جست‌وجوی استاد و پژوهشگر

Ryan T. Whiteمشاهده پروفایل

دانشیار

• Deep Learning
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Ryan T. White is an Associate Professor at Florida Institute of Technology in the Department of Mathematics and Systems Engineering within the College of Engineering and Science. He serves as Director of the NEural TransmissionS (NETS) Lab, focusing on deep learning, computer vision, and data science. He is also an Affiliate Faculty member in Electrical Engineering and Computer Science. Ph.D. in Applied Mathematics (2015) from Florida Tech His research bridges deep learning and computer vision with applications in autonomous satellite operations , physics-informed neural networks for biomedical and geoscience problems, and NLP in aerospace domains. Projects include real-time edge computing , stochastic process analysis , and generative AI for synthetic data. The NETS Lab he directs has produced 15+ recent publications in conferences like IEEE Aerospace, AIAA SCITECH, and AAS/AIAA, with funding from the U.S. Space Force, Air Force Research Lab, and NSF. His teaching spans graduate/undergraduate courses in deep learning , machine learning , probability , and honors calculus . Current advisees include Ph.D. candidates and M.S. students working on topics like 3D object detection , information-theoretic neural analysis , and geophysical signal processing . The lab’s scientific contributions include real-time satellite feature detection , physics-guided neural networks for blood flow modeling, and entropy-based visual explanations for AI interpretability. Collaborations span Georgia Tech , Mulitscale Cardiovascular Fluids Laboratory , and Engage-AI for global development projects analyzing UNDP Sustainable Development Goals.

Christos Diouمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +۳ مورد دیگر

Christos Diou is an Associate Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, Harokopio University of Athens, Greece. His academic career spans over 15 years of participation in national and international research projects, with a focus on machine learning algorithms, domain generalization, causal inference, and bias mitigation. He earned a BSc and Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research emphasizes the application of machine learning to healthcare, addressing challenges such as visual bias mitigation, causal effect estimation from observational data, and fairness-aware representation learning. Notable projects include REBECCA and RELEVIUM , both EU-funded, and MELIORA , targeting lifestyle interventions for breast cancer risk reduction. He has published extensively in top-tier venues like IEEE TPAMI, CVPR, and ICCV. Christos is a leading voice in AI ethics and healthcare innovation, with over 150 publications and best paper awards at IEEE Big Data Service 2023 and AIAI 2022. His work includes developing platforms like Effector for feature effects and Beam for behavior studies. He collaborates with institutions such as Karolinska Institutet and CERTH/ITI, and his students include PhD candidates Ioannis Sarridis and Aristotelis Ballas.

• Ophthalmology
• Neurodegenerative Diseases
• Multi-Omics Analysis
• +۷ مورد دیگر

Sanjoy K. Bhattacharya is a tenured Professor of Ophthalmology at the Bascom Palmer Eye Institute, University of Miami, with secondary appointments in Biochemistry and Molecular Biology and Neuroscience. His research focuses on intraocular pressure homeostasis, optic nerve regeneration, and neurodegenerative diseases. He leads the Miami Integrative Metabolomics Research Center and serves as the graduate program director for translational programs. Dr. Bhattacharya holds a PhD from IIT-Delhi and postdoctoral training at McGill University and Cleveland Clinic. Education: PhD in Bioengineering, Indian Institute of Technology-Delhi (1997) Master's in Biotechnology and Bioengineering, Banaras Hindu University (1992, 1990) Postdoctoral Fellowships: Pharmacology (McGill University), Structural Biology (Cleveland Clinic) Research Interests: Combines multi-omics approaches with advanced mass spectrometry and NMR to study ocular neurodegenerative diseases, lipid signaling, and optic nerve regeneration. Key areas include glaucoma mechanisms, axon remyelination, and translational therapies. Awards: ARVO Silver Fellow Chancellor’s Distinguished Award (LSU) ISER Mentor of the Month Japan Predoctoral Fellowship Teaching: Integrates experimental methods with didactic teaching, emphasizing biochemistry fundamentals and entrepreneurship. Uses creative pedagogical tools like seed germination statistics and turmeric growth experiments to teach scientific principles. Labs/Teams: Director of Miami Integrative Metabolomics Research Center, leading multidisciplinary teams in lipidomics and systems biology. Active in translational research partnerships with industry.

