Summary

Bioinformatics scientist who accelerated 50+ therapeutic programs by 40% through AI-driven antibody design and functional screening analysis. 6+ years integrating computational methods (LLMs, NN force fields) with experimental data (structure simulation, biosensor, yeast display, NGS) across 50M+ sequences. Published researcher (JCTC/JPCA/JBC) bridging in silico predictions with wet-lab validation for next-generation drug discovery.

Skills

Computational Chemistry/Biology, Bioinformatics, Chemical Engineering, Battery Reaction Simulation, Theoretical Neuroscience, NGS, Gene Editing(e.g CRISPR), Organic Synthesis, Physical Chemistry, Protein Kinetic, Pharmacokinetics, Analytical Chemistry, Data Engineering, Machine Learning, Computer Vision, Statistics, Pattern/Academic Writing.

Programming: Python, SQL, Matlab, R, Git/GitHub, Linux, Tensorflow, AWS

Scientific Data Analysis: Schrödinger, PyMol, IGOR, Gaussian, GROMACs, RDkit, Prism, Imaris, Zeiss

Language: English, Mandarin, German

Experience

Computational/AI:

Research Scientist II, Bioinformatics (2022–Present, company acquired Mar 2025)

FairJourney Biologics (formerly Charles River Laboratories), South San Francisco, CA

  • Adapted generative models (tuned LLMs) for immune library design (VHH, scFv, IgG) on phage/yeast platforms; delivered clean, internal-ready LLM-hybrid version maintaining 95% structural fidelity, accelerating discovery by 40% across 50+ therapeutic programs.

  • Built two SQL databases (screening/NGS) for 200+ antibody projects (50M+ sequences); integrated AWS ML-query interfaces, reducing data retrieval time by 60% for ML engineering and training.

  • Automated Python/R pipelines (Biopython, Scanpy) for Sanger/NGS/single-cell analysis with Tumbler visualization; processed 1,000+ samples/week, supporting 30+ oncology/immunology clients.

AI Research Scientist (Part-Time, Post-Graduation) & AI Intern (2021–2025 | 2 Publications)

Freecurve LLC (Spinout from InterX Inc.) & InterX Labs (Prof. Michael Levitt & Prof. Roger Kornberg Labs), Berkeley, CA

  • Progressed from AI Intern (2021–2022) to part-time Research Scientist (2022–2025), contributing to hybrid NN-force field models for biomolecular simulations; achieved ±0.5 kcal/mol MAE reproducing ab initio PES on methyl-capped ALA/ASP dipeptide conformations, enhancing protein torsional modeling (co-author, J. Phys. Chem. A, 2024).

  • Co-developed short-range NN corrections to ARROW force field intermolecular terms to capture nuclear quantum effects in liquid thermodynamics; enabled classical MD to match PIMD benchmarks (density, RDF, ΔH_vap, solvation free energies) for water/methane, with pairwise design scalable to proteins in drug discovery (co-author, J. Chem. Theory Comput., 2024, 20(3):1347–1357).

  • Implemented Python/PaCPAC alignment for SMILES/RDKit data; screened 10,000+ kinase compounds via Schrödinger/pyMOL docking, identifying high-affinity candidates for drug discovery. Supported CADD projects with docking simulations and preprocessing for NN training on molecular interactions.

  • Designed and maintained company's research website.

AI Researcher, Deepwise Healthcare, Beijing (2019-2020)

  • Built universal lesion detection MP3D FPN from CT slice for comprehensive disease screening. Achieved state-of-the-art detection performance on the DeepLesion dataset (3.48% absolute improvement in the sensitivity of FPs@0.5). The result was published in MICCAI2020(Medical Image Computing and Computer Assisted Intervention).

  • Facilitated the design of Bipartite graph node mapping in mammogram mass detection. Performance on DDSM dataset(%): 95@4.4, 92@1.9, 89@1.2. The result was published in CVPR2020)Computer Vision and Pattern Recognition).

  • Assisted a client with MCMC Bayesian parameter estimation using PyMC and corresponding visualizations for experimental chemistry and clinical function data.

Data Analyst, IQVIA, Taipei (Contractor) (2017-2020)

  • Experienced in analyzing large-scale unstructured/unlabeled data: analyzed drug’s clinical performance through 1000+ doctors’ reports through statistical methods (SPSS and SQL).

  • Provided clients with go-to-market insights by performing data cleaning, exploratory analysis, and PCA with clustering. (companies include Johnson & Johnson, Pfizer, Sanofi, and many more.)

  • In charge of Simultaneous Interpreting in a medical conference. Edited document translations across global branches (English/Mandarin).

Neuroscience/Chemistry:

Graduate Researcher, Dr.Yang’s Group, USF, San Francisco (2020-2022)

  • Designed and executed experiments to define and test hypotheses for cell transport mechanisms for potential drug targeting. Mastered transmembrane protein purification and quantification using FPLC with UV detectors.

  • Discovered potential new ABC transportation mechanism through Anisotropy and ATP Assay. The result was published in ASBMB2022. Currently working on following up publication (grant from NIH).

  • Received Teaching&Research Scholarship for 2 years. Assisted 20+ university-level students in learning topics in general&bio-chemistry.

Graduate Researcher, Yi’s Group, Tsinghua University, Beijing (2016-2019)

  • Investigated protein interactions with the statistical Markov model (HMM). Trained and validated with 4,000+ data published since 2000 with 85% accuracy. Performed simulation and data analysis with Python, MATLAB, and R.

  • Inspected forgetting pathway and expert in the following research techniques: NGS, Brain Imaging, Virus Infections, Immunohistochemistry, Western blot, iDisco, Gene Sequencing, and Confocal Microscope Operation.

  • Designed and conducted animal paradigms in emotions and memory formation experiments.

  • Constructed neural circuits and neural computation models in drosophila/mice for early drug screening, especially in neurogenesis and CNS disorder profiles.

  • Associate project: Reduced smoothed level rescues A-β-induced memory deficits and neuronal inflammation animal models of Alzheimer’s disease. (Immunohistochemistry)

  • Improved documentation and training for lab protocols, and maintained the internal lab wiki and Unix server.

Battery Engineer, Audi China, Beijing (Internship) (2017-2019)

This is an internship in Tsinghua University Student Formula E.

  • Managed performance, lifecycle and specialty battery testing programs.

  • Assembled high voltage battery packs and managed battery simulation using ANSYS. Used air-cooling system for thermal management

  • Built battery model and parameterize equivalent circuit that reflects the battery’s behavior on temperature, SOC, SOH, and current with Simulink.

Educations/Certificates

//Educations:

M.S. in Data Science, Massachusetts Institute of Technology, EdX (2021-2022)

M.S. in Chemistry, University of San Francisco, San Francisco (2020- 2022) 3.7/4.0 GPA

M.S. in Neuroscience, Tsinghua University, Beijing (2016-2019) 3.8/4.0 GPA

B.S. in Agricultural Chemistry, National Taiwan University, Taipei (2012-2016) 3.5/4.3 GPA

//Certificate:

Publication/Conference/Award

  1. Two stages of substrate discrimination dictate selectivity in the E. coli MetNI-Q ABC transporter system, JBC2025

  2. Neural Network Corrections to Intermolecular Interaction Terms of a Molecular Force Field Capture Nuclear Quantum Effects in Calculations of Liquid Thermodynamic Properties, J. Chem. Theory Comput. 2024

  3. Combining Force Fields and Neural Networks for an Accurate Representation of Bonded Interactions, J. Phys. Chem. A. 2024

  4. Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices, MICCAI 2020