Big Data Recommendation Systems
Friday, December 4
Recommendation systems were developed in the 90s to address the challenges of automatic and personalized selection of data from diverse and overloaded sources of information. These systems apply numerous knowledge discovery techniques on users’ historical and contextual data to suggest information, products, and services that best match the user’s preferences.
In this talk, we describe the fundamental concepts of recommendation systems, and how the techniques and methodologies are adapting in the realm of big data. As case studies, we will detail our work on developing recommendation systems for:
- Mobile social networks
- Health insurance plans
- Social venues
- Large-scale evacuations
During the discussion on the cases studies, we also will touch on issues related to the prime research concerns in big data recommendation systems, which are:
- Cold start
- Data sparseness
Samee U. Khan received a BS degree in 1999 from Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan, and a PhD in 2007 from the University of Texas, Arlington, TX, USA. Currently, he is Associate Professor of Electrical and Computer Engineering at the North Dakota State University, Fargo, ND, USA.
Prof. Khan’s research interests include optimization, robustness, and security of: cloud, grid, cluster and big data computing, social networks, wired and wireless networks, power systems, smart grids, and optical networks. His work has appeared in over 300 publications. He is on the editorial boards of leading journals, such as IEEE Access, IEEE Cloud Computing, IEEE Communications Surveys and Tutorials, and IEEE IT Pro.
He is a Fellow of the Institution of Engineering and Technology (IET, formerly IEE), and a Fellow of the British Computer Society (BCS). He is an ACM Distinguished Lecturer, a member of the ACM, and a Senior Member of the IEEE.
Friday, November 6
Dr. Gabriel Udomkesmalee, NASA
The Robotics Section of the Jet Propulsion Laboratory (JPL), California Institute of Technology, is engaged in a full spectrum of flight project and research activities. This talk will provide an overview of the efforts and discuss the recent accomplishments and future directions of them. Specific activities will be high-lighted based on their level of accomplishment, impact on the community, maturity, or novelty. Robotics activities on flight projects are a significant subset of the full effort for these large missions. Complementing flight activities is a diverse set of research efforts for NASA and other U.S. Government agencies. Future directions will be motivated by NASA and other sponsor objectives, as well as success experienced in these current endeavors.
Dr. Udomkesmalee has over twenty years of NASA/JPL experience as technical staff, task manager, group supervisor, project manager, program manager, and deputy section manager. Highlights of Dr. Udomkesmalee’s background include:
- Member of Hubble Advanced Radial Camera Fine Guidance System, Cassini Star Tracking, and Galileo Jupiter Orbit Insertion teams
- Task manager of CRAF/Cassini Target Tracking and RTOP Autonomous Feature And Star Tracking activities
- Manager of BMDO VIGILANTE and NASA/CNES Autonomous Rendezvous/Capture Demonstration projects, and Mars Science Lab Technology and Mars Sample Return Technology programs
- Group Supervisor of JPL Tracking Sensors and Deputy Manager of JPL Robotics Section
He received his Ph.D. in Systems Science (specialized in Estimation and Controls) from University of California, San Diego and worked 10 years in aerospace industry prior to joining JPL in 1992. His current research includes autonomous systems, robotics, smart sensors and spacecraft guidance, navigation, and control.
Visual Correspondences: Modern Techniques and Applications
Friday, October 9
Professor Minh N. Do, Computing Vision and Signal Processing UIUC
(Joint work with Jiangbo Lu and Dongbo Min) Finding visual correspondence across images is the cornerstone of numerous computer vision and image processing applications; including 3D reconstruction, computational photography, navigation and mapping, and scene understanding. Visual correspondence methods aim to find a set of matching pixels between two or multiple images.
In this talk we will present various fundamental techniques that have been developed recently for correspondence algorithms. We will first introduce state-of-the-art local image descriptors, which measure a matching fidelity across multiple images. We will then present recent approaches in solving for a regularized correspondence field or motion model through discrete labeling optimization techniques. In particular, we will focus on cost-volume filtering and global optimization approaches, which effectively deal with the large discrete label space by making use of efficient edge-aware filters and effective randomized search strategies. Finally, we will present the key ideas of some recent exciting applications that highlight the essential roles of visual correspondences.
Minh N. Do is a Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC). He received the B.Eng. degree in Computer Engineering from the University of Canberra, Australia in 1997, and the Dr.Sci. degree in Communication Systems from the Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland in 2001.
