Virginia Eubanks | Associate Professor of Political Science – University of Albany, SUNY
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Abstract: Today, automated systems control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data analytics, the most invasive and punitive systems are aimed at the poor. In her new book ‘Automating Inequality’, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of gut-wrenching and eye-opening stories, from a woman in Indiana whose benefits were literally cut off as she lay dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile. Join us to discuss this deeply researched, passionately written, incredibly timely book.
Bio: Virginia Eubanks is an Associate Professor of Political Science at the University at Albany, SUNY. She is the author of ‘Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor’; ‘Digital Dead End: Fighting for Social Justice in the Information Age’; and co-editor, with Alethia Jones, of ‘Ain’t Gonna Let Nobody Turn Me Around: Forty Years of Movement Building with Barbara Smith’. Her writing about technology and social justice has appeared in The American Prospect, The Nation, Harper’s and Wired. For two decades, Eubanks has worked in community technology and economic justice movements. Today she is a founding member of the Our Data Bodies Project and a Fellow at New America.
This event will be held in Rm. 1106 of Luddy Hall.
James Hendler | Tetherless World Chair of Computer, Web and Cognitive Sciences – RPI
Knowledge Representation in the Era of Deep Learning, Watson and the Semantic Web
Abstract: A burst in optimism (and unwarranted fear) has grown around a number of technologies that are high impact and able to solve problems that have challenged AI researchers for years. The over-enthusiasm that often follows such breakthroughs has caused some to declare (yet again) that it is the end of “knowledge representation” as AI moves into a world dominated by neural networks, data mining and the knowledge graph. In this talk, I argue that these technologies, while extremely powerful separately, are not only still a long way from human intelligence, but cannot get there without a level of knowledge and reasoning beyond what is currently available to these techniques. On the other hand, I also argue that taking these technologies into new and harder realms will require rethinking what traditional knowledge representation is and how it is used. Some early examples of work aimed at joining the approaches will be presented.
Bio: James Hendler is the Director of the Institute for Data Exploration and Applications and the Tetherless World Professor of Computer, Web and Cognitive Sciences at RPI. He also heads the RPI-IBM Center for Health Empowerment by Analytics, Learning and Semantics (HEALS) and serves as a Chair of the Board of the UK’s charitable Web Science Trust. Hendler has authored over 400 books, technical papers and articles in the areas of Semantic Web, artificial intelligence, agent-based computing and high performance processing. One of the originators of the “Semantic Web,” Hendler was the recipient of a 1995 Fulbright Foundation Fellowship, is a former member of the US Air Force Science Advisory Board, and is a Fellow of the AAAI, BCS, the IEEE, the AAAS and the ACM. He is also the former Chief Scientist of the Information Systems Office at the US Defense Advanced Research Projects Agency (DARPA) and was awarded a US Air Force Exceptional Civilian Service Medal in 2002. In addition he is the first computer scientist to serve on the Board of Reviewing editors for ‘Science’, co-editor-in-chief of the journal ‘Data Intelligence’, and an associate editor of ‘Big Data’. In 2010, Hendler was named one of the 20 most innovative professors in America by ‘Playboy’ magazine and was selected as an “Internet Web Expert” by the US government. In 2012, he was an inaugural recipient of the Strata Conference “Big Data” awards for his work on large-scale open government data. In 2013, he was appointed as the Open Data Advisor to New York State and in 2015 appointed a member of the US Homeland Security Science and Technology Advisory Committee. In 2016, Jim became a member of the National Academies Board on Research Data and Information. In 2017, Hendler joined the Director’s Advisory Committee for the National Security Directorate of the Pacific Northwest National Laboratory.
This event will be held in the Grand Hall of Luddy Hall.
Rich Carlton | President and COO, Data Realty
Infrastructure to insights - cutting through the hype to true business value from data analytics
Abstract: There is tremendous hype surrounding Big Data and analytics, but to truly get business value from data, there are numerous pitfalls and considerations that must be taken into account. Learn the lessons that have enabled Aunalytics to serve global organizations from here in Indiana and helped them to get indisputable value from their data.
