Students on the Market


  • Market Stata

Every year around 50% of ORC’s graduating doctoral students accept academic positions in leading universities around the world. The other 50% of graduating doctoral students and all of our Master’s and Master of Business Analytics students find jobs in leading companies around the world.


Graduate Student Directory

The Graduate Student Directory is a booklet of ORC student resumes that is compiled each year and is circulated to universities and private companies. The primary focus of this effort is on permanent job placement; however, students have also had success in finding summer jobs through this vehicle. Employers who are interested in hiring a student can contact the student directly.

Students on the Market

A listing of ORC students on the job market.



Julia Allen

Julia Allen

Degree: PhD
E: julallen@mit.edu
Advisor(s): Daniel Freund, Alexandre Jacquillat

Houssam El Cheairi

Houssam El Cheairi

Degree: PhD
E: houssamc@mit.edu
Advisor(s): David Gamarnik

Seo Yeong Kwag

Shauna Kwag

Degree: SM
E: kwags@mit.edu
Advisor(s): Andrew Lo

Brian Liu

Brian Liu

Degree: PhD
E: briliu@mit.edu
Advisor(s): Rahul Mazumder

website

Research Interests: I am broadly interested in using analytics to improve decision-making and in advancing the responsible, trustworthy application of machine learning and AI. More specifically, my research focuses on leveraging techniques from discrete and combinatorial optimization to develop efficient, explainable algorithms for machine learning. I am looking for tenure-track positions starting in the 2026-2027 academic year.

Georgios Margaritis

Georgios Margaritis

Degree: PhD
E: geomar@mit.edu
Advisor(s): Dimitris Bertsimas

website

Xiang Meng

Xiang Meng

Degree: PhD
E: mengx@mit.edu
Advisor(s): Rahul Mazumder

Matthew Peroni

Degree: PhD
E: mperoni1@mit.edu
Advisor(s): Dimitris Bertsimas

website

Alexandria Schmid

Alexandria Schmid

Degree: PhD
E: aschmid@mit.edu
Advisor(s): Alexandre Jacquillat

website

Yuan Shi

Yuan Shi

Degree: PhD
E: yuansh@mit.edu
Advisor(s): Y. Karen Zheng

website

Prem Talwai

Prem Talwai

Degree: PhD
E: talwai@mit.edu
Advisor(s): David Simchi-Levi

LinkedIn

Emily Zhang

Emily Zhang

Degree: PhD
E: eyzhang@mit.edu
Advisor(s): Georgia Perakis, Retsef Levi

website

Research Interests: My research develops new analytical methods to address critical problems in food systems, with the dual goals of reducing food waste and improving access to healthy food. I design models and algorithms that shape practical solutions while advancing general methodologies in optimization, causal inference, inventory management, and data-driven operations. I am looking for an academic position where I can further my research on food systems and teach undergraduate, graduate, or MBA students.

Jack Zhang

Daihan (Jack) Zhang

Degree: PhD
E: jackz111@mit.edu
Advisor(s): Patrick Jaillet, Saurabh Amin

Research Interests: My general research area is in the use of reinforcement learning to develop robust decision-making schemes with provable (sub) optimality guarantees, including partially observable reinforcement learning, multi-agent reinforcement learning, game theory, etc.
General job interest: I am looking for an industry research position to further my research on learning algorithms and learning theory in general and to expose myself to more relevant research areas.

Junhui Zhang

Junhui Zhang

Degree: PhD
E: junhuiz@mit.edu
Advisor(s): Patrick Jaillet

website

Research Interests: I am broadly interested in optimization, with a focus on developing (communication, oracle, memory, etc.) efficient algorithms for large scale problems and for sequential decision making. I’m open to tenure-track faculty, research faculty, postdoctoral, and research scientist positions. 

Karl Zhu

Karl Zhu

Degree: PhD
E: karlzhu@mit.edu
Advisor(s): Dimitris Bertsimas

LinkedIn

Research Interests: My general research interests are in modeling multistage optimization problems under uncertainty. I have successful applications in energy systems and healthcare, using methods including adaptive robust optimization, mixed-integer programming, Markov decision processes, deep learning, and LLMs. I’m currently looking for an industry R&D position where I can contribute with my skills in applied optimization and machine learning.