• Statistics
• Data Modeling
• Medical Research
• +۳ مورد دیگر

Hassan Doosti is a Senior Lecturer at the School of Mathematical and Physical Sciences, Macquarie University. His research focuses on statistical methodologies, particularly in flexible modeling techniques for complex datasets, with applications in medical studies and business analytics. He has authored or edited books such as Flexible Nonparametric Curve Estimation and Ethics in Statistics: Opportunities and Challenges . Research Interests Nonparametric estimation including wavelet methods and density estimation Statistical modeling of health-related data (e.g., colorectal cancer, stroke) Development of novel statistical algorithms (e.g., censored regression, numerical dependency analysis) Ethical considerations in data analysis for medical sciences Recent Projects Outside Studies Program (2025) APRIntern: Disease Risk Modelling (2019) Key Contributions His work bridges theoretical statistics with practical applications, including: Development of adaptive wavelet quantile density estimation techniques Statistical analysis of neurological and oncological data Advancing methods for handling censored and zero-inflated datasets Awards Recipient of the Faculty of Science and Engineering Award for Inter-School Collaboration (2023) for collaborative research excellence. Professional Activities Editor of multiple peer-reviewed books and active contributor to interdisciplinary projects involving healthcare, data science, and biostatistics.

Catherine Jutzelerمشاهده پروفایل

استادیار

• Spinal Cord Injury
• Pain Management
• Machine Learning in Medicine
• +۳ مورد دیگر

Prof. Dr. Catherine Jutzeler is an Assistant Professor at the Department of Health Sciences and Technology (D-HEST) at ETH Zurich. Her research focuses on spinal cord injury, pain management, and the application of machine learning in medical diagnostics and rehabilitation. She specializes in developing advanced computational methods for analyzing spinal MRI scans, predicting clinical outcomes, and optimizing therapeutic interventions. Her work bridges biomedical engineering and clinical neuroscience, with a strong emphasis on translational research. Key areas include neuroimaging analysis, serological biomarker discovery, and the development of predictive models for neurological recovery. Recent studies have explored the impact of pharmacological treatments, racial disparities in biomarker responses, and biomechanical thresholds in cervical myelopathy. Prof. Jutzeler collaborates extensively on interdisciplinary projects, integrating clinical data with machine learning frameworks. She is particularly known for pioneering diffusion-based models for lumbar spine segmentation and advancing data standards in spinal cord injury research. Her contributions aim to improve diagnostic accuracy and personalize patient care in neurotrauma and chronic pain management.

• Optimization Algorithms
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Assoc. Prof. Dr. Adnan Acan is a faculty member in the Department of Computer Engineering at Eastern Mediterranean University (EMU). He holds a PhD from Middle East Technical University, specializing in Electrical Engineering. His academic roles include teaching and supervising numerous graduate students in areas such as optimization algorithms, computer vision, and biomedical signal processing. Education: PhD, Electrical Engineering, Middle East Technical University (1992) MS, Electrical Engineering, Middle East Technical University (1985) BS, Electrical Engineering, Middle East Technical University (1983) Research Interests: His research focuses on optimization algorithms, machine learning, pattern recognition, and biomedical applications. Notable areas include metaheuristic algorithms (e.g., particle filters, great deluge), multi-agent systems, and ECG/heartbeat classification. He has contributed to advancements in computational intelligence, evolutionary algorithms, and medical diagnostics. Advising & Students: He has supervised over 30 graduate students, including PhDs such as JAMSHID TAMOUK (2021) and ZAHRA GOLRIZ KHATAMI (2019), and master’s students like MILAD ROSTAMPOUR (2023). His work spans applications in robotics, face recognition, and numerical optimization. Labs & Collaboration: His teaching and research involve courses in artificial intelligence, algorithms, and computer engineering. He actively contributes to academic service, including roles as a session chair and reviewer in conferences. His lab focuses on developing innovative solutions to complex computational and biomedical challenges.

