Technical Fellow and Director
Microsoft Research Labs, New England, New York City and Montréal
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
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
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.
Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics
Abstract: With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice, flawed models in healthcare, and black box loan decisions in finance. Interpretability of machine learning models is critical in high stakes decisions. In this talk, I will focus on one of the most fundamental and important problems in the field of interpretable machine learning: optimal scoring systems. Scoring systems are sparse linear models with integer coefficients. Such models first started to be used ~100 years ago. Generally, such models are created without data, or are constructed by manual feature selection and rounding logistic regression coefficients, but these manual techniques sacrifice performance; humans are not naturally adept at high-dimensional optimization. I will present the first practical algorithm for building optimal scoring systems from data. This method has been used for several important applications to healthcare and criminal justice. I will mainly discuss work from three papers:
- Learning Optimized Risk Scores. Journal of Machine Learning Research, 2019.
- The Age of Secrecy and Unfairness in Recidivism Prediction. Harvard Data Science Review, 2020.
- Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients. JAMA Neurology, 2017.
Professor Computer Science and Engineering Department
“Alice in Wonderland”: Navigating the Landscape of Multilinguality for NLP
Abstract: This talk is on multilingual computation. Machine Translation, Sentiment and Emotion Analysis, Information Extraction- tasks in these 3 NLP umbrella areas- have to grapple with the characteristic problem of disambiguation in the face of resource scarcity, which is the reality for most languages and also arguably for ANY language when it comes to very high end NLP tasks like, say, 100% disambiguation as demanded by pure interlingua based MT. Starting with our early work on creation of Indian Language Wordnets and Multilingual Sentiment Analysis we move to the description of our research in Low Resource Machine Translation and Conversational AI. A recurring theme is the mutual computational help amongst languages. The findings covered in this talk are based on contributions by many students and researchers over many years, reported in top conferences and journals. Bio: Dr. Pushpak Bhattacharyya is Professor of Computer Science and Engineering at IIT Bombay ... . Educated in the IIT System (B.Tech IIT Kharagpur 1984, M.Tech IIT Kanpur 1986, PhD IIT Bombay 1994), Dr. Bhattacharyya has done extensive research in Computational Linguistics/Natural Language Processing and Machine Learning. He has published more than 350 research papers and has authored/co-authored 6 books including a textbook on machine translation, and has guided more than 350 students for their PhD, Masters and Undergraduate thesis. He has received many Research Excellence Awards- Manthan award from Ministry of IT, H.H. Mathur and P.K.Patwardhan awards from IIT Bombay, VNMM award from IIT Roorkee, and substantial research grants from Government and industry. Prof. Bhattacharyya holds the Bhagat Singh Rekhi Chair Professorship of IIT Bombay, and is a Fellow of National Academy of Engineering, Abdul Kalam National Fellow, Distinguished Alumnus of IIT Kharagpur, past Director of IIT Patna and past President of Association of Computational Linguistics.
Professor, Computational & Data Sciences and Computer Science & Automationation
Abstract: We have recently defined a new query reverse-engineering problem of unmasking SQL queries hidden within opaque database applications. The diverse industrial use-cases for this problem range from resurrecting legacy code to query rewriting. It poses hard and novel intellectual challenges due to the presence of a variety of complexities such as acute dependencies between the various query clauses, schematic renaming in the output columns, result consolidation due to aggregation functions, and presence of computed column functions. In this talk, we motivate the hidden query extraction problem, and then present UNMASQUE, an extraction algorithm based on an active learning approach, that successfully exposes a complex class of hidden warehouse queries. A special feature of its design is that the extraction is completely non-invasive with respect to the application, examining only the results obtained from repeated executions on databases derived with a combination of data mutation and data generation techniques. Further, potent optimizations are incorporated to minimize the extraction overheads. A detailed evaluation over applications hosting hidden SQL queries, or their imperative versions, demonstrates that UNMASQUE correctly and efficiently extracts these queries. While the above represents a first step in query extraction, a suite of intellectually challenging open problems that remain to be solved in this domain will be covered at the end of the talk. Bio: Jayant Haritsa is on the computer science faculty at the Indian Institute of Science, Bangalore, since 1993. He received a BTech degree from the Indian Institute of Technology (Madras), and MS and PhD degrees from the University of Wisconsin (Madison). He is a Fellow of ACM and IEEE, and currently the President of ACM India.
Professor, Organizational Behavior & Human Resources Management
Will data science be the game changer for women’s participation in the workforce?
Abstract: TBA Bio: Professor Srinivasan is a Professor in the Organisational Behaviour and Human Resource Management Area at IIM Bangalore. She was an Indian Council for Cultural Relations Chair Professor for Corporate responsibility at the HHL Graduate School of Management Leipzig, Germany and also a British Council Visiting Scholar at the International Centre for Corporate Social Responsibility at the Nottingham University Business School. She has consulted extensively for both Indian and multinational companies in the field of leadership development. She has designed, developed, and delivered programmes to build the leadership pipelines especially for technology and R&D organizations. She has also designed and delivered “Tanmatra: Women in Leadership” – a leadership development programme exclusively for senior women leaders in business in collaboration with Catalyst India and IBM. A graduate of Jyoti Nivas College, Bangalore and a post-graduate from XLRI, Jamshedpur in Personnel Management and Industrial Relations, she obtained her Fellow in Management (Doctorate) from the Indian Institute of Management, Bangalore.