Ergun Simsek, Ph.D. | Data Science Graduate Program Director
Ph.D., Electrical and Computer Engineering, Duke University
M.S., Electrical and Computer Engineering, University of Massachusetts Dartmouth
B.S., Electrical and Electronics Engineering, Bilkent University (Ankara, Turkey)
Dr. Ergun Simsek earned his Ph.D. from Duke University in 2006 and worked as a post- doctoral research associate at Schlumberger Doll Research Center for the following two years. From 2008 to 2017, he was a faculty member at Bahcesehir University (Istanbul, Turkey) and the George Washington University (Washington, DC). In addition to teaching at both undergraduate and graduate levels, he also conducted research on scientific computing for different applications in electromagnetics, photonics, geophysics, material science, and data science. His research was supported by different agencies such as National Science Foundation, TUBITAK, and European Union Research Council. He has published more than 30 peer-reviewed journal papers and made more than 60 presentations at international conferences. He continues researching how to solve emerging engineering problems through efficient and robust computational techniques.
Before joining UMBC, Dr. Simsek was a manager for Exponent, where he teamed up with engineers, scientists, and regulatory specialists for solving challenging problems of consumer electronics, medical device, and IoT appliance manufacturers. Dr. Simsek is a senior member of IEEE and a licensed Professional Engineer.
Patty Delafuente | Adjunct Instructor
M.S., Analytics, Texas A&M University
M.S., Information Systems, American Sentinel University
B.S., Information Systems, American Sentinel University
Patty Stanton, an adjunct instructor 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.
Tony Diana, Ph.D. | Adjunct Instructor
Ph.D., Policy Sciences, UMBC
M.S., Policy Sciences, UMBC
Tony Diana is the Division Manager, NextGen Collaboration and Messaging at the Federal Aviation Administration. NextGen refers to advanced technologies supporting satellite-based navigation. As the Division Manager, NextGen Performance, he was involved in measuring and reporting operational outcomes from NextGen programs implemented throughout the National Airspace System. His main interests are machine learning applications to aviation, Natural Language Processing, performance evaluation and benchmarking. He received twice the Best Paper Award from the Transportation Research Forum. He is a member of the Aviation Economics and Forecasting subcommittee at the Transportation Research Board of the National Academies of Sciences, Engineering and Medicine. He is a Certified Lean Sigma Master Black Belt, a Certified Change Management Specialist, a Scrum Master, and a certified Project Management Professional.
Darin Johnson, Ph.D. | Adjunct Instructor
Ph.D., Mathematics, Southern Illinois University, Carbondale
B.S., Computer Science, Eastern Illinois University
Darin Johnson is an adjunct instructor with UMBC’s Department of Computer Science and Electrical Engineering (CSEE). He works for the Department of Defense (DoD) as an applied mathematician, where he focuses on applying data science techniques to DoD specific problems. He’s particularly interested in large scale data analysis, streaming applications and anything related to graph theory. Prior to working for DoD, he was a professor at Delaware State University, where he taught mathematics and studied probabilistic combinatorics and random graphs.
Darin graduated from Eastern Illinois University with a B.S. in Computer Science. He completed a Ph.D. in Mathematics at Southern Illinois University, Carbondale.
Jim Klucar | Adjunct Instructor
M.S., Applied and Computational Mathematics, Johns Hopkins University
B.S., Electrical Engineering, Pennsylvania State University
Adam Lippe, J.D. | Adjunct Instructor
J.D., University of Maryland Francis King Carey School of Law
B.S., Political Science, The Johns Hopkins University
Mr. Lippe is a career prosecutor who serves as the Chief of the Economic Cyber Crimes Unit, as well as the Animal Abuse Unit for the Baltimore County State’s Attorney’s Office (a jurisdiction of over 800,000 people). In this role he manages direct reports and personally works on complex embezzlements and frauds, including identity theft, internet scams, check and credit card frauds, large material thefts, organized retail crime, financial exploitation of vulnerable adults, entitlement fraud, animal cruelty, in addition to handling murders. He was a district and juvenile court prosecutor, before heading to violent crime and narcotics for many years before his current position. Mr. Lippe has been an adjunct faculty member at UMUC, as well as both the University of Maryland School of Law and University of Baltimore School of Law and several other local colleges and universities helping to teach undergraduate and graduates both on-campus and on-line. He also was a frequent lecturer at the National Advocacy Center in Columbia, South Carolina for the National District Attorney’s Association. Licensed in both the state of Maryland and New Jersey, Mr. Lippe is an alumni of the University of Maryland School of Law and The Johns Hopkins University in Baltimore, Maryland.
Ben Payne, Ph.D. | Adjunct Instructor
Ph.D., Physics, Missouri University of Science and Technology
M.S., Physics, Missouri University of Science and Technology
B.Sci., Applied Mathematics, Engineering, and Physics, University of Wisconsin at Madison
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.
