MSc(DDM) Expert Sharing Seminar Series - From Atoms to Life: A Dual-Engine AI Paradigm for Life Science Discovery

MSc(DDM) Expert Sharing Seminar Series - From Atoms to Life: A Dual-Engine AI Paradigm for Life Science Discovery
2:30pm - 3:30pm

We are excited to have Dr. Marius ZHANG, AI Scientist of SCITIX, to share the topic From Atoms to Life: A Dual-Engine AI Paradigm for Life Science Discovery”.




All HKUST members are welcome to join.




Details are as follows.



Date: 25 March 2026 (Wednesday)

Time: Starts At 2:30 PM HKT

Venue: Lecture Theatre H, HKUST

Speaker: Dr. Marius ZHANG, AI Scientist, SCITIX

Host: Prof. Sherry CHENG, Assistant Professor, Department of Chemistry, HKUST




Seminar Overview:



From Atoms to Life: A Dual-Engine AI Paradigm for Life Science Discovery - This talk presents our end-to-end AI infrastructure for life science: a closed-loop pipeline spanning multi-scale data curation, a physics-informed architecture, efficient training, and inference acceleration. We introduce a dual-architecture where Molecular Simulators serve as digital twins capturing biophysical processes with high fidelity, while Life Foundation Models act as scientific brains extracting patterns and generating hypotheses—collaborating to solve critical challenges in protein design and cellular behavior prediction. Finally, reflecting on our journey at the AI frontier, we will discuss how researchers can rapidly evolve their mindset and skillset to navigate and lead in this era of profound technological change.


About the speaker: 



Dr. Marius ZHANG is an AI Scientist at ScitiX, focusing on geometric deep learning for quantum chemistry. He develops equivariant neural architectures that bridge computational efficiency and physical rigor for molecular simulation and first-principles computation. Previously, he served as Principal Research Manager at Microsoft Research AI for Science Lab, where he led the Science Foundation Model team in exploring unified atomistic modeling; earlier in his career, he was at Microsoft Research Asia working on machine learning for industrial systems. He has published extensively in leading venues such as NeurIPS, ICML, ICLR, and Nature Communications, advancing symmetry-preserving machine learning and its scalability in atomistic modeling.



 



*Remarks:




  1. This is a face-to-face seminar.




  2. This seminar is open to all HKUST members.




  3. Please be advised that photographs and videos will be taken and recorded during the event. By entering this event, you consent to photographing, audio recording and video recording to be used for news, promotional purposes, advertising, inclusion on websites, social media, or any other purpose by HKUST MSc in Data-Driven Modeling.



Event Format
Recommended For
Alumni, Elderly, Faculty and staff, HKUST Family, PG students, UG students
Language
English
Organizer
Department of Physics
MSc Program in Data-Driven Modeling
Contact
Science & Technology