Tommi S. Jaakkola received M.Sc. in theoretical physics from Helsinki University of Technology, 1992, and Ph.D. from MIT in computational neuroscience, 1997. Following a postdoctoral position in computational molecular biology (DOE/Sloan fellow, UCSC) he joined the MIT EECS faculty 1998.
His research interests include many aspects of machine learning, statistical inference and estimation, and analysis and development of algorithms for various modern estimation problems such as those involving predominantly incomplete data sources. His applied research focuses on problems in natural language processing, computational chemistry, as well as computational functional genomics.
Our goal is to develop methods that can "explain" the behavior of complex machine learning models, without restricting their power. We seek explanations that are simple, robust and grounded in statistical analysis of the model's behavior.
We propose a novel aspect-augmented adversarial network for cross-aspect and cross-domain adaptation tasks. The effectiveness of our approach suggests the potential application of adversarial networks to a broader range of NLP tasks for improved representation learning, such as machine translation and language generation.
We develop statistical models that are prescriptive rather than predictive/descriptive. From an observational dataset, our methods learn to automatically identify beneficial actions that will improve outcomes, rather than requiring human-made decisions.
When organic chemists identify a useful chemical compound — a new drug, for instance — it’s up to chemical engineers to determine how to mass-produce it. There could be 100 different sequences of reactions that yield the same end product. But some of them use cheaper reagents and lower temperatures than others, and perhaps most importantly, some are much easier to run continuously, with technicians occasionally topping up reagents in different reaction chambers.