EDUCATION

• July 2022 - June 2023 (Boston, Massachusetts)
  Harvard University / Harvard Medical School
  Postdoctoral Research Fellow

  Multimodal ML, Time Series Modeling, Interpretable ML, Inverse Problems in Neuroimaging (MRI), Tissue Microstructure Analyses, Harmonization and Causal Inference.

• August 2017 - June 2022 (Bloomington, Indiana)
  Indiana University Bloomington, GPA 3.9/4.0
  PhD in Engineering - Intelligent Systems

  Medical Imaging, Machine Learning, Computational Topology, Random Matrix Algorithms,Deep Learning, Statistical Modeling, Software Development, Neuroscience.

• August 2017 - May 2019 (Bloomington, Indiana)
  Indiana University Bloomington
  MS in Engineering - Intelligent Systems, GPA 3.9/4.0

  Bayesian Theory, Image Processing, Representation Learning, Reinforcement Learning.

• June 2013 - June 2017 (Pune, India)
  Pune Institute of Computer Technology, University of Pune
  Bachelor of Engineering (BE): Computer Engineering, GPA 9/10

  Data Structures, Algorithms, Data Mining, Natural Language Processing, Databases,Software Engineering, Parallel & Distributed Programming, Engineering Mathematics, Artificial Intelligence.

EXPERIENCE (Research and Development)

• Jun 2023 - Present (Boston, Massachusetts)
  Janssen: Pharmaceutical Companies of Johnson & Johnson
  Postdoctoral Researcher (part of the Computer Vision Team)
  • Developed a transformers-based architecture for detecting Pulmonary Hypertension from echocardiography images.
  • Implemented a framework to integrate optical flow data into the self-supervised training of video models.
  • Introduced a novel interpretability method, 'Weiner Attributions', for enhanced comprehension of deep learning models.
• July 2022 - June 2023 (Boston, Massachusetts)
  Harvard University / Harvard Medical School (adviser: Ofer Pasternak, PhD)
  Postdoctoral Research Fellow (Leading analysis and algorithm development for U24 AMP-SCZ)
  • Developing algorithms to learn from multimodal biomedical data (dMRI, fMRI, EEG and Audio/Video Interviews).
  • Detecting and summarizing sub-types of Chronic Pain and Psychosis with NLP-transformers and Explainable AI.
  • New Deep Belief Networks-based approach to model Wisdom of Crowds data.
• May 2021 - August 2021 (Yorktown Heights, New York)
  IBM Research (adviser: Guillermo Cecchi, PhD, Amit Dhurandhar, PhD)
  PhD Research Intern
  • Robust predictive biomarkers of chronic pain using multi-view learning and expert-guided ML.
  • Studied invariant representations for denoising and data harmonization using self-supervised learning.
• May 2020 - August 2020 (Yorktown Heights, New York)
  IBM Research (adviser: Guillermo Cecchi, PhD)
  PhD Research Intern
  • Developed a multi-view learning framework for intermediate data fusion of diffusion MRI microstructure models.
  • Leverages multi-view boosting, deep learning and multiple kernel learning for neurological diseases and white matter characterizations.
• May 2018 - Present (Yorktown Heights, New York)
  Diffusion Imaging in Python (DIPY) https://dipy.org
  Software Developer (Open-Source, Core Team, Maintainer)
  • Self-supervised learning framework for denoising 4D diffusion MRI data.
  • Intravoxel Incoherent Motion using Topological Optimization and Variable Projection.
  • Constrained Deconvolution reconstruction using Spherical Harmonics.
• August 2018 - Present (Bloomington, Indiana)
  Indiana University
  Research Assistant (adviser: Prof. Eleftherios Garyfallidis)
  • Dictionary Learning for Bayesian estimation of microstructure models.
  • Random Matrix Theory-based Factor Models for Signal Recovery.
  • Combinatorial Topology for Optimization, Self-supervised Learning.
• May 2018 - August 2018 (Bloomington, Indiana)
  Google
  Summer of Code Developer, Python Software Foundation
  • Mathematical optimization techniques for nonlinear model fitting.
  • Stochastic optimization for approximating sparse exponential signals for NODDI, ActiveAx, etc.
• May 2017 - October 2017 (Juelich, Germany)
  Juelich Supercomputing Center (Institute of Advanced Simulations)
  Research Associate (Exchange Student)
  • Used OpenMP and MPI, parallelised multiple simulation models on the JURECA (Exascale Supercomputer).
  • Code optimization using Scalasca for improved Scheduling and Load Balancing.
• December 2015 - February 2016 (Pune, India)
  LiveHealth
  Software Development Intern
  • Data Integration and incremental filtering on live data for Healthcare Providers using CrossfilterJS and NodeJS.

