Lucas Rosenblatt

PhD Candidate, Center for Responsible AI @NYU.

I am a third 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 also work closely with Bill Howe (of UW) and with the Volitional AI Lab (also at UW). 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, 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.


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:
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 : )
Apr 13, 2023 I gave a talk at the excellent UCLA Synthetic Data Workshop. Thank you so much Guang Cheng for the invite and for organizing! Slides to be posted soon.
Mar 29, 2023 Very excited to have been awarded a 2023 NSF Graduate Research Fellowship!