Yu Huang

PhD, Assistant Professor
Department of Computer Science

Vanderbilt University


  • Office 4108, Sony Building
    Department of Computer Science
    Vanderbilt University
    1400 18th Ave. S
    Nashville, TN 37235

Call for Participants

My research group is actively recruiting participants for multiple studies! Those studies include different focuses on human aspects in software engineering and related fields. We firmly believe in the value of human aspects in software engineering and we aim to understand programmers' behaviors and cognitive processes to design better tools, management, and training strategies. Overall, we want to support our programming community :)

Your participation is greatly appreciated! We would love to share with you our research findings. We will also compensate your time and contribution. Our studies aim at a full range of progammers from new learners to programming to professional develoeprs.

For more information about our studies and recruitment contact, please visit our human study recruitment website: Human Study Recruitment for Huang Research Group.

About Me

Curriculum Vitae

My first name "Yu" can be confused as "You" sometimes. So people around me may also call me "HuangYu" together :)

My group's work focuses on software engineering and human factors, including user cognition, software infrastructure, sustainability for open source software, AI for software engineering, and computer science education. Broadly, we solve problems to understand and improve the effectiveness and efficiency of software engineering activities. Our work spans software, hardware, medical imaging, eye tracking, and mobile sensing, collaborating with researchers from Psychology and Neuroscience, and research labs in CS industry. We also work on social aspects in software engineering community.

I received my PhD in Computer Science at University of Michigan in 2021. My advisor was Prof. Westley Weimer. I received my MS in Computer Engineering at University of Virginia in 2015 and my BS in Aerospace Engineering from Harbin Institute of Techonology in China in 2011.

Academic News

  • Oct 2023: Congratulations to Yimeng for her paper accepted to ICSE 2024!
  • July 2023: Congratulations to Yueke for his paper accepted to ESEM 2023!
  • July 2023: Congratulations to Zihan for her paper accepted to FSE 2023!
  • Apr 2023: Congratulations to Haonan Hou for receiving the VUSRP research scholarship! We will investigate the reliability of ChatGPT on intimate relationship problems!
  • Aug 2022: Congratulations to Yifan for his doctoral proposal accepted to ASE 2022 Doctoral Symposium!
  • July 2022: We received a 4-year NSF grant to study human-like AI models for source code that leverages developers' behaviors to empower AI tools for software engineering activities. This project is in collaboration with the groups of Prof. Collin McMillan and Prof. Toby Li at Notre Dame.
  • June 2022: We received a grant from the GitHub Tech for Social Good and GitHub Policy teams to support our research on investigating global standard usage metrics! Thank you, GitHub!
  • June 2022: Our paper on using fuzzy logic to mitigate uncertainty and ambiguity in facult localization is accepted to GI 2022! Congratulations, Yueke!
  • Apr 2022: Congratulations to Anda Liang for receiving the VUSRP research scholarship! We will investigate human biases on online software engineering platforms!


Most of my research is interdisciplinary and involves many domains. I am particularly interested in improving the efficiency and effectiveness of computational activities. I like learning and using different techniques to solve impactful and interesting problems no matter it is within my nominal areas of expertise. My work has involved AI, program analysis, embedded systems, mixed-methods studies, medical imaging (fMRI, fNIRS), eye-tracking, cyber human systems, and software-hardware co-design.

Using Objective Measures to Understand Cognitive Processes in Computing Activities

My primary research interest is to understand how developers carry out computer science activities and thus help improve software engineering productivity and guide the use and development of supporting tools and environment. Previous studies have helped explore how programmers conduct computing activities, such as code comprehension and code review, but they rely on traditional survey instruments, which may not be reliable, rather than an understanding of fundamental cognitive processes. Advances in medical imaging and eye tracking have recently been applied to software engineering, supporting grounded neurobiological and visual explorations of computing activities. My research is among the first that leverages various objective measures to provide a systematic solution to understand user cognition in programming activities. I focus on understanding the role of spatial ability, fundamental processes and stereotypical associations in software engineering activities by combining medical imaging, such as fMRI and fNIRS, and eye tracking.