LEADERSHIP & COMMUNITY SERVICE

• Details
  • Core organizer and Speaker at DIPY Workshop 2019, 2021, 2022 and 2023 [link] [eg: tutorial / presentation]
  • Mentored 3 CS and Informatics Undergraduate Students at Indiana University Bloomington [2019-2020]
  • Reviewer: NeurIPS, ICML, NeuroImage, Nature Scientific Reports
  • Team member of Brainhack community [Neuron paper]

AWARDS / HONORS

• Spotlight Presentation at Neural Information Processing Systems (NeurIPS) top 2.2% (top 208 of 9454 submissions)
• Oral at International Society for Magnetic Resonance in Medicine (ISMRM) top 15% (top 878 of 5000+ submissions)
• 1st place in IEEE ISBI Competition for Biophysical Modeling of Tissue Microstructure (MEMENTO)
• Graduate Fellowship for PhD, Indiana University

PUBLICATIONS (Citations: 1476+)

• Published:
  • Fadnavis, S.*, Schilling, K.*, et. al. “Denoising of diffusion MRI in the cervical spinal cord–effects of denoising strategy and acquisition on intra-cord contrast, signal modeling, and feature conspicuity”, NeuroImage, 2022
  • Fadnavis, S., et. al., “PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization”, Machine Learning for Health, 2022 [extended abstract], under journal submission: Nature Scientific Reports [arxiv | poster]
  • Fadnavis, S. et. al., “NUQ: A noise metric for diffusion MRI via uncertainty discrepancy quantification”, Machine Learning for Health, 2022 [extended abstract], under journal submission: NeuroImage [arxiv | poster | video]
  • Fadnavis, S. el. al., “Effect of Denoising on Retrospective Harmonization of Diffusion Magnetic Resonance Images”, Neural Information Processing Systems (MedNeurIPS), 2022 [poster]
  • Fadnavis, S.et. al. “Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning”, Thirty-fourth Conference on Neural Information Processing Systems, (NeurIPS), 2020 [Spotlight Presentation] [slides | video | poster]
  • Cieslak, M. et. al., “QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI”, Nature Methods, 2021 [software package link]
  • Fadnavis, S. et. al., “Bifurcated Topological Optimization of IVIM”, Frontiers in Neuroscience, Brain Imaging Methods, 2021 [video]
  • Fadnavis, S. et. al., “MVD-Fuse: Detection of White Matter Degeneration via Multi-View Learning of Diffusion Microstructure ”, Thirty-fourth Conference on Neural Information Processing Systems, (NeurIPS), Medical Imaging Workshop, 2020 [video]
  • De Luca, et. al., “On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types”, NeuroImage, 2021 [video]
  • Hu, et.al. “Segmentation of the Brain using Direction-averaged Signal of DWI Images”, Magnetic Resonance Imaging, 2020
  • Hansen et. al., “Pandora: 4-D white matter bundle population-based atlases derived from diffusion MRI fiber tractography”, Neuroinformatics, 2020, DOI: 10.1007/s12021-020-09497-1
  • Fadnavis, S.. et. al., “NoiseFactors: Generative modeling for 3D image denoising via Randomized Factor Models”, International Society for Magnetic Resonance in Medicine, 2020
  • Fadnavis, S.. et. al., “Variable Projection Optimization for Intravoxel Incoherent Motion (IVIM) MRI estimation”, Thirty-third Conference on Neural Information Processing Systems(NeurIPS), Medical Imaging Workshop, 2019
  • Fadnavis, S.. et. al., “MicroLearn: Framework for machine learning, reconstruction, optimization and microstructure modeling”, International Society for Magnetic Resonance in Medicine, 2019
  • Fadnavis, S. “Maximizing Information of Severe Weather Events Retrieved from Satellite Images”, International Journal of Engineering Research and Applications, ISSN : 2248-9622, 2017
  • Fadnavis, S.“Optimal Partitioning Methods for Image Segmentation”, IET - Journal of Engineering ISSN : 2051-3305, 2015
  • Garyfallidis, E., et. al. "Dipy, a library for the analysis of diffusion MRI data." Frontiers in neuroinformatics 8, 2014
  • Fadnavis, S., “Image Interpolation Techniques in Digital Image Processing”, International Journal of Engineering Research and Applications, ISSN : 2248-9622, 2014
  • Fadnavis, S. “Roundoff Error Propagation in Simulation of RC Circuit”, Advances in Computing and Information Technology, ISBN : 978-981-07-8859-9, 2014
  • Garyfallidis, E., et. al. "Dipy, a library for the analysis of diffusion MRI data." Frontiers in neuroinformatics 8, 2014

