Evelyn Xiao-Yue Gong is an Assistant Professor of OM at Tepper. She has a PhD from the ORC at MIT, where she was advised by David Simchi-Levi and Jim Orlin. She spent summers at Google Research, Microsoft, HelloFresh, and DE Shaw. Her main research pertains to AI for supply chain and sustainability. She also works on assortment optimization and data-driven decision making.

In the first half of the episode, we discuss her journey from being a PhD student to an assistant professor.

In the second half of the episode, we discuss her research on reinforcement algorithms, and some recent work on using these algorithms to solve a problem for packaging at HelloFresh.

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Mariana Escallon Barrios is an Assistant Teaching Professor of Information Systems at CMU in our Heinz College. She earned her PhD in IEMS from Northwestern where she was advised by Karen Smilowitz. She worked on modeling and solution approaches to logistics problems in nonprofit settings. She is an active member of INFORMS and WORMS.

We discuss her decision to become a teaching professor at CMU, her research on scheduling volunteers at a nonprofit, her research on harvesting operations at an oil palm plantation, and her teaching philosophy.

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Woody (Shixiang) Zhu is an Assistant Professor of data analytics at Heinz College of Information Systems and Public Policy. He received his PhD in Machine Learning at Georgia Tech in ISyE. He develops models for spatio-temporal data and dynamic networks, and decision making under uncertainty. He was a finalist for the 2021 INFORMS Wagner prize and won second place in the 2019 INFORMS Doing Good with Good OR.

First 1/2:
We discuss Woody's decision to pursue a PhD in ML at Georgia Tech, and we discuss Woody's decision to become a professor at CMU.

Second 1/2:
We talk about two of Woody's recent papers. One work titled Counterfactual Generative Models for Time-Varying Treatments and the other titled Data-Driven Optimization for Atlanta Police Zone Design.

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Jourdain Bernard Lamperski is an Assistant Professor in the Department of Industrial Engineering at the University of Pittsburgh. He received a B.S. in Mathematics from the University of Pittsburgh, and a PhD in Operations Research from the Operations Research Center at MIT where he was advised by Robert M. Freund. His research interests include optimization, machine learning, and healthcare.

We discuss Jourdain's career:
- Why did he choose to go to MIT ORC?
- How did he choose what to work on and his advisor?
- Why did he choose to become a professor in Industrial Engineering at University of Pittsburgh?

And we discuss Jourdain's research:
- He explains the 'oblivious' ellipsoid method that he developed and analyzed during his PhD
- He explains a healthcare project about using optimization methods to calibrate model parameters for the progression of opioid use disorder in patients.

Thanks for listening and thanks to Jourdain!

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Bryan Wilder is an Assistant Professor in the Machine Learning Department at CMU. He received a B.S. in computer science at University of Central Florida, and then started a PhD in computer science at the University of Southern California with advisor Milind Tambe, and then they transferred over to Harvard together. His research focuses on AI for equitable data-driven decision making in high-stakes social settings, and integrating methods from machine learning, optimization, and social networks. He has won loads of awards including a Schmidt AI2050 Early Career Fellowship and Siebel Scholar award.

We discuss his project on HIV-prevention, some work on better integrating ML predictions with optimization models that have some uncertainty, and a brief but nice beginner's lesson in robust optimization.

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Michael Hamilton is an Assistant Professor of Business Analytics and Operations at the University of Pittsburgh’s Katz School of Business. He received his PhD in 2019 in OR from Columbia University advised by Adam Elmachtoub. Before that he studied math at Rutgers. He studies pricing, prescriptive analytics, and market design.

We discuss how Michael decided to study OR, reasons why he enjoys his position as a professor, his research on pricing with Loot boxes and Opaque goods, and his work on a project to help black-owned businesses in Pittsburgh called 412Connect.

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Oliver Hinder is an Assistant Professor in Industrial Engineering Department at University of Pittsburgh. Before that he was a visiting post-doc at Google in the Optimization and Algorithms group in New York and received his PhD in 2019 in Management Science and Engineering from Stanford working with professor Yinyu Ye. He studies local optimization, gradient descent, both convex and nonconvex problems, etc.

We chat about Oliver moving to the U.S. from New Zealand to start his PhD at Stanford; we talk about some of his recent work on gradient descent methods for solving LPs accurately and how using restarts can benefit algorithms like these. Finally, we touch on automated parameter tuning in ML especially in Deep Learning which is being widely used in many applications.

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Yash graduated in 2016 from a PhD program in Chemical Engineering from Carnegie Mellon University where he was advised by Dr. Nick Sahinidis . He now works as a Senior Data Scientist as Aimpoint Digital.

We discuss Yash's experience as a PhD student, some of his research on BARON, his transition to a full-time job, and his current role at Aimpoint. Yash also has some great life advice for PhD students.

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