# Alex Gittens

Email: |
gittea at rpi dot edu |

Office: |
316 Lally Building |

General Office Hours: |
email me to setup a Zoom/WebEx/in-person meeting |

Tel: |
518-276-6476 |

Address: |
Room 316 Lally, CS Department, 110 8th St, Troy, NY 12180 |

I am an assistant professor in the Computer Science department of Rensselaer Polytechnic Institute. My research focuses on using randomization to reduce the computational costs of extracting information from large datasets. My work lies at the intersection of randomized algorithms, numerical linear algebra, high-dimensional probability, and machine learning.

I earned my PhD in applied and computational mathematics at CalTech under the supervision of Prof. Joel Tropp, in 2013. From 2013 until 2015, I was a member of the machine learning research group at eBay. Following that I was a postdoctoral fellow in the AMPLab at UC Berkeley and a member of the International Computer Science Institute. I joined RPI in January of 2017.

## Research

My current research interests include, in no order:

- Randomized NLA with applications to machine learning, tensor approximation, and general optimization
- Matrix and tensor completion
- Machine learning with missing/incomplete data
- Fair machine learning
- Adversarially and outlier robust machine learning
- Federated learning

### Group

My current graduate students are Sharmishtha Duttas (PhD; ML on graphs) and Dong Hu (PhD; matrix completion and low-rank approximation).

### Publications

See Google Scholar

## Teaching

Spring 2022 | Co-teaching Trustworthy Machine Learning (course piazza), a seminar special topics course covering four aspects of trustworthy machine learning: privacy, fairness, adversarial robustness, and interpretability, by surveying the recent research literature. Students present papers, lead in-class discussions, and conduct a project in one of these directions. The aim of the class is to expose students to these other socially and legally relevant axes along which the performance of ML algorithms should be measured, give them an overview of the approaches currently taken, and stimulate their interest in working in this space. |

Fall 2021 | Taught CSCI6961/4961, "Machine Learning and Optimization". This course formulates ML problems as optimization problems, then focuses on solving them efficiently and quickly using algorithms appropriate for large-scale applications (aka first-order algorithms). Another focus of the course is on specific architectures used in modern machine learning to impose helpful inductive biases (aka, deep learning). It's a fun, challenging, and rewarding class. Take it. |

Spring 2021 | Taught CSCI2200, "Foundations of Computer Science", aka FOCS. This course serves as an introduction to discrete mathematics and the theory of computing for computer scientists. |

Fall 2020 | Taught CSCI4961/6961, "Machine Learning and Optimization". The focus is on understanding randomized optimization algorithms motivated by and focusing on applications in machine learning and data analysis. |

Spring 2020 | Taught CSCI2200, "Foundations of Computer Science", a discrete mathematics/theory of computing course. See the website for more information. |

Fall 2019 | taught CSCI6220/4030, "Randomized Algorithms". See the website for the syllabus and assignments. |

Spring 2019 | taught CSCI6971/CSCI4971, "Large Scale Matrix Computation and Machine Learning". See the website for the syllabus and assignments. |

Fall 2018 | taught CSCI6220/4030, "Randomized Algorithms". See the website for the syllabus and assignments. |

Spring 2018 | taught CSCI6971/CSCI4971, "Large Scale Matrix Computation and Machine Learning". See the website for the syllabus and assignments. |

Fall 2017 | taught CSCI6220/4030, "Randomized Algorithms". See the website for the syllabus and assignments. |

Spring 2017 | taught CSCI6971/CSCI4971, "Large Scale Matrix Computation and Machine Learning". See the syllabus. |