Junfeng Yang Professor Co-director of Software Systems Lab Department of Computer Science Columbia University 500 West 120 Street, 519 CSB Mail Code 0401 New York, NY 10027 Lab: 487 CSB Phone: (212) 939-7012 Fax: (212) 666-0140 [ Awards | Publications | Software ] [ People | Press | Teaching | Support ]

My research centers on making reliable and secure systems. Some of my current research thrusts are (1) security and robustness of machine learning, (2) tools to better protect, verify, analyze, test, and debug software, and (3) programming and runtime systems for cloud applications. After getting a BS in Computer Science from Tsinghua University, I earned my PhD in Computer Science at Stanford, where I created eXplode, a general, lightweight system for effectively finding storage system errors. This work has led to an OSDI best paper award, numerous bug fixes to real systems such as the Linux kernel, and a featured article in Linux Weekly news. In 2008, I worked at Microsoft Research Silicon Valley, extending eXplode to check production distributed systems. MoDist, the resultant system, is being transferred to Microsoft product groups. At Columbia University, my recent work on reliable multithreading was featured in sites such as ACM Tech News. I won the Sloan Research Fellowship and the AFOSR YIP award, both in 2012; and the NSF CAREER award in 2011.

I'm looking for a few graduate students, postdocs, and undergraduate interns. If you know how to build systems/tools, we should talk.

Columbia undergraduate and master students: the above applies to you, too. Also, take a look at some projects available for academic credits.

I was on sabbatical in 2016 co-founding a Columbia spin-off called NimbleDroid. NimbleDroid provides automated, comprehensive app performance analysis to help teams build performant apps. Its mission is to leverage research breakthroughs to automate mundane tasks in software engineering.

Select Awards

• IEEE S&P Test-of-Time Award, 2025
• CIDR Best Paper, 2024
• OSDI Best Paper, 2022
• USENIX ATC Best Paper, 2021
• SOSP Best Paper, 2017
• Sloan Research Fellowship, 2012
• AFOSR YIP Award, 2012
• NSF CAREER, 2011
• OSDI Best Paper, 2004

Recent Papers and Drafts

(If you're interested in a paper draft but it isn't available online, please email me for a copy.)

• Radshield: Software Radiation Protection for Commodity Hardware in Space  [bib]
  Haoda Wang, Yonatan Winetraub, Steven Myint, Vandi Verma, Junfeng Yang, Asaf Cidon
  International Conference on Architecture Support for Programming Languages and Operating Systems (ASPLOS '26), 2026
  
• PickleBall: Secure Deserialization of Pickle-based Machine Learning Models  [bib]
  Andreas Kellas, Neophytos Christou, Wenxin Jiang, Penghui Li, Laurent Simon, Yaniv David, Vasileios P. Kemerlis, James C. Davis, Junfeng Yang
  
• Learning to Rewrite: Generalized LLM-Generated Text Detection  [bib]
  Ran Li, Wei Hao, Weiliang Zhao, Junfeng Yang, Chengzhi Mao
  Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL '25), 2025
  
• EditLord: Learning Code Transformation Rules for Code Editing  [bib]
  Weichen Li, Albert Jan, Baishakhi Ray, Chengzhi Mao, Junfeng Yang, Kexin Pei
  Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025
  
• Characterizing the Prevalence of LLM-Generated Malicious Emails  [bib]
  Wei Hao, Van Tran, Vincent Rideout, Zixi Wang, AnMei Dasbach-Prisk, M. H. Afifi, Junfeng Yang, Ethan Katz-Bassett, Grant Ho, Asaf Cidon
  Proceedings of the ACM Internet Measurement Conference (IMC) 2025, 2025
  
• Diversity Helps Jailbreak Large Language Models  [bib]  (Oral presentation)
  Weiliang Zhao, Daniel Ben-Levi, Junfeng Yang, Chengzhi Mao
  Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL), 2025
  
• I Can Hear You: Selective Robust Training for Deepfake Audio Detection  [bib]
  Zirui Zhang, Wei Hao, Aroon Sankoh, William Lin, Emanuel Mendiola-Ortiz, Junfeng Yang, Chengzhi Mao
  Proceedings of The Thirteenth International Conference on Learning Representations (ICLR) , 2025
  

Select Publications (Complete list...)

