enhancing financial foresight. We seek contributions on:
Keynote: Submissions are invited for a keynote address by a renowned financial expert discussing the transformative impact of data science and AI on scenario planning, financial modeling, and predictive analytics.
Panel Discussion: Proposals for participation in a panel debate among industry leaders on the ethical implications of AI-driven financial predictions.
Workshops: Hands-on sessions focusing on advanced techniques in time series analysis, robust risk analysis and modeling, and sophisticated sentiment analysis for financial markets.
Lightning Talks: Short, impactful presentations on innovative research and novel applications within financial scenario planning, modeling, and prediction.
Algorithmic Trading and Investment Strategies
This track will explore the cutting-edge of automated trading and investment methodologies. We are particularly interested in:
Case Studies: Real-world examples demonstrating successful AI-powered trading strategies and their measurable outcomes.
Algorithm Investment & Trading: Presentations detailing the development of innovative investment and trading algorithms, including methodologies, backtesting results, and performance analysis.
Roundtable Discussions: Proposals for discussions exploring the challenges and opportunities presented by high-frequency trading, including regulatory considerations and market impact.
Fireside Chat: Submissions for a conversation with a prominent hedge fund manager on the future of AI in finance and its long-term implications for investment management.
Financial Technology (FinTech) and Innovation
This track will showcase the latest advancements and disruptive forces in financial technology. We welcome submissions related to:
Startup Showcase: Presentations from promising FinTech startups highlighting their innovative solutions and business models.
Investor Panel: Discussions on current funding trends, emerging investment opportunities, and challenges within the FinTech ecosystem.
Regulatory Roundtable: Exploring the evolving regulatory landscape for AI-driven financial services, including data privacy, compliance, and consumer protection.
Future Trends Session: Experts sharing their vision for the future of FinTech, its potential societal impact, and emerging technological paradigms.
Optional Conference Topics
In addition to the main subject areas, we also welcome submissions on the following optional, yet highly relevant, topics:
Data Science & AI in Global Finance & Investing: Explore the transformative power of data science and artificial intelligence in revolutionizing global finance and investing. Delve into cutting-edge applications such as data science and AI-powered investment decision-making, algorithmic trading, risk assessment, fraud detection, and portfolio optimization.
Central Banks Policy: Gain insights into the evolving landscape of central bank policies worldwide. Analyze the impact of monetary policies, quantitative easing, and interest rate adjustments on global financial markets and economic stability.
Emerging Markets Investing: Discover the opportunities and challenges presented by emerging markets. Understand the factors driving growth, assess investment risks, and explore strategies for navigating these dynamic economies.
BRICS Policy: Examine the collective influence of the BRICS nations (Brazil, Russia, India, China, and South Africa) on the global financial landscape. Analyze their economic policies, trade relations, and geopolitical dynamics.
US Dollar Scenarios: Evaluate the future trajectory of the US dollar as the world's reserve currency. Consider potential scenarios, including its appreciation, depreciation, or a shift towards a multipolar currency system.
Blockchain Technology: Explore the disruptive potential of blockchain technology in finance. Discuss its applications in areas such as cryptocurrency, smart contracts, decentralized finance (DeFi), and supply chain management.
Global Financial Risk: Identify and assess emerging global financial risks, including geopolitical tensions, climate change, cyber threats, and systemic failures. Develop strategies for mitigating these risks and building resilience.
Submission Types
We invite submissions across several categories to accommodate diverse contributions:
Oral Presentations (20 minutes): Share your research findings, innovative applications, case studies, or best practices.
Technical Papers (Full Length, 8-12 pages, IEEE format): Submit original, unpublished research that undergoes a rigorous peer-review process. Accepted papers will be published in the conference proceedings.
Experience & Insight Papers (4-6 pages, formatted for readability): This category is designed for practitioners, industry leaders, and innovators to share valuable lessons learned, practical case studies, real-world implementations, and insightful perspectives. These papers should focus on practical applications, best practices, and actionable insights that can benefit the broader community, even if they do not present novel research methodologies or extensive theoretical contributions. Submissions will undergo a peer-review process focused on clarity, relevance, and the practical value of the shared experiences.
Poster Presentations: Showcase preliminary results, ongoing research, or innovative concepts visually. Includes a dedicated poster session for discussion.
Panel Proposals (60 minutes): Suggest and moderate a discussion among 3-5 experts on a controversial, emerging, or complex topic in finance.
Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies.
Submission Guidelines
Abstract: All submissions (except workshop proposals) must include a concise abstract (maximum 300 words) summarizing the problem, approach, key findings/insights, and conclusions.
Author Information: Include full names, affiliations, and a brief professional biography (max 100 words per author).
Keywords: Provide 3-5 relevant keywords.
Originality: Submissions must represent original work that has not been previously published or is not under review elsewhere.
Audience Consideration: Presenters should be prepared to convey complex technical or business concepts clearly to a diverse audience, including both technical and non-technical attendees.
Formatting: Specific formatting guidelines for full papers will be provided upon submission portal opening.
Review Process
All submissions will undergo a rigorous peer-review process by the Program Committee, comprising leading experts in AI, data science, and related business domains. Submissions will be evaluated based on:
Relevance to conference themes
Originality and novelty of contributions
Technical merit and soundness (for technical papers)
Clarity, organization, and presentation quality
Potential impact and practical applicability
Further details on submission deadlines and the submission portal will be provided on the conference website. We encourage early submissions.
We look forward to your valuable contributions!
Harnessing Data Science & AI for Financial Success
Unleashing the Power of Random Forests
Join us for a 3-day immersive conference exploring the cutting-edge applications of random forests in various industries.
What to Expect:
In-depth Technical Sessions: Delve into the core concepts of random forests, including ensemble learning, decision trees, and feature importance.
