جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Computer Science
• +۳ مورد دیگر

Prof. Felix Bießmann holds a professorship in Computer Science and Media at Berlin University of Applied Sciences' Department VI. His research focuses on machine learning applications in diverse fields including healthcare, urban planning, environmental science, and robotics through his Cognitive Algorithms Lab. He teaches courses such as Machine Learning, Deep Learning, and Data Science Workflows, alongside roles at TU Berlin and Korea University. Education: PhD (Dr. rer. nat.) in Natural Sciences Research interests span machine learning theory and practical implementations across domains like computer vision, generative AI, and sensor data analysis. His work addresses challenges in automated systems, healthcare monitoring, and sustainable technologies. Recent student theses explore topics like license plate recognition, adaptive game soundtracks, and bird song detection using TinyML. Collaborations include projects with the Charité Berlin and Robert-Koch Institute. Lab: Cognitive Algorithms Lab (developing machine learning methods/applications) Contact: felix.biessmann@bht-berlin.de | Office D138, Berlin University of Applied Sciences.

Scott Thompson-Whitesideمشاهده پروفایل

استاد

• Human-centered Design
• Machine Learning
• EEG Data
• +۶ مورد دیگر

Scott Thompson-Whiteside is a Professor and Executive Dean at Torrens University Australia's School of Design and Creative Technology. With a career spanning over two decades, he contributes to research in human-centered design , machine learning , and sustainable transportation . His work bridges design innovation with technological solutions, particularly in electric bus systems and deepfake detection. Key Research Areas : Human-centered design, EEG data analysis, Industry 4.0, drone technology, design education Recent Research Trends (2022-2025): Machine learning in neurological disorder detection (e.g., epilepsy via EEG graphs) Deepfake detection techniques Design-led sustainable public transport systems in Malaysia Ethical frameworks for drone vision applications Industry 4.0 adoption challenges in creative sectors Academic standards in transnational education Academic Leadership : As Executive Dean, he actively supervises PhD students and influences higher education policy. His teaching innovations focus on design thinking and internationalization of curricula.

• Microchannel Engineering
• Proton Exchange Membrane Fuel Cell
• Two-phase Flow
• +۷ مورد دیگر

Eon Soo Lee is an Associate Professor in the Department of Mechanical and Industrial Engineering at the New Jersey Institute of Technology (NJIT). His primary research focuses on advanced materials engineering, biomedical microfluidics, and assistive technologies for individuals with disabilities. He has led federally funded projects including 'I-Corps: Multiplex Diagnostic Assay Using Interdigitated Nano-Sensing Technology' (NSF, 2023-2025) and 'Innovative Nano Catalysts for Automobile and Fuel Cell Applications' (NSF, 2018). Research Interests: Lee's work spans interdisciplinary areas including: Development of N-doped graphene/MOF composites for energy applications Microfluidic systems for blood plasma separation and antigen detection Design of accessible technologies for visually impaired users, including VR audio descriptions and remote sighted assistance systems Grants and Projects (select): National Science Foundation (2023): $500K for multiplex diagnostic assays National Science Foundation (2018): $300K for nano-catalysts in fuel cells Multiyear collaborations with industry partners on biosensor integration Innovation Highlights: Developed AIGuide: AR hand-guidance system for visual impairments Pioneered omnidirectional audio descriptions for VR music performances Published extensively in Carbon , Biomicrofluidics , and ACM/IEEE accessibility venues

