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Raunak Manekar I am an Assistant Professor at the CS & IS department at BITS-Pilani Goa Campus . I work at the intersection of computer vision, generative AI and computational imaging. I'm passionate about developing learning-based solutions for visual inverse problems in vairous fields of science and engineering. Previously, I have been a Sr. AI Scientist at GE Healthcare (Bengaluru) were I worked on medical imaging (MRI) technologies. I received my PhD from University of Minnesota, TC working with Prof. Jaideep Srivastava on generative AI for scientific imaging. After which I worked as postdoctoral researcher at UCLA (Coherent Imaging Group) with Prof. John Miao.
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Low-light phase retrieval with implicit generative priors
Raunak Manekar, Elisa Negrini, Minh Pham, Daniel Jacobs, Jaideep Srivastava, Stanley J. Osher, Jianwei Miao, Transactions on Image Processing (TIP) , 2024 Using deep learning priors for high-SNR phase retrieval at very low photon counts without any training data |
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LoDIP: Low-dose phase retrieval with deep image prior
Raunak Manekar, Elisa Negrini, Minh Pham, Daniel Jacobs, Jaideep Srivastava, Stanley J. Osher, Jianwei Miao, Machine Learning and the Physical Sciences Workshop, NeurIPS, 2023 Unsupervised, single-image technique for phase retrieval at very low photon counts |
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Breaking Symmetries in Data-Driven Phase Retrieval
Raunak Manekar, Kshitij Tayal, Zhong Zhuang, Chieh-Hsin Lai, Vipin Kumar, Ju Sun, Computational Optical Sensing and Imaging , 2021 Supervised learning for inverse problems requires a careful handling of the symmetries. |
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Deep Learning Initialized Phase Retrieval
Raunak Manekar, Zhong Zhuang, Kshitij Tayal, Vipin Kumar, Ju Sun, NeurIPS 2020 Workshop on Deep Learning and Inverse Problems , 2020 An unsupervised data-driven technique for practical phase retrieval. |
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End-to-end learning for phase retrieval
Raunak Manekar, Kshitij Tayal, Vipin Kumar, Ju Sun, ICML workshop on ML Interpretability for Scientific Discovery , 2020 Exploring dataset-bias in end-to-end learning for phase retrieval |
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Phase Retrieval using Single-Instance Deep Generative Prior
Kshitij Tayal, Raunak Manekar, Zhong Zhuang, David Yang, Vipin Kumar, Felix Hofmann, Ju Sun, Optics and Photonics for Sensing the Environment.OSA , 2021 Unsupervised single-instance method for phase retrieval |
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2D-3D CNN based architectures for spectral reconstruction from RGB images
Sriharsha Koundinya, Himanshu Sharma, Manoj Sharma, Avinash Upadhyay, Raunak Manekar, Rudrabha Mukhopadhyay, Abhijit Karmakar, Santanu Chaudhury, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2018 Hyperspectral Information restoration from RGB images |
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Convolutional neural network-based human identification using outer ear images
H Sinha Raunak Manekar, Yash Sinha, P K Ajmera, Soft Computing for Problem Solving: SocProS, 2017
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Activity recognition for indoor fall detection in 360-degree videos using deep learning techniques
Dhiraj Raunak Manekar, Sumeet Saurav, Somsukla Maiti, Sanjay Singh, Santanu Chaudhury, Neeraj, Ravi Kumar, Kamal Chaudhary, Proceedings of 3rd International Conference on Computer Vision and Image Processing: CVIP, 2018
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Service |
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Reviewer, CVPR 2022, ECCV 2022
Reviewer, Neurips 2022,2023 ICLR 2022 (Highlighted reviewer) Reviewer, AAAI '22 (Intl Workshop on Health Intelligence) Reviewer, Springer Autonomous Robots Journal |
Awards |
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Travel award- ICML '17(Sydney)
Travel award- NeurIPS '22(New Orleans) ICLR '22 Highlighted Reviewer |
Teaching |
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Teaching assistant
CSCI 5521: Machine Learning Fundamentals CSCI 5523: Data Mining CSCI 1933: Intro to Data Structures and Algorithms |