Theja Tulabandhula, Ph.D.

Interests

I am broadly interested in designing and implementing machine learning and optimization methods. 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 consumer behavior modeling, and (d) several online and reinforcement learning problems.

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.

Publications and Patents

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 patents (granted and applied) are listed on Google Patents.

• Mehrnaz Amjadi, Vijay Kamble, and Theja Tulabandhula. Managing Adoption under Network Effects. SSRN Preprint, 2019.
• Yunjuan Wang and Theja Tulabandhula. Thompson Sampling for a Fatigue-aware Online Recommendation System. ArXiv Preprint, 2019.
• Priyank Agrawal and Theja Tulabandhula. Bandits with Temporal Stochastic Constraints. ArXiv Preprint, 2018.
• Ragavendran Gopalakrishnan, Theja Tulabandhula, and Koyel Mukherjee. Sequential Individual Rationality in Dynamic Ridesharing. SSRN Preprint, 2018.
• Debjyoti Saharoy and Theja Tulabandhula. Learning Buyer Behavior under Realistic Pricing Restrictions. Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2018. Note - also presented at the INFORMS Revenue Management and Pricing Conference 2018.
• 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. Note - also presented at the 10th International Workshop on Agents in Traffic and Transportation 2018 held in conjunction with ECAI/IJCAI, AAMAS and ICML conferences.
• Mehrnaz Amjadi and Theja Tulabandhula. Block-Structure Based Time-Series Models For Graph Sequences. NIPS Workshop on Advances in Modeling and Learning Interactions from Complex Data, 2017. video. Note - also presented as a poster at the 2nd Midwest Machine Learning Symposium 2018.
• Deeksha Sinha and Theja Tulabandhula. Optimizing Revenue over Data-driven Assortments. NIPS Workshop on Nearest Neighbor Methods, 2017.
• U.N. Niranjan, Arun Rajkumar, and Theja Tulabandhula. Provable Inductive Robust PCA via Iterative Hard Thresholding. 33rd Conference on Uncertainty in Artificial Intelligence, 2017.
• 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.
• Sudeep Raja Putta and Theja Tulabandhula. Efficient Reinforcement Learning via Initial Pure Exploration. The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2017.
• Vikas Jain and Theja Tulabandhula. Faster Reinforcement Learning Using Active Simulators. NIPS Workshop: Teaching Machines, Robots, and Humans, 2017. Note - also a poster at the 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making 2017.
• 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 at the 17th ACM Conference on Economics and Computation 2016.
• Arun Rajkumar, Koyel Mukherjee, and Theja Tulabandhula. Learning to Partition Using Pairwise Compatibilities. International Conference on Autonomous Agents and Multiagent Systems, 2017.
• 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.
• 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.
• 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.
• Theja Tulabandhula. Interactions Between Learning and Decision Making. AI Matters 3(1), 2017.
• Theja Tulabandhula, Shailesh Vaya, and Aritra Dhar. Privacy-preserving Targeted Advertising. INFORMS Workshop on Data Mining and Decision Analytics, 2016.
• 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.
• 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.
• Theja Tulabandhula, Koyel Mukherjee, and Narayanan U. Edakunni. Static and Dynamic Scheduling to Minimize Passenger Waiting Times. Transportation Research Board 95th Annual Meeting, 2016.
• 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.
• 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.
• Theja Tulabandhula and Cynthia Rudin. On Combining Machine Learning with Decision Making. Machine Learning Journal 97(1-2), 2014.
• Theja Tulabandhula and Cynthia Rudin. Robust Optimization using Machine Learning for Uncertainty Sets. ArXiv Preprint, 2014.
• 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.
• Theja Tulabandhula and Cynthia Rudin. Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side Knowledge. Machine Learning Journal, 2014.
• 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.
• Theja Tulabandhula and Cynthia Rudin. Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing. Big Data 2(2), 2014.
• Theja Tulabandhula. Interactions Between Learning and Decision Making. Ph.D. thesis, Massachusetts Institute of Technology, 2014.
• Theja Tulabandhula and Cynthia Rudin. Machine Learning with Operational Costs. Journal of Machine Learning Research, 2013.
• 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..
• 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..
• Theja Tulabandhula. Some Architectures for Chebyshev Interpolation. ArXiv Preprint, 2010.
• 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.
• 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.
• 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.

Teaching

• Statistical Models and Methods (Fall 2019)
• Advanced Predictive Models (Fall 2019)
• Statistical Models and Methods (Spring 2019): notes, resources and videos available!
• Advanced Predictive Models (Spring 2019): notes, resources and videos available!
• Statistical Models and Methods (Spring 2018): notes and videos available!
• Advanced Predictive Models (Spring 2018): slides and videos available!
• Statistical Models and Methods (Fall 2017): videos available!
• Advanced Predictive Models (Spring 2017): videos available!