جست‌وجوی استاد و پژوهشگر

Pedro Ferreiraمشاهده پروفایل

استاد

• Business Model Innovation
• Cultural Management
• Digital Display Systems
• +۳ مورد دیگر

Pedro Ferreira is a Professor at Iscte Business School, ISCTE - IUL, Lisbon, Portugal. His academic work spans multiple disciplines including business model innovation, cultural management, digital display systems, and consumer behavior. Key research areas: Business Model Innovation, Cultural Management, Digital Display Systems Interdisciplinary work: Healthcare Management, Energy Efficiency, Sensory Marketing Notable collaborations: Rock in Rio, Ferreira's de Castro Cultural Complex His publications demonstrate ecosystem-based approaches to cultural economics, with applications in virtual reality exhibitions and sustainable business practices. Recent work focuses on technology integration in perfume exhibitions and energy efficiency strategies. Earlier research includes pharmacological studies on P2X7 receptors in colitis and foundational financial accounting textbooks. He has analyzed group buying systems in Portugal and explored intersections between management theory and art. Academic contributions span 45 years, with a 2024 patent on digital display systems and a 1979 report on tomato industry economics. Current affiliations include: Iscte Business School ExecED at ISCTE INDEG-ISCTE Edifício INDEG-ISCTE, Lisbon

Jacob Whitehillمشاهده پروفایل

دانشیار

• Applied Machine Learning
• Speech and Speaker Recognition
• Educational Data Mining
• +۳ مورد دیگر

Jacob Whitehill is an Associate Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), affiliated with the Learning Science & Technologies (LST) program. His research focuses on applying machine learning to education and human-computer interaction, including speech recognition, emotion analysis, and automated classroom observation. He leads the NSF-funded project Developing New Scientific Instruments for Classroom Observation and collaborates on the AI Institute for Student-AI Teaming (iSAT) . His research interests span Applied Machine Learning (e.g., multi-modal systems, speaker diarization), AI for Education (e.g., automated instructional evaluation, child speech recognition), and Emotion Recognition (e.g., affective computing in classrooms). Recent work emphasizes classroom observation tools and improving student-teacher interaction analysis through video and audio data. Notable projects include: NSF-funded classroom observation tools AI Institute for Student-AI Teaming (iSAT) Schmidt Futures-funded Hybrid Human-Agent Tutoring for math education His team includes PhD students Xinlu He (Data Science), Jiani Wang (Computer Science), and Yiwen Guan (Computer Science), along with visiting scholar Cecilia Tivir. He advises students on topics like speaker recognition, educational data mining, and computer vision in classroom settings. Grants and collaborations include NSF, Schmidt Futures, and industry partnerships. His lab develops tools for automated feedback on teaching practices, leveraging LLMs and multimodal data. For more details, contact jrwhitehill@wpi.edu .

Gady Agamمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۸ مورد دیگر

Dr. Gady Agam is an Associate Professor in the Department of Computer Science at Illinois Institute of Technology (Illinois Tech), affiliated with the College of Computing. His primary research focuses on Computer Vision, Machine Learning, and Artificial Intelligence, with applications in medical imaging, remote sensing, and security systems. He leads the Visual Computing Lab, which explores topics such as deep learning, geometric modeling, and computational methods for data analysis. Dr. Agam teaches courses including CS584 (Machine Learning), CS512 (Computer Vision), and CS577 (Deep Learning). His research has resulted in over 100 peer-reviewed publications and collaborations with organizations like SPIE. He has held leadership roles in conferences such as Document Recognition and Retrieval and has contributed to industry-relevant projects like the MuscleX software. His academic contributions include advancements in image registration, feature detection, and automated medical diagnostics. He actively supervises graduate students in his lab and maintains partnerships with academic and industrial entities to drive innovation in visual computing technologies.

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Prof. Margret Keuper is a Professor in the Department of Computer Vision and Machine Learning at the Max Planck Institute for Informatics. Her research focuses on advancing machine learning and computer vision techniques, with an emphasis on model fairness, adversarial robustness, and multimodal interactions. She leads interdisciplinary projects exploring topics such as dataset analysis, generative models, and climate action through visual narrative analysis. Research Interests: Her work bridges theoretical foundations and practical applications in domains like adversarial training, image classification robustness, and robotics perception. She explores how vision-language models can be steered to align with human biases and develops methods for data-efficient learning and interpretability. Recent Contributions: Recent work includes FAIR-TAT (model fairness via adversarial training), VSTAR (video synthesis), and TikZero (zero-shot graphics program generation). Her publications in top venues like CVPR, ICCV, and ICLR highlight contributions to both methodological innovation and real-world impact. Collaborations: Works closely with researchers across Max Planck and academic partners, focusing on projects such as sensor layout optimization, climate discourse analysis via social media imagery, and domain-aware foundation model fine-tuning.

