جست‌وجوی استاد و پژوهشگر

Claudia Califanoمشاهده پروفایل

دانشیار

• Nonlinear Systems Analysis
• Time-Delay Systems
• Digital Control
• +۵ مورد دیگر

Claudia Califano is an Associate Professor at the Department of Computer, Control and Management Engineering, University of Rome "La Sapienza". Her work focuses on nonlinear systems, time-delay systems, and digital control with applications in biomedical systems and multi-agent networks. She has held editorial roles at the IMA Mathematical Control and Information Journal and IFAC World Congress, and served as Vice Chair for Education in IFAC Technical Committee Nonlinear Systems. B.Sc. and Ph.D. in Systems Engineering from University of Rome "La Sapienza" Research Interests: Her research spans geometric control theory for nonlinear systems, dynamic feedback linearization, and time-delay systems. Recent work includes deep learning applications for non-invasive medical diagnostics and consensus protocols for multi-agent systems with communication delays. Scientific Contributions: Her publications emphasize geometric methods for nonlinear control, integrability conditions, and observer design. Key themes include accessibility-observability duality, canonical forms, and applications to diabetic gastroparesis modeling. Best Paper Presentation Award at ACC 2010 Finalist for Best Poster at IFAC'96 Senior Member of Control Systems Society Editorial board member for IEEE and IFAC Teaching Roles: • Teaching Coordinator of Master's Degree in Emergency Engineering (2001-2013) • Courses taught: Systems Theory, Fundamentals of Automatic Control, Digital Control (in English)

• Geodesy
• Geographic Information Systems (GIS)
• Spatial Modeling
• +۷ مورد دیگر

Rizwan Bulbul is a researcher at the Institute of Geodesy , Graz University of Technology (TU Graz), Austria. His work bridges geodesy, geographic information systems (GIS), and computational modeling. Research Interests : Bulbul's research focuses on geospatial modeling, artificial intelligence integration, and sustainable urban development. Key areas include smart tourism simulations, energy transition policy analysis, and off-road robotic navigation using machine learning. His work also addresses forest fire prediction uncertainty, vertical photovoltaic potential, and cattle tracking in alpine environments. Publications : His research spans spatial optimization, 3D city modeling, and semantic routing. Recent projects involve leveraging AI for tourism simulations and energy policy evaluation, demonstrating interdisciplinary applications of geospatial technologies. Contact : Email: bulbul@tugraz.at Office: TU Graz, Steyrergasse 30/I, Room ST01122

• Algorithms on strings
• Formal language theory
• Bioinformatics
• +۳ مورد دیگر

Chiara Epifanio is a researcher (INFO-01/A) at the University of Palermo , based in the Department of Mathematics and Computer Science . She holds regular office hours on Tuesdays 14:30–17:00 in Room 104, first floor, Via Archirafi 34, and can be reached at chiara.epifanio@unipa.it . Over the past fifteen years she has designed and taught a diverse portfolio of courses spanning Programming , Bioinformatics , Advanced Programming , Teaching Methodologies and Techniques for Computer Science and Pattern Discovery for Life Sciences . These offerings serve degree programmes in Mathematics, Computer Science, Statistics & Data Science, and the recently launched curricula in Data, Algorithms & Machine Intelligence and Computer Science & Artificial Intelligence . Her research lies at the intersection of combinatorics on words , string algorithms , and bioinformatics . She has made sustained contributions to the design of alignment-free distances for biological data, the theory of Sturmian words and their associated graphs, suffix automata tolerant to mismatches, and compact data structures such as linear-size suffix tries. The work repeatedly draws on deep results from formal language theory, automata theory, and discrete mathematics to solve practical problems in sequence analysis. Publications trend: From 2004 onward she has produced a steady stream of peer-reviewed articles that move from foundational combinatorial results on Sturmian words and critical factorization theorems toward application-driven studies on approximate string matching and genomic data mining. A marked acceleration is visible in the 2023 publications addressing k-Hamming and k-edit distances, reflecting current demands in large-scale biological data analytics. Scientific awards: None explicitly mentioned in the provided material. Advising & grants: No PhD or Master’s students are listed in the supplied pages, and no funded project descriptions are available. Laboratories & teams: While no specific lab is named, her teaching and research are embedded within the Department of Mathematics and Computer Science, which hosts groups in algorithms, discrete mathematics, and bioinformatics, and provides access to the university’s ATeN Center and other research infrastructures.

