CV

Research Assistant

ScopeLab, Rice University

Developed innovative machine learning and deep learning platforms integrating Raman spectroscopy, 2D materials, and advanced algorithms to enhance biomarker detection, optical characterization, virus identification, and cancer diagnosis, achieving significant breakthroughs in Alzheimer’s and pancreatic cancer research, with multiple publications, awards, and ongoing clinical collaborations.

• Artificial intelligence, Machine learning, Biomolecule and disease sensing, Materials design, Hyperspectral imaging

Chief Technology Officer & Co-founder

iDeal Technology, LLC (Startup)

Led app development and R&D teams to launch a successful mobile app for university students, achieving 15,000+ downloads, 500+ daily active users, and $5 million GMV while integrating advanced recommendation systems and winning the 2022 Asian Future Innovation Challenge.

• Tech startup, App development

Data Analyst, Intern

DataCVG

Designed and built an automated web crawler in Python and extracted 80K+ records from 5000+ listed companies, enhancing stockholder decision-making efficiency.

• Web crawler, Data analysis

Software Engineer, Intern

ATOZ Information Technology

Developed AI-powered inspection services for factory maintenance; created portable augmented reality demos on Microsoft HoloLens, showcasing industrial AI applications to stockholders.

• Artificial intelligence, Augmented reality

Ph.D.

Rice University

Electrical and Computer Engineering

B.S.

The Pennsylvania State University

Computer Science, Mathematics

Rice Synthetic Biology Institute (RSBI) Travel Award

Best Abstract Award, Pancreatobiliary Pathology Conference

Nittany AI Challenge Phase One Winner

Asian Future Innovation Challenge Seattle Winner

Best Poster Award, IndustryXchange

Schreyer Honors Scholar

Magna Cum Laude, Engineering College, Penn State

Magna Cum Laude, Science College, Penn State

Dean’s List, Engineering College, Penn State

FIRST Tech Challenge, Second Prize, China

Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression

ACS Nano

We developed PSE-LR, a peak-sensitive logistic regression algorithm optimized for spectral analysis, achieving high accuracy and interpretability in identifying subtle spectral features across diverse applications—from viral protein detection to Alzheimer’s biomarkers—while enabling the development of nanodevices and miniaturized spectrometers.

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Nanoscale

High-Dimensional Spectroscopy Analysis with Machine Learning Techniques

Strain-Level Identification and Analysis of Avian Coronavirus Using Raman Spectroscopy and Interpretable Machine Learning

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

Measuring Complex Refractive Index Through Deep-Learning-Enabled Optical Reflectometry

2D Materials

Developed the first-of-its-kind deep learning models to measure optical properties with standard optical microscopes, enabling in-operando characterization of functional and evolving materials.

Optical Properties and Emerging Phenomena of Two-Dimensional Materials

Novel Optical Materials

Raman spectroscopy on brain disorders: transition from fundamental research to clinical applications

Biosensors

Understanding the excitation wavelength dependence and thermal stability of the SARS-CoV-2 receptor-binding domain using surface-enhanced raman scattering and machine learning

ACS photonics

This study utilized surface-enhanced Raman spectroscopy (SERS) and machine learning to analyze and distinguish SARS-CoV-2 RBD with over 95% accuracy, providing insights for variant surveillance and rapid viral protein identification.

Accurate virus identification with interpretable Raman signatures by machine learning

Proceedings of the National Academy of Sciences (PNAS)

Designed convolutional and transformer networks for respiratory virus strain identification and biomolecule recognition, enabling portable virus detection devices.

Engineered 2D materials for optical bioimaging and path toward therapy and tissue engineering

Journal of Materials Research

Rapid Biomarker Screening of Alzheimer’s Disease by Interpretable Machine Learning and Graphene-Assisted Raman Spectroscopy

ACS Nano

Pioneered an interpretable machine learning (ML) biosensing platform integrating Raman spectroscopy and 2D materials, enabling rapid screening of biomarkers of Alzheimer’s disease (AD).