Find Academic Supervisors

Michael BannonView profile

Professor

• Neuroscience
• Dopamine Neurotransmission
• Parkinson's Disease
• +2 more

Professor Michael Bannon chairs research in the Department of Pharmacology at Wayne State University School of Medicine. His laboratory investigates cellular and molecular mechanisms regulating gene expression in dopamine neurons, with applications in Parkinson's disease and substance abuse disorders. His pioneering work identified subpopulation-specific differences in midbrain dopamine neurons and established regulatory principles for dopamine transporter gene expression. Research spans five interconnected domains: dopamine neuron physiology, tachykinin neuropeptide systems, dopamine transporter regulation, Parkinson's disease gene therapy using NTS-polyplex nanoparticle systems, and molecular adaptations in human addiction. Current projects focus on glial activation patterns in opioid-related deaths and neurotrophic factor delivery strategies. Publication analysis reveals consistent focus on molecular psychiatry themes, particularly epigenetic regulation in addiction and neuroprotective gene therapies. Recent advances include single-nuclei transcriptomics of postmortem brain tissue and engineered nanoparticle systems for targeted gene delivery. Authored seminal papers on dopamine transporter regulation (>100 citations) Developed neurotensin-polyplex system for targeted gene therapy in Parkinson's models Teaches graduate courses in Neuropharmacology and Recent Developments in Pharmacology. Trained at Yale University (PhD) and completed postdoctoral fellowships at Cambridge University and NIMH.

Alexander KotovView profile

Associate Professor

• Information Retrieval
• Natural Language Processing
• Health Informatics
• +3 more

Alexander Kotov is an Associate Professor in Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2011) and specializes in large-scale textual information analysis, particularly information retrieval, natural language processing, and health informatics. His research develops neural architectures for conversational entity retrieval (2024), clinical outcome prediction (2024), and behavioral intervention analysis. Key themes include knowledge graph integration, multimodal retrieval systems, and computational methods for health communication analysis, with applications in weight loss counseling and precision medicine. Research consistently bridges NLP/IR techniques with healthcare applications, exploring conversational AI for clinical support, knowledge graph retrieval, and behavioral pattern mining in therapeutic contexts. Recent projects include NIH-supported work on weight management chatbots.

Yuksel CelikView profile

Part-Time Lecturer

• Artificial Intelligence
• Cyber Security
• Optimization Algorithms
• +3 more

Dr. Yuksel Celik is an Adjunct Lecturer at the University at Albany's Massry School of Business and an Associate Professor at Karabuk University (Turkey). He holds a PhD in Computer Engineering from Selcuk University (2013) and completed postdoctoral research in Information Security and Digital Forensics at SUNY Albany. Research Focus: His work spans artificial intelligence, cybersecurity, and optimization algorithms, with applications in healthcare, genomics, and agriculture. Key methodologies include deep learning, bio-inspired computing, and computer vision for solving complex real-world problems. Publication Trends: Recent articles (2023-2025) demonstrate strong emphasis on hybrid AI models, security applications, agricultural technology, and medical diagnostics. Common themes include optimization techniques, anomaly detection, and multimodal learning approaches. Awards & Honors: Senior Member of IEEE Academic Contributions: Actively mentors graduate students and leads research projects funded by grants including the EU Erasmus+ program. Teaches courses in cybersecurity, data structures, and mobile application development.