Real-World Case Studies: Learn from industry experts as they share practical examples of how random forests have been applied to solve real-world problems.
Hands-on Workshops: Gain practical experience by working on real-world datasets and building your own random forest models.
Networking Opportunities: Connect with like-minded professionals and build valuable relationships with industry leaders.
Key Topics:
Financial Industry:
Fraud Detection
Customer Churn Prediction
Risk Assessment
Healthcare:
Disease Diagnosis
Patient Risk Stratification
Drug Discovery
Marketing and Sales:
Customer Segmentation
Market Basket Analysis
Customer Lifetime Value Prediction
Environmental Science:
Species Distribution Modeling
Climate Change Impact Assessment
Forest Fire Prediction
Cybersecurity:
Network Intrusion Detection
Malware Detection
Phishing Email Detection
Recommender Systems:
Product Recommendations
Content Recommendations
Why Attend?
Gain Expertise: Learn from top experts in the field of machine learning and data science.
Stay Ahead of the Curve: Discover the latest trends and advancements in random forest applications.
Network with Industry Leaders: Connect with like-minded professionals and build valuable relationships.
Advance Your Career: Enhance your skills and knowledge to advance your career in data science.
What to Expect:
In-depth Technical Sessions: Delve into the core concepts of random forests, including ensemble learning, decision trees, and feature importance.
Real-World Case Studies: Learn from industry experts as they share practical examples of how random forests have been applied to solve real-world problems.
Hands-on Workshops: Gain practical experience by working on real-world datasets and building your own random forest models.
Networking Opportunities: Connect with like-minded professionals and build valuable relationships with industry leaders.
Key Topics:
Financial Industry:
Fraud Detection
Customer Churn Prediction
Risk Assessment
Healthcare:
Disease Diagnosis
Patient Risk Stratification
Drug Discovery
Marketing and Sales:
Customer Segmentation
Market Basket Analysis
Customer Lifetime Value Prediction
Environmental Science:
Species Distribution Modeling
Climate Change Impact Assessment
Forest Fire Prediction
Cybersecurity:
Network Intrusion Detection
Malware Detection
Phishing Email Detection
Recommender Systems:
Product Recommendations
Content Recommendations
Why Attend?
Gain Expertise: Learn from top experts in the field of machine learning and data science.
Stay Ahead of the Curve: Discover the latest trends and advancements in random forest applications.
Network with Industry Leaders: Connect with like-minded professionals and build valuable relationships.
Advance Your Career: Enhance your skills and knowledge to advance your career in data science.
We are in the process of finalizing dates and details.
Sign up for updates - dates & details coming soon - secure your spot at this exclusive event!
Sign up for updates - dates & details coming soon - secure your spot at this exclusive event!
AI and Data Science Professionals:
Data Scientists
AI/Machine Learning Engineers
AI Researchers
Software Engineers
Product Managers
UX Designers
Business and Finance Professionals:
Financial Institutions: Banks, investment firms, and tech companies.
E-commerce Businesses: Online retailers and payment processors.
Telecommunications Companies: Providers of communication services.
Healthcare Providers: Hospitals, clinics, and health insurance companies.
Entrepreneurs and investors in the Tech space.
Academic and Research Community.
Technology Professionals:
IT Managers and CIOs: IT leaders responsible for securing organizational systems.
Network Engineers: Network administrators and security engineers.
Cybersecurity Experts: Security analysts, penetration testers, and security architects.
Cryptographers: Cryptography researchers and engineers.
Government Officials and Regulators:
Policymakers and regulators shaping the regulatory landscape.
Regulators: Policymakers responsible for overseeing AI/ML.
Data Scientists
AI/Machine Learning Engineers
AI Researchers
Software Engineers
Product Managers
UX Designers
Business and Finance Professionals:
Financial Institutions: Banks, investment firms, and tech companies.
E-commerce Businesses: Online retailers and payment processors.
Telecommunications Companies: Providers of communication services.
Healthcare Providers: Hospitals, clinics, and health insurance companies.
Entrepreneurs and investors in the Tech space.
Academic and Research Community.
Technology Professionals:
IT Managers and CIOs: IT leaders responsible for securing organizational systems.
Network Engineers: Network administrators and security engineers.
Cybersecurity Experts: Security analysts, penetration testers, and security architects.
Cryptographers: Cryptography researchers and engineers.
Government Officials and Regulators:
Policymakers and regulators shaping the regulatory landscape.
Regulators: Policymakers responsible for overseeing AI/ML.
I. Random Forest Fundamentals and Applications
A. Core Concepts
B. Hyperparameter Tuning
C. Feature Importance and Interpretation
II. Industry Applications of Random Forests
A. Finance
Fraud Detection
Customer Churn Prediction
Risk Assessment
B. Healthcare
Disease Diagnosis
Patient Risk Stratification
Drug Discovery
C. Marketing and Sales
Customer Segmentation
Market Basket Analysis
Customer Lifetime Value Prediction
D. Environmental Science
Species Distribution Modeling
Climate Change Impact Assessment
Forest Fire Prediction
E. Cybersecurity
Network Intrusion Detection
Malware Detection
Phishing Email Detection
F. Recommender Systems
Product Recommendations
Content Recommendations
Collaborative Filtering and Content-Based Filtering Techniques
III. Building a Random Forest Model (Hands-on Workshop)
A. Data Preparation and Feature Engineering
B. Model Training and Evaluation
C. Model Deployment and Monitoring
A. Core Concepts
B. Hyperparameter Tuning
C. Feature Importance and Interpretation
II. Industry Applications of Random Forests
A. Finance
Fraud Detection
Customer Churn Prediction
Risk Assessment
B. Healthcare
Disease Diagnosis
Patient Risk Stratification
Drug Discovery
C. Marketing and Sales
Customer Segmentation
Market Basket Analysis
Customer Lifetime Value Prediction
D. Environmental Science
Species Distribution Modeling
Climate Change Impact Assessment
Forest Fire Prediction
E. Cybersecurity
Network Intrusion Detection
Malware Detection
Phishing Email Detection
F. Recommender Systems
Product Recommendations
Content Recommendations
Collaborative Filtering and Content-Based Filtering Techniques
III. Building a Random Forest Model (Hands-on Workshop)
A. Data Preparation and Feature Engineering
B. Model Training and Evaluation
C. Model Deployment and Monitoring
When staying in the heart of New York City, you'll be surrounded by countless entertainment, cultural, and culinary experiences. Here are some top adventures to add to your itinerary:
Broadway Magic: Immerse yourself in the world of theater by catching a Broadway show. From musical hits like "&Juliet" to the fantastic "Hadestown", there's something for everyone.
