Ahmad Mousavi

Ahmad Mousavi

Assistant Prof. of Data Science,
Dept. of Math. and Stat.,
American University

Curriculum Vitae

Download CV (PDF)

Positions

  • American University, Washington, DC
    • Assistant Professor of Data Science, Department of Mathematics and Statistics (Fall 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 (Fall 2023–Spring 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. Nezam Mahdavi-Amiri
    • Thesis: An Inexact Newton Method for Nonconvex Equality Constrained Optimization
  • University of Guilan, Rasht, Iran — B.Sc. in Applied Mathematics, 2004–2008

Publications

Also listed on the Publications page and on Google Scholar (324 citations; h-index 9).

Journal Articles

  1. Z. Aminifard, S. Babaie-Kafaki, Z. Saeidian, and A. Mousavi. “A Dynamically Nonmonotone Trust Region Algorithm Based on Scalar Estimations of the Hessian with Application to Binary Classification with Outliers.” Journal of Computational and Applied Mathematics, Article 117998, 2026. DOI
  2. A. Mousavi, Y. Abdollahinejad, R. Corizzo, N. Japkowicz, and Z. Boukouvalas. “E-CaTCH: Event-Centric Cross-Modal Attention with Temporal Consistency and Class-Imbalance Handling for Misinformation Detection.” IEEE Transactions on Computational Social Systems, pp. 1–14, 2026. DOI
  3. H. Moosaei, M. Hladík, A. Mousavi, Z. Gao, and H. Fu. “Quadratic Surface Twin Support Vector Machine for Imbalanced Data.” International Journal of Machine Learning and Cybernetics 17(5), 250, 2026. Link
  4. Y. Abdollahinejad, S. M. Reza, B. Ashwini, and A. Mousavi. “Systematic Literature Review of Machine Learning Methods for Emotion Recognition Using EEG and Physiological Signals in Healthcare.” IEEE Access, 2026. DOI
  5. Y. Abdollahinejad, A. Mousavi, P. Siaplaouras, Z. Boukouvalas, and R. Corizzo. “EMO-CARE: EEG Multi-Scale Temporal Modeling with Channel-Aware Feature Attention for Robust Subject-Independent Emotion Recognition.” IEEE Open Journal of the Computer Society, 2026. Link
  6. A. Mousavi, M. Salahi, and Z. Boukouvalas. “Sparse Extended Mean-Variance-CVaR Portfolios with Short-Selling.” Japan Journal of Industrial and Applied Mathematics 43(1), 12, 2026. Link
  7. P. Nazari, A. Mousavi, D. Ataee Tarzanagh, and G. Michailidis. “A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming.” Automatica 173, 112039, 2025. DOI
  8. A. Mousavi and G. Michailidis. “Cardinality Constrained Mean-Variance Portfolios: A Penalty Decomposition Algorithm.” Computational Optimization and Applications, 2025. DOI
  9. A. Mousavi and G. Michailidis. “Statistical Proxy Based Mean-Reverting Portfolios with Sparsity and Volatility Constraints.” International Transactions in Operational Research 32(6), 3848–3869, 2025. DOI
  10. H. Rezapour, R. Nasiri, and A. Mousavi. “The Hyper-Zagreb Index of Trees and Unicyclic Graphs.” Iranian Journal of Mathematical Sciences and Informatics 18(1), 41–54, 2023. Link
  11. H. Moosaei, A. Mousavi, M. Hladík, and Z. Gao. “Sparse ℓ1-Norm Quadratic Surface Support Vector Machine with Universum Data.” Soft Computing 27(9), 5567–5586, 2023. DOI
  12. A. Mousavi and J. Shen. “A Penalty Decomposition Algorithm with Greedy Improvement for Mean-Reverting Portfolios with Sparsity and Volatility Constraints.” International Transactions in Operational Research 30(5), 2415–2435, 2023. DOI
  13. A. Mousavi, Z. Gao, L. Han, and A. Lim. “Quadratic Surface Support Vector Machine with ℓ1 Norm Regularization.” Journal of Industrial and Management Optimization 18(3), 1835–1861, 2022. DOI
  14. S. K. Popuri, N. K. Neerchal, A. Mehta, and A. Mousavi. “Density Estimation Using Entropy Maximization for Semi-Continuous Data.” Digital Signal Processing 116, 103107, 2021. DOI
  15. A. Mousavi, M. Rezaee, and R. Ayanzadeh. “A Survey on Compressive Sensing: Classical Results and Recent Advancements.” Journal of Mathematical Modeling 8(3), 309–344, 2020. Link
  16. A. Mousavi and J. Shen. “Solution Uniqueness of Convex Piecewise Affine Functions Based Optimization with Applications to Constrained ℓ1 Minimization.” ESAIM: Control, Optimisation and Calculus of Variations 25, 56, 2019. DOI
  17. J. Shen and A. Mousavi. “Least Sparsity of p-Norm Based Optimization Problems with p > 1.” SIAM Journal on Optimization 28(3), 2721–2751, 2018. DOI

