About me

Hi! I’m Lavender Jiang (蒋遥). I am a fifth year Data Science PhD student at New York University, co-advised by Eric Oermann and Kyunghyun Cho.

My research focuses on the science of training domain-specific models, building AI that bridges the gap between Machine Learning theory and real-world clinical deployment. My passion for healthcare was motivated by seeing my grandparents suffer from cancer, which inspires me to make a real-world impact in the field.

⭐🗓️ I’m at Neurips San Diego Dec 1 - Dec 7 and feel free to schedule an in-person meeting wth me here!

Affiliations and Honors

• Labs: OLAB and ML2
• Fellowships:
  • Apple AIML PhD fellowship
  • NYU Center for Data Science Fellowship
  • NYU CDS Meidcal Fellowship.
• Grants:
  • Seed Grant Funding Award (Apple Workshop on ML for Health 2024, $25,000). Kyunghyun Cho, Eric Oermann, Lavender Jiang.

Educational Background

09/2021 - present New York University, New York, NY PhD student in Data Science (GPA 3.979) Selected courseworks: Big Data, Machine Learning, Inference and Representation

09/2017 - 05/2021 Carnegie Mellon University, Pittsburgh, PA BSc (Honor): Electrical and Computer Engineering with an additional major in Mathematical Sciences (GPA 3.55) Selected courseworks: Probabilities and Statistics, Linear Algebra, Real Analysis, Digital Systems, Computer Systems, Signal Processing, Software Engineering, Machine Learning

Selected Publications

• 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)

• Generalist Foundation Models Are Not Clinical Enough for Hospital Operations. Lavender Y. Jiang, Angelica Chen, Xu Han, Xujin Chris Liu, Radhika Dua, Kevin Eaton, Frederick Wolff, Robert Steele, Jeff Zhang, Anton Alyakin, Qingkai Pan, Yanbing Chen, Karl L. Sangwon, Daniel A. Alber, Jaden Stryker, Jin Vivian Lee, Yindalon Aphinyanaphongs, Kyunghyun Cho, Eric Karl Oermann. In preparation.

• Language Models Can Guess Your Identities from De-identified Clinical Notes. Lavender Yao Jiang, Daniel Alexander Alber, Zihao Yang, Karl L. Sangwon, Xujin Chris Liu, Kyunghyun Cho, Eric Karl Oermann. In resubmission.

• Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction. Grace Yang, Ming Cao, Lavender Y. Jiang, Xujin C. Liu, Alexander T.M. Cheung, Hannah Weiss, David Kurland, Kyunghyun Cho, Eric K. Oermann. (ML4H 2022)

• Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model. Salman Rahman, Lavender Yao Jiang, Saadia Gabriel, Yindalon Aphinyanaphongs, Eric Karl Oermann, Rumi Chunara.

• MedG-KRP: Medical Graph Knowledge Representation Probing. Gabriel R. Rosenbaum, Lavender Y. Jiang, Ivaxi Sheth, Jaden Stryker, Anton Alyakin, Daniel Alexander Alber, Nicolas K. Goff, Young Joon Fred Kwon, John Markert, Mustafa Nasir-Moin, Jan Moritz Niehues, Karl L. Sangwon, Eunice Yang, Eric Karl Oermann. ML4H 2024.

• 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).

• 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)

Services

• Reviewer for IEEE TNNLS, Scientific Reports, BMC Health Services Research, ICLR DMLR Workshop 2024, ACL Student Research Workshop 2023, ACL 2023, All Things Attention.

• Co-organizer for Global AI Frontier Lab Workshop 2025, NYU AI School 2022

Teaching

06/2022 - 05/2025: Research mentor

I scoped and led research projects for 12 students ranging from high school interns to graduate students, guiding them to 4 accepted publications at ACL and ML4H workshops.

• Clinical Model Evaluation: Oversaw the development of new benchmarks for numerical lab measurements and knowledge graph understanding in LLMs, directly addressing gaps in current clinical AI capabilities. Analyzed the differential predictive power of clinical note types and sections to inform data prioritization.

• Model Interpretability: Led research into evaluating the alignment between Transformer sensitivity patterns with human physicians and utilizing correlation priors for automated ICD coding.

• Infrastructure & Efficiency: Guided experiments on pretraining dynamics, specifically analyzing the impact of data packing and shuffling strategies on model loss.

03/2023 - 05/2025 Teaching Assistant

Taught, graded or tutored Machine Learning (Taught by Kyunghyun Cho, Rajesh Ranganath, Mengye Ren, Ravid Shwartz-Ziv) and NLP (Profs. He He).

• Curriculum Development: Designed Python assignments bridging theoretical proofs with implementation, covering fundamental concepts (classification, backpropagation) and modern NLP (prompt engineering).

• Teaching: Delivered recitations on Transformers and ML fundamentals. Achieved top-tier student evaluations for subject mastery, organization, and clarity in answering complex technical questions.

• Mentorship: Guided 8 graduate teams through applied NLP capstone projects and supported students with proof-based theoretical concepts.

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

Students