جست‌وجوی استاد و پژوهشگر

Warren D. D'Souzaمشاهده پروفایل

استاد مدعو

• Medical Physics
• Radiation Oncology
• Machine Learning
• +۶ مورد دیگر

Warren D. D'Souza serves as Adjunct Professor in the Department of Radiation Oncology at the University of Maryland School of Medicine, where he leads the Medical Physics Division. Concurrently, he holds the position of Vice President for Enterprise Data and Analytics at the University of Maryland Medical System. His academic career spans over two decades, beginning at MD Anderson Cancer Center in 2000 before joining the University of Maryland in 2002, where he progressed from Assistant Professor to full Professor by 2014. His educational background includes: B.S. in Applied Physics (summa cum laude), Xavier University, 1995 M.S. in Medical Physics, University of Wisconsin-Madison, 1998 Ph.D. in Medical Physics, University of Wisconsin-Madison, 2000 MBA, Duke University's Fuqua School of Business, 2013 (Fuqua Scholar and Health Sector Management Scholar) Dr. D'Souza's research integrates advanced computational techniques with clinical radiation oncology, focusing on radiation treatment plan optimization through combinatorial methods derived from operations research. His work bridges medical physics with data science through machine learning applications for treatment outcomes prediction, multi-modality imaging for therapeutic response assessment, and development of novel analytic approaches in medicine. His expertise spans both theoretical optimization frameworks and practical clinical implementations. His publication record (2007-2009) reveals a strategic evolution toward integrating machine learning with radiation therapy planning, particularly in multi-plan IMRT frameworks and motion management solutions. Key thematic clusters include beam angle optimization using nested partitioning algorithms, real-time motion compensation systems, and 4D CT-based stereotactic body radiotherapy planning - demonstrating consistent innovation at the intersection of operations research, medical physics, and clinical oncology. Scientific recognition includes: Medical Physics Travel Award, American Association of Physicists in Medicine Fellow, American Association of Physicists in Medicine (2015) Fuqua Scholar designation at Duke University (top 10% of class) Health Sector Management Scholar at Duke University As Principal Investigator, he has secured substantial research funding from NIH, NSF, and industry partners to advance radiation therapy optimization and motion management technologies. His leadership extends to mentoring medical physics trainees and directing the Medical Physics Division's clinical and research operations, with six U.S. patents reflecting his translational impact. Current initiatives focus on enterprise data analytics applications within the medical system. He directs the Medical Physics Division within Radiation Oncology, fostering collaborations between radiation oncologists, medical physicists, and computer scientists. Current team projects include developing motion-synchronized treatment couches, implementing 4D CT for stereotactic radiotherapy, and creating machine learning models for predicting treatment complications - all aimed at enhancing precision radiation therapy delivery.

