Oct 31st – Nov 3rd, 2023
Machine Learning Safety & Security
Learn the latest models, advancements, and trends from the top practitioners behind two of data science’s hottest topics
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 2022 - Oct 31st – Nov 3rd
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 Previous Machine Learning Safety & Security Speakers
Dawn Song, PhD
Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning, security, and blockchain. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, blockchain and smart contracts, to the intersection of machine learning and security. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the Faculty Research Award from IBM, Google and other major tech companies, and Best Paper Awards from top conferences in Computer Security and Deep Learning. She is an IEEE Fellow. She is ranked the most cited scholar in computer security (AMiner Award). She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007. She is also a serial entrepreneur.
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Dr. Jon Krohn
Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O’Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.
Mosharaf Chowdhury, PhD
Mosharaf Chowdhury is a Morris Wellman associate professor of CSE at the University of Michigan, Ann Arbor, where he leads the SymbioticLab. His work improves application performance and system efficiency of machine learning and big data workloads. He is also building software solutions to monitor and optimize the impact of machine learning systems on energy consumption and data privacy. His group developed Infiniswap, the first scalable software solution for memory disaggregation; Salus, the first software-only GPU sharing system for deep learning; FedScale, the largest federated learning benchmark and a scalable and extensible federated learning engine; and Zeus, the first GPU energy-vs-training performance tradeoff optimizer for DNN training. In the past, Mosharaf did seminal works on coflows and virtual network embedding, and he was a co-creator of Apache Spark. He has received many individual awards and fellowships, thanks to his stellar students and collaborators. His works have received seven paper awards from top venues, including NSDI, OSDI, and ATC, and over 22,000 citations. Mosharaf received his Ph.D. from UC Berkeley in 2015.
Jack McCauley
Jack McCauley an Innovator in Residence at Jacobs Institute for Design Innovation at UC Berkeley, Professor at UC Berkeley, Co-Founder of Oculus, an American engineer, hardware designer, inventor, video game developer and philanthropist. Jack is best known for designing the guitars and drums for the Guitar Hero video game series, and as a co-founder and former chief engineer at Oculus VR. At Oculus, Jack designed and built the Oculus
DK1 and DK2 virtual reality headsets. Oculus was acquired by Facebook for $2 Billion. McCauley holds numerous U.S. patents for inventions in software, audio effects, virtual reality, motion control, computer peripherals, and video game hardware and controllers. Jack was awarded a full scholarship to attend University of California, Berkeley where he earned as BSc., EECS in Electrical Engineering and Computer Science in 1986. Jack has authored numerous research papers in the field of artificial intelligence (AI) and mathematical modeling of AI-based systems and is currently pursuing new projects at his private R&D facility and hardware incubator in Livermore, California.
Aaron Roth, PhD
Aaron Roth is the Henry Salvatori Professor of Computer and Cognitive Science, in the Computer and Information Sciences department at the University of Pennsylvania, with a secondary appointment in the Wharton statistics department. He is affiliated with the Warren Center for Network and Data Science, and co-director of the Networked and Social Systems Engineering (NETS) program. He is also an Amazon Scholar at Amazon AWS. He is the recipient of a Presidential Early Career Award for Scientists and Engineers (PECASE) awarded by President Obama in 2016, an Alfred P. Sloan Research Fellowship, an NSF CAREER award, and research awards from Yahoo, Amazon, and Google. His research focuses on the algorithmic foundations of data privacy, algorithmic fairness, game theory, learning theory, and machine learning. Together with Cynthia Dwork, he is the author of the book “The Algorithmic Foundations of Differential Privacy.” Together with Michael Kearns, he is the author of “The Ethical Algorithm”.
Raluca Ada Popa, PhD
Raluca Ada Popa is the Robert E. and Beverly A. Brooks associate professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab and SkyLab at UC Berkeley, as well as a co-founder of Opaque Systems and PreVeil, two cybersecurity companies. Raluca has received her PhD in computer science as well as her Masters and two BS degrees, in computer science and in mathematics, from MIT. She is the recipient of the 2021 ACM Grace Murray Hopper Award, a Sloan Foundation Fellowship award, Jay Lepreau Best Paper Award at OSDI 2021, Distinguished Paper Award at IEEE Euro S&P 2022, Jim and Donna Gray Excellence in Undergraduate Teaching Award, NSF Career Award, Technology Review 35 Innovators under 35, Microsoft Faculty Fellowship, and a George M. Sprowls Award for best MIT CS doctoral thesis.
