جستجوی استاد راهنمای دانشگاهی

• Genetic Programming
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Illya Bakurov is a Researcher in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. His work focuses on advancing genetic programming (GP) and its applications in machine learning, computer vision, optimization, and medical informatics. Key research themes include developing novel GP-based algorithms, exploring their use in structural similarity analysis, image quality assessment, and addressing ill-posed problems in computer vision. His contributions also extend to optimization libraries and pandemic impact studies. Bakurov's research interests span the theoretical and applied aspects of genetic programming, emphasizing its role in solving complex computational problems across disciplines. Recent efforts include studies on dynamic populations in GP, sharpness-aware minimization techniques, and reduced-order modeling for quantum systems. He has also contributed to stacked generalization frameworks and the development of open-source optimization tools. His publications reflect a strong focus on algorithmic innovation, with applications ranging from medical decision support systems to economic analysis. While no awards are explicitly listed, his prolific output indicates sustained academic engagement and interdisciplinary impact.

Kevin J. Liuمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Biology
• Comparative Genomics
• +۳ مورد دیگر

Kevin J. Liu is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University, with affiliations in the Ecology, Evolution, and Behavior (EEB) Program and the Genetics and Genome Sciences (GGS) Program. His research focuses on developing computational methodologies for comparative genomics, particularly under complex evolutionary scenarios, to connect genomic insights to biological function and phenotype. The lab employs big-data-driven approaches to generate hypotheses for biological and biomedical discoveries. Research interests include phylogenetic analysis, evolutionary genomics, computational methods for handling non-tree-like evolutionary histories, and statistical resampling techniques. Liu emphasizes interdisciplinary work at the intersection of bioinformatics and computational biology, addressing challenges in alignment-free phylogenetics and species network inference. Publications reflect a strong focus on statistical methods for phylogenetic reconstruction, error estimation in sequence alignment, and scalable genomic analysis tools like Fastnet and Coal-Miner. His work bridges computational innovation with biological interpretation, aiming to advance our understanding of evolutionary processes and genomic diversity. Prospective students and researchers interested in interdisciplinary bioinformatics are encouraged to contact him via email at kjl@msu.edu .

Leonardo Morsutمشاهده پروفایل

استادیار

• Synthetic Biology
• Stem Cell Biology
• Regenerative Medicine
• +۳ مورد دیگر

Leonardo Morsut is an Assistant Professor in the Department of Stem Cell Biology and Regenerative Medicine at the Keck School of Medicine, University of Southern California. His research focuses on synthetic biology and tissue engineering, aiming to program mammalian cells for controlled tissue morphogenesis and regeneration. He leads the Morsut Lab, which integrates computational modeling, protein engineering, and stem cell biology in an iterative framework to understand and manipulate tissue self-organization. University: University of Southern California School: Keck School of Medicine Department: Stem Cell Biology and Regenerative Medicine Academic Rank: Assistant Professor Research Interests: Dr. Morsut's work combines synthetic biology with developmental systems to engineer multicellular constructs. His lab specializes in synthetic Notch receptors, genetic circuit design, and controlling spatio-temporal patterning during tissue formation. Key areas include programmable material-to-cell pathways, juxtacrine signaling networks, and computational frameworks for morphogenesis modeling. Recent Publications Trends: His 2024-2025 studies emphasize precise spatial control of differentiation in organoids using synthetic Wnt organizers, programmable material interfaces, and dynamic cell growth feedback. Earlier works focus on transgene silencing mechanisms, mucin-driven morphogenesis, and density-dependent signaling in multicellular systems. Laboratory Mission: The Morsut Lab aims to build functional tissues through synthetic biology tools while training future regenerative medicine leaders. They utilize interdisciplinary approaches from engineering, computational biology, and developmental systems to create programmable multicellular structures.

