Starting a PhD in Computer Science and Informatics at Emory University, Fall 2026.
Moon Moon, a black cat with amber eyes curled up in a fabric storage box
Moon Moon
CurrentlyPhD Student, Emory University
Based inAtlanta, GA
Research interestsFoundation Models · Multi-modal Learning · Statistical Modeling

Tianxing Hu

View my research LinkedIn Google Scholar Curriculum Vitae Get in touch
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Coco
01About

I am a PhD student in Computer Science and Informatics at Emory University, working at the intersection of machine learning and computational biology.

My research focuses on developing and evaluating machine learning methods for biological data, with an emphasis on representation learning, foundation models, and generalization. I am particularly interested in understanding how models capture biological information, how learned representations generalize across datasets and tasks, and how to develop more reliable learning methods for single-cell and multi-omics data.

Before joining Emory, I earned an M.S. in Data Science from New Jersey Institute of Technology and a B.S. in Applied Statistics from Shanghai University of Finance and Economics.

Portrait of Tianxing Hu under a cherry blossom tree
02News
03Research
Research map

Reliable and Generalizable Machine Learning for Biological Data

01 Develop New learning methods

Representation Learning & Model Development

How can we learn meaningful biological representations?

02 Understand Model capabilities

Model Evaluation & Generalization

When and why do biological representations generalize?

  • GNEval
  • Context dependence
  • Robustness & transfer
03 Validate On biological tasks

Biological Inference & Prediction

How can ML enable reliable biological inference?

Cross-cutting foundation

Biological Data & Domain Knowledge

Data heterogeneity · Experimental design · Biological priors · Interpretation

Project Research theme / open question
04Publications
  1. 2026

    Context-specific benchmarking of foundation models for gene network inference

    Tianxing Hu, Weiwei Wu, Zhaohui S. Qin, Chang Su

    ICLR 2027 (under review)First authorUnder review

    GNEval overview: 14 Perturb-seq datasets across cell types feed foundation and embedding models (scGPT, Geneformer, scFoundation and others) plus domain baselines; static embeddings, contextual embeddings and attention scores become gene-gene score matrices and inferred gene networks, evaluated for functional and perturbational consistency and analysed for robustness, generalizability, efficiency and rank stability.
  2. 2026

    Cross-tissue graph attention networks for semi-supervised gene expression prediction ↗

    Shiyu Wang, Tianxing Hu, Mengyu He, Muran Qin, Yijuan Hu, Liang Zhao, Zhaohui Qin

    Briefings in Bioinformatics, bbag557

    GemGAT overview: genes are extracted from tissues, co-expression networks are built for source and target tissues, and a graph attention model encodes the source tissue's expression and network to predict expression in the target tissue.
  3. 2026

    Transformers for single-cell sequencing: a survey ↗

    Tianxing Hu, Zhi Wei

    Briefings in Bioinformatics 27(3), bbag332First author

  4. 2026

    GeneMamba: an efficient and effective single-cell language model ↗

    Cong Qi, Hanzhang Fang, Siqi Jiang, Xun Song, Tianxing Hu, Zhi Wei

    IJCNN 2026

  5. 2026

    LANTERN: TCR–peptide binding prediction via large language model representations ↗

    Cong Qi, Hanzhang Fang, Siqi Jiang, Tianxing Hu, Zhi Wei

    PeerJ 14, e20980

  6. 2026

    Clonal dynamics shaped by diverse drug-tolerant persister states in melanoma resistance ↗

    H Li, et al. (including Tianxing Hu)

    Molecular Cancer 25(1), 127

05Experience
Sep 2026 — Present

Research Assistant

Rollins School of Public Health, Emory University · Atlanta, GA
Sep 2024 — May 2026

Research Affiliate

University of Pennsylvania
  • Bioinformatics
  • Statistical genomics
Sep 2023 — Apr 2026

Research Assistant

New Jersey Institute of Technology
  • Generative models
  • Deep learning
  • AI for Science
  • LLMs
  • NLP
Earlier
Jan 2021 — Jun 2022

Data Analyst

East Money Information Co., Ltd · Shanghai
  • Engineered scalable financial data collection and processing pipelines, transforming large-scale datasets into structured analytical reports for investment research.
  • Led a 7-member team in designing and developing financial data interfaces, coordinating cross-functional requirements and implementation to streamline data integration.
06Education
2026 — 2031

Ph.D. in Computer Science and Informatics

Emory University · Atlanta, GA
2023 — 2026

M.S. in Data Science

New Jersey Institute of Technology · Newark, NJ
2015 — 2019

B.S. in Applied Statistics, minor in Computer Science

Shanghai University of Finance and Economics · Shanghai, China
2017 — 2018

Exchange Student, Economics

Toulouse School of Economics · Toulouse, France
07Teaching
Sep 2023 — Jan 2026

Teaching Assistant

New Jersey Institute of Technology
  • CS 732 Advanced Machine Learning
  • CS 482 Data Mining
  • CS 337 Performance Modeling in Computing
  • CS 114 Intro to Computer Science II
08Service
Peer review

Peer Reviewer

Journals
  • Bioinformatics
  • Bioinformatics Advances
  • Nucleic Acids Research
  • Briefings in Bioinformatics
  • Transactions on Machine Learning Research
  • IEEE Transactions on Systems, Man, and Cybernetics: Systems
  • Statistical Applications in Genetics and Molecular Biology
09Skills
Research
  • Foundation models
  • Multi-modal learning
  • Contrastive learning
  • Statistical modeling
  • Bioinformatics
Programming
  • Python
  • PyTorch
  • Pandas
  • scikit-learn
  • NLTK
  • spaCy
  • SQL
  • R
  • Java
  • C++
  • SAS
  • Linux
  • LaTeX
  • HPC
AI tools
  • Agentic AI
  • MCP
  • Multi-agent systems
  • RAG
  • Function calling
  • LLM integration
  • Workflow automation
  • LangChain
  • Hugging Face
  • Prompting
  • AI-assisted research & coding
Languages
  • English — full professional
  • Mandarin — native
Certifications
  • CFA Level I
  • Building with the Claude API