PeopleofSuperintelligence

Researcher

Constantine Caramanis

Constantine Caramanis is a researcher whose work connects robust optimization, statistics, and machine learning. His education includes mathematics at Harvard and EECS at MIT, and he joined UT Austin's electrical and computer engineering faculty in 2006. His papers linked robust optimization with support vector machines, developed methods for corrupted data, and explored anchored diffusion for language and code. He received an NSF CAREER Award in 2011 and became an IEEE Fellow in 2023. As of October 2026, he is a professor at UT Austin whose current interests include reinforcement learning, agents, and generative models.

Works
295
Citations
9,658
h-index
40
Badges

Publication numbers from OpenAlex, as of 19 October 2026.

Timeline

  1. 2006

    Joining UT Austin

    He joins UT Austin's Electrical and Computer Engineering department in fall 2006. His education includes an A.B. in mathematics from Harvard and M.S. and Ph.D. degrees in electrical engineering and computer science from MIT; his doctoral work is in MIT's Laboratory for Information and Decision Systems (LIDS).

  2. 2009

    Robust support vector machines

    In 2009, he and Huan Xu and Shie Mannor analyze regularized support vector machines through robustness and give a robustness-based consistency proof.

  3. 2010

    Outlier Pursuit

    In 2010, with Huan Xu and Sujay Sanghavi, he presents Outlier Pursuit, an efficient convex optimization algorithm that recovers a low-dimensional subspace and identifies corrupted data points.

  4. 2011

    An NSF CAREER award

    In 2011, he receives an NSF CAREER Award for research at the intersection of robust optimization, networks, statistics, and machine learning. The award supports a framework for analyzing high-dimensional data that may be corrupted or incomplete.

  5. 2019

    Robust M-estimation

    In a 2019 paper with Liu Liu and Tianyang Li, he defines the Robust Descent Condition and shows that robust hard thresholding is guaranteed to achieve good statistical rates when its gradient estimator satisfies the condition.

  6. 2023

    An IEEE Fellowship

    In 2023, he becomes an IEEE Fellow for contributions to robust statistics and optimization in high dimensions.

  7. 2025–2026

    Anchored diffusion for language and code

    In 2025, he coauthors the Anchored Diffusion Language Model, a two-stage framework that predicts important tokens before filling in missing text. In a 2026 preprint, AnCoder uses an abstract syntax tree to prioritize important code tokens and guide discrete diffusion generation.

  8. As of October 2026

    Professor at UT Austin

    As of October 2026, he is a professor in UT Austin's Electrical and Computer Engineering department and holds the Chandra Family Endowed Distinguished Professorship. His current interests include high-dimensional statistics and machine learning, reinforcement learning, agents, and applications to generative models.

Career

  1. Since 2006University of Texas at AustinProfessor; Chandra Family Endowed Distinguished Professor

Education

  1. Harvard UniversityA.B. in mathematics
  2. Massachusetts Institute of TechnologyM.S. and Ph.D. in electrical engineering and computer science

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

Most-cited works

  1. Theory and Applications of Robust Optimization2010 · 2,509 citations
  2. User Association for Load Balancing in Heterogeneous Cellular Networks2013 · IEEE Transactions on Wireless Communications · 1,230 citations
  3. Robust PCA via Outlier Pursuit2012 · IEEE Transactions on Information Theory · 445 citations
  4. Robustness and Regularization of Support Vector Machines2008 · 349 citationsWith Shie Mannor
  5. Robust Regression and Lasso2010 · IEEE Transactions on Information Theory · 251 citationsWith Shie Mannor

From OpenAlex, as of 19 October 2026.

  1. Google has the highest combined h-index among AI organizationsIncludes University of Texas at Austin. Our estimates of combined and median h-index at AI organizations, using current affiliations and OpenAlex records.
  2. Stanford University trained or employed 20 of the 89 AI major-prize winners, more than any other universityIncludes University of Texas at Austin. Stanford University trained or employed 20 of the 89 AI major-prize winners, more than any other university. We compare doctorate and employment links for major-prize and best-paper winners.
  3. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Massachusetts Institute of Technology. Our estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.
  4. The company share of AI researchers’ top papers rose from 2% to 32%Includes University of Texas at Austin. We estimate where leading AI researchers worked when their most-cited papers appeared. The company share rose from 2% in 1975–1979 to 32% in 2020–2024.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Constantine Caramanis | Texas ECE - Electrical & Computer Engineering at UT Austin ece.utexas.edu. Checked 19 October 2026.
  2. Constantine Caramanis caramanis.github.io. Checked 19 October 2026.
  3. Robustness and Regularization of Support Vector Machines jmlr.org. Checked 19 October 2026.
  4. Robust PCA via Outlier Pursuit arxiv.org. Checked 19 October 2026.
  5. UT ECE Professor Constantine Caramanis Receives NSF CAREER Award ece.utexas.edu. Checked 19 October 2026.
  6. High Dimensional Robust M-Estimation: Arbitrary Corruption and Heavy Tails arxiv.org. Checked 19 October 2026.
  7. Caramanis Elevated to IEEE Fellow ece.utexas.edu. Checked 19 October 2026.
  8. Anchored Diffusion Language Model arxiv.org. Checked 19 October 2026.
  9. AnCoder: Anchored Code Generation via Discrete Diffusion Models arxiv.org. Checked 19 October 2026.