SFU Data Science Research Group

Home
Admissions
Trustworthy Data Science and AI Seminars (current)
Courses

Seminar Series on Trustworthy Data Science and AI

Brought to you by SFU Data Science Research Group

Upcoming

Past

Coordinators

Jian Pei

Jian Pei

Jiannan Wang

Jiannan Wang

Tianzheng Wang

Tianzheng Wang

© Data Science Research Group, Simon Fraser University

Making an Impact? A Tale of Two Projects

Speaker:	Kevin Leyton-Brown, University of British Columbia; Canada CIFAR AI Chair, Amii
Time, Location:	2:30-4:00pm (Pacific Time), Oct 16, 2020. Online. [Recording] [Slides] [Poster]
Abstract:	How can AI researchers leverage their specialized knowledge to make a social impact? The notion is beguiling but the reality is complicated. This talk contrasts two strategies that are often employed--loosely described as “write a paper” and “be an entrepreneur”--gained via two, very different projects in electronic market design. The first project focused on developing new theoretical ideas for incentivizing local food pantries to honestly report demand to a centralized food bank. The second project was more practical; it aimed to design an electronic market for agricultural commodities in Uganda that could operate over low-end SMS phones. After discussing technical innovations, lessons learned, and lingering disappointments from both projects, the talk will conclude with some overall thoughts about strategies researchers might employ in pursuit of successful AI for Social Impact projects and how these can be taught in our courses.
Speaker Bio:	Kevin Leyton-Brown is a professor of Computer Science at the University of British Columbia and holds a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute; he is also an associate member of the Vancouver School of Economics. He holds a PhD and M.Sc. from Stanford University (2003; 2001) and a B.Sc. from McMaster University (1998). He studies artificial intelligence, mostly at the intersection of machine learning and (1) the design and operation of electronic markets and (2) the design of heuristic algorithms. He has co-written two books, "Multiagent Systems" and "Essentials of Game Theory," and over 100 peer-refereed technical articles; his work has received over 16,000 citations and an h-index of 49. He is an Fellow of the Association for the Advancement of Artificial Intelligence (AAAI; elected 2018) and a Distinguished Member of the ACM (elected 2018). He was a member of a team that won the 2018 INFORMS Franz Edelman Award for Achievement in Advanced Analytics, Operations Research and Management Science, described as "the leading O.R. and analytics award in the industry." This award recognizes a "completed, practical application that had significant, verifiable and quantifiable impact on the performance of [a] client organization," in his case the Federal Communications Commission. Leyton-Brown also received UBC's 2015 Charles A. McDowell Award for Excellence in Research, a 2014 NSERC E.W.R. Steacie Memorial Fellowship—previously given to a computer scientist only 10 times since its establishment in 1965—and a 2013 Outstanding Young Computer Science Researcher Prize from the Canadian Association of Computer Science. He and his coauthors have received paper awards from JAIR, ACM-EC, AAMAS and LION, and numerous medals for the portfolio-based SAT solver SATzilla at international SAT solver competitions (2003–15). He is Program Co-Chair for AAAI 2021, one of the top two international conferences on artificial intelligence. He has co-taught two Coursera courses on "Game Theory" to over 850,000 students (and counting!), and has received awards for his teaching at UBC—notably, a 2013/14 Killam Teaching Prize. He is past chair of the ACM Special Interest Group on Electronic Commerce, which runs the annual Economics & Computation conference. He has served as an associate editor for the Artificial Intelligence Journal (AIJ), the Journal of Artificial Intelligence Research (JAIR), ACM Transactions on Economics and Computation (ACM-TEAC), and AI Access; and was program chair for the ACM Conference on Electronic Commerce (ACM-EC) in 2012. He has held a wide range of visiting professor/researcher positions, at: Technion Israel Institute of Technology (April 2018); Microsoft Research New York City (Jan–Feb 2018); Microsoft Research New England (Mar–Jun 2016); Harvard's EconCS group (Mar–Jun 2016); the Simons Institute for the Theory of Computing at UC Berkeley (Aug–Sep, Nov 2016; Sep-Dec 2015); the Institute for Advanced Studies at Hebrew University of Jerusalem, Israel (Mar–Jun 2011); Makerere University in Kampala, Uganda (Sep 2010–Jan 2011). He currently advises Auctionomics, AI21, and Qudos. He is a co-founder of Kudu.ug and Meta-Algorithmic Technologies. He was scientific advisor to UBC spinoff Zite until it was acquired by CNN in 2011. His past consulting has included work for Zynga, Trading Dynamics, Ariba, and Cariocas.
Host:	Jian Pei