I believe that understanding the cognitive processes in software activities is exciting and essential for modern software engineering and education, because it allows us to adapt knowledge from other domains (e.g., Psychology, Biomedical Engineering) to design interventions to enhance the effectiveness in software engineering and computer science pedagogy. My research presents a systematic solution that (1) measures relevant factors objectively in computing tasks, (2) is based on rigorous cognitive (neurological and visual) evidence, (3) helps understand semantically-rich and industry-related software engineering activities (e.g, data structure manipulation, code writing and code review) and (4) provides guidance for actionable mitigations across different demographic groups. Along this line of research, I have worked on:

  • Investigating the neurological relationship between data structure manipulation and spatial ability
  • Exploring the cognitive processes of code writing via prose writing
  • Detecting biases and differences in code review: genders of reviewers and apparent authors of pull requests (human vs. machine).


I strongly value replication of research. Medical imaging studies can be costly and I would like to share our de-identified data with researchers in the community. Our data includes all the medical imaging signals (fMRI and fNIRS), eye-tracking coordinates, stimuli design, experiment interface, training videos, IRB protocols, and survey data. You can find the data and contact infomation at our main project website:

Open Source Software for Social Good

I am working with Dr. Denae Ford and Dr. Thomas Zimmermann at Microsoft Research, Redmond, on investigating chracteristics and trajectories of Open Source Software (OSS) that aims at solving societal issues.

Open source software is not only for building technical tools to support the developers. Many open-source developers use their technical skills to benefit a common societal good. An example can be medical and resource platforms for tracking COVID-19. However, this special community has been in demand but overlooked. We bring in the notion of Open Source for Social Good (OSS4SG) and present the first study to investigate the basic characterizations of this community. After conducting interviews and surveys with over 500 OSS developers and 1000 projects, we find that OSS4SG covers a very wide range of social topics, it is also distinct from traditional "technical good" OSS on many aspects, including contributors' motivations, factors to consider for project selction and evaluation, and current challenges. We also present implications for researchers, sponsors, and the OSS community to better support OSS4SG.

Ths work is featured in the GitHub Octoverse Report 2020. Currenlty, according to this work, GitHub Social Impact Sector and the Digital Public Goods Alliance are working on the nomination, identification, and verification on open source projects that aim for social good. Want to lend a hand? Contribute here: Community Sourcing Digital Public Goods

Relevant Links:

Supporting Failure Transparency for Autonomous Vehicle Systems

Autonomous vehicle systems (AVS), such as quadcopters, are facing the software engineering challenge of providing failure transparency, or the extent to which failures are invisible to users and applications. The failures can be caused by software bugs, environmental changes, and security attacks. Failure transparency is especially imporant for AVS. For example, if some security attack happens when a quadcopter is flying during a mission, how can we repair the system vulnerability and apply the repair immediately while keeping the quadcopter remain its status and resume the mission later? Furthermore, when mission resumes, how can the quadcopter system continue the mission instead of starting the mission from the beginning (i.e., fly to the home base first)?

To provide such failure transparency for AVS, I designed a type-guided selective checkpointing and restoration algorithm that allows system updates on the fly , maintains critical mission states, minimize space and time overhead compared to failure-free execution, and thus the applications can resume after failures without carrying over tainted data.

This work is under the umbrella project supported by the Air Force Research Laboratory to increase system resiliency for autonomous vehicles.

Mobile Computing and Sensing Systems for Monitoring Mental Health

Research in Psychology has shown that mental health problems (e.g., social anxiety or depression) are highly associated with impairment in academic functioning and relationships. Such mental health disorders also see a continuous increase in silicon valley. However, only a small portion of people suffereing from mental health problems seek for help. The goal of this work is to provide a non-invasive solution to monitor humans' mental health and help with real-time intervention delivery.

