Madeleine Udell

Photo of Madeleine Udell 

Assistant Professor
Richard and Sybil Smith Sesquicentennial Fellow
Operations Research and Information Engineering (ORIE)
Graduate field member, ORIE, CS, and CAM
Cornell University


office: Rhodes 227


twitter: @madeleineudell

google: madeleine.udell

skype: madeleine.udell

github: madeleineudell

news and links

June 2017. I had a great time at JuliaCon; every year I'm amazed to see new (and awesome) functionality and packages. Absurd pedant that I am, I talked about how to describe a mathematical function.

May 2017. Alex Townsend and I have posted a new paper in which we provide an answer to the question: why are so many matrices in data science of low rank?

May 2017. We had a great workshop at ACC on Control Engineering in Julia. You can find slides and demos on the workshop's GitHub repo. Thanks to my co-organizers Cristian Rojas and Mikael Johansson!

April 2017. We're running a workshop at ICDM on Data-driven Discovery of Models (D3M), together with Christophe Giraud-Carrier and Ishanu Chattopadhyay. Please submit your papers! Deadline is August 7.

March 2017. My grant proposal for research on Composable Robust Structured Data Inference was selected for funding under DARPA's program on Data Driven Discovery of Models (D3m). Looking forward to automatically constructing models for data with the other performers!

March 2017. My paper Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage with Alp Yurtsever, Joel Tropp, and Volkan Cevher was selected for an oral presentation at AISTATS 2017.

January 2017. I'll be teaching a class on Convex Optimization at Cornell in Spring of 2017. We'll be roughly following Stanford's EE364a (and some of EE364b), using the excellent textbook by Boyd and Vandenberghe, with an additional emphasis on first order methods.

November 2016. (Most) data scientists did a terrible job predicting the results of the 2016 election. Did that matter for the outcome? I analyze the data in a lecture on the limits  —  and dangers  —  of predictive modeling.

October 2016. Thanks to Cornell's Scientific Software Club for inviting me to give an introduction to Julia, and asking great questions! Here are my slides + demos, which start with basic syntax and proceed to show off advanced capabilities like multi-language integration, shared memory parallelism, and mathematical optimization packages.

September 2016. I'm teaching a new class at Cornell on Learning with Big Messy Data. Interestingly, the course itself is generating a bunch of big messy data, from lecture slides to demos to Piazza posts to project repos. Next step: train an AI to learn how to learn with big messy data from this big messy data?

June 2016. Damek Davis, Brent Edmunds and I just posted a paper on a (provably convergent) stochastic asynchronous optimization method called SAPALM for fitting generalized low rank models. It turns out asynchrony barely affects the rate of convergence (per flop), while providing a linear speedup in the number of flops per second. In other words: it's fast!

May 2016. Congratulations to Ramchandran Muthukumar and Ayush Pandey for their fantastic proposals to work with me on Convex.jl this summer through Google Summer of Code. Ayush will be adding support for complex numbers, while Ramchandran develops a fast presolve routine.

March 2016. It was great meeting incoming PhD students at the ORIE visiting student days! Here are the slides I presented to introduce students to some of my research.

November 2015. H2O is a new framework for large scale machine learning, and has just released a great implementation of generalized low rank models (engineered by Anqi Fu). Here are the slides and the video from my talk at H2O World.