CS Welcomes Four Postdoctoral Fellows

Northwestern CS looks forward to welcoming the following postdoctoral fellows to its team.

Mohammed Alam
PhD Advisor: Doug Downey
Postdoc Mentor: Kris Hammond

Mohammed Alam’s research focuses on artificial intelligence (AI), with an emphasis on machine learning (ML). He is advised by Doug Downey. His thesis shows how web information can be harnessed to predict events, from sports victories to stock prices to revolutions. To test his thesis, Alam researches ML methods that leverage the wisdom of crowds to predict sporting event outcomes using predictions crowdsourced from social media and to make financial predictions using those from a financial prediction platform. Prior to being a graduate student, Alam worked for Motorola as a software engineer for more than 10 years and at a Chicago-area startup during his doctoral pursuit. Alam received a master’s degree in computer science from Northwestern University and a bachelor’s degree in computer engineering from the Illinois Institute of Technology, where he came to study from Bangladesh. 

David Demeter
PhD Advisor: Doug Downey
Postdoc Mentor: Doug Downey

David Demeter is expected to earn a PhD in computer science from Northwestern University in August 2020. His research focuses on the composition of neural models and symbolic models, and the closely related topic of analyzing the architectural limitations of neural network language models. Prior to returning to graduate studies, David worked as an investment banker focusing on financing transactions and international mergers and acquisitions, completing over 70 transactions totaling roughly $10 billion. He also cofounded a startup specializing in derivative securities. David has a master’s degree in applied mathematics and a bachelor’s degree in electrical engineering from Northwestern University, and an MBA from the University of Chicago Booth School of Business.

Jinshuo Dong
PhD Advisor: Aaron Roth
Postdoc Mentors: Aravindan Vijayaraghavan and Jason Hartline

Jinshuo Dong joined the Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) in 2020 after obtaining a PhD in applied math from the University of Pennsylvania under the supervision of Aaron Roth. Prior to that, he obtained a bachelor's degree in mathematics from Peking University in 2014. The main theme of his research is various central limit phenomena in differential privacy. In addition to that, he is broadly interested in the mathematics of computation, information, and intelligence.

Emily Wall
PhD Advisor: Alex Endert
Postdoc Mentors: Jessica Hullman and Steve Franconeri

Emily Wall (PhD in Computer Science, Georgia Tech 2020) joins Jessica Hullman’s lab as a postdoctoral fellow to work on visualization recommendation systems for statistical inference, in collaboration with Steven Franconeri and funded by Adobe Research. Wall’s research interests are in decision making and visual analytics. She will be joining the faculty of Emory University Computer Science in Fall 2021.