• Machine Learning
• Data Mining
• Knowledge Discovery
• +۵ مورد دیگر

João Gama is a Full Professor at the School of Economics, University of Porto, Portugal, and a researcher at LIAAD - INESC TEC (Laboratory of Artificial Intelligence and Decision Support). He holds the position of Professor Emeritus at the University of Porto and serves on the board of directors of LIAAD. His professional affiliations include being a Fellow of EurIA (since 2020), IEEE Fellow (since 2021), Fellow of the Asia-Pacific AI Association, and an ACM Distinguished Speaker. Dr. Gama received his Ph.D. in Computer Science from the University of Porto in 2000. His academic journey has established him as a leading researcher in the field of machine learning and data mining, with an h-index of 67 on Google Scholar. Professor Gama's research primarily focuses on knowledge discovery from data streams , evolving data , probabilistic reasoning , and causality . His work addresses fundamental challenges in processing continuous, high-volume data streams where traditional batch processing methods are inadequate. He has made significant contributions to developing algorithms that can adapt to concept drift, handle evolving data distributions, and maintain high performance in real-time applications. His research has practical applications in diverse domains including predictive maintenance, financial analysis, transportation systems, and environmental monitoring. With over 300 publications to his name, he is the author of the influential book 'Knowledge Discovery from Data Streams' (2010). With an extensive publication record of over 300 reviewed papers in top-tier journals and conferences, Professor Gama's recent work shows a strong trend toward explainable AI for predictive maintenance , edge computing for IoT data streams , and advanced techniques for handling concept drift . His 2024-2025 publications demonstrate increasing focus on practical industrial applications, particularly in transportation systems (like the Metro do Porto case study), financial portfolio management, and resource-constrained edge devices. There's also a clear emphasis on making stream mining techniques more interpretable and applicable to real-world problems, with several papers specifically addressing how to explain anomalies and failures in complex systems. Professor Gama's scientific achievements have been recognized through several prestigious fellowships: EurIA Fellow (since 2020) IEEE Fellow (since 2021) Fellow of the Asia-Pacific AI Association ACM Distinguished Speaker As an educator and mentor, Professor Gama has supervised numerous doctoral students who have gone on to establish their own research careers. His current PhD students include Thiago Andrade, Mário Cordeiro, Shazia Tabassum, and Sofia Fernandes. Among his former students are notable researchers such as Pedro Pereira Rodrigues, Hadi Fanaee, and Elena Ikonomovska. Professor Gama has secured significant research funding through projects like MAESTRA (Learning from Massive, Incompletely annotated, and Structured Data) and Knowledge Discovery from Ubiquitous Data Streams (PTDC/EIA/098355/2008). He has also served in leadership roles for major conferences including ECMLPKDD 2005, IDA 2011, ECMLPKDD 2015, and DSAA 2017, and is currently organizing ECMLPKDD 2025. Professor Gama leads research activities at LIAAD - INESC TEC, where he heads a team focused on data stream mining and knowledge discovery. His laboratory collaborates extensively with industry partners, particularly on predictive maintenance applications as evidenced by the MetroPT-3 Dataset developed for train systems. The team has developed several influential algorithms and frameworks for processing data streams, with applications spanning transportation, finance, healthcare, and environmental monitoring. Current research directions include integrating foundational models with stream processing, enhancing explainability of stream mining results, and developing efficient techniques for edge devices that can operate with limited computational resources.

• Color Image Representation
• Color Space Selection
• Color Texture Feature Selection
• +۳ مورد دیگر

Nicolas Vandenbroucke is a researcher affiliated with the Université du Littoral Côte d'Opale. His work focuses on color and hyperspectral imaging, texture classification, and biomedical applications. Research Interests Color image representation and segmentation Hyperspectral texture descriptors for pattern recognition Dimensionality reduction via feature selection Biomedical imaging for multiple sclerosis diagnosis Machine vision applications in environmental monitoring Selected Publications Recent work on lightweight CNN models for marine plastic debris classification (2025) Hyperspectral imaging systems for aquatic drone-based waste detection (2024) Advancements in Local Binary Pattern algorithms for texture analysis (2023) Comparative studies of color vs. hyperspectral imaging (2022) Clustering-based feature selection for high-dimensional data (2021) Technical Contributions Development of remote hyperspectral imaging systems Design of compact descriptors for color texture classification Optimized hierarchical warping for medical diagnostics LBP histogram selection scores for supervised learning

Ediz ŞAYKOLمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Artificial Neural Networks
• +۷ مورد دیگر

