Constantine Caramanis


I am an Associate 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 also work on applications of machine learning and optimization to computer-aided design.

News and Announcements

NEW OPTIMIZATION SEQUENCE: In Fall 2018 - Spring 2019, Sujay

Sanghavi and I are teaching a two sequence graduate optimization course. The first course will focus on convexity, duality, the power of different formulations, special classes of convex optimization, and will see applications to combinatorial optimization. The second course will

focus on algorithms for solving convex optimization and their analysis (rates of convergence, etc.), including first and second order methods, SGD etc., and then will explore research topics in Machine Learning, related to optimization.

Undergraduate Machine Learning Club: UT now has a machine learning club - please see here for more.

Apply for a Simons Post Doc position: We are looking for our new batch of Simons Post Docs. The areas of interest are diverse, including networks, learning, optimization, stochastics, communication, and beyond.


Spring 2019 -- Large Scale & Convex Opt’n II (w/ Sujay Sanghavi)

Fall 2018 -- Large Scale & Convex Optimization I (w/ Sujay Sanghavi)

Spring 2018 -- Data Science Lab (EE379K)

Fall 2017 -- Large Scale and Convex Optimization (EE381K)

Spring 2017 -- Data Science Lab (EE372K -- w/ Alex Dimakis)

Fall 2016 -- Large Scale and Convex Optimization (EE381K)

Fall 2016 -- Data Science Lab (EE372K -- w/ Alex Dimakis)

Spring 2016 -- Large Scale Machine Learning (w/ Alex Dimakis)

Spring 2016 -- Introduction to Feedback Control (EE362K)

Fall 2015 -- Large Scale and Convex Optimization

Fall 2013 - Spring 2014: On leave - Large Scale Lin Alg @ Technion

Spring 2013 -- Large Scale Optimization and Learning Part II: Machine Learning (EE381V)

Fall 2012 -- Large Scale Optimization and Learning Part I: Convex Optimization (EE381V)

Fall 2011 -- Convex Analysis and Optimization (EE381V-11)


Chronological order -- complete list

Optimization and Applications

Machine Learning, Statistics and Applications

Wireless Networks

Other Engineering Applications


Eirini Asteri (ECE) (co-advised with Alex Dimakis)

Jessica Hoffmann (CS)
Kiyeon Jeon (ECE)

Ashish Katiyar (CS)

Jeong Yeol Kwon (ECE)

Tianyang Li (CS)

Liu Liu (ECE)

Wang Ye (co-advised with Michael Orshansky)

Jiacheng Zhuo (CS)

Post Docs and Visitors


Yudong Chen: Assistant Professor at Cornell ORIE

Doug Fearing (with C. Barnhart, MIT): Asst Prof., UT Austin B. School

Amin Abdel-Khalek (co-advised with Robert Heath): Freescale

Harish Ganapathy: Google

Aditya Gopalan (with Sanjay Shakkottai): Asst. Prof., Ind. Inst. of Sci.

Ken’ichi Kamada -- Visiting Scientist from Yokogawa Co.

Anastasios Kyrillidis (w/ S. Sanghavi & A. Dimakis): Asst. Prof., Rice CS

Ioannis Mitliagkas (with Sriram Vishwanath): Asst Prof., U. Montreal

Zrinka Puljiz (co-advised with Sanjay Shakkottai): Google

Ashish Singh (with Michael Orshansky): Terra Technology

Joe Neeman (with Sujay Sanghavi): Asst Prof., UT Austin, Math

Dohyung Park (co-advised with Sujay Sanghavi): Facebook

Srilakshmi Pattabiraman

Huan Xu (with D. Morton): Asst Prof., Georgia Tech, ISyE Dept.

Qiaoyang Ye (with Jeff Andrews): Intel

Xinyang Yi: Google

Sungho Yun: ASSIA inc.

Undergraduate Student Projects

RideShare: A senior design project by Yoni Ben-Meshulam, Garrett Cooper, Derrick Huhn, and Patrick Lowry.

Prospective Students

Interested in machine learning / statistics / optimization? Apply to the DICE track (formerly CommNetS) under ECE at UT Austin.

I am always on the lookout for motivated graduate students with a strong mathematical background and interested in theory. I am also looking for students with experience or interest in working with large scale systems -- in particular, large scale data and learning problems, and large-scale optimization.

I am looking for undergraduate students interested in doing senior design projects in the general area of energy and efficiency (including renewable energy and conservation) using tools from Machine Learning, Communications/Wireless, and Algorithms.

Brief Biography and Interests



  1. BulletNEW COURSE

  2. BulletGrad Students

  3. BulletUndergraduates

Office Hours:

Location: EER 6.820

Some Links:

  1. BulletWNCG

  2. BulletUT MINDS

  3. BulletWNCG Seminars

  4. BulletCampus Seminars

  5. BulletWNCG Student Blog

Some Links:

  1. BulletWNCG

  2. BulletWNCG Seminars

  3. BulletCampus Seminars

Associate Professor

Associate Director, WNCG

Dept of Electrical and Comp. Engineering

The University of Texas at Austin

Contact Info:

        Office: EER 6.820

          Tel: (512) 471-9269

          Mail Code: C0806

          e-mail: constantine@