GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding. - Haoyang Zeng; Tatsunori B. Hashimoto; Daniel D. Kang; David K. Gifford (Bioinform., 2016)

Optimizing Deep CNN-Based Queries over Video Streams at Scale. - Daniel Kang 001; John Emmons; Firas Abuzaid; Peter Bailis; Matei Zaharia (CoRR, 2017)

NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale. - Daniel Kang 001; John Emmons; Firas Abuzaid; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2017)

BlazeIt: Fast Exploratory Video Queries using Neural Networks. - Daniel Kang 001; Peter Bailis; Matei Zaharia (CoRR, 2018)

LIT: Block-wise Intermediate Representation Training for Model Compression. - Animesh Koratana; Daniel Kang 001; Peter Bailis; Matei Zaharia (CoRR, 2018)

Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine. - Daniel Kang 001; Peter Bailis; Matei Zaharia (CIDR, 2019)

LIT: Learned Intermediate Representation Training for Model Compression. - Animesh Koratana; Daniel Kang 001; Peter Bailis; Matei Zaharia (ICML, 2019)

Network Offloading Policies for Cloud Robotics: A Learning-Based Approach. - Sandeep Chinchali; Apoorva Sharma; James Harrison; Amine Elhafsi; Daniel Kang 001; Evgenya Pergament; Eyal Cidon; Sachin Katti; Marco Pavone 001 (Robotics: Science and Systems, 2019)

Transfer of Adversarial Robustness Between Perturbation Types. - Daniel Kang 001; Yi Sun 010; Tom Brown; Dan Hendrycks; Jacob Steinhardt (CoRR, 2019)

Testing Robustness Against Unforeseen Adversaries. - Daniel Kang 001; Yi Sun 010; Dan Hendrycks; Tom Brown; Jacob Steinhardt (CoRR, 2019)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Cody Coleman; Greg Diamos; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (CoRR, 2019)

BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics. - Daniel Kang 001; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2019)

Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark. - Cody Coleman; Daniel Kang 001; Deepak Narayanan; Luigi Nardi; Tian Zhao 001; Jian Zhang 049; Peter Bailis; Kunle Olukotun; Christopher Ré; Matei Zaharia (ACM SIGOPS Oper. Syst. Rev., 2019)

Improved Natural Language Generation via Loss Truncation. - Daniel Kang 001; Tatsunori Hashimoto (ACL, 2020)

Model Assertions for Monitoring and Improving ML Models. - Daniel Kang 001; Deepti Raghavan; Peter Bailis; Matei Zaharia (MLSys, 2020)

Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. - Peter Kraft; Daniel Kang 001; Deepak Narayanan; Shoumik Palkar; Peter Bailis; Matei Zaharia (MLSys, 2020)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Gregory F. Diamos; Cody Coleman; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (MLSys, 2020)

Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data. - Daniel Kang 001; John Guibas; Peter Bailis; Tatsunori Hashimoto; Matei Zaharia (CoRR, 2020)

Approximate Selection with Guarantees using Proxies. - Daniel Kang 001; Edward Gan; Peter Bailis; Tatsunori Hashimoto; Matei Zaharia (Proc. VLDB Endow., 2020)

Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics. - Daniel Kang 001; Ankit Mathur; Teja Veeramacheneni; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2020)

A Demonstration of Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference. - Peter Kraft; Daniel Kang 001; Deepak Narayanan; Shoumik Palkar; Peter Bailis; Matei Zaharia (Proc. VLDB Endow., 2020)

Accelerating Queries over Unstructured Data with ML. - Daniel Kang 001 (CIDR, 2021)

Proof: Accelerating Approximate Aggregation Queries with Expensive Predicates. - Daniel Kang 001; John Guibas; Peter Bailis; Tatsunori Hashimoto; Yi Sun 010; Matei Zaharia (CoRR, 2021)

Accelerating Approximate Aggregation Queries with Expensive Predicates. - Daniel Kang 001; John Guibas; Peter Bailis; Tatsunori Hashimoto; Yi Sun 010; Matei Zaharia (Proc. VLDB Endow., 2021)

VIVA: An End-to-End System for Interactive Video Analytics. - Daniel Kang 001; Francisco Romero; Peter D. Bailis; Christos Kozyrakis; Matei Zaharia (CIDR, 2022)

Finding Label and Model Errors in Perception Data With Learned Observation Assertions. - Daniel Kang 001; Nikos Aréchiga; Sudeep Pillai; Peter D. Bailis; Matei Zaharia (SIGMOD Conference, 2022)

TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data. - Daniel Kang 001; John Guibas; Peter D. Bailis; Tatsunori Hashimoto; Matei Zaharia (SIGMOD Conference, 2022)

Scaling up Trustless DNN Inference with Zero-Knowledge Proofs. - Daniel Kang 001; Tatsunori Hashimoto; Ion Stoica; Yi Sun 010 (CoRR, 2022)

ZK-IMG: Attested Images via Zero-Knowledge Proofs to Fight Disinformation. - Daniel Kang 001; Tatsunori Hashimoto; Ion Stoica; Yi Sun 010 (CoRR, 2022)

Optimizing Video Analytics with Declarative Model Relationships. - Francisco Romero; Johann Hauswald; Aditi Partap; Daniel Kang 001; Matei Zaharia; Christos Kozyrakis (Proc. VLDB Endow., 2022)

Q-Diffusion: Quantizing Diffusion Models. - Xiuyu Li; Yijiang Liu; Long Lian; Huanrui Yang; Zhen Dong 003; Daniel Kang 001; Shanghang Zhang; Kurt Keutzer (ICCV, 2023)

Identifying and Mitigating the Security Risks of Generative AI. - Clark W. Barrett; Brad Boyd; Ellie Burzstein; Nicholas Carlini; Brad Chen; Jihye Choi; Amrita Roy Chowdhury 001; Mihai Christodorescu; Anupam Datta; Soheil Feizi; Kathleen Fisher; Tatsunori Hashimoto; et al. (CoRR, 2023)

Identifying and Mitigating the Security Risks of Generative AI. - Clark W. Barrett; Brad Boyd; Elie Bursztein; Nicholas Carlini; Brad Chen; Jihye Choi; Amrita Roy Chowdhury 001; Mihai Christodorescu; Anupam Datta; Soheil Feizi; Kathleen Fisher; Tatsunori Hashimoto; et al. (Found. Trends Priv. Secur., 2023)

Accelerating Aggregation Queries on Unstructured Streams of Data. - Matthew Russo; Tatsunori Hashimoto; Daniel Kang 001; Yi Sun 010; Matei Zaharia (Proc. VLDB Endow., 2023)

InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents. - Qiusi Zhan; Zhixiang Liang; Zifan Ying; Daniel Kang 001 (ACL (Findings), 2024)

tailwiz: Empowering Domain Experts with Easy-to-Use, Task-Specific Natural Language Processing Models. - Timothy Dai; Austin Peters; Jonah B. Gelbach; David Freeman Engstrom; Daniel Kang 001 (DEEM@SIGMOD, 2024)

AIDB: a Sparsely Materialized Database for Queries using Machine Learning. - Tengjun Jin; Akash Mittal; Chenghao Mo; Jiahao Fang; Chengsong Zhang; Timothy Dai; Daniel Kang 001 (DEEM@SIGMOD, 2024)

ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs. - Bing-Jyue Chen; Suppakit Waiwitlikhit; Ion Stoica; Daniel Kang 001 (EuroSys, 2024)

Position: A Safe Harbor for AI Evaluation and Red Teaming. - Shayne Longpre; Sayash Kapoor; Kevin Klyman; Ashwin Ramaswami; Rishi Bommasani; Borhane Blili-Hamelin; Yangsibo Huang; Aviya Skowron; Zheng Xin Yong; Suhas Kotha; Yi Zeng 005; Weiyan Shi 001; et al. (ICML, 2024)

Trustless Audits without Revealing Data or Models. - Suppakit Waiwitlikhit; Ion Stoica; Yi Sun 010; Tatsunori Hashimoto; Daniel Kang 001 (ICML, 2024)

Removing RLHF Protections in GPT-4 via Fine-Tuning. - Qiusi Zhan; Richard Fang; Rohan Bindu; Akul Gupta; Tatsunori Hashimoto; Daniel Kang 001 (NAACL (Short Papers), 2024)

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. - Daniel Kang 001; Xuechen Li 005; Ion Stoica; Carlos Guestrin; Matei Zaharia; Tatsunori Hashimoto (SP (Workshops), 2024)

LLM Agents can Autonomously Hack Websites. - Richard Fang; Rohan Bindu; Akul Gupta; Qiusi Zhan; Daniel Kang 001 (CoRR, 2024)

