Ashwin Venkataraman
Assistant Professor of Operations Management
- Ashwin[dot]Venkataraman[AT]UTDallas[dot]edu
- (972) 883-5944
- JSOM 3.221
About Me
I am an Assistant Professor of Operations Management at the Naveen Jindal School of Management at University of Texas, Dallas. Previously, I was a Postdoctoral Fellow at the Institute for Quantitative Social Science (IQSS) at Harvard University. I graduated from the Courant Institute of Mathematical Sciences at New York University with a Ph.D. in Computer Science, where I was co-advised by Prof. Srikanth Jagabathula and Prof. Lakshminarayanan Subramanian.
Working Papers
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Nonparametric Estimation of Mixing Distributions in the Presence of Endogeneity
(with Sandeep Chitla and Srikanth Jagabathula) -
The Generalized Stochastic Preference Choice Model
(with Gerardo Berbeglia) -
Characterizing Food Price Fluctuations in India
(with Sunandan Chakraborty, Srikanth Jagabathula, and Lakshminarayan Subramanian)
Journal and Conference Papers
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A Conditional Gradient Approach for Nonparametric Estimation of Mixing Distributions
(with Srikanth Jagabathula and Lakshminarayanan Subramanian)Management Science, 66(8):3635-3656, 2020.
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A Model-based Embedding Technique for Segmenting Customers
(with Srikanth Jagabathula and Lakshminarayanan Subramanian)Operations Research, 66(5):1247-1267, 2018.
Identifying Unreliable and Adversarial Workers in Crowdsourced Labeling Tasks
(with Srikanth Jagabathula and Lakshminarayanan Subramanian)Journal of Machine Learning Research (JMLR), 18(93):1-67, 2017.
Predicting Socio-economic Indicators using News Events
(with Sunandan Chakraborty, Srikanth Jagabathula, and Lakshminarayan Subramanian)Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16), pp. 1455-1464, 2016.
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Reputation-based Worker Filtering in Crowdsourcing
(with Srikanth Jagabathula and Lakshminarayanan Subramanian)Advances in Neural Information Processing Systems 27 (NIPS '14), pp. 2492-2500, 2014.
Other Publications
Rethinking Customer Segmentation and Demand Learning in the Presence of Sparse, Diverse, and Large-scale Data
Ph.D. Dissertation, Sept 2018.
Data-Driven Probabilistic Framework for Student Learning
(with Rishabh Ranawat, Shiva Iyer, Srikanth Jagabathula, and Lakshminarayan Subramanian)ICML/IJCAI/AAMAS Workshop on AI and Computational Psychology: Theories, Algorithms and Applications (CompPsy), July 2018.
CollectiveTeach: Crowdsourcing Lesson Plans
(with Rishabh Ranawat, Sepehr Vakil, Jay Chen, Srikanth Jagabathula, and Lakshminarayan Subramanian)KDD Workshop on Advancing Education with Data, Aug 2017.
Education
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Ph.D. in Computer ScienceCourant Institute of Mathematical Sciences, New York UniversitySep 2014 -- Aug 2018
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MS in Computer ScienceCourant Institute of Mathematical Sciences, New York UniversitySep 2012 -- Aug 2014
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B.Tech in Computer Science and EngineeringIndian Institute of Technology (IIT) DelhiJuly 2008 -- May 2012
Awards
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Honorable Mention, INFORMS George B. Dantzig Dissertation Award (2019)Awarded to the best dissertation in any area of OR/MS
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NYU GSAS Dean's Dissertation Fellowship
(2017-18)1-year fellowship awarded to 30 Ph.D. students across NYU's Graduate School of Arts and Science (GSAS) -
Jacob T. Schwartz Ph.D. Fellowship (2015)For outstanding performance in the Ph.D. program (1 of 2 students awarded in the computer science department)
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Henry M. MacCracken Fellowship
(2012-17)For pursuing graduate studies at NYU