Hanghang Tong: Networks-as-a-Context: A New Frontier in Network Mining
Abstract: Networks not only appear in many high-impact application domains, but also have become an indispensable ingredient in a variety of data mining and machine learning problems. Often these networks are collected from different sources, at different time, at different granularities. State-of-the-art focuses on mining networks at three complementary levels, including the network-level at the coarsest granularity, the subgraph-level in the middle, and node/link-level at the finest granularity. In other words, an individual node or link is often the finest-granulated object (i.e., the atom) in a network mining model and algorithm.
In this talk, I will present our recent work on mining multiple inter-correlated networks, that allows us to go deeper inside a node or a link of a network, i.e., to model and mine networks hidden inside an atom (i.e., a node or a link). First, I will introduce a new data model for a network of networks, where the key idea is to leverage the network itself as a context to connect different networks. Second, I will present some algorithmic examples on how to perform mining with a network of networks, where the key idea is to leverage the contextual network as an effective regularizer during the mining process, including ranking and clustering. We believe that the power and beauty of networks of networks lie in thinking of networks as a context, and this vision (networks-as-a-context) goes well beyond network data. To demonstrate that, I will further introduce our other work on how to use networks as a powerful and unifying context to connect different types of data from different sources with different data mining algorithms, including a network of co-evolving time series, a network of regression models, a network of inference problems and a network of control problems.
Bio: Hanghang Tong is currently an associate professor at Department of Computer Science at University of Illinois at Urbana-Champaign. Before that he was an associate professor at School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Arizona State University. He received his M.Sc. and Ph.D. degrees from Carnegie Mellon University in 2008 and 2009, both in Machine Learning. His research interest is in large scale data mining for graphs and multimedia. He has received several awards, including ICDM Tao Li award (2019), SDM/IBM Early Career Data Mining Research award (2018), NSF CAREER award (2017), ICDM 10-Year Highest Impact Paper award (2015), four best paper awards (TUP'14, CIKM'12, SDM'08, ICDM'06), seven 'bests of conference', 1 best demo, honorable mention (SIGMOD'17), and 1 best demo candidate, second place (CIKM'17). He has published over 100 refereed articles. He is the Editor-in-Chief of SIGKDD Explorations (ACM), an action editor of Data Mining and Knowledge Discovery (Springer), and an associate editor of Knowledge and Information Systems (Springer) and Neurocomputing Journal (Elsevier); and has served as a program committee member in multiple data mining, database and artificial intelligence venues (e.g., SIGKDD, SIGMOD, AAAI, WWW, CIKM, etc.).
Murthy Rallapalli: Promise of Quantum computing in 21st century
Abstract: The promise of Quantum computers to spur the development of new breakthroughs in science is ever getting closer to the reality. This potential power of quantum computing is likely to create new medications to save lives, new machine learning methods to diagnose illnesses sooner, brand new composition of materials to make more efficient devices and structures. In addition, it could help with unique financial strategies, and algorithms to optimize resources such as ambulances.
This session agenda includes the following:
· What exactly is quantum computing, and what does it take to achieve these quantum breakthroughs?
· What is the current state of Quantum computing?
· What are some of the financial industry use cases?
· What does this decade hold for Quantum computing?
Bio: Dr. Murthy Rallapalli, is an Executive architect and Quantum ambassador at IBM based in Atlanta, GA. His research is focused on Quantum algorithms, and Cognitive computing in Cyber security, with a focus on financial industry domain. He had published number of red books in IBM in e-business architectures, Architecture design, Information privacy and Analytics reference architectures. He presented at number of international conferences in Asia, Europe and the USA in privacy, security and quantum computing including seminars in SOA (Service Oriented Architectures) security at National Security Agency (NSA) of the United States. He holds 4 patent filings in data privacy and analytics.
Dr. Rallapalli is a 2020 Fulbright scholar to help the government of Myanmar and Taunggyi university in the area of cyber security and Data sciences curriculum and teaching. Dr. Rallapalli holds a Ph.D. from Stevens Institute of Technology, Hoboken, NJ and a master’s degree from Regis University, Denver, Colorado.