April Shuمشاهده پروفایل

استادیار

• Psychiatric Genetics
• Epidemiology
• Autism Genetics
• +۴ مورد دیگر

April Shu, PhD, is an Assistant Professor in the Department of Population and Public Health Sciences and the Center for Genetic Epidemiology at the Keck School of Medicine of USC. Her research focuses on psychiatric genetics, epigenetics, and single-cell transcriptomics, leveraging machine learning to study mental and behavioral disorders. She leads the Shu Lab, which investigates autism genetics, substance use epigenetics, and precision medicine approaches in mental health. Education: PhD in Mental Health (Psychiatric Genetics), Johns Hopkins Bloomberg School of Public Health MS in Biostatistics, Johns Hopkins University MS in Epidemiology, Harvard T.H. Chan School of Public Health Research Interests: Dr. Shu’s work bridges computational methods with genetic epidemiology, emphasizing: Identification of genetic/epigenetic factors in autism and substance use disorders Machine learning applications in health data analysis Gene-environment interactions in aging and cognitive health Key Contributions: Recent work includes uncovering novel autism risk genes, developing biomarkers for opioid toxicity, and analyzing how urban greenness interacts with genetic factors in cognitive outcomes. Awards: 2020 College on Problems of Drug Dependence Early Career Award 2018 DREAM Challenge Winner (Single-cell Transcriptomics) 2016 Lucy Shum Memorial Scholarship Lab & Team: The Shu Lab collaborates across disciplines to advance computational genetics. Their work is published in top journals like Nature Genetics and Genetics in Medicine .

Francis Wyffelsمشاهده پروفایل

استادیار

• Robot control
• Embodied cognition
• Reinforcement learning
• +۵ مورد دیگر

Francis Wyffels is an early career professor at Ghent University, affiliated with the Internet Technology and Data Science Lab (IDLab). His expertise spans machine learning, robotics, and computer science education, with significant contributions to European research projects like FP7 AMARSi on robot control. Wyffels' research focuses on robot control, embodied cognition, and (deep) reinforcement learning, applied to agricultural robotics, cloth manipulation, and human-robot interaction. He actively promotes realistic public perspectives on robotics through educational initiatives, countering societal fears about automation. His work bridges theoretical neural networks with practical applications in precision agriculture and educational tools. Recent publications (2024-2025) reveal three dominant trends: agricultural robotics (e.g., intrarow weeding, crop monitoring), deformable object manipulation (e.g., cloth handling), and computational thinking education. These reflect his commitment to solving real-world problems while advancing robotic capabilities in unstructured environments. Scientific awards received: UGent Hermes award for public/scientific engagement and knowledge transfer Google RISE award Wyffels leads research within IDLab, coordinating robot-related work packages in international projects. His approach combines technical innovation with public dissemination, emphasizing robotics' societal benefits in productivity and convenience. Current projects include agricultural automation frameworks and emotion-conveying social robots. He directs the Internet Technology and Data Science Lab (IDLab), where his team develops systems for precision farming, robotic manipulation, and educational tools. Recent work includes DIY social robot platforms and compliant control systems for soft-body insertion, demonstrating IDLab's focus on accessible, real-world robotics solutions.

Mamoun T. Mardiniمشاهده پروفایل

استادیار

• Data Science
• Wearables
• Health Informatics
• +۳ مورد دیگر

Mamoun T. Mardini is an Assistant Professor at the Institute on Aging , University of Florida. His research focuses on leveraging data science, wearable technologies, and machine learning to address challenges in gerontology and healthcare. Education : Ph.D. Computer Science (2018), University of North Carolina M.S. Computer Engineering (2013), American University of Sharjah B.S. Computer Engineering (2009), Jordan University of Science and Technology Research Interests : Dr. Mardini’s work emphasizes: Development of AI-driven frailty assessment tools for surgical patients Applications of smart wearables for monitoring mobility and pain in older adults Machine learning models for predicting cardiovascular outcomes and disparities Analysis of social determinants of health using real-world data Key Contributions : His studies include: Pioneering work on reducing unnecessary TEE procedures using AI Development of the ROAMM smartwatch platform for ecological momentary assessment Investigations into racial disparities in healthcare outcomes using ML Awards : No awards explicitly mentioned in the provided data. Labs/Teams : Active in the Division of Epidemiology and Data Science in Gerontology (EDGE) .

