Talks

Fall 2022 Talks
 

Upcoming Workshops

 

Past Workshops

October 14, 2022: Elicitation Mechanisms in Practice Workshop

September 12, 2022: Theory-in-Practice Workshop

*watch the full morning event here

*watch the full after lunch event here

Speakers:

  • Harsha Simhadri, Approximate Nearest Neighbor Search algorithms for web-scale search and recommendation (video)
  • Stefan Muller, Static Prediction of Parallel Computation Graphs (video)
  • Kunal Agrawal, Scheduling Algorithms for Real-time Systems (video)
  • Jakub Łącki, Scaling Hierarchical Agglomerative Clustering to Trillion-Edge Graphs (video)
  • Uzi Vishkin, Can parallel algorithms reverse the decline of general-purpose CPUs? (video)

 

September 13, 2022: Theory-in-Practice Workshop

*Watch the full Tues. event here 

Speakers:

  • Michael Bender, Online Parallel Paging and Green Paging (video)
  • Laxman Dhulipala, Efficient Algorithms and Systems for Dynamic and Streaming Graphs (video)
  • Jeremy Fineman, Parallel Algorithms for Directed Graphs (video)

 

September 6, 2022: CS Seminar- Meena Jagadeesan

Title: Machine Learning in Digital Marketplaces: Interactions between Learners, Consumers, and Producers
Speaker: Meena Jagadeesan, University of California, Berkeley
Panopto: https://www.ideal.northwestern.edu/special-quarters/fall-2022/workshops/panopto-9-6-22

 

September 7, 2022: CS Seminar Day Two- Meena Jagadeesan

Title: Learning Equilibria in Matching Markets from Bandit Feedback
Speaker: Meena Jagadeesan, University of California, Berkeley
Panopto: https://www.ideal.northwestern.edu/special-quarters/fall-2022/workshops/panopto-9-7-22

Spring 2022 Talks

May 27, 2022: Workshop on High-Dimensional Geometry and Analysis

Speakers:

  • Arnold Filtser, Locality-Sensitive Orderings 
  • Ainesh Bakshi,  Robustly Learning a Mixture of k Arbitrary Gaussians 
  • Assaf Naor, Randomized clustering in high dimensions 
  • Erik Waingarten, The Johnson-Lindenstrauss Lemma for Clustering and Subspace Approximation 
  • Weiyun Ma, Almost 3-Approximate Correlation Clustering in Constant Rounds 

May 13, 2022: IDEAL Workshop on Algorithms for Massive Data Sets

Speakers:

  • Sami Davies, Characterizing Good Predictions for Learning-augmented Algorithms with Instance-robustness (video)
  • Sepideh Mahabadi, Non-Adaptive Adaptive Sampling in Turnstile Streams (video)
  • Elizaveta Rebrova, Randomized projection methods for corrupted data (video)
  • Jelani Nelson, Private Frequency Estimation via Projective Geometry (video)
  • David Woodruff, Memory Bounds for the Experts Problem (video)
  • Rachel Ward, Faster Johnson-Lindenstrauss embeddings via Kronecker products (video)

May 4, 2022: CS Colloquium w/ Chandra Chekuri (UIUC)

April 22, 2022: Clustering– Watch the full Friday event here

Speakers:

April 23, 2022: Clustering– Watch the full Saturday event here 

Speakers:

 
Fall 2021 Talks

September 21, 2021: Kickoff event for Fall Special Quarter on “Robustness in high dimensional statistics and machine learning”  -Opening Remarks and Overview of the Fall 2021 Special Quarter program- watch the intro and overview here

Speakers:

Spring 2021 Talks
 

May 28, 2021: CS+Law: Definitions for Algorithms

Speakers:

April 30, 2021: CS+Law: Evaluation and Accountability 

Speakers:

March 25, 2021: Technology and the Future of Courts: A Global Perspective – A Northwestern Law and Technology Initiative program

March 19, 2021: Quarterly Theory Workshop on “Algorithms and their Social Impact” 

Speakers:

 

Fall 2020 Talks
Spring 2020 Talks
  • May 5: Kickoff Workshop: Inference and Data Science on Networks
  • May 13: Dean’s Lecture: Jon Kleinberg, Fairness and Bias in Algorithmic Decision Making
    Public debates about classification by algorithms has created tension around what it means to be fair to different groups. Cornell University’s Jon Kleinberg will consider key fairness conditions at the heart of these debates, and discuss recent work on the interactions between these conditions. He will also explore how the complexity of a classification rule interacts with its fairness properties, showing how natural ways of approximating a classifier via a simpler rule can act in conflict with fairness goals.
  • May 14: Workshop: Estimation of Network Processes and Information Diffusion.
    Many important dynamic processes are determined by an underlying network structure. Examples include the spread of epidemics, the dynamics of public opinions, the diffusion of information about social programs, and biological processes such as neural spike trains. Data about these processes is becoming increasingly available which has lead to a number of different research communities to tackle related questions independently. What is the source of a rumor? Will a given disease spread widely? Who is the key player? This workshop will cover some new tools being developed to address such questions.
  • June 29: Workshop: Computational vs Statistical Tradeoffs in Network Inference.
    Network models have been used as a tool to understand the role of interconnections between entities, by diverse communities such as sociology, biology, meteorology, economics, and computer science, to name a few. 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.