Events

"Decision Analysis from Human-Generated Spatial-Temporal Data"

Presentation by Professor Yanhua Li
Department of Computer Science
Worcester Polytechnic Institute
 

Tuesday, January 28
3:30pm - Reception, Fourth Floor Lobby of the IMA Offices
4:00pm - Seminar, Lind Hall 409

About:

With the fast development of mobile sensing and information technology, large volumes of human-generated spatio-temporal data (HSTD) are increasingly collected, including taxi GPS trajectories, passenger trip data from automated fare collection (AFC) devices on buses and trains, and working traces from the emerging gig-economy services, such as food delivery (DoorDash, Postmates), and everyday tasks (TaskRabbit). Such HSTD capture unique decision-making strategies of the “data generators (e.g., gig-workers, taxi drivers). Harnessing HSTD to characterize unique decision-making strategies of human agents has transformative potential in many applications, including promoting individual well-being of gig-workers, and improving service quality and revenue of transportation service providers. In this talk, I will introduce a spatial-temporal imitation learning framework for inversely learning and “imitating the decision-making strategies of human agents from their HSTD, and present our recent works on analyzing taxi drivers passenger-seeking strategies and public transit travelers route choice strategies. Moreover, I will discuss key design challenges in spatial-temporal imitation learning, and outline various future applications in targeted training, incentive, and planning mechanisms that enhance the well-being of urban dwellers and society in terms of income level, travel and living convenience.

Bio:

Prof. Yanhua Li received two Ph.D. degrees in computer science from University of Minnesota—Twin Cities in 2013, and in electrical engineering from Beijing University of Posts and Telecommunications, Beijing in China in 2009, respectively. He joined the Department of Computer Science at Worcester Polytechnic Institute (WPI) as an assistant professor in fall 2015. His research interests are urban intelligence and smart cities in many contexts, including spatial-temporal data analytics, urban planning and optimization. Recently, Dr. Li focuses on developing data-driven approaches to inversely learn and influence the decision-making strategies of urban travelers, who take public transits, taxis, sharing bikes, etc. Dr. Li is a recipient of NSF CISE Research Initiation Initiative (CRII) Award. (http://www.wpi.edu/~yli15/)

"Service Systems with Server Compatibilities and Redundancy"

Presentation by Rhonda Righter
Professor
Department of Industrial Engineering and Operations Research
University of California, Berkeley

Wednesday, December 11
3:15pm - Refreshments, Lind Hall 305
3:30pm - Graduate Seminar, Lind Hall 305

About:

In large service systems, such as cloud computing systems, there are different classes of jobs and of servers such that each job class can only be done on a subset of the server classes, due to data locality and other constraints. Moreover, redundancy may be permitted, meaning that copies of jobs are sent upon arrival to all compatible servers. Redundant copies may be canceled once any copy begins service (cancel-on-start) or when any copy finishes service (cancel-on-completion). Under Markovian assumptions, the steady-state distributions for such systems have been shown to have a simple, “product-form” structure. I will describe a unified framework for both the cancel-on-start and cancel-on-complete models that provides a common simple proof for the product-form results at a detailed state description and provides a simple, state-aggregated, view for analyzing waiting time distributions. I will also explore conditions under which one redundancy cancellation protocol is better than the other. Joint work with Ivo Adan, Igor Kleiner, Kristen Gardner, and Gideon Weiss.

Bio:

Rhonda Righter is a Professor and past Chair of the Department of Industrial Engineering and Operations Research at the University of California, Berkeley, where she earned her PhD. Before joining the Berkeley faculty she was a Professor of Operations Management and Information Systems in the Leavey School of Business at Santa Clara University. Her primary research and teaching interests are in the general area of stochastic modeling and optimization, especially as applied to service, manufacturing, computer communication, and cloud computing systems. She has won awards for her teaching, research, and service, and is currently an associate editor for the Journal of Scheduling, Queueing Systems, Naval Research Logistics, Stochastic Models, and the INFORMS Service Science Journal. She was the founding Chair of the Applied Probability Society of INFORMS.

"Geospatial Big Data Analytics for Business and Conservation"

Presentation by Kristopher Purens
Descartes Labs
 

Wednesday, November 20
3:15pm - Refreshments, Lind Hall 305
3:30pm - Graduate Seminar, Lind Hall 305

About:

What is the role of Big Data for sustainability? With a background in paleontology, environmental science, and mass extinctions, Kristopher will talk about how corporate scientists can use geospatial analytics to help companies find sustainable solutions, while also helping the organizations become more efficient. With a rapid increase in the number of Earth observation satellites in the past decade, geospatial analysis is entering a new era where Big Data tools are necessary to analyze and create value from this data. From classic use cases of crop monitoring, new satellites sensor types are empowering organizations to understand carbon cycle, pollutant emissions, see through clouds, and more. 

Bio:

With a PhD in paleontology, Kristopher was on the forefront of using machine learning to fuse paleoenvironmental and life history data to understand ancient mass extinctions and climate catastrophes. Kristopher took that skill set to Shell Oil, where he worked as a data scientist to discover new petroleum deposits, before moving to Minnesota where he worked in the General Mills center of excellence for Data Science, seeking to find ways to improve their supply chain processes. Now, Kristopher is at Descartes Labs, a top geospatial startup, where he is working to combine his interests in conservation, efficiency, and natural science. 

"If You Can Send a Truck Anywhere, Where Would You Send It?"

Presentation by Michael Chmutov
C.H. Robinson
 

Wednesday, December 4
3:15pm - Refreshments, Lind Hall 305
3:30pm - Graduate Seminar, Lind Hall 305

About:

As one of the world’s largest third-party logistics providers, C. H. Robinson continues to manage and optimize our customers’ supply chain solutions. As the demand for automation and streamlined decision making continues to grow from our customers, so does the adoption of new technologies and practices at C. H. Robinson. From automatic bidding to digital freight matching, our data science group has led several initiatives to change the way we move freight. In this talk, we will discuss some of our current initiatives while diving deeper into the challenges of operating a semi-dedicated fleet of trucks from a third-party logistics perspective.

Bio:

Dr. Chmutov received his Ph.D. in Mathematics, specializing in abstract algebra and algebraic combinatorics, from the University of Michigan in 2014. He completed an NSF Postdoctoral Fellowship in the Math Department at the University of Minnesota in 2018. He then decided to transition to industry, and after a brief stint as an independent contractor doing deep learning, he joined C. H. Robinson in Dec. 2018 as a Data Scientist; he now works on various routing problems.