Tengfei Ma (马腾飞)
Research Staff Member
IBM T. J. Watson Research Center
Email: tengfei.ma1 (at) ibm (dot) com
I am a research staff member in IBM T. J. Watson Research Center, New York, USA. I joined IBM Research-Tokyo in 2015 and then moved to the US in 2016. Prior to that, I obtained my PhD degree from the Graduate School of Information Science and Technology, the University of Tokyo, Japan, under the supervision of Prof. Hiroshi Nakagawa. I received my M.S. from Peking University (where I was supervised by Prof. Xiaojun Wan) and my B.E. from Tsinghua University, China.
My research interests have spanned a number of different topics in machine learning and natural language processing (NLP) during my study and career, including document summarization, Bayesian nonparametrics, deep learning. Currently my research is mainly focused on graph neural networks, and I am also interested in other deep learning techniques in healthcare and NLP areas.
5/2022: One paper about knowledge graph rule learning (using cycles) accepted by ICML2022!
4/2022: One paper accepted by NAACL2022 main conference.
1/2022: Our paper "GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks" is accepted by TVCG. It is the first tool to visualize the GNN results and it can help people identify possible data and model error patterns. This tool has been integrated into DGL https://github.com/dmlc/GNNLens2
11/2021: One paper titled "MalGraph: Hierarchical Graph Neural Networks for Robust Windows Malware Detection" has been accepted by IEEE INFOCOM'22!
10/2021: Our paper "Improving Inductive Link Prediction Using Hyper-relational Facts" just won the best paper award in ISWC2021!