Since 2002, he has been on the faculty of the Department of Electrical and Computer Engineering at UIUC, and holds joint appointments with the Coordinated Science Laboratory, the Beckman Institute for Advanced Science and Technology, the Advanced Digital Sciences Center, and the Department of Bioengineering. His work covers image and multi-dimensional signal processing, wavelets and multiscale geometric analysis, computational imaging, and visual information representation, and has led to more than 50 journal papers and more than 13000 Google Scholar citations.
He received a Silver Medal from the 32nd International Mathematical Olympiad in 1991, a University Medal from the University of Canberra in 1997, a Doctorate Award from the EPFL in 2001, a CAREER Award from the National Science Foundation in 2003, a Xerox Award for Faculty Research from the UIUC College of Engineering in 2007, and a Young Author Best Paper Award from IEEE in 2008. He was an Associate Editor of the IEEE Transactions on Image Processing, and a member of the IEEE Technical Committees on Signal Processing Theory and Methods, and on Image, Video, and Multidimensional Signal Processing. He was elected as an IEEE Fellow for his contributions to image representation and computational imaging. He was a co-founder and CTO of Personify Inc., a spin-off from UIUC to commercialize depth-based visual communication.
Dr. Andreas Stefik, UNLV
Friday, October 2
Computer science has a long and complex history with programming languages. Historically, we have conducted evaluations using proofs, to ensure they give us the answers we intend, and performance data, to ensure they perform efficiently. While these two techniques are well established and important, I argue a third is fundamentally missing from the design process: a scientific analysis of impact. In the academic literature, for example, there is a near-complete lack of replicable scientific evidence regarding how the design of programming languages impacts people or communities, which has led in part to the programming language wars. In this talk, I introduce Quorum, the world's first Evidence-Oriented Programming language. Throughout it, I will discuss the specific evidence gathered on Quorum to-date and how other designers can use it to improve or evaluate their own products. Along the way, I will provide information on the history of evidence gathering through the centuries and why rigorous attention to the scientific method in this domain is crucial for the future of computer science.
Andreas Stefik is an assistant professor in the Department of Computer Science and Engineering at UNLV. Andreas received his PhD from Washington State in 2008. He is interested in working on better ways of understanding, and interacting with, computer programming languages and compilers.
Risk Based Security in the Firm
Friday, September 4
Kevin Spiares, Koch Business Solutions
What happens when an unprepared firm is suddenly beset by Nation State and Politically motivated cyber attacks? In this presentation we will discuss the circumstances and actions that resulted in the successful defense of one the worlds largest companies. We will explore the value of technology, industry experts, and how ultimately it's what you don't do that may be the difference between business as usual or a successful exploit.
Kevin Spiares, Director of Strategy and Innovation Koch Business Solutions and site Director for the Reno, Nevada office. In my role I am accountable for providing guidance and strategic direction in the areas of Compute, Storage, Networks, Internet of Things and Data Center. This is accomplished by working across Koch and externally with key partners such as Cisco, EMC, Dell, etc. as well as a variety of start-ups and venture capital firms. have been at Koch for 15 years in a variety of roles to include Cyber-Security, IT Director, Logistics Director, and Internal Business Consulting.
The Current and Emerging Global Threat Environment
Friday, May 8
Chul Yim, Director of Business Development, FireEye's U.S.
FireEye will review the overall threat landscape from their experiences and research with over 200 high profile cyber incidents in 2014 including Sony, Anthem, Target, Home Depot, etc. They will also cover security issues including targeted attacks and nation-state threat actors that have impacted government and education entities.
Tohru Watanabe, Manager, Systems Engineering
Tohru Watanabe is the Systems Engineering Manager for FireEye’s U.S. State and Local Government and Education practice. He has worked in security for over 10 years across and has consulted with the public sector and private sector organizations. He holds a Bachelor’s in Business Administration, CISM, CISSP-ISSAP, GCFA, and GREM certifications.
Chul Yim, Director of Business Development
Chul Yim is the Director for Business Development for FireEye’s U.S. State and Local Government and Education practice. He has spent the last 10 years working with the U.S. public sector space in various roles in sales, strategy, consulting and policy. He is a University of Nevada, Reno graduate (2004) with a major in computer science.