A Safe Harbor for AI Evaluation and Red Teaming. - Shayne Longpre; Sayash Kapoor; Kevin Klyman; Ashwin Ramaswami; Rishi Bommasani; Borhane Blili-Hamelin; Yangsibo Huang; Aviya Skowron; Zheng Xin Yong; Suhas Kotha; Yi Zeng 005; Weiyan Shi 001; et al. (CoRR, 2024)

LLM Agents can Autonomously Exploit One-day Vulnerabilities. - Richard Fang; Rohan Bindu; Akul Gupta; Daniel Kang 001 (CoRR, 2024)

Teams of LLM Agents can Exploit Zero-Day Vulnerabilities. - Richard Fang; Rohan Bindu; Akul Gupta; Qiusi Zhan; Daniel Kang 001 (CoRR, 2024)

Voice-Enabled AI Agents can Perform Common Scams. - Richard Fang; Dylan Bowman; Daniel Kang 001 (CoRR, 2024)

Dias: Dynamic Rewriting of Pandas Code. - Stefanos Baziotis; Daniel D. Kang; Charith Mendis (Proc. ACM Manag. Data, 2024)

LEAP: LLM-powered End-to-end Automatic Library for Processing Social Science Queries on Unstructured Data. - Chuxuan Hu; Austin Peters; Daniel Kang 001 (Proc. VLDB Endow., 2024)

REPRO-Bench: Can Agentic AI Systems Assess the Reproducibility of Social Science Research? - Chuxuan Hu; Liyun Zhang; Yeji Lim; Aum Wadhwani; Austin Peters; Daniel Kang 001 (ACL (Findings), 2025)

UTBoost: Rigorous Evaluation of Coding Agents on SWE-Bench. - Boxi Yu; Yuxuan Zhu 003; Pinjia He; Daniel Kang 001 (ACL (1), 2025)

Efficient Approximate Query Processing with Block Sampling. - Yuxuan Zhu 003; Daniel Kang 001 (CIDR, 2025)

CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities. - Yuxuan Zhu 003; Antony Kellermann; Dylan Bowman; Philip Li; Akul Gupta; Adarsh Danda; Richard Fang; Conner Jensen; Eric Ihli; Jason Benn; Jet Geronimo; Avi Dhir; et al. (ICML, 2025)

Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents. - Qiusi Zhan; Richard Fang; Henil Shalin Panchal; Daniel Kang 001 (NAACL (Findings), 2025)

Establishing Best Practices in Building Rigorous Agentic Benchmarks. - Yuxuan Zhu 003; Tengjun Jin; Yada Pruksachatkun; Andy Zhang; Shu Liu; Sasha Cui; Sayash Kapoor; Shayne Longpre; Kevin Meng; Rebecca Weiss; Fazl Barez; Rahul Gupta 001; et al. (NeurIPS, 2025)

Analytical Queries for Unstructured Data. - Daniel Kang 001 (Found. Trends Databases, 2025)

Linear-time and Logarithmically-sound Permutation and Multiset SNARKs. - Bing-Jyue Chen; Lilia Tang; David Heath 001; Daniel Kang 001 (IACR Cryptol. ePrint Arch., 2025)

Drama : Unifying Data Retrieval and Analysis for Open-Domain Analytic Queries. - Chuxuan Hu; Maxwell Yang; James Weiland; Yeji Lim; Suhas Palawala; Daniel D. Kang (Proc. ACM Manag. Data, 2025)

MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks. - Hyeonjeong Ha; Qiusi Zhan; Jeonghwan Kim; Dimitrios Bralios; Saikrishna Sanniboina; Nanyun Peng 001; Kai-Wei Chang 001; Daniel Kang 001; Heng Ji 001 (CoRR, 2025)

PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees (Technical Report). - Yuxuan Zhu 003; Tengjun Jin; Stefanos Baziotis; Chengsong Zhang; Charith Mendis; Daniel Kang 001 (CoRR, 2025)

PandasBench: A Benchmark for the Pandas API. - Alex Broihier; Stefanos Baziotis; Daniel Kang 001; Charith Mendis (CoRR, 2025)

Breaking Barriers: Do Reinforcement Post Training Gains Transfer To Unseen Domains? - Chuxuan Hu; Yuxuan Zhu 003; Antony Kellermann; Caleb Biddulph; Suppakit Waiwitlikhit; Jason Benn; Daniel Kang 001 (CoRR, 2025)

PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees. - Yuxuan Zhu 003; Tengjun Jin; Stefanos Baziotis; Chengsong Zhang; Charith Mendis; Daniel D. Kang (Proc. ACM Manag. Data, 2025)