Eunsik Kimمشاهده پروفایل

استاد

• Ergonomics
• Biomechanics
• Human Factors Engineering
• +۵ مورد دیگر

Eunsik Kim is a Professor at the University of Windsor , holding a position in the Department of Mechanical, Automotive, and Materials Engineering within the Faculty of Engineering . His research focuses on ergonomics, occupational safety, and biomechanics, with applications in automotive design, workplace safety, and human-machine interaction. He leads the Occupational Safety and Ergonomics research lab , where students explore real-world solutions for challenges such as driver fatigue, musculoskeletal risks in manual labor, and automation in agriculture. Key research interests include: Ergonomics : Optimizing workstation designs, automotive seating, and anti-vibration tools Biomechanics : Analyzing human posture, musculoskeletal loading, and workplace ergonomics Machine Learning : Developing AI-driven systems for posture recognition, workload prediction, and task automation Autonomous Vehicles : Investigating human factors in driving automation and takeover scenarios Education Innovation : Gamification in engineering labs to enhance student motivation and learning Collaborations include projects with Ewha Womans University (South Korea) on autonomous vehicle comfort and a prototype robotic system for mushroom harvesting. His work often bridges theoretical research with practical applications, addressing both industry and academic challenges. Awards and Recognition: Students Ilfeoma Michael and Elnaz Akhavan Rezaee received accolades at the Association of Canadian Ergonomists conference for their lab research Advising and Grants: Professor Kim mentors students in interdisciplinary projects, focusing on ergonomics, robotics, and AI. His research has led to innovations in workplace safety and automated systems, supported by institutional and collaborative funding. Labs/Teams: The Occupational Safety and Ergonomics Lab and partnerships with South Korean institutions drive cutting-edge studies in automotive safety, robotics, and human factors engineering.

David Towersمشاهده پروفایل

استاد

• Optical Sensing Technologies
• Fluid Mechanics
• Structural Assessment
• +۶ مورد دیگر

Professor David Towers is the Head of the School of Engineering at the University of Warwick and holds the position of Professor of Mechanical Engineering. With over 30 years of combined academic and industry experience, he leads a dynamic research group focused on optical sensing technologies, fluid mechanics, and structural assessment. His work bridges engineering, biology, and clinical practice, with significant contributions to malaria control strategies through mosquito behavior analysis. Education: BSc (1st Class) in Mechanical Engineering Science from the University of Warwick PhD in Optical Engineering from the University of Warwick Royal Society Fellowship at ETH Zürich Research Interests: Professor Towers’ research spans three core areas: fluid mechanics (turbulent flows, sprays, multi-phase mixing), structural assessment (optical interferometry for deformation and stress measurement), and clinical optical systems (mosquito tracking for malaria intervention). His multi-disciplinary approach emphasizes collaboration with end-users, particularly in developing novel optical instruments for industrial and healthcare applications. Grants & Projects: AI for Mosquito Trajectory Understanding (£X, 2022–Present) Bill & Melinda Gates Foundation: Next-Gen LLIN Development (£X, 2019–2023) MRC: Indoor Mosquito Behavior Mapping (£X, 2019–2022) Awards: Athena SWAN Silver Award (2021) for promoting gender equality in STEM. Labs & Teams: Leads the Applied Optics Laboratory and collaborates with the Vector Control Research Group, focusing on AI-driven solutions for vector-borne disease control.

Sharmin Sultanaمشاهده پروفایل

استادیار

• Multimodal Image Processing
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Sharmin Sultana, PhD, is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University's College of Engineering and Computing. Her research focuses on developing advanced computational methods for medical imaging, particularly in artificial intelligence, computer vision, and deep learning applications to improve healthcare diagnostics and treatment. PhD, Computational Modeling and Simulation, Old Dominion University (2017) Postdoctoral Research Fellow, Radiation and Oncology, Johns Hopkins University School of Medicine (2017–2020) MS and BS, Computer Science and Engineering, University of Dhaka, Bangladesh Dr. Sultana specializes in Multimodal Image Processing , Artificial Intelligence , and Medical Imaging , with a focus on creating AI-driven solutions for biomedical image analysis. Her work includes state-of-the-art techniques for image-guided cancer treatment and multimodal image registration. The 15 most recent publications highlight her contributions to prostate cancer imaging , cranial nerve segmentation , and environmentally sustainable materials . Key methodologies include convolutional neural networks, deformable models, and dual contouring for precise surgical planning and simulation.

Xiaonan Guoمشاهده پروفایل

استادیار

• Cyber-physical Systems Security
• Mobile Device Security
• IoT and Wearable Technologies
• +۳ مورد دیگر

Xiaonan Guo is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University. His research focuses on security and privacy in cyber-physical systems, mobile device security, IoT, mobile healthcare, and machine learning applications in mobile computing. He holds a PhD in Computer Science from the Hong Kong University of Science and Technology. His work spans innovative applications of mmWave technology for authentication, health monitoring, and activity recognition, alongside contributions to privacy-preserving systems and mobile deep learning optimization. Recent research emphasizes contactless human concentration monitoring, secure mobile DNN execution, and universal adversarial attacks against mmWave-based systems. Xiaonan’s publications reflect a strong focus on mobile and IoT-driven healthcare solutions, wearable device security, and cross-technology localization. His articles often bridge theoretical advancements with real-world applications in smart healthcare systems and pervasive computing environments.

