جست‌وجوی استاد و پژوهشگر

• Biomedical Informatics Systems
• Spatio-Temporal Data Integration
• Biomedical Ontologies
• +۵ مورد دیگر

Albert Georg Burger is a Professor in the School of Mathematical and Computer Sciences at Heriot-Watt University, United Kingdom. He also holds a part-time secondment to the MRC Human Genetics Unit in Edinburgh as a Biomedical Informatics Scientist. His research spans biomedical informatics systems, semantic web technologies, and spatio-temporal data integration. Education: BSc (1988), MSc (1991), PhD (1996) in Computer Science Key roles: Senior Lecturer (Heriot-Watt, 2004), Founder of the Biomedical Informatics Systems Engineering Lab (BISEL) His primary research focuses on distributed biomedical informatics systems , including the integration of data and computational biomedical models, particularly in spatio-temporal frameworks like the Edinburgh Mouse Atlas. He also works on biomedical ontologies, semantic web technologies, and eHealth applications, with a strong emphasis on knowledge transfer to biomedical end-users. Recent publications highlight his work on clinical data integration , 3D human reference atlas construction , and visual biomedical data analysis tools , often leveraging semantic web technologies and ontologies. Collaborative efforts include contributions to the Human BioMolecular Atlas Program (HuBMAP) and the Human Gut Cell Atlas roadmap. Scientific awards: Fulbright Scholarship Academic affiliations: Heriot-Watt University, MRC Human Genetics Unit Burger founded the Biomedical Informatics Systems Engineering Lab (BISEL) at Heriot-Watt University, leading interdisciplinary efforts to bridge computer science and biomedical research.

Patrick Keatleyمشاهده پروفایل

مدرس

• Renewable Energy
• Energy Policy
• Energy Storage
• +۵ مورد دیگر

Dr. Patrick Keatley is a Lecturer in Energy Policy and Infrastructure at Ulster University, affiliated with the Belfast School of Architecture & the Built Environment. His academic work focuses on the intersection of energy systems, policy, and infrastructure development within the Department of Architecture, Built Environment and Planning Research. His research interests center on renewable energy integration, with particular expertise in demand flexibility, energy storage systems, and smart grid technologies. Dr. Keatley's work addresses critical challenges in modern energy systems, including wind power integration, grid stability, and the development of sustainable energy infrastructure that supports the UN Sustainable Development Goals. Analysis of his recent publications reveals a strong focus on practical energy solutions, with significant work on battery storage systems in healthcare settings, spatio-temporal demand flexibility assessment, and comprehensive reviews of heating and cooling network modeling. His research demonstrates consistent publication output with emphasis on both theoretical frameworks and real-world applications of energy technologies. Dean's Award (2020) for collaborative research on wind power, electric power distribution, and energy storage Dr. Keatley has been actively involved in research supervision and has contributed to multiple funded projects. His consultancy work with organizations like the NI Department for the Economy demonstrates the practical application of his research. Current projects include AQUAFLEX (2020-2022) and DDCGE - Decarbonisation of Data Centres with Geothermal Energy (2021-2023), reflecting his commitment to addressing contemporary energy challenges through collaborative research initiatives. His work is closely associated with research groups focusing on energy system optimization, contributing to the development of next-generation energy infrastructure that balances technical feasibility with policy requirements.

Sayantee Janaمشاهده پروفایل

استادیار

• Data mining for Big Data
• Spatio-temporal Modelling
• AI/ML in Finance
• +۵ مورد دیگر

Sayantee Jana is a Full-time Assistant Professor in the Department of Mathematics at Indian Institute of Technology Hyderabad. She specializes in statistics and machine learning with applications across finance, healthcare, and clinical trials. PhD in Mathematical Statistics (2017) from McMaster University Postdoctoral Fellow at University of Toronto (2017-2018) Research Focus: Combines statistical theory with practical applications including: Big data analysis through data mining techniques Adaptive Gaussian Hermite Quadrature for spatio-temporal modeling Bayesian methods for skewed financial data Clinical trial design optimization Scientific Recognition: Recipient of prestigious Queen Elizabeth Scholarship Fields Institute Research Fellow Florence Nightingale Award winner (2020) Collaborations: Maintains active partnerships with researchers from University of Toronto, University of Ottawa, and IQVIA Germany. Offers funded opportunities for PhD candidates, postdocs, and summer interns.

