ZIMIN INSTITUTE

Artificial Intelligence, Machine Learning, Natural Language Semantics, Anytime Learning, Active and Selective Learning, Information retrieval, Multi-agent Systems, Adversary search, Opponent Modeling, Resource-bounded reasoning, Cost-sensitive Learning.

Contributing the theory of machine learning through connections to information theory, and computational complexity theory.

My research focuses on AI and machine learning, with an emphasis on robotics applications. My long term goal is to bring robots into human-centered domains such as homes and hospitals. Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks, and how to learn behavior from interaction in an interpretable and safe manner. Most of my work falls under the framework of reinforcement learning, and its connections to representation learning and planning.

Proteomics; Cancer Vaccines; Antigen Processing and Presentation; Big data of proteomics and peptidomics; Bioinformatics of proteomics and mass spectrometry.

Human behavior in the context of organizational service interactions. Big data and automation tools are used to objectively analyze emotion and behavior of participants in online conversations.

Closed-loop software and hardware platforms driving chemical discovery through automated hypothesis generation, refinement, validation, and revision, resulting in predictive chemical kinetic models.