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

• OSDI best paper, 2022
• USENIX ATC best paper, 2021
• Rock Star Award, Association of Chinese Scholars in Computing, 2019
• SOSP best paper, 2017
• Sloan research fellowship, 2012
• Air Force Office of Scientific Research Young Investigator Program Award (AFOSR YIP), 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.)

• kGym: A Platform and Dataset to Benchmark Large Language Models on Linux Kernel Crash Resolution
  Alex Mathai, Chenxi Huang, Petros Maniatis, Aleksandr Nogikh, Franjo Ivancic, Junfeng Yang, Baishakhi Ray
  Proceedings of the Thirty-eight Annual Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2024
  
• SemCoder: Training Code Language Models with Comprehensive Semantics
  Yangruibo Ding, Jinjun Peng, Marcus J. Min, Gail Kaiser, Junfeng Yang, Baishakhi Ray
  Proceedings of the Thirty-eight Annual Conference on Neural Information Processing Systems (NeurIPS), 2024
  
• Nazar: Monitoring and Adapting ML Models on Mobile Devices
  Wei Hao, Zixi Wang, Lauren Hong, Lingxiao Li, Nader Karayanni, AnMei Dasbach-Prisk, Chengzhi Mao, Junfeng Yang, Asaf Cidon
  Proceedings of the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2025
  
• RAFT: Realistic Attacks to Fool Text Detectors
  James Liyuan Wang, Ran Li, Junfeng Yang, Chengzhi Mao
  Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024
  
• Exploiting Code Symmetries for Learning Program Semantics   (Spotlight)
  Kexin Pei, Weichen Li, Qirui Jin, Shuyang Liu, Scott Geng, Lorenzo Cavallaro, Junfeng Yang, Suman Jana
  Proceedings of the International Conference on Machine Learning (ICML), 2024
  
• MGit: A Model Versioning and Management System
  Wei Hao, Daniel Mendoza, Rafael Mendes, Deepak Narayanan, Amar Phanishayee, Asaf Cidon, Junfeng Yang
  Proceedings of the International Conference on Machine Learning (ICML), 2024
  
• Raidar:geneRative AI Detection viA Rewriting
  Chengzhi Mao, Carl Vondrick, Hao Wang, Junfeng Yang
  Proceedings of the International Conference on Learning Representations (ICLR), 2024
  
• Interpretating and Controlling Large Vision Language Models via Text Explanations
  Haozhe Chen, Junfeng Yang, Carl Vondrick, Chengzhi Mao
  Proceedings of the International Conference on Learning Representations (ICLR), 2024
  
• GDA: Generalized Diffusion for Robust Test-time Adaptation
  Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen, Junfeng Yang, Che-Chun Su, Min Sun, Cheng-Hao Kuo
  Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
  
• SmartInv: Multimodal Learning for Smart Contract Invariant Inference
  Sally Junsong Wang, Kexin Pei, Junfeng Yang
  Proceedings of the 45th IEEE Symposium on Security and Privacy (S\&P '24), 2024
  
• Chablis: Fast and General Transactions in Geo-Distributed Systems   (Best paper award)
  Tamer Eldeeb, Philip A. Bernstein, Asaf Cidon, Junfeng Yang
  Proceedings of the Conference on Innovative Data Systems Research (CIDR), 2024
  
• RogueOne: Detecting Rogue Updates via Differential Data-flow Analysis Using Trust Domains
  Raphael J. Sofaer, Yaniv David, Mingqing Kang, Jianjia Yu, Yinzhi Cao, Junfeng Yang, Jason Nieh
  Proceedings of the 46th International Conference on Software Engineering (ICSE 2024), 2024
  
• QUACK: Hindering Deserialization Attacks via Static Duck Typing
  Yaniv David, Neophytos Christou, Andreas D. Kellas, Vasileios P. Kemerlis, Junfeng Yang
  Proceedings of the The Network and Distributed System Security Symposium (NDSS), 2024
  
• Convolutional Visual Prompt for Robust Visual Perception
  Yun-Yun Tsai, Chengzhi Mao, Junfeng Yang
  Proceedings of the Thirty-seventh Annual Conference on Neural Information Processing Systems (NeurIPS), 2023
  
• Mars Attacks! Software Protection Against Space Radiation
  Haoda Wang, Steven Myint, Vandi Verma, Yonatan Winetraub, Junfeng Yang, Asaf Cidon
  Proceedings of the Twenty-Second ACM Workshop on Hot Topics in Networksthe 18th USENIX workshop on Hot topics in operating systems (HotNets '23), 2023
  
• Chardonnay: Fast and General Datacenter Transactions for On-Disk Databases
  Tamer Eldeeb, Xincheng Xie, Philip A. Bernstein, Asaf Cidon, Junfeng Yang
  Proceedings of the 17th Symposium on Operating Systems Design and Implementation (OSDI '23), 2023
  

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 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