Dorsey Learning Hall 1106, Luddy Hall
Fran Berman | Edward P. Hamilton Distinguished Professor of Computer Science - Rensselaer Polytechnic Institute
Civilizing the Internet of Things
Abstract: The ubiquity of digital information has ushered in a new environment that is transforming how we work, do business, and live: The “Internet of Things” (IoT) is an interconnected cyber-physical-biological environment that links devices, systems, data, and people. At its best, the IoT has the potential to create a rich environment that empowers people through technology, and technology through intelligence. At its worst, the IoT can open a Pandora’s Box of inappropriate behavior, unintended consequences, and intrusiveness. To create an IoT that advances society, and not just technology, involves a holistic approach that coordinates technological design and capability, responsible social and ethical policy and regulation, and environmental sustainability. In this talk, Fran Berman discusses challenges and opportunities in developing the IoT from wild west to empowering environment that promotes the public good.
Bio: Dr. Francine Berman is the Edward P. Hamilton Distinguished Professor in Computer Science at Rensselaer Polytechnic Institute. She is a Fellow of the Association of Computing Machinery (ACM) and a Fellow of the IEEE. In 2009, Dr. Berman was the inaugural recipient of the ACM/IEEE-CS Ken Kennedy Award for “influential leadership in the design, development, and deployment of national-scale cyberinfrastructure.” In 2015, Dr. Berman was nominated by President Obama and confirmed by the U.S. Senate to become a member of the National Council on the Humanities.
Dr. Berman is former Director of the San Diego Supercomputer Center (2001 to 2009) and former Vice President for Research at Rensselaer Polytechnic Institute (2009-2012). In 2012, she became U.S. lead of the emerging Research Data Alliance (RDA), a community-driven international organization created to accelerate research data sharing world-wide, and to develop the technical, organizational and social infrastructure needed to support data-driven innovation. Dr. Berman is Chair of RDA/U.S. and Co-Chair of RDA’s leadership Council. Prior to joining Rensselaer, Dr. Berman was Professor in the UC San Diego Department of Computer Science and Engineering and first holder of the High Performance Computing Endowed Chair in the Jacobs School of Engineering. As Director of the San Diego Supercomputer Center (SDSC), she led a staff of 250+ interdisciplinary scientists, engineers, and technologists. Dr. Berman directed the National Partnership for Advanced Computational Infrastructure (NPACI), a consortium of 41 research groups, institutions, and university partners with the goal of building national infrastructure to support research and education in science and engineering.
Dorsey Learning Hall 1106, Luddy Hall
Michael Richey | Chief Learning Scientist - The Boeing Company
Advancing Personalized Learning in workplace settings
Abstract: We are living in an age transformed by intelligent systems. Data science including Machine Learning, Artificial Intelligence and Natural Processing Language methods are changing business communication and making transparent workforce expertise and culture. The concept of coupling AI and digital data between agents (learners) the system culture (formal and informal) within a real world environment (robots) enables us to see interaction patterns, agent sensing, acting and knowledge optimizing.
Changes in the skills and knowledge necessary for successful digital life and work flow are now impacting organization cultural systems as well. Organizations are inherently relational. In the jargon of complex systems, firms are complex adaptive systems composed of a network of employees bound together by contracts, who perform tasks by negotiating both internal relationships within firms, but also by observing, managing, and responding to constantly shifting external environments.
These changes driven by convergence methods promise rapid innovation for companies due to the need for a highly-educated “adaptive “workforce, but to also give company’s new opportunities to measure the flow-quality of information including key competency and agility measures. These data are starting to uncover the distributive cognition of agents within a social network, “The ghost in the machine” where thoughts are embodied in digital exhaust of agent actions (Madhavan and Richey, 2016).