My work leverages the ubiquity of smartphones to measure and monitor the mental well-being of end users via a specially-designed mobile application: Sensus. I use sensing data from modern smartphones (e.g., GPS, accelerometers, text messages, phone calls) and build a framework for integrating and analyzing users’ mobility patterns, micro-behaviors and communication patterns based on linear dynamic systems (LDS). This approach also considers the social context of users' behaviors. This line of research is done in the colaboration with Dr. Laura Barnes and psychologists at the University of Virginia.

Sensus Download

Sensus is availale in both Apple App store and Google Playstore:

Low Power VLSI Design

Before my PhD on software engineering, I worked with Prof. Benton Calhoun at the Univeristy of Virginia on low power VLSI design. I have taped out low power FPGA and level converter chips using IBM130. This series of research inlcudes new CLBs, interconnections and the dynamic voltage scaling mechanism for low power FPGA dsign, as well as an ultra low level converter design that can be applied to energy harvesting systems.


Our lab welcomes undergraduate students, graduate students, postdocs, visiting scholars, and anyone that is excited about software engineering and relevant interdisciplinary research. Everyone in my lab conducts amazing work and I am very honored to work with them :)

Current Students

Graduate Students

Yifan Zhang, PhD student
Co-advised with Prof. Leach
Zach Karas, PhD student
Zihan Fang, PhD student
Yueke Zhang, PhD student
Hanlin Chen, Master student (DS)
Garrett (Yuankai) Xue, Master student (DS)

Undergraduate Students

Eamon (Yimeng) Ma
Anda Liang
Eric Li
Haonan Hou
Danielle Page
Yuwei Yang
Ellie Li
Amanda Zhou


