Faculty
Dr. Iftekharuddin
Dr. Cetin
Dr. Chen
Dr. Diawara
Dr. Li
Dr. Shetty
Dr. Xin
Post Doctorate
Dr. Temtam
Dr. Witherow
PhD Students
Alex
Tony
Monibor
Shibly
Kwame
Walia
Meena
Joe
Yisen
MuhammadDr. Khan M. Iftekharuddin
Professor & University Eminent Scholar
Batten Endowed Chair in Machine Learning
Director, ODU Vision Lab
Department of Electrical and Computer Engineering
Research areas: Computational modeling; AI and machine learning; Medical imaging, genomics, and proteomics analysis for precision medicine; Human-machine interaction, and Cyber-physical systems and cybersecurity
Dr. Khan Iftekharuddin is a professor and Batten Endowed Chair in Machine Learning in the department of Electrical and Computer Engineering (ECE) at Old Dominion University (ODU). He concurrently serves as director of ODU Vision Lab since 2011, and inaugural director of Institute of Data Science since 2024, respectively. He served as an Interim Dean in Batten College of Engineering and Technology for 2021-2022. He served as Associate Dean for Research and Graduate Studies/Innovation (ADR) for 2017 – 2021 and 2022 – 2023. Prior to the ADR position, Dr. Iftekharuddin served as Chair of ECE Department at ODU for 2013-2017. He received his MS (1991) and PhD (1995) degrees in Electrical and Computer Engineering from University of Dayton, OH.
Dr. Iftekharuddin was the winner of the State Council of Higher Education for Virginia’s (SCHEV) outstanding faculty award for the highest standards of teaching, scholarship and service in the State of VA, 2023. He has been cited among the top 2% researchers in the globe for both career long impact and single-year impact, Research by Stanford University, published online, 2020 – present. He is awarded Old Dominion University’s 2020 Faculty Research, Scholarship and Creative Achievement Award. He obtained the best researcher award from three different academic institutions: ODU’s Batten College of Engineering and Technology Research Excellence Award for 2014; University of Memphis’s Herff Outstanding Faculty Research Award in the college of Engineering and Technology for 2011; and North Dakota State University’s Researcher of the Year Award in college of Engineering and Architecture for 2000, respectively. His lab has consistently ranked among top four teams in Global Brain Tumor Segmentation and Patient Survivability Prediction Challenges co-organized by MICCAI and NCI since 2014. He has served as the PI for over $24 million externally funded research projects and successfully led multiple national and international collaborative research projects. Different federal and private funding agencies and industries such as NSF, NIH, NASA, ARO, AFRL, NAVY, US DOT, the Whitaker Foundation, FedEx and Timken Research among others have funded his research.
Dr. Iftekharuddin coauthored a book titled, “Digital Image Processing”, in SPIE’s Field Guide series. He is the principal author of about two ad fifty hundred refereed journal papers and conference proceedings; and eleven book chapters respectively. He us a co-inventor of four US patents. He further has two patent applications pending.
Dr. Iftekharuddin has organized several symposiums, special sessions and workshops on Biologically Inspired Computational Vision in IEEE and SPIE conferences since 2003. He is a lead conference chair and organizer of an annual conference on Photonic Devices and Algorithms for Computing in SPIE’s International Symposium on Optical Science, Engineering and Instrumentation (1999-2007) and Optical Information Processing since 2008. He has been serving as the Chair of IEEE Computational Intelligence Society’s Vision and Speech Task force 2007 – 2018. He served as a senior editor for Optical Engineering journal (2017 – 2023). He currently serves as an associate editor several journals including IEEE Transaction on IoT, IEEE Transaction on Neural Networks and Learning Systems, IEEE Transaction on Man, Machine and Cybernetics, IEEE Transaction on Image Processing, Medical Imaging (SPIE), Applied Optics, and Artificial Intelligence Review. Earlier he served as an associate editor for the following journals: Optical Engineering, (2010 – 2023), International Journal of Image Processing (2008 – 2018) and International Journal of Tomography & Statistics (2006 – 2016). He also serves on the editorial board of the Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization (2012 – 2020). He served as a coeditor for multiple journal special issues including Applied Optics, Optical Engineering, Optics and Laser Technology, and Digital Signal Processing.
