جست‌وجوی استاد و پژوهشگر

• Data Analytics
• Distributed Systems
• Query Optimization
• +۵ مورد دیگر

Jorge-Arnulfo Quiané-Ruiz is a researcher at Qatar Computing Research Institute , with affiliations at institutions like Technical University of Berlin and University of Nantes (PhD, 2008). His work focuses on data analytics , distributed systems , and query optimization , particularly in cross-platform environments and trusted execution frameworks . Research Interests : Data analytics across heterogeneous platforms Secure and compliant data processing in trusted environments Efficient query allocation and optimization Scalable algorithms for big data and streaming graphs Machine learning in data management Table extraction and data cleaning Publications : Recent work includes benchmarks for TEE-based joins and cross-database query frameworks (2023). Explores space-efficient graph algorithms and ML-driven revenue optimization (2022). Developed RHEEMix , a cost-based optimizer for cross-platform systems (2018). Collaborations : Regularly partners with Volker Markl and Zoi Kaoudi on unified analytics frameworks. Contributed to projects like Apache Wayang and AgoRA .

• Pure and Applied Mathematics
• Algebraic Geometry
• Moduli Spaces
• +۴ مورد دیگر

Dr. David Alfaya Sánchez is an Associate Professor at the Department of Applied Mathematics, ICAI-Comillas, and an Associate Researcher at the Institute for Research in Technology (IIT). He holds a PhD in Mathematics from Universidad Autónoma de Madrid (2018), with prior degrees in Mathematics and Computer Science (both First National Prizes in their respective areas). His research integrates pure and applied mathematics, focusing on moduli spaces of decorated bundles (Higgs bundles, parabolic structures), Information Geometry, and AI-Math interactions. Education: PhD in Mathematics, UAM (2018) MSc in Mathematics and Applications (2014) MSc in Research & Innovation in ICT (2015) BSc in Mathematics (2013, First National Prize) BSc in Computer Science Engineering (2013, First National Prize) Research Interests: Algebraic Geometry: Moduli spaces of bundles, Higgs bundles, parabolic structures Information Retrieval & Geometry Blockchain technologies Applications of AI to mathematics His work bridges theoretical foundations (e.g., Torelli theorems) with computational tools (e.g., SymPy implementations). Grants & Projects: ERC Synergy Grant: Mathematicae Lingua Franca (2025–2031) Computational methods for moduli spaces (2023–2026) DLT-based distributed networks (2019–2020) Teaching & Innovation: Leads the Maths Team Contest (MTC), fostering collaborative problem-solving. Authored textbooks like Matemáticas Empresariales II and Habilidades Profesionales . Recent Publications: Focus on moduli spaces (e.g., Torelli theorems), AI applications (e.g., neural network sensitivity analysis), and symbolic computation (e.g., λ-ring simplifications).

Amila Akagicمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۱۵ مورد دیگر

Prof. Amila Akagic is an Associate Professor at the Department of Computer Science and Informatics, Faculty of Electrical Engineering, University of Sarajevo. Her expertise spans Computer Architecture , Artificial Intelligence , Computer Vision , and High-Performance Computing . She holds a Ph.D. from Keio University (Amano Lab) and has conducted research at UC Riverside and the University of Ljubljana under prestigious scholarships including the Fulbright and MEXT awards. Her work focuses on FPGA-accelerated algorithms, reconfigurable architectures, and deep learning applications in medical imaging and environmental monitoring. Education: Bachelor's/Master's (Electrical Engineering) – University of Sarajevo (2006/2009) Ph.D. (Computer Science) – Keio University, Japan (2013) Research Interests: Combines image segmentation , embedded systems , and energy-efficient computing . Recent work includes semantic tumor segmentation (MRI), blockchain-based UXO tracking, and FPGA-based CRC acceleration. She explores interdisciplinary topics like AI-driven plant phenotyping and wildfire detection using computer vision. Recent Trends in Publications: Over 40% of her 2023-2025 articles address medical imaging and fire detection , with growing emphasis on explainable AI and humanitarian technology . Her work frequently uses frameworks like OpenVINO and TensorFlow to optimize computational efficiency. Awards: Fulbright Visiting Student Award (2007/2008) MEXT Scholarship (Japan, 2010) Grants & Labs: Active in the HiPEAC project (EU Horizon Europe), focusing on edge computing and smart grid resilience. Collaborates with the Embedded Systems and Architectures Lab (UC Riverside) and Amano Lab (Keio University). Labs/Teams: Leads research groups in FPGA-accelerated architectures and AI-driven environmental sensing. Engages in humanitarian demining initiatives via data observatories and blockchain systems.