Getting Rid of Data

Speaker:	Tova Milo, Tel Aviv University; ACM Fellow, Member of Academia Europaea
Time, Location:	10:00am (Pacific time), Nov 12, 2020 Online. [Recording] [Slides] [Poster]
Abstract:	We are experiencing an amazing data-centered revolution. Incredible amounts of data are collected, integrated and analyzed, leading to key breakthroughs in science and society. This well of knowledge, however, is at a great risk if we do not dispense with some of the data flood. First, the amount of generated data grows exponentially and already at 2025 is expected to exceed the available storage by an order of magnitude. Second, even disregarding storage constraints, uncontrolled data retention risks privacy and security, as recognized, e.g., by the recent EU Data Protection reform. Data disposal policies must be developed to benefit and protect organizations and individuals. Retaining the knowledge hidden in the data while respecting storage, processing and regulatory constraints is a great challenge. The difficulty stems from the distinct, intricate requirements entailed by each type of constraint, the scale and velocity of data and the constantly evolving needs. While multiple data sketching, summarization and deletion techniques were developed to address specific aspects of the problem, we are still very far from a comprehensive solution. Every organization has to battle the same tough challenges, with ad hoc solutions that are application specific and rarely sharable. In this talk I will discuss the logical, algorithmic, and methodological foundations required for the systematic disposal of large-scale data, for constraints enforcement and for the development of applications over the retained information. 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.
Speaker 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 now she is 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 Logical Methods in Computer Science 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, 2017 VLDB Women in Database Research award and Weizmann award for Exact Sciences Research. The prestigious EU ERC Advanced Investigators grant.
Host:	Jian Pei

Humans in Online Job Markets

Speaker:	Sihem Amer-Yahia, Computer Scientist; Director of French National Centre for Scientific Research, Editor-in-Chief of the VLDB Journal for Europe and Africa
Time, Location:	1:00pm (Pacific time), Nov 18, 2020 Online. [Recording] [Slides] [Poster]
Abstract:	Online labor markets are increasingly becoming a destination for work. These marketplaces include freelancing platforms such as Qapa and MisterTemp' in France, and TaskRabbit and Fiverr in the USA. On those platforms, workers can find temporary jobs in the physical world such as moving furniture, or in the form of virtual micro-gigs such as helping with designing a website. I will how human factors affect algorithm design on virtual marketplaces, and a study of algorithmic fairness on those platforms. The talk will end with open questions raised a year ago at a Shonan workshop on the Future of Work.
Speaker Bio:	Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Co-Head of the Lab of Informatics of Grenoble. She works on exploratory data analysis and fairness in job marketplaces. Before joining CNRS, she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem currently leads the SIGMOD and VLDB Diversity&Inclusion initiative.
Host:	Jian Pei

Graph Representation Learning for Drug Discovery

Speaker:	Jian Tang, Mila-Quebec AI Institute; HEC Montreal, CIFAR AI Research Chair
Time, Location:	4:30pm (Pacific time), Dec 16, 2020 Online. [Recording] [Slides] [Poster]
Abstract:	Drug discovery is a very long and expensive process, taking on average more than 10 years and costing $2.5B to develop a new drug. Artificial intelligence has the potential to significantly accelerate the process of drug discovery by extracting evidence from a huge amount of biomedical data and hence revolutionizes the entire pharmaceutical industry. In particular, graph representation learning techniques---a fast growing topic in the machine learning and data mining community focusing on deep learning for graph-structured data---has seen great opportunities for drug discovery as many data in the domain are graph-structured such as molecules and biomedical knowledge graphs. In this talk, I will introduce our recent progress on graph representation learning for drug discovery including: (1) molecule properties prediction; (2) de novo molecular design and optimization; and (3) retrosynthesis prediction.
Speaker Bio:	Jian Tang is currently an assistant professor at Mila-Quebec AI Institute and HEC Montreal (business school of University of Montreal), and also a Canada CIFAR AI Research Chair. His main research interests are graph representation learning, graph neural networks, deep generative models, knowledge graphs and drug discovery. He obtained his PhD from School of EECS, Peking University in 2014, was an associate researcher at Microsoft Research Asia in 2014-2016, and was a joint postdoc fellow at University of Michigan and Carnegie Mellon University. During his PhD, he was awarded with the best paper in one of the top three machine learning conferences—ICML2014; in 2016, he was nominated for the best paper award in the top data mining conference World Wide Web (WWW); in 2020, he is awarded with Amazon and Tencent Faculty Research Award. He is one of the most representative researchers in the growing field of graph representation learning and has published a set of representative works in this field such as LINE, LargeVis, and RotatE. His work LINE on node representation learning has been widely recognized and is the most cited paper at the WWW conference between 2015 and 2019. He is the program committee and area chair of many prestige conferences in machine learning and data mining including NeurIPS, ICML, ICLR, KDD, WWW, AAAI, IJCAI, etc.
Host:	Jian Pei

