KDD 2022, a premier interdisciplinary conference in data science, will be held in Washington, D.C. starting Aug. 14 and feature Georgia Tech researchers in the two main tracks – research papers and applied data science papers. The research event is organized by the Association for Computing Machinery’s Special Interest Group on Knowledge Discovery and Data Mining.

“The annual KDD conference showcases the most innovative research and applied data science being conducted today,” said Wei Wang, SIGKDD chair, Leonard Kleinrock chair professor in computer science, and director of the Scalable Analytics Institute at University of California, Los Angeles.

“Collectively, these papers represent the future and promise of data science.”

KDD 2022 focuses on all aspects of knowledge discovery and data mining, from theoretical research on emerging science to papers assessing. This year’s papers were selected from over 2,000 papers initially submitted for consideration at KDD 2022. Topics of focus for this year’s papers include adversarial learning, anomaly detection, deep learning, text mining, and data ethics.

Georgia Tech’s presence includes the following sessions:

Research Papers track

• Graph Mining
• Interdisciplinary Applications: Medicine, Humanities and Social Good
• Potpourri Applications

Applied Data Science Papers track

• Human & Interfaces
• Search and Information Retrieval

Below are details for Georgia Tech research and paper links, when available.

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GEORGIA TECH RESEARCH @ KDD 2022

Sunday, August 14, 1-4
Data-Centric Epidemic Forecasting: A Survey
Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal, Javen Ho, Mira Patel, Suchet Sapre, B. Aditya Prakash

Monday, August 15 9am-5pm
epiDAMIK 5.0: The 5th International workshop on Epidemiology meets Data Mining
and Knowledge discovery
Co-organized by Georgia Tech faculty and PhD students

Tuesday, August 16, 1:30-3:30
SIPF: Sampling Method for Inverse Protein Folding
Tianfan Fu (Georgia Tech), Jimeng Sun (Georgia Tech)

Tuesday, August 16, 1:30-3:30
Antibody Complementary Determining Regions (CDRs) Design Using Constrained Energy Model
Tianfan Fu (Georgia Tech), Jimeng Sun (Georgia Tech)

Tuesday, August 16, 4:00-6:00
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Zijie (Jay) Wang (Georgia Tech); Alex Kale (University of Washington); Harsha Nori (Microsoft); Peter Stella (NYU Langone Health); Mark Nunnally (NYU Langone health); Duen Horng (Polo) Chau (Georgia Tech); Mihaela Vorvoreanu (Microsoft); Jennifer Wortman Vaughan (Microsoft Research); Rich Caruana (Microsoft Research)

Thursday, August 18, 10:00-12:00
Nimble GNN Embedding with Tensor-Train
Chunxing Yin (Georgia Tech); Da Zheng (Amazon); Israt Nisa (Amazon); Christos Faloutsos (Amazon); George Karypis (Amazon); Richard Vuduc (Georgia Tech)

Thursday, August 18, 10:00-12:00
Condensing Graphs via One-Step Gradient Matching
Wei Jin (Michigan State University)*; Xianfeng Tang (Amazon); Haoming Jiang (Georgia Tech)[WBA1] ; Zheng Li (Amazon); Danqing Zhang (Amazon); Jiliang Tang (Michigan State University); Bing Yin (Amazon)

Thursday, August 18, 10:00-12:00
Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition
Yinghao Li (Georgia Tech); Le Song (Biomap & MBZUAI); Chao Zhang (Georgia Tech)

Thursday, August 18, 1:30-3:30
Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction
Rongzhi Zhang (Georgia Tech); Rebecca West (The Home Depot); Xiquan Cui (Homedepot); Chao Zhang (Georgia Tech)