I'm Nanyu Luo (罗南煜), a Ph.D. student in the Developmental Psychology & Education program at the University of Toronto, supervised by Prof. Feng Ji. My education background includes a BSc in Applied Mathematics at the Chinese University of Hong Kong, Shenzhen, and an MSc in Statistical Science at the University of Oxford. My research interests focus on applying statistical machine learning and deep learning to multimodal data analysis and utilizing advanced data science and artificial intelligence (AI) in psychometrics and psychology.

🎉 Nanyu won the prestigious Connaught International Scholarship for Doctoral Students (2025-2029).

I am a PhD student in the Department of Applied Psychology and Human Development at the University of Toronto, following my pursuit of a Master’s degree in Applied Psychology from the Chinese University of Hong Kong, Shenzhen. My research interests lie in exploring eating disorders, addiction, emotion regulation, and resilience, with a specific focus on youth and adolescent populations. Recognizing the profound influence of family dynamics (e.g., parental styles, attachment types, violence) and school environments (e.g., peer relationships, bullying) on the mental and physical development of youth, I intend to expand my research to include these critical variables, such as attachment types and parental styles. My objective is to understand the intricate paths through which these factors converge to impact an individual’s well-being. With the rise of the internet and big data, I am keen to integrate advanced data analysis methodologies, such as machine learning, deep learning, and AI, into my research programs.

🎉 Yuchen won the Inlight Research Fellowship.

Linfeng Gao is currently pursuing a Master of Engineering in Electrical and Computer Engineering at the University of Toronto, with an emphasis on Data Analytics and Machine Learning. He holds a Bachelor of Applied Science in Computer Engineering from the University of British Columbia. His research experience includes optimizing YOLO models for improved insulator detection. Additionally, he has a strong background in machine learning, data analysis, and software development. Linfeng aims to advance the practical applications of artificial intelligence to improve people's daily lives.

Xiaorui (Austin) He is currently pursuing a Master of Education in the Department of Applied Psychology and Human Development at the University of Toronto. Previously Xiaorui Studied Computer Science at the Chinese University of Hong Kong, Shenzhen. His research focuses on evaluating the efficacy of various psychological scales used as screening tools for measuring teaching-related stress, particularly stress induced by external factors. He has identified that factors such as emotional regulation, family satisfaction, and teaching performance evaluations significantly impact teachers' mental health. Consequently, his research aims to integrate advanced data analysis tools such as AI and machine learning to refine or replace traditional scales to better assess these influences and improve the well-being of educators. 

Ian is a student in OISE’s Master of Teaching program, where he is receiving his teaching qualifications in intermediate/senior mathematics and physics. Prior to attending OISE, he completed a master's degree in mathematics and engineering at Queen’s University. His research interests are in math education, game theory, and educational statistics. 

Hajung is a Master of Education student in the Developmental Psychology & Education program at the University of Toronto (UofT). She received her bachelor's and master’s degrees from Yonsei University, South Korea. Before joining UofT, she worked in a post-secondary education setting, primarily focusing on program evaluation. She is interested in machine learning applications in educational measurement and evaluation, particularly using natural language processing and large language models.

Linxin Li graduated with a Bachelor of Applied Science in Computer Engineering from the University of British Columbia and is pursuing a Master of Engineering in Data Analytics and Machine Learning at the University of Toronto. He has experience in machine learning, software development, and data analysis, with a research focus on applying machine learning models to real-world problems. He has completed research projects optimizing machine learning for computer vision and developing strategies for federated learning security. Additionally, His experiences include developing a chatbot integrated with ChatGPT for language learning and contributing to financial applications using the Xinghuo Large Language Model during an internship at iFLYTEK.

Junjie is currently pursuing a Master of Science in Statistical Sciences at the University of Toronto. His primary research interests lie in statistical modelling, machine learning, and Bayesian analysis. He is particularly drawn to using advanced statistical and machine-learning techniques to develop models that provide insights into complex real-world problems. Previously, he completed his undergraduate studies in statistics with a focus on quantitative finance at the U of T.

Xinyu Song is currently pursuing a Master of Engineering in Electrical and Computer Engineering at the University of Toronto. Her research interests include Applications of AI, machine learning, AI compiler development, and unsupervised feature selection. She worked as an AI Compiler intern at AMD, and proposed an unsupervised feature selection algorithm.

