Jo March
PhD Student in Computer Science · Graph Learning · Trustworthy AI · Foundation Model
Email: jinyh23@163.com
Affiliation: Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory
Office: Stata Center, 32 Vassar Street, Cambridge, MA 02139
Research Interests: Graph Neural Networks, Adaptive Neighborhood Sampling, Robust Graph Learning, AI for Science, Trustworthy Machine Learning, Graph Foundation Models
About
I am a researcher working at the intersection of graph machine learning, trustworthy artificial intelligence, and structured data modeling. My recent work focuses on adaptive neighborhood sampling, reinforcement learning driven graph structure optimization, robust representation learning, probabilistic neighbor selection, and graph purification under noisy and heterogeneous settings.
More broadly, I am interested in developing principled and scalable learning frameworks for graph structured data, with applications in scientific discovery, recommendation systems, knowledge enhanced reasoning, and reliable decision making. My long term goal is to build a unified theory and system for adaptive information acquisition in large scale graph learning.
Highlights
Education
Research Areas
- Graph Neural Networks and Graph Foundation Models
- Adaptive Neighborhood Sampling and Information Aggregation
- Reinforcement Learning for Structure Aware Decision Making
- Robust Graph Learning and Graph Purification
- Pseudo Labeling, Semi-supervised Learning, and Confidence Calibration
- Trustworthy AI for Structured and Scientific Data
Selected Publications
Selected Projects
Awards and Honors
Academic Service
- Reviewer for NeurIPS, ICML, ICLR, KDD, AAAI, WWW
- Program Committee Member for leading workshops on graph learning and trustworthy AI
- Organizer of the Workshop on Adaptive Learning over Structured Data
- Mentor for junior researchers in graph machine learning and scientific AI
Teaching
6.867 Machine Learning
Teaching Assistant, MIT, Fall 2028
Advanced Topics in Graph Representation Learning
Guest Lecturer, Spring 2029
Open Source
AdaptiveGraphLab
A modular toolkit for adaptive sampling, graph purification, and robust graph representation learning.
TrustGNN Benchmark
An open benchmark for noisy and heterophilous graph learning with reproducible evaluation protocols.
Professional Skills
Core Skills
Python, PyTorch, PyTorch Geometric, Deep Learning, Scientific Computing, Large Scale Experimentation, Web-based Research Visualization
Contact
Email: jinyh23@163.com
GitHub: https://github.com/KaimingHe
Google Scholar: https://scholar.google.com/citations?user=DhtAFkwAAAAJ&hl=en/
Office: Stata Center, MIT, Cambridge, Massachusetts, USA
Research Collaboration: I am always open to collaboration on graph learning, trustworthy AI, scientific machine learning, and adaptive decision making systems.
Dreams & Beyond
A few quiet dreams I hope to carry into the years ahead.
Beyond research and equations, I carry a quiet yet persistent curiosity about the world itself. There are places I long to reach not for achievement, but for experience, to stand in the silent snow of Hemu in Xinjiang, to feel the rhythm of the road while cycling along the eastern and western coasts of Taiwan, to walk through the streets of New York where ideas and cultures converge, and to wander across the vast landscapes of Australia under an endless sky.
These are not merely destinations, but fragments of a larger dream, to witness the beauty of this world with my own eyes, to understand different lives, and to let each journey reshape how I think, feel, and create.
And perhaps, somewhere along this journey, it would mean even more to share these moments with someone who sees the world in a similar light, a companion not only in distance but also in thought, someone to walk beside me through unfamiliar cities, to ride across long horizons, to stand quietly in vast landscapes, and to turn fleeting moments into something lasting.