This summer, twenty engineers from the National Security Agency (NSA) joined one another at the UT Dallas Computer Science Department for a 5-day NSA funded workshop where they had the opportunity to learn about cutting edge research that is being done in the field of advanced data science. The workshop’s mission was to provide scientists and engineers, including those working in cybersecurity and operations as well as in the social/political sciences with key data science concepts, technologies, and tools. Attendees received practical and first-hand experience in working with end-to-end Big Data tools and handling (large) datasets consisting of both structured and unstructured (text) formats. UT Dallas CS Professors Drs. Latifur Khan, Bhavani Thuraisingham, Nicholas Ruozzi, and Political Science Professor, Dr. Patrick T. Brandt served as workshop facilitators.
The workshop provided attendees with the opportunity to gain experience in Scalable Advanced Data Analytics with a focus on Big Data technology and stream analytics. For this, the workshop instructors, Drs. Khan, Thuraisingham, Ruozzi, and Brandt, leveraged their research and developed coursework that emphasized hands-on practical experience through a combination of modules, each consisting of a lecture and a lab. Dr. Gopal Gupta, UT Dallas CS Department Head, provided the introductory talk on the first day of the workshop.
The over-arching objectives of the workshop were to introduce various aspects of data analytics relevant to both structured and unstructured data while applying them to datasets relevant to cybersecurity problems. To complement the course modules, the workshop instructors included accompanying course materials such as instructor notes, interactive videos and lab exercises, which served to inform and train participants to learn and apply new skills to cyber defense and data analytics. During the five-day workshop, attendees were encouraged to take part in various discussions and labs that featured topics such as text analysis and malware basis, Data Processing and Feature Extraction, On-line Anomaly detection and On-Line Stream Data Classification.
About the Instructors
Dr. Latifur Khan currently is a full tenured professor in the Department of Computer Science at the University of Texas at Dallas where he has been teaching and conducting research since September 2000. He received his Ph.D. degree in Computer Science from the University of Southern California (USC) in August of 2000. Dr. Khan is an ACM Distinguished Scientist and received the Fellow of SIRI (Society of Information Reuse and Integration) award in August of 2018. He has received prestigious awards including the IEEE Technical Achievement Award for Intelligence and Security Informatics and IBM Faculty Research Award in 2016. Dr. Latifur Khan has published over 250 papers in premier journals such as VLDB, Journal of Web Semantics, IEEE TDKE, IEEE TDSC, IEEE ISMC, and Al Research and in prestigious conferences such as AAAI, IJCAI, CIKM, ICDE, ACM GIS, IEEE ICDM, IEEE BigData, ECML/PKDD, PAKDD, ACM Multimedia, ACM WWW, ICWC, ACM SACMAT, IEEE ICSC, IEEE Could and INFOCOM. He has been invited to give keynotes and invited talks at several conferences hosted by IEEE and ACM. In addition, he has conducted tutorial sessions in prominent conferences such as SIGKDD 2017, 2016, IJCAI 2017, AAAI 2017, SDM 2017, PAKDD 2011 & 2012, ACM WWW 2005, MIS 2005, and DASFAA 2007. Currently, Dr. Khan’s research focuses on big data management and analytics, data mining and its application in cybersecurity, complex data management, including geospatial data and multimedia data. His research has been supported by grants from NSF, the Air Force Office of Scientific Research (AFOSR), DOE, NSA, IBM, and HPE. More details can be found here.
Dr. Bhavani Thuraisingham is the Louis A. Beecherl, Jr. Distinguished Professor in the Erik Jonsson School of Engineering and Computer Science at UT Dallas and the executive director of the UT Dallas Cyber Security Research and Education Institute. Dr. Thuraisingham is a fellow of the Association for Computing Machinery (ACM) and the National Academy of Inventors (NAI) as well as a Senior Research Fellow at Kings College, London, and a New America Cyber Security Policy Fellow. Her current research is on integrating cybersecurity and data science. Prior to joining UT Dallas, she worked at the MITRE Corporation for 16 years including a three-year stint as a Program Director at the NSF. While at MITRE, she served as a department head and was also an advisor to NSA/R23 as well as the CIA. She initiated the Data and Applications Security Program at NSF and was part of the Cyber Trust theme. In addition to her work at MITRE, she worked for various commercial industry companies such as Honeywell, Control Data Corporation, and the National Science Foundation as an IPA. She was also an instructor at AFCEA between 1998 and 2013. She is the recipient of numerous research awards including IEEE CS 1997 Technical Achievement Award, the ACM SIGSAC 2010 Outstanding Contributions Award, 2014 IBM Faculty Award, 2017 ACM CODASPY Lasting Research Award in data security and privacy, and the 2018 ACM SACMAT Test of Time Award. She is a 2003 Fellow of the IEEE and AAAS. She has published over 120 journal articles, 250 conference papers, 15 books, and has delivered over 130 keynote addresses and is has been awarded six patents. She has chaired conferences and workshops for women in her field, including Women in Cyber Security (WiCys) and gave a featured address at Stanford Universities in Data Science Conference (WiDS) in 2018. More details can be found here.
Dr. Nicholas Ruozzi is an Assistant Professor in the Department of Computer Science at UTD. He was previously a postdoctoral researcher and Adjunct Professor at Columbia University and a postdoctoral researcher at Ecole Polytechnique Federale de Lausanne (EPFL) in Lausanne, Switzerland. He obtained his Ph.D. at Yale University in 2011. Dr. Rouzzi’s research interests include statistical machine learning, probabilistic graphical models, approximate inference and learning, and optimization. His publications appear in the top artificial intelligence and machine learning conferences including NIPS, AAAI, UAI, and AISTATS. His work has been funded by the National Science Foundation (NSF) and the Defense Advanced Research Projects Agency (DARPA). Additional details can be found here.
Dr. Patrick Brandt is a Professor of Political Science at UT Dallas. He received an M.S. in Mathematical Methods in the Social Sciences from Northwestern (1997) and a Ph.D. in Political Science from Indiana University in 2001. Dr. Brandt’s research expertise lies in time series and multivariate data analysis for political, crime, economic, and terrorism applications. He has published widely in these areas with computer scientists, political scientists, and economists. He is a former associate editor (2013-2017) of Political Analysis, a top political science journal. He has published 28 peer-reviewed articles and books, primarily in the social sciences. More details of his work can be found here.
ABOUT THE UT DALLAS COMPUTER SCIENCE DEPARTMENT
The UT Dallas Computer Science program is one of the largest Computer Science departments in the United States with over 3,315 bachelors-degree students, more than 1,110 master’s students, 165 Ph.D. students, 52 tenure-track faculty members, and 44 full-time senior lecturers, as of Fall 2019. With The University of Texas at Dallas’ unique history of starting as a graduate institution first, the CS Department is built on a legacy of valuing innovative research and providing advanced training for software engineers and computer scientists.