Dan Putler | Chief Scientist at Alteryx
Projecting Election Polling Results to Small Geographic Areas
Abstract: Election polling generally provides a useful means of predicting voter behavior nationally and at the state level. However, due to limited sample sizes and the cost associated with polling, understanding and predicting voter behavior at a “hyper local” level is not generally done – but this information would be invaluable for better focusing grassroots campaign activities such as door-to-door canvasing, get out the vote efforts, and yard signs and other outdoor media. In this work we set out to develop a model to make election predictions at a very localized level. Our solution to the problem accomplishes this in two parts: the first is the creation of a predictive model of voter choice based on both local area factors (such as county level Partisan Voting Index values) as well as individual demographic/socioeconomic characteristics; and the second is estimating the number of individuals that fall into specific demographic/socioeconomic profiles within each small geographic unit.
Our final model predicts voting outcomes at the census tract level (which typically have populations of around 4000 people), the results of which will be available for the public to explore in an interactive web app on the Alteryx Gallery. In this talk we will be looking at the approach and methodologies used to develop these predictions around the upcoming election, which were implemented using a combination of R and Alteryx. In addition, if time permits, an assessment of the predictive capability of the approach relative to the actual November 8 election results at the county level (the lowest level of geography for which election returns are generally available) will be presented.
Bio: Dr. Dan Putler is the Chief Scientist at Alteryx, where he is responsible for developing and implementing the product road map for predictive analytics. He has over 30 years of experience in developing predictive analytics models for companies and organizations that cover a large number of industry verticals, ranging from the performing arts to B2B financial services. He is co-author of the book, “Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R”, which is published by Chapman and Hall/CRC Press. Prior to joining Alteryx, Dan was a professor of marketing and marketing research at the University of British Columbia’s Sauder School of Business and Purdue University’s Krannert School of Management.
Qiaozhu Mei | Associate Professor, University of Michigan School of Information
Learning Representations for Large-Scale Networks
Abstract: Recent successes in big data analytics and representation learning have drawn lots of attention. While deep learning techniques have demonstrated their great power in image, text, and speech data, finding methods of learning useful representations for discrete networked data remains a major challenge. In this talk, Dr. Mei will introduce recent progress made by his research group in learning the representations for large-scale network data. He will introduce efficient algorithms that embed nodes into a continuous vector space so that the local and global structural information is preserved. By further projecting the representation to a 2D space, we are able to visualize millions of high-dimensional data points meaningfully on a single slide. Learning the representation of a network as a whole demonstrates that the topological structure of a network has a predictive power for its growth.
Bio: Qiaozhu Mei is an associate professor at the School of Information, University of Michigan. He is widely interested in data mining, machine learning, information retrieval and their applications to the Web, natural language, social networks, and health informatics. He is a recipient of the NSF CAREER Award and multiple best paper awards at ICML, KDD, WSDM, and other related venues. He has co-chaired multiple research tracks of the WWW conference, has served on the editorial boards of multiple top journals, and is the general co-chair of SIGIR 2018.
Scott Swinford | Chief Operating Officer, Perscio
Analytics in Business
Abstract: The approach to analytics has changed rapidly over the past 25 years, having evolved from providing historical analysis to directing prescriptive solutions.
This talk will review the evolution of analytic tools and environments as well as how industries ranging from agriculture to hospitality leverage analytics. The talk will also include how to show value and gain acceptance for analytic efforts in business environments.
Bio: Scott is a consultant with expertise in analytics, pricing, supply chain management, and contact center management. He spent more than 20 years inside Fortune 500, publicly-traded firms such as Starwood and Wyndham where he was responsible for optimizing revenue for global business units, new products, and start up divisions. He started his career as an analyst working to uncover revenue opportunities through the use of data and analytics. This work improved management decision-making for inventory optimization, new pricing strategies, and product offerings. Scott has held senior leadership roles in both global consumer and B2B environments where he helped the large organizations he has served grow, innovate, and cut costs. He earned a B.S. in Industrial Engineering from Purdue University and a B.S. in Economics & Pre-Engineering from St. Joseph’s College.
A copy of the slides used in this talk can be found here.
