Nicholas C. Landolfi

Ph.D. Candidate, Computer Science Artificial Intelligence Laboratory (SAIL) Information Systems Laboratory (ISL) Stanford University For the immunologist (my father) see Nicholas F. Landolfi.


I am a Ph.D. candidate in the Computer Science department at Stanford. I received a B.S. in Electrical Engineering & Computer Science with honors at the University of California, Berkeley in 2018. My research focuses on algorithms for control, optimization and machine learning.


Honors & Awards

Stanford Graduate Fellowship, 2021 National Defense Science and Engineering Graduate Fellowship, 2018 Regents' and Chancellors Research Fellowship, 2017 Arthur M. Hopkin Award, 2017 Regents' and Chancellor's Scholarship, 2014 Xerox Award for Innovation and Information Technology, 2014

Open Source

midGPT: simple hackable repo for FSDP pretraining 1B+ parameter base models


Optimal dorfman group testing for symmetric distributions

Nicholas C. Landolfi, Sanjay Lall arXiv preprint, 2023 arXiv - bibtex

Unsupervised language models for disease variant prediction

Allan Zhou*, Nicholas C. Landolfi*, Daniel C. O’Neill ML for Structural Biology and Learning Meaningful Representation of Life (Spotlight) Workshops, NeurIPS 2022. arXiv - PDF - talk - slides - poster

Probabilistic Modeling Using Tree Linear Cascades

Nicholas C. Landolfi, Sanjay Lall American Control Conference (ACC), June 2022 arXiv - talk - slides - longer slides - bibtex - github code

Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences

Erdem Bıyık, Dylan P. Losey, Malayandi Palan, Nicholas C. Landolfi, Gleb Shevchuk, Dorsa Sadigh The International Journal of Robotics Research (IJRR), 2021 arXiv - bibtex

Cloud Telemetry Modeling via Residual Gauss-Markov Random Fields

Nicholas C. Landolfi, Daniel C. O’Neill, Sanjay Lall. Conference on Innovation in Clouds, Internet and Networks (ICIN). March, 2021. PDF - bibtex

A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning

Nicholas C. Landolfi, Garrett Thomas, Tengyu Ma arXiv preprint, 2019 PDF - arXiv - bibtex

Asking Easy Questions: A User-Friendly Approach to Active Reward Learning

Erdem Bıyık, Malayandi Palan, Nicholas C. Landolfi, Dylan P. Losey, Dorsa Sadigh Proceedings of the 3rd Conference on Robot Learning (CoRL), October 2019 PDF - arXiv - bibtex

Learning Reward Functions by Integrating Human Demonstrations and Preferences

Malayandi Palan*, Nicholas C. Landolfi*, Gleb Shevchuk, Dorsa Sadigh Proceedings of Robotics: Science and Systems (RSS), June 2019 PDF - arXiv - bibtex

Social Cohesion in Autonomous Driving

Nick Landolfi, Anca Dragan International Conference on Intelligent Robots and Systems (IROS), 2018 PDF - arXiv - bibtex

Planning for Cars that Coordinate with People: Leveraging Effects on Human Actions for Planning and Active Information Gathering over Human Internal State

Dorsa Sadigh, Nick Landolfi, S. Shankar Sastry, Sanjit A. Seshia, Anca D. Dragan Autonomous Robots (AURO), 2018. PDF - bibtex

Exploring Active Human Goal Inference in Shared Autonomy and Autonomous Driving

Nick Landolfi, Anca D. Dragan RSS Workshop: Mathematical Models, Algorithms, and Human-Robot Interaction, 2017. PDF - bibtex