Hybrid Gaussian process models
Combined experimental features and molecular fingerprints for Bayesian optimization.
Ph.D. researcher · NC State University
Sina Dehghan
Materials scientist combining conjugated polymer experiments, machine learning, and laboratory automation.
- 03+
- years in polymer research
- 01
- active patent application
AUTONOMOUS POLYMER LAB LIVE / BO-07
01 Selected work
Research & experience.
Ph.D. Research Assistant, NC State University · 2023—Present
Robust learning from literature data
Built a conjugated-polymer light-scattering dataset and evaluated model generalizability under distribution shift.
Stoichiometry-driven magnetic frustration
Designed an automated Bayesian optimization campaign; contributed to patent application 63/956,271.
Automated conjugated-polymer formulation
Developed mixed-solvent formulation and characterization workflows for OMIECs.
02 Publications
Selected publications.
Complete ORCID record ↗
01
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03
APL Machine Learning
Robust Learning from Literature Data: Model Generalizability and Uncertainty for Predicting Conjugated Polymer Solution Conformation
ChemRxiv · under review
Metadata Analysis to Reveal Environmental Effects in Large-Area Molecular Tunneling Junctions
Scientific Reports · 2024
Fabrication, characterization and evaluating properties of 3D-printed PLA–Mn scaffolds
03 Skills
Machine learning
Gaussian processBayesian optimizationPyTorchscikit-learnRDKit
Polymer characterization
SEC–MALSRecycling GPCUV–VisDLS / SLSHSPiP
Laboratory automation
OpentronsSiLA2FlaskDesign of experimentsActive learningLab setup
04 Education & leadership
EDUCATION
2023—Present
Ph.D. Materials Science & Engineering
North Carolina State University · Raleigh, NC
2023—2025
M.S. Materials Science & Engineering
North Carolina State University · CGPA 3.7 / 4.0
ROLES & RESPONSIBILITIES
- 2025—26President, Graduate Student Association
- 2025—26Chair, Materials Research Safety Meeting
- Since 2023Safety Officer & Lab Manager, DDOM Lab