Lavender Jiang

Lavender Jiang

PhD Student at NYU

About me

Hi! My name is Lavender Jiang (蒋遥). I am a fourth year Data Science PhD student at New York University, co-advised by Eric Oermann and Kyunghyun Cho. I work on natural language processing for clinical notes and I am interested in representation learning. I am a member with OLAB and ML2. I am honored to be the recipient medical fellowship from NYU Langone Health and the AIML PhD fellowship from Apple.

I received my BSc in Electrical and Computer Engineering and Mathematical Sciences from Carnegie Mellon, where I worked with José Moura on graph signal processing, Pulkit Grover on cortical spreading detection, and Howard Choset on sensor fusion.

If you want to discuss research with me, feel free to write me an email. (If we know each other, we can schedule a 30-minute meeting). We can meet on Zoom or in person (Center for Data Science at 60 5th Ave, Tisch Hospital at 550 1st Ave, or Washington Square Park).

I enjoy cooking, gaming and yoga. I am married to my best friend Xujin Liu. Some of my new hobbies include rollerblading, pole dancing and bass guitar. I try (and sometimes fail) to live a vegan, low-waste and minimalist lifestyle.

Publications and Talks

  • Health system-scale language models are all-purpose prediction engines. Lavender Yao Jiang, Chris Liu, Mustafa Nasir-Moin, Nima Pour Nejatian, Duo Wang, Anas Abidin, Howard Riina, Ilya Laufer, Paawan Punjabi, Kevin Eaton, Madeline Miceli, Nora C. Kim, Cordelia Orillac, Zane Schnurman, Christopher Livia, Hannah Weiss, David Kurland, Sean Neifert, Yosef Dastagirzada, Douglas Kondziolka, Alexander M Cheung, Grace Yang, Ming Cao, Mona Flores, Anthony B. Costa, Yindalon Aphinyanaphongs, Kyunghyun Cho and Eric Karl Oermann. (Nature)

  • Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction. Ming Cao¹, Grace Yang¹, Lavender Yao Jiang, Xujin Chris Liu, Alexander TM Cheung, David Kurland, Hannah Weiss, Kyunghyun Cho, Eric Oermann. (ML4H 2022)

  • Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section. Hongyi Zheng, Yixin Zhu, Lavender Yao Jiang, Kyunghyun Cho, Eric Karl Oermann. (ACL SRW 2023)

  • Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment. Zihao Yang, Chenkang Zhang, Muru Wu, Xujin Chris Liu, Lavender Yao Jiang, Kyunghyun Cho, Eric Karl Oermann. (ACL SRW 2023)

  • Attention based neural networks display human-like one-shot perceptual learning effects. Xujin “Chris” Liu, Yao “Lavender” Jiang, Mustfa Nasir-Moin, Ayaka Hachisuka, Jonathan Shor, Yao Wang, Biyu J. He, Eric K. Oermann. (Conference on Cognitive Computational Neuroscience, 2022)

  • Methods and Impact for using Federated Learning to Collaborate on Clinical Research. Alexander TM Cheung, Mustafa Nasir-Moin, Young Joon (Fred) Kwon, Jiahui Guan, Chris Liu, Lavender Jiang, Christian Raimondo, Silky Chotai, Lola Chambless, Hasan S Ahmad, Daksh Chauhan, Jang W Yoon, Todd Hollon, Vivek Buch, Douglas Kondziolka, Dinah Chen, Lama Al-Aswad, Yindalon Aphinyanaphongs, Eric Karl Oermann. (Congress of Neurological Surgeons 2022. CNS Best Data Science Award. In review for Journal of Neurosurgery)

  • 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 Chamanzar¹, Xujin Liu¹, Lavender Y. Jiang, Kimon A. Vogt, José M. F. Moura, Pulkit Grover. (International IEEE/EMBS Conference on Neural Engineering, 2021.) Patent: System and method for deep learning for tracking cortical spreading depression using eeg (WO2022235467A1).

  • 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)

Grants

Seed Grant Funding Award (Workshop on ML for Health 2024, $25,000). Kyunghyun Cho, Eric Oermann, Lavender Jiang.

Academic Services

Teaching

1/2024 - 05/2024 grader for NYU Machine Learning Course

09/2023 - 12/2023 Section leader and grader for NYU DS-GA.1011 Natural Language Processing with Representation Learning; Private tutor for NYU DS-GA.1005 Inference and Representation

03/2023 - 05/2023: Private tutor for NYU DS-GA.1003 Machine Learning

09/2020 - 12/2020 Section leader and grader for CMU 21-260 (differential equations)

06/2022 - 09/2023: Research mentor for undergraduate students (project and names are sorted by time and alphabetical order.)

Summer 2023 - Spring 2024: Evaluating clinical LM’s understanding of lab measurements.

  • Avery Hang (NYU DS+Math 24’)
  • Ruiqi Deng (NYU CS+DS 24’).

Fall 2023: Evaluating the predictive power of different note types and different note segments.

  • Hongyi Zheng (Quantitative Strategist at Akuna Captial 23’-present, NYU Math+CS+DS BA 23’)
  • Tracy Zhu (UChicago Stat MS 25’, NYU DS+Math BA 23’)

Summer 2022 - Spring 2023: evaluating token-level sensitivity of clinical language model.

  • Grace Ge’er Yang (Stanford DS MS 25’, NYU Math+DS BA 23’)
  • Ming Cao (UPenn DS MS 25’, NYU DS+CS BA 23’)

Summer 2022 - Fall 2023: using the correlation bias for automatic ICD-9 code assigment from discharge note (co-mentor with Chris Xujin Liu)

  • Gavin Yang (NYU CS+DS 24’)
  • Lucy Wu (Colombia DS MS 25’, NYU DS+CS BA 23’)
  • Stephen Zhang (UPenn DS MS 25’, NYU CS+DS BA 23’)