• Signal Processing
• Machine Learning
• Optimization
• +۷ مورد دیگر

Hiroyuki Kasai is a Full Professor at the School of Fundamental Science and Engineering, Waseda University, where he leads research in signal processing, machine learning, and optimization. He holds a B.Eng. (1996), M.Eng. (1998), and Dr.Eng. (2000) in Electronics, Information, and Communication Engineering from Waseda University. His career includes positions as Associate Professor and Professor at the University of Electro-Communications (2007-2019), Senior Policy Researcher at Japan's Cabinet Office (2011-2013), and visiting roles at Technical University of Munich and British Telecom. His research spans: Fundamental methodologies : Riemannian optimization, stochastic gradient algorithms, tensor decomposition Applied domains : Network analysis, multimedia systems, environmental sound processing, video coding Emerging areas : Low-rank modeling, manifold learning, and large-scale anomaly detection His publications focus on efficient algorithms for high-dimensional data, with recent work emphasizing Riemannian manifold optimization and real-time tensor analysis. This includes development of open-source tools like SGDLibrary (MATLAB) and McTorch (PyTorch) for optimization tasks. Awards include: IEEE ICCE Best Paper Award (2011) Yamashita Memorial Award (2003) Ericsson Young Scientist Award (2001) 電気通信普及財団賞 (2015) IEICE Service Recognition Award (2010) He maintains memberships in IEEE, IEICE, IPSJ, and JSIAM, and has contributed to over 100 peer-reviewed publications with significant citation impact (h-index 27 via Google Scholar).

• Computer Science
• Artificial Intelligence
• Explainable AI
• +۱۶ مورد دیگر

Faizan Ahmed is a Lecturer in the Formal Methods and Tools department at the University of Twente, part of the Faculty of Electrical Engineering, Mathematics and Computer Science. His work spans artificial intelligence, machine learning, and education technology. Research Focus: Explainable AI, software design, railway infrastructure analysis, and nonlinear equation optimization. Collaborations: Active in international conferences like CSEDU and FTC, with co-authors from diverse institutions. Key Contributions: Development of C-SHAP for temporal explanations, IterSHAP for feature selection, and studies on generative AI in programming education.

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Data Dependencies
• +۸ مورد دیگر

Dr. Silke Manuela Glas is an Assistant Professor specializing in Mathematical Systems Theory. Her research spans Artificial Intelligence, Knowledge Representation and Reasoning, and advanced model reduction techniques in applied mathematics. She actively contributes to interdisciplinary fields like Deep Learning and Nonlinear Systems. Recent work focuses on leveraging differential geometry and data-driven approaches for complex systems. Key trends include: Energy-stable Port-Hamiltonian Systems Nonlinear Model Reduction on Manifolds Real-time Plasma Instability Prediction Symplectic Reduction Algorithms She has presented invited talks on manifold-based model reduction and holds an h-index of 4 with 53 citations (Scopus 2022). Her collaborations span Manifold Learning, Polynomial Mappings, and Embedded Manifolds in computational science.

• Geophysics
• Remote Sensing
• Interferometric Synthetic Aperture Radar (InSAR)
• +۷ مورد دیگر

Dr. Zhong Lu is the Shuler-Foscue Professor of Earth Sciences at Southern Methodist University (SMU) , where he has served since 2013. He is a leading expert in geophysics and remote sensing, with a focus on InSAR (Interferometric Synthetic Aperture Radar) applications for monitoring geohazards such as landslides , volcano deformation , land subsidence , and human-induced geohazards . Education: Ph.D. in Geophysics, University of Alaska Fairbanks (1996) M.S. in Geophysics, Peking University (1992) B.S. in Geophysics, Peking University (1989) Research Focus: Dr. Lu's research integrates remote sensing , geodetic modeling , and machine learning to study surface deformation and geohazard mechanisms. His work spans from Alaskan volcanoes to Texas sinkholes , and from coastal subsidence to glacial landslides . Key Projects: He has led or co-led numerous NASA and USGS funded projects, including mapping land deformation over the Gulf Coast , monitoring Aleutian volcanoes , and developing InSAR-based landslide inventories for the Pacific Northwest. Awards & Honors: Elected Fellow of the American Association for the Advancement of Science (AAAS) Featured in NASA/ASF 25th Anniversary Brochure Multiple journal covers and media recognitions Teaching & Mentorship: Dr. Lu teaches courses such as Radar Interferometry , Remote Sensing Principles , and Geodynamics , and is actively recruiting Ph.D. students with interests in geohazard monitoring and modeling. Labs & Teams: He leads a research group at SMU focused on advanced InSAR techniques and collaborates with USGS, NASA, and international institutions on satellite-based geohazard monitoring.

