Quarterly Theory Workshop: 2021 Junior Theorists Workshop

December 9-10, 2021
Virtual(on Gather.Town) Please register here(free): (https://forms.gle/Kpqe4xBb9fAzGBX78) to get access to the Gather.town login information (we will send it the day before the event). https://theory.cs.northwestern.edu/events/2021-junior-theorists-workshop/

The 2021 Junior Theorists Workshop is part of the Northwestern CS Quarterly Theory Workshop Series. The focus of this workshop will be on junior researchers in all areas of theoretical computer science.

IDEAL mini-workshop on “New Directions on Robustness in ML”

November 16, 2021
Virtual
https://www.ideal.northwestern.edu/events/mini-workshop-on-new-directions-on-robustness-in-ml/

As machine learning systems are being deployed in almost every aspect of decision-making, it is vital for them to be reliable and secure to adversarial corruptions and perturbations of various kinds. This workshop will explore newer notions of robustness and the different challenges that arise in designing reliable ML algorithms. Topics include test-time robustness, adversarial perturbations, distribution shifts, and explore connections between robustness and other areas. The workshop speakers are Aleksander Madry, Gautam Kamath, Kamalika Chaudhuri, Pranjal Awasthi and Sebastien Bubeck. Please register at the webpage given below for free to participate in the virtual workshop.

IDEAL mini-workshop on “Statistical and Computational Aspects of Robustness in High-dimensional Estimation”

October 19, 2021
Virtual
https://www.ideal.northwestern.edu/events/mini-workshop-on-statistical-and-computational-aspects-of-robustness-in-high-dimensional-estimation/

Registration deadline: October 18, 2021

Today’s data pose unprecedented challenges to statisticians. It may be incomplete, corrupted or exposed to some unknown source of contamination or adversarial attack. Robustness is one of the revived concepts in statistics and machine learning that can accommodate such complexity and glean useful information from modern datasets. This virtual workshop will address several aspects of robustness such as statistical estimation and computational efficiency in the context of modern high-dimensional data analysis. The workshop speakers are Ankur Moitra, Ilias Diakonikolas, Jacob Steinhardt and Po-Ling Loh. Please register at the webpage given below for free to participate in the virtual workshop.

IDEAL Special Quarter Fall 2021 (Robustness in High-dimensional Statistics and Machine Learning)

September 21 – December 10, 2021
Virtual
https://www.ideal.northwestern.edu/special-quarters/fall-2021/

IDEAL – The Institute for Data, Econometrics, Algorithms, and Learning (an NSF-funded collaborative institute across Northwestern, TTIC and U Chicago) is organizing a Fall 2021 special quarter on “Robustness in High-dimensional Statistics and Machine Learning”.
The special-quarter activities include mini-workshops, seminars, graduate courses, and a reading group. The research goal is to explore several theoretical frameworks and directions towards designing learning algorithms and estimators that are tolerant to errors, contamination, and misspecification in data. Many of these activities will be virtual.

The kick-off event ( https://www.ideal.northwestern.edu/events/fall-2021-kickoff-event/ ) for this quarter will be held on Tuesday, September 21, 2021 at 3 pm Central time. We will briefly introduce the institute, the key personnel and information about the various activities during the special quarter. To find more information, or participate in the special quarter, please visit the webpage.

Workshop on Algorithms for Large Data (Online) 2021

August 23-25, 2021
Online
https://waldo2021.github.io/

Registration deadline: August 20, 2021

This workshop aims to foster collaborations between researchers across multiple disciplines through a set of central questions and techniques for algorithm design for large data. We will focus on topics such as sublinear algorithms, randomized numerical linear algebra, streaming and sketching, and learning and testing.

Workshop on Machine Learning for Algorithms

July 13-14, 2021
Foundations of Data Science Institute (FODSI)
https://fodsi.us/ml4a.html

In recent years there has been increasing interest in using machine learning to improve the performance of classical algorithms in computer science, by fine-tuning their behavior to adapt to the properties of the input distribution. This “data-driven” or “learning-based” approach to algorithm design has the potential to significantly improve the efficiency of some of the most widely used algorithms. For example, it has been used to design better data structures, online algorithms, streaming and sketching algorithms, market mechanisms and algorithms for combinatorial optimization, similarity search and inverse problems. This virtual workshop will feature talks from experts at the forefront of this exciting area.

Quarterly Theory Workshop: Algorithms and their Social Impact

March 19, 2021
Virtual (on Gather.town and Zoom)
https://theory.cs.northwestern.edu/events/quarterly-theory-workshop-algorithms-and-their-social-impact/

The focus of this workshop will be on the societal impacts of algorithms. From designing self-driving cars to selecting the order of news posts on Facebook to automating credit checks, the use of algorithms for decision making is now commonplace. Hence it is more important than ever to consider fairness as a key aspect while designing these algorithms to prevent unwanted bias and prejudice. The speakers for this workshop are Rakesh Vohra, Michael Kearns, Samira Samadi, Steven Wu, and Suresh Venkatasubramanian.

This workshop is part of the Northwestern Quarterly Theory Workshop series that brings in theoretical computer science experts to present their perspective and research on a common theme. This particular workshop is co-organized by the IDEAL institute, as part of the IDEAL Special Quarter on Data Science and Law.

2020 Northwestern CS Junior Theorists Workshop

December 17-18, 2020
Virtual on gather.town and zoom
https://theory.cs.northwestern.edu/events/2020-junior-theorists-workshop/

The 2020 Junior Theorists Workshop is part of the Northwestern CS Quarterly Theory Workshop Series. The focus of this workshop will be on showcasing junior researchers in all areas of theoretical computer science. The event will be held online from December 17 to 18 (Thursday to Friday), roughly from 10am to 5pm central time.

This year we have 14 excellent speakers, each delivering a 30 minutes talk. After each hour-long session, there will be an informal poster session intended for Q&A discussion on gather.town. Full details are available on the workshop homepage.

Interested participants *must* register via the link on the workshop homepage and we will send the gather.town link before the event.

2020 Virtual Telluride Neuromorphic Cognition Engineering Workshop

July 27-31, 2020
Telluride CO (virtual)
https://sites.google.com/view/telluride2020/home

We are happy to announce a Virtual Telluride Neuromorphic Cognition Engineering Workshop 2020 (https://tellurideneuromorphic.org/) this year in replacement of our usual Workshop in Telluride. The workshop will take place from July 27 to July 31 (8am to 10am PDT, or 17:00 to 19:00 CET).

The format will be a week of lectures and tutorials on current topics in neuromorphic engineering (two hours per day) followed by four hands-on, collaborative challenges which will be carried out in the month of August. The results of the challenges will be presented on September 10 and 11, 2020.

The workshop is free to attend and is open to everyone upon registration.