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Publications

Whack-a-Chip: The Futility of Hardware-Centric Export Controls

Whack-a-Chip: The Futility of Hardware-Centric Export Controls

Ritwik Gupta, Leah Walker, Andrew W. Reddie

We give the first, public evidence as to how leading PRC AI labs are effectively circumventing U.S. semiconductor export controls through better software. We question the basis and efficacy of the current export control regime.

arXiv preprint paper
Open-Source Assessments of AI Capabilities: The Proliferation of AI Analysis Tools, Replicating Competitor Models, and the Zhousidun Dataset

Open-Source Assessments of AI Capabilities: The Proliferation of AI Analysis Tools, Replicating Competitor Models, and the Zhousidun Dataset

Ritwik Gupta, Leah Walker, Eli Glickman, Raine Koizumi, Sarthak Bhatnagar, Andrew W. Reddie

China is training machine learning models to target American and Allied navel vessels, but how well do they work? In this paper, we train a state-of-the-art machine learning model on a leaked Chinese dataset that labels Aegis combat system components on military vessels. We propose a new methodology for open source assessment of adversary AI capabilities.

BRSL Tech Report paper github
xT: Nested Tokenization for Larger Context in Large Images

xT: Nested Tokenization for Larger Context in Large Images

Ritwik Gupta*, Shufan Li*, Tyler Zhu*, Jitendra Malik, Trevor Darrell, Karttikeya Mangalam

xT is a framework which lets you model extremely large images (upwards of 30,000 x 30,000 pixels) end-to-end on contemporary GPUs. You get higher accuracy with fewer parameters and less memory used per region.

International Conference on Machine Learning (ICML) 2024 website paper github
Russian Nuclear ASAT Weapons: The Fallout

Russian Nuclear ASAT Weapons: The Fallout

Sarthak Bhatnagar, Eli Glickman, Bethany Goldblum, Ritwik Gupta, Kaitlyn Lenkeit, Jane Darby Menton, Andrew Neciuk, Andrew Reddie, Vishwaa Sofat, Leah Walker

What is the state of the existing space governance regime amid concerns that Moscow is developing a nuclear-tipped anti-satellite weapon in orbit?

Lawfare paper
LAION and the Challenges of Preventing AI-Generated CSAM

LAION and the Challenges of Preventing AI-Generated CSAM

Ritwik Gupta

I examined the challenges in preventing AI-generated Child Sexual Abuse Material (CSAM), such as within the widely-used LAION-5B dataset, emphasizing the need for updated legal and technological strategies to tackle the spread of such content by generative AI technologies.

Tech Policy Press paper
See, Say, and Segment: Teaching LMMs to Overcome False Premises

See, Say, and Segment: Teaching LMMs to Overcome False Premises

Tsung-Han Wu, Giscard Biamby, David Chan, Lisa Dunlap, Ritwik Gupta, Xudong Wang, Joseph E. Gonzalez, Trevor Darrell

A method to prevent large, multimodal models (LMMs) to stop hallucinating when given false premises.

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 website paper github
Proliferate, Don't Obliterate: How Responsive Launch Marginalizes Anti-Satellite Capabilities

Proliferate, Don't Obliterate: How Responsive Launch Marginalizes Anti-Satellite Capabilities

Ritwik Gupta, Andrew W. Reddie

We analyze how the emerging responsive launch industry fundamentally shifts the strategic calculus of ASAT weapons.

War on the Rocks paper
Accelerating the Evolution of AI Export Controls

Accelerating the Evolution of AI Export Controls

Ritwik Gupta, Andrew W. Reddie

Current US AI hardware export controls are based on the best AI accelerator chip available at that time. This presents wide loopholes which allow adversarial nations to still maintain their capabilities. We propose an alternate way to set export control thresholds based on the analysis of specific ML workloads.

Tech Policy Press paper
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators

Sungduk Yu, Walter M. Hannah, Liran Peng, Mohamed Aziz Bhouri, Ritwik Gupta, Jerry Lin, Björn Lütjens, Justus C. Will, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Anima Anandkumar, Noah D. Brenowitz, Veronika Eyring, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Carl Vondrick, Rose Yu, Laure Zanna, Ryan P. Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Gunnar Behrens, Christopher S. Bretherton, Julius J. M. Busecke, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Akshay Subramaniam, Mark A. Taylor, Nathan M. Urban, Janni Yuval, Guang J. Zhang, Tian Zheng, Michael S. Pritchard

The largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers.

Best Paper Award Neural Information Processing Systems (NeurIPS) 2023 website paper github
Orbital hypersonic delivery systems threaten strategic stability

Orbital hypersonic delivery systems threaten strategic stability

Ritwik Gupta

We assess that China's development of a fractional orbital hypersonic delivery system, combining hypersonic glide vehicles with orbital bombardment, presents a concerning challenge to global stability, allowing for faster, undetectable delivery of large nuclear payloads and signaling renewed interest in first-strike capabilities.

