Unlock the Power of AI - Secure the Future

Join us for a transformative conference exploring the cutting-edge field of Adversarial Machine Learning. As artificial intelligence continues to revolutionize industries, so too do the threats to its integrity. Adversarial Machine Learning (AML) is a growing concern, where malicious actors exploit vulnerabilities in AI systems to compromise their accuracy and security.

Why Attend?

Gain a Deep Understanding: Explore the latest research and techniques in AML, including data poisoning, evasion attacks, model extraction, and backdoor attacks.
Learn Effective Defense Strategies: Discover how to mitigate these threats through adversarial training, input validation, robust optimization, and other advanced techniques.
Network with Experts: Connect with leading researchers, industry practitioners, and cybersecurity professionals.
Stay Ahead of the Curve: Learn about emerging trends and future directions in AML.

Key Topics:

Taxonomy of Attacks: A comprehensive overview of various attack techniques.
Mitigating Adversarial Attacks: Effective defense strategies.
Real-world Applications and Case Studies: Practical implications of AML.
Emerging Trends and Future Directions: Cutting-edge research and advancements.
 

Don't miss this opportunity to safeguard your AI systems and secure the future of artificial intelligence.
 

Primary Audience for Conference Attendees

Data Science & AI Professionals:

Data Scientists
AI/Machine Learning Engineers
AI Researchers
Software Engineers
Product Managers
UX Designers

Technology Professionals:

IT Managers and CIOs: IT leaders responsible for securing organizational systems.
Security Experts: Cybersecurity analysts, penetration testers, security architects, and cryptography experts.
Network Engineers: Network administrators and network security engineers.

Government Officials and Regulators:

Policymakers and Regulators: Individuals shaping the regulatory landscape for AI and machine learning.
Regulatory Agencies: Organizations responsible for overseeing AI development and deployment.

Business and Industry Professionals:

Financial Institutions: Banks, investment firms, and fintech companies.
Healthcare Providers: Hospitals, clinics, and health insurance companies.
Autonomous Vehicle Developers: Companies working on self-driving cars and trucks.
Cybersecurity Firms: Companies specializing in cybersecurity solutions.
Technology Companies: Organizations developing and deploying AI-powered products and services.

Academic and Research Community:

Researchers and Academics: Scholars studying AI, machine learning, and cybersecurity.
Students and Graduate Students: Individuals pursuing advanced degrees in related fields.

Additional Considerations:

Ethical AI Practitioners: Individuals concerned with the ethical implications of AI and machine learning.
Legal Professionals: Lawyers specializing in technology law and intellectual property.
Consultants: Cybersecurity consultants and AI consultants.

Main Conference Subject Areas

Foundations of Adversarial Machine Learning

• Welcome and Opening Remarks
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  • Introduction to the conference and its objectives
  • Overview of the conference agenda
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• A Deep Dive into Adversarial Attacks
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  • Taxonomy & Terminology of Attacks & Mitigations
  • A comprehensive overview of attack techniques: data poisoning, evasion attacks, model extraction, and backdoor attacks
  • Real-world examples of adversarial attacks and their consequences
•  
• Defending Against Adversarial Attacks
  •  
  • Overview of defense strategies: adversarial training, input validation, and robust optimization
  • Practical techniques for implementing defenses in machine learning systems
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• Panel Discussion: Challenges and Future Directions of AML research and development

Adversarial Machine Learning in Practice

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• Real-world Applications and Case Studies
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  • Case studies of adversarial attacks in various domains (e.g., autonomous vehicles, healthcare, finance)
  • Lessons learned and best practices for mitigating risks
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• Adversarial Attacks on Deep Neural Networks
  •  
  • Specific challenges and vulnerabilities of deep learning models
  • Advanced techniques for attacking and defending deep neural networks
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• Workshop: Hands-on Adversarial Attack and Defense Techniques
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  • Practical exercises to understand and implement adversarial attacks and defenses

Emerging Trends and Future Directions

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• Emerging Trends in Adversarial Machine Learning
  •  
  • Future directions in AML research
  • Novel attack and defense techniques
  • Ethical considerations in AML research
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• Adversarial Machine Learning in the Age of AI
  •  
  • The impact of AI on the landscape of adversarial attacks and defenses
  • Future challenges and opportunities in the field of AML
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• Closing Remarks and Networking
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  • Recap of key takeaways from the conference
  • Future collaborations and partnerships
  • Networking opportunities for attendees

Experiencing Telluride Slopeside

Telluride, Colorado, is a historic mining town nestled in a box canyon surrounded by towering peaks. The town's Victorian architecture and charming streets exude a timeless appeal. In winter, it transforms into a world-class ski resort with challenging slopes and breathtaking scenery.

In summer, hikers and mountain bikers flock to the area to explore the rugged wilderness. The town's lively atmosphere, with its art galleries, festivals, and gourmet restaurants, makes it a popular destination for both outdoor enthusiasts and culture seekers.

Telluride Ski Resort is a world-class ski resort nestled in the San Juan Mountains of Colorado. Known for its stunning scenery, challenging terrain, and lack of crowds, it offers a unique skiing and snowboarding experience. The resort boasts over 2,000 skiable acres, including the iconic Revelation Bowl, which offers advanced and expert terrain with breathtaking views. Telluride also caters to skiers and riders of all levels, with plenty of beginner and intermediate runs.

