Akhil Kondepudi

MD/PhD Student (M1)
University of Michigan Medical School
Department of Computational Medicine and Bioinformatics
Machine Learning in Neurosurgery Laboratory

Twitter  ·  GitHub  ·  CV

I'm a first-year medical student at the University of Michigan, as part of the Medical Scientist Training Program. My research interests are broadly in deep learning applications for translational medicine and biomedical discovery.

I'm currently a student researcher in the Machine Learning in Neurosurgery Laboratory, advised by Dr. Todd Hollon. Previously, I worked on improving the accuracy of glioma surgery at the University of California San Francisco, co-supervised by Dr. Todd Hollon and Dr. Shawn Hervey-Jumper. I completed my undergrad in neuroscience at the University of Michigan, where I studied the pathophysiology of cerebrovascular disease under Dr. Michael Wang.

I'm also working on developing AI-assisted supplemental speech therapy at Phonica.

Selected Research

A complete list of publications is available here.

Hierarchical discriminative learning improves visual representations of biomedical microscopy
Cheng Jiang*, Xinhai Hou*, Akhil Kondepudi, Asadur Chowdury, Christian W. Freudiger, Daniel A. Orringer, Honglak Lee, Todd C. Hollon
CVPR (Highlight) | 2023
project · paper · arxiv · code
Leveraging the patient-slide-patch image hierarchy present in biomedical microscopy datasets to improve self-supervised representation learning.
Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging
Todd Hollon, Cheng Jiang, Asadur Chowdury, Mustafa Nasir-Moin, Akhil Kondepudi, Alexander Aabedi, Arjun Adapa, ..., Christian Freudiger, Honglak Lee, Daniel Orringer
Nature Medicine | 2023
project · paper · arxiv · code · press
The first AI-based intraoperative molecular classification system for diffuse gliomas.
Concentration of non-myocyte proteins in arterial media of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy
Soo Jung Lee, Akhil Kondepudi, Kelly Z. Young, Xiaojie Zhang, Naw May Pearl Cartee, Jijun Chen, Krystal Yujin Jang, Gang Xu, Jimo Borjigin, Michael M. Wang
PLOS ONE | 2023
paper
Identified smooth muscle markers that may be implicated in cerebrovascular disease with a novel histopathological staining analysis workflow.
OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology
Cheng Jiang*, Asadur Chowdury*, Xinhai Hou*, Akhil Kondepudi, Christian W. Freudiger, Kyle Conway, Sandra Camelo-Piragua, Daniel A. Orringer, Honglak Lee, Todd C. Hollon
NeurIPS Datasets and Benchmarks | 2022
project · arxiv · code · talk · poster
The first publically available stimulated Raman histology dataset, for rapid intraoperative histopathological assessment of tumor specimens.
Development and Evaluation of a Natural Language Processing Annotation Tool to Facilitate Phenotyping of Cognitive Status in Electronic Health Records: Diagnostic Study
Ayush Noori*, Colin Magdamo*, Xiao Liu, Tanish Tyagi, Zhaozhi Li, Akhil Kondepudi, Haitham Alabsi, ..., M Brandon Westover, Shibani S Mukerji, Sudeshna Das
JMIR | 2022
paper
Information retrieval from electronic health records using natural language processing to improve cognitive status phenotyping.
Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence
Cheng Jiang, Abhishek Bhattacharya, Joseph Linzey, Rushikesh S. Joshi, Sung Jik Cha, Sudharsan Srinivasan, Daniel Alber, Akhil Kondepudi, ..., Honglak Lee, Todd Hollon
Neurosurgery (Cover) | 2022
paper · arxiv
Classification of skull base tumor specimens imaged with stimulated Raman histology using contrastive pretraining.

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