• Machine Learning
• Bioinformatics
• Materials Science
• +۷ مورد دیگر

Pengyu Hong is a Professor of Computer Science at Brandeis University's Michtom School of Computer Science and an affiliated faculty member at the Benjamin and Mae Volen National Center for Complex Systems. His expertise spans Machine Learning, Bioinformatics, Materials Science, and FinTech, with a focus on interdisciplinary applications in healthcare, molecular biology, and complex systems analysis. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign M.E. in Computer Science, Tsinghua University B.Eng. in Computer Science, Tsinghua University Research Interests: Hong's lab develops advanced machine learning techniques for analyzing heterogeneous data (images, text, financial data), with notable contributions in glycomaterials analysis, clinical outcome prediction, and active nematics modeling. His work bridges computational methods with biomedical and material science challenges, including NMR spectroscopy analysis and molecular property prediction. Publications: Recent work focuses on machine learning applications in glycan sequencing, fairness analysis in medical algorithms, and optical flow techniques for fluid dynamics. The lab also maintains benchmark datasets like GlycoNMR for carbohydrate analysis. Labs & Teams: Hong leads research at the Volen National Center for Complex Systems, integrating computational approaches with experimental systems biology and materials science.

Darshika G. Pereraمشاهده پروفایل

استادیار

• Computer Engineering
• FPGA-Based Hardware Acceleration
• Neuromorphic Computing
• +۵ مورد دیگر

Darshika G. Perera is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Colorado Colorado Springs (UCCS). Her research focuses on FPGA-based hardware acceleration, neuromorphic computing, and embedded systems optimization with applications in machine learning, cryptography, and battery management systems. She holds a PhD and has extensive experience in designing reconfigurable architectures for compute-intensive tasks. Her work bridges theoretical algorithms with practical hardware implementations, emphasizing efficiency and real-time performance. Key research areas include FPGA design methodologies, neuromorphic hardware for AI applications, and embedded systems security. She has published widely on topics such as hardware-software co-design for edge computing, optimization algorithms for genomics, and blockchain applications in healthcare IoT. Her contributions also span data mining hardware accelerators and dynamic reconfiguration techniques for cryptographic systems. Dr. Perera’s work is characterized by interdisciplinary collaborations, combining principles from electrical engineering, computer science, and applied mathematics. She is committed to advancing next-generation edge-computing platforms through innovative architectures and methodologies.

• Medical Image Analysis
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Dr Himashi Peiris is a Research Fellow in the Department of Data Science & AI at Monash University's Faculty of Information Technology in Australia. She holds a PhD in Biomedical Engineering from Monash University (2024) and a Bachelor's in Information Technology from the University of Moratuwa, Sri Lanka. Her work focuses on semi-supervised learning, medical image analysis, and AI-driven healthcare solutions. She has over three years of industry experience in software engineering. Research interests include developing machine learning algorithms for medical imaging challenges, particularly in scenarios with limited labeled data. Her innovations span neural networks, transformer architectures, and uncertainty-guided segmentation techniques applied to MRI, CT scans, and biomedical datasets. Her publications in Nature Machine Intelligence and MICCAI conference highlight contributions to semi-supervised segmentation and AI-driven diagnostic tools. Notable collaborations include the development of PINGU for perivascular space identification and adversarial networks for construction waste recognition. Awards include the 2023 Victorian Biomedical Imaging Capability Early Career Award and 2023 IEEE ACS Student Writing Award. Her work has been featured in media outlets and Mendeley platforms, emphasizing AI's role in medical decision-making.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Rizwan Qureshi is an active researcher and academic specializing in artificial intelligence, machine learning, and their applications in medical imaging and bioinformatics. With a robust publication record spanning from 2017 to 2025, he has established himself as a significant contributor to the fields of computer vision and biomedical AI. His research interests focus on Artificial Intelligence , Machine Learning , Medical Imaging , Computer Vision , and Biomedical Engineering . Qureshi's work demonstrates particular expertise in object detection systems (especially YOLO variants), medical image segmentation, vision-language models, and applications of AI to healthcare problems including lung cancer research and diabetic retinopathy detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward medical applications of AI, with approximately 60% of his work focusing on healthcare-related problems. His research shows increasing emphasis on model robustness, explainability, and handling distribution shifts in real-world applications. The publications span top venues including IEEE Access, IEEE Transactions on Medical Imaging, CVPR, and BIBM. Qureshi maintains extensive collaborations with researchers across multiple institutions, with frequent co-authorship with Hong Yan, Tanvir Alam, Jia Wu, and Sheheryar Khan. His work demonstrates both technical depth in machine learning methodologies and practical application to significant healthcare challenges. While specific details about his academic advising are not evident from the publication record alone, his numerous publications with multiple co-authors suggest active participation in research teams and likely supervision of graduate students. His work shows consistent funding support through publication in reputable journals and conferences.

