Agenda

Date: August 15, 2022, 8am-12pm EST

Room 102A

Scroll to the bottom for detailed agenda

Keynote Speakers

Head of Big Data Lab and Business Intelligence Lab - Baidu Research; Professor - University of Oregon

Dejing Dou is the Head of Big Data Lab (BDL) and Business Intelligence Lab (BIL) at Baidu Research. He is also a full Professor (on leave) from the Computer and Information Science Department at the University of Oregon and has led the Advanced Integration and Mining (AIM) Lab since 2005. He has been the Director of the NSF IUCRC Center for Big Learning (CBL) since 2018. He was a visiting associate Professor at Stanford Center for Biomedical Informatics Research during 2012-2013. Prof. Dou received his bachelor degree from Tsinghua University, China in 1996 and his Ph.D. degree from Yale University in 2004. His research areas include artificial intelligence, data mining, data integration, NLP, and health informatics. Dejing Dou has published more than 160 research papers, some of which appear in prestigious conferences and journals like AAAI, IJCAI, ICML, NeurIPS, ICLR, KDD, ICDM, ACL, EMNLP, CVPR, ICCV, CIKM, ISWC, TKDD, JIIS, and JoDS, with more than 5000 Google Scholar citations. His DEXA'15 paper received the best paper award. His KDD'07 paper was nominated for the best research paper award. His COLING'18 paper was Area Chair Favorites (excellent). He is on the Editorial Boards of Journal on Data Semantics, Journal of Intelligent Information Systems, and PLOS ONE. He is an Editor-in-Chief of AIMS Electronic Research Archive. Dejing Dou is a senior member of AAAI, ACM, and IEEE.

AutoDL: Research and Applications of Automated Deep Learning with Baidu’s Big Data

Big Data, Deep Learning, and huge computing are shaping up AI and are transforming our society. Especially, learning with deep neural networks has achieved great success across a wide variety of tasks. To help increase speed to solution and reduce duplication of effort, automated model construction is of great interest to provide architecture-effective and domain-adaptive deep models. On the other hand, understanding model behaviors and building trust in model prediction are essential for many applications such as autonomous driving, medical and fintech tasks. In this talk, we introduce Baidu AutoDL, which aims at building the next-generation ML system. Baidu AutoDL lowers the threshold for AI development and provides a full-stack deep learning solution, including (1) neural architecture search, (2) semi-supervised and transfer learning, (3) federated learning and (4) interpretable deep learning. State-of-the-art algorithms and industrial applications will be demonstrated in our talk.


Chief Science Officer at DataPrime.ai

Founder of Data Leadership Group LLC

Dr. Kirk Borne is Chief Science Officer at startup Data Prime Inc. since April 2021, and founder of his own freelance influencer marketing business Data Leadership Group LLC. He is a career data professional, data science leader, and research astrophysicist. From 2015 to 2021, he was Principal Data Scientist, Data Science Fellow, and Executive Advisor at Booz Allen Hamilton. Previously, Kirk was professor of Astrophysics and Computational Science at George Mason University for 12 years where he taught data science at the graduate and undergraduate levels. He has been an Adjunct Professor at the University of Maryland Global Campus and at the University of Texas in Arlington. Before that, he spent 20 years supporting data systems activities for NASA space science missions, including the Hubble Telescope and at NASA's Space Science Data Operations Office. He has a Ph.D. in astronomy from Caltech. Since 2013, he has been identified as a top worldwide influencer on social media, promoting data science, machine learning, AI, IoT strategy, and data literacy for all.

Machine Learning in the Database

We have had databases, data mining, and business intelligence for decades, and machine learning for nearly the same number of years. So what's new and different now? There is now a much greater focus on bringing these specialized methods and algorithms to the typical business worker. This democratization of machine learning is essential since nearly every business worker is now digitally enabled, confronted with data every day, and expected to deliver insights and value from those data sources. The data sources (many and diverse), computing resources (Cloud), and algorithms of machine learning are sufficiently mature that bringing the benefits of these capabilities to every person in the business is important, imperative, and impactful. I will present some examples of one particularly significant recent development: machine learning in the database. Why this? Because nearly every business worker has access to and some familiarity with (if not, expertise in) database querying. Bringing anomaly detection, classification, predictive analytics (forecasting), or other machine learning capabilities into an SQL statement is a game-changer for enterprises everywhere. With ML in the database, democratized machine learning has never been so democratized!

Associate Professor, University of Illinois Urbana-Champaign

Jingrui He is an Associate Professor in School of Information Sciences, University of Illinois at Urbana-Champaign. She received her Ph.D in Computer Science from Carnegie Mellon University in 2010, and joined UIUC in 2019. Her research focuses on heterogeneous machine learning, rare category analysis, and semi-supervised learning with applications in social network analysis, healthcare, etc. Dr. He is the recipient of the 2016 NSF CAREER Award, the 2020 OAT Award, three times recipient of the IBM Faculty Award, and was selected as IJCAI 2017 Early Career Spotlight. Her papers have been selected as Bests of the Conference by ICDM 2016, ICDM 2010, and SDM 2010.

