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- Chawin Sitawarin (Meta Research), Ph.D., 2024, New Perspectives on Adversarially Robust Machine Learning Systems.
- Yizheng Chen (University of Maryland, College Park), Postdoc, 2023.
- Nabeel Hingun (HiddenLayer), M.S., 2023, Scaling Part Models: Challenges and Solutions for Robustness on Large Datasets.
- Nathan Malkin (University of Maryland, College Park), Ph.D., 2021, Privacy Controls for Always-Listening Devices.
- An Ju, Ph.D., 2021, Generative Models as a Robust Alternative for Image Classification: Progress and Challenges.
- Alan Rosenthal (Dexterity Capital), M.S., 2021, Improving the Efficiency of Robust Generative Classifiers.
- Henry Xu, M.S., 2021, Model-Agnostic Defense for Lane Detection Against Adversarial Attack.
- Zhanyuan Zhang (Petuum), M.S., 2021, Towards Characterizing Model Extraction Queries and How to Detect Them.
- Zachary Golan-Strieb (Duolingo), M.S., 2021, Towards Evaluating and Understanding the Adversarial Robustness of Random Transformation Defenses.
- Michael McCoyd, Ph.D., 2020, Background and Occlusion Defenses Against Adversarial Examples and Adversarial Patches.
- Grant Ho (UCSD), Ph.D., 2020, Thwarting Sophisticated Enterprise Attacks: Data-Driven Methods and Insights.
- Neil Shah (Workday), M.S., 2020. A Large-Scale Analysis of Attacker Activity in Compromised Enterprise Accounts.
- Steven Chen, M.S., 2019, Stateful detection of black box adversarial attacks.
- Nicholas Carlini (Google), Ph.D., 2018, Evaluation and Design of Robust Neural Network Defenses.
- Rebecca Portnoff (Thorn), Ph.D., 2018, The Dark Net: De-Anonymization, Classification and Analysis.
- Thurston Dang (MIT), Ph.D., 2017, Towards Improved Mitigations for Two Attacks on Memory Safety.
- Chris Thompson (Google), Ph.D., 2017, Large-Scale Analysis of Modern Code Review Practices and Software Security in Open Source Software.
- Lynn Tsai (Google), M.S., 2017, TurtleGuard: Helping Android Users Apply Contextual Privacy Preferences.
- Michael Theodorides (Yahoo), M.S., 2017, Breaking Active-Set Backward-Edge Control-Flow Integrity.
- Linda Lee (Zcash), M.S., 2016, Tor's Usability for Censorship Circumvention.
- Arjun Baokar,
M.S., 2016,
A Contextually-Aware, Privacy-Preserving Android Permission Model.
- Sakshi Jain (LinkedIn), M.S., 2014, Automated Discovery of User Trackers.
- Cynthia Sturton (UNC Chapel Hill), Ph.D., 2013, Secure Virtualization with Formal Methods.
- Erika Chin (Twitter), Ph.D., 2013, Helping Developers Construct Secure Mobile Applications.
- Matthew Finifter (Uber), Ph.D., 2013, Towards Evidence-Based Assessment of Factors Contributing to the Introduction and Detection of Software Vulnerabilities.
- Adrian Mettler (Fireeye), Ph.D., 2012, Language and Framework Support for Reviewably-Secure Software Systems.
- Adrienne Porter Felt (Google), Ph.D., 2012, Towards Comprehensible and Effective Permission Systems.
- Arel Cordero, Ph.D., 2010: Enabling More Meaningful Post-Election Investigations.
- David Molnar (Microsoft Research), Ph.D., 2009: Dynamic Test Generation for Large Binary Programs.
- Chris Karlof (Mozilla), Ph.D., 2009: Human Factors in Web Authentication.
- Karl Chen (D.E. Shaw), 2008.
- Ka-Ping Yee (Wave), Ph.D., 2007: Building Reliable Voting Machine Software.
- Naveen Sastry (McKinsey), Ph.D., 2007: Verifying Security Properties in Electronic Voting Machines.
- Umesh Shankar (Google). Ph.D., 2006: Bridging the Gap between People and Policies in Security and Privacy.
- Rob Johnson (VMWare). Ph.D., 2006: Verifying Security Properties using Type-Qualifier Inference.
- Hao Chen (U.C. Davis). Ph.D., 2004: Lightweight Model Checking for Improving Software Security.
- Jason Waddle (Google). M.S., 2004: Formalizing Secure Computation for Embedded Systems.
- Ben Schwarz, M.S., 2005: Model Checking An Entire Linux Distribution for Security Violations.