Peter Bienstmanمشاهده پروفایل

استاد

• Photonics
• Neuromorphic computing
• Nanophotonics
• +۱۱ مورد دیگر

Peter Bienstman is a full professor at Ghent University, working in the Department of Information Technology (INTEC) where he has been since 1997. He is affiliated with the Photonics Research Group and also collaborates with imec. His research spans nanophotonics, neuromorphic computing, and biosensing applications. Bienstman received his electrical engineering degree from Ghent University in 1997 and completed his Ph.D. at the same institution in 2001. His doctoral work focused on "Rigorous and efficient modelling of wavelength scale photonic components." His research interests primarily revolve around nanophotonics and its applications, with specific focus areas including: Photonic Reservoir Computing for neuromorphic information processing Optical label-free biosensors based on ring resonators TE/TM biosensors for measuring conformational changes SiN biosensors operating in the visible spectrum Optical spiking neurons and neuromorphic architectures Nanophotonic information processing systems Analysis of his recent publications reveals a strong focus on advancing photonic reservoir computing for practical applications, particularly in communications signal processing and biomedical sensing. His work demonstrates how photonic systems can implement neuromorphic computing paradigms with energy efficiency advantages over traditional electronics. Recent trends show increasing integration of phase-change materials and exploration of quantum-inspired photonic computing approaches. Bienstman has received significant recognition for his work, most notably an ERC Starting Grant for the Naresco-project: "Novel paradigms for massively parallel nanophotonic information processing." This prestigious European grant supports his innovative research at the intersection of photonics and computing. As an advisor, Bienstman has supervised numerous doctoral students to completion and currently mentors a large research group with nine active PhD students and two postdoctoral researchers. His research is supported by multiple grants that enable the development of novel photonic computing architectures and biosensing platforms. The group's work bridges fundamental photonics research with practical applications in communications, healthcare, and computing. The Photonics Research Group at Ghent University, where Bienstman works, maintains state-of-the-art facilities for nanophotonic device design, fabrication, and characterization. The group collaborates extensively with imec and other international research institutions, creating a vibrant ecosystem for advancing photonic technologies from fundamental research to potential commercial applications.

Dmitry Pushinمشاهده پروفایل

دانشیار

• Neutron Physics
• Quantum Information Science
• Condensed Matter Physics
• +۴ مورد دیگر

Dmitry Pushin is an Associate Professor at the University of Waterloo and a Faculty member of the Institute for Quantum Computing (IQC). He holds a PhD in Physics from MIT (2006), and prior degrees from Moscow Institute of Physics and Technology (BSc 1995, MSc 1997). His research focuses on advancing neutron interferometry to address fundamental physics questions, including quantum information science, condensed matter physics, and dark energy studies. He explores applications like magnetic material characterization and spintronics development, with recent work on chameleon scalar field constraints via neutron experiments. Teaching includes courses such as MNS 321 (Electrical/Optical Properties of Materials), MNS 410 (Special Topics in Solid-State Materials), and graduate courses in Quantum Information Processing (QIC 890). His experimental work leverages neutron optics to study quantum coherence effects at macroscopic scales, with contributions to structured neutron waves and precision measurements. Key research areas include quantum information processing, magnetic materials, and fundamental physics inquiries like Born’s Rule verification. His team pioneered methods for neutron orbital angular momentum (OAM) generation and applied interferometry to study novel materials. Recent advancements include high-precision silicon interferometer fabrication and PROSPECT experiment contributions in antineutrino physics. Notable collaborations involve the PROSPECT reactor antineutrino experiment, focusing on short-baseline oscillations and dark matter limits. He also explores human perception of structured light phenomena, linking quantum optics to vision science.

