FOCUS AREA OVERVIEW
Pause for a moment to realize the number of machine learning models trained on crowdsourced data from social media and other web sources, and realize how easy it is to poison training data. This is one of the many treats raised when accessing machine learning safety Driven by concerns around foundational models, autonomous systems, and large-scale models, ML Safety is quickly becoming a key topic encompassing many areas of AI and ML. Adversarial attacks, backdoor model vulnerabilities, real-world deployment tail risks, risk monitoring, and boosting model defenses are a few of the topics that fall under the Machine Learning Safety umbrella.
ODSC East is one of the first applied data science and machine learning conferences to address this fast-trending topic.
ODSC WEST 2023 - Oct 30th – Nov 2nd
RegisterTOPICS YOU'LL LEARN
Transparency & Explainability in Machine Learning
Differential Privacy & Federated Learning
Cybersecurity and Machine Learning
Idenifying Bias in Machine Learning
Data Privacy and Confidentiality
Safe Machine Learning & Deep Learning
Safe Autonomous Systems Control
Ethical and Legal Consequences of Unsafe Machine Learning
Engineering Safety in Machine Learning
Identifying & Fixing Vulnerabilities in the Machine Learning
Realiabilty in Critical Machine Learning Systems
Security Risks in Machine Learning and Deep Learning
Data & Poisoning Attacks in Machine Learning
Identifying Backdoor Attacks on Machine Learning
Deep learning and Adversarial Attacks
Adverserial Attacks on Autonomous Systems
Understanding Transfer Learning Attacks
Using Machine Learning to Detect Malicious Activity
Some of Our Confirmed Machine Learning Safety & Security Speakers
Rajiv Shah, PhD
Rajiv Shah is a machine learning engineer at Hugging Face who focuses on enabling enterprise teams to succeed with AI. Rajiv is a leading expert in the practical application of AI. Previously, he led data science enablement efforts across hundreds of data scientists at DataRobot. He was also a part of data science teams at Snorkel AI, Caterpillar, and State Farm. Rajiv is a widely recognized speaker on AI, published over 20 research papers, and received over 20 patents, including sports analytics, deep learning, and interpretability. Rajiv holds a PhD in Communications and a Juris Doctor from the University of Illinois at Urbana Champaign. While earning his degrees, he received a fellowship in Digital Government from the John F. Kennedy School of Government at Harvard University. He also has a large following on AI-related short videos on Tik Tok and Instagram at @rajistics.
Evaluation Techniques for Large Language Models(Tutorial)
Anna Jung
Anna Jung is a Senior ML Open Source Engineer at VMware, leading the open source team as part of the VMware AI Labs. She currently contributes to various upstream ML-related open source projects focusing on the project’s overall health, adoption, and innovation. She believes in the importance of giving back to the community and is passionate about increasing diversity in open source. When away from the keyboard, Anna is often at film festivals supporting independent filmmakers.
Patrick Hall
Patrick Hall is an assistant professor of decision sciences at the George Washington University School of Business, teaching data ethics, business analytics, and machine learning classes. He also conducts research in support of NIST’s AI risk management framework and is affiliated with leading fair lending and AI risk management advisory firms.
Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University. He has been invited to speak on AI and machine learning topics at the National Academies of Science, Engineering, and Medicine, ACM SIG-KDD, and the Joint Statistical Meetings. He has been published in outlets like Information, Frontiers in AI, McKinsey.com, O’Reilly Ideas, and Thompson-Reuters Regulatory Intelligence, and his technical work has been profiled in Fortune, Wired, InfoWorld, TechCrunch, and others. Patrick is the lead author of the book Machine Learning for High-Risk Applications.
Prior to joining the GW School of Business, Patrick co-founded BNH.AI, a boutique law firm focused on AI governance and risk management. He led H2O.ai’s efforts in responsible AI, resulting in one of the world’s first commercial applications for explainability and bias mitigation in machine learning. Patrick also held global customer-facing roles and R&D roles at SAS Institute. Patrick has built machine learning software solutions and advised on matters of AI risk for Fortune 100 companies, cutting-edge startups, Big Law, and U.S. and foreign government agencies.
Adopting Language Models Requires Risk Management — This is How(Talk)
Parul Pandey
Parul Pandey has a background in Electrical Engineering and currently works as a Principal Data Scientist at H2O.ai. Prior to this, she was working as a Machine Learning Engineer at Weights & Biases. Parul is one of the co-authors of Machine Learning for High-Risk Applications book, which focuses on the responsible implementation of AI. She is also a Kaggle Grandmaster in the notebooks category and was one of Linkedin’s Top Voices in the Software Development category in 2019. Parul has written multiple articles focused on Data Science and Software development for various publications and mentors, speaks, and delivers workshops on topics related to Responsible AI.
Machine Learning for High-Risk Applications – Techniques for Responsible AI(Tutorial)
Nirmal Budhathoki
Nirmal Budhathoki is a Senior Data Scientist, who is currently working at Microsoft in Cloud Security. Nirmal has over 12+ years of experience in the IT industry, including 5+ years in data science. Nirmal’s strong belief in continuous learning has led him to complete three master’s degrees with majors on: Information Systems, Business Administration, and Data Science. Nirmal loves to help the data science community and has completed over 600+ free mentoring sessions with aspiring data scientists to help them navigate their data science career. Nirmal also conducts mentored learning sessions for MiT’s Data Science and Machine Learning certification program in collaboration with Great Learning. Nirmal has experience working with the US government for the Department of Navy, and he is also a US army veteran. Nirmal yearns to solve data science problems that are aligned with product strategy and business outcomes. In his free time, Nirmal loves using this data science skills in sports analytics.
Teodora Sechkova
Teodora is an open source software engineer at VMware AI Labs. During her first couple of years in VMware, as part of the Open Source Program Office, she was an active contributor and maintainer of The Update Framework (TUF) – a framework for securing software update systems. Currently, she invests her time in open source projects related to machine learning security.
Security First, Create a Robust Machine Learning Model(Talk)
New Speakers Coming Soon
You Will Meet
Top speakers and practitioners in Machine Learning Safety
Data Scientists, Machine Learning Engineers, and AI Experts interested in risk in AI
Business professionals who want to understand safe machine learning
Core contributors in the fields of Machine Learning and Deep Learning
Software Developers focused on building safe machine learning and deep learning
Technologist seeking to better understand AI and machine learning risks and vulnerabilities
CEOs, CTOs, CIOs and other c-suite decision makers
Data Science Enthusiasts
Why Attend?
Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models and advanced trends
Expand your network and connect with like-minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career
Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning
Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and its coming impact on the domains of business, society, healthcare, finance, manufacturing, and more
ODSC WEST 2023 - Oct 30th – Nov 2nd
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