keywords
- Machine Learning, Artificial Intelligence, Climate Informatics, Big Data, Climate Science, Climate Change, Renewable Energy
- Contact Info
Monteleoni, Claire Elizabeth
Positions
- Professor, Computer Science
- Professor, Computer Science
Research Areas
Research
Publications
selected publications
-
chapter
- Spatiotemporal Global Climate Model Tracking. 33-54. 2017
- Climate Informatics. 81-126. 2013
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Differentially-Private Learning of Low Dimensional Manifolds.
Lecture Notes in Artificial Intelligence.
249-263.
2013
-
conference proceeding
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Week-ahead solar irradiance forecasting with deep sequence learning
2022
- Subseasonal Solar Power Forecasting via Deep Sequence Learning 2021
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ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows
2020
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CNN-Based Forecasting of Intraseasonal Mean and Active/Break Spells for Indian Summer Monsoon.
15-21.
2020
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Variational Autoencoder Anomaly-Detection of Avalanche Deposits in Satellite SAR Imagery.
113-119.
2020
- Cooperative Online Learning: Keeping your Neighbors Updated 2020
- Can Avalanche Deposits be Effectively Detected by Deep Learning on Sentinel-1 Satellite SAR Images?. 147-151. 2019
- Causal Link Detection and the Prediction of the Indian Summer Monsoon. 136-141. 2019
- Changes in Information Hubs over the Pacific ENSO Region. 142-146. 2019
- Fused Deep Learning for Hurricane Track Forecast From Reanalysis Data. 69-72. 2018
- The 2018 Climate Informatics Hackathon: Hurricane Intensity Forecast 2018
- Beyond the Nystrom Approximation: Speeding up Spectral Clustering using Uniform Sampling and Weighted Kernel k-means. 2494-2500. 2017
- Exploiting Sparsity to Improve the Accuracy of Nystrom-based Large-scale Spectral Clustering. Proceedings of International Joint Conference on Neural Networks. 9-16. 2017
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Online Learning of Volatility from Multiple Option Term Lengths.
1-3.
2016
- On Lloyd's algorithm: new theoretical insights for clustering in practice. 1280-1289. 2016
- Environment Selection And Hierarchical Place Recognition. IEEE International Conference on Robotics and Automation. 5487-5494. 2015
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A Semi-Supervised Learning Approach To Differential Privacy.
Proceedings ... ICDM workshops IEEE International Conference on Data Mining.
841-848.
2013
- Fast Spectral Clustering via the Nystrom Method. Lecture Notes in Artificial Intelligence. 367-381. 2013
- Practical Online active learning for classification. Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3334-+. 2007
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Efficient algorithms for general active learning.
Lecture Notes in Artificial Intelligence.
650-652.
2006
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Analysis of perceptron-based active learning.
Lecture Notes in Artificial Intelligence.
249-263.
2005
- Online learning of non-stationary sequences. Advances in neural information processing systems. 1093-1100. 2003
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Week-ahead solar irradiance forecasting with deep sequence learning
2022
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journal article
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Hamiltonian learning using machine-learning models trained with continuous measurements.
Physical Review Applied.
2024
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Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data.
Frontiers in Big Data.
2020
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Research Challenges in Financial Data Modeling and Analysis.
Big Data.
177-188.
2017
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Technological Innovation, Data Analytics, and Environmental Enforcement.
Ecology law quarterly.
41-88.
2017
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Differentially-private learning of low dimensional manifolds.
Theoretical Computer Science.
91-104.
2016
- Can Topic Modeling Shed Light on Climate Extremes?. Computing in Science and Engineering. 43-52. 2015
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Can topic modeling shed light on climate extremes?.
Computing in Science and Engineering.
1-1.
2015
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Climate Informatics: Accelerating Discovering in Climate Science with Machine Learning.
Computing in Science and Engineering.
32-40.
2013
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Tracking climate models.
Statistical Analysis and Data Mining.
372-392.
2011
- Differentially Private Empirical Risk Minimization. Journal of Machine Learning Research. 1069-1109. 2011
- Analysis of Perceptron-Based Active Learning. Journal of Machine Learning Research. 281-299. 2009
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Hamiltonian learning using machine-learning models trained with continuous measurements.
Physical Review Applied.
2024
Teaching
courses taught
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CSCI 4622 - Machine Learning
Primary Instructor - Fall 2019 / Fall 2022
Introduces students to tools, methods, and theory to construct predictive and inferential models that learn from data. Focuses on supervised machine learning technique including practical and theoretical understanding of the most widely used algorithms (decision trees, support vector machines, ensemble methods, and neural networks). Emphasizes both efficient implementation of algorithms and understanding of mathematical foundations. Same as CSPB 4622. -
CSCI 5434 - Probability for Computer Science
Primary Instructor - Spring 2021 / Spring 2022
This course will introduce computer science students to topics in probability and statistics that will be useful in other computer science courses. Basic concepts in probability will be taught from an algorithmic and computational point of view, with examples drawn from computer science. Recommended prerequisite courses of APPM 1360 or MATH 2300 and CSCI 2824 or MATH 2001 or ECEN 2703 (all minimum grade B). -
CSCI 5622 - Machine Learning
Primary Instructor - Fall 2018 / Fall 2020
Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended. -
CSCI 6950 - Master's Thesis
Primary Instructor - Fall 2019 / Spring 2020 / Fall 2022 / Spring 2023
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CSCI 7000 - Current Topics in Computer Science
Primary Instructor - Spring 2019 / Spring 2020 / Fall 2021
Covers research topics of current interest in computer science that do not fall into a standard subarea. May be repeated up to 18 total credit hours.
Background
education and training
- Ph.D., Massachusetts Institute of Technology 2006
- M.S., Massachusetts Institute of Technology 2003
- B.A., Harvard University 1998
awards and honors
International Activities
geographic focus
- Austria Country
- Canada Country
- France Country
- Germany Country
- India Country
- Italy Country
- Netherlands Country
- Switzerland Country
- United Kingdom Country
- Uruguay Country