Lucas Rosenblatt

I am a fourth year PhD candidate at NYU where I am advised by Julia Stoyanovich and work closely with Christoper Musco. At NYU, I am affiliated with the NYU Center for Responsible AI and the Theoretical Computer Science @ NYU Group. I work closely with Bill Howe (of UW) and with the Volitional AI Lab (also at UW). I also work with Rachel Cummings (of Columbia) and her group. I am grateful to be supported by a NSF Graduate Research Fellowship.

Broadly, my work aims to answer open questions on data privacy, algorithmic fairness, climate, AI with social impact, and LLMs, all with an eye towards doing social good.

I was formerly a member of the Microsoft AI rotational program, working out of the New England Research and Development lab (and remotely during COVID!). In 2019 I graduated from Brown University, where I wrote a thesis about AI and self-data collection.

I happen to own a school bus that I’ve spent a lot of time converting into a mobile home and finding a permanent home for it in rural Vermont. If you like, I’ll give you some great reasons to buy a bus, and arguably some better reasons not to. I also make movies and write as much as I can.

Service

Reviewer: ICLR 2025, AISTATS 2025, Neurips 2024, SOSA 2024, CHI 2023

news

Oct 2, 2024	Teal Witter and I just posted our preprint (under submission): FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic Fairness. Really excited about some clever evaluation schemes for assessing fairness AND uncertainty under one umbrella benchmark!
Aug 11, 2024	New work that I’m very excited about, Sharper Bounds for Chebyshev Moment Matching with Applications to Differential Privacy and Beyond with Chris Musco, Cam Musco, and Apoorv Vikram Singh. Of the applications of some cool theory in this work: Chebyshev moment matching is a promising mechanism for learning private synthetic distributions! We presented an abstract of the work at TPDP 2024, and the full version is under submission.
Mar 30, 2024	Our paper Laboratory-Scale AI: Open-Weight Models are Competitive Even in Low-Resource Settings was accepted to FAccT 2024. This work was led by the indomitable Robert Wolfe; much credit to him, and happy to have been part of the team!
Feb 5, 2024	Excited to be organizing the Spring iteration of NYC Privacy Day, to be held at NYU on April 19th, 2024. Privacy Day is a seasonal event that unites privacy folks from NYC institutions; hosting rotates between Columbia, NYU, Google and Cornell Tech. RSVP through the website if you can make it!
Dec 11, 2023	Two papers forthcoming at AAAI 2024: the first, A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy is with Julia Stoyanovich and Chris Musco, and the second, I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives with Teal Witter.
Dec 4, 2023	I gave an invited talk at the first Columbia NYC Privacy Day. Thanks Rachel Cummings for inviting me (and for organizing)!
Nov 6, 2023	I gave a short talk at the NYU-Kaist Inclusive AI workshop.
Oct 27, 2023	Our paper Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology was accepted to the Tackling Climate Change with Machine Learning @ NeurIPS 2023 Workshop! Very fun collaboration with Bin Han and Bill Howe from the University of Washington, and Theresa Crimmins and Erin Posthumus from the USA-NPN.
Sep 1, 2023	Our paper Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy received the Best Experiment, Analysis, & Benchmark Paper (Runner-up) award at VLDB 2023!
Jul 7, 2023	Very happy to say that our paper Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy was accepted into the Proceedings of VLDB 2023! This was a huge team effort, so a big thanks to everyone on the paper for over a year of hard work : )