Iconic Landmarks: Explore the city's most famous landmarks, including the Statue of Liberty, Central Park, and the Empire State Building. Enjoy stunning views and learn about the city's history.
Museum Marvels: Discover the rich history and culture of New York City through its world-class museums. Visit the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), or the American Museum of Natural History.
Culinary Delights: Indulge in New York City's diverse culinary scene. From street food to Michelin-starred restaurants, there's a taste for every palate. Don't miss trying iconic dishes like pizza, bagels, and hot dogs.
Nightlife Exploration: Experience the city's vibrant nightlife by visiting trendy bars, clubs, and live music venues.
Explore neighborhoods like Times Square, SoHo, or the West Village for a memorable night out.
Shopping: Explore world-class shopping destinations like Fifth Avenue and SoHo. Fifth Avenue is synonymous with luxury shopping. Home to flagship stores of renowned brands like Tiffany & Co., Saks Fifth Avenue, Bergdorf Goodman, and Gucci, this avenue offers a dazzling array of high-end fashion, jewelry, and accessories. SoHo is a trendy neighborhood known for its bohemian atmosphere and boutique shops. Discover unique fashion designers, vintage clothing stores, and eclectic home decor shops. SoHo is also a hub for art galleries, offering a glimpse into the city's vibrant art scene.
Parks and Gardens: Enjoy the outdoors in Central Park, Bryant Park, or the High Line.
By staying in the heart of New York City, you'll have easy access to these incredible experiences and more. Make the most of your visit by exploring the city's vibrant neighborhoods and immersing yourself in its rich culture.
Broadway Magic: Immerse yourself in the world of theater by catching a Broadway show. From musical hits like "&Juliet" to the fantastic "Hadestown", there's something for everyone.
Iconic Landmarks: Explore the city's most famous landmarks, including the Statue of Liberty, Central Park, and the Empire State Building. Enjoy stunning views and learn about the city's history.
Museum Marvels: Discover the rich history and culture of New York City through its world-class museums. Visit the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), or the American Museum of Natural History.
Culinary Delights: Indulge in New York City's diverse culinary scene. From street food to Michelin-starred restaurants, there's a taste for every palate. Don't miss trying iconic dishes like pizza, bagels, and hot dogs.
Nightlife Exploration: Experience the city's vibrant nightlife by visiting trendy bars, clubs, and live music venues.
Explore neighborhoods like Times Square, SoHo, or the West Village for a memorable night out.
Shopping: Explore world-class shopping destinations like Fifth Avenue and SoHo. Fifth Avenue is synonymous with luxury shopping. Home to flagship stores of renowned brands like Tiffany & Co., Saks Fifth Avenue, Bergdorf Goodman, and Gucci, this avenue offers a dazzling array of high-end fashion, jewelry, and accessories. SoHo is a trendy neighborhood known for its bohemian atmosphere and boutique shops. Discover unique fashion designers, vintage clothing stores, and eclectic home decor shops. SoHo is also a hub for art galleries, offering a glimpse into the city's vibrant art scene.
Parks and Gardens: Enjoy the outdoors in Central Park, Bryant Park, or the High Line.
By staying in the heart of New York City, you'll have easy access to these incredible experiences and more. Make the most of your visit by exploring the city's vibrant neighborhoods and immersing yourself in its rich culture.
This event aims to bridge the gap between theoretical understanding and practical implementation, fostering a deeper knowledge of this powerful machine learning technique across diverse fields. We seek to unite researchers, practitioners, and industry leaders to share insights, discuss advancements, and explore the vast applications of Random Forests.
We welcome submissions for presentations, technical papers, and workshop proposals that delve into the cutting-edge advancements, practical applications, and developmental challenges of Random Forests. Our audience is broad, encompassing data scientists, machine learning engineers, software developers, researchers, and professionals across various industries. Submissions should offer valuable insights for both technical and non-technical attendees, covering aspects from foundational concepts to real-world deployment strategies.
Topics of Interest
We encourage submissions that align with, but are not limited to, the following themes:
I. Random Forest Fundamentals and Applications
Core Concepts: Delving into decision trees, ensemble learning, and bagging.
Hyperparameter Tuning: Optimizing model performance through effective tuning strategies.
Feature Importance and Interpretation: Understanding and explaining model decisions.
II. Industry Applications of Random Forests
Finance: Fraud detection, customer churn prediction, and risk assessment.
Healthcare: Disease diagnosis, patient risk stratification, and drug discovery.
Marketing and Sales: Customer segmentation, market basket analysis, and customer lifetime value prediction.
Environmental Science: Species distribution modeling, climate change impact assessment, and forest fire prediction.
Cybersecurity: Network intrusion detection, malware detection, and phishing email detection.
Recommender Systems: Product and content recommendations, including collaborative and content-based filtering techniques.
