Dr. Mohammad H. Rafiei, a faculty instructor of Mechanical Engineering under the Engineering for Professionals program at Johns Hopkins University (JHU), is a distinguished Artificial Intelligence (AI) scientist and Machine Learning (ML) engineer with over 12 years of experience developing and optimizing ML models AI systems. His work spans multiple domains, including Computer Vision, Natural Language Processing, Signal Processing, Civil and Mechanical Engineering, Neuroscience, and Biomedical Engineering, with significant contributions to ML/AI research and development. Dr. Rafiei’s expertise is underpinned by a robust academic background and a prolific record of peer-reviewed publications.

Dr. Rafiei earned his Ph.D. in Civil Engineering with a focus on Machine Learning and Artificial Intelligence from Ohio State University in December 2016 under Prof. Hojjat Adeli. His doctoral research laid the foundation for his innovative work in ML/AI, particularly his Neural Dynamic Classification algorithm invention. 

Professional Experience

Chief Technology Officer (CTO) – AI Whittler Startup

Columbus, OH | May 2023 – Present

Instructor & Academic Advisor – Mechanical Engineering, Johns Hopkins University

Baltimore, MD | January 2021 – Present

Postdoctoral Fellow – Computer Science, Georgia State University

Atlanta, GA | January 2021 – April 2023

Postdoctoral Fellow and Adjunct Assistant Research Scientist – Mechanical Engineering, Johns Hopkins University

Baltimore, MD | August 2018 – December 2020

Postdoctoral Researcher – Civil Engineering, Neuroscience, and Physical Medicine, Ohio State University

Columbus, OH | February 2017 – July 2018

Education History

  • PhD Civil Engineering, Ohio State University

Work Experience

Instructor, Johns Hopkins University


24. Asl, J. R., Rafiei, M. H., Alohaly, M., & Takabi, D. (2024). A Semantic, Syntactic, And Context-Aware Natural Language Adversarial Example Generator. IEEE Transactions on Dependable and Secure Computing.
23. Mohammadshirazi, A., Nadafian, A., Monsefi, A. K., Rafiei, M. H., & Ramnath, R. (2023). Novel physics-based machine-learning models for indoor air quality approximations. arXiv preprint arXiv:2308.01438.
22. Xie, B., Yao, X., Mao, W., Rafiei, M. H., & Hu, N. (2023). High-efficient low-cost characterization of composite material properties using domain-knowledge-guided self-supervised learning. Computational Materials Science, 216, 111834.
21. Ghazvinian, P., Podschwadt, R., Panzade, P., Rafiei, M. H., & Takabi, D. Poster: Packing-aware Pruning for Efficient Private Inference based on Homomorphic Encryption. Memory, 303(166), 166.
20. Podschwadt, R., Takabi, D., Hu, P., Rafiei, M. H., & Cai, Z. (2022). A survey of deep learning architectures for privacy-preserving machine learning with fully homomorphic encryption. IEEE Access, 10, 117477-117500.
19. Rafiei, M. H., Gauthier, L. V., Adeli, H., & Takabi, D. (2022). Self-supervised learning for electroencephalography. IEEE Transactions on Neural Networks and Learning Systems.
18. Rafiei, M. H., Gu, Y., & El-Awady, J. A. (2020). Machine learning of dislocation-induced stress fields and interaction forces. JOM, 72, 4380-4392.
17. Ma, C., Zhang, Z., Luce, B., Pusateri, S., Xie, B., Rafiei, M. H., & Hu, N. (2020). Accelerated design and characterization of non-uniform cellular materials via a machine-learning based framework. npj Computational Materials, 6(1), 40.
16. Rafiei, M. H., Kelly, K. M., Borstad, A. L., Adeli, H., & Gauthier, L. V. (2019). Predicting improved daily use of the more affected arm poststroke following constraint-induced movement therapy. Physical therapy, 99(12), 1667-1678.
15. Yang, Z., Rafiei, M. H., Hall, A., Thomas, C., Midtlien, H. A., Hasselbach, A., … & Gauthier, L. V. (2018). A novel methodology for extracting and evaluating therapeutic movements in game-based motion capture rehabilitation systems. Journal of medical systems, 42, 1-14.
14. Rafiei, M. H., & Adeli, H. (2018). Novel machine-learning model for estimating construction costs considering economic variables and indexes. Journal of construction engineering and management, 144(12), 04018106.
13. Rafiei, M. H., & Adeli, H. (2018). A novel unsupervised deep learning model for global and local health condition assessment of structures. Engineering Structures, 156, 598-607.
12. Rafiei, M. H., & Adeli, H. (2018). Novel machine learning model for construction cost estimation taking into account economic variables and indices. Journal of Construction Engineering and Management, 144(12), 04018106.
11. George, S. H., Rafiei, M. H., Borstad, A., Adeli, H., & Gauthier, L. V. (2017). Gross motor ability predicts response to upper extremity rehabilitation in chronic stroke. Behavioural brain research, 333, 314-322.
10. George, S. H., Rafiei, M. H., Gauthier, L., Borstad, A., Buford, J. A., & Adeli, H. (2017). Computer-aided prediction of extent of motor recovery following constraint-induced movement therapy in chronic stroke. Behavioural Brain Research, 329, 191-199.
9. Rafiei, M. H., & Adeli, H. (2017). A novel machine learning‐based algorithm to detect damage in high‐rise building structures. The Structural Design of Tall and Special Buildings, 26(18), e1400.
8. Rafiei, M. H., & Adeli, H. (2017). NEEWS: A novel earthquake early warning model using neural dynamic classification and neural dynamic optimization. Soil Dynamics and Earthquake Engineering, 100, 417-427.
7. Rafiei, M. H., Khushefati, W. H., Demirboga, R., & Adeli, H. (2017). Novel Approach for Concrete Mixture Design Using Neural Dynamics Model and Virtual Lab Concept. ACI Materials Journal, 114(1).
6. Rafiei, M. H., Khushefati, W. H., Demirboga, R., & Adeli, H. (2017). Supervised deep restricted Boltzmann machine for estimation of concrete. ACI Materials Journal, 114(2), 237.
5. Rafiei, M. H., & Adeli, H. (2017). A new neural dynamic classification algorithm. IEEE transactions on neural networks and learning systems, 28(12), 3074-3083.
4. Rafiei, M. H., Khushefati, W. H., Demirboga, R., & Adeli, H. (2016). Neural Network, Machine Learning, and Evolutionary Approaches for Concrete Material Characterization. ACI Materials Journal, 113(6).
3. Rafiei, M. H., & Adeli, H. (2016). Sustainability in highrise building design and construction. The Structural Design of Tall and Special Buildings, 25(13), 643-658.
2. Rafiei, M. H. (2016). Advanced Neural Network and Machine Learning Models for Construction, Materials and Structural Engineering (Doctoral dissertation, The Ohio State University).
1. Rafiei, M. H., & Adeli, H. (2016). A novel machine learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142(2), 04015066.

