Acting Graduate Program Director, Data Science
Professor of Computer Science and Electrical Engineering
Dr. Tim Oates is a Professor in Computer Science and Electrical Engineering (CSEE) department and Director of the University of Maryland Baltimore County’s (UMBC) Cognition, Robotics, and Learning (CoRaL) Laboratory. He received his Ph.D. in Computer Science from the University of Massachusetts Amherst in 2001 and spent a year as a Post-doc in the MIT AI Laboratory. He was appointed an Oros Family Professor of Computer Science to work on a project to improve healthcare in developing countries.He pursues research in the fields of machine learning, artificial intelligence, natural language processing, mobile healthcare, and robotics. Specifically, he is interested in developing a theoretical and algorithmic basis that will allow machines to replicate the human transition from sensors to symbols to semantics. Previously, Dr. Oates worked as aChief Data Scientist at CircleBack.
John Clemens, an adjunct professor with UMBC's Department of Computer Science and Electrical Engineering (CSEE), is a computer scientist and researcher with over 20 years of experience in operating systems, hypervisors, computer security, and machine learning.
John has been a senior staff researcher for the Johns Hopkins University Applied Physics Lab (JHU/APL) since 2009, where his main area of research is building trusted computing systems from IoT devices to cloud servers. Prior to joining APL, John was lead software architect at StackSafe, Inc., and also spent time as a kernel engineer at Sun Microsystems. He has also been active in the open source community, contributing to several open source projects including the Linux kernel. His current research interests include building adaptable software systems using machine learning. He has extensive experience with many programming languages including C, C++, Python, Rust and the machine learning frameworks Keras, TensorFlow, and PyTorch.
John graduated from Rensselaer Polytechnic Institute (RPI) in 2000 with a B.S. in Computer Engineering with a concentration in high performance parallel systems. In 2017, he achieved doctoral candidacy in Computer Science at UMBC.
B.S., Computer Science, UMBC
M.S., Computer Science, UMBC
He graduated from UMBC with a B.S. in Computer Science in 1997 and continued on in the department, earning his M.S. in 2000. His areas of focus included 3D graphics, visualization, and animation. In addition, he was an active member of Upsilon Pi Epsilon and the Computer Science Council of Majors. Jim brings a strong understanding of Machine Learning, Deep Learning, and NLP to the Data Science department at UMBC.
Ph.D., Information Engineering, Morgan State University
M.S., Computer Science, Howard University
B.S., Computer Science, Lincoln University
Amen Ra Mashariki, an adjunct professor with UMBC's Department of Computer Science and Electrical Engineering, leads Urban Analytics at Esri. Esri is a digital mapping pioneer that builds big data and analytics software that provides location intelligence and insights for almost every industry.
As the head of Urban Analytics at Esri, Dr. Mashariki is responsible for the messaging and strategy for applying data science principles to urban challenges, ensuring that data-driven decision makers will realize impactful and positive outcomes in urban policy and operations. Previously Dr. Mashariki was Chief Analytics Officer for the City of New York and the Director of the Mayor’s Office of Data Analytics. He ran the civic intelligence center that allowed one of the largest cities in the world to aggregate and analyze data from across agencies.
In 2012, Dr. Mashariki was one of eleven individuals appointed by the President of the United States to the 2012-2013 class of White House Fellows. Immediately after the Fellowship he was appointed the Chief Technology Officer for the Office of Personnel Management.Dr. Mashariki currently serves as a Fellow at the Harvard Ash Center for Democratic Governance and Innovation.
Ben Payne, an adjunct instructor with UMBC's Department of Computer Science and Electrical Engineering (CSEE), is a scientist focused on applications of data science to challenges in the Department of Defense (DoD) where he is a computer systems researcher in the area of High Performance Computing (HPC).
At the DoD, Ben's focus is on identifying new technologies and opportunities in HPC to enhance mission capabilities. This includes evaluating technical proposals for research investment, establishing baseline system requirements, as well as writing mission related software (he particularly enjoys creating Python code and Bash scripts for data analysis). Prior to working at the DoD, Ben developed software for Physics simulations and utilized large scale computers for those calculations. In addition to his extensive computational experience, Ben was an aircraft mechanic in the Air National Guard, deploying overseas twice.
Ben graduated from the University of Wisconsin at Madison in 2006 with a B.Sci. in Applied Mathematics, Engineering, and Physics. He completed a Masters and PhD in Physics at the Missouri University of Science and Technology.
Patty Stanton, an adjunct professor with UMBC’s Department of Computer Science and Electrical Engineering (CSEE), is a Data Scientist with over twenty years of database engineering, business intelligence, and analytics experience.
She performs as a lead Data Scientist to support analytic and machine learning efforts in the Advanced Data Analytics Lab at the Social Security Administration. She holds a Master of Science in Analytics from Texas A&M University and a Bachelor/Master of Science in Information Systems from American Sentinel University. She has numerous IT certifications to include Microsoft Certified Application Developer and Microsoft Certified Database Administrator. She is a frequent guest speaker at community data science related events.
In 2017, she was awarded the Texas A&M Margaret Sheather Memorial Award in Analytics for her Capstone Project, “Using Decision Trees to Analyze Patterns in Disability Fraud.”
Her interests are machine learning, text mining, and using GPUs/distributed processing to improve the performance of analyzing and processing big data. She is proficient in SQL, C#, SAS, R, and Python. She has worked with a variety of database systems to include SQL Server, Oracle, DB2, Hadoop, and Sqlite.