He had been invited to serve as a charter member for NIH’s Biomedical Computing and Health Informatics (BCHI) study section (2009- 2013) and Biomedical Imaging Technology (BMIT) study section (2015-2019), respectively. He also routinely serves on many NIH’s panels in Imaging, Data Analytics, Alzheimer and Cancer, and NSF’s EPAS, S&CC, CPS, SCH, IIS and other panels, NASA, NSERC (Canada), Swiss National Foundation, and ARO on a regular basis. He further reviewed Center grants for NSF and NIH.
He is an elected fellow of AIMBE, Optica (OSA) and SPIE and a senior member of IEEE and NNS.
E-mail: kiftekha@odu.edu
Homepage: https://www.odu.edu/directory/people/k/kiftekha
Department of Civil and Environmental Engineering
Research areas: Applications of machine learning, Intelligent Transportation Systems (ITS), connected and automated vehicles, modeling and simulation of traffic operations
Dr. Cetin is a Professor at the Civil and Environmental Engineering (CEE) Department at ODU and the Batten Chair in Transportation Systems and serves as the Director of Transportation Research Institute (TRI). He conducts research on Intelligent Transportation Systems (ITS), applications of machine learning in ITS, connected and automated vehicles (CAVs), modeling and simulation of traffic operations, system state prediction, and sensor fusion. Dr. Cetin has served as PI or CoPI on numerous projects totaling about $10M since joining ODU including an NCHRP project on Implementing and Leveraging Machine Learning at State DOTs. He received funding from various agencies including the NSF, USDOT, TRB, NOAA, FHWA’s TFHRC, VDOT, SCDOT, City of Virginia Beach, and others. Dr. Cetin published more than 100 papers in peer-reviewed journals and international conference proceedings. Dr. Cetin has also been serving the professional community for more than 20 years. He served on the TRB Committee on Artificial Intelligence and Advanced Computing Applications (AED50) for 12 years and the TRB Committee on Urban Transportation Data and Information Systems (AED20) for nine years. He chaired or co-chaired various subcommittees and activities for these committees including numerous TRB workshops on machine learning and big data. He served as the paper review chair for AED20 and managed the review process for ~45-60 papers/year (2015-2018). He is serving as an associate editor for two journals: Journal of Intelligent Transportation Systems and Data Science in Transportation. Dr. Cetin received his Ph.D. degree in Transportation Engineering from Rensselaer Polytechnic Institute (RPI) in 2002. Prior to joining ODU, Dr. Cetin had worked as an assistant professor for four years in the Department of Civil and Environmental Engineering at the University of South Carolina (USC).
E-mail: mcetin@odu.edu
Homepage: http://www.tri-odu.org/meet-the-director.html
Department of Electrical and Computer Engineering
Research areas: Robotics, automated surveillance systems, pattern recognition, image processing, artificial intelligent systems, data analysis and mining, and network performance analysis
Dr. Chung-Hao Chen received his Ph.D. in Electrical Engineering at the University of Tennessee, Knoxville in August 2009, and his B.S. and M.S. in Computer Science and Information Engineering from Fu-Jen Catholic University, Taiwan in 1997 and 1999, respectively. After receiving his M.S. degree, he was enlisted in National Military Academy from 1999 to 2001 to fulfill his civil duty/military service. In April 2001, he joined the Panasonic Taiwan Laboratory Company, Ltd. as a research and development engineer where he remained until August 2003. In August 2009, he joined in the department of Math and Computer Science at North Carolina Central University as an assistant professor. Since August 2011, he is an assistant professor in the department of Electrical and Computer Engineering at Old Dominion University. His research interests include robotics, automated surveillance systems, pattern recognition, image processing, artificial intelligent systems, data analysis and mining, and network performance analysis.
E-mail: cxchen@odu.edu
Homepage: http://www.odu.edu/~cxchen/
Department of Mathematics and Statistics
Research areas: Spatio-temporal estimation methods, Discrete choice modelling, Functional distributions
Dr. Diawara received his B.S. in Science at the University Cheick Anta Diop in Dakar, Senegal, then his Maîtrise in Mathematics at University of Le Havre, France. He then completed his Master’s in Mathematics and Statistics at University South Alabama, Mobile, Alabama and his Ph.D. in Statistics at Auburn University in Auburn, Alabama in 2006. He joined ODU then.
His research falls in the area of Applied Statistics, and it can be classified into two main areas: (1) Spatio-temporal estimation methods and (2) Discrete choice modelling. It includes estimation of the spatio-temporal behaviors, time to event, and visualization with time series data. Such research interests are needed for statistical pattern recognition with the use of copulas and spatial-temporal models. With the high volume of data, the analytic prospects in medical and engineering applications, statistics becomes much needed tool in dynamic technological and scientific advancements. Teaching is also a big part of his profession. He has a tremendous joy in seeing the faces of students when all of a sudden, they realize they can do the analysis, understand the results and meaning behind.
