Lei Wang
Northeastern University · Biomedical Engineering
Active 1984–2026
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About
Lei Wang is an Assistant Professor of Bioengineering in the College of Engineering and Biology at Northeastern University, joining the university in January 2024. She completed her postdoctoral training in the Biological Engineering Department at the Massachusetts Institute of Technology, where she specialized in mammalian synthetic biology, focusing on genetically programming human induced pluripotent stem cells (hiPSCs) to differentiate into desired cell fates inspired by natural differentiation processes. Her Ph.D. work concentrated on microfluidics and biosensors, developing a tumor-on-chip model to study anti-cancer drug efficacy and creating an ultra-sensitive biosensor for viral infection detection. Her research develops mammalian synthetic biology tools to advance anti-cancer cellular therapy, regenerative medicine, and microfluidic human organ models. She has received notable honors including the NIH NIBIB Trailblazer Award in 2025 and is a fellow of the MIT LEAPS program. Her work includes developing programmable RNA-based sensors for in situ cell type detection and response, aiming to create precise targeted therapies for diseases such as cancer.
Research topics
- Computer Science
- Artificial Intelligence
- Machine Learning
- Theoretical computer science
- Data Mining
- Engineering
- Speech recognition
- Computer vision
- Mathematics
Selected publications
Skeleton Aware Multi-modal Sign Language Recognition
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · 2021 · 260 citations
Sign language is commonly used by deaf or speech impaired people to communicate but requires significant effort to master. Sign Language Recognition (SLR) aims to bridge the gap between sign language users and others by recognizing signs from given videos. It is an essential yet challenging task since sign language is performed with the fast and complex movement of hand gestures, body posture, and even facial expressions. Recently, skeleton-based action recognition attracts increasing attention…
Adaptive Trajectory Prediction via Transferable GNN
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022 · 86 citations
Pedestrian trajectory prediction is an essential component in a wide range of AI applications such as autonomous driving and robotics. Existing methods usually assume the training and testing motions follow the same pattern while ignoring the potential distribution differences (e.g., shopping mall and street). This issue results in inevitable performance decrease. To address this issue, we propose a novel Transferable Graph Neural Network (TGNN) frame-work, which jointly conducts trajectory pred…
Composites Science and Technology · 2026-04-10 · 1 citations
article1st authorCorrespondingFigshare · 2026-04-07
articleOpen accessSI-Manuscript-Resubmit
Figshare · 2026-04-07
articleOpen accessSI-Manuscript-Resubmit
Frequent coauthors
- 47 shared
Yun Fu
- 17 shared
Can Qin
- 17 shared
Zhengming Ding
Tulane University
- 15 shared
Qinghua Han
University of Washington
- 11 shared
Yue Bai
Northeastern University
- 9 shared
Yunyu Liu
Tianjin Agricultural University
- 9 shared
Gan Sun
- 8 shared
Yan Lu
China National Petroleum Corporation (China)
Education
- 2021
Ph. D., Electrical & Computer Engineering
Northeastern University
- 2016
Master of Science in Engineering, Electronic and Information Engineering
Xi'an Jiaotong University
- 2013
Bachelor of Engineering, Electrical Engineering
Harbin Institute of Technology
Awards & honors
- NIH NIBIB Trailblazer Award (2025)
- MIT LEAPS Fellow (2022)
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