  • Yimeng Ma, Yu Huang, Kevin Leach. Breaking the Flow: A Study of Interuptions During Software Activities In ICSE 2024.
  • Zihan Fang, Madeline Endres, Thomas Zimmermann, Denae Ford, Westley Weimer, Kevin Leach, Yu Huang. A Four-Year Study of Student Contributions to OSS vs. OSS4SG with a Lightweight Intervention In Foundations of Software Engineering (ESEC/FSE 2023). pdf
  • Aakash Bansal, Zachary Eberhart, Zachary Karas, Yu Huang, Collin McMillan. Function Call Graph Context Encoding for Neural Source Code Summarization In Transactions on Software Engineering (TSE, 2023). pdf
  • Yueke Zhang, Kevin Leach, Yu Huang. Leveraging Evidence Theory to Improve Fault Localization: An Exploratory Study In ESEM 2023. pdf
  • Aakash Bansal, Chia-Yi Su, Zachary Karas, Yifan Zhang, Yu Huang, Toby Jia-Jun Li, Collin McMillan. Modeling Programmer Attention as Scanpath Prediction In ASE-NIER 2023. pdf
  • Tiger Ji, Yaxin Hu, Yu Huang, Ruofei Du, Yuhang Zhao. A Preliminary Interview: Understanding XR Developers' Needs towards Open-Source Accessibility Support In 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW 2023). pdf
  • Priscila Santiesteban, Yu Huang, Westley Weimer, Hammad Ahmad. CirFix: Automated Hardware Repair and Its Real-World Applications In Transactions on Software Engineering (TSE, 2023). pdf
  • Yifan Zhang, Junwen Yang, Haoyu Dong, Qingchen Wang, Huajie Shao, Kevin Leach Yu Huang. ASTRO: An AST-Assisted Approach for Generalizable Neural Clone Detection In InteNSE 2023 (co-located with ICSE 2023). pdf
  • Yueke Zhang, Yu Huang. Leveraging Fuzzy System to Reduce Uncertainty of Decision Making in Software Engineering Automation In Genetic Improvement (GI) 2022. pdf
  • Hammad Ahmad, Yu Huang, Westley Weimer. CirFix: Automatically Repairing Defects in Hardware Design Code In ASPLOS 2022. pdf
  • Zachary Karas, Andrew Jahn, Westley Weimer, Yu Huang. Connecting the Dots: Rethinking the Relationship between Code and Prose Writing with Functional Connectivity In Foundations of Software Engineering (ESEC/FSE) , 2021. pdf
  • Yu Huang, Hammad Ahmad, Stephanie Forrest, Westley Weimer. Applying Automated Program Repair to Dataflow Programming Languages In Genetic Improvement (GI) , 2021. pdf
  • Yu Huang, Denae Ford, Thomas Zimmermann. Leaving My Fingerprints: Motivations and Challenges of Contributing to OSS for Social Good In International Conference on Software Engineering (ICSE), 2021. pdf
  • Zohreh Sharafi, Yu Huang , Kevin Leach, Westley Weimer. Towards an Objective Measure of Developers' Cognitive Activities In ACM Trans. on Software Engineering and Methodology (TOSEM), 2021. pdf
  • Ian Bertram, Jack Hong, Yu Huang , Westley Weimer, Zohreh Sharafi. Trustworthiness Perceptions in Code Review: An Eye-tracking Study In Empirical Software Engineering and Measurement (ESEM) 2020 Emerging Results and Vision Papers. pdf
  • Yu Huang , Kevin Leach, Zohreh Sharafi, Nicholas McKay, Tyler Santander, and Westley Weimer. Biases and Differences in Code Reviews using Medical Imaging and Eye-Tracking: Genders, Humans, and Machines. In Proceedings of Foundations of Software Engineering (ESEC/FSE). FSE 2020. Sacramento, CA, USA, 2020. pdf
  • Sean Stapleton, Yashmeet Gambhir, Alexander LeClair, Zachary Eberhart, Westley Weimer, Kevin Leach, Yu Huang . A Human Study of Comprehension and Code Summarization In Proceedings of the 28th IEEE/ACM International Conference on Program Comprehension. ICPC 2020. Seoul, South Korea, 2020. pdf
  • Ryan Krueger, Yu Huang, Xinyu Liu, Tyler Santander, Westley Weimer, and Kevin Leach. Neurological Divide: An fMRI Study of Prose and Code Writing In Proceedings of the 42nd ACM/IEEE International Conference on Software Engineering. ICSE 2020. Seoul, South Korea, 2020. pdf
  • Yu Huang , Kevin Angstadt, Kevin Leach, and Westley Weimer. Selective Symbolic Type-Guided Checkpointing and Restoration for Autonomous Vehicle Repair. In Proceedings of the 1st International Workshop on Automated Program Repair. APR 2020. Seoul, South Korea, 2020. pdf
  • Yu Huang, Xinyu Liu, Ryan Krueger, Tyler Santander, Xiaosu Hu, Kevin Leach, Westley Weimer. Distilling Neural Reresentations of Data Structure Manipulation using fMRI and fNIRS. In Proceedings of the 41st ACM/IEEE International Conference on Software Engineering. ICSE 2019. Montreal, Canada, 2019. Distinguished Paper Award pdf
