Satarupa Mukherjee, Ph.D.

I am a strategic AI leader with 11+ years of experience building and scaling enterprise AI/ML solutions across Wearable Devices, Digital Health, Drug Discovery, and Computer Vision platforms. I have a proven track record in Generative AI, Multimodal Learning, and production AI systems, with leadership experience spanning R&D, productization, and cross-functional execution. I led high-impact AI initiatives at Samsung and Roche with 12 patents and multiple top-tier publications (MICCAI, MIDL, ICIP, SPIE, ISBI).

In addition, I was an adjunct faculty at the department of Electrical and Computer Engineering at the University of California, Santa Cruz from 2021 to 2024, and currently a Professor in the department of Electrical and Computer Engineering at San Francisco Bay University.

My Postdoctoral research was with the Department of Electrical Engineering and Computer Sciences in University of California, Berkeley in collaboration with Lawrence Berkeley National Laboratory (LBNL).

I have completed my PhD from the Department of Computing Science in University of Alberta, Canada, and MTech in Computer Science from the Indian Statistical Institute, Kolkata.

Apart from my professional life, I am a classical dancer. 

Degree & Academic Institution:

• PostDoc, Computer Science
  University of California, Berkeley
• PhD, Computing Science
  University of Alberta, Canada
• MTech, Computer Science
  Indian Statistical Institute, Kolkata

Published Patents:

• Satarupa Mukherjee et al., “Unique Item Count from Monocular Videos,” United States Patent. Application Serial No. 62/107,048.
• Satarupa Mukherjee et al., “Machine Learning Models for Cell Localization and Classification Learned Using Repel Coding,” United States Patent. Application Serial No. 63/065,268.
• Satarupa Mukherjee et al., “Multiclass Interactive Segmentation Graphical User Interface,” United States Patent. Application Serial No. 63/269,833.
• Satarupa Mukherjee et al., “Hybrid and Accelerated Ground-Truth Generation for Duplex Assays,” United States Patent. Application Serial No. 18/125,043.
• Satarupa Mukherjee et al., “Stain Unmixing of Multiplexed Brightfield Images,” United States Patent. Application Serial No. 63/499,098.
• Satarupa Mukherjee et al., “Unified Deep Learning Model For Color Unmixing,” United States Patent. Application Serial No. 63/667,615.

Submitted Patents:

• Satarupa Mukherjee et al., “Adaptive Learning: An Efficient and Continuous Learning Framework.”
• Satarupa Mukherjee et al., “Hybrid Unmixing Methods in Digital Pathology.”
• Satarupa Mukherjee et al., “Virtual Pathologist, Real World: Vision to Language Tool.”
• Satarupa Mukherjee et al., “Machine Learning Model Compression for Digital Pathology Analysis.”
   

Conferences/Publications:

• Satarupa Mukherjee, Jim Martin, Yao Nie, “A Generative AI Approach for Interference Study on Chromogenic Triplex Images,” SPIE 2024.
• Mohammed Adnan, Qinle Ba, Satarupa Mukherjee, Nazim Shaikh, Shivam Kalra, Auranuch Loraskul, “Structured Model Pruning for Efficient Inference in Computational Pathology,” MICCAI 2024.
• Satarupa Mukherjee, Qinle Ba, Jim Martin, Yao Nie, “A Deep-Learning Based Approach to Accelerate Groundtruth Generation for Biomarker Status Identification in Chromogenic Duplex Images,” MIDL 2023.
• Satarupa Mukherjee, Nahil Sobh, Jim Martin, Yao Nie, Erika Walker, Terry Landowski, “Analysis of Different Color Unmixing Methods for cMET-PDL1-EGFR Multiplex Assay,” ASCO 2023.
• Veena Kaustaban, Qinle Ba, Ipshita Bhattacharya, Nahil Sobh, Satarupa Mukherjee, Jim Martin, Mohammad Saleh Miri, Christoph Guetter, Amal Chaturvedi, “Characterizing Continual Learning Scenarios for Tumor Classification in Histopathology Images,” MICCAI 2022.
• Qinle Ba, Xiangxue Wang, Satarupa Mukherjee, Jim Martin, Mohamed Izady, “Generalizable Deep-Learning-Based Interactive Segmentation in Digital-Pathology Analysis,” Pathology Informatics Summit 2022.
• Satarupa Mukherjee et al., “Accelerated Groundtruth Annotation for PDL1-CK7 Duplex Assay Using HALO,” 13th Diagnostic R&D Fair 2021.
• Satarupa Mukherjee et al., “Deep Learning Algorithm for Biomarker Classification on Multiplexed Immunofluorescence Images using Repel Coding,” Pathology Visions 2020.
• Satarupa Mukherjee et al., “Digital Pathology Algorithms for Automated Characterization of TIM3, LAG3, PD1, CD8, and PanCK/Sox10 Markers in the Tumor Microenvironment by 5Plex Immunofluorescence Staining,” 12th Diagnostic R&D Fair 2019.
• Satarupa Mukherjee, Hariprasad P.S., Omar Oreifej, Brian Pugh, Eric Turner, Avideh Zakhor, “Automatic Computer Detection and Power Estimation in Indoor Environments from Imagery,” Electronic Imaging 2016.
• Satarupa Mukherjee and Nilanjan Ray, “A Novel Framework for Unique Vehicle Count for Traffic Monitoring,” VISAPP 2015.
• Nilanjan Ray, Satarupa Mukherjee, Krishna Kanth, Scott T. Acton, Silvia S. Blemker, “3D-To-2D Mapping for User Interactive Segmentation of Human Leg Muscles from MRI Data,” Global SIP 2014.
• Satarupa Mukherjee and Nilanjan Ray, “A Framework for Unique People Count from Monocular Videos,” ICIP 2014.
• Satarupa Mukherjee and Nilanjan Ray, “DTV: Detection, Tracking and Validation Framework for Unique People Count,” IJCSNS Vol. 2, No. 1.
• Satarupa Mukherjee and Nilanjan Ray, “A Novel Framework for Computing Unique People Count from Monocular Videos,” DCVISIGRAPP 2014.
• Satarupa Mukherjee, Nilanjan Ray & Scott T. Acton, “Counting Cells from Microscopy Videos Without Tracking,” ISBI 2014.
• S. Mukherjee, N. Ray & D.P. Mukherjee, “Tracking Objects with Rigid Body Templates: An Iterative Constrained Linear Least Squares Approach,” PREMI 2013.
• S. Mukherjee, B. Saha, I. Jamal, R. Leclerc, N. Ray, “A Novel Framework for Automatic Passenger Counting,” ICIP 2011.