About

Hi there! I’m Peter, a PhD Candidate in Data Science at Worcester Polytechnic Institute. I work with Professor Elke Rundensteiner as part of the DAISY Lab. 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!

KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction

Lei Ma, Jinyang Liu, Tieying Zhang, Peter M. VanNostrand, Dennis M. Hofmann, Lei Cao, Elke A. Rundensteiner, and Jianjun Chen

Proceedings of the 33rd IEEE International Conference on Data Engineering (ICDE '26)

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)

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)

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)

Reproducibility Report for ACM SIGMOD 2024 Paper: 'FACET: Robust Counterfactual Explanation Analytics'

Peter M. VanNostrand, Konstantinos Kanellis, Wan Shen Lim, and Donghyun Sohn

Reproducibility Reports of the 2024 International Conference on Management of Data (SIGMOD ARI Reports ’24)

Actionable Recourse for Automated Decisions: Examining the Effects of Counterfactual Explion anatType 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