  • Jiaqi Gong, Yu Huang, Philip I Chow, Karl Fua, Matthew Gerber, Bethany Teachman, Laura Barnes. Understanding Behavioral Dynamics of Social Anxiety Among College Students Through Smartphone Sensors. In Transactions of Information Fusion, 49:57–68, September 2019.
  • Mehdi Boukhechba, Jiaqi Gong, Kamran Kowsari, Mawulolo K Ameko, Karl Fua, Philip I Chow, Yu Huang, Bethany A Teachman, and Laura E Barnes. Physiological Changes Over the Course of Cognitive Bias Modification for Social Anxiety. In Biomedical & Health Informatics (BHI), 2018 IEEE EMBS International Conference on, pages 422–425.
  • Emily C Geyer, Karl C Fua, Katharine E Daniel, Philip I Chow, Wes Bonelli,Yu Huang, Laura E Barnes, and Bethany A Teachman. I Did OK, But Did I Like It? Using Ecological Momentary Assessment to Examine Perceptions of Social Interactions Associated with Severity of Social Anxiety and Depression. In Behavior Therapy, 49(6):866–880, 2018 .
  • Mehdi Boukhechba, Yu Huang, Philip Chow, Karl Fua, Bethany A. Teachman, and Laura E.Barnes. Monitoring Social Anxiety From Mobility and Communication Patterns. In the ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, pages 749–753. UciComp 2017.
  • Yu Huang, Jiaqi Gong, Mark Rucker, Philip Chow, Karl Fua, Matthew S. Gerber, Bethany Teachman, and Laura E. Barnes. Discovery of Behavioral Markers of Social Anxiety From Smartphone Sensor Data. In the 1st Workshop on Digital Biomarkers, DigitalBiomarkers '17, pages 9–14, New York, NY, USA, ACM.
  • Philip I. Chow, Karl Fua, Yu Huang, Wesley Bonelli, Haoyi Xiong, Laura E. Barnes, and Bethany Teachman. Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students. In J Med Internet Res, 19(3):e62, Mar 2017, Impact factor = 4.532.
  • Haoyi Xiong, Jinghe Zhang, Yu Huang, Kevin Leach, and Laura E. Barnes. Daehr: A Discriminant Analysis Framework for Electronic Health Record Data and an Application to Early Detection of Mental Health Disorders. In ACM Trans. Intell. Syst. Technol., 8(3):47:1–47:21, February 2017, Impact factor = 2.414.
  • Yu Huang, Haoyi Xiong, Kevin Leach, Yuyan Zhang, Philip Chow, Karl Fua, Bethany A Teachman, and Laura E Barnes. Assessing Social Anxiety Using GPS Trajectories and Point-of-Interest Data. In In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '16, pages 898–903. Acceptance rate = 23.7%.
  • Haoyi Xiong, Yu Huang, Laura E Barnes, and Matthew S Gerber. Sensus: a Cross-Platform, General-Purpose System for Mobile Crowdsensing in Human-Subject Studies. In In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '16, pages 415–426. Acceptance rate = 23.7%. pdf
  • Philip Chow, Wesley Bonelli, Yu Huang, Karl Fua, Bethany A Teachman, and Laura E Barnes. Demons: an Integrated Framework for Examining Associations Between Physiology and Selfreported affect Tied to Depressive Symptoms. In In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pages 1139–1143.
  • Yu Huang, Aatmesh Shrivastava, Laura E Barnes, and Benton H Calhoun. A Design and Theoretical Analysis of a 145 mV to 1.2 V Single-Ended Level Converter Circuit for Ultra-Low Power Low Voltage ICs In Journal of Low Power Electronics and Applications, 6(3):11, 2016.
  • Jinghe Zhang, Haoyi Xiong, Yu Huang, Hao Wu, Kevin Leach, and Laura Barnes. M-SEQ: Early Detection of Anxiety and Depression via Temporal Orders of Diagnoses in Electronic Health Data. In In Proceedings of the 2015 IEEE International Conference on Big Data (BigData 2015), September 2015.
  • Yu Huang, Aatmesh Shrivastava, and Benton H Calhoun. A 145 mV to 1.2 V Single Ended Level Converter Circuit for Ultra-Low Power Low Voltage ICs. In In SOI-3D-Subthreshold Microelectronics Technology Unified Conference (S3S), 2015 IEEE, pages 1–3.
  • He Qi, Oluseyi Ayorinde, Yu Huang, and Benton Calhoun. Optimizing Energy Efficient Low Swing Interconnect for Sub-Threshold FPGAs. In In Field Programmable Logic and Applications (FPL), 2015 25th International Conference on, pages 1–4. IEEE, 2015.
  • Oluseyi Ayorinde, He Qi, Yu Huang, and Benton H Calhoun. Using Island-Style Bi-directional Intra-CLB Routing in Low-Power FPGAs. In In Field Programmable Logic and Applications (FPL), 2015 25th International Conference on, pages 1–4. IEEE, 2015.


