Distinguished Speaker Series: Not All Experimental Questions Are Created Equal: Accelerating Scientific Data to Knowledge (SD2K) Transformation via Active Learning and Artificial Intelligence
Not All Experimental Questions Are Created Equal: Accelerating Scientific Data to Knowledge (SD2K) Transformation via Active Learning and Artificial Intelligence (AI)
Prof. Simon Kasif
Deciphering and understanding nature is one of the greatest challenges of science. Living organisms evolve their parts and systems to produce functions and behaviors needed to survive and flourish in natural and synthetic environments. Evolution has been often associated with life-long learning.
Scientists have been documenting and attempting to understand nature since the beginning of human existence. Thus, both knowledge and understanding of nature is part of our cumulative intelligence. It has been argued that intelligence itself is a product of natural evolution summarized nicely in the following quote "We see things as we are not as they are". We would even dare to conjecture that the necessity to decode and respond to nature and complex behaviors is what drove human intelligence. Thus, "applying" AI to formalizing and understanding life may lead to a better understanding of nature, learning, and intelligence. One of the many important distinctions between us and other living species is our ability to conduct and advance complex science.
In this talk we describe a first of a kind community project called “Computational Bridges to Experiments” (COMBREX). COMBREX aims to drive biological experiments by an intelligent system that performs many tasks including asking the most informative questions that catalyze the fastest advances in knowledge or other priorities. The prioritization of questions is driven by a rudimentary Active Learning framework. The project features a combination of many technical and logistic ideas including a new funding scheme, citizen’s science, tracking provenance and knowledge gaps, and more. COMBREX built a community of experimental and computational scientists in many universities (including Tel Aviv U.) working in concert to advance the understanding of protein function. We believe that COMBREX serves as useful and general human centered and interactive model for an open society of human and robot scientists. COMBREX was inspired and co-founded by Dr. Richard Roberts and benefitted from ideas and contributions from numerous scientists.
Bio: Prof. Simon Kasif was trained in AI and CS. His mentors in AI were Profs. Azriel Rosenfeld and Jack Minker (among the pioneers in Computer Vision and Deductive Databases respectively). He directed the AI Lab at Johns Hopkins University engaged in both theory and development of parallel AI systems, Reasoning and Machine Learning. In 1999, he joined the Human Genome Project (MIT Genome Center and CRL). He joined Boston University as the founding co-director of the Center for Advanced Genome Technology (with Charles DeLisi who started the Human Genome Project at DOE). Prof. Kasif held appointments at Children’s Hospital (Harvard-MIT program in Health Sciences and Technology) and Joslin Diabetes Center at Harvard Medical School. He also held advisory and consulting positions in academia (e.g., Columbia U., Carnegie Mellon U., U. Chicago, Johns Hopkins U.) and industry (start-ups and large companies). In Computational and Systems Biology, his contributions include popularizing graphical models for molecular biology (1992-), a widely used system for gene finding, introduction of network based protein function prediction, COMBREX and AI/network models for the study of aging, wellness and disease staging.
Host: Prof. Roded Sharan, School of Computer Science.