Dr. Mohamed Nafea

Assistant Professor, Electrical and Computer Engineering Department
Email: mnafea@mst.edu
Office: 131 Emerson Electric Co Hall;

Phone: (573) 341-4558

About Me

I am an Assistant Professor in Computer Engineering in the Electrical & Computer Engineering (ECE) department at Missouri University of Science & Technology. Before joining Missouri S&T, I was an assistant professor in the ECE department 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 from Penn State, University Park, in December 2018, under supervision of Aylin Yener. I also received a masters degree in mathematics from Penn State in 2017. Before that, I received a masters degree in wireless & information technologies from Nile University, Egypt, in 2012, and my bachelor degree in Electrical Engineering (communication and electronics specialization) 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, algorithmic fairness, explainability/interpretability, privacy, robustness and security. Specific areas of research interest include:

  1. Developing explainable & fair ML models, with emphasis on “representation learning” approaches.
  2. Federated learning & distributed optimization, with emphasis on privacy, fairness, and robustness.
  3. Causal reasoning & inference with applications to ML interpretability & fairness; social & biological sciences, and econometrics.
  4. Developing ML diagnostic models for Healthcare applications, with emphasis on explainability, privacy, robustness, trustworthiness, and fairness.
  5. Information theory; with emphasis on its applications to ML systems such as Explainable-ML & representation learning, as well as security & privacy of information processing systems.
  6. Statistical guarantees (e.g., conformal prediction, bounds & confidence intervals) for trustworthy, explainable, and fair ML models.

On the application side, I am interested in a wide range of disciplines including information processing systems, image processing, data science, health informatics, as well as social, biological, and legal 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. Application not following instructions listed will 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.

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