• Pre-prints / Under Review:
  • Fadnavis, S.*, et. al., “ViewFormer: a View-Independent Transformer Model for Disease Detection from Transthoracic Echocardiograms”, CVPR, 2024 (Under Review)
  • Park, J., Fadnavis, S., et. al. “EVAC+: Multi-scale V-net with Deep Feature CRF Layers for Brain Extraction”, submitted to Medical Image Analysis. ArXiv [code] - ISMRM’23 Oral.
  • Fadnavis, S. et. al. “Interpretable Specificity of Psychoactive Drug Effects in Informal Dialogue Using Large Language Models.” Biological Psychiatry, 2023 (under review)
  • Fadnavis, S. et. al. “PrompType: Prompting Large Language Models with Prototypes”, 2023 (under submission)
  • Fadnavis, S., el. al., “Unsupervised Ensemble Learning of Risk Predictors for Clinically High Risk Psychosis”, JAMA Psychiatry, 2023 (Under submission)
  • Fadnavis, S.,. et. al. “Patch2Self-Sketch: Self-supervised Denoising on Coresets via Matrix Sketching”, submittedto Nature Communications, 2023
  • Endres, S, et. al., “A Linear Complexity Simplicial Complex Data Structure for Fast Homology and Boundary Computation”, submitting to TMLR, 2023
  • Garyfallidis, E, Fadnavis, S, et.al. “ThetA -- fast and robust clustering via a distance parameter”, ArXiv

TEACHING EXPERIENCE

• Indiana University
  Lead Graduate Associate Instructor
  • Image Processing (ENGR-535), Engineering (Spring 2019, 2020, 2021) [Course Link]
  • Neuro-inspired AI (ENGR-506), Engineering (Fall 2019, 2020) [Course Link]

TECHNICAL SKILLS

• Languages
  Python, R, C, C++, Java, Shell Scripting, Javascript
• Big Data Tools:
  Apache Hadoop, Apache Kafka, Apache Spark, Joblib, OpenMP
• Data Analysis:
  Scikit-learn, PyTorch, TensorFlow, DIPY, SciPy, Pandas, NumPy
• NLP/CV/Web Tools:
  Large Language Models, SpaCy, NLTK, Scikit-image, Flask, Plotly Dash