E-mail: ndiawara@odu.edu
Homepage: https://ww2.odu.edu/~ndiawara/
Department of Electrical and Computer Engineering
Research areas: Machine learning, computer-aided medical diagnosis systems, medical signal/image processing, and neural networks
Dr. Jiang Li received his B.S. degree in electrical engineering from Shanghai Jiaotong University, China, in 1992, the M.S. degree in automation from Tsinghua Univey, China, in 2000, and the Ph.D degree in electrical engineering from the University of Texas at Arlington, TX, in 2004. He worked as a postdoctoral fellow at the department of radiology, National Institutes of Health, from 2004 to 2006. Dr. Li joined ODU as an assistant professor in Spring 2007. His research interests include machine learning, computer-aided medical diagnosis systems, medical signal/image processing, and neural network. His recent publications focus on computer-aided colonic polyp detection, machine learning and medical image processing. Dr. Li is a member of the IEEE and Sigma Xi.
E-mail: jli@odu.edu
Homepage: http://www.ece.odu.edu/~jli/
Virginia Modeling, Analysis & Simulation Center
Research areas: Computer networking, network security, machine learning
Sachin Shetty is an Associate Director in the Virginia Modeling, Analysis, and Simulation Center (VMASC) at Old Dominion University. He holds a joint appointment as Professor in the Department of Computational Modeling and Simulation Engineering. Sachin Shetty received his PhD in Modeling and Simulation from the Old Dominion University in 2007 under the supervision of Prof. Min Song. Prior to joining Old Dominion University, he was an Associate Professor with the Electrical and Computer Engineering Department at Tennessee State University. He was also the associate director of the Tennessee Interdisciplinary Graduate Engineering Research Institute and directed the Cyber Security laboratory at Tennessee State University. He also holds a dual appointment as an Engineer at the Naval Surface Warfare Center, Crane Indiana. His research interests lie at the intersection of computer networking, network security and machine learning. His laboratory conducts cloud and mobile security research and has received over $18 million in funding from National Science Foundation, Air Office of Scientific Research, Air Force Research Lab, Office of Naval Research, Department of Homeland Security, Department of Energy, Office of Undersecretary of Defense for Research and Engineering, MITRE, Commonwealth of Virginia, Sentara Healthcare and Boeing. He is the site lead on the Department of Defense Cyber Security Center of Excellence, Department of Defense Artificial Intelligence Center of Excellence, the Department of Homeland Security National Center of Excellence, the Critical Infrastructure Resilience Institute (CIRI), and Department of Energy, Cyber Resilient Energy Delivery Consortium (CREDC). Recently, he has developing blockchain empowered solutions to address cybersecurity issues in distributed systems for the Department of Defense, Department of Energy and Sentara Healthcare. He serves as the chair for the IEEE P2418.6 Internet of Medical Things. He has edited a book entitled “Blockchain for Distributed Systems” for Wiley-IEEE Press.
E-mail: sshetty@odu.edu
Homepage: https://sics-c.org/sachin-shetty/
Department of Electrical and Computer Engineering
Research areas: AI security and privacy, mobile computing, optical networks, networked computing, mobile and IoT security
Dr. ChunSheng Xin is a Professor in the School of Cybersecurity and Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, Virginia. He received the Ph.D. degree in Computer Science and Engineering from the State University of New York at Buffalo, Buffalo, NY, USA, in 2002. His research interests include AI security and privacy, mobile computing, optical networks, networked computing, mobile and IoT security. Dr. Xin has received over 40 external grants, with the total amount more than $14 million, from NSF, NSA, ONR, AFRL, NIST, etc. His research findings have been published over 100 papers in leading journals and conferences, including three Best Paper Awards from IEEE PerCom, Globecom, and ICCCN. He has published one technical book on wireless networks and contributed several book chapters in professional books. He also received two patents on IoT security, and control algorithms for optical networks, respectively. He has served as Co-Editor-in-Chief of an international journal and Associate Editors of several journals including IEEE Transactions on Mobile Computing. He has served as Symposium/Track Chairs of multiple international conferences including IEEE Globecom, ICCCN, and also served in the technical program and organization committees of several technical conferences/workshops, including IEEE Infocom. He has been an external consultant on cybersecurity for industry and academia. Dr. Xin is an IEEE senior member.