Rishik Dhar | Principal Data Engineer at Target
Personalization, Recommendations and Similarity - Towards Real-Time, Personalized Recommendations
Abstract: Retail industry generally operates under very stringent timelines and non-negotiable constraint of customer satisfaction. To run a retail business these days requires a combination of engineering practices and rigorous science. The next milestone for recommendations systems is to personalize recommendations in real-time driven by user interaction. To achieve this we need to rethink the conventional ideas around recommendation algorithms and the delivery mechanisms. This talk is a discussion on what we can do to make the most appropriate suggestions to consumers and make them fast enough to be relevant to their decision-making process.
Biography: Rishik Dhar is a Principal Data Engineer at Target’s Data Science and Engineering Center of Excellence in Sunnyvale, CA. He works on solving retail problems using machine learning on Target’s big data platform using distributed computing and parallel computing paradigms in his applied data science work. He received his MS in Electrical and Computer Engineering from Carnegie Mellon University, with a concentration on applications of Machine Learning in Automated Speech Recognition. Part of his job is to explore new technology areas relevant to Data Science and Engineering. He leads a team of Data Scientists and Engineers to deliver recommendations over Target’s Online Shopping experience. Rishik’s interests lie in Speech Technologies, Auditory Interfaces, Human Cognition, Assistive Technologies, Chat Bots and Social Good. In his free time, he likes to jam with his 6-year-old daughter over Bollywood songs. He is curious about Oculus VR on Samsung Gear, AI APIs, TensorFlow on GPUs and most recently WaveNet. He is also looking for engineers and scientists to join his team and help Target build state-of-the-art algorithms for solving retail problems as well as tackling general challenges in the area of applied machine learning.
A copy of the slides used in this talk can be found here.
Duru Ahanotu | Director of Corporate Measurement for Yahoo!'s Global Research and Insights Group
Organizing around Big Data
Abstract: The era of “Big Data” comes with big promises. The availability of massive amounts of data, along with the power to organize and process it, has strengthened our ability to bring evidence to bear on our decisions. I propose a model for organizing effectively around Big Data called an “Insights Supply Chain.” Embedded within a framework of experimentation and empowering data tools, an Insights Supply Chain transforms Big Data into actionable decisions. I provide some examples of how Yahoo uses Big Data to create engaging products and user experiences. I conclude with some reminders of the limits of our knowledge that have yet to change and challenge practitioners to respect the wide-reaching impacts they can have on individuals and society as a whole.
Bio: N. Duru Ahanotu, Ph.D. is the Director of Corporate Measurement for Yahoo’s Global Research and Insights group under the Marketing organization. Prior to this, Dr. Ahanotu led a data insights team for the Yahoo Advertising and Data organization. Before joining Yahoo, Dr. Ahanotu served as a Solutions Architect supporting and implmenting price optimization software used by web publishers for setting the prices for premium advertising inventory.
A copy of the slides used in this talk can be found here.
Tom Arkins | Section Chief of IT and Informatics for Indianapolis EMS
So You Have Data, Now What?
Abstract: This talk goes over how Indianapolis Emergency Medical Services use data in their daily operations. We will discuss our interactions with the police department and other public health and safety agencies, as well as the use of technologies in a disaster setting.
Bio: Tom Arkins has been in public safety since 1986, beginning his career with the White River Fire Department. He has been with Wishard/Indianapolis EMS since 1994 serving as an EMT, Paramedic, EMS Supervisor, and Tactical Paramedic. Currently he serves as the Section Chief of IT and Informatics for Indianapolis EMS.
Larry Smarr | Director of the California Institute for Telecommunications and Information Technology (Calit2)
Abstract: The human body is host to 100 trillion microorganisms, ten times the number of cells in the human body and these microbes contain 300 times the number of DNA genes that our human DNA does. The microbial component of our “superorganism” is comprised of hundreds of species with immense biodiversity. Thanks to the National Institutes of Health’s Human Microbiome Program researchers have been discovering the states of the human microbiome in health and disease. To put a more personal face on the “patient of the future,” I have been collecting massive amounts of data from my own body over the last ten years, which reveals detailed examples of the episodic evolution of this coupled immune-microbial system. An elaborate software pipeline, running on high performance computers, reveals the details of the microbial ecology and its genetic components. A variety of data science techniques are used to pull biomedical insights from this large data set. We can look forward to revolutionary changes in medical practice over the next decade.