Pinaki Sarderمشاهده پروفایل

دانشیار

• Computational Pathology
• Artificial Intelligence in Healthcare
• Kidney Disease Analysis
• +۳ مورد دیگر

Pinaki Sarder, Ph.D., is an Associate Professor in the Department of Medicine – Section of Quantitative Health at the University of Florida (UF), with affiliate roles in the Department of Electrical & Computer Engineering and Biomedical Engineering. He also serves as Associate Director for Imaging at the Intelligent Critical Care Center. Previously, he held tenure as an Associate Professor at the University at Buffalo (UB) in Pathology & Anatomical Sciences and Biomedical Engineering. His academic journey includes postdoctoral training in optical radiology at Washington University in St. Louis (WUSTL) and a Research Fellowship at Harvard University's School of Public Health. He earned his B.Tech. from IIT Kanpur and M.Sc./Ph.D. in electrical engineering from WUSTL. Dr. Sarder's research focuses on computational fusion of spatial omics data, particularly in diabetic kidney disease, leveraging AI and digital pathology. His work is funded by NIH, KPMP, and HubMAP. Key contributions include developing tools like ComPRePS for automated image analysis and the HistoLens platform for accessible pathology visualization. He is an editorial board member of the Journal of the American Society of Nephrology and Associate Editor of IEEE Journal of Biomedical Health Informatics. His awards include UB’s 2018 Exceptional Scholars – Young Investigator Award. He leads NIH-funded projects exploring kidney disease mechanisms, including the Cosmic Kidney Disease initiative investigating spaceflight-induced renal dysfunction. Dr. Sarder also contributes to educational initiatives, such as internship curricula promoting STEM diversity and consensus-based evaluation metrics for NIH programs. Research interests span computational pathology, AI-driven diagnostic tools, kidney disease biomarker discovery, and translational medical imaging. His lab develops open-source tools like DUET for collagen/elastin quantification and FUSION for multi-omics data integration with histology. Grants: NIH, KPMP Consortium, HubMAP Consortium Labs: Computational Microscopy Imaging Laboratory Teams: Intelligent Critical Care Center, Kidney Precision Medicine Project (KPMP)

Steph Haoمشاهده پروفایل

پژوهشگر

• Genetics
• Genomics
• Neurodevelopmental Disorders
• +۳ مورد دیگر

Steph Hao is a researcher specializing in genetic and genomic studies, particularly focusing on neurodevelopmental disorders such as autism spectrum disorder (ASD) and the impact of structural variations in the human genome. She is part of the Talkowski Lab, contributing to advancements in prenatal diagnostics and the understanding of genomic disorders through cutting-edge research. Her work integrates molecular biology, computational genomics, and clinical genetics to address unresolved questions in human health. Her research interests revolve around uncovering genetic mechanisms underlying neurodevelopmental disorders, including the study of rare coding variants, structural variations (SVs), and their functional consequences in neuronal models. She explores the implications of genomic dosage sensitivity and the application of non-invasive prenatal sequencing techniques, such as cell-free DNA analysis, to improve diagnostic accuracy. Her work bridges basic science and clinical applications, aiming to translate genetic findings into actionable insights for personalized medicine. Steph has presented her lab’s research at major conferences, including the AGBT Precision Health Meeting and the ACMG Annual Meeting. Her contributions span interdisciplinary collaborations, emphasizing the role of structural variation detection and genomic technologies in advancing precision medicine.

Donghoon Kimمشاهده پروفایل

دانشیار

• Cloud Computing Security
• High-Performance Computing
• Software Engineering
• +۵ مورد دیگر

Dr. Donghoon Kim is an Associate Professor in the Department of Computer Science at Arkansas State University, part of the College of Engineering & Computer Science. He holds a PhD from North Carolina State University and a Master of Science from Auburn University, following a software engineering role at Samsung Electronics in Korea. His research focuses on cloud computing security, high-performance computing, software engineering, parallel computing, and wireless network security. His work bridges theoretical advancements with practical applications in cybersecurity and machine learning. Dr. Kim’s research interests include analyzing machine learning approaches for detecting Android and IoT malware, enhancing deep hashing models for image retrieval, and improving authentication systems for digital content. He has contributed to optimizing multimedia data processing and evaluating Tor network vulnerabilities through real-time fingerprinting techniques. His teaching expertise spans software engineering and computer architecture. While his Google Scholar publications highlight cutting-edge work in cybersecurity and machine learning, no formal grants or awards are listed. His academic contributions emphasize both foundational research and applied solutions to modern computing challenges.