Martin Wynnمشاهده پروفایل

دانشیار

• Digitalisation
• Digital Transformation
• Emerging Technologies
• +۴ مورد دیگر

Martin Wynn is an Associate Professor of Information Technology at the University of Gloucestershire's School of Computing and Engineering. With 20 years of industry experience as an IT professional and senior manager at Glaxo Pharmaceuticals and HP Bulmer Drinks, he transitioned to academia with a focus on knowledge transfer partnerships and post-doctoral supervision. His research interests span digital transformation, emerging technologies, sustainability, IT strategy, and risk management. He holds a PhD in Town and Country Planning and a PGCFHE in Education. Education: BA (Hons) Geography (Durham, 1973), MA Geography (Durham, 1977), PhD Town and Country Planning (Nottingham Trent, 1981), PGCFHE Education (Gloucestershire, 2003). Research focuses on leveraging technology for sustainable development, including digitalisation in manufacturing, cybersecurity, and the interplay between technology and urban planning. Over 20 doctoral students have been supervised in areas like digitalisation, analytics, and sustainability. Publications span e-business adoption in developing countries, ERP systems in SMEs, and ICT utilization in Libyan universities. He is a PRINCE2 Practitioner and Certified Management Consultant (CMC).

Tsung-Yi Hoمشاهده پروفایل

استاد

• Design Automation for Microfluidic Biochips
• Nanometer Integrated Circuits
• Embedded Systems
• +۳ مورد دیگر

Tsung-Yi Ho is a Professor in the Department of Computer Science at National Tsing Hua University, Taiwan. He holds the Hans Fischer Fellowship at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). His primary research focuses on design automation for microfluidic biochips and nanometer integrated circuits, emphasizing reliability, optimization, and interdisciplinary applications in bioengineering. Ho received his Ph.D. in Electrical Engineering from National Taiwan University in 2005. He has held positions at National Cheng Kung University and National Chiao Tung University before joining National Tsing Hua University. His work bridges algorithmic design with practical biochip fabrication, addressing challenges like contamination control, routing optimization, and fault tolerance in microfluidic systems. His research interests span design automation for emerging technologies, including paper-based biochips and 3D microfluidic architectures. He has pioneered methods for integrating hardware-software co-design principles into biochip development, enhancing both functionality and reliability. His contributions include novel routing algorithms, contamination mitigation techniques, and reliability-aware synthesis frameworks. Ho has authored over 100 publications, including influential papers in IEEE Transactions on CAD and ACM journals. He serves on the editorial boards of multiple top-tier journals and chairs professional chapters for ACM and IEEE. His awards include the Humboldt Research Fellowship, Dr. Wu Ta-You Memorial Award, and Best Paper Awards at VLSI Test Symposium and IEEE Transactions on CAD. His current projects involve optimizing control-fluidic co-design for paper-based biochips and developing AI-driven frameworks for microfluidic functionality prediction. He collaborates widely, leading cross-disciplinary initiatives at TUM-IAS and Taiwan's academic institutions.

Stefano Buosoمشاهده پروفایل

مدرس

• Biomedical Engineering
• Cardiovascular Modeling
• Computational Fluid Dynamics
• +۴ مورد دیگر

Dr. Stefano Buoso is a Lecturer at ETH Zürich's Department of Information Technology and Electrical Engineering, specializing in biomedical imaging and computational mechanics. His research focuses on cardiovascular modeling, fluid-structure interaction, and reduced-order modeling. He holds a position in the Professur für Biomedizinische Bildgebung (Biomedical Imaging Professorship). Key research areas include computational fluid dynamics applied to cardiovascular systems, development of machine learning tools for medical imaging (e.g., MRI and echocardiography), and creation of patient-specific numerical phantoms. His work bridges engineering and medicine, with applications in clinical diagnostics and personalized medicine. Recent articles emphasize innovations like automated 3D heart modeling from 2D echocardiography, synthetic MRI data generation, and turbulence analysis in aortic flow. He has developed open-source tools like CMRsim for cardiovascular simulations. Buoso collaborates on digital twin technologies for carotid artery disease assessment and fluid dynamics modeling of biological systems. His technical contributions include methods for rapid mesh generation, stabilized reduced-order models, and MRI-guided cardiac shape modeling. Research spans from microvascular neurovascular unit studies to bio-inspired membrane wing aerodynamics, reflecting interdisciplinary expertise.

