Pascal Van Hentenryck

Last updated

Pascal Van Hentenryck (born 8 March 1963) is the A. Russell Chandler III Chair and Professor of Industrial and Systems Engineering at Georgia Tech. He is credited with pioneering advances in constraint programming and stochastic optimization, bridging theory and practice to solve real-world problems across a range of domains including sports scheduling, protein folding, kidney matching, disaster relief, power systems, recommender systems, and transportation. He has developed several optimization technologies including CHIP, [1] Numerica, [2] the Optimization Programming Language (OPL—now an IBM product), [3] and Comet. He has also published several books, including Online Stochastic Combinatorial Optimization, [4] Hybrid Optimization, [5] and Constraint-Based Local Search. [6]

Contents

Van Hentenryck is an AAAI and INFORMS fellow and has received numerous awards and honorary degrees for his contributions to optimization and artificial intelligence.

Career

Pascal Van Hentenryck was born in Belgium and obtained his PhD in Computer Science from the University of Namur in 1986. He joined Brown University’s Department of Computer Science in 1990 after spending four years at the European Computer-Industry Research Center where he led development of the CHIP system. In 1993, he was awarded an NSF National Young Investigator Award. Van Hentenryck spent the next several years at Brown developing new optimization technologies to address open problems in vehicle routing, disaster relief, [7] and power systems management. [8]

In 2009, Van Hentenryck co-founded Dynadec, [9] a spin-off company based on optimization technologies he developed at Brown. In 2012, Van Hentenryck left Brown to lead the 70-person Optimization Research Group at NICTA in Australia until its merger with CSIRO in 2015.

Van Hentenryck joined the University of Michigan in 2015. At Michigan, his work spanned several areas, including power systems, transportation, and computational social science. In 2017, Van Hentenryck ran the Seth Bonder Data Science Summer Camp for High School Students. In 2018, he launched RITMO, [10] a new transportation system for the University of Michigan’s campus. As of 2021, Van Hentenryck serves as the director of the NSF Artificial Intelligence Institute for Advances in Optimization (AI4OPT). [11]

Van Hentenryck has also taught a Massive Open Online Course on discrete optimization.

Honors and awards

In 2002, Van Hentenryck received the INFORMS ICS Award for research excellence in optimizations research and computer science, and in 2004, he was awarded an IBM Faculty Award. In 2006, Van Hentenryck received the ACP award for research excellence in Constraint Programming. In 2008, he was awarded an honorary doctorate from the University of Louvain, and in 2011, an honorary degree from the University of Nantes. He has received several best paper awards including at CP ‘03, CP ‘04, IJCAI ‘07, SEDE ‘09, AAAI ‘15, and CP ‘16. He has also received awards for outstanding contributions to teaching and education, including the Philip J. Bray Award for Teaching Excellence in 2010 at Brown, and the 2013 IFORS Distinguished Lecturer Award. He was elected to the 2016 class of Fellows of the Institute for Operations Research and the Management Sciences. [12]

Related Research Articles

Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. CSPs are the subject of research in both artificial intelligence and operations research, since the regularity in their formulation provides a common basis to analyze and solve problems of many seemingly unrelated families. CSPs often exhibit high complexity, requiring a combination of heuristics and combinatorial search methods to be solved in a reasonable time. Constraint programming (CP) is the field of research that specifically focuses on tackling these kinds of problems. Additionally, the Boolean satisfiability problem (SAT), satisfiability modulo theories (SMT), mixed integer programming (MIP) and answer set programming (ASP) are all fields of research focusing on the resolution of particular forms of the constraint satisfaction problem.

<span class="mw-page-title-main">John Cocke (computer scientist)</span> American computer scientist and mathematician

John Cocke was an American computer scientist recognized for his large contribution to computer architecture and optimizing compiler design. He is considered by many to be "the father of RISC architecture."

Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems.

In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution through a set of constraints that impose conditions that the variables must satisfy. A solution is therefore an assignment of values to the variables that satisfies all constraints—that is, a point in the feasible region.

Bart Selman is a Dutch-American professor of computer science at Cornell University. He is also co-founder and principal investigator of the Center for Human-Compatible Artificial Intelligence (CHAI) at the University of California, Berkeley, led by Stuart J. Russell, and co-chair of the Computing Community Consortium's 20-year roadmap for AI research.