• Data Science
• Precision Medicine
• Cancer Genomics
• +۳ مورد دیگر

Eric Polley is an Associate Professor in the Department of Public Health Sciences at the University of Chicago, serving as Director of the Biostatistics Laboratory. He leads the Data Science concentration in the Master of Public Health program. Previously, he held roles at the Mayo Clinic and the National Cancer Institute (NCI), where he contributed to the NCI-MATCH clinical trial, earning him the 2015 NCI Director's Award. Education: Bachelor of Arts in Mathematics, Saint John's University (2003) Master of Science in Biostatistics, Columbia University (2005) PhD in Biostatistics, University of California, Berkeley (2010) Fellowship in Computational Biology, National Cancer Institute (2012) Research Interests: Dr. Polley specializes in developing and evaluating prediction models for clinical outcomes, particularly in oncology and precision medicine. His work focuses on diagnostic/prognostic methods, germline variant analysis, and clinical trial design. He has pioneered the Super Learner ensemble prediction methodology and contributed to understanding genetic risk factors in breast and pancreatic cancers. Key Contributions: Leader in integrating machine learning with clinical data (e.g., EHR-based fracture prediction) Expert in analyzing germline variants for cancer risk stratification Co-developed the NCI-MATCH trial framework for precision oncology Grants & Labs: Directs the Biostatistics Laboratory, collaborating on genomic studies and clinical trial design. Active in multi-institutional projects addressing cancer disparities and genetic predisposition.

• Biomedical Imaging
• Optical Coherence Tomography
• Ophthalmic Diagnostics
• +۲ مورد دیگر

Myeong Jin Ju is Assistant Professor in the Department of Ophthalmology & Visual Sciences at the University of British Columbia, with joint appointment in the School of Biomedical Engineering. Research focuses on developing advanced optical imaging technologies for vision science applications. Laboratory specializes in optical coherence tomography (OCT) systems design, signal processing algorithms, and multi-modal imaging integration. Current work aims to improve clinical ophthalmic diagnostics through high-resolution retinal and choroidal visualization, automated pathology quantification, and functional assessment techniques. Applications target diabetic retinopathy, age-related macular degeneration, and inherited retinal diseases. Recent innovations include MHz-range OCT systems, polarization-diversity techniques, and computational approaches for motion correction and image enhancement. Research bridges engineering innovation with preclinical and clinical translation in vision science.

Tatum Mortimerمشاهده پروفایل

استادیار

• Antimicrobial Resistance
• Bacterial Pathogenesis
• Genomics
• +۳ مورد دیگر

Tatum Mortimer is an Assistant Professor at the Poultry Diagnostic and Research Center (PDRC), focusing on bacterial pathogen adaptation and antimicrobial resistance. Their research integrates computational genomics with whole-genome sequencing to understand pathogen transmission dynamics and guide public health interventions. Research interests include mechanisms of antibiotic resistance evolution in bacterial pathogens like Neisseria gonorrhoeae, leveraging genomic data to predict treatment outcomes and combat diagnostic evasion. The lab also explores avian pathogen genomics, such as Castellaniella ginsengisoli’s role in avian mortality events. Publications emphasize Neisseria gonorrhoeae’s resistance mechanisms, including tetM gene expansion, gyrA-based diagnostic escape, and doxycycline prophylaxis impacts. Work spans genomic epidemiology, machine learning for susceptibility prediction, and evolutionary genetics. Key contributions include developing genomic surveillance tools and predictive models for antimicrobial resistance. The PDRC lab collaborates on diagnostic innovations and public health strategies to mitigate pathogen spread.