Ruby is currently pursuing a Master of Arts in Developmental Psychology and Education (DPE) under the supervision of Dr. Ji. She received her Bachelor of Science degree in Communication, Culture, Information & Technology (CCIT), with minors in Computer Science and Mathematical Sciences from the University of Toronto Mississauga. Following this, she obtained a Master of Education in DPE from the Ontario Institute for Studies in Education, with the Program Evaluation (PE) emphasis. Xiaohan's research interests are statistical methods within psychology and education. Additionally, she has a keen interest in the integration and application of Artificial Intelligence (AI) and machine learning within these fields.

Ivy Wu is currently pursuing her Master of Engineering (MEng) in Computer Engineering at the University of Toronto. She completed her Bachelor of Applied Science in Computer Engineering at Queen's University, where she gained a strong foundation in data analysis, software development, and project management. Ivy has experience leading projects such as a dynamic parking pricing project at the University of Toronto, where her team utilized bio-inspired algorithms and machine learning models to improve prediction accuracy. She has also worked on various data analysis and machine learning projects, including face mask detection using SVM classifiers and deep learning techniques. Additionally, Ivy has experience in full-stack web development, having built and deployed web applications. 

I am a 1st year MEd student specializing in developmental psychology and education. Building on a solid foundation in traditional psychological research from my undergraduate education, I have shifted my focus toward the fascinating intersection of psychology and computer science. During my graduate journey, I have developed a strong interest in computational approaches, and I have started to apply these methods to enhance our understanding of psychological development. My research interest is mainly focused on leveraging my software engineering and computer science proficiency to address complex questions within developmental psychology. My genuine love for children drives me to tailor my technical endeavors to benefit their education and development. 

I am a fourth-year student at the University of Toronto, double majoring in both Computer Science and Psychology. My research interests include using machine learning to drive global improvements in mental health. I have developed experiences in training and fine-tuning machine learning models, developing frameworks of neural networks, managing large-scale software projects, and analyzing data distribution. Currently, my projects entail an app that helps users manage social media use and contribution to machine learning through internships in the tech industry. The application of AI in solving mental health problems by finding out the best scalable solutions that will help narrow the gap in technology and mental health support is an area of major interest for me.

My name is Jason Vaiakis (V-eye-a-kiss), a volunteer research assistant in Dr. Feng Ji’s lab. I graduated from Carleton University with an Honors Psychology degree in 2021 and I’m applying to graduate psychology programs for 2025-2026. My research interests lie in determining the effectiveness of AI-assistance in school counseling, educational assessments, and psychotherapy. I hope to research how AI tools can help with treatment planning and conduct initial assessments of patients and students. 

Yige is a third-year student at University of Toronto. He is doing Statistics Specialist and Mathematics Major. He is particularly interested in applying machine learning and advanced statistical methods to psychology and economics. Yige would like to explore some practical applications of interpretive machine learning methods in solving some interdisciplinary problems.

My name is William Yang, a senior undergraduate at University of Toronto. I am doing Mathematics and its application specialist (probability/statistics) as well as Statistics Major. My personal research interests are in Applied Statistics and Biostatistics. 

Li is a third-year Computer Science and Statistics student at the University of Toronto. He focuses on applied machine learning research, particularly in deep learning, statistical models, and data science. Li is interested in exploring how these methods can be integrated across interdisciplinary areas such as psychology, education, and finance. By leveraging AI and data-driven methods, he aims to bridge the gap between theory and real-world applications, solving complex challenges in these fields.

Xinyue (Yuki) Zhu is currently pursuing an Honours Bachelor of Science degree in Statistical Science, Mathematics and Economics at the University of Toronto. As a Dean’s List Scholar, she has been recognized for her academic excellence, receiving awards such as the University College Alumni Scholarship and Bursary Fund. As a summer intern at BNP Paribas Limited at the CEO’s office, Xinyue contributed to financial reporting and analysis for the bank. Her work involved pre-processing data and assisting in making the dashboard for key deals activities.

Xinyue also led a statistical capstone project in the field of astronomy, applying data analysis techniques using R programming to explore and validate scientific patterns relating to three astronomical questions.

I am a Ph.D. student at the School of Public Policy and Management, University of Chinese Academy of Sciences, currently a visiting student at the University of Toronto from Fall 2024 to Fall 2025. I hold a Bachelor's degree in Management from Beijing Normal University, where I developed a strong interest in educational technology and policy research. My research focuses on the application and impact of Generative AI (GenAI) tools in higher education, particularly in how these tools influence student learning behaviors, teaching practices, and institutional policies. My current work involves applying advanced data analysis methodologies, such as machine learning and deep learning, to explore the use of GenAI technologies in educational settings.

Coco is currently in his 3rd year, he is a shorthair domestic cat born in warm North Carolina, the United States.

🥥 enjoys kneading, loafing, purring, and napping.