CHIP is a constraint logic programming language developed by M. Dincbas, Pascal Van Hentenryck and colleagues in 1985 at the European Computer-Industry Research Centre (ECRC), initially using a Prolog language interface. It was the first programming language to implement constraint programming over finite domains, and subsequently to introduce the concept of global constraints.

Meinolf Sellmann is a German computer scientist, best known for algorithmic research, with a special focus on self-improving algorithms, automatic algorithm configuration and algorithm portfolios based on artificial intelligence, combinatorial optimization, and the hybridization thereof.

Murray Campbell is a Canadian computer scientist known for being part of the team that created Deep Blue; the first computer to defeat a world chess champion.

<span class="mw-page-title-main">Moshe Vardi</span> Israeli mathematicien and computer scientist

Moshe Ya'akov Vardi is an Israeli mathematician and computer scientist. He is the Karen Ostrum George Distinguished Service Professor in Computational Engineering at Rice University, United States. and a faculty advisor for the Ken Kennedy Institute. His interests focus on applications of logic to computer science, including database theory, finite model theory, knowledge of multi-agent systems, computer-aided verification and reasoning, and teaching logic across the curriculum. He is an expert in model checking, constraint satisfaction and database theory, common knowledge (logic), and theoretical computer science.

Holger H. Hoos is a German-Canadian computer scientist and a Alexander von Humboldt-professor of artificial intelligence at RWTH Aachen University. He also holds a part-time appointment as a professor of machine learning at Leiden University, and he is an adjunct professor at the Computer Science Department of the University of British Columbia, where he held a full-time professorial appointment from 2000 until 2016. His research interests are focused on artificial intelligence, at the intersection of machine learning, automated reasoning and optimization, with applications in empirical algorithmics, bioinformatics and operations research. In particular, he works on automated algorithm design and on stochastic local search algorithms. Since 2015, he is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and since 2020 a Fellow of the European Association for Artificial Intelligence (EurAI) as well as a Fellow of the Association for Computing Machinery (ACM).

David Leigh Waltz was a computer scientist who made significant contributions in several areas of artificial intelligence, including constraint satisfaction, case-based reasoning and the application of massively parallel computation to AI problems. He held positions in academia and industry and at the time of his death, was a professor of Computer Science at Columbia University where he directed the Center for Computational Learning Systems.

Jon Lee is an American mathematician and operations researcher, the G. Lawton and Louise G. Johnson Professor of Engineering at the University of Michigan. He is known for his research in nonlinear discrete optimization and combinatorial optimization.

<span class="mw-page-title-main">Francesca Rossi</span> Italian computer scientist

Francesca Rossi is an Italian computer scientist, currently working at the IBM Thomas J. Watson Research Center as an IBM Fellow and the IBM AI Ethics Global Leader.

<span class="mw-page-title-main">Shlomo Zilberstein</span>

Shlomo Zilberstein is an Israeli-American computer scientist. He is a Professor of Computer Science and Associate Dean for Research and Engagement in the College of Information and Computer Sciences at the University of Massachusetts, Amherst. He graduated with a B.A. in Computer Science summa cum laude from Technion – Israel Institute of Technology in 1982, and received a Ph.D. in Computer Science from University of California at Berkeley in 1993, advised by Stuart J. Russell. He is known for his contributions to artificial intelligence, anytime algorithms, multi-agent systems, and automated planning and scheduling algorithms, notably within the context of Markov decision processes (MDPs), Partially Observable MDPs (POMDPs), and Decentralized POMDPs (Dec-POMDPs).

Algorithm selection is a meta-algorithmic technique to choose an algorithm from a portfolio on an instance-by-instance basis. It is motivated by the observation that on many practical problems, different algorithms have different performance characteristics. That is, while one algorithm performs well in some scenarios, it performs poorly in others and vice versa for another algorithm. If we can identify when to use which algorithm, we can optimize for each scenario and improve overall performance. This is what algorithm selection aims to do. The only prerequisite for applying algorithm selection techniques is that there exists a set of complementary algorithms.