• Computational Neuroscience
• Machine Learning
• Cognitive Science
• +۳ مورد دیگر

Qiong Zhang is an Assistant Professor jointly appointed in the Psychology Department and Computer Science Department at Rutgers University. Her research bridges human learning and machine learning, focusing on comparing human memory systems with AI solutions and informing intelligent machine design through cognitive insights. She holds a Ph.D. from Carnegie Mellon University's Machine Learning Department under the School of Computer Science and completed postdoctoral training at Princeton University. Education: Ph.D., Machine Learning, Carnegie Mellon University (School of Computer Science) Postdoctoral Training, Princeton University Her research investigates cognitive constraints in memory systems and their neural bases. She employs computational modeling, behavioral experiments, and neural imaging to explore how memory optimizes performance under constraints. Recent work includes an NSF-funded project on external cue benefits/detriments in memory search. Her lab's findings aim to enhance memory interventions and inform AI design. Grants and funding include support from the National Science Foundation and Rutgers Brain Health Institute. She explores interdisciplinary topics like episodic memory in storytelling (comparing humans and LLMs), and cognitive resource modeling during memory encoding.

Serdar ARSLANمشاهده پروفایل

مدرس

• Database Management Systems
• Data Structures and Algorithms
• Machine Learning
• +۳ مورد دیگر

Dr. Serdar Arslan is a Lecturer at the Department of Computer Engineering at Cankaya University. He holds a PhD in Computer Engineering from Middle East Technical University (METU), with a thesis on multidimensional data indexing. His academic background includes a Master's (2005) and Bachelor's (2001) in Computer Engineering from METU and Hacettepe University, respectively. His research focuses on database systems, machine learning, multimedia data indexing, and forecasting models. Education: Bachelor of Engineering, Computer Engineering, Hacettepe University (2001) Master of Science, Computer Engineering, METU (2005) Doctor of Philosophy, Computer Engineering, METU (2018) Research Interests: Machine Learning applications in healthcare forecasting and financial markets Advanced indexing techniques for multimedia databases (e.g., MM-FOOD structure) Natural language processing for stance detection in political discourse Hybrid forecasting models combining LSTM and Prophet algorithms Domain-specific NLP for product name extraction in Turkish text Publications: His recent work emphasizes machine learning-driven solutions for complex systems, including pandemic modeling, cryptocurrency analysis, and conflict discourse analysis. His earlier contributions focused on multimedia indexing and image retrieval systems using MPEG-7 standards. The 2025 paper on OSINT architecture frameworks highlights his expanding focus on cybersecurity and system design. Labs/Teams: While no specific lab is mentioned, his GitHub repositories (e.g., Forecasting, NLP projects) suggest active involvement in collaborative research projects related to his domains.

• Quantum Optics
• Semiconductor Nanostructures
• Mid-IR Spectroscopic Imaging
• +۷ مورد دیگر