Towards Understanding Users' Behaviors in Multi-Armed Bandits

Multi-Armed Bandits have proven to be an effective tool in many real-world applications, such as recommender systems, online advertising, and healthcare. In these applications, the ultimate goal is to satisfy the users' need from various aspects. Therefore, in this talk, I will introduce our recent work on modeling users' behaviors in multi-armed bandits. More specifically, I will start by introducing local user clustering in multi-armed bandits, which aims at leveraging user similarity to improve the quality of reward estimation; then I will present a new problem setting with multi-facet bandits, each characterizing the users' needs from one unique aspect. Towards the end, I will discuss the trade-off between exploitation and exploration in multi-armed bandits, and how we use a pair of deep models to learn the reward function as well as the potential gains.

Engineering Manager, Pinterest

Dr. Lingfei Wu is an Engineering Manager in the Content and Knowledge Graph Group at Pinterest, where they are building the next generation Knowledge Graph to empower Pinterest recommendation/research systems across all major surfaces including Homefeed, Search, Ads, and etc. Previously, I was a Principal Scientist at JD.COM Silicon Valley Research Center, leading a team of 30+ machine learning/natural language processing scientists and software engineers to build intelligent e-commerce personalization systems. He earned his Ph.D. degree in computer science from the College of William and Mary in 2016. Previously, he was a research staff member at IBM Thomas J. Watson Research Center and led a 10+ research scientist team for developing novel Graph Neural Networks methods and systems, which leads to the #1 AI Challenge Project in IBM Research and multiple IBM Awards including three-time Outstanding Technical Achievement Award. He has published one book (in GNNs) and more than 100 top-ranked conference and journal papers, and is a co-inventor of more than 40 filed US patents. Because of the high commercial value of his patents, he has received eight invention achievement awards and has been appointed as IBM Master Inventors, class of 2020. He was the recipients of the Best Paper Award and Best Student Paper Award of several conferences such as IEEE ICC’19, AAAI workshop on DLGMA’20 and KDD workshop on DLG’19. His research has been featured in numerous media outlets, including NatureNews, YahooNews, AP News, PR Newswire, The Time Weekly, Venturebeat, TechTalks, SyncedReview, Leiphone, QbitAI, MIT News, IBM Research News, and SIAM News. He has co-organized 10+ conferences (KDD, AAAI, IEEE BigData) and is the founding co-chair for Workshops of Deep Learning on Graphs (with AAAI’21, AAAI’20, KDD’21, KDD’20, KDD’19, and IEEE BigData’19) and Deep Learning on Graphs for Natural Language Processing (with ICLR’22 and NAACL’22). He has currently served as Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and ACM Transactions on Knowledge Discovery from Data.


Graph Neural Networks: Foundations, Frontiers, and Applications

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning, have become one of the fastest-growing research topics in machine learning, especially deep learning. This wave of research at the intersection of graph theory and deep learning has also influenced other fields of science, including recommendation systems, natural language processing, program synthesis, software mining, cybersecurity, and intelligent transportation. However, as the field rapidly grows, it has been extremely challenging to gain a global perspective of the developments of GNNs. Therefore, we feel the urgency to bridge the above gap and have a comprehensive book on this fast-growing yet challenging topic. In this talk, we will talk about our recent book titled "Graph Neural Networks: Foundation, Frontiers and Applications ", one of the most comprehensive books for researchers and practitioners for reading and studying in GNNs. It covers a broad range of topics in graph neural networks, by reviewing and introducing the fundamental concepts and algorithms of GNNs, new research frontiers of GNNs, and broad and emerging applications with GNNs.

Paper Presentations


  • TIMEX: an Automatic Framework for Time-Series Forecasting-as-a-Service
    Alessandro Falcetta and Manuel Roveri

  • ACE: Adaptive Constraint-aware Early Stopping in Hyperparameter Optimization
    Yi-Wei Chen, Chi Wang, Amin Saied and Rui Zhuang

  • Mining Robust Default Configurations for Resource-constrained AutoML
    Moe Kayali and Chi Wang

  • Lightweight Automated Feature Monitoring for Data Streams
    João Conde, Ricardo Moreira, João Torres, Pedro Cardoso, Hugo Ferreira, Marco Sampaio, João Tiago Ascensão and Pedro Bizarro

Agenda

8:00 – 8:40 AM Keynote 1: Dr. Dejing Dou - AutoDL: Research and Applications of Automated Deep Learning with Baidu’s Big Data

8:40 – 9:00 AM Paper 1: Mining Robust Default Configurations for Resource-constrained AutoML - Moe Kayali and Chi Wang

9:00 – 9:40 AM Keynote 2: Dr. Jingrui He - Towards Understanding Users' Behaviors in Multi-Armed Bandits

9:40 – 10:00 AM Paper 2: ACE: Adaptive Constraint-aware Early Stopping in Hyperparameter Optimization - Yi-Wei Chen, Chi Wang, Amin Saied and Rui Zhuang

10:00 – 10:40 AM Keynote 3: Dr. Lingfei Wu - Graph Neural Networks and structure discovery, with industry applications

10:40 – 11:00 AM Paper 3: Lightweight Automated Feature Monitoring for Data Streams - João Conde, Ricardo Moreira, João Torres, Pedro Cardoso, Hugo Ferreira,

Marco Sampaio, João Tiago Ascensão and Pedro Bizarro

11:00 – 11:40 AM Keynote 4: Dr. Kirk Borne - Machine Learning in the Database

11:40 – 12:00 PM Paper 4: TIMEX: an Automatic Framework for Time-Series Forecasting-as-a-Service - Alessandro Falcetta and Manuel Roveri