An NRT-sponsored program in Data Science
Areas & Faculty
Areas & Faculty
Machine Learning and Perception
- Audio Processing
- Computer Vision
- Data Annotation and Crowdsourcing
- Deep Learning
- Image Processing
- Natural Language Processing
- Probabilistic Modeling
- Reinforcement Learning
Bowman | Bruna | Cho | Dhar | Fergus | Fernandez-Granda | Geras | Gureckis | Han | He | Lei | Kempe | Lake | Laurière | LeCun | Lindsay | Ling | Linzen | McFee | Niles-Weed | Ranganath | Ren | Savin | Silva | Simoncelli | Wilson | Zanna
Theory
- High-Dimensional Statistics
- Inverse Problems
- Optimization
- Probabilistic Modeling
- Theory of Deep Learning
Bruna | Fernandez-Granda | Han | Lei | Kempe | Laurière | Ling | Niles-Weed | Wilson
Data Engineering & Data Visualization
- AutoML
- Big Data
- Explainable Artificial Intelligence
- Visualization
Freire | Silva | Stoyanovich
Responsible Data Science
- Algorithmic Fairness & Diversity
- Data Governance
- Ethics and Legal Compliance
- Reproducibility
- Transparency & Interpretability
Dhar | Freire | Lindsay | Stoyanovich
PhD Advisory Group
- Richard Bonneau
- Sam Bowman
- Joan Bruna
- Kyunghyun Cho
- Vasant Dhar
- Rob Fergus
- Carlos Fernandez-Granda
- Juliana Freire
- Krzysztof Jerzy Geras
- Todd Gureckis
- Yanjun Han
- He He
- Julia Kempe
- Brenden Lake
- Yann LeCun
- Qi Lei
- Grace Lindsay
- Tal Linzen
- Brian McFee
- Jonathan Niles-Weed
- Rajesh Ranganath
- Mengye Ren
- Cristina Savin
- Claudio Silva
- Eero Simoncelli
- Julia Stoyanovich
- Andrew Wilson
- Laure Zanna