Biography

Akash is a PhD candidate specializing in biomedical imaging informatics and computational and systems pathology. He is interested in investigating the intrinsic characteristics of biomedical images at multi-scale resolutions using statistical modeling, computer vision, machine learning, and graph-based deep learning techniques. He has developed explainable artificial intelligence (AI) algorithms to understand the origins of diagnostic discordance in differentially diagnosing a broad spectrum of breast lesions from digitized histopathology images. He is also serving as a Member of the Review Board for Journal of Pathology Informatics (JPI) and Signal, Image and Video Processing. He is also an active member of the Digital Pathology Association (DPA).

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Interests
  • Computational and Digital Pathology
  • Spatial Biology
  • Explainable Artificial Intelligence
  • Computer Vision
  • Biomedical Informatics
  • Graph Learning
  • Statistics and Machine Learning
Education

  • PhD Candidate in Computational Biology, expected Spring 2022

    Joint Carnegie-Mellon and University of Pittsburgh, USA

  • MS in Information Science, 2018

    University of Pittsburgh, USA

  • BTech in Electronics and Communication Engineering, 2016

    R.V. College of Engineering, India

Skills

Python
Jupyter Notebook
R
Statistics
GitHub
Linux

Experience

 
 
 
 
 
Joint Carnegie-Mellon and University of Pittsburgh School of Medicine
Graduate Student Researcher
Joint Carnegie-Mellon and University of Pittsburgh School of Medicine
Aug 2018 – Present Pittsburgh, USA
  • Investigate the mechanistic underpinnings of inter- and intra-class diagnostic variability in histopathology images and spatial intratumoral heterogeneity in multiplex image data
  • Develop computational pathology tools for the challenging task of correctly classifying sub-categories present within the diagnostic spectrum of breast lesions
  • Conceptualize an explainable AI framework intended to capture the visual diagnostic thinking of the pathologists
  • Build a computational pathology-based cognitive assistant by demonstrating perceptual and planning components which are useful in bringing the above technologies to everyday pathology practice
  • Advisor: Dr. S. Chakra Chennubhotla and Committee members: Dr. Jeffrey L. Fine, Dr. Robin E.C. Lee, Dr. Min Xu, and Dr. Arvind Ramanathan
 
 
 
 
 
University of Pittsburgh
Graduate Teaching Assistant
University of Pittsburgh
Sep 2019 – Dec 2019 Pittsburgh, USA
MSC 2065 Scalable Machine Learning for Big Data Biology
 
 
 
 
 
Oak Ridge National Laboratory (ORNL)
Advanced Short-Term Research Scientist
Oak Ridge National Laboratory (ORNL)
May 2018 – Aug 2018 Oak Ridge, USA
  • Developed computational tools to analyze high-throughput, low-resolution Cryo-Electron Microscopy images for betagalactosidase, a bacterial enzyme
  • Gathered necessary skills to use RELION (Regularized Likelihood Optimization) software that uses Bayesian statistics to reconstruct three-dimensional representation of biomolecules to near atomic resolution from two-dimensional micrographs data obtained by taking snapshots of macromolecule in different orientations
  • Mentor: Dr. Arvind Ramanathan
 
 
 
 
 
Oak Ridge National Laboratory (ORNL)
Advanced Short-Term Research Scientist
Oak Ridge National Laboratory (ORNL)
May 2017 – Jul 2017 Oak Ridge, USA
  • Contributed in developing ANCA software (Anharmonic Conformational Analysis) as an extensible framework to characterize anharmonic events and enable a deeper analysis of their functional relevance
  • Contributed in developing toolbox that provides modules to measure long tail behavior of complex protein fluctuations by chasing higher order statistics
  • Mentor: Dr. Arvind Ramanathan

Recent & Upcoming Talks

Oral Talk - MICCAI 2020 Conference
Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions
Oct 6, 2020 11:00 AM Online | 4 - 8 October 2020
Akash Parvatikar
PDF Video
Oral Talk - MICCAI 2020 Conference
Guest Lecture - M.Tech. students at R.V. College of Engineering
Foundations of Statistical Machine Learning
Jan 7, 2020 1:30 PM Bengaluru | 7 January 2020
Akash Parvatikar
Guest Lecture - M.Tech. students at R.V. College of Engineering

Featured Publications

Akash Parvatikar, Om Choudhary, Arvind Ramanathan, Olga Navolotskaia, Gloria Carter, Akif Burak Tosun, Jeffrey L. Fine, S. Chakra Chennubhotla
June 2021 In MICCAI
Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions
Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions
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Recent Publications

Arvind Ramanathan, Heng Ma, Akash Parvatikar, S. Chakra Chennubhotla (2021). Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins. In Current Opinion in Structural Biology.

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Akash Parvatikar, Om Choudhary, Arvind Ramanathan, Rebekah Jenkins, Olga Navolotskaia, Gloria Carter, Akif Burak Tosun, Jeffrey L. Fine, S. Chakra Chennubhotla (2020). Prototypical models for classifying high-risk atypical breast lesions. In MICCAI.

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Arvind Ramanathan, Akash Parvatikar, S. Chakra Chennubhotla, Yang Mei, Sangita C. Sinha (2020). Transient Unfolding and Long-Range Interactions in Viral BCL2 M11 Enable Binding to the BECN1 BH3 Domain. In Biomolecules.

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Akash Parvatikar, Gabriel S. Vacaliuc, Arvind Ramanathan, S. Chakra Chennubhotla (2018). ANCA: Anharmonic Conformational Analysis of Biomolecular Simulations. In Biophysical journal.

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Popular Topics

Analytical Modeling Atypical Breast Lesions Computational Pathology Explainable AI Histological Patterns Image Similarity Lecture Machine Learning MICCAI Oral Talk Prototype-driven Recognition University of Pittsburgh

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