Nico Christianson

I am an incoming Assistant Professor in the Department of Computer Science at Johns Hopkins University (starting August 2026); I will be affiliated with the Data Science and AI Institute, the Ralph O’Connor Sustainable Energy Institute, and the Algorithms and Complexity Group. 📢 I am recruiting PhD students to join my group at Johns Hopkins in Fall 2027. Learn more →

My research lies broadly at the intersection of algorithms, machine learning, and optimization. I am particularly interested in developing theoretically-grounded algorithms and AI/ML frameworks for reliable decision-making under uncertainty, motivated by applications of broad societal importance like energy and computing systems. My recent interests include:

  • online and learning-augmented algorithms
  • uncertainty quantification and risk-aware decision-making
  • AI/ML for optimization
  • applications to energy resource optimization and data center workload scheduling

Before joining Johns Hopkins, I will be spending a year as a Stanford Energy Postdoctoral Fellow, hosted by Ellen Vitercik and Ram Rajagopal. Previously, I did my PhD in Computing and Mathematical Sciences at Caltech, where I had the good fortune of working with Adam Wierman and Steven Low. My PhD was supported in part by an NSF Graduate Research Fellowship and a PIMCO Data Science Fellowship, and my dissertation won Caltech’s Ben P.C. Chou Doctoral Prize in Information Science and Technology and Demetriades-Tsafka-Kokkalis Prize in Renewable Energy, as well as the ACM SIGEnergy Doctoral Dissertation Award. Before Caltech, I was an undergrad in applied math at Harvard.

selected publications (full list →)

  1. Optimal Robustness-Consistency Tradeoffs for Learning-Augmented Metrical Task Systems
    Nicolas Christianson, Junxuan Shen, and Adam Wierman
    International Conference on Artificial Intelligence and Statistics, 2023
  2. Prediction-Specific Design of Learning-Augmented Algorithms
    Sizhe Li, Nicolas Christianson, and Tongxin Li
    ACM SIGMETRICS, 2026
  3. Conformal Risk Training: End-to-End Optimization of Conformal Risk Control
    Christopher Yeh, Nicolas Christianson, Adam Wierman, and 1 more author
    Conference on Neural Information Processing Systems, 2025
  4. End-to-End Conformal Calibration for Optimization Under Uncertainty
    Christopher Yeh*, Nicolas Christianson*, Alan Wu, and 2 more authors
    Transactions on Machine Learning Research, 2025
  5. Fast and Reliable N−k Contingency Screening with Input-Convex Neural Networks
    Nicolas Christianson, Wenqi Cui, Steven Low, and 2 more authors
    Learning for Dynamics & Control Conference, 2025
  6. Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints
    Adam Lechowicz, Nicolas Christianson, Bo Sun, and 4 more authors
    ACM SIGMETRICS, 2025

upcoming talks

Nov 01, 2026 INFORMS Annual Meeting
Aug 06, 2026 LAMP Workshop spotlight talk

recent news (older →)

Jun 26, 2026 I had a great time attending ACM e-Energy in Banff this week! We had two full papers accepted to the main conference: one on online and learned algorithms for thermal energy network control, and one on risk-sensitive and learning-augmented algorithms for peak-aware energy scheduling (best paper finalist!). In addition, I co-organized the workshop on Physics-Informed Learning for Optimization and Control of Susatainable Energy Systems, and gave an award talk for the SIGEnergy dissertation award. It was great to see so many collaborators and friends!
Jun 12, 2026 I had a great time attending ACM SIGMETRICS in Ann Arbor this week! We had two papers on online and learning-augmented algorithms in the main conference, and I co-organized the Learning-augmented Algorithms: Theory and Applications workshop on Friday.
May 14, 2026 I am honored to have received the 2026 ACM SIGEnergy Doctoral Dissertation Award! Many thanks to my incredibly supportive PhD advisors and to all of the wonderful collaborators I worked with during my PhD, without whom this would not be possible!
Apr 15, 2026 Very excited that our team – comprising Anders Wikum, myself, and Ellen Vitercik at Stanford and Ana Rivera and Priya Donti at MIT – has won 3rd place in EPRI’s “AI-ccelerating Unit Commitment” competition! We discuss our strategy in this webinar (from 6/17/26). Congratulations to the first and second place winners and all the other participants!
Mar 19, 2026 I had a great time attending the 5th Workshop on Foundation Models for the Electric Grid at Harvard this week! I chaired the session on LLMs/Agentic AI for the grid and got the chance to learn about some very interesting emerging directions in this space.