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Boyu Zhang Personal Website Google Scholar | ORCID [email protected] Boyu Zhang Personal Website Google Scholar j ORCID Education Massachusetts Institute of Technology Cambridge, MA Master of Science, Media Lab September 2021 - June 2023 (anticipated) – Advised by Professor Rosalind Picard at the Affective Computing Group. – Awarded full research assistantship. University of Rochester Rochester, NY Bachelor of Science in Computer Science, Honors Research August 2017 - May 2021 – Highest Honors in Research (1/118), Highest Distinction, and Magna Cum Laude. – Cumulative GPA: 3:89 out of 4:00. Major GPA: 3:92 out of 4:00. – Advised by Professor Henry Kautz and Professor Ehsan Hoque. – Courses (4:00 A grade): Human-Centered AI (graduate level), Machine Learning, Statistical NLP, Time Series Analysis, AI and Mental Health, Web Data Mining for Health, Linear Algebra and Differential Equations, Probability, Discrete Mathematics, Computer Vision. Research Interests My research interest lies in Affective Computing, Machine Learning, and HCI. In particular, I focuson: • Designing explainable, robust, and efficient data-driven algorithms for high-stakes human-centered settings (e.g., emotion recognition and healthcare), minimizing the bias and opacity in automated agents. • Developing computation tools to sense human behaviors, augment human ability, and promote well-being with personalized interventions. Peer-reviewed Conferences [Ach+21] Rupam Acharyya, Ankani Chattoraj*, Boyu Zhang*, Shouman Das, and Daniel Štefankovič. “Statistical Mechanical Analysis of Neural Network Pruning”. In: 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021). 2021. url: https://www.auai.org/uai2021/pdf/uai2021.750.pdf. Journal Articles [Zha+20] Boyu Zhang, Anis Zaman, Vincent Silenzio, Henry Kautz, and Ehsan Hoque. “The Relationships of Deteriorating Depression and Anxiety With Longitudinal Behavioral Changes in Google and YouTube Use During COVID-19: Observational Study”. In: JMIR Mental Health (Nov. 2020). IF: 3.54. url: https://mental.jmir.org/2020/11/e24012. [Shi+16] Yi Shi, Xianbin Su, Kunyan He, Binghao Wu, Boyu Zhang, and Zeguang Han. “Chromatin accessibility contributes to simultaneous mutations of cancer genes”. In: Scientific reports 6.1 (Oct. 2016), pp. 1–12. url: https://www.nature.com/articles/srep35270. Page 1 of 3 Workshops and Posters [Ach+21a] Rupam Acharyya, Ankani Chattoraj*, Boyu Zhang*, Shouman Das, and Daniel Stefankovic. “Understanding Diversity Based Neural Network Pruning in Teacher Student Setup”. In: Neural Compression: From Information Theory to Applications – Workshop @ ICLR 2021. 2021. url: https://openreview.net/forum?id=Z83rOOb0pN7. [Ach+21b] Rupam Acharyya, Boyu Zhang, Ankani Chattoraj, Shouman Das, and Daniel Stefankovic. “Diversity Based Edge Pruning of Neural Networks Using Determinantal Point Processes”. In: Neural Compression: From Information Theory to Applications – Workshop @ ICLR 2021. 2021. url: https://openreview.net/forum?id=rXH8XPsWPZ. [Zam+21] Anis Zaman, Boyu Zhang, Vincent Silenzio, Ehsan Hoque, and Henry Kautz. “Individual-level Anxiety Detection and Prediction from Longitudinal YouTube and Google Search Engagement Logs”. In: 2nd Workshop on Data for the Wellbeing of Most Vulnerable at ICWSM 2021. 2021. url: https://sites.google.com/view/dataforvulnerable21. [Zha+20] Boyu Zhang, Anis Zaman, Rupam Acharyya, Ehsan Hoque, Vincent Silenzio, and Henry Kautz. “Detecting Individuals with Depressive Disorder from Personal Google Search and YouTube History Logs”. In: Workshop on Machine Learning in Public Health at NeurIPS 2020. Short Paper Lightning Talk. Oct. 2020. url: https://arxiv.org/abs/2010.15670. Research Experience Research Assistant March 2019 – May 2021 Department of Computer Science, University of Rochester Rochester, NY – Jointly advised by Professor Henry Kautz, Professor Ehsan Hoque, and Doctor Vincent