Bio: Dr. Michael Richey is the Chief Learning Scientist and a Boeing Associate Technical Fellow currently assigned to support educational technology and innovation research at The Boeing Company. Michael is responsible for leading a team conducting engineering education research projects that focus on improving the learning experience for students, incumbent engineers and technicians. His research encompasses, Sociotechnical Systems, Learning Curves, and Engineering Education Research. The online educational programs and research focus on practical understanding of human learning and the design of technology-enhanced learning environments and promoting global excellence in engineering and learning technology to develop future generations of entrepreneurially-minded engineers. Michael has served on various advisory groups including, the editorial board of the Journal of Engineering Education, Boeing Higher Education Integration Board, American Society for Engineering Education Project Board and the National Science Foundation I-UCRC Industry University Collaborative Research Center Advisory Board. Michael has authored or co-authored over 40 publications in leading journals including Science Magazine, The Journal of Engineering Education and INCOSE addressing topics in large scale system integration, learning sciences and systems engineering. Michael often represents Boeing internationally and domestically as a speaker – presenter and has authored multiple patents on Computer-Aided Design and Computer-Aided Manufacturing, with multiple disclosures focused on system engineering and elegant design. He is currently the Principle Investigator for the Boeing Internet of Learning Consortium, the Technical focal for the Boeing ASEE and manages the Boeing Santa Fee Institute Applied Complexity Network and edX Corporate Advisory Board relationship.
Additional responsibilities include providing business leadership for engineering technical and professional educational programs. This includes development of engineering programs (Certificates and Masters) in advanced aircraft construction, composites structures, systems engineering, product lifecycle management and digital manufacturing. This is achieved by partnering and investing in educational initiatives and programs between industry and institutions of higher learning. Michael has served on various advisory groups including, the editorial board of the Journal of Engineering Education, Boeing Higher Education Integration Board, American Society for Engineering Education Project Board, the National Science Foundation I-UCRC Industry University Collaborative Research Center Advisory Board and has server on the NAE as a committee member for the, “Status, Role, and Needs of Engineering Technology Education in the United States.”. He is currently the Boeing Principle Investigator for the Boeing Internet of Learning Consortium, the Technical focal for the Boeing ASEE and manages the Boeing Santa Fee Institute Action Network relationship. Michael holds a B.A and M.Sc. from ESC Lille in Program Project Management and Ph.D. from SKEMA Business School with a focus on Engineering Education Research. In addition he holds a Caltech Certificate is System Engineering, a Stanford Certified Project Management Certificate and a Masters Certificate in Project Management from Steven’s Institute of Technology.
Dorsey Learning Hall 1106, Luddy Hall
Harish Kunshetty | Data Analytics Leader - Quant Systems Inc.
Can Machine Learning Redefine Customer Experience?
Abstract: Advances in technology and processing power have combined to open
up the possibility of machines that can learn built around artificial
This talk is focused on how the customer experience is being refined with
machine learning through a series of case studies.
Bio: Harish Kunshetty is a Data Management and Advanced Analytics Leader
with 18+ years of experience in Advanced Analytics, Data Governance &
Management, and AI driven Machine Learning technologies.
Harish has worked as a Chief Data Officer at Quant Systems Inc. and
Associate Partner at IBM. Currently, working on data projects in the
Washington DC metro area.
As a North America Cognitive Architect Practice leader at IBM, Harish has
pioneered the development of multiple first-of-kind machine learning
solutions for a number of Fortune 100 clients. He has presented at various
conferences and a featured writer on data management topics.
Dorsey Learning Hall 1106, Luddy Hall
Collective Creativity: How crowds and communities explore design space
Abstract: Online communities and crowds can successfully engage in creative
activity, including the design of products. When they do so, they explore a
space of possible designs. They are affected by when others participate, and
what artifacts they are creating. And they can be affected by the design of
a site, by the recommendations shown to them. By analyzing online
communities, it is possible to gain insight into how and why novelty
appears. Moreover, it is possible to steer the exploration of design space.
Results will be presented from three ongoing NSF-funded projects that
involve both observations of online communities (including Thingiverse,
Scratch, Wikipedia) and experiments with crowds. The end goal of this
research program is to catalyze collective creativity.