Ediz ŞAYKOL is an Assistant Professor and Department Head at Beykent University's Faculty of Engineering and Architecture, Department of Computer Engineering. He also served as Vice Dean from 2012-2015. Prior to his academic career, he worked as a Senior Software Engineer at HAVELSAN A.Ş. from 2004-2010. His educational background includes a Doctorate (2001-2009), Master's degree (1999-2001), and Bachelor's degree (1995-1999), all from İhsan Doğramacı Bilkent University's Faculty of Engineering and Institute of Engineering and Science. His doctoral thesis focused on 'A scenario-based query processing framework for video surveillance'. Professor ŞAYKOL's research spans computer vision, machine learning, and video database systems, with particular emphasis on surveillance applications, medical diagnostics using neural networks, and educational technologies for special needs populations. His work bridges theoretical computer science with practical applications across multiple domains including healthcare, security, education, and entertainment. His publication portfolio shows a consistent trajectory from foundational work in video database management systems to contemporary applications of deep learning across diverse fields. The research demonstrates strong methodological continuity in content-based retrieval techniques while adapting to emerging technologies from early 2000s through present day. 2003 3. Best Paper Award, University, Türkiye Professor ŞAYKOL has supervised over 50 master's and doctoral students, with thesis topics covering computer vision applications, machine learning implementations, and innovative computing solutions for accessibility challenges. His academic leadership extends to curriculum development in computer graphics, operating systems, and computer vision courses taught in both Turkish and English. His laboratory work has focused on video processing systems, with applications in historical document analysis, security surveillance, and medical diagnostics. Current research directions include deep learning applications for behavioral analysis and specialized computing solutions for accessibility challenges.

Haoyuan Chenمشاهده پروفایل

استادیار

• Computational Chemistry
• Materials Design
• Machine Learning

Haoyuan Chen is an Assistant Professor at Southern Methodist University (2024–present), previously serving as Assistant Professor at the University of Texas Rio Grande Valley (2021–2024) and a Postdoctoral Research Associate at Northwestern University (2017–2021). He holds a Ph.D. in Chemistry from Rutgers University (2011–2017) and a B.S. from the University of Science and Technology of China (2007–2011). His research focuses on Computational Chemistry , Materials Design , and Machine Learning , with an emphasis on metal-organic frameworks (MOFs) for applications in energy storage, environmental remediation, and catalysis. He leads the Chen Research Group , advancing interdisciplinary approaches to materials innovation. Key awards include the ACS Petroleum Research Fund (2022) and a DOE Basic Energy Sciences Grant (2022) , supporting his work on sustainable materials and computational design. His research bridges simulation and experimentation, exemplified by projects like the Research Experiences via Integrating Simulations and Experiments (REVISE) initiative for undergraduate students. He has contributed to databases like MOFX-DB and pioneered studies on MOF stability, adsorption mechanisms, and upcycling waste into carbon capture materials.

M. Shahidul Islamمشاهده پروفایل

دانشیار

• Drug Discovery
• Infectious Diseases
• Cancer Immunotherapy
• +۳ مورد دیگر

M. Shahidul Islam is an Associate Professor in the Department of Chemistry at Delaware State University. He holds a PhD in Chemistry from Memorial University (2008), an MBA from the University of Illinois at Chicago (2022), and postdoctoral training at the University of Chicago and University of Waterloo. His research focuses on computationally aided drug discovery, infectious disease mechanisms (e.g., SARS-CoV-2), cancer immunotherapeutics, genetic disorders like PKU, and opto-electronic polymer design using AI/machine learning. Education Postdoctoral Fellow, University of Chicago (2016) Postdoctoral Fellow, University of Waterloo PhD, Chemistry, Memorial University (2008) MBA, University of Illinois at Chicago (2022) Research Highlights Dr. Islam’s work integrates computational methods with experimental validation to address global health challenges. Key areas include: Designing ACE2 decoy receptors to counter SARS-CoV-2 variants Investigating resistance mutations in viral proteases (e.g., 3CLpro) Developing AI-driven approaches for de novo protein design Optimizing conjugated polymers for low-cost opto-electronic applications Identifying genetic mutations underlying diseases like Tay-Sachs and PKU Grants & Collaborations NIH Grant (P30 GM145765): $100,000 to study PAH mutations in PKU NIH Grant (U54 MD015959): $1.6M for interdisciplinary cancer research NSF ACCESS Grant: National supercomputer access for SARS-CoV-2 inhibitor design Professional Activities Serves as a reviewer for Journal of Physical Chemistry , Nature Protocols , and others. Active in the Biophysical Society and American Chemical Society.