Privacy Enhancement in Biometrics
Friday, April 24
Dr. Nalini K. Ratha, IBM Thomas J. Watson Research Center, Hawthorne, New York
Biometrics, as an authentication tool, provides several advantages over conventional what you know (e.g., password, PIN) and what you possess (e.g., keys, tokens) authentication methods. However, a biometrics is an irrevocable password as we can’t change the biometrics easily. If it is compromised digitally, it is compromised for ever. Secondly, a biometrics can be easily matched against multiple databases to link identities. In order to alleviate privacy deficiencies of biometrics, IBM Research has pioneered a new technique for protecting biometrics templates that can allow for revocation and anonymous sharing. Instead of enrolling with the true biometrics, the original signal/template is intentionally and repeatably distorted using a class of non-invertible functions. The resulting “transformed” biometrics is enrolled. During verification, the same distortion transformation is applied to the biometrics signal/template to match against the enrolled template. The proposed method supports revocability and permits anonymous matching where biometrics data sharing is prohibited.
Dr. Nalini K. Ratha is a Research Staff Member at IBM Thomas J. Watson Research Center, Hawthorne NY where he is the team leader for the biometrics-based authentication research. He has over 20 years of experience in the industry working in the area of pattern recognition, computer vision and image processing. He received his B. Tech. in Electrical Engineering from Indian Institute of Technology, Kanpur, M.Tech. degree in Computer Science and Engineering also from Indian Institute of Technology, Kanpur and Ph. D. in Computer Science from Michigan State University. Before joining IBM Research, he worked at CMC R&D center and ECIL Computer Group both in India.
He has authored more than 80 research papers in the area of biometrics and has been co-chair of several leading biometrics conferences and served on the editorial boards of IEEE Trans. on PAMI, IEEE Trans. on SMC-B, IEEE Trans. on Image Processing and Pattern Recognition journal. He has co-authored a popular book on biometrics entitled “Guide to Biometrics” and also co-edited two books entitled “Automatic Fingerprint Recognition Systems” and “Advances in Biometrics: Sensors, Algorithms and Systems”.
He has offered tutorials on biometrics technology at leading IEEE conferences and also teaches courses on biometrics and security. He is Fellow of IEEE, Fellow of IAPR and a senior member for ACM. His research interests include biometrics, pattern recognition and computer vision. He is an adjunct professor at IIIT Deli, had been an adjunct for several year at Cooper Union and NYU-Poly. Currently, he is the president of the IEEE Biometrics Council (2011-2012). At IBM, he has received several awards including a Research Division Award, Outstanding Innovation Award and Outstanding Technical Accomplishment Award along with several paten achievement awards.
Interoperability Dependencies in the Internet of Things (IoT)
Friday, April 10
Antonio A. Rucci, Chief Security Officer, Ghost Systems, LLC
As business capabilities continue to develop and evolve, our dependencies on technology continue to scale while diminishing our ability to operate in its absence. Security is often an afterthought or oversight in technology design and development, resulting in complex redesign post-process. As we embrace the "Internet of Things" (IoT), we must also embrace the interoperability dependencies of adjacent, independent technologies, policies and laws, which will drive the IoT into the foreseeable future. During this talk, Don Ritzman, Chief Executive Officer; and Antonio A. Rucci, Chief Security Officer, Ghost Systems, LLC, Reno, NV, will address high level strategy and technologies which are shaping the IoT deliverables to cutting edge industries.
As The IoT evolves it will begin shaping our day-to-day lives and business processes. As we continue to grow away from password controls and "Old School" ways of thinking, we will be able to take advantage of seamless security platforms and mesh networks in trusted environments. The development requires education, planning, development and understanding from a variety of resources, never before considered in basic science and engineering. As a result of the Colloquia, our goal is for you to have a better understanding and appreciation for the interoperability and dependencies required in the development of the next generation of the Internet of Things.
With more than 31 years of counterintelligence and security experience, as a retired Counterintelligence Special Agent and US Army Warrant Officer, Tony Rucci recently followed his dream and as started his own company, /Root Technology, LLC. With a Core Emphasis on Secure Networking, Critical Infrastructure Protection, Data Center Planning, Design, and Efficiencies, /Root Technology has exceeded all our expectations of what can and will be our future. After only 13 months, Ghost Systems made a move to acquire /Root Technology and as a result, Mr. Rucci is also the Chief Security Officer @ Ghost Systems spearheading all aspects of global data center design and commissioning, core technologies development and security for a game changing, patented technology destined to change our future technological dependencies and security. Prior to founding /Root, Tony served as the Director of Data Center Services (DCS) for NJVC, LLC. Mr. Rucci was responsible for all aspects of NJVC’s data center services throughout their corporate environment, as well as NJVC’s worldwide mission supporting the DoD, U.S. Intelligence Community, as well as CIP.
