جستجوی استاد راهنمای دانشگاهی

Liqiang Wang is a Professor in the Department of Computer Science at the University of Central Florida (UCF), where he directs the Big Data Lab. Previously, he served as faculty at the University of Wyoming (2006-2015). He holds a Ph.D. in Computer Science from Stony Brook University (2006) and spent a visiting research period at IBM T.J. Watson Research Center (2012-2013). His research focuses on big data analytics, high-performance computing, parallel systems optimization, and applying deep learning to detect programming errors and enhance model robustness. Education: Ph.D., Computer Science, Stony Brook University (2006); Visiting Researcher, IBM Watson (2012-2013). Research Interests: Improving accuracy and security of big data models, optimizing parallel computing systems (HPC, Cloud, GPUs), program analysis for concurrency errors, and deep learning applications in anomaly detection and adversarial robustness. Notable projects include scalable LSQR algorithms for seismic tomography and the OpenMP Analysis Toolkit (OAT) for concurrency error detection. Key Awards: NSF CAREER Award (2011), Castagne Faculty Fellowship (2013-2015), UCF Mid-Career Refresh Award (2020), and grants including a $50K NSF CIVIC-PG grant (2022) and Google/Meta donations. Advising and Grants: Supervises over 20 Ph.D./M.S. students and has secured grants totaling over $100K. Notable collaborations include seismic tomography with NCAR and cloud computing optimization. Labs/Teams: Director of UCF’s Big Data Lab, collaborating on projects like Parallel LSQR and Anti-Neuron Watermarking.

• Big Data Analytics
• High-Performance Computing (HPC)
• Parallel Computing
• +۳ مورد دیگر

Sagar Samtani is an Associate Professor and Weimer Faculty Fellow at the Kelley School of Business , Indiana University. He serves as Director of the Kelley’s Data Science and Artificial Intelligence Lab (DSAIL) . His research focuses on Artificial Intelligence for Cybersecurity , including cyber threat intelligence, deep learning, and dark web analytics. He holds a PhD from the University of Arizona (2018), and has received prestigious awards such as the Indiana University Outstanding Junior Faculty Award (2023) and IEEE Big Data Security Junior Research Award (2023). Education : PhD in Information Systems, University of Arizona, 2018 MSMIS, University of Arizona, 2014 BSBA, University of Arizona, 2013 Research Interests : Samtani’s work addresses cybersecurity challenges through AI, including proactive threat detection, vulnerability assessment, and healthcare analytics. He emphasizes explainable AI (XAI) for transparency in cybersecurity systems. Grants & Awards : NSF Grant: CyberCorps SFS Program ($2.3M, 2020–2025) NSF Grant: AI4Cyber Research Education ($300K, 2020–2022) Multiple teaching awards, including the Trustees Teaching Award (2023) and recognition as one of Top 50 Undergraduate Professors (2022) Labs & Teams : Leads the DSAIL lab, focusing on AI-driven solutions for business and cybersecurity. Collaborates with NSF-funded initiatives on cyber AI education and threat intelligence.

• Artificial Intelligence for Cybersecurity
• Cyber Threat Intelligence
• Deep Learning
• +۵ مورد دیگر

Professor Masatoshi Okutomi is affiliated with the Department of Systems and Control Engineering at the School of Engineering, Institute of Science Tokyo. His research focuses on advanced medical imaging techniques, particularly in endoscopy and 3D reconstruction, leveraging deep learning and neural networks. Key interests include virtual chromoendoscopy for cancer detection, image restoration, and stereo matching under challenging conditions. His work bridges computer vision and healthcare, addressing real-world applications such as MRI reconstruction and foggy stereo matching. Notable contributions include developing lightweight medical segmentation networks for edge devices and advancing neural radiance fields (NeRF) for novel view synthesis. His research spans diverse domains: from improving video quality assessment to enhancing object detection in high-dynamic-range images. Collaborative projects emphasize practical solutions for medical diagnostics and robust image processing in adverse environments. Recent articles highlight advancements in temporally-consistent video restoration, few-shot view synthesis, and degraded image classification using knowledge distillation. These innovations underscore his commitment to pushing boundaries in both theoretical computer vision and applied medical technology.