Rachel Bearonمشاهده پروفایل

استاد

• Mathematical Biology
• Biophysics
• Fluid Dynamics
• +۶ مورد دیگر

Professor Rachel Bearon is the Executive Dean of the Faculty of Natural, Mathematical & Engineering Sciences (NMES) at King’s College London, where she also holds the title of Professor of Mathematical Biology. Prior to joining King’s in 2024, she served as Head of Department and Professor at the University of Liverpool’s Department of Mathematical Sciences from 2005–2023. Her research integrates mathematics with biological systems, focusing on cell motility , chemotaxis , active fluids , and quantitative systems pharmacology . Education : PhD and BA in Mathematics from the University of Cambridge Postdoctoral : Biological Oceanography at the University of Washington, Seattle Her work spans mathematical modeling of biological processes , including drug transport in hepatic spheroids , microswimmer dynamics in fluid flows , and actin filament barriers in cells . Recent publications highlight collaborations in in silico and translational medicine projects, particularly with tissue engineers and pharmacologists. Scientific contributions include: Fellow of the Institute of Mathematics and its Applications Trustee of the Academy for Mathematical Sciences (2020–2024) Leadership in interdisciplinary initiatives like the Centre for the Physical Science of Life As a leader, she emphasizes interdisciplinary collaboration , student-centered education , and EDI (Equality, Diversity, Inclusion) in academic culture. Her advisory roles include the University of Liverpool Maths School Trustee and British Science Association presidency.

• Bioinformatics
• Computational Biology
• Machine Learning
• +۵ مورد دیگر

Zafer AYDIN is an Associate Professor at the Computer Engineering Department of Abdullah Gul University. He obtained his B.Sc. and M.Sc. from Bilkent University (Turkey) and his Ph.D. from Georgia Institute of Technology (USA). His research focuses on machine learning applications in bioinformatics, health informatics, and industrial problems. PhD: Georgia Institute of Technology (2008) Postdoc: University of Washington (2008-2011) Previous: Assistant Professor at Bahcesehir University (2011-2014) Research Interests include protein structure prediction, medical imaging analysis, network security, and financial data modeling. His work spans both fundamental bioinformatics research and practical industrial applications. Scientific Awards : 2nd in T0 phase of Respiratory Viral Dream Challenge (2016-2017) 1st in both question 1 and 2 prediction tasks of COVID-19 DREAM Challenge (2020-2021) Teaching includes undergraduate courses in Bioinformatics, Machine Learning, and Algorithms, and graduate courses in Deep Learning and Pattern Recognition.

Laila Cureمشاهده پروفایل

دانشیار

• Healthcare Systems Engineering
• Operations Research
• Mathematical Modeling
• +۳ مورد دیگر

Dr. Laila Cure is an Associate Professor in the Department of Industrial, Systems and Manufacturing Engineering at Wichita State University's College of Engineering. She holds a Ph.D. from the University of South Florida. Her research focuses on applying analytics and mathematical modeling to healthcare systems, particularly in understanding human-technology interactions in uncertain environments. Key areas include healthcare operations optimization, quality improvement, and risk management. Dr. Cure teaches courses such as Probability and Statistics, Work Systems, and Data Mining and Analytics. She leads the Health Systems Engineering (HSE) Lab, which specializes in healthcare operations analysis, optimization, and data-driven decision-making. The lab employs tools like statistical analysis, simulation, and mathematical programming to improve healthcare delivery processes. Her work emphasizes practical applications, such as optimizing ambulance crew workflows, reducing hospital readmissions, and enhancing infection control through strategic facility design. She has advised students like Dr. Parisa Eimanzadeh, who pursued a tenure-track faculty position after her defense. Dr. Cure's research spans trauma care quality, clinical workflow interruptions, and healthcare technology integration, contributing to both academic and applied advancements in the field. Contact: Laila.Cure@wichita.edu , Industrial and Manufacturing Engineering Department, Wichita State University.

• Data Governance
• Semantic Modeling
• Knowledge Graphs
• +۵ مورد دیگر

Zaiwen Feng is a researcher actively contributing to data governance, semantic modeling, and causal inference. His work focuses on knowledge graphs, graph-based methods, and service-oriented architectures through collaborations with institutions like the University of Queensland and universities in China. Research Focus : Graph Differential Dependencies, Entity Resolution, Causal Effect Estimation, and Ontology Alignment Methodologies : Machine Learning, Variational Autoencoders, Prompt Engineering, and Semantic Retrieval Application Areas : Biomedical Data, Property Graph Recommendation, and Process Model Repositories Key trends in his publications include automated semantic modeling , neural approaches for entity resolution , and causal inference with graph structures . He frequently collaborates with researchers like Keqing He, Wolfgang Mayer, and Selasi Kwashie across conferences such as HPCC, BIBM, and WISE.

