About me
Hi, I am Yist! I am a PhD student working on machine learning and computational biolgoy at École polytechnique fédérale de Lausanne (EPFL). I received the EDIC fellowship and am now advised by Prof. Maria Brbic at the MLBio lab. I am recently engaged in the advanced development of machine learning and AI models, focusing on single-cell analysis, including gene expression profiles, multi-omics data, and spatial transcriptomics, to extract critical biological insights.
Before moving to Switzerland, I obtained my Bachelor of Science degree with First Class Honor at the Chinese University of Hong Kong and majored in Mathematics and Information Engineering.
I am always open for collaborations and more than willing to talk with people on possible ideas! If you want to chat or do something amazing&interesting together, please always feel free to drop me an email!
Start from Mar. 2024: I dedicate 2-3 hours weekly to mentoring female students within my fields. Should you desire guidance on your project or advice on your future academic pursuits, feel free to reach out to me. Connecting with more female scholars is something I always look forward to. TOGETHER WE RISE!
Download CV here (Last update: Dec. 2023)
Research Interest
My current research interest lies in machine learning, graph algorithms and using tools from optimization, geometry and graph theory. I love graph, probabilistic model and randomness. Central to my research philosophy is the aspiration to pioneer non-human-centered research, shifting the focus from traditional human-centric applications to broader domains. To be more technically specific:
I am dedicated to developing solid machine learning methods and graph algorithms that are underpinned by robust theoretical foundations, aiming to address practical computational challenges that resonate with my interests.
I am fascinated by the intersection of machine learning and classical information/coding theory. I am extremely curious on researching the capability of neural networks by classical theories.
Research Experience
Prior to joining EPFL, I delved into a variety of intriguing topics. While seemingly disparate at first, these areas interconnected, collectively shaping my current academic stage.
My first research exposure was with Professor Sidharth Jaggi in my freshman year. In this project, I learnt about classical Information Theory and Coding Theory, and worked on extending Zyablov Bound to general adversarial various channels. In my sophomore year, I joined Professor Li Yu’s research group where we worked on the single-cell multi-omics integration problem. Our model scMinerva turned into my first-authorship paper and won the Best Project Award among 46 undergraduate projects. In my junior year, I joined Professor Ma Shiqian’s research group at the University of California, Davis to work on Optimization, funded by the highly competitive Professor Charles K. Kao Research Exchange Scholarship. In this research, we presented a new model on the Projection Robust Wasserstein distance in unbalanced optimal transport. For my final year project, I worked with Professor Irwin King on improving the expressive power of subgraph aggregation network at the graph classification level. We presented a new graph sampling strategy for graph aggregation network based on Weisfeiler-Lehman similarity.
Throughout my undergraduate studies, I was fortunate to receive invaluable guidance from Professor Chandra Nair, the director of my pragramme. Working closely with such esteemed professors was a profound honor and an unparalleled learning experience.