• Medical Imaging
• Computer Vision
• Deep Learning
• +۵ مورد دیگر

Maria Chiara Fiorentino is a Research Fellow at the Department of Information Engineering, Polytechnic University of Marche, Italy. Her work focuses on applying deep learning techniques to medical image analysis, particularly in ultrasound, MRI, and CT imaging. Education Master’s in Biomedical Engineering, Università Politecnica delle Marche (Honors) Ph.D. in Information Engineering, Università Politecnica delle Marche (Laude) Research Interests: Dr. Fiorentino specializes in deep learning for medical imaging, with applications in diagnosing neurodegenerative diseases like Parkinson’s, cardiovascular conditions, and musculoskeletal disorders. Her recent work includes federated learning for fetal ultrasound analysis, AI-driven vocal fold pose estimation, and domain adaptation in MRI segmentation. Scientific Awards: Paolo Marziali Thesis Prize for her Master’s research Gruppo Nazionale di Bioingegneria award for her Ph.D. thesis Publications: Dr. Fiorentino’s work spans fetal brain image synthesis, zero-shot learning robustness, and machine learning for catheterization management and stenosis detection.

• Deep Learning
• Medical Image Analysis
• Biomedical Engineering
• +۲ مورد دیگر

Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۷ مورد دیگر

Ajita Rattani is an Assistant Professor in the Department of Computer Science and Engineering at the University of North Texas, affiliated with Discovery Park. Her research focuses on biometrics, AI fairness, deepfake detection, and machine learning applications in health and security. She holds a Ph.D. in Computer Science and Engineering, with expertise in facial recognition, ocular biometrics, and multimodal authentication systems. Research Interests: Her work addresses algorithmic fairness in facial attribute classification, robustness of biometric systems against adversarial attacks, and developing lightweight models for on-device authentication. She also explores applications of machine learning in health informatics, such as BMI prediction from facial images and analyzing social determinants of health. Publications Trends: Recent work emphasizes bias mitigation in AI systems (e.g., gender/racial fairness), deepfake detection through fusion of audio-visual cues, and advancing ocular biometric recognition under challenging conditions. Notable contributions include frameworks like CodeIT for data-efficient deepfake detection and PatchBMI-Net for lightweight BMI prediction. Advising & Grants: Leads research projects funded by NSF SaTC grants (e.g., probing fairness in ocular biometrics). Active in organizing competitions like VISOB 2.0 for mobile ocular biometrics evaluation. Her lab develops practical solutions for real-world challenges in biometrics and AI ethics. Labs/Teams: Involved in interdisciplinary teams addressing transdisciplinary collaboration challenges and applying AI to disaster detection (wildfires, droughts) using satellite and sensor data fusion.

• Biometrics
• Deepfake Detection
• Facial Recognition
• +۶ مورد دیگر

Eilif B. MULLER is a Professor in the Department of Neurosciences at Université de Montréal, Principal Investigator of the Architectures of Biological Learning Lab (ABL-Lab) at CHU Sainte-Justine Research Center, and Associate Faculty at Mila (Quebec AI Institute). His work bridges neuroscience and artificial intelligence, focusing on understanding how sensory perception is learned in the neocortex through biophysical simulations and deep learning models. He holds affiliations with IVADO (Institute for Data Valorization) and contributes to strategic initiatives like the UNIQUE Québec Center. His research integrates empirical neurophysiology with computational models, exploring dendritic processing and synaptic plasticity to inform both biological understanding and AI advancements. Teaches NSC-6044 and NSC-6045 (Neuroscience Colloquia) at Université de Montréal. Leads projects on neocortical learning mechanisms and their implications for neurodevelopmental disorders. Recipient of grants from CRSNG (Natural Sciences and Engineering Research Council), FRSQ (Health Research Fund), and institutional funding. Publications span topics in computational neuroscience, neural network modeling, and interdisciplinary AI-neuroscience research. Collaborates extensively across institutions to advance large-scale brain simulations and data-driven models.

• Neuroscience
• Artificial Intelligence
• Neuronal Modeling
• +۵ مورد دیگر

Shunichi Ishihara is a Professor at the School of Culture, History & Language, The Australian National University, where he leads research in forensic linguistics and computational linguistics. His work focuses on forensic text and voice comparison, authorship attribution, and Japanese linguistic studies. He holds qualifications including a PhD (ANU), MSc (Macquarie), MA (ANU), and BEd (Shizuoka). Research Interests: Forensic Voice/Text Comparison Computational Linguistics Intonational Modelling Japanese Language Processing Stylometric Analysis Research Trends: Recent work emphasizes likelihood ratio-based systems for authorship verification, fusion of acoustic and text features for forensic analysis, and applications of deep learning in text evidence evaluation. His studies often explore cross-lingual comparisons (e.g., Japanese, English, Vietnamese) and system validation methodologies. Grants & Projects: "Likelihood project on author recognition" (2024-2026) "Big Australian Speech Corpus" (2010-2015) Multiple forensic voice/text comparison initiatives Labs & Teams: Director of the Speech and Language Lab, collaborating on speech corpus development and forensic linguistic systems.

