Some News (and some not so new news)

• Our paper "Proactive Discovery of Fake News Domains from Real-Time Social Media Feeds", which was presented at the Workshop on Computational Methods in Online Misbehavior (CyberSafety), The Web Conference, 2020, received the best-paper award.
• In 2018-2019, I was on sabbatical at INRIA, visiting Ioana Manolescu and Jean-Daniel Fekete
• Our paper "A Large-scale Study about Quality and Reproducibility of Jupyter Notebooks" has received an ACM SIGSOFT Distinguished Paper Award and a FOSS Impact Paper Award.

• Dataset search. Recent years have seen an explosion in our ability to collect and catalog immense amounts of data about our environment, society, and populace. Moreover, with the push towards transparency and open data, scientists, governments, and organizations are increasingly making structured data available on the Web. Combined with advances in analytics and machine learning, the availability of such data should in theory allow us to make progress on many of our most important scientific and societal questions. However, this opportunity is often missed due to a central technical barrier: it is currently nearly impossible for domain experts to weed through the vast amount of publicly available information to discover datasets that are needed for their specific application. We are working on techniques and new kinds of queries to support effective dataset discovery. We have also built Auctus, a dataset search engine that supports a rich set of data discovery queries, including for data augmentation.
• Human-assisted automated machine learning. The Data-Driven Discovery of Models (D3M) program aims to develop automated model discovery systems that enable users with subject matter expertise but no data science background to create empirical models of real, complex processes. At the NYU VIDA Center we address two important challenges in automating machine learning: i) pipeline synthesis and model understanding, and ii) dataset search and discovery. See https://vida.engineering.nyu.edu/research/d3m for details.
• Debugging Science and Computational Pipelines. Applications in domains ranging from large-scale simulations in astrophysics and biology to enterprise analytics rely on computational pipelines. A pipeline consists of modules and their associated parameters, data inputs, and outputs, which are orchestrated to produce a set of results. If some modules derive unexpected outputs, the pipeline can crash or lead to incorrect results. Debugging these pipelines is difficult since there are many potential sources of errors including: bugs in the code, input data, software updates, improper parameter settings. We have developed algorithms and an open-source system, BugDoc , to automatically infers the root causes and derive succinct explanations of failures and behavior for black-box pipelines.
• Previous projects

• ACM SIGMOD Contributions Award, 2020.
• AAAS Fellow, 2021.
• ACM Fellow, 2014.
• IBM Faculty Award, 2014.
• Google Faculty Research Award, 2013.
• CAREER award. National Science Foundation, 2008.
• IBM Faculty Award, 2008.
• Other awards

Publications

• Quasi-complete list of publications
• Incomplete publication list from DBLP
• Citation list from Google Scholar

Patents Issued, Pending (partial list)

Recent and Selected Talks (All talks)

Current and Selected Professional Activities

• Editor in Chief for PVLDB, 2022.
• Associate Editor for PVLDB, 2020-2021.
• Member, PVLDB Executive Committee.
• Member, ACM SIGMOD Executive Committee.
• Member, Diversity and Inclusion Task in Databases, 2020--.
• Member, DBCares committee, ACM SIGMOD and VLDB, 2019--.
• Steering Committes, ACM SIGMOD Workshop on Human-In-the-Loop Data Analytics (HILDA).
• Advisory Board Member, Center for Reproducible Biomedical Modeling, 2018-.
• Member, Harvard Data Science Initiative Trust in Science External Advisory Board, 2021-.
• Elected Chair, ACM Special Interest Group on Management of Data (SIGMOD), 2017-2021.
• Council Member, Computing Research Associations Computing Community Consortium (CCC), 2017-2020.
• PI and Executive Director Moore Sloan Data Science Environment, New York University, 2015-2019.
• Past Activities