pascal-van-hentenryck-small

Pascal Van Hentenryck

By | | No Comments

Our research is concerned with evidence-based optimization, the idea of optimizing complex systems holistically, exploiting the unprecedented amount of available data. It is driven by an exciting convergence of ideas in big data, predictive analytics, and large-scale optimization (prescriptive analytics) that provide, for the first time, an opportunity to capture human dynamics, natural phenomena, and complex infrastructures in optimization models. We apply evidence-based optimization to challenging applications in environmental and social resilience, energy systems, marketing, social networks, and transportation. Key research topics include the integration of predictive (machine learning, simulation, stochastic approximation) and prescriptive analytics (optimization under uncertainty), as well as the integration of strategic, tactical, and operational models.

The video above is of a planned evacuation of 70,000 persons for a 1-100 year flood in the Hawkesbury-Nepean Region using both predictive and prescriptive analytics and large data sets for the terrain, the population, and the transportation network.

banerjee-small

Sy Banerjee

By | | No Comments

Sy Banerjee studies the impact of mobile devices on consumer behavior and on the processing of signals emerging from location-based Social Media posts. He teaches a MBA class on digital marketing and Big Data and collaborates with researchers from Business, GIS and Computer Science. Some of his recent works include:

Assessing Prime-Time for Geotargeting With Mobile Big Data, Sy Banerjee, Vijay Viswanathan, Kalyan Raman, Hao Ying, Journal of Marketing Analytics, 2013, Vol. 1(3), pp 174-183.

“Visualizing active travel sentiment in an urban context” with Greg Rybarczyk, International Conference on Transport & Health MINETA Transportation Institute, San Jose, California,¬†July 2016.

“Assigning Geo-Relevance of Sentiments Mined from Location-Based Social Media Posts”¬†with R. Sanborn and M. Farmer, in Advances in Intelligent Data Analysis XIV, LNCS

“Understanding In-Store Consumer Experiences via User Generated Content from Social Media”, working paper with Karthik Sridhar and Ashwin Aravindakshan

“Tweeted Customer Emotions as Currency for Competitive Performance: A Framework of Location-Based Social Media Listening”, working paper with Amit Poddar, Karthik Sridhar, Nanda Kumar

manchanda-small

Puneet Manchanda

By | | No Comments

My interest is in using econometrics, especially Bayesian econometrics, and machine learning methods to infer causality. I tend to work with mostly parametric models of firm and consumer behavior to assess the effectiveness of firm actions. My work spans a variety of industries such as pharmaceuticals, e-commerce, gaming and hi-technology.