CAC @ TTU Leadership Team
The CAC@TTU site works with faculty drawn from several departments and colleges throughout the university to deliver on the promise of the Cloud and Autonomic Computing effort.The participants form an ethnically diverse group that includes men and women with a variety of scientific and educational backgrounds and representing multiple fields of study. The following team leads these coordination efforts:
Site Director and PI: Yong Chen
Dr. Yong Chen is an Associate Professor and Director of the Data-Intensive Scalable Computing Laboratory in the Computer Science Department of Texas Tech University (TTU). He is also the TTu Site Director of the IUCRC Cloud and Autonomic Computing Center. His research focuses on data-intensive computing, parallel and distributed computing, high-performance computing, cloud computing, and computer systems in general. He has published over 100 research papers in international journals and conferences. His research has been funds by the National Science Foundation, Department of Defense, Department of Energy/Argonne National Laboratory, Oak Ridge Associated University Dell Inc., Nimboxx, Jabil/Stack Velocity, and NVidia. He has also served as editors, chairs, and program committee members for numerous international journals, conferences, and workshops. He received several awards for his research and teaching activities including Texas Tech University Mortar Board and Omicron Delta Kappa Outstanding Faculty Award, Texas Tech University Whitacre College of Engineering Research Award, IEE TCSC (Technical Committee on Scalable Computing) Young Achievers Award, the Ralph E. Powe Junior Faculty Enhancement Award, ACM/IEEE Outstanding High Performance Computing Ph.D. Fellowship, several Best Paper Awards and Best Paper finalist and Best Student Paper finalist at the ACM/IEEE Super-computing Conference (SC).
Co-Pi and CAC Co-Director: Alan Sill, Ph.D.
Dr. Alan Sill is the Managing Director of the High Performance Computing Center at Texas Tech University, where he is also adjunct professor of physics. He also co-directs the overall CAC center and holds a position as visiting professor of distributed computing at the University of Derby, UK. Dr. Sill has a PhD in particle physics from American University. He has an extensive track record in scientific computing and has led several large-scale distributed computing projects. His publications span topics in cloud and grid-computing, scientific computing, particle, nuclear, and cosmic ray physics, and radioisotope analysis, totaling over 600 peer-reviewed journal and conference articles. He serves as President of the Open Grid Forum, a global computing standards organization, for which he previously served as Vice President for Standards. He is an active member of IEEE, the Distributed Management Task Force, and other computing standards working groups, and serves either directly or as liaison for the Open Grid Forum on several national and international standard road-map committees. He is a past member of the editorial board for IEEE Cloud Computing and current member of the advisory board for the EU-funded StandICT international standards coordination project and several other EU-funded projects, and has chaired or co-chaired several high-profile IEEE, ACM and other international academic conferences and workshops. Further biographical information for Dr. Sill is available on EDUCAUSE.
Co-PI: Susan Mengel
Dr. Susan Mengel (firstname.lastname@example.org), is an Associate Professor at Texas Tech University. She has played strategic leadership roles in numerous multidisciplinary projects involving the delivery of innovative software and data models in sleep management, student retention, and advising, computer education, nutrition, speech therapy, big data, and cybersecurity. She has served on grants from NSF, the Texas Coordinating Board, and the Department of Agriculture. She helped to establish the Master's in Software Engineering degree program at Texas Tech University, served as Vice-President for the Texas Tech Faculty Senate, chaired the IEEE Software Engineering Education and Training Conference, served on the Steering Committee of the ACM/IEEE Computing Curriculum, and served on the IEEE Computer Society Board of Governors. She currently serves on the Texas Tech Institutional Review Board for the Protection of Human Subjects, is FY19 Outreach Chair of the Society of Women Engineers Outreach Committee, and is the Associate Editor for Computing for the IEEE Transactions on Education. She is the faculty advisor for the TTU Collegiate Chapter of the Society of Women Engineers and has been helping to guide and advise students in the formation of the Women in High-Performance Computing chapter at Texas Tech.
Co-PI: Dr. Tommy Dang
Dr. Tommy Dang is an Assistant Professor of Computer Science at Texas Tech University where he directs the interactive Data Visualization Lab (iDVL). His research on big data visualization and visual analytics have appeared in Computer Graphics Forum and IEEE Transactions on Visualization and Computer Graphics and has been presented at IEEE Information Visualization, IEEE Visual Analytics Science and Technology, EG/VGTC Conference on Visualization, among others. The mainstream of his research is on visual features for analyzing the pairwise correlation of multivariate data. Working directly with these measures, his research was able to locate the anomalous or interesting subset of variables/sub-series for massive, dynamic, and high dimensional data in scientific and social applications. He also has special interests and skills on 3D modeling, computer animation, and virtual reality. Dr. Dang has previously been a post-doc on a DARPA-funded project on biological network visualization at the Electronic Visualization Lab at the University of Illinois at Chicago which focuses on advanced virtual reality, notably the CAVE2TM hybrid reality environment and SAGETM scalable amplified group environment. In his Ph.D. research, he studied the visual characterizations of massive high dimensional data. His ongoing research targets user characterization through behaviors and translates them into visual specifications at the language level. His ambition is to effectively connect three components: Big Data-Visual Interface-Users.