March 19, 2021
Virtual (on Gather.town and Zoom)
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.
June 21-25, 2021
Submission deadline: March 15, 2021
STOC 2021 will hold workshops during the conference week, June 21–25, 2021. We invite groups of interested researchers to submit workshop proposals. The due date for proposals is March 15.
December 17-18, 2020
Virtual on gather.town and zoom
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.
September 21 – December 12, 2020
Online (https://www.ideal.northwestern.edu/special-quarters/fall-2020/) https://www.ideal.northwestern.edu/special-quarters/fall-2020/registration
There will be a Special Quarter on Theory of Deep Learning this Fall as a part of IDEAL – The Institute for Data, Econometrics, Algorithms, and Learning, runs jointly with TTIC and the University of Chicago.
The Special Quarter will be entirely online, and take place inside a virtual space on the gather.town platform. All talks, discussions, meetings and other interactions will be inside this virtual space. To register for the special quarter, please complete the registration form below.
July 27-31, 2020
Telluride CO (virtual)
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.
July 28-30, 2020
Oak Ridge National Laboratory (virtual)
ICONS 2020 will be held as a virtual conference. The goal of this conference is to bring together leading researchers in neuromorphic computing to present new research, develop new collaborations, and provide a forum to publish work in this area. Our focus will be on architectures, models, and applications of neuromorphic systems.
July 19, 2020
The objective of this workshop is to bring together researchers from multiple disciplines, ranging from physical to biological sciences, to discuss the most promising approaches and overarching goals of neuromorphic computing technologies and paradigms that have the potential to drastically improve conventional approaches. The neuromorphic computing workshop aims to establish a forum to discuss current practices; future research needs; and new principles and tradeoffs across the entire neuromorphic information processing stack with the goal to apply them holistically to future machine learning systems.
June 29, 2020
Network models have been used as a tool to understand the role of interconnections between entities in multiple research areas like sociology, biology, meteorology, economics, and computer science. Moreover emerging technological developments allow collecting data on increasingly larger networks. This leads to both computational and statistical challenges when inferring or learning the structure of such networks. This workshop will cover some of the advances in the last decade on understanding trade-offs between statistical and computational efficiency for many inference problems on large networks. The workshop speakers are Andrea Montanari, Ankur Moitra, and Liza Levina. There will be short talks from 11am-3:15pm CT and a panel discussion 3:25-4pm CT. Participants can register to join on Zoom (see link for details), or watch the live stream.
July 6, 2020
The Logic Mentoring Workshop (LMW) will introduce young
researchers to the technical and practical aspects of a career in logic research. It is targeted at students, from senior undergraduates to graduates, and will include talks and a panel session from leaders in the subject.
January 11-12, 2021
Westin Alexandria Old Town, Alexandria, Virginia, U.S.
Submission deadline: August 12, 2020
Registration deadline: December 7, 2020
Symposium on Simplicity in Algorithms is a conference in theoretical computer science dedicated to advancing algorithms research by promoting simplicity and elegance in the design and analysis of algorithms. The benefits of simplicity are manifold: simpler algorithms manifest a better understanding of the problem at hand; they are more likely to be implemented and trusted by practitioners; they can serve as benchmarks, as an initialization step, or as the basis for a “state of the art” algorithm; they are more easily taught and are more likely to be included in algorithms textbooks; and they attract a broader set of researchers to difficult algorithmic problems.