The Team
About Anna
Anna Goldenberg
Dr. Anna Goldenberg is a professor in the Departments of Computer Science and the Laboratory Medicine and Pathobiology at the University of Toronto. She is a Varma Family Chair in Biomedical Informatics and Artificial Intelligence at SickKids Research Institute as well as a CIFAR AI chair at the Vector Institute. She co-chairs the research arm of the Temerty Centre for AI at UofT and co-leads SickKids AI Services. Additionally, Dr. Goldenberg is a director of an AI in Healthcare Master’s concentration under the Master's of Applied Computer Science umbrella jointly between DCS and LMP. Dr Goldenberg trained in machine learning at Carnegie Mellon University, with a postdoctoral focus in computational biology and medicine. The current focus of her lab is on developing and deploying machine learning models to healthcare. Dr Goldenberg’s lab is strongly committed to creating responsible AI to benefit patients across a variety of conditions.
About Abhishek
Abhishek
Abhishek Moturu is a PhD candidate in the Department of Computer Science at the University of Toronto, affiliated with the Vector Institute, SickKids, and UHN KITE. He also received both his HBSc from Trinity College at U of T and his MSc from U of T. His past projects include pediatric cancer detection and facial pain detection in people with dementia. His research interests lie in using deep learning for healthcare and medicine - specifically in learning effectively with mislabeled, noisy, or difficult data. Abhishek is an Education Trainee Co-Lead at the Temerty Centre for AI Education & Research in Medicine and a Junior Fellow at Massey College. He is also interested in AI pedagogy, governance, and ethics. In his free time, he loves films, drives, racquet sports, jazz/soul/house/disco music, and exploring Toronto's food scene (ask him about his favourite restaurants!).
Awards: OGS (2023, 2024, 2025), Trinity College Chancellor William C. Graham Award (2022), QEII-GSST (2021), CGS-M (2020), NSERC USRA (2018, 2019)
Projects:
LiLAW: Lightweight Learnable Adaptive Weighting to Meta-Learn Sample Difficulty and Improve Noisy Training
LiNC: Lightweight Noise Correction via Adaptive Label Refinement
Sample Reweighting to Effectively Use Synthetic Data during Model Training
Github: https://github.com/moturuab
LinkedIn: https://www.linkedin.com/in/abhishek-moturu
Google Scholar: https://scholar.google.com/citations?user=pr333dYAAAAJ
About Jerry
Jerry
Zongliang (Jerry) Ji is a PhD student who began his studies in the fall of 2022. He focuses on biomedical machine learning, particularly with multi-modality and ICU datasets, and creating conversational agents for clinical assistive tools. He earned his undergraduate degree from Union College and a master’s degree from the University of Waterloo. His research experience includes developing an ambient assistant that helps primary care physicians with evidence-based medicine , and a framework that recommends ICU lab tests to reduce unnecessary procedures while maintaining patient safety. He also created a method that uses unpaired multimodal learning to link biological datasets and built computational pathology systems in previous roles. His work emphasizes translating methods into practical clinical tools by focusing on interpretable objectives, rigorous evaluation, and practical guidance.
Awards: OGS 2023
Projects:
Lab test ordering for ICU patient
Towards Developing Medical Guideline Agent
Explore Unpair Mutilmodal Learning for Biological Dataset
Google Scholar: https://scholar.google.com/citations?user=eyyWQqAAAAAJ
Website: https://jerryji007.github.io
About Jingyi
Jingyi Zhao
Jingyi am a PhD student in Computer Science at the University of Toronto, advised by Professor Anna Goldenberg. Her research focuses on machine learning for healthcare and biomedical applications. Before joining U of T, she completed her MPhil in Machine Learning and Machine Intelligence at the University of Cambridge. Prior to that, she earned her B.Sc. from NYU Shanghai, double-majoring in Computer Science and Mathematics.
Projects:
Foundation models for wearable times series data
LinkedIn: https://www.linkedin.com/in/jingyi-zhao-688873283/
Google Scholar: https://scholar.google.ca/citations?user=R7Rb2x4AAAAJ
About Kaden
Tina
As a doctoral candidate supervised by Dr. Anna Goldenberg and co-supervised by Dr. Bo Wang, Kaden conducts translational machine learning research for digital health—building knowledge‑enhanced foundation models & multimodal LLMs fusing physiological time‑series, EHR data, and clinical text. From in‑hospital monitors to consumer wearables, he aims to engineer & deploy AI that delivers measurable clinical impact, bridging academic innovation, MedTech entrepreneurship, and frontline care!