• Multi-task Learning Increases Adversarial Robustness  [bib]  (Oral presentation)
  Chengzhi Mao, Amogh Gupta, Vikram Nitin, Baishakhi Ray, Shuran Song, Junfeng Yang, Carl Vondrick
  Proceedings of the 16th European Conference on Computer Vision (ECCV '20), August, 2020
  
  We present both theoretical and empirical analyses that connect the adversarial robustness of a model to the number of tasks that it is trained on. Experiments on two datasets show that attack difficulty increases as the number of target tasks increase. Moreover, our results suggest that when models are trained on multiple tasks at once, they become more robust to adversarial attacks on individual tasks. While adversarial defense remains an open challenge, our results suggest that deep networks are vulnerable partly because they are trained on too few tasks.
• DeepXplore: Automated Whitebox Testing of Deep Learning Systems  [bib]  (Best paper award)
  Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana
  Proceedings of the 26th ACM Symposium on Operating Systems Principles (SOSP '17), October, 2017
  
  We increasingly rely on deep learning and deep neural networks (DNNs) in safety- and security-critical applications such as self-driving, medical diagnosis, face-based identification, and malware detection, but it remains an open challenge to thoroughly test DNNs for robustness and security. We propose Neuron Coverage, the first testing coverage metric to empirically understand how much decision logic a testing input set has exercised in a DNN. We design and build DeepXplore, the first systematic testing framework for DNNs. Given a test input, DeepXplore applies physically realizable transformations (e.g., darkening an image) to the inputs (as opposed to noise in prior adversarial ML work) to generate new inputs to maximize neuron coverage. It found thousands of flaws in state-of-art self-driving and malware detection DNNs and improved their neuron coverage by over 50%. (Also appeared in MLSec '17.)
• Shuffler: Fast and Deployable Continuous Code Re-Randomization  [bib]
  David Williams-King, Graham Gobieski, Kent Williams-King, James P Blake, Xinhao Yuan, Patrick Colp, Vasileios P Kemerlis, Junfeng Yang, William Aiello
  Proceedings of the Twelfth Symposium on Operating Systems Design and Implementation (OSDI '16), 2016
  
  Describes Shuffler, a system that continuously randomizes an application's binary code at runtime, defeating code-reuse attacks. Shuffler is fast: it shuffles all code within tens of milliseconds, whereas cutting-edge ROP attacks need 10--100x more time to discover gadgets. Shuffler is egalitarian: leveraging the insight that randomization doesn't require a higher privilege authority, Shuffler shuffles itself, reducing trusted computing base and making the approach applicable to kernels and hypervisors. Shuffler is deployable: its augmented binary analysis requires no modifications to OS, compilers, and linkers.
• Determinism Is Not Enough: Making Parallel Programs Reliable with Stable Multithreading  [bib]
  Junfeng Yang, Heming Cui, Jingyue Wu, Yang Tang, Gang Hu
  Communications of the ACM (2014)
  
  This paper is geared toward a general audience. If you have time to read just one paper on our concurrency work, this is the paper to read. It describes our vision of stable multithreading (StableMT), a radical approach to making multithreading reliable, and summarizes our last five years of work on designing, building, and applying stable multithreading systems. The final version of this paper will appear in CACM.
• EXPLODE: a Lightweight, General System for Finding Serious Storage System Errors  [talk | bib]
  Junfeng Yang, Can Sar, Dawson Engler
  Proceedings of the Seventh Symposium on Operating Systems Design and Implementation (OSDI '06), November, 2006
  
  Describes our in-situ model checking approach, which made it easy to thoroughly check real systems. We applied eXplode to 17 storage systems and found serious data-loss errors in every system checked. This paper is my favorite in describing our model checking approach, which forms the basis of my PhD thesis work.

Software

• Crane, our transparent state machine replication system.
• AppDoctor, our Android app checker.
• Parrot, our latest stable and deterministic multithreading system. It has two goals: (1) be practical and (2) be fast. By default, it schedules synchronizations in a round-robin manner, vastly reducing the set of schedules for reliability. When needed, it allows developers to add performance hints for speed. Together with the code, we also released a benchmark suite with 100+ multithreaded programs, and Parrot's complete results on these programs.
• NeonGoby, a system for effectively detecting errors in alias analysis, one of the most crucial and widely used program analyses. If you have an LLVM-based alias analysis you want to check, give NeonGoby a try.
• Loom, a "live-workaround" system designed to quickly and safely bypass various types of concurrency errors at runtime. It contains a generic engine for live-updating multithreaded programs without restarts, which you can leverage if you want to build a live-update tool.
• eXplode, a storage system checker. It uses an approach we call in-situ model checking to thoroughly check general systems software in a lightweight manner.

People

I'm fortunate to work or have worked with these brilliant people.