III. Building a Random Forest Model (Hands-on Workshop)
Data Preparation and Feature Engineering: Techniques for preparing data for Random Forest models.
Model Training and Evaluation: Methodologies for training and evaluating Random Forest models.
Model Deployment and Monitoring: Best practices for deploying and monitoring Random Forest models in production.
Submission Types
Oral Presentations (20 minutes): Share your research findings, innovative applications, case studies, or best practices.
Technical Papers (Full Length, 8-12 pages, specified format): Submit original, unpublished research that undergoes a rigorous peer-review process. Accepted papers will be published in the conference proceedings.
Experience & Insight Papers (4-6 pages, formatted for readability): This category is designed for practitioners, industry leaders, and innovators to share valuable lessons learned, practical case studies, real-world implementations, and insightful perspectives on the challenges and successes encountered in the development, deployment, or strategic adoption of Random Forest models. These papers should focus on practical applications, best practices, and actionable insights that can benefit the broader community, even if they don't present novel research methodologies or extensive theoretical contributions. Submissions will undergo a peer-review process focused on clarity, relevance, and the practical value of the shared experiences.
Poster Presentations: Showcase preliminary results, ongoing research, or innovative concepts visually. This includes a dedicated poster session for discussion.
Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies related to Random Forest development or deployment.
Submission Guidelines
Abstract: All submissions (except workshop proposals) must include a concise abstract (maximum 300 words) summarizing the problem, approach, key findings/insights, and conclusions.
Author Information: Include full names, affiliations, and a brief professional biography (max 100 words per author).
Keywords: Provide 3-5 relevant keywords.
Originality: Submissions must represent original work that has not been previously published or is not under review elsewhere.
Audience Consideration: Presenters should be prepared to convey complex technical or business concepts clearly to a diverse audience, including both technical and non-technical attendees.
Formatting: Specific formatting guidelines for full papers will be provided upon submission portal opening.
Review Process
All submissions will undergo a rigorous peer-review process by the Program Committee, comprising leading experts in data science and machine learning. Submissions will be evaluated based on:
Relevance to conference themes
Originality and novelty of contributions
Technical merit and soundness (for technical papers)
Clarity, organization, and presentation quality
Potential impact and practical applicability
Ready to share your expertise in Random Forests? We look forward to your valuable contributions!
We welcome submissions for presentations, technical papers, and workshop proposals that delve into the cutting-edge advancements, practical applications, and developmental challenges of Random Forests. Our audience is broad, encompassing data scientists, machine learning engineers, software developers, researchers, and professionals across various industries. Submissions should offer valuable insights for both technical and non-technical attendees, covering aspects from foundational concepts to real-world deployment strategies.
Topics of Interest
We encourage submissions that align with, but are not limited to, the following themes:
I. Random Forest Fundamentals and Applications
Core Concepts: Delving into decision trees, ensemble learning, and bagging.
Hyperparameter Tuning: Optimizing model performance through effective tuning strategies.
Feature Importance and Interpretation: Understanding and explaining model decisions.
II. Industry Applications of Random Forests
Finance: Fraud detection, customer churn prediction, and risk assessment.
Healthcare: Disease diagnosis, patient risk stratification, and drug discovery.
Marketing and Sales: Customer segmentation, market basket analysis, and customer lifetime value prediction.
Environmental Science: Species distribution modeling, climate change impact assessment, and forest fire prediction.
Cybersecurity: Network intrusion detection, malware detection, and phishing email detection.
Recommender Systems: Product and content recommendations, including collaborative and content-based filtering techniques.
III. Building a Random Forest Model (Hands-on Workshop)
Data Preparation and Feature Engineering: Techniques for preparing data for Random Forest models.
Model Training and Evaluation: Methodologies for training and evaluating Random Forest models.
Model Deployment and Monitoring: Best practices for deploying and monitoring Random Forest models in production.
Submission Types
Oral Presentations (20 minutes): Share your research findings, innovative applications, case studies, or best practices.
Technical Papers (Full Length, 8-12 pages, specified format): Submit original, unpublished research that undergoes a rigorous peer-review process. Accepted papers will be published in the conference proceedings.
Experience & Insight Papers (4-6 pages, formatted for readability): This category is designed for practitioners, industry leaders, and innovators to share valuable lessons learned, practical case studies, real-world implementations, and insightful perspectives on the challenges and successes encountered in the development, deployment, or strategic adoption of Random Forest models. These papers should focus on practical applications, best practices, and actionable insights that can benefit the broader community, even if they don't present novel research methodologies or extensive theoretical contributions. Submissions will undergo a peer-review process focused on clarity, relevance, and the practical value of the shared experiences.
Poster Presentations: Showcase preliminary results, ongoing research, or innovative concepts visually. This includes a dedicated poster session for discussion.
Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies related to Random Forest development or deployment.
Submission Guidelines
Abstract: All submissions (except workshop proposals) must include a concise abstract (maximum 300 words) summarizing the problem, approach, key findings/insights, and conclusions.
Author Information: Include full names, affiliations, and a brief professional biography (max 100 words per author).
Keywords: Provide 3-5 relevant keywords.
Originality: Submissions must represent original work that has not been previously published or is not under review elsewhere.
Audience Consideration: Presenters should be prepared to convey complex technical or business concepts clearly to a diverse audience, including both technical and non-technical attendees.
Formatting: Specific formatting guidelines for full papers will be provided upon submission portal opening.
Review Process
All submissions will undergo a rigorous peer-review process by the Program Committee, comprising leading experts in data science and machine learning. Submissions will be evaluated based on:
Relevance to conference themes
Originality and novelty of contributions
Technical merit and soundness (for technical papers)
Clarity, organization, and presentation quality
Potential impact and practical applicability
Ready to share your expertise in Random Forests? We look forward to your valuable contributions!
