Associate Professor, Zhongguancun Academy
On duty for the AI + Education direction.
夏应策 / Yingce Xia, Ph.D.
Foundation models for natural and social systems.
Associate Professor at Zhongguancun Academy and former Principal Research Manager at Microsoft Research. His research focuses on AI for Science and AI for Social Science, especially foundation models and real-world applications in drug discovery, protein design, large language models, and education/social systems.
Research
AI for Science and AI for Social Science
My research focuses on AI for Science and AI for Social Science: building foundation models for both natural and social systems.
In AI for Science, my work centers on life science, including chemistry, drug discovery, and protein design. I have led and contributed to several foundation-model and generative-AI systems for scientific discovery, including NatureLM, a science foundation model for cross-domain scientific generation; BioGPT, a generative pre-trained model for biomedical text generation and mining; and TamGen, a target-aware molecular generation model for drug discovery. More broadly, my work studies how generative models can support molecular generation, target-aware drug design, lead optimization, and protein engineering.
In AI for Social Science, I am developing an emerging research agenda that extends foundation-model techniques to social systems. This includes modeling human behavior, institutional mechanisms, social interactions, educational processes, and the effects of interventions, while exploring how AI systems can support social understanding, simulation, evidence-based decision-making, and educational innovation.
I also initiated the research line of dual learning, a general framework for learning from primal-dual task structures and feedback loops. It shaped my long-standing interest in AI systems that learn through interaction, consistency, and feedback, rather than static supervision alone.
Experience
Appointments, education, and leadership
Principal Research Manager, Microsoft Research
Led research programs in generative AI, AI for Science, drug discovery, and NLP.
Joint Ph.D. program between University of Science and Technology of China
and Microsoft Research Asia.
Supervisors: Prof. Tie-Yan Liu and Prof. Nenghai Yu.
Microsoft Research Asia PhD Fellowship, 2016.
Research topics: dual learning and machine translation.
Thesis: A Theoretical and Empirical Study of Dual Learning.
B.S., University of Science and Technology of China
School of Information Science and Technology.
Research Contributions
Selected research contributions
- NatureLM The first science foundation model for universal scientific entity generation, translating natural language goals into molecules, proteins, materials, RNA, and text, with wet-lab validation in infectious disease drug discovery and de novo DNA binding protein design. Paper: Nature Language Model: Deciphering the Language of Nature for Scientific Discovery. Technical report, 2025.
- TamGen A target aware molecular generation model developed with GHDDI to accelerate Gates Foundation supported tuberculosis drug discovery. Paper: TamGen: drug design with target-aware molecule generation through a chemical language model. Nature Communications, 2024.
- BioGPT The first GPT style generative pretrained Transformer model specialized for biomedical text generation and mining. Paper: BioGPT: generative pre-trained transformer for biomedical text generation and mining. Briefings in Bioinformatics, 2022.
- Dual Learning A pioneering closed-loop learning paradigm for inverse NLP tasks, later integrated into MSRA's WMT'19 systems with 8 first-place wins and transferred to real-world translation products. Papers: Dual Learning for Machine Translation, NeurIPS 2016; Dual Supervised Learning, ICML 2017; Microsoft Research Asia's Systems for WMT19, ACL WMT 2019.
For the full publication list, please see Google Scholar.
Highlights
Awards and achievements
- 2025 Released Nature Language Model, serving as project lead for a 50-member virtual team across China, the U.S., and the U.K.
- 2024 Developed TamGen and delivered it to GHDDI and additional institutions; designed hit and lead compounds targeting tuberculosis.
- 2023 Released BioGPT, the first AI model to achieve human parity on the PubMedQA benchmark.
- 2019 Contributed to Microsoft Research Asia's WMT'19 systems with 8 first-place finishes; received Excellent PhD Thesis of Chinese Academy of Sciences.
- 2018 Received the Chinese Academy of Sciences President Award; contributed to human parity on Chinese-to-English news translation.
- 2016 Microsoft Research Asia PhD Fellowship.
Community
Teaching and professional service
Professional service
- 2023-2026 Senior Area Chair for NeurIPS ED track 2026; Area Chair for NeurIPS, ICML, and ACL.
- 2020-2023 Senior Program Committee member for AAAI, IJCAI, and AAMAS.
- 2018-2025 Reviewer for Nature Methods, Nature Communications, CVPR, ICCV, ECCV, EMNLP, and TPAMI.
Teaching
- 2023 Chinese Academy of Sciences: advanced machine learning, including deep learning basics, meta learning, generative adversarial networks, and dual learning.
- 2021, 2022 AI School China and Microsoft: advanced machine learning, automatic machine learning, dual learning, and natural language processing.
- 2019, 2021 Tsinghua University: advanced machine learning topics including meta learning, generative adversarial networks, dual learning, and NLP.