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 →)
- Optimal Robustness-Consistency Tradeoffs for Learning-Augmented Metrical Task SystemsInternational Conference on Artificial Intelligence and Statistics, 2023
- Prediction-Specific Design of Learning-Augmented AlgorithmsACM SIGMETRICS, 2026
- Conformal Risk Training: End-to-End Optimization of Conformal Risk ControlConference on Neural Information Processing Systems, 2025
- End-to-End Conformal Calibration for Optimization Under UncertaintyTransactions on Machine Learning Research, 2025
- Fast and Reliable N−k Contingency Screening with Input-Convex Neural NetworksLearning for Dynamics & Control Conference, 2025
- Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline ConstraintsACM SIGMETRICS, 2025
upcoming talks
| Nov 01, 2026 | INFORMS Annual Meeting |
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| 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! |
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| 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. |