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

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

• Control Systems
• Machine Learning
• Robotics
• +۶ مورد دیگر

Marko Tanasković is a researcher at Singidunum University's Faculty of Informatics and Computing, specializing in control systems, robotics, and electrical engineering. He holds a PhD from ETH Zurich (2015) in Information Technology and Electrical Engineering, following degrees from University of Belgrade (BEng, 2009) and ETH Zurich (MEng, 2011). His research focuses on adaptive control systems, machine learning applications in engineering, and sensorless motor control. Key contributions include: Development of predictive algorithms for traffic systems and industrial automation Innovations in rotor orientation determination for PMSM motors Integration of AI in fraud detection and building climate control Recent work includes a 2024 study on wearable health monitoring devices and a 2022 paper on drone forensics. He has authored/co-authored over 15 peer-reviewed articles and holds patents in motor control technologies. Current affiliations include Singidunum University's Department of Electrical Engineering and Collaboration with ETH Zurich alumni networks. Active in international conferences such as Sinteza and IEEE events.

Urs Hengartnerمشاهده پروفایل

دانشیار

• Information Privacy
• Computer Security
• Network Security
• +۳ مورد دیگر

Urs Hengartner is an Associate Professor at the Department of Computer Science, University of Waterloo. His research focuses on information privacy, computer and network security with emphasis on smartphones, IoT, and machine learning-based authentication systems. He holds a Ph.D. (2005) and M.Sc. (2003) from Carnegie Mellon University, and a Diploma from ETH Zürich (1997). His work spans Adaptive security attacks on ML systems Implicit user authentication frameworks Privacy-preserving technologies for location and genomic data Secure authentication systems resilient to voice/spoofing attacks Recent publication trends show a strong focus on adversarial attack detection (e.g., watermarking evasion, diffusion model attacks) and context-aware authentication systems . His frameworks like MRAAC and SHRIMPS address multi-stage authentication challenges in mobile ecosystems. Key contributions include frameworks for evaluating multi-user authentication systems (SHRIMPS), risk-aware access control (MRAAC), and novel defense strategies against collaborative robot traffic fingerprinting. His work bridges security mechanisms with user-centric design principles.

• Oncology
• Digital Pathology
• Machine Learning in Healthcare
• +۳ مورد دیگر

Yalai Bai is a Research Scientist and Technical Director of Tissue Distribution and Analysis Service in the Pathology Department at Yale School of Medicine. She holds a PhD and MD from Peking University Health Science Center (1996 and 1990, respectively). Her research focuses on quantitative biomarker analysis, tumor immunology, and machine learning applications in pathology. Key projects include developing automated algorithms for tumor-infiltrating lymphocyte (TIL) scoring and standardizing Ki67 and HER2 expression measurements. She collaborates internationally on clinical trials, such as the EORTC 10994/BIG 1-00 breast cancer trial, and contributes to translational studies on cancer metastasis and therapeutic response prediction. Education : PhD in Pathology, Peking University Health Science Center (1996) MD, Peking University Health Science Center (1990) Labs/Teams : Rimm Lab (specializing in quantitative pathology and computational oncology) Tissue Procurement & Distribution team (supporting large-scale clinical studies) Her work bridges clinical pathology and computational methods, addressing challenges in reproducibility and standardization. Recent studies highlight spatial tumor-immune microenvironment analysis and AI-driven biomarker validation across breast, head & neck, and gynecologic cancers. Contributions also include optimizing pre-analytic variables for antigen stability and advancing open-source tools for TIL quantification in triple-negative breast cancer.