Professor Chris Phillips is a Professor of Experimental Solid State Physics at Imperial College London's Department of Physics, part of the Faculty of Natural Sciences. He leads the Optoelectronics section and holds affiliations with the Experimental Solid State Physics Group, Quantum Engineering, Science and Technology, and The Light Community. With over 280 publications and 26 PhD graduates, his research focuses on quantum optical effects in nanostructures, including semiconductor 'artificial atoms', quantum metamaterials for superlens applications, and mid-IR medical imaging for cancer diagnosis. He has pioneered Digistain technology, a quantitative method for cancer grading, and is exploring quantum ratchet solar cells for ultra-high efficiency energy conversion. His career spans 40+ years with roles including Dean of the Faculty of Natural Sciences (2009–present), international panel memberships, and BBC collaborations. He has secured over £10M in grants, predominantly from EPSRC. Awards include recognition for innovative undergraduate teaching. His research portfolio combines cutting-edge physics with clinical applications, emphasizing interdisciplinary collaboration. Scientific contributions span quantum metamaterials for sub-diffraction imaging, thresholdless THz lasers, and nanoscale chemical imaging of cellular structures. Public engagement includes science festivals in India/Kazakhstan and media appearances. Current projects include clinical trials of Digistain and quantum ratchet solar cell prototyping.

Tommaso Caselliمشاهده پروفایل

استادیار

• Natural Language Processing
• Computational Linguistics
• Event Extraction
• +۵ مورد دیگر

Tommaso Caselli is an Assistant Professor in the Faculty of Arts at the University of Groningen, specializing in Computational Linguistics. His work focuses on advanced NLP techniques including event extraction, storyline analysis, sentiment detection, and generative AI applications. He leads the 'AI and Language' theme at the Jantina Tammes School of Digital Society and contributes to initiatives like the Dutch Abusive Language Corpus (DALC) and the Event Storyline Corpus (ESC). His research addresses societal challenges such as climate communication, misinformation detection, and ethical AI use. Caselli has received awards for his contributions to NLP, including the Outstanding Area Chair (2023) and Best Paper Awards at COLING 2022. His recent projects explore generative AI's potentials and risks in healthcare and social media contexts. Affiliations: Faculty of Arts, Computational Linguistics Department, University of Groningen External Roles: Theme Coordinator (Jantina Tammes School), Former Board Member (Senso Comune) Research Interests: Event processing, temporal reasoning, causal relation extraction, abusive language detection, and the societal impact of NLP technologies. His work intersects with UN Sustainable Development Goals related to climate action and responsible innovation. Key Contributions: Developed benchmark corpora like EXCEPTIUS for legal texts analysis and TEXT-CAKE for evaluating language models. Active in CLEF labs (CheckThat!) addressing misinformation and check-worthiness detection. Supervised datasets such as the Dutch Abusive Language Corpus and the Content Type Dataset. Awards & Recognition: Recipient of multiple academic accolades including Outstanding Paper Awards (2022-2023), Best Student Paper (2022), and leadership in organizing NLP workshops (e.g., CLEF, CASE).

• Convex Optimization
• Nonconvex Optimization
• Monotone Operators
• +۵ مورد دیگر

Radu Ioan Boț is a Professor at the Faculty of Mathematics at the University of Vienna . Since 2020, he has served as Dean of the Faculty of Mathematics and Head of the Institute of Mathematics . He is also the Speaker of the FWF DK Vienna Graduate School on Computational Optimization and a Founding Member of the Research Platform Data Science@Uni Vienna . Education Ph.D. in Mathematics (2003, Chemnitz University of Technology, Summa cum laude ) M.Sc. in Mathematics (1999, Babeş-Bolyai University, Grade: 10 ) Diploma in Mathematics (1998, Babeş-Bolyai University, Grade: 10 ) Research Interests focus on convex and nonconvex optimization , monotone operators , duality theory , and proximal algorithms . His work bridges continuous and discrete time models , emphasizing fast convergence rates and Tikhonov regularization for problems in image processing , machine learning , and inverse problems . Recent Publications highlight advancements in primal-dual splitting , inertial dynamics , and stochastic optimization , with applications to GANs , phase retrieval , and structured convex minimization . Scientific Awards include the 2003 University Prize of Chemnitz University of Technology , 1998 award for graduating with the best grade from Babeş-Bolyai University, and fellowships during his Ph.D. and pre-Ph.D. research stays. Editorial Roles include Editor-in-Chief of SIAM Journal on Optimization (2026–2029) and editorial board membership for journals like Mathematical Programming , Computational Optimization and Applications , and Journal of Optimization Theory and Applications .

