0000-0003-4810-352X About
Phaneendra Yalavarthy received B.Sc. and M.Sc. degrees in Physics from Sri Sathya Sai University, Puttaparthy, India, in 1999 and 2001, respectively. He also obtained an M.Sc. degree in Engineering from Indian Institute of Science, Bangalore, India, in 2004. He received a Ph.D., working as a U.S. Department of Defense Breast Cancer Pre-doctoral Fellow, in Biomedical Computation from Thayer School of Engineering, Dartmouth College, Hanover, USA in 2007. He worked as a post-doctoral research associate in the Department of Radiation Oncology, School of Medicine, Washington University in St. Louis, USA between 2007-2008. He leads TANUH: The AI-Centre of Excellence in Healthcare funded by Ministry of Education at Indian Institute of Science, Bangalore. He is a Professor in the Department of Computational and Data Sciences, Indian Institute of Science, Bangalore. He served as the chair of the department for the period 2016-2018 as well as the Institute Coordinator of the Prime Minister’s Research Fellowship (PMRF) scheme scheme for the period 2018-2020. He was the Chair of the Office of International Relations (OIR) for the period 2018-2021 and the Chair of the Office of Development and Alumni Affairs (ODAA) for the period 2020-2023. He had two industrial sabbaticals, first one in 2015, involved developing post-processing algorithms for clinical MR Imaging at Samsung R&D Institute, Bangalore. Another sabbatical in 2023-2024, he focused on applying AI techniques to enhance CT imaging at GE HealthCare’s Edison AI Advanced Technology Group (ATG) in Bangalore.
He was a recipient of the Apple Laureate award in 2009. He also received the Department of Atomic Energy Young Scientist Research Award in 2010 and the coauthor of the work chosen for the International Society for Magnetic Resonance in Medicine (ISMRM) Merit Award (Summa Cum Laude) in 2012. He is a recipient of the Indian National Academy of Engineering (INAE) Young Engineer award and Innovative Young Biotechnologist Award (Government of India) for the year 2013. He also received the National Academy of Sciences, India (NASI)Young Scientist Platinum Jubilee Award in 2014. He received S. Ramachandran-National Bioscience Award for the year 2020 from Department of Biotechnology, Government of India.
His research interests include computational methods in medical imaging, deep learning in medical imaging, medical image processing (reconstruction/analysis), physiological signal processing, diffuse optical imaging, and photoacoustic imaging. He is a senior member of IEEE, OSA, and SPIE. He serves as an associate editor of IEEE Transactions on Medical Imaging as well as an academic editor of PLOS Digital Health.
Employment
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Indian Institute of Science Professor2020 - Present
Education
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Dartmouth College2004 - 2007
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Indian Institute of Science M.Sc. (Engg.)2001 - 2004
Projects & Funding
Projects & funding information is unavailable.
Publications (93)
- Noise-aware dynamic convolution for improved generalizability of retinal disease diagnosis using optical coherence tomography images Save
- Integrating Fourier analysis and deep learning for robust detection of deep fake brain magnetic resonance images Save
- Comparative analysis of loss functions for uncertainty quantification in medical image segmentation Save
- Energy-regularized self-supervised deep network for speckle noise reduction in optical coherence tomography Save
- Inference time correction based on confidence and uncertainty for improved deep-learning model performance and explainability in medical image classification Save
- Information Geometric Approaches for Patient-Specific Test-Time Adaptation of Deep Learning Models for Semantic Segmentation Save
- Model resolution‐based deconvolution for improved quantitative susceptibility mapping Save
- Transformer-Based Automated Segmentation of the Median Nerve in Ultrasound Videos of Wrist-to-Elbow Region Save
- Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping Save
- Two‐step morphology‐based denoising and non‐local means smoothing improves micro‐computed tomography digital rock images Save