Technical Fellow and Director
Microsoft Research Labs, New England, New York City and Montréal
Prof. Susan Dumais
The Importance of Context in Search
Abstract: Traditionally search engines have returned the same results to everyone who asks the same question. However, using a single ranking for everyone in every context at every point in time limits how well a search engine can do in providing relevant information. In this talk I present a framework to quantify the "potential for personalization” which we use to characterize the extent to which different people have different intents for the same query. I describe several examples of how we represent and use different kinds of contextual features to improve search quality for individuals and groups. Finally, I conclude by highlighting important challenges in developing personalized systems at Web scale including privacy, transparency, serendipity, and evaluation. Bio: Susan Dumais is a Technical Fellow and Director of the Microsoft Research Labs in New England, New York City and Montréal, and an adjunct professor at the University of Washington. ... Prior to joining Microsoft, she was a Member of Technical Staff at Bell Labs and Bellcore. Her research is at the intersection of human-computer interaction, information retrieval, and web and data science. A common theme that runs through her work is the importance of understanding and improving information systems from an interdisciplinary and user-centered perspective. She is a co-inventor of Latent Semantic Analysis, a well-known word embedding technique, which was designed to mitigate the disagreement between the words that authors use writing and those that searchers use to find information. Her research spans a wide range of topics in information systems, including email spam filtering, user modeling and personalization, context-aware information systems, temporal dynamics of information, and large-scale behavioral interactions. She has worked closely with several Microsoft product teams (Bing, Windows Search, SharePoint, and Office Help) on search-related innovations, and has published widely in the fields of information retrieval, human-computer interaction, and cognitive science. Susan is an ACM Fellow and was elected to the National Academy of Engineering (NAE), the American Academy of Arts and Sciences (AAAS), and the ACM SIGCHI and SIGIR Academies. She received the ACM Athena Lecturer Award for fundamental contributions to computer science, the SIGIR Gerard Salton Award for lifetime achievement in information retrieval, the Tony Kent Strix Award for outstanding contributions to information science, the ACM SIGCHI Research Award for lifetime achievement in human-computer interaction, and the lifetime achievement award from Indiana University Department of Psychological and Brain Science.
Professor of Computer Science
Dean, Faculty of Exact Sciences
Tel Aviv University
Prof. Tova Milo
Data Disposal by Design
Abstract: Big data analytics is changing our lives. Huge amounts of data are being collected, transformed, integrated and analyzed, leading to incredible breakthroughs in medicine, commerce, transportation, and science. This data-centered revolution is fueled by the massive amounts of data being constantly generated, but at the same time the very same information flood threatens to overwhelm us. First, the amount of data generated is growing exponentially and, in spite of advances in storage technology, is expected to exceed storage production by an order of magnitude by 2025. Second, uncontrolled data retention risks security and privacy, as recognized, e.g., by the EU Data Protection reform. Retaining the knowledge hidden in the data while respecting storage, processing and regulatory constraints is a great challenge. Traditional techniques for data sketching, summarization and deletion were developed for specific aspects of the problem, mostly related to query answering. But modern data analysis pipelines are far more complex, consisting of multiple analysis steps, including data integration, cleaning, visualization and machine learning. In this talk I will discuss the logical, algorithmic, and methodological foundations required for the systematic disposal of large-scale data over the processing pipeline. I will overview relevant related work, highlighting new research challenges and potential reuse of existing techniques, as well as the research performed in this direction in the Tel Aviv Databases group. Bio: Tova Milo is the Dean of the Faculty of Exact Science at Tel Aviv University. She received her Ph.D. degree in Computer Science from the Hebrew University, Jerusalem, in 1992. After graduating she worked at the INRIA research institute in Paris and at University of Toronto and returned to Israel in 1995, joining the School of Computer Science at Tel Aviv university, where she is now a full Professor and holds the Chair of Information Management. She served as the Head of the Computer Science Department from 2011-2014. Her research focuses on large-scale data management applications such as data integration, semi-structured information, Data-centered Business Processes and Crowd-sourcing, studying both theoretical and practical aspects. Tova served as the Program Chair of multiple international conferences, including PODS, VLDB, ICDT, XSym, and WebDB, and as the chair of the PODS Executive Committee. She served as a member of the VLDB Endowment and the PODS and ICDT executive boards and as an editor of TODS, IEEE Data Eng. Bull, and the LMCS Journal. Tova has received grants from the Israel Science Foundation, the US-Israel Binational Science Foundation, the Israeli and French Ministry of Science and the European Union. She is an ACM Fellow, a member of Academia Europaea, a recipient of the 2010 ACM PODS Alberto O. Mendelzon Test-of-Time Award, the 2017 VLDB Women in Database Research award, the 2017 Weizmann award for Exact Sciences Research, and of the prestigious EU ERC Advanced Investigators grant.
Chief Scientist, IBM Research; IBM Fellow; Vice President
Dr. Ruchir Puri
Engineering the future of software with AI
Abstract: Recent advances in AI are starting to transform every aspect of our society from healthcare, manufacturing, environment, and beyond. Future of AI for enterprises will be engineered with success along three foundational dimensions. We will dive deeper along these dimensions - Automation of AI; Trust of AI; and Scaling of AI - and conclude with the opportunities and challenges of AI for businesses. Bio: Dr. Ruchir Puri is the Chief Scientist of IBM Research, an IBM Fellow, and Vice-President of IBM Technical Community. He led IBM Watson as its CTO and Chief Architect from 2016-19 and has held various technical, research, and engineering leadership roles across IBM’s AI and Research businesses. Dr. Puri is a Fellow of the IEEE, and has been an ACM Distinguished Speaker, an IEEE Distinguished Lecturer, and was awarded 2014 Asian American Engineer of the Year. Ruchir has been an adjunct professor at Columbia University, NY, and a visiting scientist at Stanford University, CA. He was honored with John Von-Neumann Chair at Institute of Discrete Mathematics at Bonn University, Germany. Dr. Puri is an inventor of over 70 United States patents and has authored over 100 scientific publications on software-hardware automation methods, microprocessor design, and optimization algorithms. He is the chair of AAAI-IAAI conference that focused on industrial applications of AI. Ruchir’s technical opinions on the adoption of AI by society and businesses have been featured across New York Times, Wall Street Journal, Forbes, Fortune, IEEE spectrum among other.