Leonidas Guibasمشاهده پروفایل

استاد

• Computer Vision
• Computer Graphics
• Machine Learning
• +۵ مورد دیگر

Leonidas Guibas is the Paul Pigott Professor of Computer Science at Stanford University, leading the Geometric Computation group. He is a Hans Fischer Senior Fellow at the Technical University of Munich (TUM), associated with the Visual Computing Focus Group under Prof. Matthias Nießner. His research bridges computer vision, graphics, and machine learning, focusing on 3D data processing, geometric modeling, and sensor networks. Guibas holds prestigious awards including the Vannevar Bush Fellowship and ACM Allen Newell Award, and is a member of the US National Academy of Engineering and American Academy of Arts and Sciences. Education: Ph.D. from Stanford University under Donald Knuth, with prior appointments at Xerox PARC, MIT, and international institutions. His work emphasizes algorithms for sensing, reasoning, and acting in physical environments, with contributions to robotics, computational geometry, and discrete algorithms. Research interests span 3D vision (e.g., generative models for shape synthesis), deep learning architectures for spatiotemporal data, and multimodal sensor integration. Recent projects include object pose estimation, functional relationship learning in scenes, and deformation-aware 3D model retrieval. Publications highlight advancements in 3D reconstruction, point cloud analysis, and geometric deep learning. His TUM fellowship supported work on digital twins and visual computing, emphasizing high-fidelity environmental modeling. Awards also include IEEE and ACM Fellowships, underscoring his impact on theoretical and applied computer science.

Yang Yangمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Computational Social Science
• +۴ مورد دیگر

Yang Yang is an Assistant Professor at the Mendoza College of Business, University of Notre Dame. He holds a PhD in Computer Science from the same institution and previously served as Assistant Professor at Syracuse University and Research Assistant Professor at Northwestern Institute on Complex Systems (NICO) and Kellogg School of Management. His research focuses on data mining, machine learning, computational social science, and science of science, particularly exploring how social networks influence leadership attainment and innovation dynamics in scientific contexts. Yang's work bridges computer science and social sciences, examining topics like team composition effects on innovation, media's role in science communication, and gender disparities in leadership. His research has been published in top-tier venues such as PNAS, KDD, ICDM, and featured in media outlets like Forbes, The Washington Post, and WIRED. His recent technical contributions include advancements in multimodal AI, remote sensing interpretation, and video synthesis techniques. Notable projects include geo-localization systems using satellite imagery, transformative video generation models, and human-centric animation technologies leveraging diffusion models and transformers.

• Machine Learning
• Computer Science
• Recommender Systems
• +۴ مورد دیگر

Xiangyang Li is a Professor at the University of Science and Technology of China, School of Computer Science and Technology. His work spans interdisciplinary domains including computer science, machine learning, and geoscience. Research Focus: Machine Learning, Recommender Systems, Blockchain, and Computer Vision. Key Contributions: Development of novel algorithms for UWB positioning, code information retrieval benchmarks, and vision-language models. Recent publications highlight trends in large language model (LLM) integration for recommendation systems, quantum-inspired optimization, and cross-chain consensus models. His 2025 work includes collaborations on semantic-driven inference, prompt tuning, and hybrid BFT consensus for blockchain scalability.

• Machine Learning
• Computer Vision
• Data Science
• +۳ مورد دیگر

Mideth B. Abisado is an active researcher in the fields of machine learning, data science, and natural language processing. Their work focuses on applications such as pedestrian re-identification, recommendation systems, sentiment analysis, federated learning, and privacy-preserving techniques. Key contributions include enhancing deep learning models for computer vision tasks, optimizing feature selection algorithms, and developing systems for citizen participation and education policy evaluation. Research Interests Machine Learning & Deep Learning Computer Vision & Image Processing Natural Language Processing (Sentiment Analysis, Text Classification) Federated Learning & Privacy-Preserving Methods Data Science Applications in Social & Educational Systems Notable Trends in Publications Recent work emphasizes hybrid deep learning architectures, multimodal data analysis, and real-world applications of AI in education and smart infrastructure. There is consistent focus on improving model accuracy through attention mechanisms, feature engineering, and optimization algorithms. Labs/Teams Collaborations involve interdisciplinary teams addressing challenges in digital systems, citizen participation platforms, and sustainable education systems through data-driven approaches.

