Theja Tulabandhula, Ph.D.
Bio
- I am currently an Assistant Professor at the University of Illinois Chicago. Here, I work on research themes related to machine learning and sequential decision making (see below).
- I was a Research Scientist at Xerox Research (Machine Learning and Statistics, Data Analytics Lab) from 2014-2016.
- I received a PhD in Electrical Engineering and Computer Science from MIT, and was advised by Cynthia Rudin. While there, I was also a Fulbright Science and Technology Scholar.
- Before that, I was an undergrad at IIT Kharagpur, where I was also awarded the Prime Minister's Gold Medal.
Teaching
Research Interests
I am broadly interested in artificial intelligence, operations research and their applications to multiple scientific and business domains. I have worked on theoretical and empirical aspects of: (a) supervised learning in the presence of operational information, (b) transportation problems including scheduling and fair ride-sharing, (c) assortment optimization, and (d) several online and reinforcement learning problems.
Publications
In addition to below, you can also find my publications at Arxiv or DBLP. For some of these, source-code is made available on GitHub. Further, a few of my patent applications are listed on Google Scholar.
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Debjyoti Saharoy and Theja Tulabandhula.
Learning Buyer Behavior under Realistic Pricing Restrictions.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics,
2018.
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Arpita Biswas, Ragavendran Gopalakrishnan, Theja Tulabandhula, Koyel Mukherjee, Asmita Metrewal, and Raja Subramaniam Thangaraj.
Impact of Detour-Aware Policies on Maximizing Profit in Ridesharing.
Transportation Research Board 97th Annual Meeting,
2018.
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Deeksha Sinha and Theja Tulabandhula.
Optimizing Revenue over Data-driven Assortments.
Arxiv preprint,
2017.
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U.N. Niranjan, Arun Rajkumar, and Theja Tulabandhula.
Provable Inductive Robust PCA via Iterative Hard Thresholding.
33rd Conference on Uncertainty in Artificial Intelligence,
2017.
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Theja Tulabandhula, Shailesh Vaya, and Aritra Dhar.
Privacy-preserving Targeted Advertising.
Arxiv preprint,
2017.
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Anuj Mahajan and Theja Tulabandhula.
Symmetry Detection and Exploitation for Function Approximation in Deep Reinforcement Learning.
The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making,
2017.
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Sudeep Raja Putta and Theja Tulabandhula.
Efficient Reinforcement Learning via Initial Pure Exploration.
The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making,
2017.
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Vikas Jain and Theja Tulabandhula.
Faster Reinforcement Learning Using Active Simulators.
The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making,
2017.
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Ragavendran Gopalakrishnan, Koyel Mukherjee, and Theja Tulabandhula.
The Costs and Benefits of Ridesharing: Sequential Individual Rationality and Sequential Fairness.
INFORMS Transportation and Logistics Society Conference,
2017.
Note - also, a poster was presented at the 17th ACM Conference on Economics and Computation 2016.
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Arun Rajkumar, Koyel Mukherjee, and Theja Tulabandhula.
Learning to Partition Using Pairwise Compatibilities.
International Conference on Autonomous Agents and Multiagent Systems,
2017.
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Anuj Mahajan and Theja Tulabandhula.
Symmetry Detection and Exploitation for Function Approximation in Deep Reinforcement Learning.
International Conference on Autonomous Agents and Multiagent Systems,
2017.
Note - superceded.
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Sudeep Raja Putta and Theja Tulabandhula.
Pure Exploration in Episodic Fixed-Horizon Markov Decision Processes.
International Conference on Autonomous Agents and Multiagent Systems,
2017.
Note - superceded.
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Arpita Biswas, Ragavendran Gopalakrishnan, Theja Tulabandhula, Koyel Mukherjee, Asmita Metrewal, and Raja Subramaniam Thangaraj.
Profit Optimization in Commercial Ridesharing.
International Conference on Autonomous Agents and Multiagent Systems,
2017.
