Constantine Caramanis Photo 

    Constantine Caramanis

    Professor, Dept. of Electrical and Computer Engineering

    Chandra Family Endowed Distinguished Professorship in Electrical and Computer Engineering

    Member of the Computer Science Graduate Studies Committee

    Office: 2501 Speedway, EER Building Room 6.820

    e-mail: constantine at utexas.edu



I am a Professor in the ECE department of The University of Texas at Austin. I received a PhD in EECS from The Massachusetts Institute of Technology, in the Laboratory for Information and Decision Systems (LIDS), and an AB in Mathematics from Harvard University. I received the NSF CAREER award in 2011.


My current research interests focus on decision-making in large-scale complex systems, with a focus on learning and computation. Specifically, I am interested in robust and adaptable optimization, high dimensional statistics and machine learning, and applications to large-scale networks, including social networks, wireless networks, transportation networks, and energy networks. I have also worked on applications of machine learning and optimization to computer-aided design.


I am affiliated with the NSF Institute for Foundations of Machine Learning.


Teaching

I have also created two classes which I have made available online.


Research Group

Current Group

Group Alumni


The last ten publications, chronologically…

  1. Hoffmann, Jessica, Matt Jordan, and Constantine Caramanis. “Quarantines as a Targeted Immunization Strategy.” Preprint, 2021.
  2. Katiyar, Ashish, Soumya Basu, Vatsal Shah, and Constantine Caramanis. “Robust Estimation of Tree Structured Markov Random Fields.” Preprint, 2021.
  3. Zhuo, Jiacheng, Jeongyeol Kwon, Nhat Ho, and Constantine Caramanis. “On the Computational and Statistical Complexity of over-Parameterized Matrix Sensing.” Preprint, 2021.
  4. Kwon, Jeongyeol, Yonathan Effroni, Constantine Caramanis, and Shie Mannor. “RL for Latent MDPs: Regret Guarantees and a Lower Bound.” Advances in Neural Information Processing Systems (NeurIPS), 2021.
  5. ———. “Reinforcement Learning in Reward-Mixing MDPs.” Advances in Neural Information Processing Systems (NeurIPS), 2021.
  6. Papadigenopoulos, Orestis, and Constantine Caramanis. “Recurrent Submodular Welfare and Matroid Blocking Bandits.” Advances in Neural Information Processing Systems (NeurIPS), 2021.
  7. Kwon, Jeongyeol, Nhat Ho, and Constantine Caramanis. “On the Minimax Optimality of the Em Algorithm for Learning Two-Component Mixed Linear Regression.” In International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 2021.
  8. Basu, Soumya, Orestis Papadigenopoulos, Constantine Caramanis, and Sanjay Shakkottai. “Contextual Blocking Bandits.” In International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR, 2021.
  9. Atsidakou, Alexia, Orestis Papadigenopoulos, Soumya Basu, Constantine Caramanis, and Sanjay Shakkottai. “Combinatorial Blocking Bandits with Stochastic Delays.” In International Conference on Machine Learning (ICML). PMLR, 2021.
  10. Caramanis, Constantine, Paul Duetting, Matthew Faw, Federico Fusco, Philip Lazo, Stefano Leonardi, Orestis Papadigenopoulos, Emmanouil Pountourakis, and Rebecca Reiffenhauser. “Single Sample Prophet Inequalities via Greedy-Ordered Selection.” Symposium on Discrete Algorithms (SODA), 2021.