• Image Processing
• Facial Expression Recognition
• Pattern Recognition
• +۳ مورد دیگر

Prof. Dr. Hasan Demirel is a faculty member in the Department of Electrical and Electronic Engineering at Eastern Mediterranean University (EMU). He holds a PhD in Electrical and Electronic Engineering from Imperial College London (2003), an MS in Computer Science from EMU (1993), and a BS in Electrical and Electronic Engineering from EMU (1992). His research focuses on image and video processing, facial expression recognition, pattern recognition, and biomedical applications. He has authored/co-authored over 100 peer-reviewed publications and supervised numerous PhD and MS students in areas such as Alzheimer's disease detection using MRI, SAR target recognition, and deep learning for emotion synthesis. Prof. Demirel's awards include multiple EMU Publication Citation Awards (2017–2021). He has led projects on multimodal emotion recognition and developed systems for real-time face recognition using robotics platforms like NAO humanoid robots. His work integrates machine learning techniques with medical imaging, remote sensing, and computer vision. He currently serves as a faculty member at EMU, contributing to academic programs and advising students in electrical engineering and biomedical engineering. His research labs focus on advancing AI-driven solutions for healthcare diagnostics and visual surveillance systems.

Stefan Brucknerمشاهده پروفایل

استاد

• Biomedical Visualization
• Volume Rendering
• Visual Data Exploration
• +۱ مورد دیگر

Stefan Bruckner is Professor of Visualization at the University of Bergen, specializing in biomedical visualization, volume rendering, and visual data exploration. His work develops novel techniques for analyzing complex scientific datasets across meteorology, medicine, and materials science. Dr. Bruckner's research group develops interactive visual analytics tools for weather forecasting, medical diagnostics, and ensemble data analysis. His methodological innovations include GPU-accelerated rendering, visual parameter exploration, and uncertainty visualization. He received the 2011 Eurographics Young Researcher Award for contributions to illustrative visualization. Professional service includes program committee roles for IEEE VIS, Eurographics, and ECRTS conferences. His pedagogical contributions span visualization, computer graphics, and programming languages at institutions including École normale supérieure and École polytechnique.

Kai Hormannمشاهده پروفایل

استاد

• Computational Sciences
• Geometric and Visual Computing
• Informatics
• +۳ مورد دیگر

Kai Hormann is a full professor in the Faculty of Informatics at Università della Svizzera italiana (USI Lugano). He earned his Ph.D. in computer science from the University of Erlangen-Nürnberg in 2002 and has held positions at Clausthal University of Technology, Caltech, and the CNR in Pisa. His research focuses on mathematical foundations of geometry processing, including barycentric coordinates, subdivision algorithms, and rational interpolation. Hormann has authored over 100 publications and serves as an associate editor for journals like Computer Aided Geometric Design and Dolomites Research Notes on Approximation . In 2024, he received the prestigious John A. Gregory Award for his contributions to geometric modeling. Education : Ph.D. in Computer Science, University of Erlangen-Nürnberg (2002) Diploma in Mathematics, University of Erlangen (1997) Abitur, Leibniz-Gymnasium Bad Schwartau (1992) Research Interests : Hormann’s work bridges computer science, applied mathematics, and engineering. His current projects include interactive shape deformations, time-dependent object processing, 3D visualization, and GPU-accelerated algorithms. His research areas encompass geometry processing, computational sciences, and numerical analysis. Publications : His recent articles focus on barycentric coordinates, subdivision schemes, and surface reconstruction. Themes include transfinite coordinates, curvature continuity, and efficient interpolation methods. Awards : John A. Gregory Award (2024) Chair of SIAM Activity Group on Geometric Design (2017–2018) Advising & Grants : He has advised numerous PhD students, including work on barycentric rational curves and subdivision schemes. He has organized conferences such as the SIAM GD and GMP series, reflecting his leadership in computational geometry. Labs & Teams : Hormann is affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), collaborating on interdisciplinary projects in AI and geometry processing.

