Nico Christianson
I am an incoming Assistant Professor in the Department of Computer Science at Johns Hopkins University (starting in Fall 2026); I will be affiliated with the Data Science and AI Institute and the Ralph O’Connor Sustainable Energy Institute.
📢 I am recruiting PhD students and postdocs to join my group at Johns Hopkins in Fall 2026. 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 for risk-aware decision-making
- machine learning 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 the Ben P.C. Chou Doctoral Prize in Information Science and Technology and the Demetriades-Tsafka-Kokkalis Prize in Renewable Energy. 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
- Conformal Risk Training: End-to-End Optimization of Conformal Risk ControlNeural Information Processing Systems, 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
- Online Algorithms with Uncertainty-Quantified PredictionsInternational Conference on Machine Learning, 2024
upcoming talks
| Feb 17, 2026 | RAIN Seminar at Stanford |
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recent news (older →)
| Dec 12, 2025 | Two papers accepted to ACM SIGMETRICS 2026: one on online smoothed demand management and another on prediction-specific design of learning-augmented algorithms. Many thanks to my wonderful collaborators! |
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| Dec 02, 2025 | I am at NeurIPS in San Diego this week! Our paper on End-to-End Conformal Risk Control will be presented in the poster session on Wednesday. |
| Nov 05, 2025 | I gave a talk on risk-sensitive online algorithms in the CS Theory Seminar at UMass Amherst. Thanks to my wonderful collaborators Adam Lechowicz and Mohammad Hajiesmaili for hosting me! |
| Oct 27, 2025 | It was great attending INFORMS in Atlanta and and catching up with so many collaborators and friends! I gave a talk on end-to-end learning for contingency screening in the session “Human-Centric Energy Markets.” |
| Oct 22, 2025 | Our paper End-to-End Conformal Calibration for Optimization Under Uncertainty was accepted at Transactions on Machine Learning Research |