جست‌وجوی استاد و پژوهشگر

Ayesha Aliمشاهده پروفایل

استاد

• Graphical Markov models
• Pollination networks
• Multivariate statistics
• +۷ مورد دیگر

Ayesha Ali is a Professor of Statistics and Director of the Master of Data Science program at the University of Guelph. She holds a PhD in Statistics from the University of Washington (2002) and has expertise in statistical methods for complex high-dimensional systems, including ecological networks, causal inference, and bioinformatics. Her research integrates graphical Markov models, machine learning, and statistical computing to address challenges in plant-pollinator networks, livestock genetics, and disease risk modeling. Education: B.Sc. Honours in Statistics and Actuarial Science, University of Western Ontario (1996) M.Sc. in Statistics, University of Toronto (1998) Ph.D. in Statistics, University of Washington (2002) Research Interests: Graphical Markov models and ecological networks Causal inference and longitudinal data analysis Machine learning and high-dimensional predictive modeling Statistical methods for livestock genetics and animal health Computational statistics and bioinformatics Articles Trends: Her recent work spans interdisciplinary applications, including veterinary oncology biomarker discovery, remote sensing for agricultural suitability, and pipeline development for cross-species transcriptomics. She emphasizes graphical structure exploitation in regression and predictive modeling, with contributions to both theoretical and applied statistical methodologies. Awards: Canadian Journal of Statistics Award (2020) for groundbreaking work on doubly sparse regression NSERC Discovery Grant (2018) NSERC Collaborative Research and Development Grant (2015) Advising & Grants: She has supervised numerous graduate and undergraduate students on projects ranging from plant-pollinator network analysis to bioinformatics. Her grants include NSERC-funded research on milk fatty acid genetics and statistical methods for clustered data. Labs/Teams: Involved in the Bioinformatics program at the University of Guelph, contributing to interdisciplinary research collaborations in ecology and animal science.

Tony Buiمشاهده پروفایل

پژوهشگر ارشد

• Trustworthy Machine Learning
• Generative Models
• Artificial Intelligence
• +۳ مورد دیگر

Tony Bui is a Research Fellow at the Department of Data Science & AI, Monash University. His research focuses on the intersection of Generative AI and Trustworthy Machine Learning, particularly addressing ethical and security challenges in AI models such as ChatGPT and Stable Diffusion. He is actively involved in developing methodologies to prevent harmful outputs from generative models. Research Interests: Trustworthy Machine Learning Generative Models Adversarial Machine Learning Model Security Ethical AI Current Project: TMLGenAI: Trustworthy Generative AI (2024–2026), a collaborative initiative exploring safe and aligned foundation models. As a Chief Investigator, he contributes to advancing the theoretical and practical aspects of ethical AI. Advising/Grants: Tony Bui has been accepting PhD students since 2020 and is a key researcher in projects funded by Monash University. His work bridges foundational theory with real-world applications, emphasizing robustness and ethical considerations in AI systems.

Jonathan Blazekمشاهده پروفایل

استادیار

• Cosmology
• Astrophysics
• Galaxy Clustering
• +۵ مورد دیگر

Jonathan Blazek is an Assistant Professor of Physics at Northeastern University's College of Science, specializing in observational and theoretical cosmology. His research focuses on large-scale astronomical surveys to understand cosmic structure and dark energy, particularly through galaxy clustering and weak gravitational lensing. He is a key member of the Dark Energy Survey and Vera C. Rubin Observatory collaborations, leading efforts to combine multi-wavelength datasets for cosmological insights. Blazek earned his Ph.D. from UC Berkeley and completed postdoctoral fellowships at EPFL (Switzerland) and Ohio State University. Education: Ph.D. in Physics, University of California, Berkeley Postdoctoral Fellowships: EPFL (Switzerland), Ohio State University Research Interests: His work centers on cosmological modeling using galaxy surveys, particularly refining analytic and numerical methods to connect observations with theoretical frameworks. Key areas include: Weak gravitational lensing and galaxy clustering Combined-probe cosmology (integrating datasets across wavelengths) Dark matter and dark energy dynamics Large-scale structure formation Publications & Grants: Blazek has authored over 50 peer-reviewed articles, including foundational work on intrinsic alignment modeling and cosmic shear analysis. He leads the NSF CAREER grant project exploring dark sector physics with galaxy surveys. His recent publications address baryonic feedback effects, CMB lensing cross-correlations, and next-generation survey methodologies. Labs & Collaborations: He contributes to the Northeastern Cosmology Group and the Dark Energy Science Collaboration, advancing projects like the Legacy Survey of Space and Time (LSST) at Vera Rubin Observatory.