Prior to NJVC, Mr. Rucci was an Intelligence Programs Manager at Oak Ridge National Laboratory (ORNL), Department of Energy, Oak Ridge, Tennessee where he spearheaded the design and technical accreditation of the MultiProgram Research Facility (MRF) then served as the Director of Cyber Initiatives reaching into to the U.S. Intelligence Community developing programs for the Global Security Directorate.
Prior to ORNL, Mr. Rucci retired after 21 years as a United States Army Counterintelligence (CI) Warrant Officer / Special Agent, having served in a variety of leadership positions and conducted numerous security and espionage investigations to protect our national interests, culminating with his final assignment as the Counterintelligence Operations Officer for the Director of Security, White House Military Office.
Beyond Cyber-Physical Era: What's Next?
Friday, April 3
Dr. Sajal K. Das, Dept. of Computer Science, Missouri University of Science & Tech.
We live in an era of “Internet of Things” where our physical and personal environments are becoming increasingly smarter as they are immersed with sensing, networking, computing and communication capabilities. The availability of rich mobile devices like smartphones and wireless sensors have also empowered humans as an integral part of cyber-physical systems. This synergy has led to cyber-physical-social convergence exhibiting complex interactions, interdependencies and adaptations among devices, machines, systems/environments, users, human behavior, and social dynamics. In such a connected and mobile world, almost everything can act as information source, analyzer and decision maker. This talk will highlight some of the emerging research challenges and opportunities in cyber-physical-social convergence, and then present some novel solutions to tackle them. It will also reflect on a fundamental question: “What’s Next?”
Dr. Sajal K. Das, an IEEE Fellow, is the Chair of Computer Science Department and Daniel St. Clair Endowed Chair in Computer Science at the Missouri University of Science and Technology, Rolla. During 2008-2011, he served the National Science Foundation as a Program Director in the Computer Networks and Systems Division. Prior to 2013 he was a University Distinguished Scholar Professor of Computer Science and Engineering, and founding director of the Center for Research in Wireless Mobility and Networking (CReWMaN) at the University of Texas at Arlington.
His research spans theory and practice of wireless and sensor networks, mobile and pervasive computing, participatory sensing, cyber-physical systems and smart environments, distributed and cloud computing, security and privacy, biological and social networks. He has published extensively in this areas with more than 600 research articles in high quality journals and conference proceedings, and 51 book chapters. He coauthored four books – Smart Environments: Technology, Protocols, and Applications (2005); Handbook on Securing Cyber-Physical Critical Infrastructure: Foundations and Challenges (2012); and Mobile Agents in Distributed Computing and Networking (2012), and Principles of Cyber-Physical Systems (2015). His h-index is 65 with more than 18,500 citations according to Google Scholar.
Dr. Das holds 5 US patents and received 10 Best Paper Awards in prestigious conferences like ACM MobiCom’99, IEEE PerCom’06, and IEEE SmrtGridComm’12. He is also a recipient of numerous awards for teaching, mentoring and research including the IEEE Computer Society’s Technical Achievement Award for pioneering contributions to sensor networks and mobile computing. Dr. Das serves as the founding Editor-in-Chief of Pervasive and Mobile Computing journal, and Associate Editor of IEEE Transactions on Mobile Computing, ACM Transactions on Sensor Networks, Journal of Parallel and Distributed Computing, and Journal of Peer to Peer Networking and Applications. He is a co-founder of IEEE PerCom, WoWMoM, and ICDCN conferences.
Vision-Based Monitoring of Behavioral Disorders
Saturday, March 7
Prof. Nikos Papanikolopoulos, Dept. of Computer Science University of Minnesota
This work involves algorithms to assist with the early diagnosis of children who are at risk of developing behavioral disorders. Previous research has indicated that two critical areas of behavioral investigation for use in identifying at-risk children have been abnormalities in motor activities and emotional range displays, especially of the face. Motor abnormalities are based on the observation that motor control involves the circuits of the brain associated with dopamine; these are also implicated in behavioral disorders. Many different disorders share the observation of disruption in the emotional range regulation, so facial expressions are included in the study. To date, assessments of motor and emotional range have been done by the experts who view and rate videos of an individual. However, these expert, subjective ratings limit the analysis of behavioral conditions to only a narrow range of behaviors, work only for small populations of individual subjects, and are both costly and dependent on the observer's particular expertise. In order to enable wider population screening, automation is required. Innovative ways of capturing and quantifying the expertise of experts are accompanied by metrics for assessing the evolution of the behavior. In addition, new computational tools support evaluation of the effectiveness of interventions.
