Patrick Jaillet




Dr. Patrick Jaillet is the Dugald C. Jackson Professor in the Department of Electrical Engineering and Computer Science and a member of the Laboratory for Information and Decision Systems at MIT. He is also co-Director of the MIT Operations Research Center. He was Head of Civil and Environmental Engineering at MIT from 2002 to 2009, where he currently holds a courtesy appointment. From 1991 to 2002 he was a professor at the University of Texas in Austin, the last five years as the Chair of the Department of Management Science and Information Systems within the McCombs School of Business School. He co-founded and was Director of UT Austin's Center for Computational Finance. Before his appointment in Austin, he was a faculty and a member of the Center for Applied Mathematics at the Ecole Nationale des Ponts et Chaussée in Paris. He received a Diplôme d'Ingénieur from France (1981), and then came to MIT where he received an SM in Transportation (1982) followed by a PhD in Operations Research (1985).

Dr. Jaillet's research interests include on-line problems; real-time and dynamic optimization; network design and optimization; and probabilistic combinatorial optimization. His research is currently being funded by NSF, ONR, and Singapore. Professor Jaillet's teaching includes subjects such as algorithms; mathematical programming; network science and models; and probability. Dr. Jaillet's consulting works include supply chain strategy, logistics and distribution optimization, electronic marketplace design, and development of optimization solutions in various industries, including financial, defense, healthcare, and information technology.

Dr. Jaillet was a Fulbright Scholar in 1990 and has received several awards including most recently the Glover-Klingman Prize. He is a Fellow of the Institute for Operations Research and Management Science Society (INFORMS) and a member of the Society for Industrial and Applied Mathematics (SIAM). He is currently an Associate Editor for Networks, Transportation Science, and Naval Research Logistics, and has been an Associate Editor for Operations Research from 1994 until 2005.

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Jan 17th 2017

An introduction to probabilistic models, including random processes and the basic elements of statistical inference. The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.

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