Silenzio. – Developed ubiquitous computing frameworks for personalized mental disorder sensing through longitudinal private Google Search and YouTube data. – Extended the above frameworks for COVID-19 and found significant correlations between deteriorating mental health profiles and online behavior shifts during the pandemic (r ranging between 0:47 and 0:75, p ≤ :03). – Simulated the semantics and stochasticity of user online engagements with mutually exciting Hawkes Processes to detect major depressive disorder (F1: 0:77 ± 0:04, AUROC: 0:81 ± 0:02). – Extracted interpretable semantic and temporal features, including novel representations for content entropy and interevent times, from personal online data; customized a Gaussian Process to continuously predict generalized anxiety disorder (MSE: 1:87 ± 0:14). – Designed a Bayesian graphical model with MCMC methods that leverages the latent information of self-esteem and online activities to identify past suicidal ideation (F1: 0:82, AUROC: 0:79). – Collaborated with the URMC and recruited more than 200 volunteers; periodically collected clinically validated mental health evaluation results and individual online activity logs from the study cohort. Research Assistant October 2019 – December 2020 Department of Computer Science, University of Rochester Rochester, NY – Advised by Professor Daniel Štefankovič. – In a group of 2, completed theoretical proofs of the generalization error bounds for several diversity and weight-based pruning methods on feed-forward neural networks under a statistical mechanics framework. – Proposed a novel diversity-based edge pruning algorithm based on Determinantal Point Process; improved network compression performances from previous diversity-based methods on both synthetic and real-world datasets. Research Assistant February 2019 – August 2019 Department of Linguistics, University of Rochester Rochester, NY Page 2 of 3 – Advised by Professor Aaron Steven White at the Formal and Computational Semantics Lab. – Implemented three deep graph encoders over WordNet for word sense embedding: a child-sum graph bi-LSTM, a matrix decomposition method with graph kernels, and a Graph Convolutional Networks. – Developed a sequence-to-sequence model with von Mises-Fisher loss for word sense disambiguation (WSD); improved the model to work on unknown words by incorporating the Supersense relations from WordNet. The best model achieved 75% accuracy on the Universal Decompositional Semantics Word Sense dataset. Scholarships and Awards • Excellence in Undergraduate Research, University of Rochester Spring 2021 • Finalist, 2021 CRA Outstanding Undergraduate Researcher Award Fall 2020 • Student Research Credits ($5; 000), Google Cloud Platform Fall 2019 • Discover Grant for Undergraduate Researches ($1; 750), University of Rochester Summer 2019 • Dean’s List, University of Rochester All eligible semesters Teaching Teaching Assistant Department of Computer Science, University of Rochester Rochester, NY – Machine Learning - CSC 246 Spring 2021 & Spring 2020 ∗ Designed midterm and final exams for a class of 53 students. – Introduction to Artificial Intelligence - CSC 242 Fall 2019 – Introduction to Computer Science - CSC 171 Spring 2019 Skills • Programming Language: – Proficiency: Python, Java, and C. – Familiarity: MATLAB, R, and C++. • Packages and Tools: PyTorch, PyMC3, LATEX, Git, TensorFlow, Docker, SLURM, and MTurk. Extracurricular Activities • Kukkiwon-style Taekwondo 2008–present – 1st dan Black Belt awarded by World Taekwondo. – 1st poom Black Belt at age 14. References • Henry Kautz, Professor of Computer Science University of Rochester [email protected] • Ehsan Hoque, Associate Professor of Computer Science University of Rochester [email protected] • Vincent M. Silenzio, MD, MPH, Professor of Urban-Global Public Health Rutgers University [email protected] Page 3 of 3.
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