E-mail: cxin@odu.edu
Homepage: https://ww1.odu.edu/eng/programs/ccni
Department of Electrical and Computer Engineering
Research areas: Deep learning, Image processing and analysis, Spectrum Sensing, Cognitive Radio Networks, MIMO systems
Dr. Temtam received his Ph.D. degree in Electrical and Computer Engineering from Old Dominion University in December 2014, Postgraduate Diploma in Microelectronic Systems from University of Liverpool, England and M.Sc. from the Department of the Electronics Engineering from Bradford University, England, UK, in 2002 and 2004, respectively. After earning his M.Sc., he worked as faculty at Electrical and Electronics Engineering AlJabel Algarbi University, Gharyan, Libya til 2009, after obtaining his Ph.D., Dr. Temtam worked at Reliable Business Communications Inc. Virginia Beach, VA til end of 2016 also worked as an adjunct Faculty at Electronics Engineering Technology Faculty, ECPI University, Newport News. His research interests include deep learning, Image processing and analysis, Spectrum Sensing, Cognitive Radio Networks, MIMO systems.
References:
- Temtam A, S Sadique MS, Rahman MM, Farzana W, Iftekharuddin KM. “Pediatric Brain Tumor Segmentation using Multiresolution Fractal Deep Neural Network”. In International MICCAI Workshop 2023 Oct 7.
- Temtam, A., L. Pei, and K. Iftekharuddin. “Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentation.” arXiv e-prints (2023): arXiv-2306.
- A. Temtam, L. Ma, F. Gerard Moeller, M. S. Sadique, and K. M. Iftekharuddin. “Opioid use disorder prediction using machine learning of fMRI data.” In Medical Imaging 2023: Computer-Aided Diagnosis, vol. 12465, pp. 142-146. SPIE, 2023.
- A. Temtam, E. J Cruz, H. Okhravi, D. Strock, M. Sternick and K. M. Iftekharuddin, “Advanced Machine Learning Approach to Increase Diagnostic Accuracy in Atypical Alzheimer’s Disease Cases” Accepted to Alzheimer’s Association International Conference (AAIC) 2022.
E-mail: atemt001@odu.edu
Research Areas: Computer Vision, Deep Learning, Affective Computing
Dr. Megan Witherow is a Post-Doctoral Research Associate with the Research Foundation appointed to the Vision Lab, Dept. of Electrical & Computer Engineering, Old Dominion University. She received her Ph.D. in Electrical and Computer Engineering in May 2024 and her B.S. in Computer Engineering in May 2018 from Old Dominion University, Norfolk, VA, USA. From September 2020 to May 2024, she was a National Science Foundation Graduate Research Fellow. Her research interests include computer vision, deep learning, human-computer and human-robot interaction, affective computing, medical image analysis, and responsible AI.
References:
- M. A. Witherow, N. Diawara, J. Keener, J. W. Harrington, and K. M. Iftekharuddin, “Pilot Study to Discover Candidate Biomarkers for Autism based on Perception and Production of Facial Expressions,” arXiv. 2024, doi: 10.48550/arXiv.2404.16040.
- M. A. Witherow, C. Butler, F. Ilgin, N. Diawara, J. Keener, J. W. Harrington, and K. M. Iftekharuddin, “Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) for Improved User Engagement,” arXiv, 2024. doi: 0.48550/arXiv.2403.07314.
- M. A. Witherow, M. D. Samad, N. Diawara, H. Y. Bar and K. M. Iftekharuddin, “Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features,” in IEEE Transactions on Affective Computing, 2023, doi: 10.1109/TAFFC.2023.3297075.
- M. A. Witherow, M. D. Samad, N. Diawara, and K. M. Iftekharuddin “Facial landmark feature fusion in transfer learning of child facial expressions”, Proc. SPIE 12227, Applications of Machine Learning 2022, 122270P (3 October 2022); https://doi.org/10.1117/12.2641898.
- M. A. Witherow and K. M. Iftekharuddin, “Fundamentals of 3D Human Face Imaging and Automated Expression Analysis”, Field Guide to Education of Optics: A Tribute to John Greivenkamp, SPIE. August 21, 2022. Available: https://spie.org/Publications/Book/2635871.
- M. A. Witherow, W. J. Shields, M. D. Samad, and K. M. Iftekharuddin “Learning latent expression labels of child facial expression images through data-limited domain adaptation and transfer learning”, Proc. SPIE 11511, Applications of Machine Learning 2020, 115110E (21 August 2020); https://doi.org/10.1117/12.2569454.