• Statistics
• Bayesian Methodology
• Mixture Models
• +3 more

Deborah Kunkel is an Assistant Professor in the Department of Mathematical and Statistical Sciences at Clemson University's College of Science. Her research focuses on Bayesian methodology, mixture models, and their applications in medicine, behavioral science, and cognitive psychology. She holds a Ph.D. in Statistics from The Ohio State University (2018), an M.S. in Statistics (2014), and a B.A. in Business from the University of Dallas (2011). Education: Ph.D., Statistics, The Ohio State University, 2018 M.S., Statistics, The Ohio State University, 2014 B.A., Business, University of Dallas, 2011 Her research interests span statistical inference with Bayesian models, allometric data analysis, and interdisciplinary applications in health and social sciences. Recent publications explore polygenic prediction, E-cigarette effects on opioid users, and cognitive processes in virtual reality environments. She teaches courses including Bayesian Statistics, Probability Theory, and Statistical Methods. Dr. Kunkel has contributed to studies on adolescent dating violence, febrile infant diagnostics, and program comprehension in VR. Her work bridges statistical theory with practical applications in medicine and behavioral research.

Ming-Ying LeungView profile

Professor

• Bioinformatics
• Computational Biology
• Structural Bioinformatics
• +7 more

Ming-Ying Leung is a Professor in Mathematical Sciences at the University of Texas at El Paso (UTEP) and Director of the Bioinformatics and Computational Science Programs. Her career spans structural bioinformatics, RNA sequence analysis, and mathematical modeling of biomolecules. PhD in Mathematics, Stanford University (1989) MS in Computer Science, Stanford University (1988) Her research focuses on probabilistic modeling and statistical bioinformatics , particularly Markov models and scan statistics for DNA sequence analysis. She develops algorithms to predict RNA secondary structures and functional genomic sites, with applications in agricultural science and biomedical research . Recently, her work has expanded to cancer genomics , studying single nucleotide variants in leukemia and prostate cancer, and GPCR ligand binding mechanisms across families. Dr. Leung's funded projects include NSF , NIH , and USDA grants for genomic studies. She leads initiatives like Bioinformatics Education for Agricultural Science (BEAS) , Bioinformatics Computing Laboratory (BCL) , and Undergraduate Participation in Bioinformatics Training (UPBiT) , emphasizing student research involvement. Student Choice Award for Outstanding Teaching (2017) Two Outstanding Performance Awards in Extramural Funding (2015, 2008) Professor Y.C. Wong Visiting Lectureship at University of Hong Kong (2004) Andrew Mellon Foundation Research Award (1986) She mentors students through doctoral and graduate research courses, including Mathematical & Computational Modeling and Stochastic Processes . Her lab maintains tools like RNAVLab , ISOGlyP , and GPCR-PEnDB , advancing computational methods for genomic and structural analysis.

Sheridan HoughtenView profile

Professor

• Combinatorial Optimization
• Algorithms
• Computational Intelligence
• +5 more

Sheridan Houghten is a Professor in the Department of Computer Science at Brock University, part of the Faculty of Mathematics & Science. He holds a BSc, MSc, and PhD in Mathematics and Computer Science from Concordia University. His research focuses on combinatorial optimization, computational intelligence, and bioinformatics, with applications in coding theory, graph compression, and epidemiological modeling. He actively supervises graduate and undergraduate students in these areas. Education: BSc (Mathematics) from Concordia University (1991); MSc (Computer Science) from Concordia (1993); PhD (Computer Science) from Concordia (1999). Research interests include developing algorithms for bioinformatics problems (e.g., disease gene association, gait analysis in Parkinson's patients), optimizing error-correcting codes, and using evolutionary computation for network modeling. His work bridges theoretical computer science with practical applications in healthcare and cybersecurity. Recent research trends involve epidemic modeling using weighted networks, AI-driven cybersecurity analysis, and compression techniques for game graphs and biomedical data. He has contributed to tools like GADGIT for disease gene identification and collaborated on projects analyzing epidemic variants and vaccination strategies. Advising and grants: Supervised over 40 students, emphasizing interdisciplinary approaches. Active in IEEE committees including the Computational Intelligence Society. Coaches Brock's International Collegiate Programming Contest teams. Labs/Teams: Leads a research group exploring computational intelligence applications in bioinformatics, cybersecurity, and network science. Collaborates with interdisciplinary teams on projects involving evolutionary algorithms and biomedical data analysis.

