جستجوی استاد راهنمای دانشگاهی

Patrick Doncasterمشاهده پروفایل

استاد

• Population ecology
• Conservation biology
• Forest ecosystems
• +۷ مورد دیگر

Professor Patrick Doncaster is an academic faculty member at the School of Biological Sciences, University of Southampton, within the Faculty of Environmental and Life Sciences. His research spans population ecology, conservation management of forest and freshwater lake ecosystems, and development of conservation technology and data analysis tools. BSc in Environmental Sciences (1st-class) from University of East Anglia D.Phil. in Zoology from University of Oxford Research interests include: Population ecology Forest biodiversity Human-predator-shared prey interactions Acoustic ecology Open-source conservation technology Early warning signals for ecosystem tipping points Climate change mitigation mechanisms Robust evidence-based analysis methods His publications from 2023-2025 demonstrate expertise in statistical ecology, conservation technology, and ecosystem dynamics across terrestrial, freshwater, and marine environments. He develops tools for ANOVA analysis, radio-tracking data, and evidence-based conservation. Academic contributions include: Active projects on lake ecosystem recovery pathways Completed NERC and PTES-funded field studies Methods development for statistical power and ecological modeling Interdisciplinary collaborations across environmental science and technology Teaching materials on statistics and evolutionary genetics Leadership in the Ecology and Evolution Centre and Institute for Life Sciences He supervises PhD students and maintains active research groups focused on ecological theory and conservation practice.

Reza Arghaمشاهده پروفایل

مدرس

• Biomedical Engineering
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Reza Argha is a Lecturer at the Graduate School of Biomedical Engineering, UNSW, Sydney. His research focuses on AI-driven biomedical signal processing, machine learning, and digital health. He holds a PhD from UTS (2017), with earlier degrees from Shiraz University, Iran. His work includes developing algorithms for fall detection, blood pressure estimation, and ECG-based cardiovascular diagnostics. Education: BSc and MSc in Electrical Engineering, Shiraz University (Iran) PhD in Biomedical Engineering, University of Technology Sydney (2017) Research Interests: AI in healthcare, biomedical signal/image processing, telehealth systems, fall detection using mmWave radar, non-invasive blood pressure monitoring, and arrhythmia detection via ECG. Grants & Awards: ARC Research Hub for Connected Sensors for Health ($5M) ARC Industrial Transformation Research Hubs Multiple CVMM Collaborative Grants (2023-2024) Awarded 2017 UTS Chancellor’s Best PhD Thesis UTS FEIT Research Publication Awards (2014-2015) Teaching & Supervision: Teaches Biological Signal Analysis (BIOM9621) and Engineering Projects (ENGG2600). Supervises 6 current PhD students, with 1 successful completion. Seeks new students in digital health, AI clinical decision support, and radar-based fall prediction. Labs & Projects: Leads projects in mmWave radar for unobtrusive elderly monitoring, AI for blood pressure estimation, and deep learning for medical image analysis. Collaborates with industry partners on translational health tech solutions.

• Explainable Artificial Intelligence
• Type-2 Fuzzy Logic Systems
• Computational Intelligence
• +۱۲ مورد دیگر

Hani Hagras is a Professor in the School of Computer Science and Electronic Engineering at the University of Essex, UK , where he leads pioneering research in type-2 fuzzy logic systems and explainable artificial intelligence (XAI) . His work bridges theoretical advances with real-world applications in smart infrastructure, healthcare, telecommunications, and ambient intelligence. His research interests include explainable AI, type-2 fuzzy logic, computational intelligence, machine learning, human-computer interaction, and intelligent systems. He investigates how fuzzy systems can enhance the transparency, robustness, and adaptability of AI models, particularly in uncertain and dynamic environments. His recent work explores applications in genomics, predictive maintenance, and smart grid optimization. The publication trends show a strong focus on integrating fuzzy logic with deep learning and optimization techniques to create interpretable models. His work frequently appears in top-tier journals such as IEEE Transactions on Fuzzy Systems , IEEE Transactions on Artificial Intelligence , and Neurocomputing . He emphasizes real-world applicability, often collaborating with industry partners like BT. ORCID: 0000-0002-2818-5292 He has advised numerous PhD and Master’s students, including Mehrin Kiani, Ashish Bhatia, and Hugo Leon-Garza, many of whose research projects focus on workforce optimization, healthcare AI, and intelligent systems. His research has been supported by major grants, particularly in smart environments and telecommunications. Hani Hagras is actively involved in leading research labs and teams at the University of Essex, including work on augmented reality for field service support, georeferenced data visualization , and ambient-assisted living systems. His team develops fuzzy logic frameworks for real-time decision-making in complex environments.