Bruno SICILIANOمشاهده پروفایل

استاد

• Robotics
• Automation
• Control Systems
• +۴ مورد دیگر

Prof. Bruno Siciliano is a full Professor of Automatics and Robotics at the University of Naples Federico II's Department of Electrical Engineering and Information Technology (DIETI). He directs the PRISMA Lab and chairs the Scientific Council of the ICAROS Center. His research focuses on robotics, automation, and human-robot interaction, with notable contributions to medical robotics, deformable object manipulation, and AI integration. He has received prestigious awards such as the 2024 IEEE RAS Pioneer Award and is a Fellow of multiple international organizations. Affiliations: Director, PRISMA Lab Chair, ICAROS Center Scientific Council Member, ACN Technical-Scientific Committee Research Interests: His work spans robotics education, surgical robotics, non-prehensile manipulation, and AI-driven robotic systems. Notably, his projects include the ERC-funded EndoTheranostics initiative for robotic colonoscopy and RoDyMan for dynamic manipulation. His contributions bridge robotics with healthcare, automation, and cognitive systems. Awards & Honors: 2024 IEEE RAS Pioneer in Robotics and Automation Award Genio Award (2024) for pioneering Italian robotics Fellowships: IFAC, IEEE RAS, and international AI associations Grants & Leadership: ERC Synergy Grant (2023, 10M€) and Advanced Grant (2013) Leadership roles in IEEE RAS and international robotics initiatives Labs & Teams: PRISMA Lab focuses on robotics paradigms like design, knowledge, and human-robot interaction, with projects in medical robotics and autonomous systems.

• Microwave Imaging
• Dielectric Properties
• Antenna Engineering
• +۵ مورد دیگر

Mehmet Çayören is a Professor at Istanbul Technical University in the Department of Electronics and Communication Engineering. His research focuses on applying microwave imaging techniques to medical diagnostics, particularly for early detection of breast cancer. He leads the development of the SAFE (Screening and Early Detection) microwave imaging system, which has shown promising results in clinical investigations. His work bridges electrical engineering, biomedical applications, and machine learning for improved cancer screening. Dr. Çayören's research interests span multiple domains within electromagnetic applications for medical imaging: Microwave imaging systems for breast cancer detection and screening Determination of dielectric properties of biological tissues Development of open-ended coaxial probe techniques for material characterization Application of machine learning algorithms (XGBoost, SVM) to enhance medical imaging Monitoring of intracerebral hemorrhage using microwave imaging Development of tissue-mimicking phantoms for medical device validation His recent publications demonstrate a strong trend toward integrating advanced machine learning techniques with microwave imaging systems to improve diagnostic accuracy. The SAFE platform represents a significant advancement in non-invasive breast cancer screening, particularly for dense breast tissue where traditional mammography has limitations. His work also extends to neurological applications, with research on microwave-based monitoring of brain hemorrhages. Scientific awards received by Dr. Çayören include: Teknoloji Ödülü (Technology Award) in 2014 Dr. Çayören has supervised 24 students and leads multiple research projects funded by various sources including TUBITAK. His current projects focus on microwave imaging systems for breast cancer screening, monitoring of intracerebral hemorrhage, and hardware design for microwave imaging systems. He collaborates extensively with medical professionals to validate his imaging systems in clinical settings. Dr. Çayören leads a research group focused on microwave imaging applications in medicine. His team develops both hardware systems (like the SAFE platform) and advanced signal processing algorithms to improve medical diagnostics. The group maintains close collaborations with hospitals for clinical validation of their imaging systems.