Note - superceded by an arxiv preprint.
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Theja Tulabandhula.
Interactions Between Learning and Decision Making.
AI Matters 3(1),
2017.
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Theja Tulabandhula and Narendra Annamaneni.
Optimal Automated Booking of On-demand Transportation in Multi-modal Journeys.
Twenty-third World Congress on Intelligent Transportation Systems,
2016.
Note - full paper will be linked soon.
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Narayanan U. Edakunni, Koyel Mukherjee, Theja Tulabandhula, Kaushik Baruah, Tuhin Bhattacharya, and Geetha Manjunath.
Demand Sensitive Scheduling of Public Transport using Past Ticketing Data.
Twenty-third World Congress on Intelligent Transportation Systems,
2016.
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Theja Tulabandhula, Koyel Mukherjee, and Narayanan Unny.
Static and Dynamic Scheduling to Minimize Passenger Waiting Times.
Transportation Research Board 95th Annual Meeting,
2016.
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Prabuchandran K.J., Tejas Bodas, and Theja Tulabandhula.
Reinforcement Learning Algorithms for Regret Minimization in Structured Markov Decision Processes.
International Conference on Autonomous Agents and Multiagent Systems,
2016.
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Theja Tulabandhula.
Learning Personalized Optimal Control for Repeatedly Operated Systems.
NIPS workshop on Machine Learning From and For Adaptive User Technologies: From Active Learning and Experimentation to Optimization and Personalization,
2015.
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Theja Tulabandhula and Cynthia Rudin.
On Combining Machine Learning with Decision Making.
Machine Learning Journal 97(1-2),
2014.
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Theja Tulabandhula and Cynthia Rudin.
Robust Optimization using Machine Learning for Uncertainty Sets.
ArXiv Preprint,
2014.
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Theja Tulabandhula and Cynthia Rudin.
Robust Optimization using Machine Learning for Uncertainty Sets.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics,
2014.
Note - superceded by an arxiv preprint.
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Theja Tulabandhula and Cynthia Rudin.
Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side Knowledge.
Machine Learning Journal,
2014.
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Theja Tulabandhula and Cynthia Rudin.
Generalization Bounds for Learning with Linear and Quadratic Side Knowledge.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics,
2014.
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Theja Tulabandhula and Cynthia Rudin.
Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing.
Big Data 2(2),
2014.
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Theja Tulabandhula.
Interactions Between Learning and Decision Making.
Ph.D. thesis, Massachusetts Institute of Technology,
2014.
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Theja Tulabandhula and Cynthia Rudin.
Machine Learning with Operational Costs.
Journal of Machine Learning Research,
2013.
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Theja Tulabandhula and Cynthia Rudin.
The Influence of Operational Cost on Estimation.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics,
2012.
Note - superceded by a journal version..
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Theja Tulabandhula, Cynthia Rudin, and Patrick Jaillet.
The Machine Learning and Traveling Repairman Problem.
Proceedings of the Second International Conference on Algorithmic Decision Theory,
2011.
Note - superceded by a journal version..
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Theja Tulabandhula.
Some Architectures for Chebyshev Interpolation.
ArXiv Preprint,
2010.
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Theja Tulabandhula and Yujendra Mitikiri.
A 20MS/s 5.6 mW 6b Asynchronous ADC in 0.6$\mu$m CMOS.
Proceedings of the 22nd International Conference on VLSI Design,
2009.
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Theja Tulabandhula, Samuel Antao, and Leonel Sousa.
A Class of Software-Hardware Processors for Fingerprint Matching on the Fourier Domain.
Proceedings of the Third HiPEAC Workshop on Reconfigurable Computing,
2009.
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Theja Tulabandhula, Amit Patra, and Nirmal B Chakrabarti.
Design of a Two Dimensional PRSI Image Processor.
Proceedings of the 11th EUROMICRO Conference on Digital System Design: Architectures, Methods and Tools,
2008.