• Digital Image Processing
• Aquaculture
• Selective Breeding
• +۳ مورد دیگر

Adrian Lapico is a researcher affiliated with James Cook University , contributing to interdisciplinary projects in digital image processing and aquaculture. His work focuses on applying computational techniques to enhance selective breeding practices in marine organisms, particularly pearl oysters. Research Interests: Adrian's research bridges computer science and marine biology, emphasizing automated measurement systems, machine vision, and data-driven approaches in aquaculture. His 2019 publication at DICTA highlights applications of digital image computing for biological data analysis. Collaborative Work: He collaborates with multidisciplinary teams, including co-authors from institutions like James Cook University, to advance technologies in marine resource management.

Liang Xueمشاهده پروفایل

استادیار

• Applied Cryptography
• Cybersecurity
• Data Security and Privacy
• +۴ مورد دیگر

Liang Xue is an Assistant Professor in the School of Information Technology at York University. She holds a PhD in Electrical and Computer Engineering from the University of Waterloo (2022) and completed a postdoctoral fellowship at the University of Guelph’s School of Computer Science (2022–2024). Her research focuses on applied cryptography, blockchain security, privacy-preserving AI, and cybersecurity in cloud and IoT systems. She has published in top-tier journals like IEEE Transactions on Dependable and Secure Computing, and conferences such as IEEE International Conference on Communications. Her work addresses challenges in data privacy, secure authentication, and regulatory compliance in decentralized systems. Recent projects include privacy-enhancing technologies for access control, blockchain-based data trading frameworks, and federated learning with privacy guarantees. She actively contributes to standards for cybersecurity in smart cities and next-generation wireless networks.

Ceyhun Eksinمشاهده پروفایل

دانشیار

• Networked Multi-Agent Systems
• Game Theory
• Distributed Optimization
• +۵ مورد دیگر

Ceyhun Eksin is an Associate Professor and the Corrie and Jim Furber '64 Faculty Fellow at the Texas A&M University Industrial & Systems Engineering Department. He is also affiliated with the Electrical & Computer Engineering Department. His research focuses on networked multi-agent systems, integrating game theory, distributed optimization, and control theory to address challenges in autonomous systems, energy systems, and epidemiological modeling. Education: Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2015), followed by a postdoctoral fellowship at Georgia Institute of Technology (hosted by Professors Jeff S. Shamma and Joshua S. Weitz). Research interests emphasize the design and analysis of complex systems, including distributed algorithms for autonomous teams, epidemic dynamics influenced by behavioral changes, and optimization in smart grids. His work bridges theoretical foundations with practical applications in cyber-physical systems and social networks. Notable awards include the NSF CAREER Award (2023) and TAMIDS Career Initiation Fellowship (2023). His research has been published in top journals like Proceedings of the National Academy of Sciences and IEEE Transactions . Lab activities center on the NetMaS (Networked Multiagent Systems) Lab, focusing on theoretical and algorithmic innovations for multi-agent systems. Collaborations span academia and industry, addressing real-world challenges in energy, healthcare, and robotics.

• Neuroscience
• Neurocardiology
• Gut-Brain Axis
• +۵ مورد دیگر

Rui Chang is an Associate Professor at Yale University School of Medicine , jointly appointed in the Department of Neuroscience and Department of Cellular and Molecular Physiology . His research focuses on organ-to-brain circuits and neurocardiology with specific interest in Parkinson's disease . He completed his B.S. in Biological Sciences at Tsinghua University (2005) Ph.D. in Neuroscience at University of Southern California (2011) Postdoctoral training in the lab of Stephen Liberles at Harvard Medical School (2017) Research Highlights: The Chang lab employs single-cell gene expression profiling , virus-based anatomical mapping , and optogenetics to investigate vagal interoceptive systems and their roles in cardiovascular regulation and gut-brain axis dysfunction in neurodegenerative diseases. Their work has revealed a multidimensional coding architecture for visceral sensory signals. Notable Scientific Awards: McKnight Neurobiology of Brain Disorders Award (2021) NIH Director’s New Innovator Award (2019) Kavli Faculty Innovative Research Award (2019) NIH K01 Mentored Research Scientist Award (2017) Keystone Symposia Future of Science Scholarship (2016) Collaborative Network: Chang collaborates with experts in neurodegeneration (David A. Hafler, MD), biostatistics (Hongyu Zhao, PhD), and neural imaging (Le Zhang, PhD). His work intersects with the Stephen & Denise Adams Center for Parkinson’s Disease Research and Kavli Institute for Neuroscience .