Portrait
Yinghua Yao
Research Scientist
A*STAR Centre for Frontier AI Research (CFAR), Singapore
About Me

I am a Research Scientist at the A*STAR Centre for Frontier AI Research (CFAR), Singapore. I received my Ph.D. in Machine Learning from the University of Technology Sydney, advised by Prof. Ivor Tsang and Prof. Xin Yao.

My research focuses on generative AI, controllable generative models, and AI for bioscience, especially models that align generation with human preferences and scientific objectives.

Education
  • University of Technology Sydney
    Australia
    Ph.D. in Machine Learning
    Aug. 2018 - Sept. 2023
  • Southern University of Science and Technology
    Shenzhen, China
    B.Eng. in Computer Science and Technology
    Sept. 2014 - July 2018
Experience
  • A*STAR Centre for Frontier AI Research (CFAR)
    Singapore
    Research Scientist
    Sept. 2023 - Present
  • A*STAR Centre for Frontier AI Research (CFAR)
    Singapore
    Research Intern
    July 2022 - Aug. 2023
News
2026
Paper accepted to KDD 2026: From Structure to Function.
May
Paper accepted to IJCAI 2026: Learning Well-Structured Logits.
Apr
Awarded A*STAR CDF for AI for protein sequence-structure co-design.
Mar
Paper accepted to CVPR 2026: SEA-Flow3D.
Mar
Paper accepted to ICLR 2026: Sample Reward Soups.
Jan
Paper accepted to ICLR 2026: TS2.
Jan

Projects

  • AI for enzyme engineering
    MTC IRG, Singapore
  • AI for protein sequence-structure co-design
    A*STAR CDF
  • AI for drug discovery
    AIDD, Singapore
  • LLM alignment
    NMLP, AISG

Awards

  • Best Poster Award, AI for Science and Nobel Turing Challenge Initiative Conference
    2024
  • Best Paper Award, Conference on Parsimony and Learning
    2024
  • Excellent Undergraduate Thesis Research
    2018
  • SUSTech Undergraduate Scholarship
    2014-2018

Services

Session Chair
  • IJCAI, 2025
Conference Reviewer
  • ICML
  • NeurIPS
  • ICLR
  • AAAI
  • ACML
  • ICONIP
Journal Reviewer
  • IEEE Transactions on Information Theory
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • IEEE Transactions on Neural Networks and Learning Systems
  • Machine Learning
  • IEEE Transactions on Big Data
  • IEEE Transactions on Emerging Topics in Computational Intelligence
  • Neurocomputing

Invited Talks

  • "Protein Design with Generative AI: Innovations and Applications"
    11 March 2025
    SupercomputingAsia 2025, Singapore
  • "Generative AI and its Application in Science"
    24 June 2024
    The Third RIKEN AIP & A*STAR-CFAR Joint Workshop on Machine Learning and Artificial Intelligence, Singapore

Students

PhD Students
  • Samal Mukhtar (PhD candidate at University of Manchester, A*STAR ARAP joint PhD scholarship)
  • Ziming Wang (PhD candidate at SUSTech)
  • Chunyi Li (PhD candidate at SJTU)
Master Students
  • Kehan Liu (NUS)
  • Nilufer Tamatgar (NTU)
  • Lehan Hong (TUST)
Alumni
  • Bai Yucheng (NTU master)
  • Xu Ziyang (NUS master)
  • Ananthu Rajendran Pillai (NTU master)
  • Soobin Park (Yonsei University bachelor)
  • Guo Huixuan (NTU bachelor, Singapore; now PhD candidate at MIT)
Selected Publications (view all )
From Structure to Function: Preference Alignment for Function-Aware Protein Inverse Folding

N. Tamatgar*, S. Park*, Y. Yao*#, X. Chen, Y. Pan

SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2026 CCF A

From Structure to Function: Preference Alignment for Function-Aware Protein Inverse Folding

N. Tamatgar*, S. Park*, Y. Yao*#, X. Chen, Y. Pan

SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2026 CCF A

Sample Reward Soups: Query-efficient Multi-Reward Guidance for Text-to-Image Diffusion Models

Y. Yao, Y. Pan, G. Fu, I. Tsang

International Conference on Learning Representations (ICLR) 2026 CCF A

[PDF] [Code]

Sample Reward Soups: Query-efficient Multi-Reward Guidance for Text-to-Image Diffusion Models

Y. Yao, Y. Pan, G. Fu, I. Tsang

International Conference on Learning Representations (ICLR) 2026 CCF A

[PDF] [Code]

TS^2: Training with Sparsemax+, Testing with Softmax for Accurate and Diverse LLM Fine-Tuning

Z. Xu*, A. R. Pillai*, Y. Yao#, Y. Pan

International Conference on Learning Representations (ICLR) 2026 CCF A

[PDF] [Code]

TS^2: Training with Sparsemax+, Testing with Softmax for Accurate and Diverse LLM Fine-Tuning

Z. Xu*, A. R. Pillai*, Y. Yao#, Y. Pan

International Conference on Learning Representations (ICLR) 2026 CCF A

[PDF] [Code]

Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space

Y. Yao, Y. Pan, X. Chen

International Joint Conference on Artificial Intelligence (IJCAI) 2025 CCF A

[PDF] [Code]

Generative Co-Design of Antibody Sequences and Structures via Black-Box Guidance in a Shared Latent Space

Y. Yao, Y. Pan, X. Chen

International Joint Conference on Artificial Intelligence (IJCAI) 2025 CCF A

[PDF] [Code]

Generative Adversarial Ranking Nets

Y. Yao, Y. Pan, J. Li, I. Tsang, X. Yao

Journal of Machine Learning Research 2024 CCF A

[PDF] [code]

Generative Adversarial Ranking Nets

Y. Yao, Y. Pan, J. Li, I. Tsang, X. Yao

Journal of Machine Learning Research 2024 CCF A

[PDF] [code]

PROUD: PaRetO-gUided Diffusion Model for Multi-Objective Generation

Y. Yao, Y. Pan, J. Li, I. Tsang, X. Yao

Machine Learning 2024 Best Poster Award

[PDF] [code]

PROUD: PaRetO-gUided Diffusion Model for Multi-Objective Generation

Y. Yao, Y. Pan, J. Li, I. Tsang, X. Yao

Machine Learning 2024 Best Poster Award

[PDF] [code]

PC-X: Profound Clustering via Slow Exemplars

Y. Pan, Y. Yao, I. Tsang

Conference on Parsimony and Learning 2024 Best Paper Award

[PDF] [code]

PC-X: Profound Clustering via Slow Exemplars

Y. Pan, Y. Yao, I. Tsang

Conference on Parsimony and Learning 2024 Best Paper Award

[PDF] [code]

All publications