Bio: Jeffrey V. Nickerson is professor and associate dean of research in the
School of Business at Stevens Institute of Technology. His research focuses
on different aspects of collective creativity, in particular the way crowds
and communities design digital artifacts: 3D printing designs, systems
designs, source code, and articles. Before joining Stevens, he worked in
industry as a systems designer and software developer: he held positions at
Time Inc, AT&T, Bear Stearns, Salomon Inc., and was a partner at
PricewaterhouseCoopers. He has a Ph.D. in Computer Science and an M.F.A in
Graphic Design. His most recent NSF-funded project looks at the effects of
artificial intelligence on work design: more information can be found at
Dorsey Learning Hall 1106, Luddy Hall
Rahul Basole | Associate Professor and Director - Georgia Institute of Technology
Understanding Complex Ecosystems using Visual Analytics
Abstract:In today’s complex, hyperconnected business environment, successful transformation requires a system perspective. In this talk, I will discuss the challenges in navigating transformations from an ecosystem perspective and present the value of novel data-driven visual analytic and computational methods and tools that can augment and accelerate decision makers’ ability to discover, make sense, and analyze innovation opportunities and competitive strategies. My talk will draw on many examples, including our work on software stacks, APIs, and microservices as well as domain-specific studies such as FinTech and AI. I will present a suite of tools that have been developed in the Enterprise Science Lab, including ecoxight, pulse, graphicle, and epheno. I will discuss challenges associated with the identification and curation of relevant data, novel methods to extract knowledge from unstructured data, and the importance of a human-centered approach to tool development and design.
Bio: Dr. Basole is an Associate Professor in the School of Interactive Computing, the Associate Director for Enterprise Transformation in the Tennenbaum Institute/IPaT at the Georgia Institute of Technology and an affiliated faculty member in the GVU Center and the Health Systems Institute. His research fuses system science and visualization to study technology strategy, innovation management, and transformation of complex enterprise systems. He has received several best paper awards and his work has been published in leading management, engineering, and computer science journals, conference proceedings, and books.
In previous roles, he was the CEO, Founder, and VP Research of a Silicon Valley-based wireless research and consulting firm, the Director of Research and Development at a software firm, and a Senior Analyst at a leading IT management consulting firm. He currently serves as a director or advisor for several technology firms.
He received a B.S. degree in industrial and systems engineering from Virginia Tech, has completed graduate studies in engineering economic systems, operations research, and management information systems at Stanford University and the University of Michigan, and received a Ph.D. degree in industrial and systems engineering from the Georgia Institute of Technology, concentrating in IT and operations management.
Dorsey Learning Hall 1106, Luddy Hall
Nathan Sanders | Chief Scientist
The Role of Inference and Prediction in Data Science for Entertainment
Abstract: The emerging field of data science, and the notion that statistical modeling and machine learning can unlock latent value in data held or acquired by businesses, has already had a transformative impact on many industries and disciplines. In particular, many companies have benefited from accurate predictive systems deployed at scale. Hollywood studios leverage predictive modeling to target advertisements, recommend content, and more. Such predictive applications have proven successful enough that ‘data science’ has in some cases become synonymous with ‘prediction.’ However, inferential applications of modeling are equally important. Statistical inference provides the basis for people to learn from models and improve decision making, in both scientific and industrial contexts. In entertainment, this includes assessing the impact of a blockbuster film’s marketing campaign and inferring the thematic composition of content like TV shows. In this talk, I will introduce applications of both predictive and inferential statistical and machine learning systems in the entertainment industry and highlight the critical importance of investment in interpretation and communication to enable data and models to be leveraged efficiently.