Conference Papers

  1. Md Toufikuzzaman, A. Mousavi, and D. Lee. “BLADE: Bilevel Low-Rank Augmented-Lagrangian Erasure for LLM Unlearning.” To appear in EMNLP 2026 (Main Conference). arXiv
  2. J. Rajabi, S. Okechukwu, A. Mousavi, R. Corizzo, C. C. Cavalcante, and Z. Boukouvalas. “Event-Based Multi-Modal Fusion for Online Misinformation Detection in High-Impact Events.” Proceedings of the IEEE International Conference on Big Data, pp. 3301–3308, 2024. Link

Submitted and Non-Peer-Reviewed Manuscripts

  1. R. Ayanzadeh, A. Mousavi, A. Basareh, N. Alavisamani, and K. Taram. “Enigma: Beyond Circuit-Level Security through Application-Layer Obfuscation for Quantum Optimization.” Submitted to EuroSys 2027 Fall (September 2026). arXiv
  2. S. Babaie-Kafaki, M. Khoshsimaye-Bargard, and A. Mousavi. “A family of spectral conjugate gradient algorithms derived by least-squares approximations based on a modified quasi-Newton update with application to a revised robust binary classification model.” arXiv preprint arXiv:2609.13526, 2026. arXiv
  3. A. Mousavi and M. Alikhani (equal contribution), Y.-C. Lee, R. Corizzo, and Y. Abdollahinejad. “MURAL: Multimodal Uncertainty-Aware Recommendation via Adaptive Edge Learning.” arXiv preprint arXiv:2609.04574, 2026. arXiv
  4. M. Soltanalian and A. Mousavi. “The Rank-Collapse Principle for Quadratic Optimization.” arXiv preprint arXiv:2608.07828, 2026. arXiv
  5. A. S. Kumar, Y. Abdollahinejad, S. Khosravi, M. Alikhani, H. Talebighadikolahi, and A. Mousavi. “CAM-Soft: Scalable Multimodal Fusion for Soft Hate Speech Detection via Camouflaged-Implicature Modeling and Counterfactual Invariance.” Manuscript in preparation (intended for IEEE BigData).
  6. K. Jayathilake, A. Mousavi, and R. Ayanzadeh. “Weaver: A Hybrid Quantum-Classical Optimization Method for Binary Compressive Sensing under Matrix Uncertainty.” Submitted to AAAI 2027 (July 2026). OpenReview
  7. M. Esparza, A. Ayanzadeh, A. Mousavi, and A. Mostafavi. “Physics-Conditioned Attention Bias and Retrieval-Augmented Correction for Next-Day Wildfire Spread Prediction.” Preprint, July 2026.
  8. P. Yang, J. Luo, A. Mousavi, R. Corizzo, and Z. Gao. “An Ensemble Learning Approach Based on Random Sparse Quadratic Twin Parametric-Margin SVMs.” SSRN 7022004, 2026 (submitted). SSRN
  9. Y. Abdollahinejad, A. Mousavi, N. Hassan, K. Shu, N. Japkowicz, S. Khosravi, and A. Karami. “MOMENTA: Mixture-of-Experts over Multimodal Embeddings with Neural Temporal Aggregation for Misinformation Detection.” arXiv:2604.16172, 2026 (submitted). arXiv
  10. A. Mousavi, M. Kimiaei, S. Babaie-Kafaki, and V. Kungurtsev. “An Efficient Penalty Decomposition Algorithm for Minimization over Sparse Symmetric Sets.” arXiv:2601.12383, 2026 (under review at Mathematical Programming Computation). arXiv
  11. A. Mousavi, R. Zandvakili, and Z. Gao. “ℓ0-Regularized Quadratic Surface Support Vector Machines.” arXiv:2501.11268, 2025; Research Square journal version under review, 2026. arXiv
  12. J. Shen and A. Mousavi. “Exact Support and Vector Recovery of Constrained Sparse Vectors via Constrained Matching Pursuit.” arXiv:1903.07236, 2019. arXiv
  13. R. Ayanzadeh, A. Mousavi, M. Halem, and T. Finin. “Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty.” arXiv:1901.00088, 2019. arXiv

Book

  • Hassan Rezapour and Ahmad Mousavi. Probability and Statistics (a comprehensive book for M.S. nationwide examination in economics and management fields), Sobhan-e Mehr Publication, 2013 (in Farsi).

