CV

A one-page PDF résumé is available on request. Full publication list on the Publications page.

Education

• Ph.D. in Computational Biology, Joint CMU–Pitt Ph.D. Program in Computational Biology (CPCB), 2019–2025 (thesis defended November 2025)
  • Program spans Carnegie Mellon’s School of Computer Science (2019–2020) and the University of Pittsburgh School of Medicine (2021 onward), within the joint program.
  • Collaborated with the Xing lab and the Xu lab on live-cell imaging and cryo-EM deep-learning research.
• M.S. in Computer Science, University of California, Santa Barbara, 2017–2019
  • Research focus on natural language processing and understanding.
• B.S. in Computer Science, University of California, Santa Barbara, 2015–2017
  • Major GPA 3.96/4.00; Dean’s Honors 2015–2017.

Experience

Descriptions are kept intentionally brief; details available on request.

• Amazon, Applied Scientist, Seattle, WA (2026–Present)
  • Multimodal and large language models; mechanistic interpretability; generative AI for data efficiency and robustness.
• Independent Research, Agentic Systems (self-funded), Remote (2024–2025)
  • Built LLM-in-a-loop multi-agent systems, including a tool-use code agent, progressing toward FARS.
• Carnegie Mellon University & University of Pittsburgh, Research Assistant, Pittsburgh, PA (2019–2025)
  • Deep-learning computer-vision frameworks for live-cell imaging (LivecellX, LivecellAction); open-source contributions.
• Shanghai Ragamuffin Networks Inc., Engineering Lead, Shanghai, China (2020–2021)
  • Led delivery of a cloud-based operations platform.
• FusionTech (Startup), Co-Founder / ML Research Engineer, China (2020)
  • AI-driven medical imaging diagnostics; the startup was later acquired.
• University of California, Santa Barbara, Research / Teaching Assistant, Santa Barbara, CA (2016–2017)
  • NLP research with Prof. William Yang Wang; TA for parallel computing, C++, and a graduate deep-learning course.
• Arista Networks, Software Engineer Intern, Santa Clara, CA (2017)
• UCSB L&S IT, Software Engineering Intern, Santa Barbara, CA (2016)

Selected Publications

LivecellX: A Scalable Deep Learning Framework for Single-Cell Object-Oriented Analysis

Ke Ni, et al. (in review, Nature Communications). LivecellX: A Scalable Deep Learning Framework for Single-Cell Object-Oriented Analysis. BioRxiv.

LivecellAction: Guiding Spatiotemporal Deep Learning Models for Precise Detection of Rare Single-Cell Actions

Ke Ni, et al. (in preparation). LivecellAction: Guiding Spatiotemporal Deep Learning Models for Precise Detection of Rare Single-Cell Actions.

Predictive Modeling of Single Cell Transcriptomic Dynamics with Dynamo

Zehua Zeng, Sichao Yu, Ke Ni (co-first authors), Yan Zhang, Ukyeon Shin, Cinlong Huang, Natalie Cao, Jonathan Weissman, Jianhua Xing, Xiaojie Qiu. (in review, Nature Protocols). Predictive Modeling of Single Cell Transcriptomic Dynamics with Dynamo.

Learning to Explain Non-standard English

Ke Ni, William Yang Wang. (2017). Learning to Explain Non-standard English. ACL-IJCNLP 2017 (Oral, Taipei).

See the Publications page for the complete list, including co-authored papers.

Skills & Training

• Research areas: Multimodal models (MLLMs/VLMs), large language models, mechanistic interpretability, reinforcement learning & post-training (SFT, PEFT, RLHF/RLAIF), computer vision, natural language processing, agentic systems, computational genomics.
• Graduate coursework: reinforcement learning, machine learning, probabilistic graphical models, evolution dynamics modeling, computational genomics (CMU); structural biology, protein structure prediction, biophysics (Pitt).
• Master’s coursework: parameterized algorithms, quantum computing, advanced linear algebra, numerical analysis, cryptoengineering, computer graphics, augmented reality, distributed systems, graph/network analysis, computer vision, NLP, runtime systems, FPGA (Vivado, VHDL).

Awards & Service

See the Service & Activities page for talks, awards, and professional involvement.