- M. A. Witherow, M. D. Samad, and K. M. Iftekharuddin “Transfer learning approach to multiclass classification of child facial expressions”, Proc. SPIE 11139, Applications of Machine Learning, 1113911 (6 September 2019); https://doi.org/10.1117/12.2530397.
- M. A. Witherow, C. Sazara, I. M. Winter-Arboleda, M. I. Elbakary, M. Cetin and K. M. Iftekharuddin, “Floodwater Detection on Roadways from Crowdsourced Images,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2018. https://doi.org/10.1080/21681163.2018.1488223.
- M. A. Witherow, M. I. Elbakary, K. M. Iftekharuddin and M. Cetin, “Analysis of Crowdsourced Images for Flooding Detection,” in VIPImage 2017, Porto, Portugal, 2017. https://doi.org/10.1007/978-3-319-68195-5_15.
E-mail: mwith010@odu.edu
BS in Computer Engineering, Old Dominion University
MS in Electrical and Computer Engineering, Old Dominion University
Current Program: PhD Electrical and Computer Engineering
Research Topics: Machine Learning, Computer Vision
Research Project: Action Recognition and Person Identification using Lidar Video
Advisor: Dr. Khan Iftekharuddin
References:
- Glandon, A., Vidyaratne, L., Dhar, N. K., Familoni, J. O., Sadeghzadehyazdi, N., Acton, S. T., & Iftekharuddin, K. M. (2024). 3D far-field Lidar sensing and computational modeling for human identification. Applied Optics, 63(8), C15-C23.
- Glandon, A. M., Zalameda, J., & Iftekharuddin, K. M. (2023, June). Transfer learning using infrared and optical full motion video data for gender classification. In Infrared Technology and Applications XLIX (Vol. 12534, pp. 292-301). SPIE.
- A. Glandon, L. Vidyaratne, N. Sadeghzadehyazdi, N. Dhar, J. Familoni, S. Acton and K. Iftekharuddin, ” 3D Skeleton Estimation and Human Identity Recognition Using Lidar Full Motion Video,” in Proc. Of 2019 International Joint Conference on Neural Networks (IJCNN) and IEEE, July 2019.
- A. Glandon, S. Ullah, L. Vidyaratne, M. Alam, C. Xin and K. M. Iftekharuddin, “Prediction of Spatial Spectrum in Cognitive Radio using Cellular Simultaneous Recurrent Networks”, IEEE World Congress on Computational Intelligence (WCCI), International Joint Conference on, Rio De Jenerio, Brazil, July 2018.
E-mail: aglan001@odu.edu
BS in Electrical Engineering, U.S. Coast Guard Academy
MS in Electrical Engineering, U.S. Naval Postgraduate School
Current Program: PhD Electrical and Computer Engineering
Research Topics: Machine Learning, Computer Vision
Research Project: Skeleton Extraction from LIDAR range scans: Improved Anatomical and Biomechanical Validity with Biometric Applications
Advisor: Dr. Khan Iftekharuddin
References:
- Anthony H. Hawes and Khan M. Iftekharuddin “An improved silhouette for human pose estimation”, Proc. SPIE 10395, Optics and Photonics for Information Processing XI, 1039512 (12 September 2017); https://doi.org/10.1117/12.2274449
E-mail: ahawe002@odu.edu
B.Sc. in Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Bangladesh
Current Program: PhD Electrical and Computer Engineering
Research Topics: Machine Learning, Deep Learning, Medical Image Processing
Advisor: Dr. Khan Iftekharuddin
References:
- Md. M. Rahman, K. Iftekharuddin, A. Carpenter, C. Tennant, “SRF cavity fault prediction using deep learning at Jefferson Lab”, 15th International Particle Accelerator Conference, 18-24 May, 2024 (Press).
- M. Rahman, L. Vidyaratne, A. Carpenter, C. Tennant, and K. Iftekharuddin, “Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals”, International Joint Conference on Neural Networks (IJCNN), June 18 – 23, 2023, Queensland, Australia.
- M. Rahman, K. M. Iftekharuddin, A. Carpenter, T. S. McGurkin, C. Tennant, L. S. Vidyaratne. “Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning”, NAPAC 2022, Albuquerque, NM, USA, 07-12 August 2022, doi:10.18429/JACoW-NAPAC2022-WEPA29.
