Patrick Jaillet - Research

Please contact me directly if you are interested in learning more about one of these activities.

main interests these days:

online and data-driven optimization problems
dynamic and real-time optimization in networks
applications: internet, transportation, energy, finance

research group:


Setareh Borjian, master student, MIT CEE & ORC, 2012-now
Maximilien Burq, doctoral student, MIT ORC, starting 9/2014
Jie Chen, post-doc, SMART, Singapore, 2013-now
Arthur Flajolet, doctoral student, MIT ORC, 2013-now
Virgile Galle, master student, MIT ORC, 2013-now
Chong Yang Goh, doctoral student, MIT ORC, 2012-now
Swati Gupta, doctoral student, MIT ORC, 2011-now
Dawsen Huang, doctoral student, MIT EECS, 2011-now
Sanjay Jena, post-doc, SMART, Singapore, starting 9/2014
Nikita Korolko, doctoral student, MIT ORC, 2012-now
Maokai Lin, doctoral student, MIT ORC, 2009-now
Yi Yin Ma, master student, MIT LGO-EECS, 2014-now
Sebastien Martin, doctoral student, MIT ORC, starting 9/2014
Andrew Mastin, doctoral student, MIT EECS, 2010-now
Konstantina Mellou, doctoral student, MIT ORC, starting 9/2014
Phong Nguyen, doctoral student, NUS CS, Singapore, 2013-now
Ali Oran, post-doc, SMART, Singapore, 2011-now
Xiaomin Wang, undergraduate-super-urop, MIT EECS, starting 9/2014


Vahideh Manshadi, post-doc, MIT EECS and ORC, 2011-14 (now, Yale University)
Augusta Niles, master student, MIT LGO-EECS, 2013-14
Thibaut Vidal, post-doc, MIT EECS and ORC, 2013-14 (now, Univ. Rio de Janeiro, Brazil)
Yossiri Adulyasak, post-doc, SMART, Singapore, 2013-14 (now, JDA Software, Montreal)
David Wyrobnik, undergraduate, MIT EECS, 2013
Jin Hao Wan, M.Eng., MIT EECS and Math, 2011-13
Iain Dunning, doctoral student, MIT ORC, 2011-13
Xin Lu, doctoral student, MIT ORC, 2009-13 (now, Amazon, Seattle)
Nicolai Ludvigsen, undergraduate-urop, MIT EECS, 2012
Rico Zenklusen, post-doc, MIT EECS and Math, 2011-12 (now, ETH Zurich)
Christian Therkelsen, m.eng, MIT EECS, Spring 2011
Shen Shen, master student, MIT EECS, 2011-12
Brian Crimmel, master student, MIT ORC/Draper, 2010-12
Jacob Cates, master student, MIT ORC/Draper, 2009-11

research interns

Antoine Legrain, Ecole Centrale de Paris, MIT EECS, 1/2011-7/2011
Thibault Lehouillier, ENSIMAG, MIT EECS, 2/2011-8/2011
Pierre Jeremie, Ecole Polytechnique Paris, MIT EECS, 3/2011-6/2011
Alexandre Hollocou, Ecole Polytechnique Paris, MIT EECS 4/2012-8/2012
Chen Yao Liu, NUS, SMART 6/2012-8/2012
Yee Sian Ng, NUS, SMART 6/2013-5/2014
Maximilien Burq, Ecole Polytechnique, MIT EECS, 4/2014-8/2014

current research projects:

Below are some specific topics that could each lead to the definition of several research topics of interests for either interns/urops, graduate students, and/or postdocs.
1. Online Traveling Salesman and Hamiltonian Path Problems: Concentrating on autonomous spatial exploration and information harvesting problems, this research considers online version of classical combinatorial optimization problems (TSP, Hamiltonian path) with (i) incomplete and uncertain input streams and (ii) time-sensitive objectives. Goal is to design and analyze rigorous algorithmic solution strategies for these canonical problems.

2. Online Resource Allocation Problems: Similar in spirit to the previous project, but motivated by applications from healthcare (kidney exchange), social interactions (online dating, job market), and telecom/internet (sponsored search auctions and online auctions, load balancing for content delivery networks, distributed caching problems, on-demand video/movie requests). Analyis of online versions of classical bipartite matching problems, as well as of other related problems such as the matroid secretary problems as well as more general linear programming problems. Include also issues associated with incentives for market participation.

3. Real-time Paths Tracking/Predictions and On-Demand Route Guidance Under Uncertainty: This overall activity is to develop novel algorithms using real-time data (from many heterogeneous sources) in order to (i) track and predict paths in dynamic transportation networks, and (ii) provide on-demand route guidance under uncertainty. Based on a combination of optimization, data-fusion, machine learning, and novel behavioral techniques. The aim is to develop rigorous data-driven algorithms and methodologies with provable properties, and practical implementations.

4. Port Optimization: Our overarching goal is to develop data-centric models and algorithms for real-time port optimization. Our work is to include the development and application of a number of different methodologies, including data fusion and mining, model formulation and validation, on-line algorithmic development, and assessment of solution quality and impacts. We plan to apply these methodologies in the context of import and export-heavy and/or transshipment operations. An example research project in which we are interested involves using historical and current information to allocate containers to locations and subsequently plan container movements in order to minimize the number of unproductive moves. By predicting operational bottlenecks and providing tools for fully automated systems, we envision our approaches to be used by operators for real-time decision making.

current funding:

National Science Foundation (NSF): Online Optimization for Dynamic Resource Allocation Problems (2010-15)

Singapore MIT Research Alliance (SMART): Future Urban Mobility: Real-time Paths Tracking/Predictions and On-Demand Toute Guidance under Uncertainty (2010-15)

Office of Naval Research (ONR): Online and Dynamic Optimization Problems under Uncertainty (2011-14)

Office of Naval Research (ONR): Decentralized Online Optimization in Multi-Agent Systems in Dynamic and Uncertain Environments (2012-17)

Anonymous: Port Optimization (2012-15)

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Last modified July 2014.