Seminar Spring 2026

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  • Theme: AI for Health
  • Instructor: Prof. Chenyang Lu
  • Semester: Spring 2026
  • Time: Wednesday at 8:30 am
  • Location: McKelvey 1020
  • Guidelines:
  • Recommended sources: NeurIPS ([1]), ICML ([2]), AAAI ([3]), SIGKDD ([4]), IJCAI ([5]), IMWUT ([6]), HEALTH ([7]), Digital Medicine ([8]), Lancet Digital Health ([9]), NEJM AI ([10])

Presentation Schedule

---Jan 14 ---

Liuyi

GLOSS: Group of LLMs for Open-ended Sensemaking of Passive Sensing Data for Health and Wellbeing

https://dl.acm.org/doi/epdf/10.1145/3749474

---Jan 21 ---

Ben

A multimodal sleep foundation model for disease prediction

https://www.nature.com/articles/s41591-025-04133-4

---Jan 28 ---

Patrick

A Foundation Transformer Model with Self-Supervised Learning for ECG-Based Assessment of Cardiac and Coronary Function

https://ai.nejm.org/doi/10.1056/AIoa2500164

---Feb 4 ---

Shengxin

Vgent: Graph-based retrieval-reasoning-augmented generation for long video understanding

https://arxiv.org/pdf/2510.14032

---Feb 11 ---

Ziqi

the foundational wearable models

https://arxiv.org/pdf/2507.00191 https://research.google/blog/sensorlm-learning-the-language-of-wearable-sensors/

---Feb 18 ---

Sihang Zeng

---Feb 25 ---

Skipped

---Mar 4 ---

Skipped

---Mar 11 ---

Allen

---Mar 18 ---

Claire

Conformal selective prediction with cost aware deferral for safe clinical triage under distribution shift. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40637-w

---Mar 25 ---

Cancelled

---Apr 1 ---

Peiqi (Oral Exam Practices)

Paper 1: A visual–language foundation model for pathology image analysis using medical Twitter (https://www.nature.com/articles/s41591-023-02504-3)

Paper 2: CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10655627)

Paper 3: A multimodal whole-slide foundation model for pathology (https://www.nature.com/articles/s41591-025-03982-3)

---Apr 8 ---

Zichen (Oral Exam Practices)

Paper 1: Genome-wide prediction of disease variant effects with a deep protein language model (https://www.nature.com/articles/s41588-023-01465-0)

Paper 2: Accurate proteome-wide missense variant effect prediction with AlphaMissense (https://www.science.org/doi/10.1126/science.adg7492)

Paper 3: A disease-specific language model for variant pathogenicity in cardiac and regulatory genomics (https://www.nature.com/articles/s42256-025-01016-8)

---Apr 15---

Hangyue

H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking

https://www.nature.com/articles/s41746-025-01741-9

---Apr 22---

Liuyi


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