<span class="mw-page-title-main">Bruce Hajek</span> American electrical engineer

Bruce Edward Hajek is a Professor in the Coordinated Science Laboratory, the head of the Department of Electrical and Computer Engineering, and the Leonard C. and Mary Lou Hoeft Chair in Engineering at the University of Illinois Urbana–Champaign. He does research in communication networking, auction theory, stochastic analysis, combinatorial optimization, machine learning, information theory, and bioinformatics.

<span class="mw-page-title-main">Ashok Goel</span> Indian-American computer scientist

Ashok K. Goel is a professor of computer science and human-centered computing in the School of Interactive Computing at Georgia Institute of Technology, and the chief scientist with Georgia Tech's Center for 21st Century Universities. He conducts research into cognitive systems at the intersection of artificial intelligence and cognitive science with a focus on computational design and creativity. Goel is also the executive director of National Science Foundation's AI Institute for Adult Learning and Online Education and an editor emeritus of AAAI's AI Magazine.

<span class="mw-page-title-main">Carla Gomes</span> Portuguese-American computer scientist

Carla Pedro Gomes is a Portuguese-American computer scientist and professor at Cornell University. She is the founding Director of the Institute for Computational Sustainability and is noted for her pioneering work in developing computational methods to address challenges in sustainability. She has conducted research in a variety of areas of artificial intelligence and computer science, including constraint reasoning, mathematical optimization, and randomization techniques for exact search methods, algorithm selection, multi-agent systems, and game theory. Her work in computational sustainability includes ecological conservation, rural resource mapping, and pattern recognition for material science.

<span class="mw-page-title-main">Thomas Dean (computer scientist)</span> American computer scientist

Thomas L. Dean is an American computer scientist known for his work in robot planning, probabilistic graphical models, and computational neuroscience. He was one of the first to introduce ideas from operations research and control theory to artificial intelligence. In particular, he introduced the idea of the anytime algorithm and was the first to apply the factored Markov decision process to robotics. He has authored several influential textbooks on artificial intelligence.

References

  1. Dincbas, Mehmet; Hentenryck, Pascal Van; Simonis, Helmut; Aggoun, Abderrahmane; Herold, Alexander (1988-05-23). The CHIP System: Constraint Handling In Prolog. Springer-Verlag. pp. 774–775. ISBN   978-3540193432.
  2. Numerica. MIT Press. 25 April 1997. ISBN   9780262720274 . Retrieved 2018-05-03.
  3. "IBM Knowledge Center". www.ibm.com. Retrieved 2018-05-03.
  4. Online Stochastic Combinatorial Optimization. MIT Press. 13 October 2006. ISBN   9780262220804 . Retrieved 2018-05-03.
  5. Hybrid Optimization - The Ten Years of CPAIOR | Pascal van Hentenryck | Springer. Springer Optimization and its Applications. Springer. 2011. ISBN   9781441916433.
  6. Constraint-Based Local Search. MIT Press. 23 September 2005. ISBN   9780262220774 . Retrieved 2018-05-03.
  7. Hentenryck, Pascal Van; Bent, Russell; Coffrin, Carleton (2010-06-14). "Strategic Planning for Disaster Recovery with Stochastic Last Mile Distribution". Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. Lecture Notes in Computer Science. Vol. 6140. Springer, Berlin, Heidelberg. pp. 318–333. Bibcode:2010LNCS.6140..318V. CiteSeerX   10.1.1.227.4402 . doi:10.1007/978-3-642-13520-0_35. ISBN   9783642135194.
  8. Carleton, Coffrin; Pascal, Van Hentenryck (2012-06-16). "A Linear-Programming Approximation of AC Power Flows". arXiv: 1206.3614 [cs.AI].
  9. Roushe, Wade (2009-06-15). "Getting Better Answers Faster: Providence Software Startup Dynadec Goes Way Beyond the Traveling Salesman Problem".
  10. "New app reinvents University bus system to be more like Uber". The Michigan Daily. Retrieved 2018-05-03.
  11. "Team Led by ISyE's Pascal Van Hentenryck Awarded $20M NSF Grant to Fund Center for Study of AI and Optimization". www.isye.gatech.edu.
  12. Fellows: Alphabetical List, Institute for Operations Research and the Management Sciences, archived from the original on 2019-05-10, retrieved 2019-10-09