  • Fall23, TR, 11:00pm - 12:15pm. ESB 001
  • Spring23, TR, 1:15pm - 2:30pm. University Club 117
  • Fall22, TR, 11:00am - 12:15pm. Featheringill Hall 203
  • Spring22, MW, 2:30pm - 3:45pm. Engineering & Science 048

Advising and Research Opportunities

I would love to meet with students to discuss their research interests and career plans. Unfortunately, faculty's schedule is not always open (in fact, most of the time we are busy with who knows what is going on). So please check my calendar below for potential meeting slots: I really prefer putting such meetings on Monday, Tuesday and Thursday so that I can focus on other work on Wednesday and Friday. Please only consider Wednesday/Friday if that is your only choice.

My calendar can be viewed here.

If you want to meet with me, please find one or multiple open slots that work for you on my calendar and email me the slots, meeting purposes and whether it is in person or zoom. If I didn't reply your email within a week, please feel free to send another email to remind me.

Research Opportunities

For undergraduate and master students who are interested in research, you are very welcome to schedule a meeting with me. Please send me your CV when you schedule the meeting with me.

For potential PhD students, please send me an email first with your CV and research interests. Then over emails we will talk about next steps (i.e., if we should schedule a meeting).

Please read the FAQ below first before contacting me.

  • Can I do undergraduate research for credits?
  • Yes. You can register CS2860, CS3860 and CS3861 for research credits. You can also use undergraduate research to fill your Immersion requirement.

  • Can I immediately register for research credits with you before talking to you?
  • No. The system requires my approval anyway. But more importantly, I only take undergraduate researchers that are motivated and willing to commit to our project plans: to evaluate if it is a good fit, you need to schedule a meeting with me first.

  • When can I start doing undergraduate research with you?
  • In theory, it is never too late/early to try research as long as you are interested. In practice, I suggest students to consider it after (or at least when you have started) taking CS2201 Data Structures. Also, I suggest you consider it no later than the begining of the second semester of your junior year especially if you want to apply for graduate school.

  • I am an undergrad in CS and I want to get a software development job after graduation. Do I have to do undergrad research to get that type of job?
  • No. You don't have to do research to get that type of job. But yes, the experience you gained from your research project can speak for your capabilities. But I personally don't think undergrad research is more appealing to recruiters than getting an industry internship in this context.

  • I am currenly an undergrad student in another country. Can I do remote research with you?
  • Our lab is open to remote collaboration. Please email me first about your interest.

  • Do you have PhD openings?
  • In general, it depends on my grant situation. But it is case by case. So please email me first.

  • How should I call you?
  • My students usually call me "Dr. Huang" or "Prof. Huang". This is not because I care about the title that much, but more about "this person's first name (Yu sounds like You) is a disaster in conversation". In general, you can call me "Yu, HuangYu, Dr. Huang, Prof. Huang" or whatever as long as I can tell you are trying to talk to me.

Undergraduate Advising

For my undergraduate advisees (i.e., I am assigned as your academic advisor), I usually schedule meetings with all of you (as needed) at the beginning of every semester. Please try your best to make those meetings if you want to discuss your course plans. If you cannot make that meeting or have other questions during the semester, please feel free to email me and/or schedule a meeting as needed.

I collected some FAQs for my undergrad advisees. Please read them below first when you have a question (because you can probably find an answer really fast):

  • Where can I find the general guidance for my course requirement?
  • Here you can find all the documents and resources for CS: Course Requirement.

  • Where can I find the general information for all advising-related questions?
  • Here you can find most of information for undegrad advising: Engineering Academic Advising.

  • Can I study aboard? Can I transfer the credits from my abroad program?
  • This is a good starting point: Study Abroad.

  • Where can I find the form to (1) apply for a major/minor (2) audit a class (3) wavie prerequisite and (4) basically anything about your undegrad education?
  • All the forms are listed here: Forms for Students.

  • Can you help me change XYZ in YES?
  • Sadly I don't have any permission to edit anything in YES associated with your audit. For such requests, you will need to contact the Engineeging OAS via email: engineering-oas at vanderbilt.edu.

  • I really need to enroll in course CS XXXX but it is full. Can you add me to the course?
  • Sadly I don't have any permission to add a studnet to a full class. If you really need this course (e.g., "I really need to take this course this semester, otherwise I won't be able to graduate on time."), please email the instructor and me about your situation.