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 Hennessy, and Kennya Jiles. Multimodal Emergency Department Diagnosis Discordance — Led students in designing multimodal models integrating clinical notes, vitals/labs time series, and medical imaging (MIMIC-IV) to predict diagnostic discordance between ED admissions and discharge diagnoses.
    • Spring 2026: Data Science Practicum (DATA-793). Detecting Errors in Medical Diagnoses with Machine Learning — Supervised practicum students in building a multimodal system for diagnostic-error detection, integrating sequential labs and vitals, clinical notes, and medical images (MIMIC-III and DDXPlus) with deep learning and reinforcement learning for proactive misdiagnosis intervention.
  • Independent Study and Capstone
    • Fall 2024: Spencer Grewe (DATA 690). Medical Computer Vision — Supervised architectural and empirical analysis of U-Net for biomedical image segmentation, leading to development of LFASNet.
  • External and cross-institutional supervision
    • Spring 2026–present: Yanjia Zhang, Pooja Jitendra Shah, and 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.
    • 2026–present: Aruthra Satish Kumar (George Mason University). CAM-Soft: Scalable Multimodal Fusion for Soft Hate Speech Detection via Camouflaged-Implicature Modeling and Counterfactual Invariance — Mentoring research on this manuscript (in preparation; intended for IEEE BigData).
    • Spring 2025–present: Yeganeh Abdollahinejad (Michigan State University, Department of Biology; mentorship began while she was at Pennsylvania State University). Robust Multi-Modal and Multi-Scale Learning under Distribution Shift — Mentoring work on EMO-CARE, E-CaTCH, MOMENTA (submitted), and CAM-Soft (manuscript in preparation; intended for IEEE BigData), including model design, experiments, and manuscript preparation.
    • Fall 2025–present: Md Toufikuzzaman (Pennsylvania State University), co-advised with Dr. Dongwon Lee (Pennsylvania State University). BLADE: Bilevel Low-Rank Augmented-Lagrangian Erasure for LLM Unlearning — Co-advising the student in the constrained bilevel unlearning framework (accepted to the EMNLP 2026 main conference), including theoretical formulation, implementation, and evaluation on TOFU, MUSE, and KnowUndo.
    • 2025–present: Majid Alikhani (independent researcher, Toronto). Multimodal Recommendation and Soft Hate Speech Detection — Supervised research on MURAL (arXiv:2609.04574) and CAM-Soft (manuscript in preparation; intended for IEEE BigData).
    • 2026–present: Miguel Esparza (Texas A&M University) and Aydin Ayanzadeh (UMBC), co-mentored with Dr. Ali Mostafavi (Texas A&M University). Physics-Conditioned Attention Bias and Retrieval-Augmented Correction for Next-Day Wildfire Spread Prediction — Co-mentoring research on this preprint (July 2026).

Review Experience

Review for the following journals and conferences: Neural Networks, NeurIPS 2025 Workshop on Constrained Optimization in Machine Learning (COML), IEEE Transactions on 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.

Conference And Workshop Presentations

  • (Poster presentation) E-CaTCH: Event-Centric Cross-Modal Attention with Temporal Consistency and Class-Imbalance Handling for Misinformation Detection, AU Research Conference, American University, Washington, DC, USA, May 11, 2026.
  • (Presentation) Exploring Sparsity: Modeling and Algorithmic Insights in Machine Learning and Finance, Departmental Seminar, Department of Mathematics, Semnan University, October 13, 2025.
  • (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.
  • (Presentation) A Mathematical Introduction to Compressive Sensing, University of Maryland, Baltimore County, Optimization Seminar, Baltimore, MD, USA, 2016.

Service

  • Member, 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.
  • Member, Honors Committee, Department of Mathematics and Statistics, American University, Fall 2026–present.
  • Program Committee Member, 5th IEEE Big Data Workshop on Multimodal AI (MMAI 2025), IEEE International Conference on Big Data, 2025.
  • Program Committee Member, Multimodal AI (MMAI) Workshop, IEEE International Conference on Big Data, 2026.
  • External PhD Thesis Reviewer and Defense Examiner, Computer Science, Free University of Bozen-Bolzano, in-person defense September 24, 2026.
  • 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.

Selected Honors And Awards

  • CAS Faculty Grant for Scholarship and Creative Inquiry, College of Arts and Sciences, American University, $2,500 (“Fair Collaborative Learning via Synthetic Payloads in Imbalanced Settings”), awarded December 2025.
  • Outstanding Graduate Research Award in Mathematics, College of Natural and Mathematical Sciences, UMBC, 2019.

Miscellaneous

Computer skills

  • R
  • Python
  • MATLAB
  • LaTeX

Memberships

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