Emilija Perkovicمشاهده پروفایل

استادیار

• Causal Inference
• Graphical Models
• Probabilistic Graphical Models
• +۳ مورد دیگر

Emilija Perkovic serves as the Dorothy Gilford Early Career Endowed Professor in Mathematical Statistics within the Department of Statistics at the University of Washington. She joined the department in Autumn 2018 as an Acting Assistant Professor and advanced to a tenure-track Assistant Professor position in Autumn 2020, establishing herself as a core faculty member in statistical methodology. Her academic foundation includes: B.Sc. in Mathematics from the University of Belgrade (2012) M.Sc. in Statistics from ETH Zürich (2014) Ph.D. in Statistics from ETH Zürich (2018) under Professor Marloes Maathuis Perkovic's research pioneers causal inference methodologies through probabilistic graphical models, focusing on intuitive frameworks for causal identification and estimation from observational data. Her work bridges theoretical statistics with practical applications, particularly in covariate adjustment techniques and Markov equivalence class analysis. She actively integrates expert knowledge to refine causal models, making complex inference accessible for real-world data challenges where experimental intervention is impossible. Her publication record reveals a concentrated evolution in causal graph theory, with recent preprints advancing adjustment criteria for diverse graph structures (MPDAGs, MAGs, ancestral graphs). Key trends include developing sound/complete identification rules, efficient estimation techniques under linearity, and minimal effect enumeration in equivalence classes—collectively enhancing robustness in observational causal analysis. Her scientific recognition includes: Best Poster Award, Semantic Statistics (SEMSTAT), Statistical Network Science Workshop (2017) While specific advising details and grant histories remain unreported in source materials, Perkovic's research trajectory indicates active mentorship through co-authored preprints with junior collaborators like Sara LaPlante and F. Richard Guo. Her Dorothy Gilford Endowed Professorship likely supports ongoing methodological innovation in causal statistics. No laboratory or team structures are explicitly referenced in her professional profile, suggesting independent or collaborator-driven research within the Statistics Department framework.

Gita Alaghbandمشاهده پروفایل

استاد

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Professor Gita Alaghband is Chair and PhD Director at the Department of Computer Science and Engineering, University of Colorado Denver. Her work bridges high-performance computing with AI applications in computer vision, facial recognition, and deep learning optimization. Current research focuses on real-time multi-human tracking for autonomous systems Develops explainable AI solutions for medical and financial domains Leads the Parallel Distributed Systems (PDS) Lab Key publication trends include: 2025: Medical imaging AI for pediatric trauma analysis 2024: Financial forecasting with causal econometrics and adversarial defense systems 2023: Trajectory prediction models and HEVC compression frameworks Scientific recognition: IRC Best Paper Award (2020) for facial recognition research Students advised include industry leaders at Google, VMWare, and Nissan, with research spanning parallel computing, medical imaging AI, and autonomous systems.

Thanassis Tiropanisمشاهده پروفایل

استاد

• Decentralised information systems
• Decentralised information retrieval
• Data observatories
• +۷ مورد دیگر

Thanassis Tiropanis is a Professor at the University of Southampton's School of Electronics and Computer Science (ECS), specifically within the Web and Internet Science Group. He serves as enterprise champion for ECS and impact champion for computer science. His academic affiliations extend to the Institute for Life Sciences and the Southampton Marine and Maritime Institute, reflecting his interdisciplinary research approach. Professor Tiropanis specializes in decentralised information systems and infrastructures, with research interests spanning decentralised information retrieval, data and web observatories, bias in datasets, inaccessible algorithms, and IoT analytics. His work focuses on the technical and socio-technical aspects of the Web and Internet as evolving socio-technical artifacts, particularly examining how individuals can maintain control over their personal data through decentralised architectures. His recent publications reveal a strong research trajectory in decentralised search technologies, particularly through the ESPRESSO framework for personal online datastores. The publications demonstrate expertise in federated learning, privacy-preserving techniques, and bias detection in machine learning systems. His work bridges theoretical computer science with practical applications in health data, cybersecurity, and social linked data environments. Fellow of BCS Chartered IT professional with BCS Senior member of IEEE Fellow of the Higher Education academy in the UK Member of ACM Member of the Technical Chamber of Greece Professor Tiropanis currently supervises PhD students including Engin Kocak and Wenxuan Huang, while leading active research projects such as ESPRESSO (EPSRC-funded) focused on efficient search over personal repositories. His completed projects include significant EU-funded initiatives like NoBIAS (Artificial Intelligence without Bias) and IoT Lab. He is affiliated with multiple research centers including the Centre for Internet of Things and Pervasive Systems, Centre for Machine Intelligence, and Centre for Digital Finance.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +۳ مورد دیگر

