Ahmad Mousavi

Ahmad Mousavi

Assistant Professor of Data Science,
Department of Mathematics and Statistics,
American University

Curriculum Vitae

Positions

  • American University, Washington, DC
    • Assistant Professor, Department of Mathematics and Statistics (2024–present)
    • Affiliate Faculty, Institute of Artificial Intelligence, Kogod School of Business (2024–present)
    • Faculty Fellow, Center for Data Science, School of Public Affairs (2026–present)
    • Professorial Lecturer, Department of Mathematics and Statistics (2023–2024)
  • University of Florida, Gainesville, FL
    • Postdoctoral Associate, Informatics Institute (2021–2023)
  • University of Minnesota, Minneapolis, MN
    • Industrial Postdoctoral Fellow, Institute for Mathematics and its Applications (2019–2021)

Education

  • University of Maryland, Baltimore County, Baltimore, MD — Ph.D. in Applied Mathematics, 2013–2019
    • Advisor: Prof. Jinglai Shen
    • Thesis: Topics in Sparse Recovery via Constrained Optimization: Least Sparsity, Solution Uniqueness, and Constrained Exact Recovery
  • University of Maryland, Baltimore County, Baltimore, MD — M.Sc. in Applied Mathematics, 2013–2015
  • Sharif University of Technology, Tehran, Iran — M.Sc. in Applied Mathematics, 2008–2011
    • Advisor: Prof. Nazam Mahdavi-Amiri
    • Thesis: An Inexact Newton Method for Nonconvex Equality Constrained Optimization
  • University of Guilan, Rasht, Iran — B.Sc. in Applied Mathematics, 2004–2008

Work experience

  • Research and Development Intern, Precima, R&D division, Chicago, IL (Summer 2019)

Supervising experience

  • Data Science Practicum
    • Fall 2024: Yen-Chun Lin, Sean Hsu, Ting Yi Chuang, and Po-Yu Lai. Fairness in Imbalanced Data — Guided students in exploring Universum-based quadratic and linear SVM models to address bias in imbalanced datasets, with applications such as fraud and disease detection.
    • Spring 2025: Meet Patel, Dwanith Venkat Girish. Neural Network Pruning — Supervised the development and evaluation of structured and unstructured pruning methods to improve the efficiency and scalability of deep learning models.
    • Fall 2025: Peter Ozo-Ogueji, Kaitlin Works-Figueroa, Aidan Hennesey. Multimodal Misdiagnosis Detection — Led students in designing an AI system integrating clinical notes, laboratory data, and medical imaging using deep learning and reinforcement learning to proactively identify diagnostic errors.
    • Spring 2026–present: Kennya Jiles, Kaitlin Works-Figueroa, Aidan Hennesey. Chronic Disease Detection — Led students in designing an AI system integrating clinical notes, laboratory data, and medical imaging using deep learning methods to detect chronic diseases at early stages.
  • Capstone
    • Fall 2024: Spencer Grewe. Understanding U-Net Architecture — Supervised a detailed architectural and empirical analysis of U-Net for biomedical image segmentation.
  • External and cross-institutional supervision
    • Spring 2026–present: Katherine Lessard (American University), Yanjia Zhang (George Mason University), Pooja Jitendra Shah (George Mason University), Sai Praneet Reddy Chinthala (George Mason University), co-supervised with Dr. Lindi Liao (George Mason University). Fair Bilevel Optimization with Synthetic Data for Collaborative Classification — Guiding students in developing and implementing an efficient bilevel optimization algorithm for fairness-aware collaborative learning using synthetic data generation.
    • Spring 2025–present: Yeganeh Abdollahinejad (Pennsylvania State University). Robust Multi-Modal and Multi-Scale Learning under Distribution Shift — Leading and mentoring the development of advanced attention-based architectures for subject-independent EEG emotion recognition (EMO-CARE) and event-centric cross-modal misinformation detection (E-CaTCH). Provided conceptual guidance on multi-scale temporal modeling, channel-aware and cross-modal attention mechanisms, temporal consistency regularization, and class-imbalance handling. Supervised theoretical framing, algorithm design, experimental validation under rigorous evaluation protocols, and manuscript preparation for peer-reviewed publication.
    • Fall 2025–present: Mohammed Toufikuzzaman (Pennsylvania State University), co-supervised with Dr. Dongwon Lee (Pennsylvania State University). Sparse Bilevel Augmented Lagrangian for Unlearning — Mentoring the student in understanding, implementing, and extending the Sparse Bilevel Augmented Lagrangian framework for machine unlearning with theoretical and computational analysis.

