جست‌وجوی استاد و پژوهشگر

Patrick Dennyمشاهده پروفایل

دانشیار

• Computer Vision
• Automotive Systems
• Medical Imaging
• +۳ مورد دیگر

Patrick Denny is an Associate Professor at the University of Limerick, affiliated with the Department of Computer Science & Information Systems, the Centre for Sustainable Digital (Re)Manufacturing, and Lero – the Research Ireland Centre for Software. His research focuses on computer vision, automotive systems, and medical imaging, with notable contributions to object detection, instance segmentation, and image processing in autonomous vehicles and healthcare. He holds patents in automotive camera systems and has authored over 38 research papers. His work addresses challenges such as rain impact on automated vehicle perception, medical image classification using graph neural networks, and optimizing camera exposure for automotive applications. He collaborates extensively on projects involving V2X communications and intelligent transportation systems. Denny’s research also extends to waste management through computer vision and medical imaging innovations. Patents: Over 10 patents in automotive imaging, including systems for calibrating image-capturing devices and thermal infrared sensors. Key Research Themes: Automotive perception, computer vision algorithms, medical image analysis, and sensor optimization. He actively engages in interdisciplinary projects, combining machine learning with real-world applications in transportation and healthcare.

Francesco Iorioمشاهده پروفایل

پژوهشگر

• Computational Biology
• Pharmacogenomics
• Cancer Biology
• +۳ مورد دیگر

Francesco Iorio is a Research Group Leader in Computational Biology at Human Technopole (Milan, Italy), leading research in computational cancer pharmacogenomics and therapeutic target discovery. He holds a PhD from the University of Salerno and TIGEM (Naples), focusing on computational methods for drug discovery. Previously, he was a Senior Bioinformatician at EBI, leading CRISPR-Cas9 screen analyses across cancer cell lines to identify synthetic lethalities and therapeutic targets. He led the Cancer Dependency Map Analytics team at Wellcome Sanger Institute, developing algorithms for integrating large-scale cancer datasets. Since 2019, he serves as a Scientific Advisor to the Cancer Research Horizon–AstraZeneca Functional Genomics Centre. His research integrates computational methods with experimental data to uncover cancer dependencies, drug mechanisms, and biomarkers. Key contributions include tools like CRISPRcleanR for bias correction in CRISPR screens, and CELLector for selecting cancer cell line models. His work spans pharmacogenomics, functional genomics, and CRISPR technologies, with a focus on translating findings into clinical applications. Publications highlight advancements in CRISPR screen analysis, cancer dependency mapping, and drug repositioning. Current projects aim to advance precision medicine through computational frameworks for understanding tumor heterogeneity and therapeutic vulnerabilities.

Glenn Harvelمشاهده پروفایل

استاد

• Diagnostic Techniques
• Energy Systems
• Nuclear Design
• +۸ مورد دیگر

Dr. Glenn Harvel is a Professor in the Department of Energy and Nuclear Engineering at Ontario Tech University, located in Oshawa, Ontario. He holds a PhD in Nuclear Engineering (McMaster University, 1995) and has over three decades of academic and research experience. His expertise spans nuclear safety, energy systems optimization, and advanced diagnostic techniques. Education: PhD in Nuclear Engineering, McMaster University (1995) Master of Engineering in Engineering Physics, McMaster University (1991) Bachelor of Engineering, McMaster University (1989) Research Focus: Dr. Harvel’s research integrates Diagnostic Techniques (neutron radiography, ultrasonics), Energy Systems (EHD-driven pollution control and heat transport), and Nuclear Design (small modular reactors and plant aging mitigation). Recent work includes supercritical fluid thermal analysis and non-destructive material evaluation. Recent Research Trends: His 2013 publications emphasize supercritical fluid dynamics and advanced reactor cooling systems. Collaborations with industry (e.g., Toyota, CSA) and organizations (NSERC, OCE) highlight applied research in sustainable energy and nuclear safety. Affiliations and Memberships: Member of Canadian Nuclear Society Member of ASME Member of IEEE-DEIS Licensed Professional Engineer (PEO) Teaching: He instructs courses in nuclear plant design, safety, thermalhydraulics, and reactor engineering. Research Infrastructure: Based at the Energy Systems and Nuclear Science Research Centre (ERC), his lab focuses on experimental and computational analysis of energy systems and nuclear components.