Ram Hariharanمشاهده پروفایل

عضو هیئت علمی

• AI in Healthcare
• Machine Learning Applications
• Computational Biology
• +۳ مورد دیگر

Ram Hariharan is an Associate Teaching Professor and Director of Programs for the College of Engineering and Data Science Faculty at Northeastern University's Seattle campus. He specializes in experiential data science education and AI applications in healthcare, focusing on the intersection of technology, business leadership, and stakeholder collaboration. His work emphasizes real-world case studies and AI-driven solutions for healthcare challenges. Education: PhD in Computational Biology (Statistical Modeling and Simulation of Biophysical Data, Machine Learning), University of Kerala, India (2008) Research Interests: Ram’s research explores AI in healthcare, particularly using machine learning to predict cancer treatment responses, improve wellness for cancer survivors, and democratize access to precision medicine. His projects often involve collaborations with institutions like the Institute for Systems Biology and the University of Washington. Awards and Recognition: British Council Exchange Program award Young Scientist Award from the Government of India Reviewer for grants from the Governments of Poland and Hong Kong Advising and Grants: Ram advises graduate students in AI and data science programs, emphasizing hands-on learning through initiatives like AI Immersion Week. His industry experience includes leading AI teams at MACRO-EYES (now Pendulum Systems) and contributing to AI longevity research via the Artificial Intelligence Longevity Toolbox (AI-LOT) project. Labs and Teams: He collaborates with the Institute for Experiential AI and contributes to interdisciplinary teams at RIKEN (Japan), the University of Oxford, and Singapore’s A* Bioinformatics Institute.

• Epidemiology
• Biostatistics
• Infectious Diseases
• +۴ مورد دیگر

Forrest W. Crawford is an Associate Professor Adjunct in Biostatistics at the Yale School of Public Health (YSPH). He holds affiliations with the Computational Biology and Bioinformatics Program, the Center for Biomedical Data Science, and the Public Health Modeling initiative. His research focuses on mathematical and statistical methods for discrete structures and stochastic processes, with applications in epidemiology, public health, biomedicine, and evolutionary science. Dr. Crawford earned his PhD and MS in Biostatistics from the University of California, Los Angeles (2012 and 2009). His work bridges statistical theory and applied public health challenges, emphasizing network analysis, infectious disease modeling, and computational methods. He leads the Crawford Lab, which develops tools for understanding complex systems like opioid epidemics, HIV transmission dynamics, and pandemic response strategies. Key research interests include: - Network-based epidemiological modeling - Causal inference under contagion - Public health data science - Statistical methods for hidden populations - Stochastic processes in biological systems Recent work includes studies on fentanyl-related mortality trends in Connecticut, SARS-CoV-2 transmission dynamics, and vaccine efficacy analyses. His interdisciplinary collaborations span public health, computer science, and mathematics. Dr. Crawford has advised numerous research initiatives and contributed to pandemic response efforts through real-time modeling. His lab's projects include software development for public health surveillance (e.g., the covidestim tool) and methodological advancements in respondent-driven sampling for hidden populations. Professional affiliations include the American Statistical Association, Society for Mathematical Biology, and editorial roles in statistical and epidemiological journals.

Laurie A. Boyerمشاهده پروفایل

استاد

• Developmental Biology
• Cardiovascular Biology
• Chromatin Remodeling
• +۴ مورد دیگر