Zubair Shafiq: Ad-Mageddon - The Next Frontier in Online Privacy
Abstract: While online advertising supports the "free" web, it relies on a complex and opaque tracking ecosystem that surveils users across the web. Hundreds of millions of users rely on ad-blocking and anti-tracking tools to counter the negative externalities of online advertising and tracking. Perhaps unsurprisingly, advertisers are increasingly retaliating against the users of such tools -- prompting an arms race. In this talk, I will first discuss the pain points of the state-of-the-art ad-blocking and anti-tracking tools. I will then describe our recent work on building effective and robust countermeasures against online advertising and tracking using machine learning techniques. I will highlight the unique challenges and opportunities in deploying ad-blocking and anti-tracking tools in web browsers as well as mobile and IoT systems. I will conclude with a discussion of my future research vision for a privacy-respecting web.
Bio: Zubair Shafiq is an assistant professor of computer science at the University of Iowa. Prior to this, he received his Ph.D. from Michigan State University in 2014. His research focuses on building privacy-enhancing tools to counter online tracking and surveillance. More broadly, his work takes a data-driven approach to addressing emerging online privacy and security threats. He is a recipient of the NSF CAREER Award (2018), Andreas Pfitzmann PETS Best Student Paper Award (2018), ACM IMC Best Paper Award (2017), NSF CRII Award (2015), Fitch-Beach Outstanding Graduate Research Award (2013), IEEE ICNP Best Paper Award (2012), and the Dean's Plaque of Excellence for undergraduate research (2007, 2008). More information at cs.uiowa.edu/~mshafiq
Sean Forman: The Death of Intangibles - How Sports are Measuring the Previously Unmeasurable
Abstract: Analytics, wearables, and new technologies are changing how teams and athletes train and play. Every industry is facing these trends, but sports tend to be on the leading edge given the well-defined relationship between improvement and success and the larger competitive trends that force teams to constantly seek improvement. We will review some of the general trends and discuss the impact this is having on sports and those who play them.
Bio: Dr. Sean Forman earned his Ph.D. from the Applied Math & Computational Sciences Program at the University of Iowa in 2001. After six years as a professor at Saint Joseph's University in Philadelphia, he began working full-time on his site Baseball-Reference.com and with three others founded Sports Reference LLC. He has been company president since its founding. Sports Reference now runs statistical websites for seven sports, has eleven full-time employees, and had over twenty million users last year. Dr. Forman grew up in Manning, Iowa and got his start in sports statistics keeping the tackle chart for his dad's high school football team.
Junaed Sattar: Perception, Learning, and Systems for Underwater Human-Robot Collaboration
Abstract: Autonomous robots are making great headways in a wide range of applications, including diverse areas such as manufacturing, healthcare, and surveillance. In many domains, however, autonomous robots are required to work alongside humans to ensure safety or augment human performance. The underwater domain is unique in many ways, but as an application area of autonomous robots, it stands out with numerous challenges -- in sensing, control, and human-robot interaction -- that can justifiably be considered extreme. Our research at the Interactive Robotics and Vision Lab at the The University of Minnesota looks into numerous issues in robust underwater human-robot collaboration. Specifically, we investigate underwater bidirectional human-robot communication, underwater imagery enhancement, localization/mapping of underwater objects of interest using multimodal sensing, and biological and non-biological object tracking. We primarily investigate computational solutions to these problems, and use methods from robotics, machine vision, stochastic reasoning, and (deep) machine learning. This talk will present a brief overview of our research and present an in-depth discussion of some recent projects in underwater human-robot interaction and imagery enhancement.
Bio: I'm an assistant professor at the Department of Computer Science and Engineering at the University of Minnesota, and a MnDrive (Minnesota Discovery, Research, and Innovation Economy) faculty. I am the founding director of the Interactive Robotics and Vision Lab, where we investigate problems in field robotics, robot vision, human-robot communication, assisted driving and applied (deep) machine learning, and develop rugged robotic systems. My graduate degrees are from McGill University in Canada, and I have a BS-in-Engineering degree from the Bangladesh University of Engineering and Technology. Before coming to the UoM, I worked as a post-doctoral fellow at the University of British Columbia where I worked on service and assistive robotics, and at Clarkson University in upstate New York as an Assistant Professor. Find me at junaedsattar.org, and the IRV Lab at irvlab.cs.umn.edu, @irvlab on Twitter, and our YouTube page.