Curriculum Vitae
Suhas Sreehari, PhD
Assistant Professor
Data Science Institute & Biomedical Engineering
Medical College of Wisconsin, Milwaukee, USA
Contact:
Phone: +1 (865) 985-1885
Email: suhas.sreehari@ieee.org
Web: www.ornl.gov/staff-profile/suhas-sreehari
Education
Ph.D., Electrical Engineering (2012–2017)
Purdue University, West Lafayette, USA
First Rank, PhD Qualifying Exam in Signal Processing
Thesis: “Advanced Prior Modeling for Nano-scale Imaging”
Advisor: Prof. Charles A. Bouman
Master of Applied Science (M.A.Sc.), Electrical Engineering (2010–2012)
University of Windsor, Canada
GPA: 12.5/12 (A = 12)
Thesis: “Fast Modular Reduction for Large-Integer Multiplication”
Advisors: Prof. Majid Ahmadi and Prof. Huapeng Wu
Bachelor of Engineering, Telecommunications Engineering (2005–2010)
Visvesvaraya Technological University, India
First Class with Distinction
Professional Experience
Assistant Professor (Dec 2025–Present)
Data Science Institute and Biomedical Engineering
Medical College of Wisconsin, Milwaukee, WI
- Multimodal modeling, computational imaging, biomedical informatics
- Member, MCW Center for Imaging Research
Chief Scientist (Jun 2025–Apr 2026)
Pelagia Labs (Stealth Mode)
- Multimodal analytics, computational imaging, differential geometry for explainable AI
Assistant Professor (Aug 2023–May 2025)
Electrical Engineering and Computer Science
The University of Tennessee, Knoxville, TN
- Joint Faculty Appointment with Oak Ridge National Laboratory
Research Staff Scientist (Jun 2021–May 2025)
Oak Ridge National Laboratory, TN
- Robust latent space representations in autoencoders for biological data
- Lead investigator on federated learning for medical image super-resolution
- Privacy-preserving face ID algorithms using generative networks
- AI/ML solutions for anomaly detection in healthcare
Assistant Vice President, Quantitative Analytics (Oct 2019–Apr 2021)
Wells Fargo, San Francisco, CA
- AI/ML anomaly detection models for financial crime/fraud
- Explainable ML techniques and surrogate models for neural networks
- Automated ML pipeline development
Quantitative Analytics Specialist (Jul 2018–Oct 2019)
Wells Fargo, San Francisco, CA
Quantitative Analytics Associate (Jun 2017–Jul 2018)
Wells Fargo, San Francisco, CA
Research Intern (May 2016–Aug 2016)
Siemens Healthineers, Princeton, NJ
- Multi-prior Bayesian image reconstruction for MRI scanners
- Pushed into production in 2017 – led to two inventions
Graduate Research Assistant (Jan 2013–Apr 2017)
Purdue University, West Lafayette, IN
- Developed Plug-and-Play Priors algorithm
- Created reconstruction algorithms for super-resolution, electron tomography
- Funded by US Air Force Research and MURI
Honors and Awards
- China-America Frontiers of Engineering, National Academy of Engineering (2026)
- Distinguished Alumnus, B.M.S. College of Engineering, India (2024)
- IEEE Senior Member (2022)
- SIAM Imaging Sciences Best Paper Prize (2020)
- Wells Fargo Medallion for Most Innovative Patent (2020)
- IEEE Signal Processing Society Young Author Best Paper Award (2018)
- SPIE Best Student Paper Award (2017)
- Presidential Scholar Award, Microscopy and Microanalysis (2016)
- 1st Rank, PhD Qualifying Exam in Signal Processing, Purdue (2013)
Research Impact
- Plug-and-Play Priors: Used in 100+ scientific and engineering research labs worldwide
- Commercial Deployment: Samsung Galaxy smartphone fingerprint sensors
- Research Funding: Led to $1.2M NSF grant
- 20+ Publications: Highly cited work in computational imaging and ML
- 3 US Patents: Including innovative face recognition and imaging technologies
Professional Service
- Area Chair, IEEE ICASSP Computational Imaging (2025, 2026)
- Elected Member, IEEE Technical Committee on Computational Imaging (2025–)
- Chairman, IEEE East Tennessee Section (2022–2023)
- Reviewer: IEEE Trans. on Image Processing, Signal Processing, Optics Express
- Consultant: Imaging and AI (Hypercubes Global, 2020–2021)
Technical Expertise
Research Areas:
Generative AI, multi-modal models, Bayesian machine learning, deep learning, computational imaging, federated learning, explainable AI, optimization, inverse problems
Computing:
Python, PyTorch, TensorFlow, Spark, MATLAB, C++
Specializations:
MRI reconstruction, electron microscopy, super-resolution imaging, anomaly detection, privacy-preserving ML, differential geometry for AI
Selected Publications
-
Buzzard et al., “Plug-and-play unplugged: Optimization-free reconstruction using consensus equilibrium,” SIAM J. Imaging Sciences, 2018
-
Sreehari et al., “Plug-and-play priors for bright field electron tomography,” IEEE Trans. Computational Imaging, 2016 ⭐ Best Paper Awards
-
Sreehari et al., “Multi-resolution data fusion for super-resolution electron microscopy,” IEEE CVPR Workshops, 2017
Full publication list available
Contact
For collaboration inquiries, speaking engagements, or prospective students:
suhas.sreehari@ieee.org