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

Hrushikesh Mhaskarمشاهده پروفایل

استاد پژوهش

• Approximation Theory
• Computational Harmonic Analysis
• Machine Learning
• +۳ مورد دیگر

Hrushikesh Mhaskar is a Research Professor of Mathematics at Claremont Graduate University (CGU) since 2012, with a prior 32-year tenure at California State University, Los Angeles. He holds a PhD in Mathematics from Ohio State University, alongside an MS in Computer Science and an MSc from the Indian Institute of Technology, Mumbai. His research focuses on approximation theory, computational harmonic analysis, machine learning, and signal processing, with significant contributions to neural network theory and kernel-based methods. Mhaskar has authored over 150 papers, two books, and five edited volumes. His work includes pioneering studies on weighted polynomial approximation, Fourier domain conversions, and manifold learning. He currently serves on editorial boards for journals like Applied and Computational Harmonic Analysis and Journal of Approximation Theory , and collaborates with institutions like the University of California, Santa Barbara. Awards include five Alexander von Humboldt Fellowships and a John von Neumann Distinguished Professorship. His research is supported by the NSF and previously by the U.S. Air Force and intelligence agencies. Notable contributions include developing eignets for function approximation on manifolds and analyzing deep vs. shallow networks' approximation capabilities. His work bridges theoretical mathematics with practical applications in biomedical data analysis (e.g., blood glucose prediction) and signal processing.

Ehsan Mobarakiمشاهده پروفایل

پژوهشگر ارشد

• Graph Neural Networks
• Explainability Methods
• Data Analytics
• +۴ مورد دیگر

Ehsan Mobaraki is a Research Fellow at the Department of Computer Science within Aalborg University 's Technical Faculty of IT and Design. His work focuses on Graph Neural Networks (GNNs) , Explainable AI (XAI) , and Data Analytics , with particular emphasis on uncertainty quantification and interpretability in machine learning systems. Research Trends: Recent publications highlight his contributions to reducing uncertainty in GNNs, developing interpretability frameworks for deep learning, and advancing data management techniques. His work spans theoretical and applied aspects of Machine Learning and Neural Networks , with applications in network analysis and subgraph modeling. Collaborations: Active collaborations with researchers like Arif Khan , Francois Bonchi , and Ylli Velaj demonstrate his engagement with the broader data science community. His research has been presented at prominent venues such as DSAA and GRADES-NDA . Labs & Teams: Affiliated with the Data Engineering, Science and Systems group at Aalborg University, Mobaraki contributes to cutting-edge research in AI and data science, focusing on enhancing transparency and robustness in learning systems.

Luigi Palopoliمشاهده پروفایل

استاد

• Hybrid Systems
• Real-Time Scheduling
• Quality of Service Control
• +۱۲ مورد دیگر

Luigi Palopoli is a Full Professor and Director at the Department of Information Engineering and Computer Science (DISI), University of Trento, Italy. He is also a member of the University Council. His research spans hybrid systems, real-time scheduling, quality of service control, robotics, embedded systems, and cyber-physical systems. His research interests include: Hybrid Systems and Real-Time Scheduling Service and Assistive Robotics for Elderly Support AI Applications in Robotics and Embedded Systems Outlier Detection and Anomaly Explanation Audio Super-Resolution and Signal Processing Biological Network Analysis and Graph Alignment His recent publications reflect a strong interdisciplinary focus, combining AI, robotics, real-time systems, and bioinformatics. Trends show increasing work in explainable AI, assistive technologies, and multimodal learning (e.g., vision transformers for audio). He has made notable contributions to robotic navigation for the elderly, real-time embedded systems, and logic-based outlier detection. Scientific awards include: Outstanding Paper Award at RTAS 2017 He advises students and collaborates widely, especially with Daniele Fontanelli, Luca Abeni, and other researchers in robotics and AI. He maintains an active research group, evidenced by a dedicated YouTube channel for the Embedded Intelligence and Robotic Systems group. He is involved in teaching, including courses on Robot Planning and Real-Time Operating Systems, and participates in AI seminar series. There is no indication of retirement or former status; he is actively engaged in research, teaching, and academic service.

