About Me

I am an Assistant Professor in Computer Engineering at Missouri University of Science & Technology. Before joining Missouri S&T, I was an assistant professor in ECE at University of Detroit. Prior to that, I spent a year as a postdoctoral research fellow at Georgia Tech, ECE.

I received my Ph.D. degree in electrical engineering (EE) from Penn State, University Park, in December 2018, under supervision of Dr. Aylin Yener. I also received a masters degree in mathematics from Penn State in 2017. Before that, I received a masters degree in wireless & info. technologies from Nile University, Egypt, in 2012, and my bachelor degree in EE (communication & electronics) from Alexandria University, Egypt, in 2010.

My research lies at the intersection of statistical learning, information and data sciences, and causal reasoning, and aims to solve problems in responsible development of machine learning models encompassing issues of reliability & trustworthiness, explainability & interpretability, privacy, robustness & security, as well as algorithmic fairness. Specific areas of research interest include:

1. Explainable, interpretable, and fair ML models; with emphasis on ‘representation learning’ and ‘data engineering’ frameworks.
2. Federated learning & distributed optimization; with emphasis on balancing privacy, personalization, robustness, and fairness.
3. Causal reasoning & inference;with applications to ML interpretability & fairness. 
4. ML diagnostic models for healthcare (HC); with foucus on responsible, timely development & deployment in HC domain.   
5. Information theory; with focus on its application in ML systems, as in explainable-ML, representation learning, as well as security/privacy of information processing systems.

On the application side, I am interested in a wide range of disciplines including, but not limited to, information processing systems, data science, health informatics, wireless communications, image/signal processing, engineering management, as well as social and regulatory sciences.

Join US! I am always looking for excelled and self-motivated PhD students to join our research group. If you are interested, please check out this flyer and apply accordingly. Applications not adhering to instructions will likely not be reviewed.

If you are already a student at Missouri S&T, then taking a course that I am offering and doing very well is a great plus.

News!

• November 25: I attended IEEE ICDM 2025 in Washington, DC; and presented our work on “benchmarking statistical & information theoretic measures for pre- & post- hoc feature attribution in explainable & fair ML via Shapley-value aggregation framework”.
• October 25: I served as a reviewer for ICLR 2026, Rio de Janeiro, Brazil.
• October 25: I delivered a talk titled “Toward responsible development of learning systems” at the CReWMaN Lab at Missouri S&T; directed by Dr. Sajal Das.
• September 25: I served as a reviewer for the Machine Learning for Health (ML4H 2025) conference, San Diego, CA.
• September 25: Our paper “Shapley-value Based Feature Attribution for Explainability and Algorithmic Fairness: A Model-agnostic Benchmark” is accepted for publication at the adaptable, reliable and responsible (ARRL) workshop at IEEE ICDM 2025. Congrats to Sokrat!
• August 25: I am introducing a new course on Explainable Machine Learning in Spring 2026.
• May 25: I attended ICLR 2025 in Singapore, SP; and presented our work on “developing, theoretically justifying, and empricially validating, novel information -theoretic measures for fairness auditing at the Data Foundation Models workshop.
• April 25: Our paper Benchmarking deep learning architectures for ECG-based multi-label heart disease prediction using MIMIC-IV database. is accepted for publication in the 38^th IEEE International Symposium on Computer based Medical Systems (CBMS), Madrid, Spain.
• March 25: Our paper “Information-theoretic quantification of inherent discrimination bias in training data for supervised learning”” is accepted for presentation at the 2nd Workshop on Navigating and Addressing Data Problems for Foundation Models (DATA-FM @ ICLR 2025), in conjunction with the 13th International Conference on Learning Representations ICLR 2025.
• March 25: Our paper “Centralized and federated heart disease classification using UCI dataset: A benchmark with interpretability analysis” is accepted for publication and oral presentation at the 2025 IEEE Evolution conference, to be held in Boston, MA, June 2025.
• February 25: I delivered a talk at the 2025 Information Theory and Application (ITA2025) Workshop, titled “Information-theoretic quantification of inherent discrimination bias in training data for supervised learning”.
• January 25: Junior undergrad, computer science, student Yeva Vainerman joined our research group. Welcome Yeva!
• October 24: Our paper “Causal discovery in linear models with unobserved variables and measurement error” is accepted for presentation at the Causal Representation Learning workshop at the 2024 Neural Information Processing Conference (CRL@NeurIPS24).
• August 24: I started a new position as an Assistant Professor in Computer Engineering at Missouri University of Science & Technology, ECE department.
• August 24: I was elevated to an IEEE senior member!
• August 24: My master’s student, Mario Rodriguez, has successfully defended his master’s thesis!
• July 24: Our paper “Causal Discovery in Linear Models with Unobserved Variables and Measurement Error” is available on arXiv.
• May 24: PhD, computer engineering, student Sokrat Aldarmini joined our research group. Welcome Sokrat!
• April 24: I delivered a virtual talk titled “Towards Responsible AI: Learning with Biased, Imperfect, and Decentralized Data” to the Computer Science Department at Texas Tech University.
• March 24: I visited the ECE department at University of New Haven and delivered the same talk.
• February 24: I visited the ECE department at Missouri S&T and delivered a talk titled “Towards Responsible AI: Learning with Biased, Imperfect, and Decentralized Data”