• In-Memory Computing
• Energy-Efficient Systems
• Machine Learning Accelerators
• +۳ مورد دیگر

Prof. Naresh R. Shanbhag holds the Jack S. Kilby Professorship in Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. He is affiliated with the Coordinated Science Lab and the Information Trust Institute, both part of the College of Engineering. His research focuses on energy-efficient computing architectures, in-memory computing, nanoelectronics, and secure machine learning systems. Prof. Shanbhag has pioneered work on statistical error compensation techniques and resistive crossbar-based architectures, addressing critical challenges in low-power, high-performance computing. Education and career details are not explicitly provided in the text, but his extensive publications (313+) and honors indicate deep academic involvement. His research interests emphasize the intersection of hardware architecture and machine learning, with a focus on emerging technologies like MRAM and SRAM-based in-memory computing. Key awards include the IEEE Fellow designation (2006) and the prestigious Semiconductor Industry Association (SIA) University Research Award (2018). His recent work explores security vulnerabilities in in-memory architectures, energy-accuracy trade-offs in resistive systems, and adversarial robustness of neural networks. Prof. Shanbhag’s contributions span over 20 years, bridging theoretical computer science with practical hardware implementations. His lab (Coordinated Science Lab) and collaborations drive innovations in nanoscale information processing and trustworthy computing systems.

• Medical Imaging
• Image Processing
• Machine Learning
• +۴ مورد دیگر

Anna Breger is a Senior Postdoctoral Researcher at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, and a Research Fellow leading the iDeal project at the Medical University of Vienna. She holds the prestigious Hertha Firnberg Fellowship from the Austrian Science Fund, focusing on image quality assessment and medical imaging applications. Her work bridges theoretical mathematics with practical challenges in healthcare and cultural heritage preservation. Research interests include mathematical image processing for medical diagnostics, data representation, and cultural heritage restoration. She has pioneered AI-driven methods for analyzing historical sheet music and medical imaging data, collaborating with institutions like the Fitzwilliam Museum and Cambridge University Library. Key Projects: iDeal (Medical Image Quality), C2D3-funded Cultural Heritage AI, AIX-COVNET collaboration for X-ray analysis. Awards: Hertha Firnberg Fellowship, City of Vienna Promotion Award, L’OREAL Fellowship. Grants: C2D3 Accelerate, Austrian Science Fund. Publications emphasize advancing IQA metrics for medical images and developing clustering algorithms (visClust). She also contributes to interdisciplinary initiatives like Her Math’s Story and the AI for Cultural Heritage Hub (ArCH).

Ling Dingمشاهده پروفایل

دانشیار

• Biochemistry
• Chemistry
• Molecular Biology
• +۷ مورد دیگر

Ling Ding is an Associate Professor at the Department of Biotechnology and Biomedicine, Technical University of Denmark, and a key member of the DTU Microbes Initiative and Center for Microbial Secondary Metabolites. Their research focuses on microbial ecology, secondary metabolite biosynthesis, and antifungal compound discovery. Recent work involves genome mining in Streptomyces species, NMR spectroscopy, and studying microbial interactions such as actinobacterial and Pseudomonas fluorescens systems. Projects explore bioactive phosphorus-containing molecules, in situ detection of metabolites, and data-driven biosynthesis mechanisms. Their research contributes to UN Sustainable Development Goals (SDGs) related to health and environmental sustainability, with publications spanning microbial chemistry, drug discovery, and synthetic biology. Supervised PhD projects include omics-driven antifungal metabolite discovery and microbial interaction studies. Key publication trends include Streptomyces-derived bioactive compounds (e.g., lydicamycins, alligamycin), azoxy/polyketide metabolites, and computational approaches for fungal interaction classification. Methodologies emphasize NMR, genome mining, and deep learning models. Advising: PhD students: M. Haahr, A. Kostoglou, D. J. Otto, A. Svetlova, A. Kaltenyte Projects: Active roles in omics-driven antifungal metabolite discovery, phosphorus-containing molecule mining, and secondary metabolite biosynthesis mechanisms.

