Keynote Session
Follow Doris Lee at:
Designing Practical Visual Exploration Assistants for All
Abstract: Visual exploration assistants guide users through the overwhelming number of decisions that an analyst needs to make during the visual data exploration process to identify trends and patterns, generate and verify hypotheses, and detect outliers and anomalies. In this talk, I will discuss a few of the research systems that we developed during my Ph.D. that explores the design space of visual exploration assistants. As part of these studies, we uncovered several adoption challenges that present a barrier to the practical usage of visual exploration assistants in real-world settings. These findings inspired the design of Lux, an effortless visualization tool for exploring pandas dataframes in Jupyter notebooks. Lux has over 3.8K stars on GitHub and has been used by data scientists in a variety of industries and sectors. To conclude, I will share my experience and advice for Ph.D. students interested in pursuing a user-centered, adoption-driven research agenda.
Doris Lee is the co-founder and CEO of Ponder. She graduated with her Ph.D. from the School of Information at UC Berkeley in 2021. During this time, she developed several data science tools to accelerate insight discovery, including Lux, a lightweight visualization tool on top of pandas dataframes. She is the recipient of the Facebook Ph.D. Fellowship in Systems for Machine Learning in 2020.
Panel Session
Career Paths after Ph.D. – Perspectives from Senior and Junior Researchers
Follow Anastasia Ailamaki at:
Anastasia Ailamaki is a Professor of Computer and Communication Sciences at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, as well as the co-founder and Chair of the Board of Directors of RAW Labs SA, a Swiss company developing systems to analyze heterogeneous big data from multiple sources efficiently. She earned a Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000. She has received the 2019 ACM SIGMOD Edgar F. Codd Innovations Award and the 2020 VLDB Women in Database Research Award. She is also the recipient of an ERC Consolidator Award (2013), the Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), an NSF CAREER award (2002), and ten best-paper awards in international scientific conferences. She has received the 2018 Nemitsas Prize in Computer Science by the President of Cyprus and the 2021 ARGO Innovation Award by the President of the Hellenic Republic. She is an ACM fellow, an IEEE fellow, a member of the Academia Europaea, and an elected member of the Swiss, the Belgian, the Greek, and the Cypriot National Research Councils.
Follow Guoliang Li at:
Guoliang Li is a full professor at Department of Computer Science, Tsinghua University, Beijing, China. His research interests include database systems, large-scale data cleaning and integration, and learning-based data management. He got VLDB 2017 Early Research Contribution Award, TCDE 2014 Early Career Award, CIKM 2017 Best Paper Award, VLDB 2020 Best papers, KDD 2018 Best Papers, ICDE 2018 Best Papers, DASFAA 2014 Best Paper Runnerup, APWeb 2014 Best Paper Award. He was SIGMOD 2021 General Chair, VLDB 2021 Demo Chair, ICDE 2022 Industry Chair, SIGMOD 2023 Tutorial Chair.
Follow Wang-Chiew Tan at:
Wang-Chiew Tan is a research scientist manager at Meta AI. Before she was the Head of Research at Megagon Labs, where she led the research efforts on building advanced technologies to enhance search by experience. This included research on data integration, information extraction, text mining and summarization. Prior to joining Megagon Labs, she was a Professor of Computer Science at University of California, Santa Cruz. She also spent two years at IBM Research – Almaden.
Follow Yixiang Fang at:
Yixiang Fang is an Associate Professor at the School of Data Science in the Chinese University of Hong Kong, Shenzhen. He received the Ph.D. Degree from the University of Hong Kong in 2017. Afterwards, he worked as a research fellow in the University of Hong Kong during 2017-2018 and University of New South Wales, Australia during 2018-2020. He once was a visiting scholar in Nanyang Technological University in 2016.
Dr. Fang’s general research interests mainly focus on the areas of data management, data mining, and artificial intelligence over big data. In particular, he has worked on the topics of data management, data mining, graph neural network, representation learning over big graph data, and keyword search, geo-social network mining, and trajectory query over big spatial data. He has published 60 papers in the areas of database, data mining, and artificial intelligence, and most of them were published in top conferences (e.g., PVLDB, SIGMOD, ICDE, NeurIPS, and IJCAI) and journals (e.g., TODS, VLDBJ and TKDE). Particularly, one of his representative papers was selected as the One of the four Best Papers in SIGMOD 2020 (~4/458) and awarded as the 2021 ACM SIGMOD Research Highlight Award. He is an editorial board member of the journal of Information & Processing Management (IPM). He has also served as program committee members for several top conferences (e.g., PVLDB, ICDE, KDD, AAAI, and IJCAI) and invited reviewers for top journals (e.g., TKDE and VLDBJ) in the areas of database and data mining.
Follow Olgo Poppe at:
Olgo Poppe is a Senior Scientist in Gray Systems Lab in Azure Data. Her research interests include system building and big data analytics. Prior to joining Microsoft, she received PhD in computer science from Worcester Polytechnic Institute in 2017. Her dissertation focused on Event Stream Analytics.
Follow Sebastian Schelter at:
Sebastian Schelter is an Assistant Professor with the University of Amsterdam, where he is affiliated with the Intelligent Data Engineering Lab and manages the ‘AI for Retail Lab’, a joint industry lab between the University of Amsterdam and Dutch e-commerce and retail companies. He is conducting research at the intersection of data management and machine learning (ML), where he addresses data-related problems that occur in the real world application of ML. Examples are data debugging for ML pipelines, the design of efficient recommender systems, and responsible data management, e.g., developing ML applications that can efficiently forget data. His research is often accompanied by scalable open source implementations, which are applied in real world use cases, for example in the AWS SageMaker Model Monitor service. In the past, he has been a Faculty Fellow with the Center for Data Science at New York University and a Senior Applied Scientist at Amazon Research, after obtaining his PhD at TU Berlin with Volker Markl.