• Food Security
• Political Economy
• Agrarian Reform
• +۵ مورد دیگر

Dr. Hannah Wittman is a Professor at the University of British Columbia (UBC), holding dual appointments in the Institute for Resources, Environment and Sustainability (IRES) and the Faculty of Land and Food Systems . Her research focuses on food sovereignty, agrarian reform, and socio-ecological transitions. She leads projects on farmland access, agroecological practices in Latin America, and institutional procurement strategies. Wittman co-founded the LiteFarm open-source platform for sustainable agriculture and directs the Centre for Sustainable Food Systems at UBC Farm. Her work bridges academia and community action, emphasizing participatory research methods. Notable initiatives include the Indigenous Foodlands Conservation Areas project and the Agroecological Transitions Research Cluster . Wittman teaches graduate courses such as LFS 500 and contributes to sustainability education through ASIC 220 . Education: PhD in Rural Sociology (implied from academic standing) Key Projects: Farmland Access in BC Agroecology in Ecuador & Brazil Urban Agriculture in Metro Vancouver Digital Agroecology Tools (LiteFarm) Wittman’s research integrates political economy analysis with grassroots activism, advocating for equitable food systems that center Indigenous rights and smallholder farmers. Her interdisciplinary approach spans policy, technology, and community-based solutions.

• Synthetic Biology
• Chemical Engineering
• Biomedical Engineering
• +۹ مورد دیگر

Dr. Francesca Ceroni is a Senior Lecturer in the Department of Chemical Engineering at Imperial College London, part of the Faculty of Engineering. She holds affiliations with the Centre for Synthetic Biology, the CRUK Convergence Science Centre, and the UKRI Artificial Intelligence for Engineering Biology Consortium, among others. Her research focuses on synthetic biology, host-aware cell engineering, and the integration of computational biology with AI for synthetic design. She has developed tools to mitigate cellular burden in engineered systems and pioneered work on synthetic cells for therapeutic applications. Dr. Ceroni earned her BSc, MRes, and PhD in Bioengineering from the University of Bologna. Her postdoctoral training included a Visiting Research Fellowship at Harvard Medical School and roles at Imperial College London, where she became a Junior Research Fellow in 2016. Her work bridges engineering principles with biological systems, emphasizing ethical and biosecurity considerations in synthetic biology. Her research spans six core areas: host-aware engineering of mammalian and bacterial cells, computational biology/AI-driven design, engineered synthetic cells, gene expression control, and biosecurity governance. Key contributions include identifying resource competition in engineered cells and developing non-invasive assays for cell growth characterization. Her team collaborates with industry partners like AZ and Bayer to translate findings into biomedical applications. Education: BSc in Pharmaceutical Biotechnologies, University of Bologna (2001–2004) MRes in Bioengineering, University of Bologna (2004–2007) PhD in Bioengineering, University of Bologna (2008–2011) Dr. Ceroni has been recognized with the 2016 Junior Research Fellowship from Imperial College London. Her work is published in high-impact journals like Nature Communications , Nature Reviews Bioengineering , and Science , with a focus on advancing synthetic biology’s technical and ethical dimensions.