Affiliations: Univeristy of Toronto, SickKids, UHN
Projects:
ECG-FM: An Open Electrocardiogram Foundation Model
ECG-CRED: ECG-powered Cross-modal Robust Evidence-driven Diagnosis
Github: https://github.com/KadenMc
LinkedIn: https://www.linkedin.com/in/kadenmckeen/
Google Scholar: https://scholar.google.ca/citations?user=LGPnpjYAAAAJ
About Tina
Tina
Tina is a 3rd-year PhD student in Computer Science at the University of Toronto, supervised by Anna Goldenberg. Her research focuses on the intersection of AI and healthcare, with experience in healthcare trajectory prediction, out-of-distribution detection, representation learning for digital pathology, time-series anomaly detection, and biometric identification. She is passionate about solving challenging problems in generative ML, time-series analysis, computer vision, and graph learning. Outside of research, she loves playing tennis, reading, painting, and heading out for camping adventures.
Awards: NSERC PGS-D 2025
Projects:
Dynamic Out-of-Distribution Detection
Discharge Readiness Prediction
LLM-Based Information Retrieval from Wearable and Survey Data
Github: https://github.com/Tinbeh97
LinkedIn: https://www.linkedin.com/in/tinbeh97
Google Scholar: https://scholar.google.com/citations?hl=en&user=a67z4YIAAAAJ
Website: https://tinbeh97.github.io/tinabehrouzi.github.io/
About Andrew
Andrew
Andrew is a Computer Science MSc-PhD student advised by Prof. Anna Goldenberg at the Vector Institute for Artificial Intelligence and the University of Toronto, from which he graduated with an HBSc triple-majoring in Computer Science, Cognitive Science, and Physics. He is broadly interested in the synergy between computational and biological problem-solving. His research focuses on AI and ML methods for solving problems in medical bio-physics, while he is symmetrically also excited by bio-inspired computation. Thus far, he has mostly worked on bio-signal processing (clinical time-series & transcriptomics) with deep learning. In the interim, he also took an excursion to work with LLMs trained on the world’s largest computer chip during his co-op internship at Cerebras Systems.
Awards: University of Toronto Scholar (2020), Trinity College 6T5 Scholarship (2021), Dean's List Scholar (2020-2024)
Past Projects:
Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
scGPT-spatial: Continual Pretraining of Single-cell Foundation Model for Spatial Transcriptomics
Speculative decoding for LLMs with unstructured sparsity
Current Projects:
Forecasting patient deterioration in the Paediatric ICU
Prescreening neurodegenerative diseases with wearable device signals
Causal discovery for time series
Github: https://github.com/a663E-36z1120
Twitter: https://twitter.com/a663e_36z1120
Google Scholar: https://scholar.google.com/citations?user=rW_3XVUAAAAJ
Website: https://a663e-36z1120.github.io//
About Aditya
Aditya
Aditya is an undergraduate student at the University of Toronto studying data science and statistics. His research interests and background are primarily in mathematical statistics, techniques for correlated data, and sampling algorithms. Aditya's goal is to develop robust methods for doing inference, in the service of being able to make actionable decisions from data. He occasionally also dabbles in other fields and applications (e.g. computational linguistics). At the Goldenberg Lab, he is currently studying machine learning methods for lab tests ordering, particularly in the presence of distribution shifts. In his free time, you’ll find him reading a book or (more likely) annoying his friends.
Awards: DCS Engagement Award (2025), Dean's List Scholar (2022-2024), NSERC USRA (2024), Samuel Beatty Scholarship (2023), Chancellor’s Scholarship (2022), University of Toronto Scholars Award (2021)
Projects:
ICU patient forecasting
Github: https://github.com/AdK0101
Google Scholar: https://scholar.google.com/citations?user=uI9I9qYAAAAJ
Website: https://adk0101.github.io
About Haochen
Haochen
Haochen is a 4th-year undergraduate at the University of Toronto, in Data Science Specialist & Computer Science Major. His research interests focus on machine learning applications, like healthcare. He is currently developing a multi-modal model that predicts in-hospital discharge readiness at the Vector Institute. He is also conducting research on fairness in machine learning in the statistical sciences department. Outside of academics, he enjoys playing badminton and guitar.
Awards: NSERC USRA (2022, 2025)
Projects:
In-hospital Discharge Readiness Prediction
Github: https://github.com/Dennis-Ding1
LinkedIn: https://www.linkedin.com/in/haochen-ding-17039a295/
About Kai
Kai
Kai is a 4th year undergraduate student studying Engineering Science at UofT, majoring in machine intelligence. His research interests include machine learning for healthcare, spanning multiple disciplines including histology, radiology, physiology, and more. Past research topics include computational pathology, remote heartrate detection (rPPG), surgical scene reconstruction, and radiological segmentation. In his free time, Kai enjoys spending time with family and friends, travelling, and exploring.