Unleashing the Power of Random Forests
Unlocking Insights from Data: Fundamentals of Mining Massive Datasets
Join us for an immersive conference exploring the processes, techniques and technologies to efficiently extract valuable insights from massive datasets. This conference will delve into the fundamental concepts and techniques of Mining Massive Datasets (MMD), exploring how to effectively harness the power of big data.
What to Expect:
Fundamental concepts and techniques of data mining and machine learning for massive datasets.
Explore the underlying technologies, algorithms, and tools to extract valuable insights from large-scale data.
Gain hands-on experience in processing, analyzing, and modeling massive datasets.
Networking Opportunities: Connect with like-minded professionals and build valuable relationships with industry leaders.
Key Topics:
Distributed Systems and Frameworks:
Deep dive into HDFS and Cloud Storage for efficient data storage.
Comparative analysis of Apache Spark and Apache Flink for parallel data processing.
Understanding batch and stream processing paradigms.
Data Mining Techniques:
Similarity Search: Mastering minhashing and locality-sensitive hashing for recommendation systems and anomaly detection.
Frequent Itemset Mining: Discovering association rules and market-basket analysis for retail and healthcare.
Clustering: Grouping data points with k-means, hierarchical clustering, and DBSCAN for customer segmentation and image clustering.
Advanced Topics:
Graph Analytics: Unraveling complex relationships with social network analysis and graph mining for fraud detection and knowledge graph construction.
Dimensionality Reduction: Simplifying data with SVD and LSI for text mining and feature engineering.
Machine Learning for Massive Datasets: Scaling machine learning algorithms and leveraging cloud-based platforms.
Why Attend?
Master the Fundamentals: Gain a solid understanding of the core concepts and techniques of data mining and machine learning.
Learn from Experts: Hear from practicing data scientists who are shaping the future of data science.
Gain Expertise: Learn from top experts in the field of machine learning and data engineering.
Stay Ahead of the Curve: Discover the latest trends and advancements.
Network with Industry Leaders: Connect with like-minded professionals and build valuable relationships.
Advance Your Career: Enhance your skills and knowledge to advance your career in data science.
What to Expect:
Fundamental concepts and techniques of data mining and machine learning for massive datasets.
Explore the underlying technologies, algorithms, and tools to extract valuable insights from large-scale data.
Gain hands-on experience in processing, analyzing, and modeling massive datasets.
Networking Opportunities: Connect with like-minded professionals and build valuable relationships with industry leaders.
Key Topics:
Distributed Systems and Frameworks:
Deep dive into HDFS and Cloud Storage for efficient data storage.
Comparative analysis of Apache Spark and Apache Flink for parallel data processing.
Understanding batch and stream processing paradigms.
Data Mining Techniques:
Similarity Search: Mastering minhashing and locality-sensitive hashing for recommendation systems and anomaly detection.
Frequent Itemset Mining: Discovering association rules and market-basket analysis for retail and healthcare.
Clustering: Grouping data points with k-means, hierarchical clustering, and DBSCAN for customer segmentation and image clustering.
Advanced Topics:
Graph Analytics: Unraveling complex relationships with social network analysis and graph mining for fraud detection and knowledge graph construction.
Dimensionality Reduction: Simplifying data with SVD and LSI for text mining and feature engineering.
Machine Learning for Massive Datasets: Scaling machine learning algorithms and leveraging cloud-based platforms.
Why Attend?
Master the Fundamentals: Gain a solid understanding of the core concepts and techniques of data mining and machine learning.
Learn from Experts: Hear from practicing data scientists who are shaping the future of data science.
Gain Expertise: Learn from top experts in the field of machine learning and data engineering.
Stay Ahead of the Curve: Discover the latest trends and advancements.
Network with Industry Leaders: Connect with like-minded professionals and build valuable relationships.
Advance Your Career: Enhance your skills and knowledge to advance your career in data science.
We are in the process of finalizing dates and details.
Sign up for updates - dates & details coming soon - secure your spot at this exclusive event!
Sign up for updates - dates & details coming soon - secure your spot at this exclusive event!
AI and Data Science Professionals:
Data Scientists
AI/Machine Learning Engineers
Data Engineers
AI Researchers
Software Engineers
Product Managers
UX Designers
Business and Leadership Audience:
C-Suite Executives (CEOs, CTOs, CIOs): Leaders who need to understand the strategic implications of big data and data-driven decision-making.
Business Analysts: Professionals who use data to analyze business performance and identify opportunities.
Product Managers: Individuals responsible for developing data-driven products and services.
Investors, Traders and Venture Capitalists:
Investors: Benefit from optimizing investment decision-making processes.
Traders: Benefit from processing, analyzing, and modeling massive datasets.
Venture Capitalists: Professionals who fund early-stage technology companies.
Government and Policymakers:
Government Officials: Policymakers who need to understand the impact of big data on society and regulations.
Data Privacy Officers: Professionals responsible for ensuring data privacy and security.
Regulatory Agencies: Organizations that oversee data usage and compliance.
Other Potential Attendees:
Academics and Researchers: Individuals from universities and research institutions.
Students and Aspiring Data Professionals: Students and early-career professionals interested in learning about big data.
Consultants: Professionals who advise organizations on data strategy and implementation.
Data Journalists: Individuals who use data to tell stories and inform the public.
Data Scientists
AI/Machine Learning Engineers
Data Engineers
AI Researchers
Software Engineers
Product Managers
UX Designers
Business and Leadership Audience:
C-Suite Executives (CEOs, CTOs, CIOs): Leaders who need to understand the strategic implications of big data and data-driven decision-making.
Business Analysts: Professionals who use data to analyze business performance and identify opportunities.
