Nicholas C. LandolfiPh.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
Optimal dorfman group testing for symmetric distributionsarXiv preprint, 2023 arXiv - bibtex
Unsupervised language models for disease variant predictionML for Structural Biology and Learning Meaningful Representation of Life (Spotlight) Workshops, NeurIPS 2022. arXiv - PDF - talk - slides - poster
Probabilistic Modeling Using Tree Linear CascadesAmerican 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 PreferencesThe International Journal of Robotics Research (IJRR), 2021 arXiv - bibtex
Cloud Telemetry Modeling via Residual Gauss-Markov Random FieldsConference on Innovation in Clouds, Internet and Networks (ICIN). March, 2021. PDF - bibtex
A Model-based Approach for Sample-efficient Multi-task Reinforcement LearningarXiv preprint, 2019 PDF - arXiv - bibtex
Asking Easy Questions: A User-Friendly Approach to Active Reward LearningProceedings of the 3rd Conference on Robot Learning (CoRL), October 2019 PDF - arXiv - bibtex
Learning Reward Functions by Integrating Human Demonstrations and PreferencesProceedings of Robotics: Science and Systems (RSS), June 2019 PDF - arXiv - bibtex
Social Cohesion in Autonomous DrivingInternational 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 StateAutonomous Robots (AURO), 2018. PDF - bibtex
Exploring Active Human Goal Inference in Shared Autonomy and Autonomous DrivingRSS Workshop: Mathematical Models, Algorithms, and Human-Robot Interaction, 2017. PDF - bibtex
Unsupervised Language Models for Disease Variant Prediction (Dec. 2022) Efficient Disease Screening Using Group Testing and Symmetric Probability (Oct. 2022) PCA 2 Ways (for teaching EE263) (Aug. 2022) Tree Linear Cascades @ ACC (Jun. 2022) Probabilistic Modeling Using Tree Linear Cascades (Apr. 2022) Latent Variable Models for Genomic Data (Mar. 2022) Basic mathematical genomics (Jan. 2022) Dorfman-Rosenblatt Testing (Dec. 2021) Tree Densities (Mar. 2021) Cloud Telemetry Modeling (Mar. 2021) Tree Distributions (Jan. 2021) Directed Information (Oct. 2020) Causal Models (Jul. 2020)