• Bioengineering
• Biomaterials
• 3D Bioprinting
• +۷ مورد دیگر

Dr. Tomas Gonzalez-Fernandez is an Assistant Professor at Lehigh University's Department of Bioengineering within the Rossin College of Engineering and Applied Science. His research focuses on developing smart biomaterials and stem cell engineering strategies for musculoskeletal regeneration, integrating CRISPR technology with 3D/4D bioprinting innovations. PhD in Mechanical and Manufacturing Engineering from Trinity College Dublin Postdoctoral Fellow at UC Davis Leach Laboratory The TGF Lab specializes in three main research areas: CRISPR integration in 3D bioprinting for tissue repair, immunomodulatory materials development, and smart bioink engineering for 4D printing. Their work combines synthetic biology with advanced manufacturing to address cartilage, bone, and tendon regeneration challenges. Recent research trends show strong emphasis on CRISPR delivery systems, machine learning applications in gene editing, and immunomodulatory biomaterials for tissue repair. The lab's publications span topics from vascularized bone regeneration to machine learning-driven CRISPR target discovery. American Heart Association Postdoctoral Fellowship Columbia University Rising Star in Engineering and Health Lindau Nobel Laureate Meeting Young Scientist (2023) The lab has secured multiple research grants including NSF GRFP awards for graduate students and institutional research programs. Dr. Gonzalez-Fernandez actively participates in professional societies like BMES and TERMIS, serving as Biomanufacturing Track Chair for BMES 2023 Annual Meeting.

Ashok Dahalمشاهده پروفایل

استادیار

• Artificial Intelligence
• Geo Sciences
• Deep Learning
• +۳ مورد دیگر

Ashok Dahal is an Assistant Professor in the Department of Applied Earth Sciences, specializing in landslide hazard modeling, InSAR data analysis, and deep learning applications for geoscience. His research integrates artificial intelligence with geological data to address challenges in environmental risk assessment. Key Research Areas: Artificial Intelligence, Geo Sciences, Deep Learning, Landslide Hazard Modeling, Computer Vision, Data Dependencies. Recent work focuses on space-time landslide modeling, physics-informed neural networks, and explainable AI for susceptibility mapping. His publications highlight collaborations across Europe and Turkey, emphasizing geohazard mitigation. Scientific Awards: EscherPrijs (2021) ITC Publication Award (2023)

Alastair Lambمشاهده پروفایل

دانشیار مهمان

• Prostate Cancer
• Cancer Evolution and Genomics
• Spatial Biology
• +۵ مورد دیگر

Dr. Alastair Lamb is a Senior Clinical Lecturer at the Barts Cancer Institute , Queen Mary University of London, and a Visiting Associate Professor at the University of Oxford. He serves as an Honorary Consultant Urological Surgeon at Guy's and St Thomas' NHS Foundation Trust and leads the SPACE Research Group focused on transforming prostate cancer decision-making through spatial molecular techniques and machine learning. Affiliations: Barts Cancer Institute (Queen Mary University of London), University of Oxford, Guy's and St Thomas' NHS Trust Research Themes: Cancer Evolution, Genomics, Spatial Biology, Tumor Microenvironment His work integrates spatial transcriptomics , clonal dynamics analysis , and machine learning to address critical challenges in prostate cancer diagnostics and treatment selection. He has developed innovative approaches for capturing circulating tumor cells and analyzing tissue heterogeneity to improve clinical outcomes. Recent publications highlight advancements in spatial multiomics , clonal lethality identification , and integrating imaging with genomic data . These studies demonstrate his focus on scalable solutions for precision oncology. Scientific Awards: Cancer Research UK Advanced Clinician Scientist Fellowship (2025–2029) Cancer Research UK Clinician Scientist Fellowship (2017–2024) Dr. Lamb serves on the editorial boards of BJUI and European Urology , and is a Trustee for the UCare charity. He actively collaborates with international societies including the British Association of Urological Surgeons (BAUS) and American Urological Association (AUA) .

• Deep Learning
• Graph-based Learning
• Infrastructure Failure Prediction
• +۷ مورد دیگر

