Siddarth

Siddarth Asokan

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Biography

Dr. Siddarth Asokan is currently a Senior Researcher at the Microsoft Research Lab (MSR) in Bengaluru, India. Prior to joining MSR, he received both a Ph.D. and M.Tech. (Research) Degree from the Department of Cyber Physical Systems at the Indian Institute of Science in 2023, and a Bachelor of Engineering (B.E.) degree in Electronics and Communication Engineering from the M.S. Ramaiah Institute of Technology, Bangalore in 2017. During his Ph.D., he worked on generative machine learning for images and developed strong theoretical foundations for the widely popular generative adversarial networks (GANs) and diffusion model frameworks with significant contributions to high-dimensional interpolation, Fourier approximations, and partial differential equations in very large dimensional space. His doctoral research was awarded the IUPRAI Doctoral Dissertation Award 2023, and the Prof. Satish Dhawan Research Award 2024. He has received various accolades in the past, including the IEI Young Engineers’ Award 2024-25, the Qualcomm Innovation Fellowship in 2019, 2021, 2022, and 2023, the Robert Bosch Center for Cyber Physical Systems Fellowship in 2020 and 2021, and the Microsoft Research Fellowship in 2018. He also has several high-profile publications at NeurIPS, ICML, CVPR, and JMLR. He currently works at the intersection of generative modeling and large-scale information retrieval models. His research interests lie broadly in the space of signal processing, image processing, information retrieval, and generative machine learning, with a focus on building mathematically well-founded generative learning frameworks.

Publications

2024

  1. ICASSP 24
    Variational Analysis of Adversarial Regularization for Solving Inverse Problems
    , , , and 
    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2024
    Cite Download .bib
    @inproceedings{bhandiwad2024variational, author = {Bhandiwad, A. S. and Kamath, A. J. and Asokan, S. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {Variational Analysis of Adversarial Regularization for Solving Inverse Problems}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:GFxP56DSvIMC}, year = {2024} }
  2. ICASSP 24
    Momentum-Imbued Langevin Dynamics (MILD) for Faster Sampling
    , M. Bandla , , , and 
    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2024
    Cite Download .bib
    @inproceedings{shetty2024momentum, author = {Shetty, N. and Bandla, M. and Neema, N. and Asokan, S. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {Momentum-Imbued Langevin Dynamics (MILD) for Faster Sampling}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:ZzlSgRqYykMC}, year = {2024} }

2023

  1. JMLR 23
    Euler-Lagrange analysis of generative adversarial networks
    , and 
    Journal of Machine Learning Research, Apr 2023
    Cite Download .bib
    @article{asokan2023euler, author = {Asokan, S. and Seelamantula, C. S.}, journal = {Journal of Machine Learning Research}, number = {126}, pages = {1--100}, title = {Euler-Lagrange analysis of generative adversarial networks}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:yMeIxYmEMEAC}, volume = {24}, year = {2023} }
  2. arXiv 23
    Data Interpolants–That’s What Discriminators in Higher-order Gradient-regularized GANs Are
    , and 
    arXiv preprint arXiv:2306.00785, Apr 2023
    Cite Download .bib
    @article{asokan2023data, author = {Asokan, S. and Seelamantula, C. S.}, journal = {arXiv preprint arXiv:2306.00785}, title = {Data Interpolants--That's What Discriminators in Higher-order Gradient-regularized GANs Are}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:r_AWSJRzSzQC}, year = {2023} }
  3. CVPR 23
    Spider GAN: Leveraging friendly neighbors to accelerate GAN training
    , and 
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Apr 2023
    Cite Download .bib
    @inproceedings{asokan2023spider, author = {Asokan, S. and Seelamantula, C. S.}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, title = {Spider GAN: Leveraging friendly neighbors to accelerate GAN training}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:fFSKOagxvKUC}, year = {2023} }
  4. arXiv 23
    Gans settle scores!
    , , A. Srikanth , and 
    arXiv preprint arXiv:2306.01654, Apr 2023
    Cite Download .bib
    @article{asokan2023gans, author = {Asokan, S. and Shetty, N. and Srikanth, A. and Seelamantula, C. S.}, journal = {arXiv preprint arXiv:2306.01654}, title = {Gans settle scores!}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:LhH-TYMQEocC}, year = {2023} }
  5. ICASSP 23
    A Game of Snakes and Gans
    , F. S. Mohammed , and 
    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2023
    Cite Download .bib
    @inproceedings{asokan2023game, author = {Asokan, S. and Mohammed, F. S. and Seelamantula, C. S.}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title = {A Game of Snakes and Gans}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=200&pagesize=100&citation_for_view=1g1i1B4AAAAJ:OR75R8vi5nAC}, year = {2023} }

2022

  1. NeurIPS-W 22
    LSGANs with gradient regularizers are smooth high-dimensional interpolators
    , and 
    In First Workshop on Interpolation Regularizers and Beyond at NeurIPS, Apr 2022
    Cite Download .bib
    @inproceedings{asokan2022lsgans, author = {Asokan, S. and Seelamantula, C. S.}, booktitle = {First Workshop on Interpolation Regularizers and Beyond at NeurIPS}, title = {LSGANs with gradient regularizers are smooth high-dimensional interpolators}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:mNrWkgRL2YcC}, year = {2022} }
  2. DLDE-II 22
    Bridging the gap between Coulomb GAN and gradient-regularized WGAN
    , and 
    In The Symbiosis of Deep Learning and Differential Equations II, Apr 2022
    Cite Download .bib
    @inproceedings{asokan2022bridging, author = {Asokan, S. and Seelamantula, C. S.}, booktitle = {The Symbiosis of Deep Learning and Differential Equations II}, title = {Bridging the gap between Coulomb GAN and gradient-regularized WGAN}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&cstart=100&pagesize=100&citation_for_view=1g1i1B4AAAAJ:cWzG1nlazyYC}, year = {2022} }

2020

  1. NeurIPS 20
    Teaching a gan what not to learn
    , and 
    Advances in Neural Information Processing Systems, Apr 2020
    Cite Download .bib
    @article{asokan2020teaching, author = {Asokan, S. and Seelamantula, C.}, journal = {Advances in Neural Information Processing Systems}, pages = {3964--3975}, title = {Teaching a gan what not to learn}, url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=1g1i1B4AAAAJ&pagesize=100&citation_for_view=1g1i1B4AAAAJ:LO7wyVUgiFcC}, volume = {33}, year = {2020} }