Awards: NSERC USRA (2022, 2025)
Projects:
Foundation Models for Wearables
Github: https://github.com/kailathan
Google Scholar: https://scholar.google.ca/citations?user=peirP6sAAAAJ
About Phoebe
Phoebe
Phoebe is a fourth-year Computer Science Specialist student at the University of Toronto with a minor in Statistics. Her research interests lie at the intersection of AI/ML and healthcare. She is currently working on an LLM-based data querying system for the 4YouandMe BUMP study. Beyond academics, she loves dancing and playing guitar!
Projects:
Natural Language Querying System for BUMP Data
Github: https://github.com/ShihHsin0723
LinkedIn: https://www.linkedin.com/in/shih-hsin-chuang/
About Sabrina
Sabrina
Sabrina is a 4th year undergraduate student at University of Toronto in Bioinformatics and Computational Biology and Computer Science. She is interested in using technological tools to solve biological problems and her current project involves leveraging machine learning to predict patient discharge. Outside of the lab, you can find her reading the latest fantasy novel or at the pool.
Projects:
Predicting In-Hospital Discharge Readiness from Multi-Model EHR Data
About Yuchen
Yuchen
Yuchen is an undergraduate student majoring in Computer Science and Statistics at the University of Toronto. He is working with Anna and Jerry on optimizing laboratory test utilization for ICU patients using deep learning methods, aiming to minimize resource overuse and alleviate patient discomfort. His research interests include deep learning, reinforcement learning, and their applications in healthcare and data-driven decision making. Outside of academics, he enjoys playing badminton.
Awards: University of Toronto Scholar (2025), The Charles P. McTague Scholarship (2024)
Projects:
ICU patient forecasting
Github: https://github.com/richardwang1236
LinkedIn: https://www.linkedin.com/in/yuchen-wang1236/
Past Lab Members
- Sujay Nagaraj (University of Toronto)
- Sana Tonekaboni (Broad Institute, MIT and Harvard)
- Jennifer Yu (Tri-I Institution, Weill Cornell Medicine, Memorial Sloan Kettering Cancer Center)
- Lauren Erdman (Cincinnati Children's Hospital Medical Center, University of Cincinnati)
- Alex Adam (GPTZero)
- Bret Nestor (Harvard School of Public Health)
- Kopal Garg (Cartography Biosciences)
- Gabriela Morgenshtern (University of Zurich)
- Soren Sarvestany (Tradera)
- Addison Weatherhead (SurveyMonkey)
- Erik Drysdale (Boston Consulting Group GAMMA)
- Jaryd Hunter (UHN)
- Chun-Hao (Kingsley) Chang
- Sulagshan Raveendrakumar (Google)
- Alex Chang (Medical student at the University of Montreal)
- Vinith M. Suriyakumar (PhD MIT)
- Melissa McCradden (Bioethicist at Sickkids)
- Daniel Hidru
- Julyan Keller-Baruch
- Farnush Farhadi (Layer 6)
- Carson McLean (Georgian partners)
- Ladislav Rampasek (Postdoc at MILA)
- Shalmali Joshi (Harvard)
- Angeline Yasodhara (Georgian partners)
- Jennifer Guo (University of Toronto)
- Aziz Mezlini (PhD, now PostDoc at Harvard Medical School/MassGen)
- Lebohang Radebe (Masters, now in South Africa)
- Walter Nelson (Undergrad, now McMaster Health System)
- Hannah Li (Undergrad, now grad student at Stanford)
- Yasaman Mahdaviyeh (Undergrad, PhD University of Toronto)
- Shems Saleh (Masters, now Vector)
- Daniel Cole (Undergrad, now Shopify)
- Won June (Undergrad, now Shopify)
- Tal Friedman (Undergrad, now PhD candidate at UCLA).
- Ekansh Sharma (Undergrad, PhD at University of Toronto).
- Hareem Naveed (Masters, now at Data Science for Social Good).
- Cheng Zhao (Masters, now at Google)
- Bo Wang (Illumina)
- Feyyaz Saigin Demir (Masters, now at Google)
- Ying Li (bioinformatician)
- Beyrem Khalfaoui (Now PhD student with JP Vert)
- Anne Keller (Masters, now Masters in epidemiology)
- Mohsen Hajiloo (Amazon)
Past Visitors
- Vanessa Lima (Brazil)
- Xin Wang (China)
- Seunghak Lee (CMU)
- Lloyd Elliot (Oxford Stats)