The town of Telluride itself is a charming historic district with a variety of dining, shopping, and cultural attractions. Here are some cool adventures in Telluride:

Heli-skiing: Experience an adrenaline-pumping adventure through pristine powder and breathtaking mountain scenery.

Skiing and Snowboarding at Telluride Ski Resort: Experience world-class skiing and snowboarding on over 2,000 acres of terrain, including the iconic Revelation Bowl.

Hiking and Mountain Biking: Explore the stunning San Juan Mountains on a variety of trails, from easy hikes to challenging mountain bike routes.

Whitewater Rafting: Embark on a thrilling whitewater rafting adventure on the Animas River, with options for all skill levels.

Jeep Tours: Take a guided jeep tour through the stunning mountain scenery, learning about the area's history and geology.

Scenic Gondola Ride: Ride the gondola to the top of the mountain for panoramic views of the town and surrounding peaks.

Heli-Skiing Adventure (Optional)

Experience the ultimate powder skiing adventure with a helicopter tour to remote, pristine snowfields.

Join us for a thrilling day of heli-skiing in the Colorado Rockies. We will fly you to remote, pristine snowfields where you will experience the ultimate powder skiing adventure. Our experienced guides will lead you to the best terrain, ensuring a safe and unforgettable experience.

Call for Presentations & Papers

This conference is a critical forum for AI, technology and data science professionals, security experts, business and industry professionals and government officials and regulators to address the evolving challenges of securing machine learning systems. It will serve as a hub for exploring the latest adversarial attacks, defense strategies, and ethical considerations in this rapidly growing field.

We invite you to contribute your expertise and join a community dedicated to building more robust and trustworthy AI. We are seeking compelling presentations, insightful papers, interactive workshops, and concise lightning talks that showcase original research, practical applications, and forward-looking analyses.

Main Conference Subject Areas

Foundations of Adversarial Machine Learning

This track will establish a common understanding of the adversarial landscape, from basic concepts to complex attack methodologies. We encourage submissions on:

• Welcome and Opening Remarks: We are seeking a keynote that provides a comprehensive introduction to the conference and an overview of the current state of adversarial machine learning.
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• A Deep Dive into Adversarial Attacks:
  •  
  • A comprehensive taxonomy and terminology of attacks and mitigations.
  • An overview of key attack techniques, including data poisoning, evasion attacks, model extraction, and backdoor attacks.
  • Real-world examples of adversarial attacks and a discussion of their consequences across different sectors.
•  
• Defending Against Adversarial Attacks:
  •  
  • Overviews of defense strategies such as adversarial training, input validation, and robust optimization.
  • Practical techniques for implementing effective defenses in real-world machine learning systems.
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• Panel Discussion: Proposals for a panel that debates the challenges and future directions of AML research and development.

Adversarial Machine Learning in Practice

This track focuses on the real-world application and impact of adversarial machine learning across various domains. We are particularly interested in:

• Real-world Applications and Case Studies:
  •  
  • Case studies of adversarial attacks in specific domains like autonomous vehicles, healthcare, and finance.
  • Sharing lessons learned and best practices for mitigating adversarial risks in practical settings.
•  
• Adversarial Attacks on Deep Neural Networks:
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  • Discussions on the unique challenges and vulnerabilities of deep learning models.
  • Advanced techniques for both attacking and defending deep neural networks.
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• Workshop: Proposals for hands-on sessions where attendees can get practical experience with adversarial attack and defense techniques.

Emerging Trends and Future Directions

This track will explore the cutting-edge of adversarial machine learning and its future trajectory. We welcome submissions on:

• Emerging Trends in Adversarial Machine Learning:
  •  
  • Future directions in AML research and predictions for the field.
  • Novel attack and defense techniques that are currently in development.
  • Ethical considerations and responsible conduct in AML research.
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• Adversarial Machine Learning in the Age of AI:
  •  
  • The broader impact of AI on the landscape of adversarial attacks and defenses.
  • Future challenges and opportunities for securing increasingly complex AI systems.
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• Closing Remarks and Networking: We are also seeking a closing speaker to provide a summary of the conference's key takeaways.

Submission Types

We welcome a variety of contributions to ensure a rich and diverse program:

• Oral Presentations (20 minutes): Share your research findings, innovative applications, or case studies in a focused presentation.
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• 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.
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• Experience & Insight Papers (4-6 pages, formatted for readability): This category is designed for practitioners and industry leaders to share valuable lessons learned, practical case studies, and insightful perspectives on the challenges and successes of dealing with adversarial threats in real-world systems. Submissions will be peer-reviewed for clarity, relevance, and practical value.
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• Poster Presentations: Visually showcase preliminary results, ongoing research, or innovative concepts. There will be a dedicated poster session for interactive discussions.
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• Panel Proposals (60 minutes): Suggest and moderate a discussion among 3-5 experts on a controversial, emerging, or complex topic within adversarial machine learning.
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• Workshop Proposals (60 minutes): Propose an interactive, hands-on session focused on practical skills, tools, or methodologies related to AML.

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 theoretical 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 machine learning, cybersecurity, and related fields. 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 your valuable contributions and to a stimulating discussion on securing the future of AI!