- Rahman, M.M., Sadique, M.S., Temtam, A.G., Farzana, W., Vidyaratne, L., Iftekharuddin, K.M. (2022). Brain Tumor Segmentation Using UNet-Context Encoding Network. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham. https://doi.org/10.1007/978-3-031-08999-2_40.
E-mail: mrahm006@odu.edu
B.S. in Electrical and Electronic Engineering, University of Dhaka
M.Sc. (Eng.) in Electrical and Electronic Engineering, University of Dhaka
Current Program: PhD Electrical and Computer Engineering
Research Topics: Biomedical Image Processing, Machine learning, Deep learning
Advisor: Dr. Khan M. Iftekharuddin
References:
- Sadique MS, Farzana W, Temtam A, Lappinen E, Vossough A, Iftekharuddin KM. Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction. arXiv preprint arXiv:2306.03270. 2023 Jun 5.
- S Sadique MS, Rahman MM, Farzana W, Glandon A, Temtam A, Iftekharuddin KM. Local Synthesis of Healthy Brain Tissue Using an Enhanced 3D Pix2Pix Model for Medical Image Inpainting In International MICCAI Brainlesion Workshop 2023 Oct 7.
- S Sadique MS, Rahman MM, Farzana W, Glandon A, Temtam A, Iftekharuddin KM.Brain Tumor Segmentation: Glioma Segmentation in Sub-Saharan Africa Patients Using nnU-Net. In International MICCAI Brainlesion Workshop 2023 Oct 7.
- S. Sadique,W. Farzana,A. Temtam, and K. M. Iftekharuddin “Class activation mapping and uncertainty estimation in multi-organ segmentation”, Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650V (7 April 2023); https://doi.org/10.1117/12.2655508
- S. Sadique, M.M. Rahman, A. G. Temtam, W. Farzana, and K. M. Iftekharuddin, “Brain Tumor Segmentation using Neural Ordinary Differential Equations with UNet-Context Encoding Network”, In press, 2023.
- S. Sadique , M. M. Rahman, W. Farzana, A. Glandon, A. G. Temtam, and K. M. Iftekharuddin. “Abdominal Multi-Organ Segmentation of CT Images with nnUNet”, Consortium paper of Abdominal Multi-Organ Segmentation Challenge, 2022.
- S. Sadique, M. M. Rahman, W. Farzana, A. Glandon, A. G. Temtam, and K. M. Iftekharuddin. “Automated Abdominal Multi-organ Segmentation using Cross-Modality (CT and MRI) with nnUNet?”, Consortium paper of Abdominal Multi-Organ Segmentation Challenge, 2022.
- M. S. Sadique, A. Temtam, E. Lappinen, and K. M. Iftekharuddin. “Radiomic texture feature descriptor to distinguish recurrent brain tumor from radiation necrosis using multimodal MRI.” In Medical Imaging 2022: Computer-Aided Diagnosis, vol. 12033, pp. 654-660. SPIE, 2022.
E-mail: msadi002@odu.edu
B.Sc. in Computer Engineering, Kwame Nkrumah University of Science and Technology
M.Sc. in Convergence Engineering, Hanbat National University
Current Program: PhD Electrical and Computer Engineering
Research Topics: Computer Vision and Machine Learning
Research Project: Scalable Modeling and Adaptive Real-time Trust-based Communication (SMARTc) System for Roadway Inundations in Flood-Prone Communities
Advisor: Dr. Khan M. Iftekharuddin
References:
- K. Ampofo, M. A. Witherow, A. M. Glandon, M. M. Rahman, A. Temtam, M. Cetin, K. M. Iftekharuddin, “Automated flood extent and depth estimation on roadways”, Proc. SPIE 13136, Optics and Photonics for Information Processing XVIII, 13136-1, San Diego, California, USA, 2024.
E-mail: kampo001@odu.edu
B.Sc. in Electrical and Electronic Engineering, Chittagong University of Engineering & Technology
Current Program: PhD Electrical and Computer Engineering
Research Topics: Medical Image Processing, Machine Learning, Deep Learning
Advisor: Dr. Khan M. Iftekharuddin
References:
- W. Farzana, M. M. Basree, N. Diawara, Z. A. Shboul, S. Dubey, M. M. Lockhart, M. Hamza, J.
