Akash Srivastava
I am a PI at the MIT-IBM AI Research Lab, and the Chief Architect of Large Language Model Alignment at IBM Research, where I (1) oversee the tuning and alignment of IBM’s foundation models and (2) lead the synthetic data generation efforts. My lab is located on campus, at 314 Main St (new MIT Museum building) in Cambridge.
Before joining MIT-IBM, I obtained my PhD at the University of Edinburgh where I worked with Prof Charles Sutton and Prof Michael U. Gutmann on variational inference for generative models and deep learning.
News:
- I am hiring! If you work on alignment of LLMs and want to join my team (full-time or as a research intern (PhD Students)) to help us train very large language models, please reach out!
- 7 papers accepted at Neurips 2023
- Read about how my team and colleagues are working on alignment of LLMs at IBM Research
- Read about how MIT-IBM lab and IBM Research are using synthetic data generation methods to tackle real-world problems in this blog post. It also features work from our work on generative models for engineering design problems.
- Our project on generative models for inverse linkage synthesis was recently featured in MIT’s spectrum magazine.
- Read about our on-going work on making the last mile deliveries greener, in this blog post
Current Projects
- PI, MIT-IBM, 2023: Generative Modeling for Complex Mechanical Systems with Constraints. w/ Prof. Faez Ahmed (MIT)
- PI, MIT-IBM, 2023: Synthetic data and randomness in business and societal decision-making. w/ Prof. Dean Eckles (MIT)
- PI, MIT-IBM, 2023: Generative active learning of atomistic simulators for silica materials. w/ Prof. Rafael Gomez-Bombarelli (MIT)
- PI, MIT-IBM, 2023: Rethinking the vehicle routing problem under the lens of modern machine learning techniques. w/ Dr. Matthias Winkenbach (MIT)
- co-PI, MIT-IBM, 2023: Teaching Foundation Models 3D. Led by Prof. Vincent Sitzmann (MIT) and Dr. Leonid Karlinsky (MIT-IBM Research)
- PI, Climate Change AI, 2022: Towards greener last-mile operations: Supporting cargo-bike logistics through optimized routing of multi-modal urban delivery fleets.
- PI, MIT-IBM, 2021: Hybrid Generative Models. w/ Prof. Faez Ahmed (MIT)
- Co-PI, MIT-IBM, 2021: Representation Learning as a Tool for Causal Discovery. Led by Prof. Caroline Uhler (MIT) and Dr. Kristjan H Greenewald (MIT-IBM Research)
- PI, MIT-IBM, 2020: Learning Priors for Transfer. w/ Prof. Pulkit Agarwal (MIT)
- Co-PI, DARPA, 2019: Machine Common Sense. Led by Prof. Josh Tenenbaum (MIT) and Dr. Dan Gutfreund (MIT-IBM Research)