Bio: Nathan Sanders is the Chief Scientist at Warner Media Applied Analytics. He leads a team of social, physical, and computational scientists engaged in deploying data science and human subjects research techniques to better understand and serve consumers of entertainment products including film, TV, and digital video. Prior to that, Nathan built and led the Quantitative Analytics team at Legendary Entertainment’s Applied Analytics division, which was acquired by Warner Media in 2018. Nathan is also a co-founder and Leadership Team Chair of ComSciCon (http://comscicon.org/), the international workshop series on science communication for graduate students. Nathan did his undergraduate work at Michigan State University and earned his PhD in Astronomy and Astrophysics from Harvard University.
Tyler Foxworthy | Chief Scientist - DemandJump Inc.
Analyzing Dynamic Networks with Topological Data Analysis
Abstract: The internet, social networks, the global transportation system… Complex networks are the substrate and substance of modern life. Unlike static networks, the structure and function of these systems evolves dynamically with time, which has traditionally posed significant challenges to analysts seeking to predict their behavior or optimize their performance. In recent years, Topological Data Analysis (TDA) has been demonstrated to be a meaningful platform for computing topological features of a wide variety of complex networks at multiple scales. Although relatively unknown by data scientists working in industry, TDA has over the last decade been gaining strong acceptance in the research community and seen a surge of growth in a wide variety of application areas, particularly when coupled with kernel-based machine learning techniques. The purpose of this talk is to discuss both the theoretical and practical aspects of applying TDA to the study of dynamic networks by demonstrating its application with Python and a years’ worth of real-time airline scheduling data.
Biography: Tyler Foxworthy is a mathematician and computational scientist working on a wide range of problems in machine learning and marketing optimization. An alumnus of Purdue University, his research is largely focused on the development of high performance algorithms for understanding complex networks, machine learning, and natural language processing. Tyler currently serves as the Chief Scientist of DemandJump Inc, and is a scientific advisor to many technology companies and investors in the Midwest. Tyler’s previous experience includes leadership and research roles in academia, biotech, and management consulting. Additionally, Tyler maintains an active research program and is a regular speaker at international scientific and industry conferences.
James "Jimi" Shanahan |
How Gradient and Autodiff are Transforming Deep Learning
Abstract: Just like electricity, the automobile, the Internet, and mobile phones transformed the 20th century, deep learning is transforming the 21st century, changing how people perceive and interact with technology, enabling machines perform a wider range of tasks, in many cases doing a better job than humans. These applications include: voice assistants on our smartphones, product recommendation engines, self-driving cars, deep fakes, high frequency stock market trading, applications for social good (combating crime), playing games (from Go to Atari), preventing credit card fraud, filtering out spam from our email inboxes, detecting and diagnosing medical diseases, the list goes on and on. Large companies, such as Amazon, Apple, Facebook, Google, Microsoft, and venture capitalists alike are investing heavily in deep learning research and applications.
This talk focuses primarily on one of the key enablers of deep learning, that of optimization theory’s gradient descent and its sidekick, autodiff. Shakespeare might have structured such a talk as follows and used the lens of reverse mode autodiff to aid with understanding:
Act 1: Hack it up
Act 2: BackProp: theory to the rescue
Act 3: Layer by layer learning, a medieval pastime
Act 4: Introspection: better init. and activation functions
Act 5: Express-laning the gradient: Skip Connections, the SoTA frontier (LSTMs, ResNet, Highway Nets, DenseNets)
These five acts will be supported by examples and Jupyter notebooks in Python and TensorFlow. In addition, this talk will show how reverse mode autodiff provides an efficient and effective calculus framework that is transforming how we do machine learning and how we should teach it.
Bio: Jimi has spent the past 25 years developing and researching cutting-edge artificial intelligent systems splitting his time between industry and academia. He has (co) founded several companies including: Church and Duncan Group Inc. (2007), a boutique consultancy in large scale AI which he runs in San Francisco; RTBFast (2012), a real-time bidding engine infrastructure play for digital advertising systems; and Document Souls (1999), a document-centric anticipatory information system. In 2012 he went in-house as the SVP of Data Science and Chief Scientist at NativeX, a mobile ad network that got acquired by MobVista in early 2016. In addition, he has held appointments at AT&T (Executive Director of Research), Turn Inc. (founding chief scientist), Xerox Research, Mitsubishi Research, and at Clairvoyance Corp (a spinoff research lab from CMU). He also advises several high-tech startups (including Quixey, Aylien, ChartBoost, DigitalBank you.co, VoxEdu, and others).
