Data To AI Lab Reading Group
We organize a data science reading group to read, discuss, and present research broadly related to data science. For up-to-date details and correspondence related to the reading group, request to join our mailing list. You can also email one member of the group to request to join the list if you don’t want to use a Google account.
Our regular meeting schedule is
Monday 10:00 pm - 11:00 pm On Zoom or in D707
We generally meet every week during the semester. Occasionally, we hold DAI Lab-member only reading group sessions for draft reviews and skill sharing. These will be clearly indicated on the Google calendar. All other sessions are open to anyone who is interested. Please join us!
For up-to-date meeting information, subscribe to our calendar at right.
Past Meetings
Monday, January 22, 2024
Presenter: Ola Zytek, Sara Pidò
Paper: Are Large Language Models Post Hoc Explainers?
(Kroeger, Ley, Krishna, Agarwal, Lakkaraju, 2023)
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Monday, March 16, 2020
Presenter: Iván Ramirez
Paper: V-Matrix Method of Solving Statistical Inference Problems
(Vapnik, Izmailov, 2015)
Monday, February 24, 2020
Presenter: Micah Smith
Paper: Understanding User-Bot Interactions for Small-Scale Automation in Open-Source Development
(Liu, Smith, Veeramacheneni, 2020)
Monday, February 10, 2020
Presenter: Ola Zytek
Paper: Questioning the AI: Informing Design Practices for Explainable AI User Experiences
(Liao, Gruen, Miller, 2020)
Monday, November 25, 2019
Presenter: Dongyu Liu
Papers:
1. explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
(Spinner, Schlegel, Schafer, El-Assady, 2019)
2. FairSight: Visual Analytics for Fairness in Decision Making
(Ahn, Lin, 2019)
Monday, November 18, 2019
Presenter: Alicia Yi Sun
Papers:
1. A New Defense Against Adversarial Images: Turning a Weakness into a Strength
(Yu, Hu, Guo, Chao, Weinberger, 2019)
2. Adversarial Examples Are Not Bugs, They Are Features
(Ilyas, Santurkar, Tsipras, Engstrom, Tran, Madry, 2019)
Monday, October 28, 2019
Presenter: Lei Xu
Topic: ML Robustness
Papers:
1. When Robustness Doesn’t Promote Robustness: Synthetic vs. Natural Distribution Shifts on ImageNet
(Taori, Dave, Shankar, Carlini, Recht, Schmidt, 2020)
2. Breaking Certified Defenses: Semantic Adversarial Examples With Spoofed Robustness Certificates
(Ghiasi, Shafahi, Goldstein, 2020)
Monday, October 21, 2019
Presenter: Ola Zytek
Topic: ML model deployment
Paper: A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions
(Chouldechova, Putnam-Hornstein, Benavides-Prado, Fialko, Vaithianathan, 2018)
Monday, September 30, 2019
Presenter: Ola Zytek
Topic: Better figures
Paper: Ten SImple Rules for Better Figures
(Rougier, Droettboom, Bourne, 2014)
Thursday, April 4, 2019
Presenter: Micah Smith
Topic: AutoML Comparison
Paper: A Strategy for Ranking Optimization Methods using Multiple Criteria
(Dewancker, McCourt, Clark, Hayes, Johnson, Ke, 2016)
Thursday, March 21, 2019
Presenter: Lei Xu
Topic: GANs and the evaluation of generative models
Paper: A note on the evaluation of generative models
(Theis, van der Oord, Bethge, 2016)
Thursday, March 14, 2019
Presenter: Kevin Zhang
Topic: Methods for learning representations of graphs
Papers:
Node2Vec: Scalable Feature Learning for Networks
(Grover and Leskovec, 2016)
Representation Learning on Graphs: Methods and Applications
(Hamilton, Ying, Leskovec, 2018)
Thursday, March 7, 2019
Presenter: Alicia Yi Sun
Topic: Graph neural networks
Paper: Relational inductive biases, deep learning, and graph networks
(Battaglia et. al., 2018)
Thursday, February 28, 2019
Presenter: Ola Zytek
Topic: Machine learning interpretability applied to time series and LSTMs.
Paper: Techniques for visualizing LSTMs applied to electrocardiograms
(Van Der Westhuizen and Lasenby, 2018)
Tuesday, December 18, 2018
Presenter: Gaurav Sheni (Feature Labs, Boston, MA)
Topics:
Data Curation at Scale: The Data Tamer System
(Stonebraker et al, 2013)
Prediction Factory: Automated Development and Collaborative Evaluation of Predictive Models
(Sheni et al, 2017)
Thursday, December 13, 2018
Presenter: Lei Xu
Topics:
VizML: A Machine Learning Approach to Visualization Recommendation
(Hu et al, 2018)
DeepEye: Towards Automatic Data Visualization
(Luo et al, 2018)
Tuesday, November 27, 2018
Presenter: Micah Smith
Topics:
Random Search for Hyper-Parameter Optimization
(Bergstra and Bengio, 2012)
Practical Bayesian Optimization of Machine Learning Algorithms
(Snoek et al, 2012)
Tuesday, November 8, 2018
Presenter: Lei Xu
Topics:
Differentially Private Generative Adversarial Network
(Xie et al, 2018)
Chorus: Differential Privacy via Query Rewriting
(Johnson et al, 2018)
A Demonstration of Sterling: A Privacy-Preserving Data Marketplace
(Hynes et al, 2018)
Tuesday, November 1, 2018
Presenter: Alicia Yi Sun
Topic: Fairness in Machine Learning
Tuesday, October 23, 2018
Presenter: Micah Smith
Topic: Streaming feature selection and collaborative feature engineering
Tuesday, April 23, 2018
Presenter: Lei Xu
Topic: MaskGAN: Better Text Generation via Filling in the ______
(Fedus et al, 2018)
Tuesday, March 20, 2018
Presenter: Micah Smith
Topic: Learning Features from Relational Data
(Lam et al, 2018)
Tuesday, March 6, 2018
Presenter: Alicia Yi Sun
Topic: Learning to Compose Domain-Specific Transformations for Data Augmentation
(Ratner et al, 2017)
Tuesday, February 20, 2018
Presenter: Lei Xu
Topic: Synthetic Data for Social Good
(Howe et al, 2017)
Friday, December 8, 2017
Presenter: Toshiyuki Shimono (Digital Garage, Tokyo, Japan)
Topic: Make Accumulated Data in Companies Eloquent by SQL Statement Constructors
(Shimono et al, 2017)
Tuesday, November 28, 2017
Presenter: Micah Smith
Topic: Decibel: The Relational Dataset Branching System
(Maddox et al, 2016)
Monday, April 10, 2017
Presenter: Micah Smith
Topic: Ava: From Data to Insights Through Conversation
(John et al, 2017)