ACM SIGMOD Philadelphia, USA, 2022
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ACM SIGMOD 2022 New Researcher Symposium

The Dos and Don’ts for Sharing Research

Time slot and date: 18:30 – 20:30 on Tuesday, June 14, 2022

Session Chair:

Leilani Battle, University of Washington, USA
Xiaolan Wang, Megagon Labs, USA


Every year, the SIGMOD Conference includes a symposium with advice on starting a career in data management. These symposia are both informative and entertaining; they are geared towards graduate students and junior researchers but they often attract a broader audience. This year's SIGMOD New Researcher Symposium is on "The Dos and Don’ts for Sharing Research". We have put together an exciting panel with diverse viewpoints and perspectives on how to effectively engage in such research. The panel will begin by each panelist presenting their perspective and thoughts on sharing research, followed by a moderated Q&A and then an open-forum for the audience to ask their questions.


Wang-Chiew Tan is a research scientist at Meta AI. Prior to joining Meta AI, she led the research efforts at Megagon Labs with the goal of building advanced technologies to enhance search by experience. Prior to that, she was a Professor of Computer Science at University of California, Santa Cruz. She also spent two years at IBM Research - Almaden. She is a co-recipient of the 2014 ACM PODS Alberto O. Mendelzon Test-of-Time Award, the 2018 ICDT Test-of-Time Award, and the 2020 Alonzo Church Award. She was the program committee chair of ICDT 2013, PODS 2016, and the program committee co-chair of SIGMOD 2020. She received the 2019 VLDB Women in Database Research Award. She was on the VLDB Board of Trustees (2014-2019) and she is a Fellow of the ACM.

Eugene Wu is broadly interested in technologies that help users play with their data. His goal is for users at all technical levels to effectively and quickly make sense of their information. He is interested in solutions that ultimately improve the interface between users and data, and uses techniques borrowed from fields such as data management, systems, crowd sourcing, visualization, and HCI. Eugene Wu received his Ph.D. from MIT, B.S. from Cal, and was a postdoc in the AMPLab. Eugene Wu has received the VLDB 2018 10-year test of time award, best-of-conference citations at ICDE and VLDB, the SIGMOD 2016 best demo award, the NSF CAREER, and the Google and Amazon faculty awards.

Sudeepa Roy is an Assistant Professor in Computer Science at Duke University. She works in the area of databases, with a focus on foundational aspects of big data analysis, which includes causality and explanations for big data, data provenance, probabilistic databases, and applications of database techniques in other domains. Prior to Duke, she did a postdoc at the University of Washington, and obtained her Ph.D. from the University of Pennsylvania. She is a recipient of an NSF CAREER Award and a Google PhD Fellowship in Structured Data.

Ce Zhang is an Assistant Professor in Computer Science at ETH Zurich. The mission of his research is to make machine learning techniques widely accessible while being cost efficient and trustworthy to everyone who wants to use them to make our world a better place. He believes in a system approach to enabling this goal, and his current research focuses on building next generation machine learning platforms and systems that are data centric, human centric, and declaratively scalable. Before joining ETH, Ce finished his PhD at the University of Wisconsin Madison and spent another year as a postdoctoral researcher at Stanford, both advised by Christopher Ré. His work has received recognitions such as the SIGMOD Best Paper Award, SIGMOD Research Highlight Award, Google Focused Research Award, an ERC Starting Grant, and has been featured and reported by Science, Nature, the Communications of the ACM, and a various media outlets such as Atlantic, WIRED, Quanta Magazine, etc.

Anna Fariha is a Researcher at Microsoft. She obtained her Ph.D. from the College of Information and Computer Sciences (CICS), University of Massachusetts, Amherst under the supervision of Prof. Alexandra Meliou. Her primary area of research revolves around data management; but, the application areas of her research have been interdisciplinary, spanning from program synthesis and software engineering to machine learning, natural language processing, and human-computer interaction. She is interested in designing mechanisms for enhancing system usability and developing intelligent tools towards boosting productivity for a diverse group of users, ranging from end-users to data scientists and developers.

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