Keum Joo Kim

Research Interests

Discrete optimization; nonlinear decision-making; innovative reasoning; emergent behavior; intent inferencing; bioinformatics

Education

• BS, Computer Science and Engineering, Ewha Womans University, South Korea
• MS, Computer Science and Engineering, Ewha Womans University, South Korea
• PhD, Computer Engineering, Dartmouth 2009

Research Projects

• Connections Hypothesis Provider in NCATS

  Connections Hypothesis Provider in NCATS

  Connections Hypothesis Provider (CHP) service built by Dartmouth College (PI – Dr. Eugene Santos) and Tufts University (Co-PI – Joseph Gormley) in collaboration with the National Center for Advancing Translational Sciences (NCATS). CHP aims to leverage clinical data along with structured biochemical knowledge to derive a computational representation of pathway structures and molecular components to support human and machine-driven interpretation, enable pathway-based biomarker discovery, and aid in the drug development process. In its current version, CHP supports queries relating to genetic, therapeutic, and patient clinical features (e.g. tumor staging) contribution toward patient survival, as computed within the context of our test pilot: a robust breast cancer dataset from The Cancer Genome Atlas (TCGA). We are using this as a proving ground for our system’s basic operations as we work to incorporate structured pathway knowledge and pathway analysis methods into the tool.

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• Nonlinear decision-making

  Nonlinear decision-making

  To advance the science of decision-making as it pertains to how people learn to make decisions and how this process can be captured computationally, we are specifically addressing the challenge of how nonlinear decisions can be learned from data, experience, and even interactions with other decision-makers. Nonlinear thinking is a prized ability we, humans, have that is ubiquitously applied across any and all domains when the problems are challenging, and known solutions or ways of addressing the problems all fail to provide an adequate solution – e.g., All available choices are bad choices, must we settle for the least bad one? The ability to discover a new choice has been called being nonlinear, innovative, intuitive, emergent, or “outside-the-box.” It is well-documented that humans can often excel at such thinking in situations when there is a scarcity/overflow of data, significant uncertainty, and numerous contradictions in what is known or provided. However, how this can be replicated computationally for a machine has yet to be fully addressed or understood in extant research.

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