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.
Murat Guner, Ph.D. | Assistant Graduate Program Director
Ph.D., Mathematics, University of Rochester
M.Sc., Mathematics, Bogazici University (Istanbul, Turkey)
B.Sc., Mathematics, Bogazici University (Istanbul, Turkey)
Murat Guner got his Ph.D. in 2019 from the University of Rochester, in New York. During his doctoral studies, he studied geometrical analysis. After finishing his Ph.D. he worked as the Lead Data Science Instructor at Flatiron School. His current interests include unstructured data in machine learning, especially NLP techniques and audio data.
Huthaifa Ashqar, Ph.D. | Adjunct Instructor
Ph.D., Virginia Tech
Dr. Huthaifa Ashqar earned his Ph.D. at Virginia Tech in 2018 and since then he has worked as a transportation systems engineer at Booz Allen Hamilton with a primary focus on Intelligent Transportation Systems. He has co-authored various publications for different journals and conferences, and has been a presenter at several events. His areas of interest include Intelligent Transportation Systems, Traffic Signal Systems and Operations, Traffic Safety, Traffic Control Strategies, Traffic Flow Theory, Connected and Autonomous Vehicles, and Urban Computing.
Philip Bogden, Ph.D. | Senior Lecturer
Ph.D., University of California
Philip Bogden, Ph.D., specializes in custom data analytics and visualization for a range of clients, including companies in the oil and gas industry and financial services sector. He was previously CEO of a nonprofit organization that provided real-time environmental data as a public service, and spent two years helping develop and implement open data policy and cyber-infrastructure for the National Science Foundation. He is currently a senior lecturer teaching data visualization in the graduate GIS program at UMBC, and previously served on the faculty of Geology and Geophysics at Yale. He received his Ph.D. from the University of California and a Bachelors from Harvard.
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.
Matthew Esworthy | Graduate Faculty
J.D., Dickinson School of Law of the Pennsylvania State University
B.A., Skidmore College
A seasoned trial lawyer and Partner with the law firm of Bowie & Jensen, LLC, Mr. Esworthy has successfully advocated on behalf of business owners, in-house legal teams, and individuals in state and federal courts. Prior to entering private practice, Mr. Esworthy served the Maryland Court of Special Appeals as a judicial clerk to Hon. Joseph F. Murphy, Jr.
Mr. Esworthy holds numerous leadership roles with the American Bar Association related to cybersecurity, including the Co-Chair of the Cybercrime Committee for the Criminal Justice Section. Dedicated to the Greater Baltimore community, he is a member of the board of directors and acting general counsel for the Clarence H. “Du” Burns Memorial Fund, Inc.
For almost two decades, businesses and individuals in the region have relied on Mr. Esworthy for representation in a variety of matters at the trial and appeals court levels. This work includes complex commercial litigation, trade secret disputes, real estate and partnership disputes, as well as counsel and representation in cybersecurity matters. He also has considerable experience representing business owners and individuals accused of white-collar crimes. This includes defense against allegations of mail and wire fraud, bank fraud, antitrust, election fraud, and cybercrimes.
Mr. Esworthy is licensed to practice in Maryland, the District of Columbia, and Pennsylvania. He has been admitted to the U.S. District Court, Maryland; U.S. District Court, District of Columbia; U.S. Court of Appeals, Fourth Circuit; U.S. Supreme Court; and U.S. Bankruptcy Court, Maryland. Mr. Esworthy is alumni of The Dickinson School of Law of the Pennsylvania State University, where he maintains an active leadership role within the Penn State alumni association, and Skidmore College in Saratoga Springs, New York.
Since 2016, Mr. Esworthy has been selected to the annual list of Top 100 Maryland Super Lawyers and to the annual list of Best Lawyers in America in the field of Commercial Litigation and Technology Law. In 2019, Best Lawyers named Mr. Esworthy the Lawyer of the Year in Technology Law. In 2019, Mr. Esworthy was also a winner of The Daily Record’s Leadership in Law Award, 2019.
Charles Givre | Adjunct Instructor
M.A., Near Eastern and Judaic Studies, Brandeis University
B.S., Computer Science, University of Arizona
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.
Donghwa Kim | Instructor
M.P.S., Data Science, UMBC
B.S., Computer Science, Rutgers College
Donghwa Kim received his BS in CS degree from Rutgers and MPS in Data Science from UMBC. He has 20 years experience in designing and development of complex and innovative systems for financial and healthcare industries with proven track records of successful projects delivery for small, mid and Fortune 500 companies. Currently he’s the vice president of engineering at NewWave.
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.
Jyotsna Potarazu | Adjunct Instructor
M.S., Computer Science, the George Washington University
B.S., Physics, Bombay University
Professor Jyotsna Potarazu is a senior specialist in KPMG’s Federal Advisory Practice with over 20 years of experience with business and information technology leadership in data-driven enterprise strategy and data management.
Edward Raff, Ph.D. | Visiting Assistant Professor
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 Chief 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.
Mehmet Sarica | Adjunct Instructor
M.P.S., Data Science, UMBC
B.S., Computer Education & Educational Technology, Bogazici University
Mehmet Sarica is an experienced Software Engineer with over 10 years of experience. He has designed & developed various desktop and web applications in the education industry. In his current work, Mehmet develops software for the healthcare industry.
Mehmet earned his M.P.S. in Data Science from UMBC. His interests include working on random projects, machine learning, artificial intelligence and mentoring young adults
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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