The Bulletin of Atomic Scientists paper
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning

Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning

Ritwik Gupta*, Colorado Reed*, Shufan Li*, Sarah Brockman, Christopher Funk, Brian Clipp, Kurt Keutzer, Salvatore Candido, Matt Uyttendaele, Trevor Darrell

A pre-training method to make encoders robust to imagery captured at varying satellite resolutions. State-of-the-art multi-scale pre-training method and the largest satellite imagery foundation model, to date.

Nominated for Best Paper International Conference on Computer Vision (ICCV) 2023 website paper github
Emerging Technology and Policy Co-Design Considerations for the Safe and Transparent Use of Small Unmanned Aerial Systems

Emerging Technology and Policy Co-Design Considerations for the Safe and Transparent Use of Small Unmanned Aerial Systems

Ritwik Gupta, Alexander Bayen, Sarah Rohrschneider, Adrienne Fulk, Andrew Reddie, Sanjit A. Seshia, Shankar Sastry, Janet Napolitano

With the meteoric rise of small unmanned aerial systems, we discuss policy shortcomings in integrating sUAS technology in a safe fashion into our society. We suggest technology and policy co-design approaches to addressing these gaps in our systems.

Center for Security in Politics, UC Berkeley paper
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery

xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery

Ritwik Gupta*, Fernando Paolo*, Tsu-ting Tim Lin*, Bryce Goodman, Nirav Patel, Daniel Kuster, David Kroodsma, Jared Dunnmon

The largest labeled dataset for training ML models to detect and characterize vessels and ocean structures in synthetic aperture radar imagery. xView3 is built to help control illegal, unreported, and unregulated fishing.

Satlas: A Large-Scale, Multi-Task Dataset for Remote Sensing Image Understanding

Satlas: A Large-Scale, Multi-Task Dataset for Remote Sensing Image Understanding

Favyen Bastani, Piper Wolters, Ritwik Gupta, Joe Ferdinando, Aniruddha Kembhavi

A foundational remote sensing dataset with over 290M labels under 137 categories and seven label modalities for pre-training large machine learning models.

International Conference on Computer Vision (ICCV) 2023 website paper
Snowpack Estimation in Key Mountainous Water Basins from Openly-Available, Multimodal Data Sources

Snowpack Estimation in Key Mountainous Water Basins from Openly-Available, Multimodal Data Sources

Malachy Moran, Kayla Woputz, Derrick Hee, Manuela Girotto, Paolo D'Odorico, Ritwik Gupta, Daniel Feldman, Puya Vahabi, Alberto Todeschini, Colorado J Reed

We fuse satellite and weather data to estimate snowpack depth in key mountainous regions and beat single-source estimation by 5.0 inches RMSE.

CVPR 2022 Workshop on Multimodal Learning for Earth and Environment paper github
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data

WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data

Rupa Kurinchi-Vendhan, Björn Lütjens, Ritwik Gupta, Lucien Werner, Dava Newman

An extensible benchmark of deep learning-based super-resolution techniques on wind and solar data. We accompany the benchmark with a novel public, processed, and machine learning-ready dataset for benchmarking super-resolution methods on wind and solar data.

Climate Change AI Workshop at NeurIPS 2021 website paper github youtube
Region-level Active Detector Learning

Region-level Active Detector Learning

Michael Laielli, Giscard Biamby, Dian Chen, Ritwik Gupta, Adam Loeffler, Phat Dat Nguyen, Ross Luo, Trevor Darrell, Sayna Ebrahimi

A new strategy that subsumes previous Image-level and Object-level approaches into a generalized, Region-level approach.

arXiv preprint paper
xBD: A Dataset for Assessing Building Damage from Satellite Imagery

xBD: A Dataset for Assessing Building Damage from Satellite Imagery

Ritwik Gupta, Richard Hosfelt, Sandra Sajeev, Nirav Patel, Bryce Goodman, Jigar Doshi, Eric Heim, Howie Choset, Matthew Gaston

The foundational dataset for assessing building damage after natural disasters from very-high resolution satellite imagery with over 850,000 annotations across 45,000 square kilometers.

Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery

Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery

Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, Matthew Gaston

Preliminary work discussing xBD, the foundational dataset for assessing building damage after natural disasters from very-high resolution satellite imagery with over 850,000 annotations across 45,000 square kilometers.

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019 website paper github
Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery

Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery

Ritwik Gupta, Carson D. Sestili, Javier A. Vazquez-Trejo, Matthew E. Gaston

Focused around the IARPA Functional Map of the World Challenge, this work discusses how to scale deep learning at an academic lab for geospatial analysis.

2018 IEEE International Conference on Big Data paper
Open Problems in Robotic Anomaly Detection

Open Problems in Robotic Anomaly Detection

Ritwik Gupta, Zachary T. Kurtz, Sebastian Scherer, Jonathon M. Smereka

Motivated by the development of ROS 2, this work discusses open problems in the field of robotic anomaly detection and presents an inverse reinforcement learning-based approach to detecting anomalous motion.

arXiv preprint paper