Dr. Andy Guo is a Senior Research Fellow at the Data Science Institute within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). With extensive experience in data science and machine learning applications, he collaborates closely with industry partners on critical infrastructure projects including transportation systems, water management, and financial markets. His work bridges advanced AI research with practical industry solutions. Dr. Guo received his PhD in Computer Science from the Faculty of Engineering and Information Technology at UTS between April 2012 and November 2015. Prior to his current position, he worked at several prestigious research institutions including Data61, CSIRO, and NICTA, building a strong foundation in both theoretical and applied data science. His research focuses on deep learning, graph-based learning, and infrastructure failure prediction, with particular expertise in developing solutions for railway operations optimization, water infrastructure management, and financial market analysis. Dr. Guo's work combines advanced machine learning techniques with domain-specific knowledge to address complex real-world problems across multiple sectors. His research has significant practical implications for improving infrastructure reliability and operational efficiency. Analysis of Dr. Guo's recent publications reveals a strong focus on applying graph neural networks and spatio-temporal modeling to infrastructure and transportation challenges. His work consistently demonstrates how machine learning can be deployed in real-world settings to solve complex problems in railway systems, sewer infrastructure, and financial markets. The trend shows increasing sophistication in handling imbalanced data and developing consensus-based recommendation systems that address both accuracy and fairness concerns. Best Overall Paper Award at PAKDD 2021 Sydney Trains project selected as finalist for ITS Australia National Award 2020 Dr. Guo actively supervises Masters and PhD students and serves on program committees for prestigious conferences including ICDM, IJCAI, KDD, and AAAI. His funded research portfolio includes multiple projects with iMOVE CRC, Sydney Water, Sydney Trains, Telstra, and NBN, focusing on infrastructure analytics, railway operations, and financial market analysis. These projects demonstrate his ability to secure competitive funding and translate research into practical applications. As a core member of the Data Science Institute at UTS, Dr. Guo collaborates with cross-disciplinary teams to develop innovative AI solutions for infrastructure management. His work with industry partners has led to deployed systems that improve railway punctuality, predict sewer failures, and enhance financial market monitoring, demonstrating the real-world impact of his research.

Penny Hudsonمشاهده پروفایل

مدرس ارشد

• Biomechanics
• Sport Biomechanics
• Human Sprinting Performance
• +۳ مورد دیگر

Dr Penny Hudson is a Senior Lecturer in Sport and Exercise Biomechanics at the University of Chichester, specializing in human sprinting performance and high-speed locomotion biomechanics. Her research bridges insights from cheetah biomechanics to human athletic performance, with active involvement as a trampoline gymnastics coach providing practical biomechanical support to athletes. Her educational background includes: BBSRC Funded PhD (2007-2011) from the University of London: 'The Structural and Functional Specialisations of Locomotion in the Cheetah (Acinonyx jubatus)' BSc (Hons) Bioveterinary Sciences (2004-2007) from the Royal Veterinary College Dr Hudson's research focuses on understanding the biomechanical limits of running speed through comparative analysis of cheetahs and humans. She investigates tissue mechanics, assistive device design, and movement optimization in sports contexts, particularly examining how insights from animal locomotion can enhance human sprinting performance. Her work integrates experimental biomechanics with practical athletic applications. Analysis of her 15 most recent publications (2011-2024) reveals three dominant research thrusts: (1) Sports biomechanics with emphasis on soccer kicking mechanics and energy transfer strategies; (2) Clinical biomechanics examining weight loss surgery outcomes and joint loading; and (3) Continued exploration of cheetah locomotion using advanced motion capture techniques. This trajectory shows evolution from pure animal biomechanics toward human performance applications while maintaining her foundational interest in high-speed movement. As Academic Advisor and module coordinator for Anatomy and Kinesiology (SPL103) and Tissue Mechanics and Assistive Devices (MSCBM02), she mentors students through independent projects. Her research has been supported by BBSRC funding during her PhD and DARPA funding during postdoctoral work at the Royal Veterinary College where she collaborated with Boston Dynamics on robotic design inspired by cheetah locomotion. Dr Hudson maintains active professional engagement through Society for Experimental Biology and Society for Integrative and Comparative Biology memberships, serves as Research Fellow for the Zoological Society of London, and provides journal reviewing services for Mammalian Biology. Her public outreach includes BBC News features, Science Museum exhibitions, and Channel 4's BAFTA-winning 'Inside Nature's Giants' series.

Roberto Amatoمشاهده پروفایل

پژوهشگر

• Genomic Epidemiology
• Infectious Disease Evolution
• Population Genetics
• +۵ مورد دیگر