Fabio Valdésمشاهده پروفایل

پژوهشگر

• Spatial Databases
• Trajectory Data Mining
• Pattern Recognition
• +۲ مورد دیگر

Dr. Fabio Valdés is a researcher at FernUniversität in Hagen's Faculty of Mathematics and Computer Science, specializing in spatial databases and trajectory data analysis. He teaches core modules including Softwaresysteme, Databases and Security on the Internet, and Data Mining while leading advanced research in metric space indexing and pattern recognition. His research focuses on symbolic trajectories , efficient pattern matching for time-dependent data, and metric space indexing . Key contributions include the N-tree index structure for trajectory similarity search and frameworks for spatio-textual trajectory mining. His work bridges theoretical foundations with industrial applications in aircraft tracking, wildlife monitoring, and industrial image processing systems. Analysis of his 15 most recent publications reveals an evolution from foundational symbolic trajectory models (2013-2017) to advanced indexing structures (2020-2025). Current work integrates machine learning for neural network interpretability and energy price forecasting, maintaining strong ties to ACM SIGSPATIAL and IEEE venues while expanding into AI-driven applications. Best Demo Award at DASFAA 2013 Dr. Valdés has supervised over 40 Master's and Bachelor's theses since 2013, covering machine learning, reinforcement learning, and data engineering across finance, autonomous systems, and medical applications. His students have developed solutions for investment decision systems, autonomous agricultural robots, and medical NER systems while addressing industrial challenges in packaging machinery optimization and cockpit staffing.

Mohammad Taher Pilehvarمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Lexical Semantics
• Semantic Representation
• +۳ مورد دیگر

Mohammad Taher Pilehvar is a Senior Lecturer at Cardiff University's School of Computer Science and an Affiliated Lecturer at the University of Cambridge's Department of Theoretical and Applied Linguistics. His research focuses on lexical semantics and semantic representation in NLP, particularly in word sense disambiguation and embedding space isotropy. He has held academic positions at Tehran Institute for Advanced Studies (TeIAS) and Sapienza University of Rome, where he completed his PhD under Dr. Roberto Navigli. Key Research Interests Natural Language Processing (NLP) Lexical Semantics & Semantic Representation Contextualized Embeddings & Word Sense Disambiguation Knowledge-Infused Language Models His recent publications (2021-2013) demonstrate expertise in semantic similarity frameworks, metonymy resolution, and biomedical NLP applications like PheneBank. He supervised PhD students including Jose Camacho-Collados and Costanza Conforti, now at top institutions. Awards 2023 AIJ Prominent Paper Award 2014 Sapienza Starting Grant 2015 Best PhD Paper (Sapienza University) He has collaborated with the University of Cambridge's Language Technology Lab on MRC-funded projects and co-organized SemEval tasks. Current roles include editorial board membership at JNLE and co-chairing ACL 2025.

• Medical AI
• Computational Neuroimaging
• Computational Geometry
• +۳ مورد دیگر

Dr. Martin Reuter serves as Assistant Professor of Radiology and Neurology at Harvard Medical School and Massachusetts General Hospital, with additional affiliation at the Martinos Center for Biomedical Imaging and the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany. His educational background includes a PhD in Computational and Differential Geometry from the University of Hanover (2005, summa cum laude) and a Diplom (MSc) in Mathematics with Computer Science from Leibniz University of Hanover (2001). Postdoctoral training at MIT (2006-08) was supported by a Feodor-Lynen fellowship. Dr. Reuter's research program develops AI technologies for medical imaging analysis, focusing on: Deep learning-based methods for automated brain MRI analysis Computational extraction of preclinical biomarkers from neuroimaging Quantification of structural changes in neurodegenerative diseases Distribution of open-source tools through FastSurfer and FreeSurfer His publications demonstrate expertise in brain morphology characterization, longitudinal image analysis, and neuroanatomical asymmetry detection, primarily applied to dementia research. Major recognitions include: NIH K25 Career Award (2014) Most Cited Paper Award for 'ShapeDNA' (2009) Feodor-Lynen Fellowship (2006) Leibniz University Research Prize (2006) His Deep-MI Lab develops resource-efficient neural network architectures that bridge computational methods with clinical applications for precision medicine in neurodegenerative disorders, collaborating with population scientists, neuroscientists, and clinicians worldwide.

