Agentic AI—systems that move beyond generating content to taking autonomous, goal-directed actions in real time—is top of mind in the news, in research, and in business.
MIT CSAIL has a very strong Agentic AI research ecosystem, with PIs exploring last mile AI, security, deployment, cost, physical AI, and so many aspects of the agentic space.
Join us for a day-long summit and be a part of what’s next in artificial intelligence.
Add to calendarAmerica/New_YorkMIT CSAIL Agentic AI Summit11/09/2026
Agentic AI—systems that move beyond generating content to taking autonomous, goal-directed actions in real time—is top of mind in the news, in research, and in business.
MIT CSAIL has a very strong Agentic AI research ecosystem, with PIs exploring last mile AI, security, deployment, cost, physical AI, and so many aspects of the agentic space.
Join us for a day-long summit and be a part of what’s next in artificial intelligence.
Engage directly with MIT CSAIL researchers defining the next generation of AI. What comes next with agents? From research talks, to industry perspective, to small group lunches you’ll leave with a clear view of what Agentic AI can mean for your organization, where the technology is heading, and what questions remain unsolved.
This event is for CSAIL Alliances Members, the MIT Community, and companies interested in connecting with CSAIL research.
Update: The in-person event is at capacity and only CSAIL Alliances members may join in-person at this date. Registration for the live stream is still open.
Join CSAIL Alliances member Susquehanna in Stata to explore how deep learning has become central to modern quantitative trading.
Add to calendarAmerica/New_YorkSusquehanna Tech Talk: Deep Learning in Quantitative Trading 11/16/2026
Join CSAIL Alliances member Susquehanna in Stata to explore how deep learning has become central to modern quantitative trading.
Susquehanna machine learning researcher Tony Yang will share some of the research problems the team works on, from modeling large-scale market data to developing models that can adapt to changing market conditions. He’ll discuss what makes financial markets different from more familiar machine learning data modalities and benchmarks, how research ideas translate into trading signals, and how the academic research background has helped him explore new and interesting research directions in industry.
Whether you're considering an internship or full-time role, or have never seriously considered research outside academia, you're invited to join this open conversation. Tony and other members of the Susquehanna research team will offer Q&A and informal discussion after the talk.
PhD students in EECS, mathematics, statistics, physics, and related fields are especially welcome. No prior knowledge of finance or trading is needed.
