Research

My research interests are broadly in machine learning, statistics, and information theory, and their applications. My goal is to draw the most benefit from the smallest amount of data while addressing the statistical, computational, and societal aspects of modern data analysis (what is data science?). I am currently interested in two main problems: designing learning algorithms that adapt to structure in data and making non-discriminatory algorithmic decisions both computationally feasible and aware of their long-term societal impact.

News

•  October 2026: My former student Kenya Andrews, currently a postdoc at Brown University, will be presenting her findings along with my colleage Elena Zheleva, at the Artificial Intelligence, Ethics, and Society (AIES) conference. She showed how an intersectional perspective causally leads to unjust language that could obsucate the need of patients in medical records and proposed a preliminary approach to remedy this and improve the visibility of patients.

• December 2025: My student Zahra Rahimi Afzal had a poster paper at NeurIPS 2025. Along with Tara Esmaeilberg and my colleague Mojtaba Soltanalian, she figured out how to forecast the impact of fine tuning using a linearized (NTK) version of a neural network at initialization (pretrained).

Recent Past Activity

- December 2024: Very proud of my student Usama Muneeb for his spotlight paper at NeurIPS 2024. He figured out how to correctly incorporate feature-restricted models in the training of larger ones.

- November 2024: My student Shayan Vassef and I, along with Aloni Cohen from UChicago, organized a workshop on Fairness in Generative AI: Protecting and Compensating Content Producers, as part of the IDEAL Institute.

-  In June 2023,  my mentor and one of my PhD advisors, Sanjoy Mitter, passed away.  Sanjoy welcomed me into LIDS. To this day, I remember our first meeting in old Building 35, which felt like MIT’s proverbial ``drinking from a firehose.’’ I soon realized he had simply mined and forged together the fragments of my ideas, using his vast knowledge and experience. This infectious intellectual passion and rigor made me a better researcher. Our later discussions on world politics, language, and culinary preferences made me a more astute observer of life and humanity. Thank you, Sanjoy, for being a guide and beacon. I will forever carry your memory with me and endeavor to pass on your teachings.

- May 2023: We held the 4th Midwest Machine Learning Symposium (MMLS) at UIC!
- April 2023: Our paper on fairness auditing using data combination with Jingyi Yang and Joel Miller is accepted at FAccT 2023!
- December 2022: Zohreh  Ovaisi presented our work on balancing item-fairness and utility in recommender systems in the presence of biases, as an extended abstract poster presentation at the NeurIPS 2022 Workshop on Algorithmic Fairness through the Lens of Causality and Privacy.

- October 2022: Kenya Andrews presented our work on modeling access for fair resource allocation as a full-paper oral presentation at EAAMO 2022 (ACM Conference on Equity and Accountability in Algorithms, Mechanisms, and Optimization).

- March 2022 : Very honored to receive the NSF CAREER award for my project titled "CAREER: From Rare Events to Competitive Learning Algorithms"! This is both the continuation and the new beginning of my vision in research and education, and I am very excited to work toward its success along with my students. I am very grateful for this support and appreciation.

- March 2021: Kenya presented a poster at the Women in Data Science (WiDS) Cambridge conference, titled "Equity, Access, and Vulnerability in COVID-19 Vaccine Allocation".

- March 2021: I gave an invited talk at ISI at USC.

- November 2020: I gave an invited talk as part of the LUCID seminar at UW Madison.

- My research group is now called Data, Information, and Computation, Equitably (DICE). I welcomed 3 excellent graduate students: Usama Muneeb, Jingyi Yang, and Kenya Andrews!

- August 2020: "Towards competitive N-gram smoothing", with M. Falahatgar, A. Orlitsky, and V. Pichapati, will appear in AISTATS 2020!

- July 2020: "Fair Learning with Private Demographic Data", with H. Mouzannar and N. Srebro, will appear in ICML 2020!

- Spring 2020: I taught a new course: ECE 491 Information and Learning.

- January 2020:  Honored and grateful to receive support from NSF and Amazon for "FAI: Addressing the 3D Challenges for Data-Driven Fairness: Deficiency, Dynamics, and Disagreement" with my excellent co-PIs from the CS department at UIC!

- December 2019: We had an oral presentation on "Fair Learning with Private Demographic Data", with H. Mouzannar and N. Srebro, at the NeurIPS 2019 Workshop on ML with Guarantees.

- Fall 2019: I taught ECE 341, an introductory probability course for engineers.

- August 2019: I joined the Department of Electrical and Computer Engineering at UIC!

- August 2019: I was invited and gave a talk at Elucid8 at UW Madison

- July-August 2019: I taught (for the second time!) "How Machines Learn" at Summer Lab, to rising 7th and 8th graders.

- April 2019: I was invited and gave talks on "From Fair Decisions to Social Benefit" at the ML Seminar at the University of Chicago and the SINE Seminar at UIUC

- October 2018: "From Fair Decision Making to Social Equality", with H. Mouzannar and N. Srebro, accepted at ACM FAT* 2019.

- June-July 2018: I developed and taught a three-week activity-based course introducing machine learning and Python coding to rising 7th and 8th grade students at the University of Chicago Lab Schools' Summer Lab program.

- June 2018: I gave an invited talk titled "One (categorical) distribution estimator for all dimensions" at the 2018 Midwest Machine Learning Symposium.

Recent Teaching

UIC

ECE 491. Information and Learning (See link to samply syllabus. Spring 2020–2026)

ECE 341. Probability and Stochastic Processes for Engineers (Fall 2019–2023, 2025, 2026, SU 2026)

ECE 559. Neural Networks (Fall 2025)

Earlier 

Summer 2018 and Summer 2019: "How Machines Learn" at Summer Lab at the University of Chicago Lab Schools (course for rising 7th and 8th grade students)

Fall Quarter 2017: TTIC 31150/CMSC 31150 Mathematical Toolkit at TTIC and the University of Chicago

Address: Mesrob I. Ohannessian

                 Electrical and Computer Engineering

                 University of Illinois at Chicago

                 851 S. Morgan St., 1030 SEO (MC 154)

                 Chicago,IL 60607-7042