Curriculum Vitae

Education

• Columbia University, New York, US
  PhD in Computer Science M.S. in Computer Science (Feb 2026)
  B.A. in Computer Science (May 2024, GPA: 3.73)
  Honors: Academic Scholarship, Dean’s List

• Institut d’études Politiques (IEP) de Paris, Paris, FR
  B.A. in Economics (May 2024, GPA: 4.00)

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Experience

• Columbia University, Research Assistant — New York, US
  Jan 2024 – May 2025
  Formal Verification, Program Synthesis, and AI.
  • Applied synthesis methods to increase safety and predictability of LLM-generated code.
  • Used natural language to generate complex arbiters through LLMs.
• Columbia University, Research Assistant — New York, US
  Aug 2023 – May 2025
  Deep Reinforcement Learning for Finance.
  • Lead RA on SIRS/STAR-funded project improving LLM understanding of code and tabular data.
  • Built GPU-parallel RL frameworks for financial trading.
  • Managed team of six researchers and coordinated ICAIF’24 financial AI competition.
• IBM Research, Research Assistant — New York, US
  Oct 2024 – Dec 2024
  Co-design of resistive processing units (RPU) for analog in-memory compute systems.
  • Improving energy-efficient AI through neural architecture search (NAS) using IBM AIHWKit.
  • Developing robust analog deep learning architectures accounting for nonlinearities.
• Columbia University, Course Assistant — Data Structures and Algorithms
  Sep 2023 – May 2024
  • Assisted in course delivery for 400+ students; ran review sessions and recitations.
• Baqend GmbH, Full-Stack Developer Intern — Hamburg, DE
  Aug 2020 – Sep 2021
  • Built proof-of-concept full-stack apps for five enterprise clients.
  • Supported production systems used by millions of users weekly.

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Publications

1. Holzer, N., Fishell, W., Ray, B., & Santolucito, M. (2025). Mechanics of Learned Reasoning 1: TempoBench, A Benchmark for Interpretable Deconstruction of Reasoning System Performance. https://arxiv.org/abs/2510.27544
2. Wang, K., Holzer, N., Xia, Z., Cao, Y., Gao, J., Walid, A., Xiao, K., & Yanglet, X. Y. L. ( 2025 ). FinRL Contests: Data‐Driven Financial Reinforcement Learning Agents for Stock and Crypto Trading . Artificial Intelligence for Engineering .
3. Holzer, N., Wang, K., Xiao, K., & Yanglet, X.-Y. L. ( 2025 ). Revisiting ensemble methods for stock trading and crypto trading tasks at ACM ICAIF FinRL Contest 2023-2024 . ArXiv Preprint ArXiv:2501.10709 .
4. Murphy, W., Holzer, N., Qiao, F., Cui, L., Rothkopf, R., Koenig, N., & Santolucito, M. ( 2024 ). Combining llm code generation with formal specifications and reactive program synthesis . ArXiv Preprint ArXiv:2410.19736 .

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Teaching