Nikolaos P. Papanikolopoulos (IEEE Fellow) received the Diploma degree in electrical and computer engineering from the National Technical University of Athens, Athens, Greece, in 1987, the M.S.E.E. in electrical engineering from Carnegie Mellon University (CMU), Pittsburgh, PA, in 1988, and the Ph.D. in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, PA, in 1992. Currently, he is a Distinguished McKnight University Professor in the Department of Computer Science at the University of Minnesota and Director of the Center for Distributed Robotics and SECTTRA. His research interests include computer vision, sensors for transportation applications, robotics, and control. He has authored or coauthored more than 350 journal and conference papers in the above areas (seventy refereed journal papers).
Visual Exploration of Big Urban Data
Tuesday, February 17
Huy Vo, Center for Urban Science and Progress, New York University
About half of humanity lives in urban environments today and that number will grow to 80% by the middle of this century. Cities are thus the loci of resource consumption, of economic activity, and of innovation; they are the cause of our looming sustainability problems but also where those problems must be solved. Data, along with visualization and analytics can help significantly in finding these solutions. In this talk, I will discuss the challenges of visual exploration of big urban data; and showcase our approaches in a study of New York City taxi trips. Taxis are valuable sensors and can provide unprecedented insight into many different aspects of city life. But analyzing these data presents many challenges. The data are complex, containing geographical and temporal components in addition to multiple variables associated with each trip. Consequently, it is hard to specify exploratory queries and to perform comparative analyses. This problem is largely due to the size of the data. There are almost a billion records of taxi trips collected in a 5-year period. I will present TaxiVis, a tool that allows domain experts to visually query taxi trips at an interactive speed and performing tasks that were unattainable before. This is thanks to our visual query model and efficient indexing of spatio-temporal data.
Dr. Vo is a Research Scientist at the Center for Urban Science and Progress (CUSP), New York University. His research focuses on large-scale data analysis and visualization, big data systems, and scalable displays. He is also a Research Assistant Professor of Computer Science and Engineering at NYU's Polytechnic School of Engineering since 2011. He is one of the co-creators of VisTrails, an open-source scientific workflow and provenance management system, where he led the design of the VisTrails Provenance SDK. He received his B.S. in Computer Science (2005) and PhD in Computing (2011) from the University of Utah and was a two time recipient of the NVIDIA Fellowship awards (2009-2010 and 2010-2011).
The Digital Assault on Privacy
Friday, February 13
Dr. Hal Berghel, Professor of CS at University of Nevada, Las Vegas
George Orwell and Aldous Huxley are frequently mentioned in the context of the recent spate of surveillance leaks from the NSA. While both Orwell and Huxley feared big government and big controls, they feared it for different reasons. This difference will set the tone for this talk. We will begin with the history of the U.S. involvement in surveillance, from the early analog days to the latest digital technologies. We'll explain the motivations, technologies and civil libertarian consequences of some noteworthy surveillance programs like Echelon, Carnivore, Narusinsight, Magic Lantern, ThinThread, Trailblazer, Stellar Wind/Ragtime, and TAO (Tailored Access Operations) to name but a few. The speaker will also cover corporate surveillance by high tech companies and cyber intelligence mercenaries. The speaker will conclude with speculation on future directions for government and private surveillance programs the privacy implications that will arise therefrom. (50 slides; 45-50 minutes plus Q&A: categories: digital security and privacy, privacy legislation, privacy safeguards, personally identifiable information).
