Professional Profile

Bioinformatics scientist with a product mindset, turning complex high-throughput omics datasets into biologically meaningful insights across viral infectious diseases, immune diseases, and plant stress biology. Specializing in multi-omics integration for molecular signal identification, and building reproducible computational pipelines for target discovery. Extended into applied LLM research: built an autonomous literature mining system and a reasoning framework estimating tissue-specific PPI likelihood from RNA, IHC, mass-spectrometry, and subcellular-localization data. Proficient in Python, R, NGS data analysis, Git, HPC/SLURM, and collaborating with multidisciplinary teams to translate complex data into biological tools and insights.

Experience

Postdoctoral researcher

Institute of Network Biology, Helmholtz Munich, Germany | Jan 2018 – present (parental leave: May 2022 – Feb 2023 and Nov 2024 – Aug 2025)

  • Developed and implemented automated computational pipelines for the analysis of high-throughput SARS-CoV-2-human protein interaction networks, enabling a proteome-scale map (co-first author, Nature Biotechnology 2022) for viral target prioritisation.
  • Developed new computational approaches to integrate multi-omics datasets, connecting molecular signals to clinical phenotypes in asthma-relevant immune mechanisms.
  • Prototyped and implemented reproducible data processing tools and workflows, turning complex ideas into robust code across heterogeneous omics sources.
  • Collaborated closely with multidisciplinary teams, leading scientific questions forward to address data quality concerns, and presented analytical findings at internal and external meetings.

Tissue-specific PPI Estimation via LLM Reasoning

Ongoing Project

  • Developed an LLM reasoning framework that estimates the tissue-specific likelihood of protein-protein interactions by prompting models (Claude, GPT-5, gpt-oss, Gemma) to jointly reason over RNA expression, IHC protein abundance, mass-spectrometry data, and subcellular localization.
  • Built an autonomous literature mining system spanning PubMed abstract retrieval, gene mention filtering, PMC full-text download, and open-ended LLM extraction of experimentally validated PPIs with tissue context.
  • Engineered a provider-agnostic API abstraction supporting Claude, OpenAI, and Ollama backends with structured JSON output, self-confidence scoring, and resumable HPC/SLURM batch execution.

Visiting scholar

National Institute for Basic Biology, Japan | May 2016 – Jun 2016

Gene co-expression network clustering (WGCNA) to identify functional gene modules.

Research and development engineer

ProMD Biotech Co., Ltd., Taiwan | Jul 2010 – Jun 2012

Probiotics identification, functional assays, and validation of new probiotic formulations for commercial health products.

Research assistant

Academia Sinica, Taiwan | Aug 2007 – Feb 2010

Investigated nutrient deficiency effects in Arabidopsis and cucumber via microarray; identified impacted genetic pathways.

Education

Ph.D., Life Sciences

National Cheng Kung University, Taiwan | Jul 2012 – Sep 2017

Thesis: "Transcriptome analysis provide insights into environmental stresses in plants"

M.S., Life Sciences

National Cheng Kung University, Taiwan | Sep 2003 – Jun 2005

Thesis: "The effect of vanadate and zinc on rice root cell signal transduction"

Skills

  • Bioinformatics: Multi-omics data integration, linking omics to clinical outcomes, NGS data analysis, target discovery, proteomics, microbiome profiling
  • Programming & Tools: Python, R, method prototyping, building analytical tools, Perl, SQL, Git, Bash
  • AI/LLM Engineering: Structured prompt engineering, multi-provider API integration, tissue-specific PPI reasoning with multi-modal evidence, resumable HPC/SLURM batch pipelines
  • Data Engineering & Pipelines: Automated QC pipelines, reproducible data analysis workflows (Nextflow), large-scale data curation, SQLite
  • Environments: HPC/SLURM, Docker, high-performance computing, cloud-aware workflows