Jimi has been affiliated with the University of California at Berkeley and at Santa Cruz since 2008 where he teaches graduate courses on big data analytics, machine learning, deep learning, and stochastic optimization. In addition, he is currently visiting professor of data science at the University of Ghent, Belgium. He has published six books, more than 50 research publications, and over 20 patents in the areas of machine learning and information processing. Jimi received his PhD in engineering mathematics from the University of Bristol, U. K., and holds a Bachelor of Science degree from the University of Limerick, Ireland. He is a EU Marie Curie fellow. In 2011 he was selected as a member of the Silicon Valley 50 (Top 50 Irish Americans in Technology).
Peng Wong | Chief Data Science Officer, Omeda
Abstract: Are companies thinking about AI? How does a company innovate with data science, and why should they? The purpose of this talk is to showcase how businesses leverage data science for growth, and in the process answer these questions. We will look at how organizations effectively compete using data science in their journey towards innovation with AI.
Bio: Peng’s role as the Chief Data Science Officer at Omeda is to provide leadership in launching new products and accelerate the pace of innovation. He possesses over 29 years of experience, having led analytics teams and strategic initiatives focused on business transformation and growth. Omeda is a leading audience relationship management platform, providing a real-time, single view of a company’s audience through 24/7 data storage, management, matching and activation. He helped Omeda build their first data science team and advanced analytic capabilities. Peng previously worked at Angie’s List where he was Senior Director of Advanced Analytics. Prior to Angie’s List, he co-founded Beyond Predictive, an advanced analytics consulting company, and has worked in many industries helping companies of various sizes transform their data and analytic strategies. Peng received his B.A. and M.S in Computer Science from Southern Illinois University.
This event will be held in Rm. 1106 of Luddy Hall.
Bruno Miguel Tavares Goncalves | Moore-Sloane Data Science Fellow, NYU
Spatio-temporal Analysis of Language Use
Abstract: The advent of large-scale online social services coupled with the dissemination of affordable GPS-enabled smartphones resulted in the accumulation of massive amounts of data documenting our individual and social behavior. Using large datasets from sources such as Twitter, Wikipedia, Google Books and others, this talk will present several recent results on how languages are used across both time and space. In particular, we will analyze the role of multilinguals in social networks and how language dialects can be defined empirically based on how they are used in the real world. Finally, we will also analyze how English usage changes from place to place and over time and how languages can be used to identify communities within the urban environment.
Bio: Bruno Gonçalves is a Data Science fellow at NYU’s Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Université. His expertise is in using large-scale datasets for the analysis of human behavior. After completing his joint Ph.D. in Physics and MSc in C.S. at Emory University of Atlanta, GA, in 2008 he joined the Center for Complex Networks and Systems Research at Indiana University as a Research Associate. From September 2011 until August 2012 he was an Associate Research Scientist at the Laboratory for the Modeling of Biological and Technical Systems at Northeastern University. Since 2008 he has been pursuing the use of Data Science and Machine Learning to study human behavior. By processing and analyzing large datasets from Twitter, Wikipedia, web access logs, and Yahoo! Meme, he studied how we can observe both overall and individual human behavior in an unobtrusive and widespread manner. The main applications of this research have been towards the study of Computational Linguistics, Information Diffusion, Behavioral Change and Epidemic Spread. Bruno is the author of 60+ publications with over 5200+ Google Scholar citations and an h-index of 30. In 2015 he was awarded the Complex Systems Society’s 2015 Junior Scientific Award for “outstanding contributions in Complex Systems Science” and he is the editor of the book Social Phenomena: From Data Analysis to Models (Springer, 2015).
This talk will be held in Rm. 1106 of Luddy Hall.
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