• Data Visualization
• Educational Resources
• Library Science
• +۳ مورد دیگر

Judy Schmitt serves as faculty within the University Library at the University of Missouri-St. Louis, specializing in educational resource development with a focus on data visualization and statistical literacy. Her work appears in the institution's Library Faculty Works repository, demonstrating her active role in creating instructional materials for academic use. Dr. Schmitt's research interests center on effective data representation methods and educational resource design. She has developed numerous Open Educational Resources focusing on statistical visualization techniques including bar charts, stem-and-leaf plots, histograms, and box plots. Her work emphasizes proper visualization practices, identifying common misrepresentations, and developing accessible educational materials for statistics instruction. This specialization bridges library science with quantitative education, supporting students' statistical literacy through well-designed visual resources. Her publication pattern reveals a concentrated effort in November 2021 to create a comprehensive suite of statistical visualization educational materials, followed by the 'Qualitative/COVID Journals' work in February 2022. These publications demonstrate her focus on creating practical, accessible resources that help students understand proper data representation techniques and identify visualization pitfalls. The materials serve as teaching tools that address both foundational statistical concepts and contemporary issues like pandemic-related research. As a library faculty member, Schmitt contributes to academic support through the development of specialized educational resources rather than traditional laboratory research. Her work supports information literacy initiatives within the university, particularly in the area of quantitative reasoning and data comprehension. These contributions help bridge the gap between library resources and statistical education needs across various academic disciplines.

Daniel J Grahamمشاهده پروفایل

استاد

• Neuroaesthetics
• Vision Science
• Computational Neuroscience
• +۷ مورد دیگر

Daniel J. Graham is a Professor of Psychological Science at Hobart & William Smith Colleges (HWS), where he has been a faculty member since 2012. He is affiliated with the Department of Psychological Science within the School of Humanities and Sciences, contributing to interdisciplinary research and teaching in vision science, brain networks, and neuroaesthetics. Graham holds a Ph.D. in Psychology and an M.S. in Physics from Cornell University, and a B.A. in Physics from Middlebury College, reflecting his strong foundation in both the natural and cognitive sciences. Ph.D. in Psychology, Cornell University M.S. in Physics, Cornell University B.A. in Physics, Middlebury College His research integrates computational, behavioral, and theoretical approaches to understand how the brain processes visual information, particularly in natural scenes, art, and faces. He is a leading proponent of the 'internet metaphor' for brain function, proposing that neural communication operates similarly to packet-switched networks. His work emphasizes efficiency, statistical regularities, and network dynamics in cortical and whole-brain systems. Key research themes include efficient coding, neuroaesthetics, and models of neural communication. The most recent publications reveal a strong trend toward interdisciplinary synthesis, combining neuroscience, computer science, and psychology. His work increasingly explores machine learning models to predict human affective responses to visual stimuli, critiques of dominant theoretical frameworks like the free energy principle, and educational innovation in perception teaching. The research spans from foundational vision science to philosophical reflections on brain function. Scientific Awards and Recognition: Winner, Outstanding Student Presentation Award at MAA MathFest (2021) Invited speaker at numerous national and international conferences, including the Redwood Neuroscience Institute and the Bernstein Conference Media coverage of PNAS work in Nature , NPR , BBC , and IEEE Spectrum Teaching and Advising: Graham mentors numerous undergraduate students, many of whom co-author his publications. He has developed innovative lab courses involving electrophysiology, perceptual experiments, and creative demonstrations. He teaches core courses such as Introduction to Psychology, Sensation and Perception, and advanced seminars on art and neuroscience. His teaching philosophy emphasizes critical thinking, interdisciplinary reasoning, and active student participation. Research Labs and Collaborations: Graham collaborates closely with Prof. Yan Hao (HWS Mathematics) on modeling neural communication. His research group involves students in computational modeling, data analysis, and experimental design. He is involved in conferences and workshops focused on the mathematics of neuroscience and AI, reflecting his commitment to cross-disciplinary science.