Review experience

Review for the following journals and conferences: Neural Networks, NeurIPS, IEEE Signal Processing, Journal of Optimization Theory and Applications, Mathematical Methods of Operations Research, Optimization Methods and Software, Digital Signal Processing, Physica A: Statistical Mechanics and its Applications, Numerical Algorithms, Set-Valued and Variational Analysis, Mathematical Problems in Engineering, Infor: Information Systems and Operational Research, Complex and Intelligent Systems, and so on.

Conference and workshop presentations and attendance

  • (Attendance) StatConnect@AI, Georgetown University, Washington, DC, USA, 2026.
  • (Attendance) NeurIPS 2025, San Diego, CA, USA, 2025.
  • (Attendance) IEEE International Conference on Data Mining (ICDM 2025), Washington, DC, USA, 2025.
  • (Attendance) Workshop on Intersections between Control, Learning, and Optimization, UCLA, Los Angeles, CA, USA, 2019.
  • (Poster presentation) Solution Uniqueness of Convex Piecewise Affine Functions Based Optimization with Applications to Constrained L1 Minimization, Princeton Day of Optimization, 2018; ICERM Computational Imaging Workshop, 2019; ICERM Optimization Methods in Computer Vision and Image Processing Workshop, 2019.
  • (Presentation) Some Topics in Sparse Optimization, Norbert Wiener Center seminars, University of Maryland, College Park, MD, USA, 2018.
  • (Presentation) Solution Uniqueness of Convex Piecewise Affine Functions Based Optimization with Applications to Constrained L1 Minimization, SIAM Annual Meeting session, Portland, OR, USA, 2018.
  • (Attendance) SIAM Annual Meeting, Pittsburgh, PA, USA, 2017.
  • (Presentation) A Mathematical Introduction to Compressive Sensing, University of Maryland, Baltimore County, Optimization Seminar, Baltimore, MD, USA, 2016.
  • (Attendance) American Mathematical Society Spring Eastern Sectional Meeting, Baltimore, MD, USA, 2014.
  • (Attendance) 3rd and 4th Workshop on Optimization and its Applications, Tehran, Iran, 2011 and 2012.
  • (Attendance) 3rd and 4th International Conference of Iranian Operations Research Society, Tehran and Rasht, Iran, May 2010 and 2011.
  • (Attendance) 40th Annual Iranian Mathematics Conference, Tehran, Iran, 2009.

Service

  • Member, CAS AI Steering Committee, American University, Fall 2025–present.
    • I also serve as a member of both the Student Engagement and Scholarship Group and the Responsible AI Group within the committee.
  • Member, Student Research Committee, Department of Mathematics and Statistics, American University, Fall 2025–present.
  • Judge, Smith Analytics Consortium Datathon, Robert H. Smith School of Business, University of Maryland (UMD–American University student analytics competition sponsored by Deloitte), 2025 and 2026.
  • Vice President, Mathematics and Statistics Graduate Student Association (MSGSA), University of Maryland, Baltimore County, 2015.
  • Orientation Advisor, University of Maryland, Baltimore County, Summer 2017.

Miscellaneous

Computer skills

  • R
  • Python
  • MATLAB
  • LaTeX
  • Microsoft Office

Memberships

  • Society for Industrial and Applied Mathematics (SIAM)
  • IEEE (Regular Member)

Download full CV (PDF)