Young Changمشاهده پروفایل

استادیار

• Precision agriculture automation
• Cost-effective camera/sensor systems
• Biosystems robotics
• +۴ مورد دیگر

Young Chang is an Assistant Professor in the Department of Agricultural and Biosystems Engineering at South Dakota State University. He holds a Ph.D. in Agricultural Biotechnology from Seoul National University. His academic journey includes postdoctoral research at UC Davis, North Dakota State University, and Nova Scotia Agricultural College, alongside roles at Samsung Electronics' Living System Laboratory. He founded the Bio-systems Automation and Robotics Lab at Dalhousie University, Canada. His research focuses on precision agriculture automation, cost-effective sensor systems, and agricultural robotics. He actively collaborates on projects like cybersecurity in smart agriculture (Cyber-Ag-Law, $249k) and edge computing for agroecosystems (SDAES, $125k). Education: Ph.D./M.S./B.S. in Food Engineering (Seoul National University) Key Labs: Bio-systems Automation and Robotics Lab (Dalhousie), Raven Precision Ag Center (SDSU) Grants: Over $1M in funded projects including USDA grants and industry partnerships Research interests span precision agriculture automation, drone/UAV systems, and IoT-driven livestock management. His work integrates machine learning (e.g., YOLOv4 for strawberry counting) and FPGA-based real-time image processing. Recent articles emphasize cybersecurity for agricultural data and phenotypic trait analysis in soybeans. Awards include NSERC Discovery Grants and Mitacs Accelerate funding. Patents: Variable rate sprayer systems and kimchi ripening control Teaching: Courses on precision agriculture tech (AST-426) and farm machinery diagnostics (PRAG-304)

Jiwei Zhaoمشاهده پروفایل

دانشیار

• Semiparametric Statistics
• Missing Data Analysis
• Causal Inference
• +۱۰ مورد دیگر

Jiwei Zhao is an Associate Professor at the University of Wisconsin-Madison, jointly appointed in the Department of Statistics (School of Computer, Data & Information Sciences) and the Department of Biostatistics & Medical Informatics (School of Medicine & Public Health). He leads the Zhao Research Group, focusing on statistical and machine learning methods for biomedical data analysis. His work is supported by multiple NIH and NSF grants, including an NIH R01 project on patient-reported outcomes in vocal fold paralysis and a Distinguished Research Fellows Program at UW-Madison. Dr. Zhao earned his PhD in Statistics from UW-Madison in 2012 under Dr. Jun Shao. Prior to his current position, he was a Postdoctoral Researcher at Yale University (2012-2013) and held Assistant and Associate Professor roles at SUNY Buffalo (2014-2020). His academic leadership includes serving as Associate Editor for journals like the Annals of Applied Statistics and Scandinavian Journal of Statistics , and as Area Chair for AISTATS (2024-2025). His research interests span semiparametric methodologies, missing data analysis, causal inference, and domain adaptation. Key applications include patient-reported outcomes, electronic health records, clinical trials, and studies on aging, mental health, and cancer. His group collaborates with institutions like the Data Science Institute, Institute on Aging, and Center for Demography of Health and Aging. Recent work emphasizes efficient parameter estimation under privacy constraints, robust learning in nonignorable missing data scenarios, and leveraging machine learning for medical imaging and genetic studies. He actively recruits researchers across postdocs, PhD, and undergraduate levels to join his team.

Michael McCulloughمشاهده پروفایل

پژوهشگر ارشد

• Computational Neuroscience
• Neural Coding
• Machine Learning
• +۵ مورد دیگر

Dr. Michael McCullough is a Jubilee Joint Fellow at ANU's John Curtin School of Medical Research (JCSMR), affiliated with the Eccles Institute of Neuroscience and School of Computing. His research focuses on computational neuroscience, developing novel methods to analyze neural activity and behavior using machine learning, network science, and complex systems analysis. Education: BEng (Hons) and BMus (Hons) from UWA (2013), PhD in applied mathematics (2018). Postdoctoral experience includes work at the UWA Young Lives Matter Foundation and Queensland Brain Institute. Current affiliations include leadership of The McCullough Group and membership in the Centre for Computational Biomedical Sciences and Division of Neuroscience. Research interests emphasize large-scale neural data analysis, particularly in developing algorithms to interpret patterns in neural activity and behavioral recordings from awake animals. Techniques include topological data analysis, network dynamics, and computational ethology. Collaborates internationally on projects involving neural coding, sensory processing, and brain development. Publications span computational neuroscience, biomedical signal processing, and nonlinear dynamics. Active in translating computational methods to clinical mental health applications and neurodevelopmental studies. Lab leadership includes supervision of research assistants and associates in the McCullough Group.