Establishing Best Practices for Building Rigorous Agentic Benchmarks. - Yuxuan Zhu 003; Tengjun Jin; Yada Pruksachatkun; Andy Zhang; Shu Liu; Sasha Cui; Sayash Kapoor; Shayne Longpre; Kevin Meng; Rebecca Weiss; Fazl Barez; Rahul Gupta 001; et al. (CoRR, 2025)

ZKTorch: Compiling ML Inference to Zero-Knowledge Proofs via Parallel Proof Accumulation. - Bing-Jyue Chen; Lilia Tang; Daniel Kang 001 (CoRR, 2025)

Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation. - Sayash Kapoor; Benedikt Stroebl; Peter Kirgis; Nitya Nadgir; Zachary S. Siegel; Boyi Wei; Tianci Xue; Ziru Chen; Felix Chen; Saiteja Utpala; Franck Ndzomga; Dheeraj Oruganty; et al. (CoRR, 2025)

Visual Backdoor Attacks on MLLM Embodied Decision Making via Contrastive Trigger Learning. - Qiusi Zhan; Hyeonjeong Ha; Rui Yang 010; Sirui Xu 002; Hanyang Chen; Liang-Yan Gui; Yu-Xiong Wang; Huan Zhang 001; Heng Ji 001; Daniel Kang 001 (CoRR, 2025)

ELT-Bench: An End-to-End Benchmark for Evaluating AI Agents on ELT Pipelines. - Tengjun Jin; Yuxuan Zhu 003; Daniel Kang 001 (Proc. VLDB Endow., 2025)

AIA Forecaster: Technical Report. - Rohan Alur; Bradly C. Stadie; Daniel Kang 001; Ryan Chen; Matt McManus; Michael Rickert; Tyler Lee; Michael Federici; Richard Zhu; Dennis Fogerty; Hayley Williamson; Nina Lozinski; et al. (CoRR, 2025)

Measuring Agents in Production. - Melissa Z. Pan; Negar Arabzadeh; Riccardo Cogo; Yuxuan Zhu 003; Alexander Xiong; Lakshya A. Agrawal; Huanzhi Mao; Emma Shen; Sid Pallerla; Liana Patel; Shu Liu; Tianneng Shi; et al. (CoRR, 2025)

MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Knowledge Poisoning Attacks. - Hyeonjeong Ha; Qiusi Zhan; Jeonghwan Kim; Dimitrios Bralios; Saikrishna Sanniboina; Nanyun Peng 001; Kai-Wei Chang 001; Daniel Kang 001; Heng Ji 001 (ACL (1), 2026)

Behavioral Fingerprints for LLM Endpoint Stability and Identity. - Jonah Leshin; Manish Shah; Ian Timmis; Daniel Kang 001 (CAIS, 2026)

Text-to-SQL Benchmarks are Broken: An In-Depth Analysis of Annotation Errors. - Tengjun Jin; Yoojin Choi; Yuxuan Zhu 003; Daniel Kang 001 (CIDR, 2026)

SafeSearch: Do Not Trade Safety for Utility in LLM Search Agents. - Qiusi Zhan; Angeline Budiman-Chan; Abdelrahman Zayed; Xingzhi Guo; Daniel Kang 001; Joo-Kyung Kim (EACL (Findings), 2026)

Teams of LLM Agents can Exploit Zero-Day Vulnerabilities. - Yuxuan Zhu 003; Antony Kellermann; Akul Gupta; Philip Li; Richard Fang; Rohan Bindu; Daniel Kang 001 (EACL (Volume 1: Long Papers), 2026)

Pervasive Annotation Errors Break Text-to-SQL Benchmarks and Leaderboards. - Tengjun Jin; Yoojin Choi; Yuxuan Zhu 003; Daniel Kang 001 (Proc. VLDB Endow., 2026)

Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards. - Yuxuan Zhu 003; Daniel Kang 001 (CoRR, 2026)

Accelerating Approximate Analytical Join Queries over Unstructured Data with Statistical Guarantees. - Yuxuan Zhu 003; Tengjun Jin; Chenghao Mo; Daniel Kang 001 (CoRR, 2026)

SODIUM: From Open Web Data to Queryable Databases. - Chuxuan Hu; Philip Li; Maxwell Yang; Daniel Kang 001 (CoRR, 2026)

ReViSQL: Achieving Human-Level Text-to-SQL. - Yuxuan Zhu 003; Tengjun Jin; Yoojin Choi; Daniel Kang 001 (CoRR, 2026)