• Forensic Voice/Text Comparison
• Computational Linguistics
• Intonational Modelling
• +۶ مورد دیگر

Pooya Davari is a Professor and Head of the Section for Applied Power Electronic Systems at Aalborg University , Denmark. He leads the EMI/EMC in Power Electronics Research Group and serves as Vice Chair of the Energy Efficiency Mission. His research focuses on electromagnetic interference (EMI) and harmonic mitigation in power electronic systems, with over 200 publications and significant contributions to renewable energy integration. Education: B.Sc. and M.Sc. in Electronic Engineering (2004, 2008), Ph.D. in Power Electronics from Queensland University of Technology (2013) Prior Roles: Lecturer at QUT (2013–2014), Postdoc at AAU (2014) Research Interests: Harmonic and EMI analysis in grid-tied converters High power density converter design Signal processing for converter modeling Reliability of power electronic systems Article Trends: Recent work emphasizes EMI/EMC in renewable energy systems, wide bandgap semiconductors (SiC/GaN), and reliability modeling for EVs and hydrogen production via electrolysis. Sub-fields include converter topologies, grid integration challenges, and AI-driven diagnostics. Scientific Awards: Equinor 2022 Prize (Denmark’s oldest engineering award) IEEE EMC Society Young Professional Award (2020) World’s Top 2% Highly Cited Scientist (Stanford, 2021–2025) Multiple best paper awards (IEEE, Applied Sciences, etc.) Grants & Editorial Roles: Recipient of grants from Innovation Fund Denmark (Supra-EMC project), Horizon Europe (SOLARIS), and industry partnerships. Serves as Area Editor for IEEE Transactions on Transportation Electrification , Associate Editor for IEEE Transactions on Power Electronics , and Editor-in-Chief of Circuit World Journal (2020–2025). Labs & Standards: Coordinator of the EMC Laboratory at Aalborg University. Member of IEC standardization Working Groups 6 and 8 (TC77A), focusing on EMC strategies for power grids.

• Harmonic and EMI analysis and mitigation in grid-tied converters
• Electromagnetic interference and compatibility
• High power density power electronic systems
• +۲ مورد دیگر

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

• Machine Learning
• Data Mining
• Bioinformatics
• +۱۱ مورد دیگر

Monowar Bhuyan is an Associate Professor in the Department of Computing Science at Umeå University, Sweden, leading the Cyber Analytics and Learning Group within ADSLab. He holds a Ph.D. in Computer Science from Tezpur University and has held academic positions at Assam Kaziranga University and Umeå University. His research focuses on machine learning, anomaly detection, edge AI, and distributed systems security. He has secured over 35 MSEK in grants from WASP, STINT, and EU Horizon programs. Education Ph.D. in Computer Science and Engineering, Tezpur University (2014) M.Tech. in Information Technology, Tezpur University (2009) B.E. in Computer Science and Engineering, IETE (2007) Research Interests Distributed/Federated/Responsible Machine Learning Cybersecurity and Anomaly Detection in Edge Clouds AI for DDoS Defense and Cyber Resilience Edge AI and Serverless Computing Recent Contributions His recent work addresses secure federated learning, DDoS attack detection in cloud-edge systems, and responsible AI. Key publications include novel frameworks for VSI-DDoS detection and federated learning optimizations. Awards & Grants Best Paper Awards at ICONIP 2023 and ACM ICACCI 2012 WASP NEST Grant (AIR2 Project, 5 MSEK) EU Horizon Europe Grant (SovereignEdge.Cognit, 8.27 MSEK) Lab & Collaborations He leads the Cyber Analytics and Learning Group (ADSlab), collaborating with institutions like KTH, Linköping University, and Nara Institute of Science and Technology (NAIST). The lab focuses on AI-driven security solutions for distributed systems.

• Machine Learning
• Anomaly Detection
• Systems and AI Security
• +۶ مورد دیگر

Gökhan Alcan is an Assistant Professor in Robotics and Machine Learning at the Automation Technology and Mechanical Engineering Unit of Tampere University, Finland. He leads the Advanced Learning, Control and AutomatioN (ALCAN) Research Group, focusing on safe model predictive control, constrained optimal control theory, reinforcement learning, and their applications to dynamical systems. His research addresses challenges in robotic manipulation, safe navigation, and human-robot collaboration through projects like the Aurora initiative on automated and connected machines. Education: B.Sc., M.Sc., and Ph.D. in Mechatronics Engineering from Sabanci University (2008–2019). Postdoctoral research at Sabanci University (2019) and Aalto University (2020–2024). Research Interests: Robotics, control theory, system identification, autonomous systems, and machine learning applied to robotic manipulation, autonomous vehicles, and safety-critical systems. Notable work includes trajectory optimization for hybrid systems, magnetic manipulation for medical applications, and sim-to-real gap analysis in cloth manipulation. Awards: Third Place in Aalto Open Science Award 2023, Elginkan Foundation Technology Award (2016), and multiple scholarships. Advised Ph.D. student David Blanco Mulero, who defended his thesis on robotic manipulation of deformable objects. Labs/Teams: ALCAN Research Group, former roles in Aalto University's Intelligent Robotics Group and Sabanci University's Control, Vision, and Robotics (CVR) Group.