Product Managers: Individuals responsible for developing data-driven products and services.
Investors, Traders and Venture Capitalists:
Investors: Benefit from optimizing investment decision-making processes.
Traders: Benefit from processing, analyzing, and modeling massive datasets.
Venture Capitalists: Professionals who fund early-stage technology companies.
Government and Policymakers:
Government Officials: Policymakers who need to understand the impact of big data on society and regulations.
Data Privacy Officers: Professionals responsible for ensuring data privacy and security.
Regulatory Agencies: Organizations that oversee data usage and compliance.
Other Potential Attendees:
Academics and Researchers: Individuals from universities and research institutions.
Students and Aspiring Data Professionals: Students and early-career professionals interested in learning about big data.
Consultants: Professionals who advise organizations on data strategy and implementation.
Data Journalists: Individuals who use data to tell stories and inform the public.
Scaling Data Processing: Distributed Systems and Frameworks
Distributed File Systems: The Foundation of Big Data
Deep dive into HDFS, Cloud Storage, and their role in storing massive datasets.
Explore the advantages and trade-offs of different storage solutions.
Distributed Processing Frameworks: Unleashing the Power of Parallelism
Comparative analysis of Apache Spark and Apache Flink.
Hands-on demonstrations of real-world data processing pipelines.
Data Processing Paradigms: Batch vs. Stream
Discuss the strengths and weaknesses of batch and stream processing.
Explore hybrid approaches and real-time analytics use cases.
Uncovering Hidden Patterns: Data Mining Techniques
Similarity Search: Finding Your Matches in the Haystack
In-depth exploration of minhashing and locality-sensitive hashing.
Practical applications in recommendation systems and anomaly detection.
Frequent Itemset Mining: Discovering the Rules of the Game
Dive into association rule mining and market-basket analysis.
Learn how to apply these techniques to retail, healthcare, and other domains.
Clustering: Grouping Together What Belongs Together
Explore various clustering algorithms, including k-means, hierarchical clustering, and DBSCAN.
Hands-on exercises in customer segmentation and image clustering.
Advanced Topics in Data Mining and Machine Learning
Graph Analytics: Unraveling Complex Relationships
Social network analysis and graph mining techniques.
Real-world applications in fraud detection and knowledge graph construction.
Dimensionality Reduction: Simplifying the Complex
In-depth discussion of SVD and LSI.
Practical applications in text mining and feature engineering.
Machine Learning for Massive Datasets: Scalable Algorithms and Techniques
Explore scalable machine learning algorithms, including gradient descent and distributed deep learning.
Hands-on experience with cloud-based machine learning platforms.
Distributed File Systems: The Foundation of Big Data
Deep dive into HDFS, Cloud Storage, and their role in storing massive datasets.
Explore the advantages and trade-offs of different storage solutions.
Distributed Processing Frameworks: Unleashing the Power of Parallelism
Comparative analysis of Apache Spark and Apache Flink.
Hands-on demonstrations of real-world data processing pipelines.
Data Processing Paradigms: Batch vs. Stream
Discuss the strengths and weaknesses of batch and stream processing.
Explore hybrid approaches and real-time analytics use cases.
Uncovering Hidden Patterns: Data Mining Techniques
Similarity Search: Finding Your Matches in the Haystack
In-depth exploration of minhashing and locality-sensitive hashing.
Practical applications in recommendation systems and anomaly detection.
Frequent Itemset Mining: Discovering the Rules of the Game
Dive into association rule mining and market-basket analysis.
Learn how to apply these techniques to retail, healthcare, and other domains.
Clustering: Grouping Together What Belongs Together
Explore various clustering algorithms, including k-means, hierarchical clustering, and DBSCAN.
Hands-on exercises in customer segmentation and image clustering.
Advanced Topics in Data Mining and Machine Learning
Graph Analytics: Unraveling Complex Relationships
Social network analysis and graph mining techniques.
Real-world applications in fraud detection and knowledge graph construction.
Dimensionality Reduction: Simplifying the Complex
In-depth discussion of SVD and LSI.
Practical applications in text mining and feature engineering.
Machine Learning for Massive Datasets: Scalable Algorithms and Techniques
Explore scalable machine learning algorithms, including gradient descent and distributed deep learning.
Hands-on experience with cloud-based machine learning platforms.
When staying in the heart of New York City, you'll be surrounded by countless entertainment, cultural, and culinary experiences. Here are some top adventures to add to your itinerary:
Broadway Magic: Immerse yourself in the world of theater by catching a Broadway show. From musical hits like "&Juliet" to the fantastic "Hadestown", there's something for everyone.
Iconic Landmarks: Explore the city's most famous landmarks, including the Statue of Liberty, Central Park, and the Empire State Building. Enjoy stunning views and learn about the city's history.
Museum Marvels: Discover the rich history and culture of New York City through its world-class museums. Visit the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), or the American Museum of Natural History.
Culinary Delights: Indulge in New York City's diverse culinary scene. From street food to Michelin-starred restaurants, there's a taste for every palate. Don't miss trying iconic dishes like pizza, bagels, and hot dogs.
Nightlife Exploration: Experience the city's vibrant nightlife by visiting trendy bars, clubs, and live music venues.
Explore neighborhoods like Times Square, SoHo, or the West Village for a memorable night out.
Shopping: Explore world-class shopping destinations like Fifth Avenue and SoHo. Fifth Avenue is synonymous with luxury shopping. Home to flagship stores of renowned brands like Tiffany & Co., Saks Fifth Avenue, Bergdorf Goodman, and Gucci, this avenue offers a dazzling array of high-end fashion, jewelry, and accessories. SoHo is a trendy neighborhood known for its bohemian atmosphere and boutique shops. Discover unique fashion designers, vintage clothing stores, and eclectic home decor shops. SoHo is also a hub for art galleries, offering a glimpse into the city's vibrant art scene.
