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: Speed, Guidance, and In-Place Editing with Diffusion Models for Text

Progress in diffusion models for discrete data has posed a useful alternative to the dominant autoregressive approach to language modeling. This new paradigm holds the promise of accelerated inference, more steerable generation, and higher quality outputs. In this talk we will discuss a prominent approach for diffusion language modeling, masked diffusion, and explain why it also poses a challenge to materializing the promised benefits of diffusion for text. We will then cover recent work for mitigating the issues of masked diffusion and different model parameterizations that can help  realize the benefits of speed, control, and iterative refinement of diffusion language models.

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.