• Robotics
• Machine Learning
• Control Theory
• +۵ مورد دیگر

Professor Jouni Mattila is a leading academic in Machine Automation at Tampere University's Faculty of Engineering and Natural Sciences, affiliated with the Automation Technology and Mechanical Engineering department. He is part of the IHA-Innovative Hydraulics and Automation research group. His expertise spans autonomous mobile working machines, nonlinear control engineering, and safety-critical systems like those in the ITER project. He holds a Technical Editor role in ASME/IEEE Transaction on Mechatronics (2015-2020). Research interests include real-world autonomous systems, whole-body motion control for rough-terrain robots, energy-efficient actuators, and teleoperation systems. His work integrates advanced control theory, AI, and robotics for heavy-industry applications. Recent publications focus on robust control frameworks, LiDAR-inertial SLAM navigation, and fault-tolerant systems for mobile robots. Publications highlight advancements in hydraulic/electromechanical actuator systems, visual-inertial feedback control, and energy-efficient robotics. Awards/recognitions are not explicitly listed, but his contributions are evident through collaborations with Finnish industry and big science projects. Advising focuses on MSc and Dr (Tech) students in robotics and automation, with a mission to bridge academia and industry for high-tech innovation. Labs/teams include the Intelligent Hydraulics and Automation (IHA) group, emphasizing practical R&D in cleantech and heavy-duty robotics. Ongoing projects address challenges in autonomous rock-breaking systems, exoskeleton control, and energy-efficient robotic actuators.

• Machine Automation
• Autonomous Mobile Working Machines
• Heavy-Duty Robotics
• +۴ مورد دیگر

Dr. Wenjuan Yu is a Lecturer at the School of Computing and Communications (SCC), InfoLab21, Lancaster University, UK. She holds a PhD in Communication Systems from Lancaster University and has held prior roles, including Research Fellow at the 5G Innovation Centre (5GIC), University of Surrey (2018–2020), and part-time Research Officer at the University of Essex (2017–2018). She is a Fellow of the Higher Education Academy and a Senior Member of IEEE. Her research focuses on communication systems, with emphasis on radio resource management, mMTC, low-latency communications, B5G/6G, MEC, and machine learning. She actively contributes to IEEE conferences as a Technical Program Committee (TPC) member and holds editorial roles, including Executive Editor for Transactions on Emerging Telecommunications Technologies (2019–2022). Current teaching includes CNSCC141 Professionalism in Practice and CNSCC365 Advanced Networking . Dr. Yu supervises PhD students in EE/CS, particularly welcoming applicants from China via CSC scholarships. Her projects include DSI-funded initiatives on sustainable AI-driven resource allocation for 6G and Smart Multi-RAT Traffic Steering for V2X systems. She leads research groups in Security Lancaster (Networks, Systems, Distributed Systems).

• Communication Systems
• Radio Resource Management
• Massive Machine Type Communications (mMTC)
• +۴ مورد دیگر

Gustav Henter is an Assistant Professor in Intelligent Systems at KTH Royal Institute of Technology, specializing in Machine Learning. He is affiliated with the Division of Speech, Music and Hearing (TMH) within the School of Electrical Engineering and Computer Science. His research focuses on deep generative models for applications like speech synthesis, 3D character animation, and human-computer interaction. He holds a Docent degree from KTH and has held post-doctoral positions at the University of Edinburgh and the National Institute of Informatics in Tokyo. Education: PhD in Electrical Engineering (KTH, 2013), MSc in Engineering Physics (KTH, 2007). He supervises doctoral students in areas like gesture synthesis and multimodal interaction. His work is supported by grants from the Wallenberg AI, Autonomous Systems, and Software Program (WASP) and South Korea's MOTIE. He co-founded Motorica AB to commercialize motion synthesis research. Awards include Best Paper Awards at ICMI 2020 and IVA 2020, and recognition for student theses. His research spans generative AI, perceptual evaluation, and robust statistical models. He organizes the GENEA Challenge and Workshop series for gesture generation benchmarking.

• Deep Generative Models
• Speech Synthesis
• 3D Character Animation
• +۳ مورد دیگر