I am a Ph.D. student in Computer Science at Information Sciences Institute, University of Southern California. I am fortunate and honored to have Dr. Emilio Ferrara as my advisor. Prior to USC, I got my Master’s Degree in Computer Science at University of Chinese Academy of Sciences in 2017.
My current research lies at the intersections of Human Behavior Understanding and AI Fairness, and I am particularly interested in understanding the heterogeneity of human behaviors, and designing fairer machine learning systems for human behavior estimation. I am broadly instrested in Computational Social Science.
Fairness-Aware Machine Learning for Multimodal Human Behavior Data
Behavioral modeling has found broad applicabilitiy in many decision-making domains, including health and job performance evaluation, financial and employment scrutiny, etc. Hence, guaranteeing model fairness in behavioral modeling is an open challenge for real-world applications. We propose and develop machine learning models that will identify, account for, and mitigate the biases of multimodal human behavior modeling methods.
Tracking Individual pErformance with Sensors (TILES)
TILES is a project focused on the analysis of stress, task performance, behavior, and other factors pertaining to professionals engaged in a high-stress workplace environment. Biological, environmental, and contextual data is collected from hospital nurses and staff both in the workplace and at home over a period of 10 weeks. Labels of human experience are collected using a variety of psychologically validated questionnaires sampled on a daily basis at different times during the day.
We design machine learning models to estimate human behaviors from the sensory data, which are aiming to provide promising solutions to generalizable, trust, and fair mobile health systems.
Fair Class Balancing: Enhancing Model Fairness without Observing Sensitive Attributes
S. Yan, H.-T Kao, and E. Ferrara
29th ACM International Conference on Information and Knowledge Management (CIKM'20)
Mitigating Biases in Multimodal Personality Assessment
S. Yan, D. Huang, and M. Soleymani
22nd ACM International Conference on Multimodal Interaction (ICMI'20)
Human Behavior Understanding & Healthcare Informatics
User-Based Collaborative Filtering Mobile Health System
H.-T Kao, S. Yan, H. Hosseinmardi, S. Narayanan, K. Lerman and E. Ferrara
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IWMUT)
Estimating Individualized Daily Self-Reported Affect with Wearable Sensors
S. Yan, H. Hosseinmardi, H.-T Kao, S. Narayanan, K. Lerman and E. Ferrara
7th IEEE International Conference on Healthcare Informatics (ICHI'19)
Political polarization drives online conversations about COVID-19 in the United States
J. Jiang, E. Chen, S. Yan, K. Lerman, and E. Ferrara
Human Behavior and Emerging Technologies
Understanding Cyberbullying on Instagram and Ask.fm via Social Role Detection
H.-T Kao, S. Yan, D. Huang, N. Bartley, H. Hosseinmardi, and E. Ferrara
4th Workshop on Computational Methods in Online Misbehavior Co-located with The Web Conference (CyberSafety'19)
DynaEgo: Privacy-preserving collaborative filtering recommender system based on social-aware differential privacy
S. Yan, S. Pan, W.-T. Zhu, and K. Chen
18th International Conference on Information and Communication Security (ICICS'16)
Towards privacy-preserving data mining in online social networks: Distance-grained and item-grained differential privacy
S. Yan, S. Pan, Y. Zhao, and W.-T. Zhu
21st Australasian Conference on Information Security and Privacy (ACISP'16)