Confidential Data Computing and Collaboration for Data Scientists(Keynote)
Scott Zoldi, PhD
Scott Zoldi is chief analytics officer at FICO responsible for advancing the company's leadership in artificial intelligence (AI) and analytics in its product and technology solutions. At FICO Scott has authored more than 120 analytic patents, with 71 granted and 49 pending. Scott is actively involved in the development of analytics applications, Responsible AI technologies and AI governance frameworks, the latter including FICO's blockchain-based [SZ1] model development governance methodology. Scott is a member of the Board of Advisors of FinRegLab, a Cybersecurity Advisory Board Member of the California Technology Council, and a Board Member of Tech San Diego and the San Diego Cyber Center of Excellence. He is also a member of the CNBC Technology Executive Council. Scott received his Ph.D. in theoretical and computational physics from Duke University.
Veena Mendiratta, PhD
Bio Coming Soon!
Using Change Detection Algorithms for Detecting Anomalous Behavior in Large Systems(Talk)
Johnathan Roy Azaria
Experienced Data Scientist and Tech Lead at Imperva’s threat research group where I work on creating machine learning algorithms to help protect our customers against web app and DDoS attacks. Before joining Imperva, I obtained a B.Sc and M.Sc in Bioinformatics from Bar Ilan University.
Aric LaBarr, PhD
A Teaching Associate Professor in the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern workforce to wisely communicate and handle a data-driven future at the nation’s first Master of Science in Analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and led a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.
Advanced Fraud Modeling & Anomaly Detection with Python & R part 1(Training)
Advanced Fraud Modeling & Anomaly Detection with Python & R part 2(Training)
Meg Kurdziolek, PhD
Meg is currently the Lead UXR for Intrinsic.ai, where she focuses her work on making it easier for engineers to adopt and automate with industrial robotics. She is a “Xoogler”, and prior to Intrinsic worked on the Explainable AI services on Google Cloud. Meg has had a varied career working for start-ups and large corporations alike, and she has published on topics such as user research, information visualization, educational-technology design, voice user interface (VUI) design, explainable AI (XAI), and human-robot interaction (HRI). Meg is also a proud alumnus of Virginia Tech, where she received her Ph.D. in Human-Computer Interaction.
Balaji Lakshminarayanan, PhD
Balaji is currently a Staff Research Scientist at Google Brain working on Machine Learning and its applications. Previously, he was a research scientist at DeepMind for 4.5+ years. Before that, he received a PhD in machine learning from Gatsby Unit, UCL supervised by Yee Whye Teh. His research interests are in scalable, probabilistic machine learning. More recently, he has focused on: – Uncertainty and out-of-distribution robustness in deep learning – Deep generative models including generative adversarial networks (GANs), normalizing flows and variational auto-encoders (VAEs) – Applying probabilistic deep learning ideas to solve challenging real-world problems.
Practical Tutorial on Uncertainty and Out-of-distribution Robustness in Deep Learning(Tutorial)
Adam Breindel
Adam Breindel consults and teaches widely on Apache Spark and other technologies. Adam’s experience includes work with banks on neural-net fraud detection, streaming analytics, cluster management code, and web apps, as well as development at a variety of startup and established companies in the travel, productivity, and entertainment industries. He is excited by the way that Spark and other modern big-data tech remove so many old obstacles to system design and make it possible to explore new categories of interesting, fun, hard problems.
Celia Cintas, PhD
Celia Cintas is a Research Scientist at IBM Research Africa – Nairobi. She is a member of the AI Science team at the Kenya Lab. Her current research explores subset scanning for anomalous pattern detection under generative models and the improvement of ML techniques to address challenges in Global Health. Previously, a grantee from the National Scientific and Technical Research Council at LCI-UNS and IPCSH-CONICET. She holds a Ph.D. in Computer Science from Universidad del Sur (Argentina). More info https://celiacintas.github.io/about/
A Tale of Adversarial Attacks & Out-of-Distribution Detection Stories in the Activation Space(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 31st – Nov 3rd
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