Steven Dillmann

PhD Candidate at Stanford University working on AI for Scientific Discovery & Terminal-Bench-Science

sd_github.png

Stanford Artificial Intelligence Laboratory

Gates Computer Science, Room 338

Stanford University

Reach out at stevendi@stanford.edu
about research or potential collaborations.

I am a PhD candidate in Computational Mathematics at Stanford University, advised by Sanmi Koyejo (CS) and Risa Wechsler (Physics) and working closely with Ludwig Schmidt (CS). My research focuses on ai for science and ai evaluation: how foundation models and agents can accelerate scientific discovery, and how to rigorously evaluate their scientific capabilities. Currently, I lead Terminal-Bench-Science, a benchmark for evaluating AI agents on real research workflows across scientific domains. My PhD is supported by the Stanford Interdisciplinary Graduate Fellowship (SIGF) and the Stanford HAI Data Science Scholars Program.

I am affiliated with the Stanford AI Lab, Stanford HAI, Stanford ICME, KIPAC, SLAC, and the Center for Decoding the Universe @ Stanford. Previously, I obtained an MPhil in Data Intensive Science from the University of Cambridge and an MEng in Aerospace Engineering from Imperial College London. I have also done research in scientific machine learning and astrophysics at Harvard, NASA JPL, ESA, DLR, and interned at Ai2, Amazon, Airbus, and BMW.

Outside of research, I love football (soccer), tennis, poker, chess, and cinema & filmmaking – check out my favorite movies on letterboxd.

selected publications

  1. Terminal-Bench-Science: Evaluating AI agents on research workflows across scientific domains
    Terminal-Bench-Science Team
    agentsai evaluationai for science
    2
  2. Terminal-bench: Benchmarking agents on hard, realistic tasks in command line interfaces
    Mike Merrill, Alexander Shaw, Nicholas Carlini, Boxuan Li, Harsh Raj, Ivan Bercovich, Lin Shi, Jeong Shin, Thomas Walshe, E Kelly Buchanan, Junhong Shen, Guanghao Ye, Haowei Lin, Jason Poulos, Maoyu Wang, Marianna Nezhurina, Di Lu, Orfeas Menis Mastromichalakis, Zhiwei Xu, Zizhao Chen, Yue Liu, Robert Zhang, Leon Liangyu Chen, Anurag Kashyap, Jan-Lucas Uslu, Jeffrey Li, Jianbo Wu, Minghao Yan, Song Bian, Vedang Sharma, Ke Sun, Steven Dillmann, Akshay Anand, Andrew Lanpouthakoun, Bardia Koopah, Changran Hu, Etash Guha, Gabriel Dreiman, Jiacheng Zhu, Karl Krauth, Li Zhong, Niklas Muennighoff, Robert Amanfu, Shangyin Tan, Shreyas Pimpalgaonkar, Tushar Aggarwal, Xiangning Lin, Xin Lan, Xuandong Zhao, Yiqing Liang, Yuanli Wang, Zilong Ryan Wang, Changzhi Zhou, David Heineman, Hange Liu, Harsh Trivedi, John Yang, Junhong Lin, Manish Shetty, Michael Yang, Nabil Omi, Negin Raoof, Shanda Li, Terry Yue Zhuo, Wuwei Lin, Yiwei Dai, Yuxin Wang, Wenhao Chai, Shang Zhou, Dariush Wahdany, Ziyu She, Jiaming Hu, Zhikang Dong, Yuxuan Zhu, Sasha Cui, Ahson Saiyed, Arinbjörn Kolbeinsson, Christopher Rytting, Ryan Marten, Yixin Wang, Jenia Jitsev, Alex Dimakis, Andy Konwinski, Ludwig Schmidt
    International Conference on Learning Representations 2026, 40903-40986 agentsai evaluation
    563
    2026
  3. Genome modelling and design across all domains of life with Evo 2
    Garyk Brixi, Matthew G Durrant, Jerome Ku, Mohsen Naghipourfar, Michael Poli, Gwanggyu Sun, Greg Brockman, Daniel Chang, Alison Fanton, Gabriel A Gonzalez, Samuel H King, David B Li, Aditi T Merchant, Eric Nguyen, Chiara Ricci-Tam, David W Romero, Jonathan C Schmok, Ali Taghibakhshi, Anton Vorontsov, Brandon Yang, Myra Deng, Liv Gorton, Nam Nguyen, Nicholas K Wang, Michael T Pearce, Elana Simon, Etowah Adams, Zachary J Amador, Euan A Ashley, Stephen A Baccus, Haoyu Dai, Steven Dillmann, Stefano Ermon, Daniel Guo, Michael H Herschl, Rajesh Ilango, Ken Janik, Amy X Lu, Reshma Mehta, Mohammad RK Mofrad, Madelena Y Ng, Jaspreet Pannu, Christopher Ré, John St. John, Jeremy Sullivan, Joseph Tey, Ben Viggiano, Kevin Zhu, Greg Zynda, Daniel Balsam, Patrick Collison, Anthony B Costa, Tina Hernandez-Boussard, Eric Ho, Ming-Yu Liu, Thomas McGrath, Kimberly Powell, Sudarshan Pinglay, Dave P Burke, Hani Goodarzi, Patrick D Hsu, Brian L Hie
    Nature 652 (8112), 1349-1361 ai for sciencefoundation modelsscience
    716
    2026
  4. Representation learning for time-domain high-energy astrophysics: Discovery of extragalactic fast X-ray transient XRT 200515
    Steven Dillmann, Juan Rafael Martínez-Galarza, Roberto Soria, Rosanne Di Stefano, Vinay L Kashyap
    Monthly Notices of the Royal Astronomical Society 537 (2), 931-955 ai for sciencefoundation modelsscience
    28
    2025
  5. The impact of satellite trails on Hubble Space Telescope observations
    Sandor Kruk, Pablo García-Martín, Marcel Popescu, Ben Aussel, Steven Dillmann, Megan E Perks, Tamina Lund, Bruno Merín, Ross Thomson, Samet Karadag, Mark J McCaughrean
    Nature Astronomy 7 (3), 262-268 science
    79
    2023

news

  1. Honored to be named a Stanford HAI Data Science Scholar.
  2. Released Terminal-Bench-Science 0.1, a benchmark to evaluate AI agents on scientific research workflows.
  3. Honored to receive the Stanford Interdisciplinary Graduate Fellowship (SIGF).
  4. Discovery of XRT 200515, a new extragalactic fast X-ray transient, featured by the Royal Astronomical Society, Space.com, Phys.org, and SciTechDaily.
  5. Started a PhD in Computational Mathematics at Stanford University.
  6. Graduated from the University of Cambridge with an MPhil in Data Intensive Science.
  7. Graduated from Imperial College London with an MEng in Aeronautics with Spacecraft Engineering, receiving the Head of Department Award.