Dr. Bo Li is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana–Champaign. She is the recipient of the MIT Technology Review TR-35 Award, Alfred P. Sloan Research Fellowship, NSF CAREER Award, IJCAI Computers and Thought Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star award, Symantec Research Labs Fellowship, Rising Star Award, Research Awards from Tech companies such as Amazon, Facebook, Intel, and IBM, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, security, machine learning, privacy, and game theory. She has designed several scalable frameworks for robust machine learning and privacy-preserving data publishing systems. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.

Keynote: Trustworthy Machine Learning via Learning with Reasoning

Abstract. Advances in machine learning have led to the rapid and widespread deployment of learning based methods in safety-critical applications, such as autonomous driving and medical healthcare. Standard machine learning systems, however, assume that training and test data follow the same, or similar, distributions, without explicitly considering active adversaries manipulating either distribution. For instance, recent work has demonstrated that motivated adversaries can circumvent anomaly detection or other machine learning models at test-time through evasion attacks, or can inject well-crafted malicious instances into training data to induce errors during inference through poisoning attacks. Such distribution shift could also lead to other trustworthiness issues such as generalization. In this talk, I will describe different perspectives of trustworthy machine learning, such as robustness, privacy, generalization, and their underlying interconnections. I will focus on a certifiably robust learning approach based on statistical learning with logical reasoning as an example, and then discuss the principles towards designing and developing practical trustworthy machine learning systems with guarantees, by considering these trustworthiness perspectives in a holistic view.

Ali Makhdoumi is an Associate Professor in the Decision Sciences area at Fuqua School of Business, Duke University. He has received a BSc in Electrical Engineering, a BSc in Mathematics from Sharif University of Technology, and a Ph.D. in Electrical Engineering and Computer Science from MIT. His current research interests include data and information markets, privacy, algorithmic game theory, and network economics.

Keynote: Too Much Data: Prices and Inefficiencies in Data Markets

Abstract. When a user shares her data with online platforms, she reveals information about others. In such a setting, externalities depress the price of data because once a user's information is leaked by others, she has less reason to protect her data and privacy. These depressed prices lead to excessive data sharing. We characterize conditions under which shutting down data markets improves welfare. Platform competition does not redress the problem of excessively low data price and too much data sharing, and may further reduce welfare. We propose a scheme based on mediated data-sharing that improves efficiency.

Ari Juels is the Weill Family Foundation and Joan and Sanford I. Weill Professor in the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion and a Computer Science faculty member at Cornell University. He is a Co-Director of the Initiative for CryptoCurrencies and Contracts (IC3). He is also Chief Scientist at Chainlink Labs.

He was the Chief Scientist of RSA, Director of RSA Laboratories, and a Distinguished Engineer at EMC (now Dell EMC), where he worked until 2013. He received his Ph.D. in computer science from U.C. Berkeley in 1996.

His recent areas of interest include blockchains, cryptocurrency, and smart contracts, as well as applied cryptography, cloud security, user authentication, and privacy.

Keynote: Non-Fungible Tokens (NFTs): How Should the Apes Evolve?

Rosanna is a Postdoctoral Scholar at Cornell Tech in New York City. She has a passion for innovative digital approaches to detecting tech abuse and financial abuse in intimate contexts. She co-leads the Clinic to End Tech Abuse (CETA), a volunteer-run service that works directly with victim-survivors of intimate partner violence to address their safety and security concerns. She provides technical assistance to abusive partner interventions and is motivated to design support services that are trauma-informed and responsive to harm.

Keynote: Hidden in Plain Sight: Detecting Technology-Enabled Financial Abuse in Intimate Partnerships

Abstract. The role of technology in connecting people with financial resources is growing. They offer new ways to apply for jobs, earn money, pay bills, send gifts, and track spending. However, survivors of intimate partner violence (IPV) — who may also experience technology-facilitated abuse — are especially vulnerable to risks of the digitization of financial resources. The purpose of this talk is to discuss how technologies actively facilitate and motivate abusers of IPV to exploit, restrict, monitor, and sabotage survivors' finances, while highlighting potential responses by financial services to this devastating form of abuse.

Camelia Simoiu is a Data Scientist at Waymo where her work focuses on developing computational models of human behavior and methodology for the safety evaluation of autonomous vehicles. Prior to Waymo, she completed a PhD in computational social science at Stanford University, where her research focused on designing algorithmic tools to aid human decision-making in cybersecurity and criminal justice. In her spare time, she likes to bike California's back roads and climb mountains.

Invited Talk: Who is targeted by email-based phishing and malware? Measuring factors that differentiate risk

Kevin Lee is a research scientist at J.P. Morgan AI Research, where he works on bettering user safety practices. Kevin recently joined after earning his Ph.D. in Computer Science from Princeton University, and was also affiliated with the Center for Information Technology Policy. His work in usable security has revealed flaws in authentication practices that impact millions of people, and has motivated ongoing legislation and improvements to companies' policies.

Kevin is currently involved in several user safety policy projects at JPMorgan Chase, including audits of existing security policies and exploration into financial abuse by intimate partners.

Invited talk: The Research-Practice Gap in User Authentication