Education
- PhD in Data Science, New York Univerisity, 2026 (expected)
- BSc in Electrical and Computer Engieering, with double major in Mathematical Sciences , Carnegie Mellon University, 2021
Work experience
- Sep 2021 - Present: PhD Student
- Member of OLAB at NYU Langone Health and ML2 at Center for Data Science
- Work on developing a natural language model for clinical predictive problems with the goal of deployment at NYU Langone Health
- Supervisor: Eric Oermann, Kyunghyun Cho
- Jan 2019 - June 2021: Research Assistant
- Moura’s Research Group
- Work on applying graph sampling to graph convolutional neural network
- Supervisor: José Moura, John Shi, Pulkit Grover, Alireza Chamanzar
- Feb 2018 - Jan 2019: Research Assistant
- Biorobotics Lab
- Wrote a modular linear Kalman filter to solve the yaw drifting problem in IMU
- Devloped a calibration GUI for IMU and signal GUI for proximity sensor
- Supervisor: Howie Choset, Changsheng Shen, Lu Li
- Jan 2017: Winter Intern
- Da Jiang Innovations
- a 10-day robotics winter camp as one of DJI’s youngest interns
- Appointed as the only female team leader. Placed first in engineering problem sets.
- Supervisor: Shuo Yang
Course Works
- Signal Processing}
- Digital Signal Processing(18-491)
- Image and Video Processing (18-793)
- Mathematics
- Real Analysis I (21-355)
- Algebraic Structure (21-373)
- Combinatorics (21-301)
- Computer Science
- Electrical Engineering
- Introduction to Computer System (18-213)
- Structure and Design of Digital System (18-240)
- Complex Systems
- Introduction to Complexity
- Nonlinear Dynamics (enrolled)
Skills
- Programming Languages
- C, Java, Python, SML
- Software & Tools
Publications and Talks
NYUTron: Health System-scale Language Models for Clinical Operations: 30-day Readmissions. Lavender Y. Jiang, Nima P. Nejatian, Anthony B. Costa, Chris X. Liu, Yindalon Aphinyanaphongs, Mona G. Flores, Kyunghyun Cho, Eric K. Oermann. (NVIDIA GTC, 2022)
Automated, Scalable and Generalizable Deep Learning for Tracking Cortical Spreading Depression Using EEG. Alireza Chamanzar, Xujin Liu, Lavender Y. Jiang, Kimon A. Vogt, José M. F. Moura, Pulkit Grover. (International IEEE/EMBS Conference on Neural Engineering, 2021)
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification. Lavender Y. Jiang, John Shi, Mark Cheung, Oren Wright, José M.F. Moura. (Proceeding of Asilomar Conference on Signals, Systems, and Computers 2020)
“Graph Signal Processing and Deep Learning”, Mark Cheung, John Shi, Yao Jiang, Oren Wright and José Moura. (IEEE Signal Processing Magazine Special Issue on Graph Signal Processing)
“Pooling in Graph Convolutional Neural Networks”, Mark Cheung, John Shi, Oren Wright, Yao Jiang and José Moura. (Proceeding of Asilomar Conference on Signals, Systems, and Computers 2019)