VISION Faculty Fellows

At the heart of the VISION program pilot are the Faculty Fellows. A VISION Advisory Panel comprised of recognized experts and leaders in computing research, selected 8 faculty from 4 minority-serving institutions to be part of the inaugural 2021 VISION Faculty Fellows Program. These faculty were selected for their commitment to the teaching and research mission of their home institution and for their potential to accelerate research in areas of national need at their school. Over the next two years, Faculty Fellows will engage in training and mentoring, and build connections to expand research capacity at their university through the VISION program.

2021 Cohort

Wei-Bang Chen, Ph.D. | Associate Professor, Department of Engineering and Computer Science, Virginia State University

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Wei-Bang Chen is an associate professor and graduate program coordinator of the Department of Computer Science at Virginia State University. He also serves as a research scientist and technical advisory council (TAC) committee member at Commonwealth Center for Advanced Manufacturing (CCAM), Virginia. Chen has a Ph.D. in computer science from the University of Alabama at Birmingham. In addition, he has an MS in genetics from National Yang-Ming University, Taiwan, and an MS in computer science from the University of Alabama at Birmingham.

His research interests include multimedia data mining, image processing, computer vision, machine learning, artificial intelligence, laboratory automation, bioinformatics, and cyber security. Chen has published several book chapters and more than 60 papers in top-tier journals and conferences. He has served as the finance and registration chair for more than 5 years for the IEEE International Conference on Information Reuse and Integration for Data Science (IRI) and the IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR). He also reviewed many manuscripts for journals and conferences. Chen’s research has been sponsored by the NSF.

Frances Chevonne Dancer, Ph.D. | Assistant Professor, Department of Computer Science, Jackson State University

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Frances Chevonne Dancer received her B.S. in Computer Science with a minor in Mathematics (Magna Cum Laude) from Jackson State University. She was accepted to Mississippi State University in a dual M.S./Ph.D. degree program where she received her M.S./Ph.D. in Computer Science with an emphasis in Computer Forensics. Dr. Dancer currently works as an Assistant Professor in the Department of Electrical and Computer Engineering & Computer Science at Jackson State University where she is the advisor for the student chapter of ACM. Currently, her research interests include mobile device forensics, process modeling, and computer education of which she is the PI on an NSF grant entitled Excellence in Research: Teaching Problem-Solving and Deductive Skills to K12 Students Through a Forensics Course Based on Mobile Devices. She is also the Co-PI and Senior Personnel on several grants by these foundations: DHS, DOE, and NSF. She is also the reviewer of IEEE Technologies for Homeland Security, IJCSES (International Journal of Computer Science Education in Schools), IJCSDF (International Journal of Cyber-Security and Digital Forensics), and NSF.

Edward Dillon, Jr., Ph.D. | Assistant Professor, Department of Computer Science, Morgan State University

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Dr. Dillon is an Assistant Professor in the Department of Computer Science at Morgan State University. His research focuses on human factors in computing with emphasis on societal and educational aspects. One notable aspect of his research explores mental models that are developed by early CS majors as they learn to program, while also infusing industry-based practices that could aid in their ability to become proficient programmers. This particular work is currently funded by the National Science Foundation. Through his research, Dr. Dillon mentors and funds several undergraduate students, in particular ones who come from underrepresented backgrounds in computing. Moreover, some of these students have recently been accepted into graduate programs (both Master and Ph.D. level) across the country.

Naja Mack, Ph.D. | Assistant Professor, Department of Computer Science, Morgan State University

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Dr. Naja A. Mack is currently a tenure-track Assistant Professor of Computer Science at Morgan State University. Naja attended Clafin University in Orangeburg, South Carolina, where she was a member of the Alice Carson Tisdale Honors College and graduated Cum Laude with a Bachelor of Science in Computer Engineering. She then attended Clemson University, where she earned a Master of Science in Computer Science with a concentration in Interactive Computing in the Fall of 2013. In April 2021, Naja received her Ph.D. in Human-Centered Computing, under the advisement of Dr. Juan E. Gilbert, from the Computer and Information Science Engineering Department in the University of Florida's Herbert Wertheim College of Engineering. Her research interests lie in Human-Centered Computing, Broadening Participation in Computing, Artificial Intelligence, Intelligent Conversational User Interfaces, and Advanced Learning Technologies.

Jose Lugo-Martinez, Ph.D. | Assistant Professor, Department of Computer Science, University of Puerto Rico - Río Piedras

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Dr. Lugo-Martinez is a Lane Fellow at the Computational Biology Department in the School of Computer Science at Carnegie Mellon University, co-hosted by Professors Ziv Bar-Joseph and Robert F. Murphy. In August 2021, Jose will start his independent career in the Department of Computer Science at the University of Puerto Rico-Rio Piedras (UPR-RP). Dr. Lugo-Martinez received the PhD degree in computer science with a minor in bioinformatics from Indiana University under the supervision of Professor Predrag Radivojac. Prior to that, he received dual BSc degrees in computer science and mathematics at UPR-RP, and MSc degree in computer science at the University of California-San Diego. His research interests include machine learning, data science, computational biology, and bioimage informatics.

Venkata Melapu, Ph.D. | Assistant Professor, Department of Computer Science, Jackson State University

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Dr. Venkata Kiran Melapu is a Bioinformatician with insights into functional genomics and systems biology and currently serving as an assistant professor of Computer Science department at Jackson State University, Jackson, Mississippi. He is involved in research that is mainly focused on identification and validation of biomarkers for diagnosing Alzheimer’s disease and other neurological diseases such as Parkinson’s disease. He is also involved in computational study and analysis of molecular markers associated with neurological disorders such as Huntington’s disease, Multiple Sclerosis, Tauopathies disorder and prion diseases. He is also interested and has expertise on applications of Monte Carlo simulations in protein structure prediction.