Lucia Bandieraمشاهده پروفایل

پژوهشگر ارشد

• Systems Biology
• Synthetic Biology
• Bioengineering
• +۳ مورد دیگر

Dr. Lucia Bandiera is a Chancellor’s Fellow in Engineering Biology at the University of Edinburgh’s School of Engineering. Her research focuses on cybergenetic platforms to model and control biological systems, with applications in precision medicine and synthetic biology. She holds a PhD in Bioengineering from the University of Bologna (2013–2016) and has held postdoctoral positions at the University of Edinburgh’s Centre for Synthetic and Systems Biology. Key roles include Deputy Head of Graduate School (Postgraduate Experience) and Deputy Biological Safety Officer. Her academic qualifications include a BSc and MSc in Biomedical Engineering from the University of Bologna (2007–2012). She is an Associate Fellow of the Higher Education Academy (AFHEA, 2020) and has received prestigious funding such as the EPSRC Postdoctoral Fellowship (2017) and a tenure-track fellowship (2021). Her teaching includes courses on Lab-on-Chip Technologies and Engineering Principles. Research interests include mathematical modeling, optimal experimental design, and microfluidics. She leads projects like CONDSYC (EPSRC-funded), focusing on synthetic biology systems design. Her work spans biomolecular systems, cyber-physical platforms, and applications in liver disease and pandemic mitigation. Notable achievements include organizing the Institute for Bioengineering’s seminar series and contributing to datasets like Face coverings and respiratory tract droplet dispersion . Her lab develops tools for quantitative analysis of biological networks and precision medicine.

• Dairy Science
• Pasture Management
• Animal Nutrition
• +۳ مورد دیگر

Professor Sergio Garcia (Yani) is a globally recognized expert in dairy science at the University of Sydney. He holds the title of Professor of Dairy Science and leads key roles including Director of the Dairy Research Foundation and Group Leader of the Dairy Science Group. His affiliations include membership in the Sydney Environment Institute, Sydney Institute of Agriculture, and Charles Perkins Centre. He earned his Agr. Eng (B.Ag.Sc) from Universidad de Mar del Plata, Argentina, and a PhD from Massey University, New Zealand. Research focuses on pasture-based dairy systems, including grazing management, forage utilization, crop-pasture integration, and automation in dairy farming. He supervises the FutureDairy Project and explores innovations like robotic milking and remote sensing technologies. His work addresses sustainability challenges such as reducing greenhouse gas emissions and optimizing feed efficiency. Recent grants include projects on dairy business resilience post-bushfires and climate-adaptive farming strategies. He has authored over 100 publications, with recent topics covering methane emissions monitoring, Napier grass applications, and automated feeding systems. Garcia’s labs and teams collaborate on platforms like FutureDairy and DairyUp, advancing precision agriculture and livestock systems.

• Bayesian statistics
• Hierarchical modeling
• Image analysis for super-resolution microscopy
• +۴ مورد دیگر

Dr. Miguel Juarez is a Lecturer in Statistics at the University of Sheffield's School of Mathematical and Physical Sciences. He holds a PhD in Mathematical Sciences from Universidad de Valencia (2004), an MSc in Economics from CIDE (Mexico), and a BSc in Actuarial Sciences from ITAM (Mexico). His research focuses on Bayesian hierarchical modeling for panel/longitudinal data with applications in biology, medicine, and econometrics. Key projects include the STriTuVaD initiative for integrating computer simulations with clinical trials and developing models for super-resolution microscopy image analysis. He has contributed to advancing in silico trial methodologies through the UISS-TB simulator and works on objective Bayesian methods for non-Gaussian data. Professional activities include teaching MAS2010 Statistical Inference and Modelling. His recent work emphasizes accelerating tuberculosis vaccine development via augmented clinical trials and establishing credibility frameworks for in silico trials. He collaborates with interdisciplinary teams in systems biology and biomedical informatics. Research outputs span Bayesian statistical theory, computational epidemiology, and medical technology innovation. Notable collaborations include the Warwick Systems Biology Centre and EU-funded H2020 projects.

