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

I am recruiting Ph.D. students, undergraduate researchers and visiting scholars starting Spring 2023 and Fall 2023. Please email me with your CV if you are insterested in joining my group! For more information on the recruitment, please check the recruitment document. I realized some emails may go to the outlook quarantine list and I may easily miss them. If I haven't replied your emails within one week, please feel free to email me again.

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

  • 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!
  • Nov 2021: Our paper on using automated program repair techniques to fix HDL bugs is accepted to ASPLOS 2022!


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
Zachary Karas, PhD student
Zihan Fang, Master student

Undergraduate Students

Eamon (Yimeng) Ma
Anda Liang
Eric Li
Haonan Hou
Danielle Page
Yueke Zhang
Southwest University


  • 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.


  • 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