91����

Bio

Subrata Mukherjee graduated from Michigan State University in 2023 with a PhD in Electrical Engineering. After completing his PhD, he served as a Postdoctoral Fellow in the Division of Imaging, Diagnostics, and Software Reliability at the US Food and Drug Administration until July 2024. Currently, he is a Postdoctoral Research Associate at 91����, a position he has held since July 2024.

Subrata has research and industrial experience focused on algorithm development, non-invasive imaging, non-destructive evaluation, data analysis, signal and image processing, machine learning, statistical inference, and fault diagnostics. At ORNL, his research focuses on sensors and automation in complex real-world environments. This includes developing specialized algorithms and software tools for sensor management, fault detection and correction, modeling, optimization, and applying Machine Learning (ML) and Artificial Intelligence (AI) to enhance fault diagnostics, process monitoring, and advanced control methods. These sophisticated data-driven and physics-based models and tools have applications across various fields, including electrical grid monitoring, smart manufacturing, water treatment, desalination, and other non-destructive monitoring applications.

A terse description of his research activities is available .
 

Professional Experience

Postdoctoral Research Associate - 91����
Sensors and Embedded Systems Group [July 2024 – Present]
At ORNL, he is working in the Sensors and Embedded Systems Group of the Electrification and Energy Infrastructures Division (EEID). His primary responsibility involves the development and application of Machine Learning (ML) and Artificial Intelligence (AI) algorithms to enhance fault diagnostics, process monitoring, and advanced control methods. These sophisticated data-driven and physics-based dynamic models find application across diverse fields such as electrical grid monitoring, smart manufacturing, water treatment, and desalination.

Postdoctoral Fellow - US Food and Drug Administration
Division of Imaging, Diagnostics, and Software Reliability [August 2023 – July 2024]
At FDA, he worked on the project “Prediction of Response to Therapy for Metastatic Breast Cancer: Joint Analysis of Radiologic and Genomic Data Using Machine Learning”. He developed an image registration-based automated lesion correspondence and matching (RAMAC) algorithm and designed it into a regulatory science tool (RST). This algorithm dynamically tracks both target and non-target entities, addressing the variability across different timepoints and radiologists in longitudinal data analysis. He developed joint modeling of the longitudinal and time-to-event data for metastatic breast cancer (mBC) progression risk prediction, performing lesion and organ segmentation using deep learning frameworks. He also actively participated in reviewing AI/ML consults related to medical imaging devices and diagnostics and attended regulatory discussion meetings at the FDA.

Research Associate - Michigan State University
Department of Electrical and Computer Engineering [August 2018 – July 2023]
During his PhD at MSU, he worked as a research assistant (RA) where he developed robust automated defect detection and identification algorithms using data collected from novel near-field electromagnetic sensors. He led various projects funded by industrial partners such as Electric Power Research Institute (EPRI), Gas Technology Institute (GTI), and other DOT, DOE-based PHMSA projects. His responsibilities included developing flexible deep learning frameworks, spatially adaptive denoising algorithms, efficient data augmentation, digital twinning and fusion schemes, sophisticated data compression algorithms, defect tracking algorithms, and nondestructive sensor development for inline inspection (ILI).

Advanced Algorithm Researcher - Analog Garage
[May 2022 – September 2022]
 At Analog Garage, he worked on the Advanced Battery Monitoring (ABM) project, where he developed novel statistics-based transfer learning algorithms for electric vehicle battery state of health (SOH) estimation across various battery types. Additionally, he developed synthetic battery aging models in Ansys following complex test protocols similar to real experiments.

Solution Integrator - Ericsson
[September 2015 – May 2018]
He worked in R&D and operations for Workforce Management (WFM) at Ericsson in Kolkata, India. He developed optimized algorithms and new customized routines and policies for mobile touch customization using C#, Click, and JQuery. 
 

Awards

Recipient of the 2018 Engineering Distinguished Scholar Award at Michigan State University.
 

Education

• Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering: Michigan State University, 2023

  Dissertation:  

• Bachelor of Technology (B. Tech) in Electronics & Communication Engineering, 2015

Professional Service

Reviewer: 91���� Sensors, 91���� Transaction on Instrumentation and Measurement, Springer Nature-Journal of Nondestructive Evaluation (JNDE), Research in Nondestructive Evaluation (RNDE), 91���� PHM, IMECE (ASME), British Journal of Radiology (BJR), and Medical Physics

Other Publications