Bio: Larry Smarr is the founding Director of the California Institute for Telecommunications and Information Technology (Calit2), a UC San Diego/UC Irvine partnership, and holds the Harry E. Gruber professorship in the Department of Computer Science and Engineering (CSE) of UCSD’s Jacobs School of Engineering. Before that he was the founding director of the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Champaign-Urbana. He is a member of the National Academy of Engineering, as well as a Fellow of the American Physical Society and the American Academy of Arts and Sciences. In 2006 he received the IEEE Computer Society Tsutomu Kanai Award for his lifetime achievements in distributed computing systems and in 2014 the Golden Goose Award. He served on the NASA Advisory Council to 4 NASA Administrators, was chair of the NASA Information Technology Infrastructure Committee and the NSF Advisory Committee on Cyberinfrastructure, a member of the DOE Advanced Scientific Computing Advisory Committee and ESnet Policy Board, and for 8 years he was a member of the NIH Advisory Committee to the NIH Director, serving 3 directors. His personal interests include growing orchids, snorkeling coral reefs, and quantifying the state of his body.
Pedro Alves | Director of Data Science at Sentient Technologies
Practical Data Science
Abstract: This talk will cover practical points in data science that come up in industry as well as some real-world examples of projects and problems. The problems discussed in this talk will focus on aspects of projects that are usually not taught in data science courses but are where a considerable portion of work is done. This talk will also briefly cover the history and theory behind ensembles and why they work so well.
Biography: Pedro has experience in predicting, analyzing and visualizing data in the fields of: genomics, gene networks, cancer metastasis, insurance fraud/costs, hospital readmissions, soccer strategies, joint injuries, social graphs, human attraction, spam detection and topic modeling, among others. Pedro is incredibly passionate about all aspects of data science and is constantly creating new techniques and algorithms to suit the problems at hand. At Banjo, his efforts were geared towards detecting and interpreting everything that is happening in the world in real-time, from major concerts and sporting events to major and minor news. Now he leads the data science efforts at Sentient.ai where they use evolutionary algorithms and massively scaled deep learning to solve problems such as trading and visual comprehension of consumer products.
Woodburn Hall 200
Mauro Martino | Cognitive Visualization Lab at IBM Watson Cambridge, MA
Point, Line & Data: New methods for understanding complex data, from storytelling to machine learning
Abstract: The aesthetics of science is changing, the diffusion of data visualization tools is enabling a revival of beauty in scientific research. More and more papers are presented with seductive images, convincing videos, and sharp interactive tools. Scientific storytelling will be discussed with 2 case studies: “Charting Culture, 2014”, and “Rise of partisanship, 2015”. In the second part of the talk we explore the connection between Machine Learning & Data Visualization. We will see together 3 projects: News Explorer – exploration of real-time news, Ted Watson – exploration of a large corpus of videos, and Watson 500 – the analysis of relationships between entities and topics in a specific corpus of date. We encourage the public to use these tools before the talk:
Biography: Mauro Martino is an Italian expert in data visualization based in Boston. He created and leads the Cognitive Visualization Lab at IBM Watson in Cambridge, Massachusetts, USA. Martino’s data visualizations have been published in the scientific journals Nature, Science, and the Proceedings of the National Academy of Sciences. His projects have been shown at international festivals including Ars Electronica, and art galleries including the Serpentine Gallery, UK, GAFTA, USA, and the Lincoln Center, USA.
Jointly organized by the Data Science program and the Cyberinfrastructure for Network Science Center, this talk is partially supported by Indiana University’s Consortium for the Study of Religion, Ethics and Society, a consortium sponsored by the Vice President for Research Office
Talk details can be found at http://cns.iu.edu/cnstalks. All talks will take place in the new Social Science Research Commons, Woodburn Hall 200 (unless otherwise noted).
Abe Weston | Associate Principal performing in Fraud Analytics
Predictive Modeling of Pay-Per-Click Keywords Bid Value
Abstract: Pay-Per-Click (PPC) keywords are purchased by advertisers, preferably at minimum cost, in order to maximize profit. Data sets with predictors are generated using data collected from the Google Search API for Shopping and the Microsoft Ad Intelligence Service. Machine learning is applied to successfully predict the future bid value.
Biography: Abe Weston works for Google as an Associate Principal performing in fraud analytics. He has an undergraduate degree in Industrial Technology, MS degrees in Telecommunications Systems and Data Mining, and a graduate certificate in Statistics. He enjoys traveling, camping, reading, hiking, biking, yoga, playing guitar, and spending time with his wife and three kids.
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