Amir Ghalamzanمشاهده پروفایل

دانشیار

• Robot Learning
• Robot Grasping and Manipulation
• Teleoperation
• +۳ مورد دیگر

Amir Ghalamzan is an Associate Professor at the University of Surrey's School of Computer Science and Electronic Engineering, leading the Intelligent Manipulation Lab. His research focuses on robot learning, robotic grasping, teleoperation, and agri-food robotics. Previously, he held roles at the University of Lincoln and University of Birmingham. Education: PhD in Robot Learning (Politecnico di Milano, 2015), M.Sc. in Mechanical Engineering (Iran University of Science and Technology, 2009). Research Interests: He addresses real-world challenges like labor shortages in agriculture through robots for extreme conditions, such as strawberry picking. His work integrates advanced perception, AI, and control systems. Notable projects include developing autonomous strawberry-picking robots and haptic-guided teleoperation systems. Key Projects: Led the £310K CERES Agtech project (2020-2021) for autonomous strawberry robots, and contributed to the £1.5M Agri Open-Core project (2023-2027) for open-source harvesting software. Collaborated with institutions like Imperial College London and companies like Saga Robotics. Labs/Teams: Directs the Intelligent Manipulation Lab, focusing on tactile sensors (e.g., AST Skin) and soft robotics innovations.

• Image and Video Processing
• Multimedia Analysis
• Computer Vision
• +۵ مورد دیگر

Prof. Tan Yap Peng is a Professor and Chair of the School of Electrical & Electronic Engineering at Nanyang Technological University (NTU), Singapore. He holds the President's Chair in Electrical and Electronic Engineering and serves as Associate Vice President (Lifelong Learning – Postgraduate Programmes by Coursework). His research focuses on multimedia analysis, computer vision, machine learning, and data analytics. He earned his B.S. from National Taiwan University and M.A./Ph.D. from Princeton University. He has led major initiatives including the INFINITUS Infocomm Research Centre and contributed to IEEE technical committees. His over 200 publications span image/video processing, neural network robustness, and cross-modal systems. Awards include IEEE Fellow status. Education: B.S. Electrical Engineering (NTU), M.A./Ph.D. (Princeton) Research interests emphasize interactive digital media, content-based analysis, and AI-driven solutions for visual and signal processing. His work addresses challenges in adversarial attacks, video generation, and low-light image enhancement. He has held editorial roles at IEEE Transactions and EURASIP journals. Conference leadership includes chairs for ICME and ICIP. His contributions bridge academia and industry through collaborative research networks.

Young Leeمشاهده پروفایل

مدرس ارشد

• Edge Computing
• Blockchain Technology
• Fog Computing
• +۳ مورد دیگر

Young Lee is a Senior Lecturer in Computing at Macquarie University, affiliated with the School of Computing and three research centers: Data Horizons Research Centre, Future Communications Research Centre, and Smart Green Cities Research Centre. His research focuses on edge computing, blockchain technology, fog computing, and resource scheduling in distributed systems, with a strong emphasis on applications in IoT, healthcare, and sustainability. He has led or contributed to over 150 research outputs since 2005, including influential works on edge-based video analytics, blockchain frameworks, and cloud-edge resource optimization. Dr. Lee's projects include 'Extreme-Scale Computing for Big Data Analytics' (2016–present) and 'Synergising the Power of the Cloud with the Power of the Crowd' (2016–present), demonstrating expertise in scalable computing and cloud-crowd integration. His work bridges theoretical advancements in scheduling algorithms with practical implementations in energy-efficient data centers and mobile edge environments. Research interests span edge computing architectures, blockchain oracles, and fog caching strategies, with recent contributions to carbon-conscious travel systems and malware detection frameworks. His interdisciplinary approach addresses challenges in resource allocation, network security, and distributed system efficiency.

Qiongkai Xuمشاهده پروفایل

مدرس

• Natural Language Processing
• Machine Learning
• Security
• +۳ مورد دیگر

Dr. Qiongkai Xu is a Lecturer in Natural Language Processing (NLP) at Macquarie University's School of Computing, with an honorary fellowship at the University of Melbourne. He holds a PhD in NLP from the Australian National University (ANU). His research focuses on auditing machine learning models, particularly addressing privacy/security issues in NLP/ML models and developing evaluation frameworks. Key areas include text watermarking, adversarial attacks, data leakage mitigation, and model authentication. Recent publications emphasize robustness against backdoor attacks, generative model watermarking, and healthcare applications of LLMs. His work bridges theoretical security advancements with practical NLP challenges. Awards: DAAD Scholarship (2022), Research Pitching Session Winner (MQ, 2022 & 2024) Grants: Leading the Climate Litigation Risk project on AI-driven greenwashing detection. Labs/Teams: Data Horizons Research Centre, Future Communications Research Centre, and Frontier AI Research Centre.