10. Ma, C., Zhang, Z., Luce, B., Gul, B., Rafiei, M.H., & Hu, N. “Design of Architected Materials by Machine Learning.” 56th Annual Technical Meeting, The Society of Eng. Science, Washington University, St. Louis, MO, October 13-15, 2019, Accepted 06/2019.
9. Rafiei, M.H. & Mohammadshirazi, F. “A Novel Multi-Paradigm Deep Learning-Based Model for Condition Assessment of Bridges Using Vibration Records from Smartphones.” 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure, St. Louis, MO, August 04-07, 2019, Accepted 10/2018.
8. Ma, C., Zhang, Z., Luce, B., Gul, B., Rafiei, M.H., & Hu, N. “A Machine Learning-Based Framework for Accelerated Design in Architected Materials.” Eng. Mechanics- Institute Conference 2019, California Institute of Technology, Pasadena, CA, Presented 06/2019.
7. Rafiei, M.H. & El-Awady, J. (2019) “A Machine Learning Approach for Estimating the Stress Field & Dislocation-Dislocation Interactions in Two-Dimensional Discrete Dislocation Dynamics.” 2019 MACH Conference, The Hopkins Extreme Materials Institute, Annapolis, MD, Presented 04/2019.
6. Rafiei, M.H. (2018). “Smart Systems for Damage & Plasticity Assessment of Materials.” Mechanics of Materials Seminar Series, Johns Hopkins University, Baltimore, MD, Presented 09/2018.
5. Rafiei, M.H. (2018). “Multi-Paradigm Smart Systems & Crowdsensing for Health Monitoring of Structures.” S.C. Lab, Massachusetts Institute of Technology, Boston, MA, Presented 05/2018.
4. Rafiei, M.H. (2018). “How to Analyze Large Neuroimaging Datasets Using Advanced Data Analytics.” The American Congress of Rehabilitation Medicine Webinar, Columbus, OH, Presented 04/2018.
3. Rafiei, M.H. (2018). “Advanced Data Analytics for Neuroimaging Problems.” The Brain Health & Performance Summit, Columbus, OH, Presented 04/2018.
2. George, S. H., Rafiei, M.H., Gauthier, L., Borstad, A., Buford, J. A., & Adeli, H. (2017). “Predicting the Extent of Post-Stroke Motor Recovery Under Two Modalities of Constraint-Induced Movement (CI) Therapy Using Enhanced Probabilistic Neural Networks (EPNN).” OSU Life Sciences Interdisciplinary Graduate Programs Symposium 2017, Columbus, OH, Presented 05/2017.
1. Rafiei, M.H. & Adeli, H. (2014). “A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units.” The Graduate Eng. Research Colloquium, Ohio State University, Columbus, OH, Presented 09/2014.

Professional Organizations

American Society of Civil Engineers (ASCE)