Bio: Rich Carlton leads Data Realty as President and COO after twenty plus years of leadership experience in data and technology-based businesses. There are three corporate brands under his purview: the data center organization Data Realty, the Big Data and Analytics organization Aunalytics, and the Cloud Hosting and Managed Services company MicroIntegration. Carlton works towards driving the overall organization to meet the mission of harnessing the power of data to fuel the economic engine of growing companies, communities, and people. The organization hopes to lead clients toward a culture of data-driven decision-making, differentiating them within their industry and providing true competitive advantage.
Carlton earned a Bachelor of Science degree from Indiana University Kelly School of Business in 1992 with a focus in Computer and Information Systems. He is past Chairman of the Board for the St. Joseph County Chamber of Commerce. In addition, he serves on the Indiana state Chamber of Commerce Technology board, the Quality Committee of St. Joseph Regional Medical Center, the Entrepreneurship board of the South Bend-Elkhart Regional Development Authority, and the Indiana University South Bend School of Medicine Foundation board.
This event will be held in Wells Library Rm. 001.
Prabhat | Head of Data and Analytics Services team at NERSC, U.C. Berkeley
Top 10 Data Analytics Problems in Science
Abstract: Lawrence Berkeley National Lab and NERSC are at the frontier of scientific research. Historically, NERSC has provided leadership computing for the computational science community, but we now find ourselves tackling Big Data problems from an array of observational and experimental sources. In this talk, I will review the landscape of Scientific Big Data problems at all scales, spanning astronomy, cosmology, climate, neuroscience, bioimaging, genomics, material science and subatomic physics. I will present a list of Top 10 Data Analytics problems from these domains, and highlight NERSC’s current Data Analytics strategy and hardware/software resources. I will highlight opportunities for engaging with NERSC, Berkeley Lab and the scientific enterprise.
Bio: Prabhat leads the Data and Analytics Services team at NERSC. His current research interests include scientific data management, parallel I/O, high performance computing and scientific visualization. He is also interested in applied statistics, machine learning, computer graphics and computer vision. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.
Liangjie Hong | Head of Data Science at Etsy
A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation
Abstract: Recommending personalized content to users is a long-standing challenge to many online services, including Facebook, Yahoo!, LinkedIn and Twitter. Traditional recommendation models such as latent factor models and feature-based models are usually trained for all users and optimize an “average” experience for them, yielding sub-optimal solutions. Although multi-task learning provides an opportunity to learn personalized models per user, learning algorithms are often tailored to specific models (e.g., generalized linear model, matrix factorization), creating obstacles for a unified engineering interface, which is important for large Internet companies. This talk will present an empirical framework to learn user-specific personal models for content recommendation by utilizing gradient information from a global model, which potentially benefits any model that can be optimized through gradients, offering a lightweight yet generic alternative to conventional multi-task learning algorithms for user personalization. The effectiveness of the proposed framework is demonstrated by incorporating it in three popular machine learning algorithms including logistic regression, gradient boosting decision tree, and matrix factorization. An extensive empirical evaluation shows a significant improvement in the efficiency of personalized recommendations in real-world datasets.
Bio: Liangjie Hong is Head of Data Science at Etsy Inc., managing a group of data scientists to deliver cutting-edge scientific solutions for: Search and Discovery, Personalization and Recommendation, and Computational Advertising. Previously, he was Senior Manager of Research at Yahoo Research from 2013 to 2016, leading science efforts for Personalization and Search Sciences. Liangjie has published papers in all major international conferences in data mining, machine learning and information retrieval, such as SIGIR, WWW, KDD, CIKM, AAAI, WSDM, RecSys and ICML, winning WWW 2011 Best Poster Paper Award, WSDM 2013 Best Paper Nominated and RecSys 2014 Best Paper Award, as well as serving as a program committee member in KDD, WWW, SIGIR, WSDM, AAAI, EMNLP, ICWSM, ACL, CIKM, IJCAI and several workshops. In addition, he constantly reviews articles in prestigious journals such as DMKD, TKDD, TIST, TIS, and TKDE. Liangjie co-founded the User Engagement Optimization Workshop, which has been held in conjunction with CIKM 2013 and KDD 2014. Prior to Yahoo Research, he obtained his Ph.D. (2013) and M.S. (2010) from Lehigh University and B.S. (2007) from Beijing University of Chemical Technology, all in Computer Science.