Edward Raff, Ph.D. | Adjunct Instructor
Ph.D., Computer Science, UMBC
M.S., Computer Science, Purdue University
B.S., Computer Science, Purdue University
Dr. Edward Raff is an adjunct instructor of Computer Science at the University of Maryland, Baltimore County. Edward is currently working as a Senior Lead Scientist at Booz Allen Hamilton.
He received his Ph.D. in Computer Science from UMBC in 2018, and a M.S. and B.S. from Purdue in 2013 and 2012. Dr. Raff’s research includes work in new methods for malware detection and similarity analysis, high-performance machine learning, biometric fingerprint recognition, and algorithmic fairness. In his spare time, he is also the author of the JSAT machine learning library.
Jared Sylvester, Ph.D. | Adjunct Instructor
Ph.D., Computer Science, University of Maryland
M.S., Computer Science, University of Maryland
B.S., Computer Science, University of Notre Dame
Jared Sylvester, an adjunct instructor with UMBC’s Department of Computer Science and Electrical Engineering (CSEE), has been programming computers for almost two dozen years and conducting scientific research for the last fifteen. Jared is currently a data scientist with Booz Allen Hamilton’s Strategic Innovation Group. His work at Booz Allen focuses on machine learning research, which has lead him to publishing papers on deep learning for non-traditional data types including malware detection as well as the development of new machine learning algorithms to mitigate bias.
Prior to joining Booz Allen, Jared got his doctorate in AI at the University of Maryland. He worked in the Computer Science department doing neural network cognitive modeling, and in the Marketing department doing social network analytics. His dissertation focused on bridging the gap between traditional, symbolic cognitive modeling techniques and neurally-inspired, biologically grounded techniques.
When he is not doing research, Jared enjoys creating algorithmic art, and when he is not in front of a computer he likes to practice calligraphy.
John Wan | Adjunct Instructor
M.B.A., Georgia Institute of Technology
B.S., Georgia Institute of Technology
John is a Senior Program Analyst in the Analytics Center of Excellence (ACE) at Social Security
Administration where he provides technical advice and consultation to project teams, technical
experts, and senior leaders related to multi-disciplinary data science and data analytic projects
supporting the agency’s priorities.
Before joining ACE, John was a held senior position in the Office of Performance Management and
Business Analytics. He began his SSA career as a Computer Scientist in the Hardy-Apfel IT Fellows
program where he rotated through various SSA components in Office of the CIO, Office of
Operations and Office of Systems.
Prior to joining the agency, he was the lead product engineer for several scientific instruments and
heavy machinery companies that serve customers across various sectors, ranging in size from small
privately-owned businesses to Fortune 100 companies. A certified Six Sigma Black Belt, he also
conducted marketing/business analytics to improve several companies’ operational efficiency. With
his proven analytical, technical, and problem-solving skills, he is passionate about performance
improvement through data-driven decision-making.
John graduated with an MBA with concentrations in IT Management and Operations and a
bachelor’s in Computer Engineering both from Georgia Institute of Technology. He enjoys traveling
and cooking in his spare time. He is married with two sons.
Chaojie (Jay) Wang, D.Sc. | Adjunct Instructor
D.Sc., Information Systems & Communications, Robert Morris University
M.B.A., Finance, Loyola University Maryland
M.S., Statistics, M.A., Economics, The University of Toledo
B.E., Management Information System, Tsinghua University (Beijing, China)
Dr. Chaojie (Jay) Wang is a seasoned software engineer, systems architect, data scientist, researcher, and project manager with over thirty-year experience in industry, government, and academia. Jay currently works for the MITRE Corporation, an international think tank and operator of Federally Funded Research and Development Centers (FFRDC). Previously Jay worked for Lockheed Martin Corporation and participated in the design and development of multiple large-scale federal IT systems.
Jay is a certified Java Programmer, a certified Project Management Professional (PMP), and a certified SAFe Agilist (SA). Jay’s research interests include diverse and interdisciplinary subjects including Data Analytics, Knowledge Management, Health IT & Informatics, Big Data, and Artificial Intelligence. Jay is the Editor-in-Chief of the International Journal of Patient-Centered Healthcare (IJPCH) and serves on the Editorial Review Board of the Issues in Information System (IIS).
Waleed Youssef, Ph.D. | Adjunct Instructor
Ph.D., Computer Science, UMBC
M.S., Computer Science, The Pennsylvania State University
B.S., Engineering, Alexandria University
Waleed Youssef is an adjunct instructor in the UMBC’s Department of Computer Science and Electrical Engineering (CSEE). He joined UMBC as an adjunct instructor teaching graduate level data science classes. He uses his 20+ years of experience in the IT industry to provide students with professional engineering experience and hands-on experience in the field. Dr. Youssef works at IBM, as a Chief Architect. He joined IBM in 2008 after earning his Ph.D. degree from UMBC in Computer Science. Dr. Youssef also has a Master’s degree in Computer Science from Penn State University. His undergraduate studies were in Computer Engineering.
Dr. Youssef has many published research papers in cognitive computer and wireless sensor networks. His current work areas include cloud architecture, data science, AI, and IoT. His areas of expertise include Cognitive Computing, Data Science, and Artificial Intelligence.
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