July 25-29, 2022
Los Angeles, USA
Registration deadline: April 15, 2022
The goal of this summer school is to present an in-depth introduction to post-quantum and quantum cryptography for advanced
undergraduate and graduate students, as well as young researchers, in mathematics, computer science, and physics. Lecturers in the school will discuss both topics hand in hand: post-quantum
cryptography, or the art of analyzing security of classical
cryptosystems against attacks, and quantum cryptography, or the art of leveraging quantum effects to develop new cryptographic schemes that are made possible by quantum information.
October 17-21, 2022
Submission deadline: May 22, 2022
LATIN is devoted to different areas in theoretical computer science including, but not limited to: algorithms (approximation, online, randomized, algorithmic game
theory, etc.), analytic combinatorics and analysis of algorithms, automata theory and formal languages, coding theory and data compression, combinatorial algorithms, combinatorial optimization, combinatorics and graph theory, complexity theory,
computational algebra, computational biology, computational geometry, computational number theory, cryptology, databases and information retrieval, data structures, formal methods and security, foundations of data science and theoretical machine learning, Internet and the web, parallel and distributed computing, pattern matching, programming language theory, quantum computing, and random structures.
July 11-15, 2022
Prague, Czech Republic
Registration deadline: March 1, 2022
Prague Summer School on Discrete Mathematics 2022 will feature lecture series on
statistical physics methods in combinatorics (Will Perkins) and the container method (Wojciech Samotij). The School is primarily intended for PhD students and postdocs, but students and researchers in other stages of their careers may also participate. A limited number of stipends to cover travel and stay for participants is available.
June 20-24, 2022
Submission deadline: January 15, 2022
STOC/TheoryFest 2022 will hold workshops during the conference week, June 20–24, 2022. We invite groups of interested researchers to submit workshop proposals. The due date for proposals is February 15, 2022. Submission instructions can be found on the STOC’22 website.
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.
November 16, 2021
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.
October 19, 2021
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.
September 21 – December 10, 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.
September 13-17, 2021
Aims and Scope:
The school provides an introduction to some of the main topics of the trimester program on discrete optimization. The lectures will address the interface between tropical geometry and discrete optimization; recent developments in continuous optimization with applications to combinatorial problems; topics in approximation algorithms; and fixed parameter tractability. The lectures will be mainly directed towards PhD students and junior researchers.
Michał Pilipczuk (Warsaw University): Introduction to parameterized algorithms and applications in discrete optimization
Aaron Sidford (Stanford University): Introduction to interior point methods for discrete optimization
Ngoc Mai Tran (UT Austin): Tropical solutions to hard problems in auction theory and neural networks, semigroups and extreme value statistics
Rico Zenklusen (ETH Zürich): Approximation algorithms for hard augmentation problems
Abstracts can be found here:
Schedule can be found here:
Interested in attending the School?
Here is the link for the online (and free) registration!
This Summer School is part of the HIM trimester in Discrete Optimization
Organizers: Daniel Dadush (Amsterdam), Jesper Nederlof (Utrecht), Neil Olver (London), Laura Sanità (Eindhoven), László Végh (London)
August 23-25, 2021
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.