AI/ML Lecture & Networking

​A monthly ML research colloquium by the New York Machine Learning Research Guild — this session in collaboration with our partner, Intuit.

​Diffusion Language Models

​Yair Schiff · PhD Candidate, Computer Science, Cornell University · NVIDIA

​Topic: TBD

​This session is a close-quarters look at that theory, with plenty of room to push back and dig in.

​About Yair

​Yair Schiff is a PhD candidate in computer science at Cornell University, based at Cornell Tech and advised by Volodymyr Kuleshov, and is currently at NVIDIA. Yair works on generative modeling, optimal transport, and AI for science.

​Recent work centers on diffusion language models, which generate text by iteratively unmasking tokens rather than writing left to right

• ​MDLM, a simple and effective masked diffusion recipe for language modeling (NeurIPS 2024)

• ​Guidance mechanisms for discrete diffusion models (ICLR 2025)

• ​Remasking for inference-time scaling, and an encoder-decoder design for faster training and inference (both NeurIPS 2025)

• ​d2, improved techniques for training reasoning diffusion language models (ICML 2026)

​Other work includes Caduceus (ICML 2024), a bidirectional, equivariant DNA language model, and DySLIM (ICML 2024) for learning stable models of chaotic systems.

​Before the PhD, Yair was a developer on IBM Watson Machine Learning, contributing to IBM Research's Trusted AI work and the winning entry of the 2020 VizWiz Grand Challenge. Yair has also been a student researcher at Google and a research intern at InstaDeep, and holds an MS in computer science from NYU Courant and a BA in economics from the University of Pennsylvania. Yair's honors include a Best Paper Award from IEEE JETCAS and a Harold & Inge Marcus PhD Fellowship.

​Who this is for

​NY-MLR colloquia are small by design. The room is capped, and we keep it that way so the discussion stays substantive and everyone in it can contribute.

​We'd love to see you if you're:

• ​A practitioner working seriously on ML, in industry or in a lab

• ​A researcher or graduate student in ML, mathematics, or an adjacent field

• ​A highly motivated student or self-directed learner of any age or background, with real depth of interest

​Sincere interest is enough to belong — that's one of our core principles. What we ask is that you come ready to engage. More on the Guild and our ethos at nymlr.com.

​Schedule

• ​5:00 – 5:30 PM · Doors open, arrivals, mingling

• ​5:30 – 6:30 PM · Talk

• ​6:30 – 7:00 PM · Moderated Q&A

• ​7:00 – 8:00 PM · After hours, mingling, wind-down

• ​8:00 PM · Lights out

​Venue: Manhattan NYC (Details TBD)

​A note on no-shows

​Seats are strictly limited by room capacity, and every in-person RSVP takes one from someone else who wanted it.

​If you register to attend in person and your plans change, please amend your registration or cancel on Luma. It takes ten seconds and frees the seat.

​Registering in person and not showing up without updating your RSVP will forfeit your access to future NY-MLR events. Please mark your attendance mode accurately.