Dr. Roberto Amato is Head of Data Analysis and Translation at the Wellcome Sanger Institute, where he leads a team focused on genomic analysis of infectious diseases. His work bridges computational biology, epidemiology, and public health to address global health challenges, particularly in malaria and pandemic response. His educational background includes: PhD in Computational Biology and Bioinformatics from the University of Naples "Federico II" (2012) Visiting student at the Department of Human Genetics, University of Chicago MSc in Computer Science BSc in Computer Science Dr. Amato specializes in understanding the evolutionary dynamics of infectious diseases through population genetics. His research blends theoretical and empirical approaches to investigate pathogen evolutionary dynamics, with particular emphasis on how human interventions influence these processes. He has made significant contributions to understanding antimalarial resistance evolution and has been instrumental in genomic surveillance during the COVID-19 pandemic through his involvement with the COG-UK consortium. His publication record demonstrates a consistent focus on malaria genomics and epidemiology, with increasing attention to SARS-CoV-2 during the pandemic. His work spans fundamental research on parasite evolution, development of genomic surveillance tools, and direct application of genomic data to inform public health interventions. Dr. Amato has held progressive research positions at the Sanger Institute since 2015, advancing from Staff Scientist to Senior Staff Scientist and ultimately to his current leadership role. Prior to this, he was a Research Associate in Statistical Genomics at the University of Oxford's Wellcome Centre for Human Genetics. He is deeply involved in major international collaborative efforts including MalariaGEN and the COG-UK consortium, where his team's analyses of genomic data using cutting-edge methodologies have directly influenced surveillance activities and intervention strategies for global health organizations.

Megan Heymanمشاهده پروفایل

دانشیار

• Nonparametric Statistics
• Time-Series Analysis
• Climate Change Modeling
• +۲ مورد دیگر

Megan Heyman is an Associate Professor in the Department of Mathematics at Rose-Hulman Institute of Technology, specializing in nonparametric statistical methods and time-series data analysis for climate change and environmental phenomena. Her educational qualifications include: PhD in Statistics from the University of Minnesota (2016) MS in Statistics from the University of Minnesota (2014) BS from Rose-Hulman Institute of Technology (2008) Dr. Heyman's research centers on bootstrapping methods for spatio-temporal data and nonparametric environmental modeling , with significant contributions to climate informatics. She develops innovative statistical frameworks for tree-ring analysis, crop yield prediction, and climate model validation, while integrating pedagogical approaches like rubber chicken demonstrations for teaching statistical inference. Her publication record reveals a consistent trajectory in applying bootstrap techniques to environmental datasets, evolving from foundational R package development (lmboot, WiSEBoot) toward interdisciplinary climate applications. Recent work emphasizes probabilistic evaluation of climate models and spatio-temporal relationships in ecological records, demonstrating strong collaboration with NASA's Jet Propulsion Laboratory. Dr. Heyman has not listed any scientific awards in her current profile. As co-director of the Independent Project/Research Opportunity Program, she mentors undergraduate researchers in statistics and environmental projects. Her NASA collaboration represents significant external research engagement, though specific grant details are not provided in the source material. She operates within environmental statistics research networks focused on climate informatics, collaborating with NASA JPL on data analysis initiatives while maintaining active statistical consulting practice.

Yongqi Dongمشاهده پروفایل

پژوهشگر

• Automated Vehicles
• Smart Mobility
• Shared Mobility
• +۵ مورد دیگر

Dr. Yongqi Dong is a Research Group Leader at RWTH Aachen University in the domain of AI and Automated Mobility, and a guest researcher at Delft University of Technology. He holds a Ph.D. in Traffic Systems Engineering from Delft University of Technology (2025), an M.Sc. in Control Science and Engineering from Tsinghua University (2017), and a B.Sc. in Telecommunication from Beijing Jiaotong University. His research centers on three pillars: Deep Learning for sensing and anomaly detection in transportation systems Deep Reinforcement Learning for vehicle control and decision-making Big Data Analytics for spatial-temporal pattern mining in smart mobility He aims to develop socially compliant automated driving systems through interdisciplinary approaches combining AI with transportation research. Dong's publications primarily focus on autonomous vehicles, intelligent transportation systems, and spatial-temporal pattern analysis. His recent work shows increasing emphasis on transformer architectures, self-supervised learning, and social compliance in mixed traffic environments. Awards and Fellowships: 2023 IEEE TAB Committee on Standards Seed Funding 2024 IEEE ITSS New Initiatives Proposal Funding 2023 Fellowships for Young Researchers (ITS) 2024 IEEE ITSC Conference Support Program China National Scholarship He leads a research group focused on AI and Automated Mobility, actively supervising graduate students and seeking collaborations. His team works on projects involving deep reinforcement learning for autonomous driving, multi-agent systems in mixed traffic, and vision-language models for transportation applications.

