Yu Huang

PhD, Assistant Professor
Department of Computer Science

Vanderbilt University


  • 2909 Bob and Betty Beyster Building
    Computer Science and Engineering
    Univeristy of Michigan, Ann Arbor
    2260 Hayward Street
    Ann Arbor, MI 48109-2121

About Me

Curriculum Vitae

I will be an assistant professor in the CS department at Vanderbilt University in Jan 2022! I am recruiting Ph.D. students, undergraduate researchers and visiting scholars. Please email me your CV if you are insterested in joining my group!

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, open source software, 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. 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. 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

  • June 2021: I successfully defended my PhD dissertation!
  • June 2021: Our paper on investigating code writing using functional connectivity analysis is accepted to FSE 2021!
  • May 2021: I am co-chairing the Diversity and Inclusion panel of ICSE 2021. Come and join us on May 27, 2021!
  • Mar 2021: Our paper on applying automatated program repair to dataflow programming languages is accepted to GI-ICSE 2021!
  • Dec 2020: Our paper on open source software for social good is accepted to ICSE 2021!
  • Dec 2020: Our work on OSS for social good is featured in the GitHub Octoverse Report 2020!
  • Dec 2020: Presented my work on investigating developers' cognition at the madPL seminar at the University of Wisconsin-Madison!
  • Nov 2020: Presented my work on investigating developers' cognition at University of Michigan (PPFP Candidate)!
  • Nov 2020: Presented my work on investigating developers' cognition at Clemson University!
  • Oct 2020: Presented my work on open source software for social good at the MERL Center (via GitHub Social Impact Sector)!
  • Oct 2020: Selected as one of the EECS Rising Stars, 2020, hosted by UC Berkeley!
  • May 2020: Started my internship at Microsoft Research with Dr. Denae Ford! We will work on OSS4SG with Dr. Tom Zimmermann.
  • May 2020: Our paper on biases and differences in code review is accepted in FSE2020!
  • Feb 2020: Received the Google Faculty Research Award to support our study to understand bias in code review using medical imaging!


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 program analysis, embedded systems, mixed-methods studies, medical imaging (fMRI, fNIRS), eye-tracking, cyber human systems, and hardware 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:

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.

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:

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.


  • 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. To Appear.
  • Yu Huang, Hammad Ahmad, Stephanie Forrest, Westley Weimer. Applying Automated Program Repair to Dataflow Programming Languages In Genetic Improvement (GI) , 2021. To Appear.
  • 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. To Appear.
  • 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. To Appear.
  • 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. To Appear.
  • 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. To Appear.
  • 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. To Appear.
  • 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. To Appear.
  • 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. To Appear.
  • 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
  • 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%.
  • 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.


EECS 481
Software Engineering
  • Winter 2018: Discussion Sessions, Office Hours, Grading, Homework and Exam Design
CS 4330
Computer Architecture
  • Fall 2012: Lab, Office Hours, Grading