Paromita Nath, Ph.D.

Paromita Nath, Ph.D.

Paromita Nath, Ph.D.
Assistant Professor

Paromita Nath, Ph.D.
Mechanical Engineering

Contact Info
Rowan Hall 138


Website: Google Scholar

Ph.D., Civil Engineering, Vanderbilt University
M.S., Civil Engineering, Vanderbilt University
M.E., Civil Engineering - Structural Engineering, Birla Institute of Technology and Science, Pilani, India
B.E., Civil Engineering, Assam Engineering College, India

Impactful Research Areas:
    Computational Health
    Sustainable Materials & Processes
    Digital Engineering
    Sensors & Robotics

Research Expertise:
Uncertainty Quantification, Additive Manufacturing, Bayesian Inference, Additive Manufacturing, Process Design and Control under Uncertainty     

Dr. Nath is interested in uncertainty quantification and management with applications in additive manufacturing, health care, and power systems.  Dr. Nath’s current research focuses on building and implementing a probabilistic digital twin for additive manufacturing and also on studying the process-structure-property relationships in additive manufacturing.

Professional Memberships:
American Society of Mechanical Engineers (ASME)                                                                                              
American Society for Engineering Education (ASEE) 

Recent Publications:
Nath, P., Sato, M., Karve, P., & Mahadevan, S. (2022). Multi-fidelity Modeling for Uncertainty Quantification in Laser Powder Bed Fusion Additive Manufacturing. Integrating Materials and Manufacturing Innovation, 1-20.

Nath, P., & Mahadevan, S. (2022). Probabilistic Digital Twin for Additive Manufacturing Process Design and Control. Journal of Mechanical Design, 1-15. 

Kapusuzoglu, B., Nath, P., Sato, M., Mahadevan, S., & Witherell, P. (2022). Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering8(1), 011112.

Mahadevan, S., Nath, P., & Hu, Z. (2022). Uncertainty Quantification for Additive Manufacturing Process Improvement: Recent Advances. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering8(1), 010801.

Nath, P., & Mahadevan, S. (2021). Probabilistic predictive control of porosity in laser powder bed fusion. Journal of Intelligent Manufacturing, 1-19.