D. Palmer, and K. M. Iftekharuddin, “Prediction of rapid early progression and survival risk with
pre-radiation MRI inWHO grade 4 glioma patients,” Cancers, vol. 15(18), 2023. - W. Farzana, M. A. Witherow, I. Longoria, M. Sadique, A. Temtam, and K. M. Iftekharuddin, “Domain adaptive federated learning for multi-institution molecular mutation prediction and bias identification,” in Medical Imaging 2024, Computer-Aided Diagnosis. SPIE, 2024.
- W. Farzana, A. Temtam, and K. M. Iftekharuddin, “Wavelet based harmonization of local and
global model shifts in federated learning for histopathological images,” in Medical Imaging 2024, Clinical and Biomedical Imaging. SPIE, 2024. - Walia Farzana, Mustafa M Basree, Norou Diawara, Zeina A Shboul, Sagel Dubey, Marie M
Lockhart, Mohamed Hamza, Joshua Palmer, Khan M Iftekharuddin, NIMG-04. PRE-RADIATION PROGRESSION PREDICTION IN GLIOBLASTOMA PATIENTS, Neuro-Oncology, Volume 25, Issue Supplement_5, November 2023, Pagesv184–v185, https://doi.org/10.1093/neuonc/noad179.0700 - W. Farzana, A. G. Temtam, Z. A. Shboul, M. M. Rahman, M.S. Sadique, K.M. Iftekharuddin. (2022). Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized Hyperparameters. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_32
- W. Farzana, Z. A. Shboul, A. Temtam, and K. M. Iftekharuddin. “Uncertainty estimation in classification of MGMT using radiogenomics for glioblastoma patients.” In Medical Imaging 2022: Computer-Aided Diagnosis, vol. 12033, pp. 365-371. SPIE, 2022.
- W. Farzana, A. Temtam, M. M. Rahman, M. S. Sadique, and K. M. Iftekharuddin.” Radiogenomic Prediction of MGMT Status using Transfer Learning” Vision Lab, ECE, Old Dominion University. Accepted to RSNA-ASNR-MICCAI BraTS 2021 challenge.
E-mail: wfarz001@odu.edu
B.S. in Electrical and Computer Engineering, Virginia Commonwealth University
M.S. in Electrical Engineering, Old Dominion University
Current Program: PhD Electrical and Computer Engineering
Research Topics: Machine Learning, Communications
Research Project: Automatic Classification of Modulation Schemes and Jamming Signals of Received Signals
Advisor: Dr. Khan M. Iftekharuddin
E-mail: msree001@odu.edu
B.S. in Computer Engineering, Old Dominion University
Current Program: PhD Electrical and Computer Engineering
Research Topics: Machine Learning, Computer Vision
Research Project: Trust quantification of neural networks using reinforcement learning approaches
Advisor: Dr. Khan M. Iftekharuddin
References:
- J. G. Zalameda, M. Witherow, A. Glandon, J. Aguilera, K. M. Iftekharuddin. “Attack Assessment and Augmented Identity Recognition for Human Skeleton Data.” International Joint Conference on Neural Networks (IJCNN), June 18 – 23, 2023, Queensland, Australia.
- J. G. Zalameda, A. Glandon, K. M. Iftekharuddin. “Adaptive critic network for person tracking using 3D skeleton data”. In Press (May 30, 2023). SPIE, 2023.
- J. G. Zalameda, B. Kruse, A. M. Glandon, M. A. Witherow, S. Shetty and K. M. Iftekharuddin, “Generalized Adversarial and Hierarchical Co-occurrence Network based Synthetic Skeleton Generation and Human Identity Recognition,” 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1-8, doi: 10.1109/IJCNN55064.2022.9892887.
E-mail: jzala001@odu.edu
B.Sc. in Computer Engineering, Henan University and Old Dominion University
Current Program: PhD Electrical and Computer Engineering
Research Topics: Computer Vision and Machine Learning
Research Project: Urban road flood depth prediction using drones based on machine learning
Advisor: Dr. Khan M. Iftekharuddin
E-mail: yzhan003@odu.edu
BS Electrical (Electronics) Engineering, COMSATS Institute of Information Technology, Pakistan
MS Electrical and Computer Engineering, Antalya Science University, Turkey
MS Electrical and Computer Engineering, George Mason University, USA
Current Program: PhD Electrical and Computer Engineering
Research Topics: Computer Vision, Machine Learning
Research Project: Scalable Modeling and Adaptive Real-time Trust-based Communication (SMARTc) System for Roadway Inundations in Flood-Prone Communities
Advisor: Dr. Khan M. Iftekharuddin
E-mail: mumai001@odu.edu
Previous PhD Graduates
PhD Electrical and Computer Engineering
Graduation: Spring 2024 (Dissertation Defense: April 4, 2024).