Jun WanView profile

Associate Professor

• Bioinformatics
• Computational Biology
• Systems Biology
• +6 more

Dr. Jun Wan is an Associate Professor of Medical & Molecular Genetics at Indiana University School of Medicine and holds an Adjunct Associate Professor position at the Luddy School of Informatics, Computing, and Engineering. He directs the Collaborative Core for Cancer Bioinformatics (C3B), shared between the Indiana University Simon Comprehensive Cancer Center and Purdue University's cancer research center. His research focuses on bioinformatics and computational systems biology, particularly gene regulatory networks involving epigenetics, transcription, and translation. Dr. Wan has authored over 120 peer-reviewed publications and secured grants from NIH, DoD, and other institutions. Education: Bachelor's in Physics, Shanghai Jiaotong University Master's in Physics, Fudan University PhD in Physics, Queen’s University Postdoctoral training in Bioinformatics, Johns Hopkins University School of Medicine Research Interests: Gene regulatory networks, epigenetic mechanisms, computational systems biology, and integrative '-omics' data analysis. Key Collaborations: Over 100 interdisciplinary research groups across universities and institutes. His work bridges physics and biology, applying data mining and deep learning to decode genetic/epigenetic codes and identify master regulators in disease contexts. Notable achievements include developing the GESS database for SARS-CoV-2 sequences and discovering critical roles of epigenetic elements in cancer and stem cell biology.

Hasan JamilView profile

Associate Professor

• Databases
• Knowledge Representation and Reasoning
• Data Science
• +7 more

Hasan Jamil is an Associate Professor in the Department of Computer Science at the University of Idaho, where he also serves as Director of Graduate Studies. He holds a Ph.D. in Computer Science from Concordia University (Canada, 1996), and earlier degrees from the University of Dhaka, Bangladesh (M.Sc. 1984, B.Sc. 1982). Affiliations: College of Engineering, University of Idaho Roles: Academic Leadership | Research Mentorship | Curriculum Development His research focuses on databases , knowledge representation , bioinformatics , health informatics , and educational technologies . Recent work emphasizes AI-driven systems for medical diagnosis (e.g., Explainable AI for heart disease), large language model integration in scientific workflows, and educational platforms like SQL tutoring systems (ViSQL). Publications span 1993–2025, with notable contributions to computational biology pipelines (VisFlow), health question-answering systems (ClinLearning), and materials design (MatFlow). His NSF/NIH-funded projects bridge data science with healthcare, education, and life sciences. Key Projects: MatFlow (materials design), ClinLearning (medical education), SociaLens (digital journalism) Labs/Teams: Leads interdisciplinary teams in computational biology and educational AI

Jinrui XuView profile

Assistant Professor

• Computational Biology
• Genome Evolution
• Gene Regulation
• +3 more

Jinrui Xu is an Assistant Professor in the Department of Biology at Howard University's College of Arts & Sciences. A computational biologist, their research focuses on genome evolution, gene regulation, and cancer genomics. They develop bioinformatics tools to analyze complex biological data, such as GRAM for predicting non-coding variant effects and CytoSVM for cytokine-receptor interactions. Education: PhD in Bioinformatics from the University of Michigan, Ann Arbor (2015). Research interests include evolutionary mechanisms of genetic variation, regulatory networks in cancer, and protein structure analysis. Their work emphasizes functional genomic data interpretation and experimental methodology improvements, such as optimizing ChIP-seq analysis protocols. Key contributions include advancing understanding of pseudogene functionality, protein fold classification, and regulatory element differentiation between wolf/dog populations. Collaborations span institutions like Yale University and the University of Washington, reflecting interdisciplinary research engagement.

