Curriculum vitae
CV
General information
- Full name
- Chih-Hsuan (Bella) Yang
- Current position
- Postdoctoral Researcher, Argonne National Laboratory
- Research identity
- Multi-Agent Systems · AI for Science · Materials Discovery
- bellayang@anl.gov
- Profiles
- Google ScholarORCIDGitHub
Research experience
- Jul 2025–present
Postdoctoral Researcher
Argonne National Laboratory · Lemont, Illinois
Develops evaluation and post-training methods for reliable multi-agent scientific reasoning, including protocol routing, critique uptake, credit assignment, and large-scale experimentation on leadership computing systems.
- Sep 2024–Jan 2025
Visiting Scientist
Corteva Agriscience · Des Moines, Iowa
Applied RLHF with preference-based insights for domain-specific model customization.
- Sep–Dec 2023
AI Resident
X, Google LLC (Google X) · Mountain View, California
Developed multi-agent collaboration frameworks using a Mixture-of-Experts architecture with multimodal data streams.
- 2022
Deep Learning Engineer Intern
FLX AI · New York, New York
Worked on deep representation learning and forecasting methods for image and time-series applications.
- Jan 2020–Jun 2025
AI Graduate Researcher
Iowa State University · Ames, Iowa
Led and contributed to research in molecular language modeling, materials design, multi-fidelity learning, neural PDE solvers, and multimodal AI for biodiversity.
Education
- 2025
Ph.D. · Mechanical Engineering; co-major in Computer Engineering; minor in Applied Mathematics
Iowa State University
- 2025
M.S. · Computer Science
Iowa State University
- 2019
B.S. · Bioenvironmental Systems Engineering
National Taiwan University
Research areas
- Collaborative AIEvaluate when multi-agent interaction helps, identify which messages and agents contribute, and turn trajectories into learning signals.
- Materials & molecular discoveryDevelop generative, preference-based, and multi-fidelity learning methods for molecular and materials design.
- Scientific machine learningBuild multimodal foundation models, scientific datasets, and physics-informed learning methods for real scientific workflows.
Scholarly work
Google Scholar ↗- 2026
- 2026
- 2026
- 2026MolGen-Transformer: A Molecule Language Model for the Generation and Latent Space Exploration of Organic MoleculesComputational Materials Science 269, 114549, 2026
- 2025Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM AgentsLAW 2025 Workshop at NeurIPS 2025; revised 2026
- 2025Evaluating Molecular Similarity Measures: Do Similarity Measures Reflect Electronic Structure Properties?Journal of Chemical Information and Modeling 65(9), 4311–4319, 2025
- 2024BioTrove: A Large Curated Image Dataset Enabling AI for BiodiversityAdvances in Neural Information Processing Systems 37, Datasets and Benchmarks Track, Spotlight, 2024
- 2024Neural PDE Solvers for Irregular DomainsComputer-Aided Design 172, 103709, 2024
- 2024Google Trends as an Early Indicator of African Swine Fever Outbreaks in Southeast AsiaFrontiers in Veterinary Science 11, 1425394, 2024
- 2024In the Mix: A Workshop Merging Computational Chemistry and Electrochemistry Alongside Data ScienceJournal of Chemical Education 101(11), 5060–5067, 2024
- 2023Deep Reinforcement Learning Exploration in Continuous Latent Space for Molecular DesignAAAI Workshop on AI to Accelerate Science and Engineering, 2023
- 2022Multi-Fidelity Machine Learning Models for Structure–Property Mapping of Organic ElectronicsComputational Materials Science 213, 111599, 2022
- 2021Fast Inverse Design of Microstructures via Generative Invariance NetworksNature Computational Science 1, 229–238, 2021
Mentoring & collaboration
Alongside hands-on research, I mentor students working on collaborative AI and AI for Science. I am open to research collaborations across multi-agent systems, scientific foundation models, and AI-enabled scientific discovery. Team member profiles will be added with their permission.