Quality-driven Analytics on Scientific Data

Speaker:	Angela Bonifati, Full Professor; Lyon 1 University
Time, Location:	11:30am (Pacific time), Apr 14, 2021 Online. Registration [Poster]
Abstract:	One of the key principles of data analytics in data science pipelines is that the quality of the results is dependent on the quality of the input data analyzed. Data collection and acquisition in scientific domains, such as for instance in healthcare and life sciences domains, tend to often introduce errors into the data, such as violations of business rules, typos, missing values and replicated entries. Moreover, data collected for the patients' signals might exhibit peculiar features that need to be taken into account within the analytical and inference processes. I will present our latest results on enhancing the quality of querying and inference processes on scientific data and beyond. Among the others, we operate on real-life data of patients from several hospitals in the EU and provide the domain experts with useful data management and learning techniques that can help them with their diagnoses and analyses. First, inconsistency-aware annotations can enhance the data input to analytical processes. These annotations are further exploited during query processing in order to enhance the output of queries with inconsistency degrees. Second, feature-based similarities among time series corresponding to patients’ signals help to better identify groups of patients and to assess their risks. Third, logic-based declarative privacy-preserving data integration allows to migrate clinical data from one hospital to another while ensuring privacy guarantees. In all cases, our research aims at providing the caregivers with a better understanding of their clinical data thanks to the improved outcomes of the performed analytics. In the talk, I will overview the research performed in my group on this topic as well as the related work and the future directions of investigation.
Speaker Bio:	Angela Bonifati is a Professor of Computer Science at Lyon 1 University and affiliated with the CNRS Liris research lab since 2015. In 2019 and 2020, she has been on leave at INRIA. Prior to that, she was working as a Professor at Lille 1 University (2011-2015) and as a researcher at CNR, Italy since 2003. She received her Ph.D. from Politecnico di Milano in 2002 and right after she was a postdoctoral researcher at INRIA Rocquencourt in Paris for one year. Her current research interests are on the interplay of relational and graph-oriented data paradigms, particularly on data integration and Big Data curation for life sciences, query processing and learning for structured and unstructured data models. She is the Principal Investigator of the ANR research project QualiHealth: Enhancing the Quality of Healthcare Data (2018-2023) and was the recipient of the IDEX Palse Impulsion Individual grant in 2016. She is involved in several grants at Lyon 1 University, including a EU H2020 grant on data quality and data integration in a clinical data lake. She has also co-authored several publications in first-rate venues of the data management field along with two books (edited by Springer in 2011 and Morgan&Claypool in 2018) and an invited paper in ACM Sigmod Record 2018. She is the Program Chair of ACM Sigmod 2022 and was the Program Chair of EDBT 2020. She is Associate Editor of PVLDB in 2020-2022 and Associate Editor of IEEE ICDE 2021, 2018 and 2011. She is Associate Editor of the VLDB Journal, ACM TODS, Distributed and Parallel Databases and Frontiers in Big Data. She is the President of the EDBT Executive Committee and a member of the ICDT council. She holds many visiting scholar positions in foreign universities in both Europe and North America, the latest of which at the University of Waterloo (Canada) in 2019. She is currently Adjunct Professor in the latter university.
Host:	Jian Pei

Towards Democratizing Data Science

Speaker:	Carsten Binnig, Full Professor, TU Darmstadt; Adjunct Associate Professor, Brown University
Time, Location:	11:30am (Pacific time), Mar 24, 2021 Online. Registration [Poster]
Abstract:	Technology has been the key enabler of the current Big Data movement. Without open-source tools like TensorFlow and Spark, as well as the advent of cheap, abundant computing and storage in the cloud, the trend toward datafication ofalmost every field in research and industry could never have happened.However, the current Big Data tool set is ill-suited for an efficient knowledge discovery by domain experts with only limited IT skills and thus represents a major bottleneck in our data-drivensociety. In this talk, I will present an overview of my current research efforts to revisit the Big Data stack from the user interface to the underlying hardware for making Big Data tools more efficient and easier to use for domain experts and thus enablethe democratization of data science.
Speaker Bio:	Carsten Binnig is a Full Professor in the Computer Science department at TU Darmstadt and an Adjunct Associate Professor in the Computer Science department at Brown University. Carsten received his PhD at the University of Heidelberg in 2008.Afterwards, he spent time as a postdoctoral researcher in the Systems Group at ETH Zurich and at SAP working on in-memory databases. Currently, his research focus is on the design of data management systems for modern hardware as well as modern workloads suchas interactive data exploration and machine learning. His work has been awarded with a Google Faculty Award, as well as multiple best paper and best demo awards for his research.
Host:	Jian Pei