• Natural Language Processing
• Computational Linguistics
• Multilingual Embeddings
• +۵ مورد دیگر

Simon Clematide is a Professor in the Department of Computational Linguistics within the Faculty of Arts and Social Sciences at the University of Zurich. With a publication record spanning over two decades from 2001 to 2025, he has established himself as a leading researcher in computational linguistics, particularly focusing on multilingual natural language processing, historical document analysis, and OCR applications. His research interests encompass Natural Language Processing, Computational Linguistics, Multilingual Embeddings, Historical Document Analysis, OCR Processing, and Text Normalization. Clematide's work demonstrates a strong emphasis on practical applications of NLP in digital humanities, with significant contributions to processing historical texts and multilingual resources. His recent work has increasingly incorporated large language models and transformer architectures for tasks ranging from text embedding evaluation to historical document processing. Clematide's publication trends show a consistent focus on multilingual NLP with particular expertise in Germanic languages and historical text processing. His work bridges theoretical linguistics with practical computational approaches, often addressing challenges in low-resource language scenarios and historical document digitization. Recent publications demonstrate increasing integration of LLMs into traditional NLP pipelines for tasks like paraphrase detection, job advertisement analysis, and historical text normalization. His collaborative work includes significant contributions to shared tasks and benchmarks in the NLP community, particularly through the HIPE (Historical Information Processing Evaluation) shared tasks. These efforts have helped establish standardized evaluation frameworks for named entity recognition in historical documents. Clematide has been actively involved in several major research projects related to historical newspaper content mining, including the impresso project which focused on cross-lingual historical document processing. His work often involves interdisciplinary collaboration between computer scientists, linguists, and historians.

Minge Xieمشاهده پروفایل

استاد

• Statistical Inference
• Data Science Foundations
• Meta-Analysis
• +۶ مورد دیگر

Minge Xie is a Distinguished Professor and Director of the Office of Statistical Consulting at Rutgers, The State University of New Jersey . His research focuses on foundational aspects of statistical inference, data science, and interdisciplinary applications. He serves as a leader in the field of statistics, contributing to methodologies in meta-analysis, reproducibility, and computational inference. Education : Ph.D. from the University of Illinois at Urbana-Champaign Xie’s research spans statistical theory, computational methods, and practical applications in data science. He has pioneered approaches in confidence distributions , fusion learning , and high-dimensional modeling , with notable work on zero-inflated data, conformal prediction, and reproducibility frameworks. His recent publications emphasize the integration of Bayesian, frequentist, and fiducial paradigms, advancements in meta-analysis for rare events, and scalable methods for big data analysis. Xie’s work bridges theoretical rigor with real-world applications, including microbiome studies, nuclear detection, and maritime threat modeling. Scientific Awards : ASA Fellow IMS Fellow ISI Fellow Xie actively contributes to statistical education and interdisciplinary collaboration, leading the Office of Statistical Consulting at Rutgers. His methodological innovations continue to influence fields requiring robust and interpretable statistical inference.

• Computational Linguistics
• Natural Language Processing
• Artificial Intelligence
• +۸ مورد دیگر

Bing Qin is a Researcher specializing in computational linguistics, artificial intelligence, and multimodal learning. Their work focuses on enhancing large language models' capabilities in temporal knowledge graph forecasting, cross-lingual alignment, and safety mechanisms. Core Research Areas: Knowledge graphs, multimodal systems, reasoning frameworks Technical Innovations: Analogical replay, gain signal estimation, cross-modal attention intervention Recent Trends: 2025 publications emphasize training-free methods and preference alignment in LLMs

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +۷ مورد دیگر

Jing Jiang is a prominent researcher at Singapore Management University, specializing in Natural Language Processing (NLP), Computational Linguistics, and Artificial Intelligence. His work spans diverse areas including Vision-Language Models, Machine Translation, Knowledge Graph Reasoning, Sentiment Analysis, and Social Media Discourse Modeling. Key contributions include frameworks for consistent client simulation in mental health counseling and counterfactual contrastive prefix-tuning for many-class classification. He has pioneered methods in zero-shot VQA with interpretable reasoning graphs , cross-lingual understanding with universal syntax , and modularized zero-shot architectures . His research often combines theoretical insights with practical implementations, as seen in works on stereotypical bias in vision-language models (VLStereoSet), tensorized self-attention for dependency modeling, and collaborative relation-augmented attention for knowledge graph completion. Jing Jiang's collaborations span global experts in NLP and AI, with co-authors from institutions like SMU, Waseda University, and Microsoft Research.

• Data Mining
• Social Network Analysis
• Machine Learning
• +۳ مورد دیگر

Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.