David JuedesView profile

Professor

• Algorithms
• Complexity Theory
• Approximation Algorithms
• +4 more

David Juedes is a Professor of Electrical Engineering and Computer Science and Associate Dean for Academics at Ohio University's Russ College of Engineering and Technology. He also directs the Avionics Engineering Center and is part of the Center for Scientific Computing and Immersive Technologies. Juedes holds a Ph.D. from Iowa State University (1994) and has taught courses ranging from introductory computer science to advanced graduate topics like computational complexity and formal languages. His research focuses on algorithms, complexity theory, approximation algorithms, and computational biology. Notable contributions include work on secure embedded systems (e.g., d-GUARD and DAGGER frameworks) and parameterized algorithm analysis. He has advised multiple graduate and undergraduate students in areas like parameterized algorithms and bioinformatics. Juedes has received awards including ACM Senior Membership (2010) and the Russ College Outstanding Research Paper Award (2004). His work spans 30+ years with over 80 publications in top journals/conferences. He actively promotes STEM education through initiatives like cybersecurity workshops for K-12 teachers and curriculum development for real-time systems and CORBA-based resource management. Labs/Teams: Director of Avionics Engineering Center, Core member of Center for Scientific Computing and Immersive Technologies.

Demetris P. GerogiannisView profile

Researcher

• Computer Vision
• Image Processing
• Image Segmentation and Registration
• +7 more

Demetris P. Gerogiannis is a researcher in the Department of Computer Science & Engineering at the University of Ioannina, Greece. His academic work focuses on Computer Vision and Image Processing, with specific expertise in image registration, pointset registration, and feature extraction. He has maintained a consistent publication record from 2007 through 2020, primarily collaborating with Christophoros Nikou and A. Likas. Gerogiannis' research interests span multiple areas within Computer Vision including Image Segmentation and Registration, Pointset Registration, Feature Extraction, Object Detection and Recognition, Pattern Recognition, Automatic Image Annotation, and Machine Learning applications. His work demonstrates a consistent focus on developing robust algorithms for geometric problems in vision, with particular attention to handling noise and outliers in visual data. He has made significant contributions to point set registration, shape representation, and vanishing point detection. His publication history reveals a strong trend toward developing mathematically rigorous approaches to Computer Vision problems, particularly utilizing statistical models like Student's t-distributions for robust registration. His work bridges theoretical computer vision with practical applications, including consumer-facing technologies like QR code systems. The 15 most recent publications show progression from foundational work in image registration to more applied research in texture analysis, word spotting, and biomedical applications. Gerogiannis is also an active entrepreneur, currently coordinating QReca!, a startup focused on consumer engagement through QR codes and NFC technology. He has previously worked on a Video On Demand startup and is developing a spin-off to commercialize his Computer Vision research through an API framework. He maintains a commitment to open academic knowledge by providing Matlab code for non-commercial use related to his publications. He has volunteered for the Athens 2004 Olympic Games and maintains a passion for Mathematics, particularly Number Theory and Euclidean Geometry. His academic profile suggests a researcher who bridges theoretical computer vision with practical applications and entrepreneurial ventures.

• Cloud Computing
• Grid Computing
• Health Informatics
• +5 more

David Levine serves as an Associate Professor in the Computer Science and Engineering Department within the College of Engineering at The University of Texas at Arlington. His academic career spans decades with continuous teaching and research contributions, evidenced by his ongoing course instruction through Fall 2025 and active grant leadership. Levine's research spans cloud and grid computing, health informatics, bioinformatics, pervasive computing, and secure programming. His work integrates computational techniques with practical applications in healthcare (Smart Hospital for Elderly Care), high-energy physics (Fermilab collaborations), and accessibility infrastructure. He has pioneered curriculum development in emerging fields including Cloud Computing, Secure Programming, Mobile Computing, and Data Mining, with courses consistently reaching maximum enrollment capacity and establishing distance learning records within the engineering college. His publication portfolio demonstrates significant interdisciplinary impact across IEEE Supercomputing, health informatics, genetics, and pervasive computing. Recent work focuses on spatial data visualization for public health (2022), hardware simulation platforms (2017), and scalable notification frameworks for healthcare systems (2011). His research has secured over $5 million in funding from NSF, NIH, DoE, and industry partners including Luminant Power. Computer Science Department Teaching Award recipient Student teams won ATT University Challenge, Nokia Symbian Challenge, and Department of Energy Challenge ($200k+ total) Levine has mentored 4 PhD students as co-supervisor, chaired 6 Master's theses, and advised numerous undergraduate researchers. His laboratory work focuses on grid computing applications for high-energy physics and bioinformatics, with current projects including the GAANN Doctoral Fellowships in IoT and Smart Hospital infrastructure development. He serves as NTT Hiring Committee Chair and Faculty Senate Chair, demonstrating significant institutional leadership.