Laurie A. Boyer is a Professor of Biology and Biological Engineering at the Massachusetts Institute of Technology (MIT), affiliated with the Koch Biology Building. She serves as Co-Undergraduate Officer and leads research on gene regulatory mechanisms in heart development and regeneration using embryonic stem cells and mouse models. Her work integrates genomic, genetic, biochemical, and cell biological approaches to understand chromatin dynamics and lineage commitment, with applications in cardiac tissue engineering and regeneration. Education: PhD, 2001, University of Massachusetts Medical School BS, 1990, Biomedical Science, Framingham State University Research Interests: Dr. Boyer’s research focuses on the interplay between chromatin remodeling, epigenetic regulation, and transcriptional networks that drive heart development. She investigates how ATP-dependent chromatin remodelers influence lineage specification and explores methods to stimulate cardiac regeneration. Her lab combines advanced genomic techniques with tissue engineering to model 3D cardiac architecture, aiming to uncover regulatory circuits underlying normal and diseased heart development. Publications: Her recent work includes breakthroughs in high-throughput expansion microscopy for scalable super-resolution imaging, metabolic modulation of mesenchymal stem cells for cartilage repair, and the role of long noncoding RNAs like Braveheart in cardiovascular lineage commitment. These studies span molecular mechanisms of chromatin dynamics to translational applications in regenerative medicine. Awards & Honors: Pew Scholars Award in the Biomedical Sciences (2008) Smith Family Award for Excellence in Biomedical Science (2009) Scientific American World’s 50 Top Leaders in Research (2006) Irvin and Helen Sizer Career Development Award (2012) Grants & Collaborations: Dr. Boyer oversees grants from organizations like the NIH and American Heart Association. She collaborates with teams on interdisciplinary projects, including tissue engineering and stem cell therapies. Her lab also develops innovative microscopy techniques to advance cellular imaging scalability. Labs & Teams: Her laboratory at MIT’s Koch Institute integrates computational biology, bioengineering, and molecular biology. The lab focuses on translating fundamental discoveries into clinical applications for heart disease and regeneration.

Daeyoung Kimمشاهده پروفایل

استاد

• Statistics
• Asymmetric Dependence
• Categorical Data Modelling
• +۴ مورد دیگر

Daeyoung Kim is a Full Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. He holds a Ph.D. in Statistics from The Pennsylvania State University (2008), and M.S. and B.S. degrees in Statistics from Korea University (2002, 2000). His research focuses on statistical dependence modeling via copulas, likelihood inference in categorical data, mixture modeling, and interdisciplinary applications in food science and environmental engineering. He has authored/co-authored numerous publications, including a book on Direction Dependence in Statistical Modeling . His work spans statistical methodology development and collaborative research in environmental and biomedical sciences. Education: Ph.D. in Statistics, The Pennsylvania State University, 2008 M.S. in Statistics, Korea University, 2002 B.S. in Statistics, Korea University, 2000 Research Interests: Kim’s work emphasizes asymmetric dependence analysis, categorical data modeling, and mixture models. He applies these methods to food science (e.g., lipid metabolism) and environmental challenges (e.g., cyanobacterial bloom modeling). His statistical contributions include copula-based regression and likelihood inference techniques for incomplete data. Collaborative Projects: Kim has contributed to studies on antimicrobial additives’ effects on colonic inflammation, lipid metabolism modulation in disease models, and the role of dietary fats in tumorigenesis. His interdisciplinary collaborations span environmental engineering, nutrition science, and biomedical research. Awards: Noted for award-winning teaching and research excellence, though specific honors are not detailed in the provided text.

David Hallidayمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Computing
• Intelligent Systems
• +۳ مورد دیگر

Professor David Halliday holds a position at the University of York as a Professor and Chair of the Research Committee. His research focuses on interdisciplinary fields of Computational Neuroscience and Neural Computing, with a particular emphasis on neural networks and neurophysiological signal analysis. He leads the Intelligent Systems and Nano-science Group and maintains an active research profile with over 100 publications. His work integrates theoretical modeling with experimental techniques to investigate topics such as motor control, neural oscillations, and clinical applications of machine learning. David Halliday's academic training includes a BSc and PhD, though specific institutions are not detailed in the provided text. His research interests span computational modeling of neural circuits, development of neuro-inspired algorithms, and application of signal processing techniques to neurological disorders. His homepage at http://www-users.york.ac.uk/~dh20 provides further details, including access to his ResearcherID profile (RID: A-3848-2009). Key research contributions include studies on cortico-muscular coherence in movement disorders, astrocyte-neuron interactions, and fault-tolerant spiking neural networks. His publications demonstrate interdisciplinary collaboration across computational neuroscience, biomedical engineering, and clinical diagnostics. Notably, his work combines experimental neurophysiology with advanced computational methods, such as non-parametric directionality analysis and wavelet-based coherence estimation. Recent studies have addressed clinically relevant topics like gait disturbance in spinal cord injury and biomarker identification in bladder cancer using machine learning approaches.