Dr. Melapu received his Ph.D. in Bioinformatics from University of Arkansas at Little Rock (UALR) in conjunction with University of Arkansas for Medical Sciences (UAMS), Little Rock, Arkansas. He did his postdoctoral research on DNA methylation studies at University of Arkansas for Medical Sciences (UAMS). He also received his master’s degree in Bioinformatics from UALR/UAMS, Little Rock, Arkansas.

He is teaching graduate courses in computer science at Jackson State University, Jackson, Mississippi. His present research at JSU include Big Neuroimaging data analysis and his collaboration with University of Mississippi Medical Center (UMMC) is established through a Vanguard Data mining and research center located at JSU. This serves as the source of translating BD2K (Big Data to Knowledge). He is serving in various academic committees and as a member in various graduate student’s master’s and PhD thesis committees. He has been an author and co-author of research papers in many journal publications as well as research proceedings.

Patricia Ordóñez, Ph.D. | Associate Professor, Department of Computer Science, University of Puerto Rico - Río Piedras

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Dr. Ordóñez is an Associate Professor in the Computer Science faculty at the University of Puerto Rico Río Piedras, thereby fulfilling two lifelong and one unexpected midlife dreams: living in a Spanish speaking country, having the opportunity to make a greater difference in the world, and becoming a professor. Her research interests are in applying machine learning, data mining, and visualization to multivariate time series analysis, specifically to large repositories of clinical data (now know as Biomedical Data Science). There are so many techniques that have been applied to financial and marketing data that can make a significant impact to improve the way medicine is practiced today. Through collaboration among computational scientists, medical professionals, and others, tools can be developed to create clinical decision support systems so that medical providers make better diagnosis and treatment plans by learning from the experience of previous providers who have had similar patients. She founded the Symposium of Health Informatics for Latin America and the Caribbean as a major step in addressing that objective through international collaboration. She is passionate about making quality health and education accessible to all. She is also passionate about diversifying the field of computer science and thereby am active in mentoring and developing mentoring communities at the elementary,middle, and high school level, college and graduate school level, and in creating assistive technologies for programming and communication so that computer science is for all.

Dr. Ordóñez received a PhD from the department of Computer Science at the University of Maryland, Baltimore County. She defended her dissertation on March 29, 2012 and her doctoral degree was conferred on May 18, 2012. She was a non-traditional student, meaning she took a huge leap of faith while experiencing a close to mid-life crisis. Ages ago as an undergraduate, she was scared off by electrical engineering (computer science was just starting then). After years of teaching high school math and Spanish and even more years being a technical trainer, she decided to go back to school full-time to pursue a graduate degree and explore her mind's fullest potential. She applied for the PhD program realizing that without external funding she would not be able to accomplish this task. UMBC took a chance on me and accepted her with full funding in Fall of 2005 even though she did not have a degree in Computer Science. Thank you, CSEE department at UMBC! After two years, she was blessed and pleasantly surprised to win a fellowship from the National Science Foundation while she struggled to keep up with all the twenty somethings in graduate school. Thank you, NSF! It's hard to believe she is now a tenured Associate Professor. But, this road was not traveled alone. She gives credit to UMBC's PROMISE program and to her family, friends, peers, mentors, and advisors for all the support she has received from them. Credit also goes to the CRA-W, the CDC, Google, and the Anita Borg Institute for the wonderful programs they have to keep the underrepresented motivated. Thank you to the National Science Foundation, the CSEE department, the eBiquity laboratory, Verizon, Xerox, and the Hispanic Scholarship Fund for helping to fund her graduate school experience.

Joseph Shelton, Ph.D. | Assistant Professor, Department of Engineering and Computer Science, Virginia State University

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Dr. Joseph Shelton is an assistant professor in the Computer Science department at VSU. He is from Virginia, and he obtained his computer science Ph.D. from North Carolina Agricultural and Technical (NCAT) State University in the Fall of 2015. He has published over 30 publications that incorporated artificial intelligence techniques, a vital role in data analytics. Additionally, he has participated in a number of activities that emphasized teaching STEM principles to a young audience. Though Dr. Shelton focused on innovating his research, he also has a passion for educating any audience in his research. His life’s goal is to expand the horizons of his research area to educate as well as educate future researchers and practitioners of the computer sciences.

Dr. Shelton's biometric based research focuses on using a genetic algorithm technique to evolve feature extraction for biometric recognition and for mitigating biometric based replay attacks. Replay attacks focus on capturing an individual's biometric feature vectors (FVs) during transmission, and replaying it for illegal access. Genetic and Evolutionary Feature Extraction (GEFE), developed by Dr. Shelton, recently showed promising results in mitigating replay attacks in combination with a feature selection algorithm. Currently, Dr. Shelton's latest work is focused on biometric-based presentation attacks. Presentation attacks are another biometric system vulnerability primarily focused on presenting a biometric sample of quality to illegally gain access to secured data. Although biometric authentication strengthens security protocols through unique feature extraction, presenting a biometric sample of quality to illegally gain access to a biometric system is feasible. A 2D face system with no anti-spoofing measures can be easily spoofed by presentation attacks. Dr. Shelton's most recent work involves using the novel deep learning technique of Generative Adversarial Networks (GANs) to generate synthetic (spoofing) samples. Once properly trained, the synthetic images are used to create spoofing datasets. The GEFE technique is used in combination with the deep learning technique of GANs to generate improve anti-spoofing feature extractors optimized to mitigate presentation attacks.