Manuel Bergerمشاهده پروفایل

مدرس

• Fluid Dynamics
• Medical Engineering
• Computational Fluid Dynamics (CFD)
• +۵ مورد دیگر

Manuel Berger is a Lecturer at the MCI Management Center Innsbruck, specializing in Fluid Dynamics and Medical Engineering. He holds a PhD in Image Guided Diagnosis and Therapy (IGDT) from the Medical University of Innsbruck (2021) and advanced degrees in Mechatronics/Mechanical Engineering (M.Sc., 2014) and Mechatronics (B.Sc., 2011). His academic journey includes roles as a scientific staff member (2014–2024) and dissertant at MCI. He has taught courses such as Advanced Computational Modeling, Medical Image Processing, and Fluid Dynamics. His research focuses on fluid dynamics applications in medical technologies, including nasal airflow simulations, hydrocyclone optimization, and biomedical device development. He has pioneered tools like a sensorized guide wire for cardiovascular surgery and CFD-based pre-surgery planning for nasal interventions. His work also addresses environmental challenges, such as biogas production from waste and microplastic separation. Berger has received awards including the MCI Teaching Award (2024) and the Wilhelm-Auerswald-Preis (2022 finalist). He actively supervises bachelor’s and master’s theses on topics ranging from CFD applications to renewable energy systems. His research group collaborates on projects like plasma actuation studies, hydrocyclone scale-up, and AI-driven aerodynamic analysis. Additional expertise includes fluid mechanics in hydropower, laser anemometry validation, and interdisciplinary education initiatives. He is currently pursuing Chinese language training and has conducted research stays in Taiwan via an OeAD scholarship (2022).

Zhiguang Caoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Science
• Decision Making & Optimization
• +۳ مورد دیگر

Dr. Zhiguang Cao is an Assistant Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). With a PhD from Nanyang Technological University (2017), his work bridges Artificial Intelligence , Deep Learning , and Combinatorial Optimization to tackle complex logistics and urban mobility challenges. Research Advisor to PhD/Master's students: XIAO Xiangjie, YI Hang, ZHANG Ni Recipient of the World’s Top 2% Scientist recognition (2024), IEEE Transactions on Industrial Informatics Outstanding Paper Award (2022), and Nobert Wiener Review Award (2022) Completed degrees: PhD (NTU), MSc (NTU), BEng (GDUT, China) Research Interests focus on Learning to Optimize (L2Opt) and Neural Combinatorial Optimization , with applications in: Vehicle Routing Problems (TSP, CVRP, PDP) Job Shop Scheduling and Multimodal Optimization UAV Routing , Drone Delivery , and Stochastic Programming His 15 most recent publications (2024-2025) span top venues like NeurIPS , ICML , IJCAI , and IEEE Transactions journals, covering topics from multi-task vehicle routing to graph domain adaptation and learning with large language models . Scientific Awards : World’s Top 2% Scientist (Stanford, 2024) IEEE Transactions on Industrial Informatics Outstanding Paper (2022) Nobert Wiener Review Award (2022) BMW Summer School Excellent Research Work (2014) IEEE Singapore Chapter Tennis Prize (2012) Dr. Cao advises the L2Opt Research Group , which recruits PhD students and remote interns. He has served as Area Chair for ICLR, SIGKDD, ICML, and Senior PC for AAAI and IJCAI. His ongoing projects include Trustworthy Human-AI Optimization (AISG, 2025-2027) and Mobile Crowdsourcing Emergency Supplies (SMU-UofT, 2025-2026).

