Hello!

I am a graduate student in Computer Science at the University of Maryland, College Park advised by Prof. Tom Goldstein. My broader research focus lies at the intersection of Machine Learning (ML) and Computer Vision with the aim of building practical ML systems that are interpretable and robust. My recent works range from developing novel deep learning architectures for diverse domains such as tables, images, and graphs, to building tools to explain and analyze the success/failure modes of common deep learning models. I am a recipient of the 2021 Kulkarni Fellowship and 2022 Amazon Internship Fellowship.

Before starting my Ph.D., I worked in the industry for 8 years in various product and engineering roles and received my bachelor's from IIT Madras. My pivot to machine learning happened during my start-up years when I was building ML tools for fashion assistance. I’ve since been working towards building interpretable machine learning models with real-world applications.

I am always up for new collaborations, drop me an email if you want to chat! If you are looking for mentorship, drop an short email introducing yourself and the topic of your interest.

Recent News

• Mar 2024: Awarded Ann-Wylie Fellowship! 🎉
• Dec 2023: Gave a short talk at NeurIPS Diffusion Workshop!
• Sep 2023: 3 papers accepted to NeurIPS'23! 🎉
• Aug 2023: Presented Diffusion Replication work at Google Research.
• July 2023: Understanding copying in diffusion models is chosen as Spotlight at ICML GenLaw workshop.
• Apr, May 2023: Presented Diffusion Replication work at ML Collective, TikTok US.
• Feb 2023: Replication in diffusion models paper got into CVPR'23! 🎉
• Feb 2023: Data Augmentations paper got into ICLR'23!
• Dec 2022: Replication in diffusion models covered in TechCrunch and others! Techcrunch, The Decoder,The Next Web
• Dec 2022: Awarded Amazon Internship Fellowship!
• Nov 2022: Presented SAINT at G-research!
• Mar 2022: Can NNs learn the... paper is accepted as CVPR'22 Oral!

Research Highlights

	Measuring Style Similarity in Diffusion Models Preprint PAPER CODE MINI PODCAST Can we measure the style similarity between images? We propose a way to extract style from images. We call this Contrastive Style Descriptors (CSD). Using this model, we study the style replication in image generation models. Gowthami Somepalli,Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava , Tom Goldstein.
	Understanding and Mitigating Copying in Diffusion Models NeurIPS 2023 PAPER CODE We study why diffusion models copy and ways to mitigate the same. We found text conditioning plays a major role along with training data duplication. One way to mitigate is to use multiple captions per data point during training. Gowthami Somepalli, Vasu Singla , Micah Goldblum, Jonas Geiping, Tom Goldstein.
	Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models CVPR 2023 PROJECT PAGE PAPER MEDIA COVERAGE INTERVIEW CODE Understanding the training data replication in diffusion models. Examined DDPM models on Celeb-A and Oxford flowers and LAION-Aesthetics on Stable Diffusion v.1.4. Gowthami Somepalli, Vasu Singla , Micah Goldblum, Jonas Geiping, Tom Goldstein.
	Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective CVPR 2022, Oral PROJECT PAGE PAPER CODE VIDEO MEDIA COVERAGE Understanding the reproduciblity of various architectures from the decision boundary perspective. We also examine how the decision boundaries change as we increase the model capacity in the case of double-descent. Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping Yeh-Chiang, Yehuda Dar, Richard Baraniuk , Micah Goldblum, Tom Goldstein.
	SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training NeurIPS 2022, TRLW PROJECT PAGE PAPER CODE Improving predictions on structured tabular data using intersample attention and contrastive learning. Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, Tom Goldstein.
	PatchGame - Learning to Signal Mid-level Patches in Referential Games NeurIPS 2021 PAPER CODE VIDEO Emergent communication via mid-level patches in a referential game played on a large-scale image dataset. Kamal Gupta, Gowthami Somepalli, Anubhav Gupta, Vinoj Jayasundara, Matthias Zwicker, Abhinav Shrivastava
	Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders UAI 2021, Long Presentation PAPER CODE We proposed mirrored wasserstein loss along with latent space regularization to recognize anomalies in case where there are no/ a few anomalies present during training time. Gowthami Somepalli, Yexin Wu, Yogesh Balaji, Bhanukiran Vinzamuri, Soheil Feizi.
	Prioritizing and characterizing functionally relevant genes across human tissues PLOS Computational Biology 2021 In this work, we try to understand and predict which genes are important in any given tissue. We show that the gene expression is not the only important factor, but the location of the gene in PPI network also plays a role. Gowthami Somepalli, Sarthak Sahoo, Arashdeep Singh, Sridhar Hannenhalli.
	What Doesn't Kill You Makes You Robust (er): Adversarial Training against Poisons and Backdoors Preprint 2021 In this paper, we desensitize networks to the effects of poisoning by creating poisons during training and injecting them into training batches. Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein.