Women in KDD Workshop

Schedule (Sunday, August 15, 2021; all times PDT):

  • 9-10 am Keynote by Jennifer Neville, Microsoft Research

  • 10-11:00 am KDD Women's Panel

    • Topic: Adapting Data Science and AI Research in a Fast-Changing World

    • Moderator: Nadia Fawaz, Applied Research Scientist, Pinterest

    • Panelists:

    • 11-11:15am Break

    • 11:15 am - noon Mentoring Round Tables organized by WiML


Title: Improving productivity with Graph ML over content-interaction networks

Abstract: Workplace content has increased significantly, particularly over the pandemic due to coordination of effort across multiple electronic channels asynchronously. At the same time, widespread use of socio-technical systems in work environments provides a unique source of graph data to address the challenge of content overload—in the data trails that record the way that people interact with content and with each other. These interactions are a source of linguistic pragmatics (cues to language meaning implied by social interactions). Graph ML methods are uniquely positioned to be able to learn from these data trails and extend semantic understanding extracted via more conventional NLP methods—by jointly considering multiple modalities of interaction data, and collectively propagating pragmatics across teams/organizations. In this talk, I will briefly present some recent research on graph ML methods that learn from textual content in relational interactions, and discuss why I joined Microsoft Research to pursue this long-range research direction with the goal of significantly improving search and recommendation over workplace signals.


Dr. Jennifer is a Senior Principal Researcher at Research at Microsoft Redmond and the Samuel Conte Chair Professor of Computer Science and Statistics at Purdue University. Her research focuses on developing data mining and machine learning techniques for complex relational and network domains, including social, information, and physical networks. This work has produced over 100 publications with 10K citations. Her awards include an NSF Career Award (2012), ICDM Best Paper (2009), and IEEE’s “10 to Watch in AI” (2008). She was an elected member of the AAAI Executive Council from 2015-2018 and a member of the DARPA Computer Science Study Group in 2007-2008. She was PC chair of the SIAM International Conference on Data Mining in 2019 and the ACM International Conference on Web Search and Data in 2016.


This panel brings together accomplished scientific leaders who are working on cutting-edge innovation in data science and artificial intelligence and their applications to various domains. We will hear about how they shaped their research agenda and career, and discuss how data science and AI research are evolving to adapt to a fast-changing world.

Panelists and Panel Moderator:

Dr. Aarti Singh is an Associate Professor in the Machine Learning Department at Carnegie Mellon University. Her research lies at the intersection of machine learning, statistics, and signal processing, and focuses on designing statistically and computationally efficient algorithms that can interactively leverage inherent structure in the data, and its application to scientific domains. She received a B.E. in electronics and communication engineering from the University of Delhi in 1997, and a M.S. and Ph.D. in electrical engineering from the University of Wisconsin-Madison in 2003 and 2008, respectively. Dr. Singh was a postdoctoral research associate at the Program in Applied and Computational Mathematics at Princeton University, before joining CMU in 2009. Her work is recognized by an NSF Career Award, a United States Air Force Young Investigator Award, A. Nico Habermann Junior Faculty Chair Award, Harold A. Peterson Best Dissertation Award, and four best student paper awards. Her service honors include serving as program chair for the International Conference on Machine Learning (ICML) 2020, program chair for Artificial Intelligence and Statistics (AISTATS) 2017 conference, associate editor for IEEE Transactions of Information Theory and IEEE Transactions on Signal and Information Processing over Networks, expert team member for ONR/NIST TMS S&T study on AI for Materials and Manufacturing innovation, steering committee for NSF innovation lab on data-driven chemistry, and the National Academy of Sciences (NAS) committee on Applied and Theoretical Statistics.

Dr. Barbara Poblete is an Associate Professor at the Computer Science Department of the Universidad de Chile and an Amazon Visiting Scholar at Alexa Shopping Research. She is also a Researcher at the Millennium Institute on Data (IMFD Chile), where she co-leads the "Fake News and Misinformation" multidisciplinary research group. Formerly, she was a researcher at Yahoo! Labs. Her research areas are Social Network Analysis, Web Data Mining, Crisis Informatics and Applied Machine Learning. Her influential work "Information Credibility on Twitter" was awarded the 2021 IW3C2 Seoul Test of Time Award at The Web Conference. This was the first paper to address misinformation in social media and has been widely cited, featured in SciAM, WSJ, Slate, The Huffington Post, BBC News and NPR, among others. She holds 7 US Patents and has over 80 peer-reviewed articles. Currently (and according to Google Scholar), she is the most cited female Computer Scientist in Chile and among the top cited in Latin America. She is also co-founder of ChileWiC, the first and now main event in Chile that gathers women in CS and Technology, which is a yearly event running for 8-years.

Dr. Mounia Lalmas-Roelleke is a Director of Research and Head of Tech Research in Personalization at Spotify, leading a team of researchers in content personalization and discovery. Prior to that, she was Director of Research at Yahoo London. She also holds an Honorary Professorship at University College London. Mounia’s work focuses on studying user engagement in areas such as native advertising, digital media, social media, and search, and now audio (music and talk). She is a frequent conference speaker, author, and organizer whose research has appeared at many ACM conferences, including CIKM, SIGIR, SIGKDD, WSDM, WWW, and more.

Dr. Vidhya Navalpakkam is a Principal Scientist (Director) at Google Research. She leads an interdisciplinary team on modeling human attention and behavior at scale. She's also interested in applications of attention for healthcare (e.g., smartphone-based screening for health conditions). Her work has been published at top venues including Nature Neuroscience, Nature communications, and PNAS. Before joining Google in 2012, she was a Research Scientist at Yahoo labs and a postdoc in Computational Neuroscience at Caltech. She obtained her PhD in Computer Science at USC, and Bachelors in Computer Science from the Indian Institute of Technology, Kharagpur.

Dr Xin Luna Dong is the Head Scientist for Facebook AR/VR Assistant. Prior to joining Facebook, she was a Senior Principal Scientist at Amazon, leading the efforts of constructing Amazon Product Knowledge Graph, and before that one of the major contributors to the Google Knowledge Vault project, and has led the Knowledge-based Trust project, which is called the “Google Truth Machine” by Washington’s Post. She has co-authored books "Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases" and “Big Data Integration”, was awarded ACM Distinguished Member, and VLDB Early Career Research Contribution Award for “advancing the state of the art of knowledge fusion”. She serves in the VLDB endowment and PVLDB advisory committee, and is a PC co-chair for WSDM 2022, VLDB 2021, KDD'2020 ADS Invited Talk Series, and Sigmod 2018.​

Dr Nadia Fawaz is an applied research scientist and the tech lead for Inclusive AI at Pinterest. Her research and engineering interests include machine learning for personalization, AI fairness and data privacy. Her work leverages techniques from AI, information theory, fairness and privacy theory, and aims at bridging theory and practice. She was named one of the 100 Brilliant Women in AI Ethics 2021. She was a winner of the ACM RecSyS challenge on Context-Aware Movie Recommendations CAMRa2011, her 2012 UAI paper was featured in an MIT TechReview article as “The Ultimate Challenge For Recommendation Engines”, and her work on inclusive AI was featured in many press outlets, including The Wall Street Journal, Fast Company and Vogue Business. Earlier, she was a Staff Software Engineer in Machine Learning and the tech lead for the job recommendations team at LinkedIn, a principal research scientist at Technicolor Research lab, Palo Alto, and a postdoctoral researcher at the Massachusetts Institute of Technology, Research Laboratory of Electronics. She received her Ph.D. in EECS in 2008 and her Diplome d'ingenieur (M.Sc.) in EECS in 2005 both from Ecole Nationale Superieure des Telecommunications de Paris and EURECOM, France. She is a member of the IEEE and of the ACM.

About the Workshop

The event brings together members of the academic and industry research landscape and provides faculty, research scientists, applied scientists, data science practitioners, and graduate students who self-identify as women or non-binary an opportunity to connect, exchange ideas, and learn from each other. Male allies who might be interested in learning more and supporting the events in the workshop are also welcomed. The workshop is a half-day event with an invited speaker, a panel, and a mentoring round table sessions to discuss current research trends and career choices in KDD.

The workshop is co-organized by WiML and ACM SIGKDD.


Contact: edi2021@kdd.org