• Empirical Finance
• Econometrics
• Financial Markets
• +۳ مورد دیگر

Erik Hjalmarsson is a Professor at the University of Gothenburg , affiliated with the Centre for Finance . His research lies at the intersection of empirical finance and econometrics, with a strong focus on return predictability, algorithmic trading, and market microstructure. He has published extensively in top-tier journals such as the Journal of Finance and Journal of Financial and Quantitative Analysis . Research Interests: Return predictability using long-horizon regressions Algorithmic trading and its impact on market volatility and liquidity Pairs trading and co-movement in equity prices Portfolio choice under skewness and long-run return dynamics Tax policy effects on investor behavior and capital gains His recent work includes the widely cited paper "Nonstandard Errors" (2024), co-authored with over 100 researchers, which investigates the reliability of standard errors in multi-analyst studies. Another notable publication, "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market" (2015), explores the role of algorithmic trading in FX markets. Scientific Impact: His SSRN profile shows over 31,000 total citations and a top 2,151 ranking in total downloads, reflecting significant academic influence.

• Statistical Learning
• Game Theory
• Decision-Making Algorithms
• +۶ مورد دیگر

David Leslie is a Professor of Statistics and Director of Engagement in the Department of Mathematics and Statistics at Lancaster University. He specializes in statistical learning, decision-making algorithms, and game theory, with applications in real-time website optimization through bandit algorithms. Previously, he served as a Senior Lecturer at the University of Bristol and co-directed a cross-disciplinary decision-making research group. He has led significant projects such as the EPSRC/NERC-funded DSNE initiative and contributed to strategic partnerships like ALADDIN (with BAE Systems and EPSRC) and NG-CDI (with BT). His work emphasizes bridging theoretical research with practical industry solutions. Education details are not explicitly provided, but his career trajectory includes prior roles at prestigious institutions. Research interests focus on statistical methodologies, AI-driven decision systems, and environmental data science. He advises multiple PhD students in areas like Statistical AI and Extreme Value Theory. Notable projects include AI Hub, NABS+, and ProbAI, reflecting his engagement with cutting-edge technologies and interdisciplinary collaboration. David actively participates in research groups such as STOR-i Centre for Doctoral Training and the Statistical Artificial Intelligence group. His contact information includes an office at B73 in the PSC building and a direct email address. No specific scientific awards are listed, but his extensive project leadership and contributions to foundational AI research highlight his academic impact.

Carlos José Díaz Basoمشاهده پروفایل

پژوهشگر ارشد

• Solar Physics
• Bayesian Statistics
• Deep Learning
• +۳ مورد دیگر

Carlos José Díaz Baso is a Research Fellow at the Rosseland Centre for Solar Physics (RoCS), part of the Institute of Theoretical Astrophysics at the University of Oslo. His research focuses on solar chromospheric phenomena, Bayesian statistics, and deep learning applications in solar physics. Education: Ph.D. in Astrophysics (2014-2018, Universidad de La Laguna, Spain), followed by postdoctoral positions at Stockholm's Institute for Solar Physics (2018–2022) and currently at RoCS (2022–present). Research emphasizes analyzing solar spectra and magnetic field dynamics using advanced statistical and machine learning techniques. Key projects include the ISSRESS initiative studying small-scale solar reconnection events. Recent publications explore spectral resolution impacts, coronal oscillations, and sunspot light bridges. Active in international collaborations using instruments like SST/CRISP and SolO/EUI. Engaged in developing observational strategies for solar telescopes and improving data analysis methodologies.