Parks and Gardens: Enjoy the outdoors in Central Park, Bryant Park, or the High Line.
By staying in the heart of New York City, you'll have easy access to these incredible experiences and more. Make the most of your visit by exploring the city's vibrant neighborhoods and immersing yourself in its rich culture.
Broadway Magic: Immerse yourself in the world of theater by catching a Broadway show. From musical hits like "&Juliet" to the fantastic "Hadestown", there's something for everyone.
Iconic Landmarks: Explore the city's most famous landmarks, including the Statue of Liberty, Central Park, and the Empire State Building. Enjoy stunning views and learn about the city's history.
Museum Marvels: Discover the rich history and culture of New York City through its world-class museums. Visit the Metropolitan Museum of Art, the Museum of Modern Art (MoMA), or the American Museum of Natural History.
Culinary Delights: Indulge in New York City's diverse culinary scene. From street food to Michelin-starred restaurants, there's a taste for every palate. Don't miss trying iconic dishes like pizza, bagels, and hot dogs.
Nightlife Exploration: Experience the city's vibrant nightlife by visiting trendy bars, clubs, and live music venues.
Explore neighborhoods like Times Square, SoHo, or the West Village for a memorable night out.
Shopping: Explore world-class shopping destinations like Fifth Avenue and SoHo. Fifth Avenue is synonymous with luxury shopping. Home to flagship stores of renowned brands like Tiffany & Co., Saks Fifth Avenue, Bergdorf Goodman, and Gucci, this avenue offers a dazzling array of high-end fashion, jewelry, and accessories. SoHo is a trendy neighborhood known for its bohemian atmosphere and boutique shops. Discover unique fashion designers, vintage clothing stores, and eclectic home decor shops. SoHo is also a hub for art galleries, offering a glimpse into the city's vibrant art scene.
Parks and Gardens: Enjoy the outdoors in Central Park, Bryant Park, or the High Line.
By staying in the heart of New York City, you'll have easy access to these incredible experiences and more. Make the most of your visit by exploring the city's vibrant neighborhoods and immersing yourself in its rich culture.
We're excited to announce our Call for Presentations & Papers for a premier conference dedicated to the cutting-edge of scalable data processing and data mining. This event will bring together leading data professionals and innovators to share their insights, discoveries, and practical applications in this rapidly evolving field. We invite you to contribute your expertise and join us in exploring the future of big data.
We're looking for compelling presentations, papers, workshops, and lightning talks that offer original research, innovative solutions, real-world case studies, and insightful analyses.
Main Conference Subject Areas
Scaling Data Processing: Distributed Systems and Frameworks
This track focuses on the foundational and advanced aspects of processing large-scale datasets efficiently. We encourage submissions on:
Distributed File Systems: The Foundation of Big Data
Deep dives into HDFS, Cloud Storage, and their crucial role in storing massive datasets.
Explorations of the advantages and trade-offs of different storage solutions in various contexts.
Distributed Processing Frameworks: Unleashing the Power of Parallelism
Comparative analyses of Apache Spark and Apache Flink, including performance benchmarks and use cases.
Hands-on demonstrations of real-world data processing pipelines built with these frameworks.
Data Processing Paradigms: Batch vs. Stream
Discussions on the strengths and weaknesses of batch and stream processing.
Explorations of hybrid approaches and real-time analytics use cases across different industries.
Uncovering Hidden Patterns: Data Mining Techniques
This track delves into methodologies for extracting valuable insights and patterns from large datasets. We're particularly interested in:
Similarity Search: Finding Your Matches in the Haystack
In-depth explorations of minhashing and locality-sensitive hashing.
Practical applications in recommendation systems and anomaly detection.
Frequent Itemset Mining: Discovering the Rules of the Game
Deep dives into association rule mining and market-basket analysis.
How to apply these techniques to various domains like retail, healthcare, and cybersecurity.
Clustering: Grouping Together What Belongs Together
Explorations of various clustering algorithms, including k-means, hierarchical clustering, and DBSCAN.
Hands-on exercises in practical applications such as customer segmentation and image clustering.
Advanced Topics in Data Mining and Machine Learning
This track covers cutting-edge techniques and scalable approaches for complex data challenges. We welcome submissions on:
Graph Analytics: Unraveling Complex Relationships
Social network analysis and advanced graph mining techniques.
Real-world applications in fraud detection and knowledge graph construction.
Dimensionality Reduction: Simplifying the Complex
In-depth discussions of SVD and LSI.
Practical applications in text mining and feature engineering.
Machine Learning for Massive Datasets: Scalable Algorithms and Techniques
Explorations of scalable machine learning algorithms, including gradient descent and distributed deep learning.
Hands-on experiences and best practices with cloud-based machine learning platforms.
Submission Types
We invite various types of submissions to ensure a rich and diverse program:
Oral Presentations (20 minutes): Share your research findings, innovative applications, case studies, or best practices in a focused presentation.
Technical Papers (Full Length, 8-12 pages, IEEE format): Submit original, unpublished research that will undergo a rigorous peer-review process. Accepted papers will be published in the conference proceedings.
Experience & Insight Papers (4-6 pages, formatted for readability): This category is for practitioners, industry leaders, and innovators to share valuable lessons learned, practical case studies, real-world implementations, and insightful perspectives on the challenges and successes encountered in scalable data processing and data mining. These papers should focus on practical applications, best practices, and actionable insights. Submissions will be peer-reviewed for clarity, relevance, and practical value.
Poster Presentations: Visually showcase preliminary results, ongoing research, or innovative concepts. There will be a dedicated poster session for interactive discussions.