This talk will be held in Lindley Hall, Rm. 102.
Kimberly Van Auken | Database Curator, WormBase and Gene Ontology Consortium
Data Curation in the Biomedical Sciences: from Text to Databases to Knowledge Discovery
Abstract: Knowledge discovery in the biomedical sciences depends on accurate, consistent representation of data in knowledgebases. Over the past two decades the biocuration community, including the Model Organism Databases (MODs) and the Gene Ontology Consortium (GOC) (http://geneontology.org/), have been at the forefront of biological knowledge management. The vast and rapidly increasing amount of biomedical data, however, makes biocuration a very challenging task. In my talk, I’ll discuss methods we’ve employed at WormBase (https://wormbase.org) and the GOC to help meet these challenges, with specific emphasis on the use of: 1) text mining tools, such as Textpresso (http://textpresso.org/), to identify suitable papers and evidence sentences for curation, 2) controlled vocabularies and ontologies to model biological data, and 3) data capture, visualization, and analysis tools to engage users and foster knowledge discovery.
Bio: Kimberly Van Auken, Ph.D. is a Database Curator for WormBase, the online database housing the genetics, genomics and biology of Caenorhabditis elegans and other nematodes. She serves as an ontology editor and co-manager of the Annotation Working Group for the Gene Ontology Consortium and is a member of the editorial board for ‘Database: The Journal of Biological Databases and Curation’. She holds a B.S. in Biochemistry from the University of Rochester, Rochester, NY and a Ph.D. in Molecular, Cellular, and Developmental Biology from the University of Colorado, Boulder.
This event will be held in Rm. 102 of Lindley Hall until further notice.
Stephen Kobourov | Professor of Computer Science, University of Arizona
Analyzing the Language of Food on Social Media
Abstract: In this lecture we investigate the predictive power behind the language of
food on social media from a collected corpus of over three million
food-related posts from Twitter. Using this, we will demonstrate that many latent
population characteristics can be directly predicted from this data:
overweight rate, diabetes rate, and political leaning. We analyze
which textual features have most predictive power for these datasets,
providing insight into the connections between the language of food,
geographic locale, and community characteristics. Lastly, we describe
and demonstrate an online system for real-time query and visualization of the
dataset. Visualization tools, such as geo-referenced heatmaps, semantics-preserving wordclouds,
and temporal histograms allow us to discover more complex, global patterns mirrored in the language of food.
Bio: Stephen Kobourov is a Professor of Computer Science at the University
of Arizona. He completed BS degrees in Mathematics and Computer
Science at Dartmouth College in 1995, and a PhD in Computer Science at
Johns Hopkins University in 2000. He has worked as a Research
Scientist at AT&T Research Labs, and is a Humboldt Fellow at the University
of Tübingen in Germany as well as a Distinguished Fulbright Chair at Charles
University in Prague.
This event will be held in Rm. 102 of Lindley Hall.
Ellie Symes | CEO, The Bee Corp.
Data Driven Beekeeping
Abstract: The Bee Corp is a benefit corporation founded in Bloomington, IN that applies data analytics to beekeeping. Our mission is to drive innovation on traditional beekeeping practices through scientific research and technology in order to foster sustainable honeybee populations. Because of advancements in sensor technology, the Internet of Things is permeating traditional industries. Increasingly, beekeepers are adopting technology to discover insights about their hives. The Bee Corp uses the sensor data to derive hive health insights, allowing beekeepers to track the health of their hive in real time. This talk will focus on the current knowledge and future possibilities of precision beekeeping.
The talk will be held in Lindley Hall Rm. 102.