Andreas Athenodorouمشاهده پروفایل

پژوهشگر

• Theoretical Particle Physics
• Lattice Gauge Theories
• Computational Physics
• +۴ مورد دیگر

Andreas Athenodorou is an associate research scientist at the Computation-based Science & Technology Research Center (CaSToRC) of The Cyprus Institute. He holds a PhD in Theoretical Particle Physics from the University of Oxford (2004–2008) and has held postdoctoral positions at DESY Zeuthen, Swansea University, and the University of Cyprus. Prior to his current role, he was a Marie Sklodowska Curie Individual Fellow at the University of Pisa (2019–2021). He currently leads a work package in the NI4OS-Europe project. His research focuses on theoretical and computational aspects of particle physics, including lattice gauge theories, large-Nc limits, hadron structure, and beyond Standard Model physics. He specializes in applying high performance computing (HPC), data science, and machine learning to solve complex problems in quantum field theory. Notable areas include confinement dynamics via effective string theory, glueball spectroscopy, and topological susceptibility studies in QCD. His computational work spans lattice QCD simulations, development of reproducible open science workflows, and application of machine learning to phase transition analysis. He has contributed to understanding mass hierarchies in strongly-coupled systems and the neutron electric dipole moment using advanced simulation techniques. Key contributions include advancing effective string theory descriptions of flux tubes, analyzing Z_N gauge theories, and exploring β-function behaviors in SU(2) adjoint fermion systems. His work bridges fundamental theory with computational innovation, addressing both particle physics questions and methodological challenges in large-scale simulations.

Julia Schnabelمشاهده پروفایل

استاد

• Medical Image Processing
• Machine Learning
• Image Reconstruction
• +۴ مورد دیگر

Prof. Dr. Julia Schnabel is the TUM Liesel Beckmann Distinguished Professor and Helmholtz Distinguished Professor at TUM's TUM School of Computation, Information and Technology. Her research focuses on computational imaging and AI in medicine, including medical image processing, machine learning, motion modeling, and quantitative imaging. She holds IEEE, Ellis, and MICCAI Society fellowships, and has pioneered work in image reconstruction, artifact correction, and AI-based diagnostics. Educations: Bachelor/Master from TU Berlin (1993) PhD from University College London (1998) Postdocs at UMC Utrecht, King's College London, and UCL Her research interests span medical AI, deep learning for medical imaging, and clinical evaluation methodologies. Key contributions include frameworks for motion artifact correction in MRI, physics-informed neural networks, and benchmark datasets like NOVA for anomaly detection in brain MRI. She has authored over 100 publications, with recent work advancing unsupervised anomaly detection and federated learning in healthcare. Prof. Schnabel leads interdisciplinary projects at TUM and Helmholtz Zentrum München, focusing on AI-driven solutions for diagnostic and therapeutic challenges. Her labs develop tools for real-time cardiac imaging, histopathology segmentation, and trustworthy AI guidelines (FUTURE-AI initiative).

Michel Sannerمشاهده پروفایل

استاد

• Molecular Interactions
• Molecular Graphics
• Component-Based Software
• +۳ مورد دیگر

MICHEL SANNER is a Professor of Molecular Biology at the Department of Integrative Structural and Computational Biology at Scripps Research. He holds a PhD in Computer Science from the University of Haute Alsace, France (1992). His research focuses on computational methods for molecular interactions, molecular graphics, and component-based software development. Notable contributions include the AutoDock suite (for molecular docking), PMV (a molecular visualization environment), and Vision (a visual programming tool). His research group develops tools like AutoDock CrankPep for peptide docking and F2Dock for protein-protein interactions. These tools are widely used in drug discovery and structural biology. His work emphasizes software engineering principles to create adaptable computational pipelines for analyzing macromolecular structures and simulating interactions. Publications span topics like peptide-docking methodologies, ligand-binding site prediction, and GPU-accelerated docking algorithms. His articles highlight advancements in computational methods for understanding protein-ligand interactions, with applications in anticoagulant research and HIV/FIV protease inhibition. Collaborations include work with Arthur J. Olson and David S. Goodsell on docking methodologies.

Shaurya Agarwalمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• Intelligent Transportation Systems
• Smart Cities
• +۷ مورد دیگر