Panel Proposals (60 minutes): Suggest and moderate a discussion among 3-5 experts on a controversial, emerging, or complex topic within scalable data processing and data mining.
Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies related to data processing, data mining, or machine learning for massive datasets.
Submission Guidelines
Abstract: All submissions (except workshop proposals) must include a concise abstract (maximum 300 words) summarizing the problem, approach, key findings/insights, and conclusions.
Author Information: Include full names, affiliations, and a brief professional biography (max 100 words per author).
Keywords: Provide 3-5 relevant keywords that best describe your submission.
Originality: Submissions must represent original work that has not been previously published or is not currently under review elsewhere.
Audience Consideration: Presenters should be prepared to convey complex technical or business concepts clearly to a diverse audience, including both technical and non-technical attendees.
Formatting: Specific formatting guidelines for full papers will be provided upon the submission portal opening.
Review Process
All submissions will undergo a rigorous peer-review process by the Program Committee, comprising leading experts in data science, distributed systems, machine learning, and related domains. Submissions will be evaluated based on:
Relevance to conference themes
Originality and novelty of contributions
Technical merit and soundness (for technical papers)
Clarity, organization, and presentation quality
Potential impact and practical applicability
We look forward to receiving your valuable contributions and seeing you at the conference!
We're looking for compelling presentations, papers, workshops, and lightning talks that offer original research, innovative solutions, real-world case studies, and insightful analyses.
Main Conference Subject Areas
Scaling Data Processing: Distributed Systems and Frameworks
This track focuses on the foundational and advanced aspects of processing large-scale datasets efficiently. We encourage submissions on:
Distributed File Systems: The Foundation of Big Data
Deep dives into HDFS, Cloud Storage, and their crucial role in storing massive datasets.
Explorations of the advantages and trade-offs of different storage solutions in various contexts.
Distributed Processing Frameworks: Unleashing the Power of Parallelism
Comparative analyses of Apache Spark and Apache Flink, including performance benchmarks and use cases.
Hands-on demonstrations of real-world data processing pipelines built with these frameworks.
Data Processing Paradigms: Batch vs. Stream
Discussions on the strengths and weaknesses of batch and stream processing.
Explorations of hybrid approaches and real-time analytics use cases across different industries.
Uncovering Hidden Patterns: Data Mining Techniques
This track delves into methodologies for extracting valuable insights and patterns from large datasets. We're particularly interested in:
Similarity Search: Finding Your Matches in the Haystack
In-depth explorations of minhashing and locality-sensitive hashing.
Practical applications in recommendation systems and anomaly detection.
Frequent Itemset Mining: Discovering the Rules of the Game
Deep dives into association rule mining and market-basket analysis.
How to apply these techniques to various domains like retail, healthcare, and cybersecurity.
Clustering: Grouping Together What Belongs Together
Explorations of various clustering algorithms, including k-means, hierarchical clustering, and DBSCAN.
Hands-on exercises in practical applications such as customer segmentation and image clustering.
Advanced Topics in Data Mining and Machine Learning
This track covers cutting-edge techniques and scalable approaches for complex data challenges. We welcome submissions on:
Graph Analytics: Unraveling Complex Relationships
Social network analysis and advanced graph mining techniques.
Real-world applications in fraud detection and knowledge graph construction.
Dimensionality Reduction: Simplifying the Complex
In-depth discussions of SVD and LSI.
Practical applications in text mining and feature engineering.
Machine Learning for Massive Datasets: Scalable Algorithms and Techniques
Explorations of scalable machine learning algorithms, including gradient descent and distributed deep learning.
Hands-on experiences and best practices with cloud-based machine learning platforms.
Submission Types
We invite various types of submissions to ensure a rich and diverse program:
Oral Presentations (20 minutes): Share your research findings, innovative applications, case studies, or best practices in a focused presentation.
Technical Papers (Full Length, 8-12 pages, IEEE format): Submit original, unpublished research that will undergo a rigorous peer-review process. Accepted papers will be published in the conference proceedings.
Experience & Insight Papers (4-6 pages, formatted for readability): This category is for practitioners, industry leaders, and innovators to share valuable lessons learned, practical case studies, real-world implementations, and insightful perspectives on the challenges and successes encountered in scalable data processing and data mining. These papers should focus on practical applications, best practices, and actionable insights. Submissions will be peer-reviewed for clarity, relevance, and practical value.
Poster Presentations: Visually showcase preliminary results, ongoing research, or innovative concepts. There will be a dedicated poster session for interactive discussions.
Panel Proposals (60 minutes): Suggest and moderate a discussion among 3-5 experts on a controversial, emerging, or complex topic within scalable data processing and data mining.
Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies related to data processing, data mining, or machine learning for massive datasets.
Submission Guidelines
Abstract: All submissions (except workshop proposals) must include a concise abstract (maximum 300 words) summarizing the problem, approach, key findings/insights, and conclusions.
Author Information: Include full names, affiliations, and a brief professional biography (max 100 words per author).
Keywords: Provide 3-5 relevant keywords that best describe your submission.
Originality: Submissions must represent original work that has not been previously published or is not currently under review elsewhere.
Audience Consideration: Presenters should be prepared to convey complex technical or business concepts clearly to a diverse audience, including both technical and non-technical attendees.
Formatting: Specific formatting guidelines for full papers will be provided upon the submission portal opening.
Review Process
All submissions will undergo a rigorous peer-review process by the Program Committee, comprising leading experts in data science, distributed systems, machine learning, and related domains. Submissions will be evaluated based on:
Relevance to conference themes
Originality and novelty of contributions
Technical merit and soundness (for technical papers)
Clarity, organization, and presentation quality
Potential impact and practical applicability
We look forward to receiving your valuable contributions and seeing you at the conference!