Saifuddin Syedمشاهده پروفایل

پژوهشگر ارشد

• Computational Statistics
• Bayesian Inference
• Machine Learning
• +۳ مورد دیگر

Saifuddin Syed is a Florence Nightingale Bicentennial Fellow in computational statistics and machine learning at the University of Oxford’s Department of Statistics. He is also a member of the Algorithms and Inference Working Group for the Next Generation Event Horizon Telescope (ngEHT). His research focuses on developing robust and scalable algorithms for statistical inference and generative modeling, with applications in astrophysics, computational biology, and nuclear fusion. Syed holds a PhD in Statistics from the University of British Columbia (2022), an MSc in Mathematics (2016), and a BMath in Pure & Applied Mathematics from the University of Waterloo (2014). His research interests include parallel tempering, annealing algorithms, scalable Bayesian inference, and AI-driven scientific discovery. He has contributed to high-impact projects such as imaging black holes (e.g., Sagittarius A* and M87), modeling genetic variations in malaria, and analyzing plasma dynamics in nuclear fusion reactors. Syed’s work has been recognized with awards like the Pierre Robillard Award and the Cecil Graham Doctoral Dissertation Award. Syed’s methodologies, such as non-reversible parallel tempering (NRPT), have been implemented in software like Pigeons.jl, enabling distributed sampling for complex statistical problems. He collaborates with interdisciplinary teams, including the Event Horizon Telescope collaboration, to advance computational methods for challenging scientific problems. Awards: Pierre Robillard Award, Cecil Graham Doctoral Dissertation Award, Savage Award Honourable Mention Key Projects: Black hole imaging, malaria genetics modeling, plasma dynamics inference Software: Pigeons.jl (distributed sampling framework)

• Aerospace Engineering
• Computational Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Professor Andy Keane is a Professor of Computational Engineering at the University of Southampton , focusing on advanced computational methods for aerospace system design and optimization. He is affiliated with the Computational Engineering and Design Group and has led numerous EPSRC and EU-funded research projects, including DECODE , DYNAMO , and ICAReS . Research spans Aerospace Engineering , Artificial Intelligence , and Computer-Aided Design (CAD) Key projects include Novel Passive Vibration Isolators and Multi-fidelity Simulation for Aero Engine Seals Active in mentoring PhD students such as Gerico Vidanes and Petru-Cristian Cimpoesu Research Trends : Recent publications highlight applications of surrogate modeling , generative adversarial networks , and hybrid machine learning in aerospace and CAD. Themes include uncertainty estimation , meta-learning , and vibration isolation . Scientific Awards : Fellow of the Institution of Mechanical Engineers (1998) Fellow of The Royal Institution of Naval Architects (2011) Collaborations : Partnered with institutions across the UK and EU on projects like CRESCENDO and E-Break . Supervision of PhD students focuses on AI-driven design and optimization . Labs & Teams : Member of the Computational Engineering and Design Group , integrating analytical, computational, and experimental techniques for multidisciplinary engineering.

• Computer Vision
• Multimedia Systems
• Machine Learning Applications
• +۷ مورد دیگر

Timothy K. Shih is an active academic researcher with over 30 years of scholarly contributions, evidenced by his extensive publication record from 1991 through 2025. With more than 380 publications spanning numerous prestigious venues including IEEE Access, Multimedia Tools and Applications, and Lecture Notes in Computer Science, he maintains a robust research profile with consistent annual output (20+ papers in peak years). His work demonstrates leadership through frequent senior/corresponding author positions and collaborations with numerous researchers across international institutions. Dr. Shih's research interests encompass a diverse range of computer science disciplines with particular emphasis on Computer Vision , Human-Computer Interaction , and AI Applications . His work bridges theoretical advancements with practical implementations in educational technology, accessibility solutions, and multimedia systems. Recent publications reveal a strategic focus on applying deep learning techniques to solve real-world problems in sign language recognition, gesture analysis, and wireless sensing applications. Analysis of his publication trends over the past five years shows increasing specialization in multimodal AI systems, with significant contributions to sign language technology (including Arabic Sign Language recognition), WiFi-based human activity recognition, and music technology applications. His research demonstrates strong interdisciplinary connections between computer vision, machine learning, and human-centered computing, with practical applications spanning educational technology, accessibility solutions, and smart environments. Through his mentorship, Dr. Shih has guided numerous junior researchers who have become frequent collaborators, including Chih-Yang Lin, Hsin-Hung Cho, and Tipajin Thaipisutikul. His research program appears well-funded through consistent publication output across multiple project areas, suggesting successful grant acquisition in computer vision, AI, and educational technology domains. Current work indicates active involvement in cutting-edge research on diffusion models for audio processing, enhanced sign language recognition systems, and novel approaches to WiFi-based human interaction analysis.