• Penghui Li, Postdoc research scientist
• Tamer Eldeeb, PhD student
• Wei Hao, PhD student
• Yun-Yun Tsai, PhD student
• Andreas Kellas, PhD student
• Raphael Jedidiah Sofaer, PhD student
• Harry Haoda Wang, PhD student
• Alex Mathai, PhD student
• Jinjun Peng, PhD student
• Hailie Mitchell, PhD student
• Hideaki Takahashi, PhD student
• Jihwan Kim, PhD student

• Yaniv David, Postdoc research scientist, 2021-2024, joined Meta and then Technion as a professor
• Sally Junson Wang, MS, 2024, joined Stanford with Stanford Graduate Fellowship
• Weichen Li, MS, 2024, joined University of Chicago for PhD
• Kexin Pei, PhD, 2023, jointed University of Chicago as a Neubauer Family professor
• Chengzhi Mao, PhD, 2023, joined Google and then Rugters as a professor
• Lingmei Weng, PhD, 2023, joined Google
• David Williams-King, PhD, 2020, joined Elpha Secure as CTO
• Scott K. Geng, Undergraduate, 2023, jointed University of Washington for PhD, NSF Graduate Research Fellow
• Yang Tang, PhD, 2019, joined laioffer as Director of Education and Curriculum Development then NYU as a lecturer
• Gang Hu, PhD, 2018, joined Google
• Xinhao Yuan, PhD, 2019, joined Google
• Yinzhi Cao, Postdoc research scientist, 2014-2015, joined Lehigh and then John Hopkins University as a professor
• Heming Cui, PhD, 2015, joined the University of Hong Kong as a professor
• Jingyue Wu, PhD, 2014, joined Google
• Yan Cui, Postdoc research scientist, 2013-2015, joined Intel
• Oren Laadan, Postdoc research scientist, 2010-2011, founded Cellrox
• Rui Gu, MS, 2017
• Linjie Zhu, MS, 2019
• Georgios Koloventzos, MS, 2016
• Karthik Jayaraman, MS, 2016
• Chuliang Weng, Visiting research scientist, 2012
• John Gallagher, MS, 2011, joined FourSquare
• Chia-che Tsai, MS, 2011, joined Stony Brook for PhD
• Neetha Maria Sebastian, MS, 2011, joined Google
• Yunling Wang, MS, 2010, joined Microsoft
• Ben Warfield, MS, 2010, joined Wireless Generation
• Nathan Murith, MS, 2010, joined Autodesk
• Maoliang Huang, MS, 2010, joined FlexTrade Systems
• Patrick Huang, MS, 2009, joined Sony

I co-advise some students in the SSL lab.

Articles and Discussions about Research

DIVID: Columbia Engineering
Raidar: Science News Explores, Columbia Engineering, Columbia Magazine
DeepXplore: Scientific American, IEEE Spectrum, CACM Research Highlight (Video), Newsweek, TechRadar, Columbia News, China News, Sohu, Sina, CCTV's Hello AI documentary
Shuffler: Network World, ACM Tech News
Machine unlearning: The Stack, EurekAlert, The Atlantic, KurzweilAI, ACM Tech News
Peregrine: CACM, ACM Tech News, The Register, Columbia Engineering, TG Daily, Physorg.com
MoDist: Softpedia
eXplode: Linux Weekly News
Static analysis: Linux Kernel Mailing List

Teaching

Fall 2025 -- W4152: Engineering Software-as-a-Service
Fall 2025 -- E6113: Agent for Work

Fall 2024 -- Sabbatical leave
Spring 2024 -- Sabbatical leave
Fall 2023 -- W4152: Engineering Software-as-a-Service
Fall 2023 -- E6998: Engineering Blockchain and Web3 Apps
Fall 2022 -- W4152: Engineering Software-as-a-Service
Fall 2022 -- E6998: Engineering Blockchain and Web3 Apps
Fall 2021 -- W4995: Engineering Software-as-a-Service
Fall 2021 -- E6998: Security and Robustness of ML systems
Spring 2021 -- W4156: Advanced Software Engineering
Spring 2021 -- E6998-003: Security and Robustness of ML systems
Spring 2020 -- W4156: Advanced Software Engineering
Spring 2020 -- E6998-010: Security and Robustness of ML systems
Spring 2019 -- E6121: Reliable Software
Spring 2019 -- E6998-001: Security and Robustness of ML systems
Spring 2018 -- E6121: Reliable Software
Spring 2018 -- E6998-009: Security and Robustness of ML systems
Spring 2017 -- E6121: Reliable Software
Fall 2016 -- Teaching leave
Spring 2016 -- Sabbatical leave
Fall 2015 -- Sabbatical leave
Spring 2015 -- Teaching leave
Fall 2014 -- E6121: Reliable Software
Spring 2014 -- Teaching leave
Fall 2013 -- W4118: Operating Systems I
Spring 2013 -- Teaching leave
Fall 2012 -- E6121: Reliable Software
Spring 2012 -- W4118: Operating Systems I
Fall 2011 -- E6121: Reliable Software
Spring 2011 -- W4118: Operating Systems I
Fall 2010 -- E6998-1: Reliable Software
Spring 2010 -- W4118: Operating Systems I
Fall 2009 -- E6998-1: Reliable Software
Spring 2009 -- W4118: Operating Systems I
Fall 2008 -- E6998-2: How to Make Reliable Software

Support for Research and Teaching