Lunch-n-Learn

TADS Lunch-n-Learn is a weekly meeting of researchers interested in theoretical and applied data science. This group meets on a weekly basis on Thursdays from 12-1pm. The agenda varies but includes a presentation of recent research results, discussion about upcoming funding opportunities, etc. A subset of group participants also frequently form teams to go after funding opportunities and write papers together. These weekly meetings provide a platform for building camaraderie and a shared vocabulary in theoretical and applied data science.

To request an invitation or to recommend a researcher, please contact Hridesh Rajan at hridesh@iastate.edu.

2023

Jelani Nelson, UC Berkeley, New local differentially private protocols for frequency and mean estimation
Monday, April 3, 2023 - 1:00pm to 2:00pm

2022

Dr. Jia (Kevin) Liu, 'Incentivized Bandit Learning under Self-Reinforcing User Preference for Online Recommender Systems', TADS Lunch-n-Learn
Thursday, April 28, 2022 - 12:00pm to 1:00pm
Dr. Kuldeep Meel, 'Next Gen Automated Reasoning: Beyond “SAT Revolution” to “Beyond SAT” Revolution', TADS Lunch-n-Learn
Thursday, February 17, 2022 - 12:00pm to 1:00pm

2021

Dr. Eric Price, 'Instance-Optimal Compressed Sensing via Conditional Resampling', TADS Lunch-n-Learn
Thursday, April 29, 2021 - 12:00pm to 1:00pm
Dr. Liangxiu Han, 'Deep Learning and its Application to Remotely Sensed Data', TADS Lunch-n-Learn
Thursday, April 22, 2021 - 12:00pm to 1:00pm
Dr. Cynthia Rudin, 'The Extremes of Interpretability in Machine Learning: Sparse Decision Trees, Scoring Systems and Interpretable Neural Networks', TADS Lunch-n-Learn
Thursday, April 8, 2021 - 12:00pm to 1:00pm
Dr. Ryan Martin, 'Valid Inferential Models and Conformal Prediction', TADS Lunch-n-Learn
Thursday, March 18, 2021 - 12:00pm to 1:00pm

2020

Dr. Andrew McGregor, 'Recent Results on Cycle Counting in the Data Stream Model', TADS Lunch-n-Learn
Thursday, December 17, 2020 - 12:00pm to 1:00pm
Dr. Xiaoming Huo, 'A Homotopic Method to Solve the Lasso Problems with an Improved Upper Bound of Convergence Rate', TADS Lunch-n-Learn
Thursday, December 3, 2020 - 12:00pm to 1:00pm
Dr. Tianbao Yang, 'Deep AUC Maximization and Applications in Medical Image Classification', TADS Lunch-n-Learn
Thursday, November 19, 2020 - 12:00pm to 1:00pm
Dr. Hongfeng Yu, 'Exploring and Evaluating Edge Bundling for Large Graph Visualization', TADS Lunch-n-Learn
Thursday, November 12, 2020 - 12:00pm to 1:00pm
Dr. Tamal Dey, 'Computational Topology and Data Analysis: A New Way of Looking at Data', TADS Lunch-n-Learn
Thursday, November 5, 2020 - 12:00pm to 1:00pm
Dr. Stephen Wright, 'Second-order Methods for Nonconvex Optimization with Complexity Guarantees', TADS Lunch-n-Learn
Thursday, October 22, 2020 - 12:00pm to 1:00pm
Dr. In-Ho Cho, 'Parallel Fractional Hot Deck Imputation for Large/Big Missing Data Curing for Improving Machine Learning and Statistical Inference', TADS Lunch-n-Learn
Thursday, October 15, 2020 - 12:00pm to 1:00pm
Dr. Lev Reyzin, 'Differential Privacy, Adaptive Data Analysis, and Free Speedups via Sampling', TADS Lunch-n-Learn
Thursday, October 1, 2020 - 12:00pm to 1:00pm
Dr. Vipin Kumar, 'Physics Guided Machine Learning: A New Framework for Accelerating Scientific Discovery', TADS Lunch-n-Learn
Thursday, September 24, 2020 - 12:00pm to 1:00pm
Rangeet Pan, 'On Decomposing a Deep Neural Network into Modules', TADS Lunch-n-Learn
Thursday, September 17, 2020 - 12:00pm to 1:00pm
Dr. Vinod Variyam, 'Learning and Sampling of Atomic Interventions from Observations', TADS Lunch-n-Learn
Thursday, August 27, 2020 - 12:00pm to 1:00pm
Yan Wang, 'Wasserstein subsampling: Theory and empirical performance', TADS Lunch-n-Learn
Thursday, August 20, 2020 - 12:00pm to 1:00pm
Dr. Hongyang Gao, 'Graph Neural Networks: A Feature and Structure Learning Approach', TADS Lunch-n-Learn
Thursday, August 13, 2020 - 12:00pm to 1:00pm

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