Shaurya Agarwal is an Associate Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida (UCF), where he has been a faculty member since 2018. He is the founding director of the URBANITY Lab (Urban Intelligence and Smart City Lab) and currently serves as the director of the Future City Initiative. Prior to joining UCF, he was an Assistant Professor in the Electrical and Computer Engineering Department at California State University, Los Angeles (2016–2018). Ph.D. in Electrical Engineering, University of Nevada, Las Vegas (2015) Postdoctoral Research, New York University (2016) B.Tech. in Electronics and Communication Engineering, Indian Institute of Technology (IIT), Guwahati Dr. Agarwal's research lies at the intersection of cyber-physical systems, intelligent transportation systems, and smart cities. He employs interdisciplinary methodologies integrating control theory, data-driven techniques, physics-informed machine learning, and mathematical modeling to address challenges in connected and autonomous mobility. His work emphasizes real-world applications such as traffic state estimation, signal-free intersections, and pedestrian safety using LiDAR perception. His recent publications demonstrate a strong trend in applying physics-informed deep learning and Koopman operator theory to model complex traffic dynamics. These works leverage both Lagrangian and Eulerian data frameworks and aim to improve accuracy under sparse sensor conditions. The research spans transportation, public health, and social systems, indicating a broad interdisciplinary impact. Dr. Agarwal is a senior member of IEEE and serves as an Associate Editor for IEEE Transactions on Intelligent Transportation Systems . His research has been funded by agencies including the Federal Highway Administration (FHWA), Florida Department of Transportation (FDOT), and Oculus. Senior Member, IEEE Associate Editor, IEEE Transactions on Intelligent Transportation Systems He actively mentors Ph.D. students in the Civil, Environmental, and Construction Engineering Department and leads the URBANITY Lab, a research team focused on next-generation urban mobility solutions. The lab develops real-time 3D object detection algorithms, operates a small-scale CAV test-bed, and explores hybrid approaches bridging theory, simulation, and practice.

• Bioengineering
• Systems Glycobiology
• Thrombosis & Hemostasis
• +۳ مورد دیگر

Sriram Neelamegham is the UB Distinguished Professor of Chemical & Biological Engineering, Biomedical Engineering, and Medicine at the University at Buffalo, SUNY. His research focuses on applying engineering principles to study molecular mechanisms of blood cell interactions in diseases such as inflammation, thrombosis, and cancer. He leads the Bioengineering Laboratory within the School of Engineering and Applied Sciences. Education: PhD in Chemical/Biomedical Engineering, Rice University, 1996 B.Tech in Chemical Engineering, Indian Institute of Technology Delhi, 1991 Research Interests: Systems Glycobiology: Investigating glycan biosynthesis and its role in disease Leukocyte and Platelet Adhesion Dynamics under Fluid Flow Von Willebrand Factor (VWF) Structure-Function Relationships Engineering Glycoengineered Therapeutics and Diagnostic Tools Key Contributions: Developed computational models and experimental tools for glycosylation pathway analysis Discovered mechanisms of VWF conformational changes under shear stress Pioneered glycoengineering strategies for stem cell targeting Awards & Recognition: NIH Independent Scientist Award (2015) SUNY Chancellor's Award for Excellence (2015) AIMBE Fellow (2012) and BMES Fellow (2019) 2018 Schoellkopf Medal (ACS) Lab Activities: Recruitment of postdocs, full-time, and part-time research technicians Development of glycan-engineered technologies for drug delivery and diagnostics Collaborations with biomedical industries and academic institutions

Qi Chenمشاهده پروفایل

استاد

• Remote Sensing
• LiDAR Applications
• Geographic Information Systems
• +۵ مورد دیگر

Qi Chen is a Professor in the Department of Geography at the University of Hawaii at Mānoa, specializing in remote sensing and geospatial technologies. His office is located in Saunders Hall, and he teaches undergraduate and graduate courses including GEO 370 (UAV and Aerial Photography), GEO 388 (Introduction to GIS), GEO 470 (Remote Sensing), GEO 489 (Applied GIS), and GEO 762 (Research Seminar: Remote Sensing. His research focuses on transforming earth observation data into actionable knowledge for environmental monitoring. Primary interests include: LiDAR applications for vegetation analysis and biomass estimation Climate change impacts on land cover and coastal systems Machine learning integration with geospatial data High-resolution mapping of agricultural and forest ecosystems Drone and satellite-based environmental assessment Chen's recent publications (2020-2025) demonstrate a strong focus on advancing remote sensing methodologies, particularly through: AI-driven approaches (GANs for vegetation indices, deep learning for marine debris) Multi-sensor fusion (LiDAR with camera systems, hyperspectral-multispectral integration) Novel applications in precision agriculture and infrastructure monitoring Hawaii-specific environmental studies incorporating indigenous knowledge systems He leads the Smart Remote Sensing Lab (smartremotesensing.org) where he mentors graduate students in developing cutting-edge geospatial solutions for ecological and societal challenges.

