Draft:Harry B. Hunt, III

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• Comment: I am uncertain about this as while he has reasonable citations, there are no indications of major awards. Ldm1954 (talk) 14:51, 2 January 2025 (UTC)

Harry Bowen Hunt, III (born July 01, 1949) is an American computer scientist and professor at the University at Albany, SUNY. He is known for his contributions to theoretical computer science, particularly in the areas of complexity theory, automata theory, and formal language theory. Over the course of his career, Hunt has authored and co-authored research papers in journals such as the Journal of the ACM, SIAM Journal on Computing, Acta Informatica, and Theoretical Computer Science.^[3]

Hunt graduated with a B.S and M.S. in mathematics from Case Western Reserve University and then received his Ph.D. from Cornell University in 1973 after completing a doctoral dissertation, titled On the Time and Tape Complexity of Languages, under the supervision of John Hopcroft.^[1] At Cornell University, Hunt and Juris Hartmanis published a paper studying whether deterministic and non-deterministic context-sensitive languages are equivalent.^[4] Their work is a contribution to computational complexity theory. Hunt is now a professor of Computer Science at the University at Albany, which is part of the State University of New York.^[5] His collaborations with eminent scientists, including Richard E. Stearns, a co-recipient of the 1993 Turing Award^[6], and Daniel J. Rosenkrantz, have produced results that have advanced the field of theoretical computer science, including the development of NC-approximation schemes for various NP- and PSPACE-Hard problems in geometric graphs.^[7]. Hunt and Stearns also investigated sum-of-products problems where the addition and multiplication operators are drawn from commutative semirings. They demonstrated that such problems can be solved using fewer operations if the problem exhibits favorable structural properties captured by a structure tree. In this context, "favorable structural properties" refers to either a small weighted depth for top-down processing or a small channelwidth for bottom-up processing. Channelwidth corresponds to the concept of treewidth when sum-of-products problems are visualized graphically, but the structure tree offers a clearer perspective for algebraic interpretations. Furthermore, with the inclusion of an algebraic condition, they extended the structure tree framework to handle quantified formulas. Finally, they established a strong connection between sub-linear space complexity and OR-of-AND problems with sub-linearly bounded treewidth.^[6]