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Laurent Michel, Ph.D. - University of Connecticut. Storrs, CT, US

Laurent Michel, Ph.D. Laurent Michel, Ph.D.

Associate Professor of Computer Science and Engineering | University of Connecticut

Storrs, CT, UNITED STATES

Professor Laurent Michel is an expert in the areas of constraint programming, voting technology and cybersecurity

Biography

Co-director of the Connecticut Cybersecurity Center, which conducts research on all aspects of cybersecurity, including the emerging fields of mobile technology security and web-based services and applications, in addition to longer-established concerns with hardware and software vulnerabilities, voting technology, and cyber industry partnerships

Areas of Expertise (3)

Computer Science

Voting Technology & Security

Cybersecurity

Education (3)

Brown University: Ph.D., Computer Science 1999

Brown University: M.Sc., Computer Science 1997

Universtaires Notre-Dame de la Paix: B.S., Computer Science

Affiliations (4)

  • Connecticut Academy of Science and Engineering
  • Director, Synchrony Financial Center of Excellence in Cybersecurity
  • Past-president, International Association for Constraint Programming
  • Founding Member, Connecticut Voting Technology Research Center

Accomplishments (1)

Synchrony Financial Chair in Cybersecurity (professional)

2019-12-11

Established by a generous donation from Synchrony Financial in 2016, this position is aimed at supporting a leader focused on the advancement of education and research in cybersecurity. Michel’s appointment was approved by the UConn Board of Trustees during their meeting on December 11, 2019.

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Laurent Michel, Ph.D. Publication

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Media Appearances (1)

Black Hats, Cyber Bots, Zombies, and You

UConn Magazine  

2016-05-31

Cybersecurity specialists, led by John Chandy, an electrical and computer engineering professor, and Laurent Michel, an associate professor of computer science and engineering, is working to protect our information. UConn’s Comcast Center of Excellence for Security Innovation is advancing research to strengthen the nation’s electronic information networks and training a new generation of hardware, software, and network security engineers to protect the integrity of everything from small consumer electronics to the complex computer systems running our major industrial, financial, and transportation systems.

Secured behind passcode-protected entry doors, the Comcast lab is embedded deep inside one of UConn’s main academic buildings. Getting there can be an adventure.

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Articles (5)

An efficient approach to short-term load forecasting at the distribution level Authors IEEE Transactions on Power Systems

2015

Short-term load forecasting at the distribution level predicts the load of substations, feeders, transformers, and possibly customers from half an hour to one week ahead. Effective forecasting is important for the planning and operation of distribution systems. The problem, however, is difficult in view of complicated load features, the large number of distribution-level nodes, and possible switching operations.

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Activity-Based Search for Black-Box Constraint Programming Solvers International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming

2012

Robust search procedures are a central component in the design of black-box constraint-programming solvers. This paper proposes activity-based search which uses the activity of variables during propagation to guide the search. Activity-based search was compared experimentally to impact-based search and the wdeg heuristics but not to solution counting heuristics.

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Corrective line switching with security constraints for the base and contingency cases IEEE Transactions on Power Systems

2011

Following a line outage, the fast corrective operations of transmission line switching might be used to regain N-1 security of the system without generation re-dispatch or load shedding. The problem to find feasible switching operations can be formulated as a constraint satisfaction problem (CSP). Feasibility checking, however, is difficult since changes in load flows caused by line switching operations are discontinuous, and many contingency cases need to be examined.

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Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks IEEE Transaction on Power Systems

2009

In deregulated electricity markets, short-term load forecasting is important for reliable power system operation, and also significantly affects markets and their participants. Effective forecasting, however, is difficult in view of the complicated effects on load by a variety of factors. This paper presents a similar day-based wavelet neural network method to forecast tomorrow's load.

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Constraint-Based Local Search The MIT Press

2009

The ubiquity of combinatorial optimization problems in our society is illustrated by the novel application areas for optimization technology, which range from supply chain management to sports tournament scheduling. Over the last two decades, constraint programming has emerged as a fundamental methodology to solve a variety of combinatorial problems, and rich constraint programming languages have been developed for expressing and combining constraints and specifying search procedures at a high level of abstraction.

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