• Social Policy
• Social Work
• Poverty and Social Exclusion
• +۷ مورد دیگر

Fernanda Rodrigues is an Affiliate Professor at the Faculty of Psychology and Educational Sciences of the University of Porto (FPCEUP) and a guest lecturer at the Faculty of Human Sciences, Universidade Católica Portuguesa. She is a full researcher at the Centre for Educational Research and Intervention (CIIE), where she contributes to interdisciplinary research on social policy and education. Her academic journey includes a PhD in Social Work from the Pontifical Catholic University of São Paulo, Brazil, and degrees in Social Work and Sociology from Portuguese institutions. PhD in Social Work – Pontifical Catholic University of São Paulo, Brazil (1999) Degree in Social Work – ISSS Porto, Portugal Degree in Sociology – ISCTE, Portugal Her research interests span Social Policy, Social Exclusion, University Social Responsibility, Community Development, and Social Work . She has led and participated in numerous international projects, particularly within the Erasmus+ framework, focusing on social inclusion, lifelong learning, and intercultural conflict resolution. Her recent publications highlight a strong emphasis on the role of universities in promoting social responsibility and ethical engagement with communities. The analysis of her recent articles reveals a consistent focus on partnerships, institutional accountability, and social justice . Her work bridges academic research with practical policy development, especially in the context of European and Latin American collaborations. Themes such as poverty alleviation, educational equity, and civic engagement recur across her publications, reflecting a deep commitment to transformative social research. Although no specific scientific awards are listed, her leadership roles in national and European policy frameworks—such as serving on the Scientific Committee for poverty strategies in the Azores and as a UNICEF consultant—demonstrate significant recognition in her field. Fernanda Rodrigues has extensive experience in advising and grant-funded research, particularly through EU-funded projects like YOUNG_ADULLLT, UNIBILITY, and SOLVINC. She has supervised or collaborated with numerous researchers and students, though specific advisees are not named. Her work is closely tied to the CIIE, where she contributes to research teams focused on social inclusion and educational policy. She also plays key roles in public service, including as President of the Porto Delegation of the Portuguese Red Cross and former city councillor in Porto, integrating academic expertise with civic leadership. Her affiliated research units include the Centre for Educational Research and Intervention (CIIE) and participation in networks such as CoPIn (PCEP - Participation, Communities and Political Education). These labs and teams focus on participatory democracy, community-based education, and policy innovation.

• Structural Biology
• Molecular Biophysics
• Cryo-Electron Microscopy
• +۹ مورد دیگر

David Agard is a Professor in the Department of Biochemistry and Biophysics at the University of California San Francisco (UCSF), where he leads a research group focused on uncovering the structural basis of biological function at the molecular and cellular levels. His lab specializes in advanced cryo-electron microscopy (cryo-EM) and fluorescence light microscopy, developing novel imaging technologies to study dynamic cellular processes. His research interests span structural biology , molecular chaperone function (particularly Hsp90 and its role in disease), microtubule nucleation , centrosome and cilium structure , and the structure of phage-encoded tubulins . He is deeply involved in methodological innovations in cryo-EM data processing , including deconvolution, heterogeneous reconstruction, and tomography, enabling atomic-level insights into complex biological systems. Recent publications highlight his lab’s work on the structural mechanisms of Hsp90-client regulation, microtubule organization, phage nucleus formation, and high-resolution imaging techniques using functionalized graphene-oxide grids. His work increasingly integrates AI-driven structural modeling and in situ approaches to understand macromolecular complexes in their native cellular context. Dr. Agard has made seminal contributions to understanding the ATPase cycle of Hsp90, the architecture of the gamma-tubulin complex, and the structural dynamics of viral and cellular tubulins. His research bridges biochemistry, biophysics, and cell biology, with implications for cancer, neurodegeneration, and antimicrobial strategies.

Elif Akمشاهده پروفایل

پژوهشگر

• Digital Twin
• Smart City
• Waste-to-energy
• +۳ مورد دیگر

Elif Ak is a Researcher at Istanbul Technical University's Department of Computer Engineering, College of Engineering. Her work focuses on cutting-edge network technologies and digital twin systems. Current research in 6G communication frameworks Active in AI-enabled network management Digital twin methodology specialist Her research interests span Digital Twin , 6G Networks , and Machine Learning applications in telecommunications. Recent publications highlight advancements in backbone network security , UWB localization , and semantic communication systems. Key publication trends show 7 Scopus citations with 33 Mendeley readers, featuring collaborations with international experts in IEEE Transactions and Communications Magazine . Research outputs (21 total) demonstrate consistent annual contributions since 2019.