Hi there! I’m Peter, a PhD Candidate in Data Science at Worcester Polytechnic Institute. My research focuses on building systems and technologies for the ethical and responsible use of machine learning with an eye towards using data science for social good. My most recent works study generating and evaluating different types of explanations to bring transparency and recourse to consequential automated decisions.

As a researcher with an engineering background, I’m passionate about expanding on emerging concepts in machine learning to build systems and solutions that are practically useful to real users. I’ve leveraged this background to become a fast learner and taken technologies from initial research to design and development of scalable systems and human-computer interfaces to evaluation through A/B crowd sourced user testing.

My research has been published in conferences such as FAccT, SIGMOD, KDD, and VLDB with a variety of incredible collaborators. I’ve also had the honor of serving as a mentor to several undergraduate teams and new graduate students.

Learn more about my work here or check out my CV for more about me!

Publications

Click here for more detail about my work!

 Agree to Disagree: Robust Anomaly Detection with Noisy Labels
Dennis M. Hofmann, Peter M. VanNostrand, Lei Ma, Huayi Zhang, Joshua C. DeOliveira, Lei Cao, Elke A. Rundensteiner
Proceedings of the ACM on Management of Data 2025 (SIGMOD ‘25)

 Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data
Lei Ma, Lei Cao, Peter M. VanNostrand, Dennis M. Hofmann, Elke A. Rundensteiner
Proceedings of the ACM on Management of Data 2025 (SIGMOD ‘25)

 Counterfactual Explanation Analytics: Empowering Lay Users to Take Action Against Consequential Automated Decisions
Peter M. VanNostrand, Dennis M. Hofmann, Lei Ma, Belisha Genin, Randy Huang, and Elke A. Rundensteiner
Proceedings of the VLDB Endowment (VLDB ‘24)

 Actionable Recourse for Automated Decisions: Examining the Effects of Counterfactual Explanation Type and Presentation on Lay User Understanding
Peter M. VanNostrand, Dennis M. Hofmann, Lei Ma, Elke A. Rundensteiner
Proceedings of ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘24)

 FACET: Robust Counterfactual Explanation Analytics
Peter M. VanNostrand, Huayi Zhang, Dennis M. Hofmann, Elke A. Rundensteiner
Proceedings of the ACM on Management of Data 2023 (SIGMOD ‘23)

 A Demonstration of AutoOD: A Self-Tuning Anomaly Detection System
Dennis Hofmann, Peter VanNostrand, Huayi Zhang, Yizhou Yan, Lei Cao, Samuel Madden, Elke Rundensteiner
Proceedings of the VLDB Endowment 2022 (VLDB ‘22)

 ELITE: Robust Deep Anomaly Detection with Meta Gradient
Huayi Zhang, Lei Cao, Peter VanNostrand, Samuel Madden, Elke A. Rundensteiner
Proceedings ACM SIGKDD Conference on Knowledge Discovery & Data (KDD ‘21)

 Confidential Deep Learning: Executing Proprietary Models on Untrusted Devices
Peter M. VanNostrand, Ioannis Kyriazis, Michelle Cheng, Tian Guo, Robert J. Walls
arXiv:1908.10730 2019

Education

PhD in Data Science

Worcester Polytechnic Institute, Ongoing

Masters in Data Science

Worcester Polytechnic Institute, 2022

BS in Electrical and Computer Engineering

University at Buffalo, 2016