Project: “Computational Modeling and Analysis of Facial Expressions and Gaze for Discovery of Candidate Behavioral Biomarkers for Children and Young Adults With Autism Spectrum Disorder”.
PhD Electrical and Computer Engineering
Graduation: Fall 2022 (Dissertation Defense: November 14, 2022).
Project: “Detection, Tracking, and Classification of Aircraft and Birds from Multirotor Small Unmanned Aircraft Systems”.
PhD Electrical and Computer Engineering
Graduation: Spring 2020 (Dissertation Defense: May 28, 2020).
Project: “Model-Based Approach for Diffuse Glioma Classification, Grading, and Patient Survival Prediction”.
PhD Electrical and Computer Engineering
Graduation: Spring 2020 (Dissertation Defense: March 30, 2020).
Project: “Longitudinal Brain Tumor Tracking, Tumor Grading, And Patient Survival Prediction Using MRI”.
PhD Electrical and Computer Engineering
Graduation: Fall 2019 (Dissertation Defense: November 18, 2019).
Project: “Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing”.
PhD Electrical and Computer Engineering
Graduation: Fall 2018 (Dissertation Defense: November 12, 2018).
Project: “Deep Recurrent Learning For Efficient Image Recognition Using Small Data”.
D.Eng. Electrical and Computer Engineering
Graduation: Fall 2017.
Project: “Statistical Analysis and Comparison of Optical Classification of Atmospheric Aerosol Lidar Data”.
PhD Electrical and Computer Engineering
Graduation: Summer 2017 (Dissertation Defense:June 12, 2017).
Dissertation: “Computational Modeling for Abnormal Brain Tissue Segmentation, Brain Tumor Tracking and Grading”.
PhD Electrical and Computer Engineering
Graduation: Spring 2016 (Dissertation Defense:May 19, 2016 ).
Dissertation: “Computational Modeling of Facial Response for Detecting Differential Traits in Autism Spectrum Disorders”.
PhD Electrical and Computer Engineering
Graduation: Spring 2014 (Dissertation Defense:April 23, 2014 ).
Dissertation: “Manifold learning of large-scale data sets”.
PhD Electrical and Computer Engineering
Graduation: Spring 2012
Dissertation: “Creation of Large Scale Face Dataset Based on Single Images”.
PhD Electrical and Computer Engineering
Graduation: 2011 (Advisor: Dr. Iftekharuddin).
Dissertation: “An Information theoretic Approach for Feature Selection and Posterior Fossa Tumor Segmentation”
PhD Electrical and Computer Engineering
Graduation: 2011 (Advisor: Dr. Iftekharuddin)
Dissertation: “Novel Fractal Features for Improved Glaucoma Detection and Glaucomatous Progression Prediction”
PhD Electrical and Computer Engineering
Graduation: Summer 2010 (Dissertation Defense: July 21, 2010)
Dissertation: “Multi-scale Edge Detection Algorithms and Their Information-theoretic Analysis in the Context of Visual Communication”
PhD Electrical and Computer Engineering
Graduation: Fall 2009 (Dissertation Defense: September 14, 2009)
Dissertation: “A Subspace Projection Methodology for Nonlinear Manifold Based Face Recognition”
PhD Electrical and Computer Engineering
Graduation: 2008 (Advisor: Dr. Iftekharuddin)
Dissertation: “Computer Aided Pediatric Brain Tumor Detection, Prediction and Statistical Validation Using Structural MRI and Gene Expression Data”
PhD Electrical and Computer Engineering
Graduation: Fall 2007 (Dissertation Defense: November 14, 2007)
Dissertation: “Power Aware Design Methodologies for FPGA Based Implementation of Video Processing Systems”
PhD Electrical and Computer Engineering
Graduation: Fall 2007 (Dissertation Defense: May 10, 2007)
Dissertation: “Neighborhood Defined Feature Selection Strategy for Improved Face Recognition in Different Sensor Modalities”
PhD Electrical and Computer Engineering
Graduation: Summer 2006 (Dissertation Defense: May 3, 2006)
Dissertation: “Learning as a Nonlinear Line of Attraction for Pattern Association, Classification and Recognition”
PhD Electrical and Computer Engineering
Graduation: Summer 2006 (Dissertation Defense: December 12, 2005)
Dissertation: “Multimodal Enhancement Techniques for Visibility Improvement of Digital Images”