Horst Bischofمشاهده پروفایل

دانشیار

• Computer Vision
• Medical Imaging
• Pattern Recognition
• +۳ مورد دیگر

Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Horst Bischof is an Associate Professor at Vienna University of Technology (TU Wien). His work focuses on computer vision, medical imaging, and pattern recognition. He has contributed to projects in automated medical diagnosis (e.g., rheumatoid arthritis assessment), 3D reconstruction, and cultural heritage preservation through advanced segmentation techniques. He has supervised numerous students including S. Veigl, A. Belbachir, and G. Langs. Bischof has edited proceedings like the 2005 Computer Vision Winter Workshop and published extensively on topics like Markov Random Fields, Active Appearance Models, and Canonical Correlation Analysis. His research bridges computer science and biomedical applications, with notable contributions to anatomical structure localization and robust model learning. He collaborates with institutions like the European Society of Musculoskeletal Radiology and has developed systems such as the RheumaCoachCAD for automated scoring of bone erosions. Education: Dipl.-Ing. (Master of Engineering) and Dr.techn. (Doctor of Technical Sciences) from TU Wien. Key Projects: CAD systems for rheumatoid arthritis, 3D rock-art segmentation, and MRF-based medical image analysis. Research interests emphasize integrating computational methods into clinical diagnostics and heritage conservation. His work on sparse MRF models and group-wise learning has advanced automated medical image processing techniques.

• Bayesian Statistics
• Spatio-temporal Data Analysis
• Environmental Statistics
• +۳ مورد دیگر

Sujit Sahu is a Professor of Statistics at the University of Southampton with a focus on Bayesian modeling and spatio-temporal data analysis. He co-leads the Statistics group within the Statistical Sciences Research Institute (S3RI) , contributing to environmental and health-related statistical research. Books : Author of Bayesian modeling of spatio-temporal data with R (Chapman & Hall/CRC Press) and Introduction to Probability, Statistics & R: Foundations for Data-Based Sciences (Springer Nature, 2024). Research Themes : Applied Bayesian modeling, environmental/climate statistics, spatio-temporal uncertainty quantification, and statistical software development (R package bmstdr ). Grants : Sole investigator for EPSRC grant EP/J017485/1 (2013-2016, £365k) and key contributor to multi-institution projects like NERC grant NE/J012238/1 (2012-2015, £475k). Publication Trends : 15 most recent works show a consistent focus on Bayesian spatio-temporal methods applied to epidemiology, environmental monitoring, and public health policy. Key subfields include disease mapping, air pollution exposure assessment, oceanographic data analysis, and statistical learning algorithm development. Scientific Recognition : His 2022 Bayesian spatio-temporal textbook was honored as one of the Book Authority Best Bayesian Statistics Books of All Time .

Harry Kai-Ho Chanمشاهده پروفایل

مدرس

• Data Mining
• Data Science
• Big Data
• +۷ مورد دیگر

Dr. Harry Kai-Ho Chan is a Lecturer in Data Science at the University of Sheffield's School of Information, Journalism and Communication. He holds a BEng, MPhil, and PhD from The Hong Kong University of Science and Technology (HKUST). His research focuses on data mining, big data analytics, and spatial data management, with particular emphasis on spatio-textual data, indoor location-based services, and applying machine learning in databases. Before joining Sheffield in 2022, he was a Postdoc researcher on the "Data Management for Indoor Location-based Services" project at Roskilde University (2020-2022). He currently serves as the Interim Deputy Programme Coordinator for the BSc Data Science program and the Department Employability Lead for Data Science. His work addresses efficient query processing for spatial data, co-location pattern mining, and uncertainty-aware systems in indoor environments. He has contributed to over 15 peer-reviewed publications in top venues like IEEE TKDE and VLDB. Teaching responsibilities include courses like INF114 (Communicating Data), INF217 (Databases and beyond), and INF4000 (Data Visualization). He actively reviews for leading conferences (e.g., SIGKDD, ICDE) and journals (e.g., IEEE TKDE, GeoInformatica).

Yidan Zhangمشاهده پروفایل

پژوهشگر ارشد

• Spatial-Temporal Data Sensemaking
• Human-Computer Interaction
• Immersive Analytics
• +۵ مورد دیگر

Yidan Zhang is a Research Fellow at Monash University's Department of Human Centred Computing, specializing in spatial-temporal data analysis within immersive environments. She holds a PhD from Monash (2024), MSc in Information Technology (2019), and a BA in Arts from Jinan University (2016). Her work focuses on embodied interaction, immersive analytics, and energy visualisation, contributing to UN SDGs in education and sustainable energy. Research Interests : Spatio-Temporal Data Sensemaking Immersive Analytics Embodied Provenance Eye Tracking in Immersive Systems Energy Efficiency Visualisation Recent Research Trends : Her publications emphasize embodied interaction design, immersive data exploration tools, and energy-aware visualisation systems. Notable contributions include frameworks for tracking user interactions in immersive environments and improving user engagement in energy management. Lab/Teams : Member of the Embodied Visualisation research group, collaborating on multidisciplinary projects involving immersive technology and human-computer interaction.