Hal Berghel is currently Professor of Computer Science at the University of Nevada, Las Vegas where he has previously served as Director of both the Schools of Computer Science and Informatics, and as Associate Dean of the College of Engineering. He created and directed the first CyberSecurity degree programs (Bachelors, Masters and PhD) in Nevada in 2005. This program became an NSA Center for Academic Excellence two years later. He was the founding Director of the Identity Theft and Financial Fraud Research and Operations Center and CyberSecurity Research Center. His research interests are wide-ranging within the binary and digital ecosystem, ranging from logic programming and expert systems, relational database design, algorithms for non-resolution based inferencing, approximate string matching, digital watermarking and steganography, and digital security and privacy. Since the mid-1990's he has applied his work in digital security to law enforcement and intelligence gathering, particularly with respect to digital crime, digital money laundering, information warfare and trusted identities. His research has been supported by both industry and government for over thirty years. His most recent work in secure credentialing technology was funded by the Department of Justice. In addition to his academic positions, Berghel is also a popular columnist, author, frequent, talk show guest, inventor, and keynote speaker. For nearly fifteen years he wrote the popular Digital Village column for the Communications of the ACM, and has written the Out-of-Band column for IEEE Computer since 2011. Berghel is a Fellow of both the Institute for Electrical and Electronics Engineers and the Association for Computing Machinery, and serves both societies as a Distinguished Visitor and Distinguished Lecturer, respectively. He has received the ACM Outstanding Lecturer of the Year Award four times and was recognized for Lifetime Achievement in 2004. He has also received both the ACM Outstanding Contribution and Distinguished Service awards. He is also the founder and owner of Berghel.Net, a consultancy serving government, business and industry. Berghel is a member of the Nevada Technology Crimes Advisory Board and Chairs the Nevada Privacy Subcommittee.
Thursday, February 12
Wei Cheng, University of North Carolina, CS
Traditional sparse regression problems in data mining and machine learning consider both predictor variables and response variables individually, such as sparse feature selection using LASSO. In many emerging applications, both predictor variables and response variables are not independent of each other, and we may maintain some prior knowledge, such as the correlation structures between predictors and correlation structures between response variables. Thus, we can leverage these prior relationships to improve the regression accuracy. The task, however, is challenging because of the inherent characteristics of the prior information:
- Variety (e.g., complex structures, heterogeneous types and data sources)
- Poor quality
- Massive volume
In this talk, I will present our research efforts to use big data technologies to design robust and integrative regression algorithms for biological eQTL mapping problem. First, I will begin by presenting the work of robust regression algorithm that integrates multi-source heterogeneous data for eQTL mapping. Next, I will present the work on integrative analyzing multi-domain noisy heterogeneous data for establishing a more accurate and robust knowledge base.
Wei Cheng is a Ph.D. candidate in Computer Science at University of North Carolina at Chapel Hill. He has been visiting Department of Computer Science of UCLA since 2013. He received a Master's and Bachelor's degree from Tsinghua University and Nanjing University, in 2010 and 2006, respectively. His research interests include big data, data mining, bioinformatics, computational biology, and machine learning. He is especially interested in scalable data analysis problems for data science with an emphasis on biological applications. His research has been published in top tier conferences including ISMB, SIGKDD, IEEE ICDM, CIKM, SDM and journals including Nature, TKDE, Bioinformatics, BMC Bioinformatics, KAIS and so forth. His paper was nominated for Best Paper Award at SDM'12. He has one patent pending in the area of large scale table recognition in unstructured documents. He has also served as a program committee member for several top tier conferences including SIGKDD'14, IJCAI'15, etc. Previously, he also conducted research at Microsoft Research and IBM Research as an intern.
Data-Driven Scientific Discovery in the Big Data Era
Thursday, February 12
Dr. James Faghmous, Dept. of Computer Science University of Minnesota
Data science has become a powerful tool to extract knowledge from the large data. However, despite massive data growth in the sciences, it remains unclear whether Big Data can lead to scientific breakthroughs. In this talk, I present a new knowledge discovery paradigm -- theory-guided data science -- that brings together novel data analysis methods and powerful scientific theory to extract knowledge from complex spatio-temporal data. The principles of this paradigm will be demonstrated with a data mining application to oceanography.
James Faghmous is a Research Associate at the University of Minnesota where he develops new data science methods for scientific discovery. In 2015, James received an inaugural NSF CRII Award for junior faculty and his doctoral dissertation received the "Outstanding Dissertation Award" in Science and Engineering at the University of Minnesota. James received his Ph.D. from the University of Minnesota in 2013 where he was part of a 5-year $10M NSF Expeditions in Computing project to understand climate change from data. He graduated Magna Cum Laude in 2006 with a B.Sc. in computer science from the City of College of New York where he was a Rhodes and a Gates Scholar nominee.