Dingzhu DuView profile

Professor

• Computer Science
• Wireless Networks
• Sensor Networks
• +5 more

Dingzhu Du is a Professor of Computer Science at the University of Texas at Dallas (UTD) and concurrently serves as Dean of Science at Xi'an Jiaotong University. He holds a Ph.D. in Mathematics from the University of California, Santa Barbara (1985) and an M.S. in Operations Research from the Chinese Academy of Sciences (1982). His research focuses on optimization, wireless networks, sensor networks, and computational biology. He has pioneered work in network topology control, fault tolerance, DNA screening algorithms, and combinatorial optimization with applications in bioinformatics. His contributions to the Steiner ratio conjecture and relay sensor placement have been internationally recognized. Awards include the CSTS Prize (1998), 2nd Class National Natural Science Prize (China, 1996), and multiple best paper awards. He has held leadership roles such as NSF Program Director (2002–2005) and editorial board memberships in journals like Theoretical Computer Science and Journal of Combinatorial Optimization . His work bridges theoretical computer science and practical applications in wireless systems, security protocols, and algorithm design. Ongoing research explores secure communication protocols (e.g., GeoSENS) and efficient network design methodologies.

• Bioinformatics
• Genomics
• Multiomics Data Integration
• +3 more

Dr. Trupti Joshi is an Associate Professor in the Department of Biomedical Informatics, Biostatistics and Medical Epidemiology at the University of Missouri-Columbia's School of Medicine, with joint appointments in Plant Sciences and Technology and affiliations with multiple research institutes. Her research focuses on bioinformatics, genomics, multiomics data integration, and machine learning applications in precision medicine and agriculture. She has over 20 years of experience and 157+ publications. Her work spans computational methodologies for biomarker discovery, deep learning frameworks for phenotype prediction, and translational bioinformatics for COVID-19 genomics. Recent articles emphasize epigenetic profiling, plant stress responses, immunotherapy prediction, and cross-modality genomic data portals, reflecting interdisciplinary innovation.

• Developmental Psychology
• Epidemiology
• Mental Health
• +7 more

Rebecca M Pearson is a Professor of Developmental Psychology and Epidemiology at Manchester Metropolitan University, affiliated with the School of Psychology and the Institute for Children’s Futures. Previously, she held positions at the University of Bristol and Oxford University, working on global longitudinal cohorts. Education: BSc in Applied Psychology (1st Class), Cardiff University PhD in Psychiatric Epidemiology, University of Bristol (Bristol Medical School) Her research focuses on intergenerational transmission of mental health disorders , combining developmental psychology and epidemiology to study parenting relationships, genetic liability, and environmental influences. She innovates in measurement tools (e.g., wearable cameras, TIDAL software) and intervention design for maternal self-efficacy. Recent publications highlight her work on genetic pathways , breastfeeding-related mental health , and urban environmental impacts on perinatal depression. Her methodological contributions include automated facial coding, thin-slice sampling, and stakeholder-engaged protocol design. Her research has informed national and international policies on maternal mental health, including UK NHS guidelines, WHO recommendations, and Swedish maternity protocols. She actively supervises students and collaborates across 7 international cohorts.