Bio: Born in St. Louis, Missouri, Ellie Symes, CEO, grew up in a number of cities across the Midwest and Canada. Ellie is a part-time graduate student at Indiana University pursuing dual candidacy for a Master of Public Affairs and Master of Science in Environmental Science from the School of Public and Environmental Affairs.
This event will be held in Rm. 102 of Lindley Hall.
Lisa Shaler | Deputy chief, Program Budget Data Management Division, Army G-8 Program Analysis and Evaluation Directorate
Army Data Science Initiatives
Abstract: From humanitarian relief after natural disasters at home and abroad, to planning budgets, designing new vehicles, recruiting soldiers and civilians, planning operations, and analyzing cyber and intelligence events, the U.S. Army uses data science to analyze and evaluate options. The goal is to give Army senior leaders the information needed to make difficult decisions. While the time required for these data science analyses can sometimes be sufficient, it can also demand quick, accurate responses. Using effective data visualization to present data science results to clarify options and tradeoffs is essential. Modern data science tools and cloud infrastructures enable the Army to build collaborative teams, solving complex problems together.
Bio: Lisa Shaler has supported soldiers and the Army with technology for more than 20 years. She led Army Intel with “Big Data” technology efforts including Analytics, Cloud Computing, and Cyber Security. At the Army G-8 Program Analysis & Evaluation Directorate, she focuses on Data Migration to the Cloud, and the Army G-8 Data Science Pilot. She earned master’s degrees from MIT, National Defense University, and Virginia Tech. Her research interests include computational social science and complexity studies.
This talk will be held in Lindley Hall Rm. 102.
Kuansan Wang | Managing Director, MSR Outreach Innovation
Abstract: Cognition is defined as the “process of acquiring knowledge and understanding through thought, experience, and the senses,” and often “encompasses processes such as knowledge, attention, memory, judgment, evaluation, reasoning and computation.” Based on this definition, humans appear destined to be surpassed by machines equipped with massive memory and perfect recall, capable of remaining attentive with perpetual endurance and exercising judgment/reasoning through computations in a much faster and precise fashion. With massive amounts of data and computation powers, the machine has made great strides in exhibiting intelligent behavior. But has it beaten humans in acquiring and utilizing knowledge yet? In this talk, I will describe Microsoft Academic, a research project to create a cognitive agent that can be simultaneously proficient in more than 50,000 fields of study by reading more than a century’s worth of scholarly publications from the web. At the core of the agent is a virtuous cycle based on reinforcement learning where the machine is aided by a knowledge graph to extract salient entities and their relationships from publications that are then fed back to the knowledge graph, thereby enriching its coverage and further improving machine reading capabilities. We will show how the cognitive agent, currently at age two, can be publicly accessed, and how the knowledge accumulated has played a role in our daily activities inside Microsoft Research.
Bio: Kuansan Wang is a principal researcher and managing director of Microsoft Research Outreach where he is responsible for engaging with the global academic community on jointly advancing the state-of-the-art accomplishments in the areas where MSR conducts research. He currently leads a team that explores web-scale machine reading, intelligent inference, deep semantic analytics and user behavior modeling. In addition to contributing to the development of Microsoft Bing and Cortana, the technologies developed at his team can also be seen in Microsoft Academic services, including a search engine at academic.microsoft.com and the Academic Knowledge API available through Microsoft Cognitive Services. Dr. Wang joined MSR in 1998 as a researcher in speech technology group where he conducted research in language modeling and multimodal interactions. He then became a software architect for Microsoft speech product group, responsible for Microsoft Speech Server and Response Point, and represented Microsoft at W3C, ECMA and ISO to help author international standards in speech, language and communication areas. He returned to MSR to work on web search in 2007 and has been a key driving force to evolve web search from a keyword-based to a semantic-based paradigm. Kuansan received his BS from National Taiwan University and MS and PhD as an NSF Fellow from University of Maryland, College Park, all in electrical engineering.
This talk will be held in Lindley Hall, Rm. 102.
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