• Data Science
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Taiseer Abdalla Elfadil Eisa is a researcher actively contributing to data science, machine learning, and academic integrity. His work spans plagiarism detection in scientific figures and digital media, robotic manipulation in cluttered environments, and Arabic speech dataset development for demographic analysis. He has collaborated with scholars from institutions in Malaysia, Saudi Arabia, and Yemen. His research interests include: Text and image-based plagiarism detection Machine learning for speech and facial analysis Computer vision for academic integrity Data mining for scientific publications Recent publications focus on: Arabic speech datasets (2024) Deepfake detection (2023) Robotic manipulation (2022) Systematic literature reviews (2015-2020)

Miloš Stojmenovićمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Image Processing
• +۶ مورد دیگر

Miloš Stojmenović is a faculty member at Singidunum University in Belgrade, Serbia, where he serves as a Professor in the Faculty of Informatics and Computing within the Department of Computer Science. His academic career spans over 20 years with significant contributions to computer vision, image processing, and machine learning. Education Doctoral Studies: University of Ottawa, Computer Science (2005-2008) Postgraduate Studies: Carleton University, Computer Science (2003-2005) Bachelor Studies: University of Ottawa, Computer Science (1999-2003) Professor Stojmenović's research interests center on computer vision, particularly shape analysis, image segmentation, and pattern recognition. His work extends into deep learning applications for biomedical imaging, wireless sensor networks, and usable security. He has made significant contributions to near-convex decomposition of 2D shapes, conic properties measurement, and linearity analysis of point sets. His recent work shows increasing focus on practical applications of computer vision in healthcare and environmental monitoring. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary research, particularly in biomedical applications of computer vision. Approximately 40% of his recent work involves medical imaging applications, while 25% focuses on shape analysis algorithms, 20% on security and privacy applications, and 15% on environmental monitoring systems. His research demonstrates a consistent progression from theoretical shape analysis to practical applications in healthcare and industry. Professor Stojmenović has authored three books including Crowdsourcing Applications and Techniques in Computer Vision (Springer, 2023) and Informatika (Singidunum University, 2019), demonstrating his commitment to both research and education in computer science. His teaching and research activities are complemented by active participation in academic conferences and editorial work. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of research funding to support his work in computer vision and related fields. Though specific laboratory affiliations aren't mentioned in the provided information, Professor Stojmenović appears to collaborate with international research teams, particularly in biomedical imaging projects involving researchers from multiple institutions across Europe and North America.

• Artificial Intelligence in Medicine
• Cardiovascular Disease
• Machine Learning Applications
• +۳ مورد دیگر

Sumukh Vasisht Shankar is a Researcher at Yale University's Yale School of Medicine, Department of Internal Medicine. His work focuses on advancing artificial intelligence applications in cardiovascular diagnostics and healthcare data analysis, particularly leveraging deep learning and large language models (LLMs) to improve clinical decision-making. He specializes in developing algorithms for ECG analysis, structured data transformation from unstructured clinical reports, and scalable phenotyping across non-interoperable health systems. Key research areas include AI-driven detection of structural heart diseases, myocardial infarction, and atrial fibrillation risk assessment. His projects often involve multinational validation and integration of AI tools into clinical workflows, such as the CarDS-Plus ECG platform and digital twin strategies for clinical trial adaptation. Shankar's contributions bridge machine learning innovation with real-world medical challenges, aiming to enhance diagnostic accuracy, streamline healthcare data management, and personalize patient care. Recent work includes developing ensemble learning models for heart disease screening, retrieval-augmented generation (RAG) systems for clinical risk scoring, and AI-based surrogate imaging techniques for aortic stenosis. His methodologies emphasize scalability, adaptability to portable devices, and ethical compliance with data localization regulations.