جست‌وجوی استاد و پژوهشگر

Nasser Kehtarnavazمشاهده پروفایل

استاد

• Signal and Image Processing
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Nasser Kehtarnavaz is an Erik Jonsson Distinguished Professor and Director of the Embedded Machine Learning (EML) Laboratory at the University of Texas at Dallas's Erik Jonsson School of Engineering and Computer Science. He holds a PhD from Rice University (1987) and has over 40 years of academic and industrial experience. His research focuses on signal/image processing, machine learning, biomedical applications, and real-time embedded systems. He has authored/co-authored 11 books and over 450 publications, and supervised 37 PhD and 35 MS students. Leadership roles: Editor-in-Chief of Journal of Real-Time Image Processing , SPIE Conference Chair, IEEE Fellow (2013), AAIA Fellow Industrial collaborations: Texas Instruments, AT&T Bell Labs, US Army TACOM Research Lab Key contributions: Smartphone-based signal processing labs, hearing aid amplification algorithms, and FPGA-based embedded systems Research interests include real-time implementation on embedded processors, biomedical signal analysis, and smartphone-based laboratories. He has pioneered educational tools like anywhere-anytime labs using smartphones for signal processing education. Recipient of numerous awards including IEEE Region 5 Professional Leadership Award (2015), Outstanding Engineering Educator Award (2013), and community recognition from SPIE (2020-2021). His funded projects (62 as PI/Co-PI) span healthcare tech, energy systems, and semiconductor manufacturing. Director of the EML Lab and previously the Signal and Image Processing Lab. Teaches graduate courses like Machine Learning and Pattern Recognition and undergraduate courses in signals and systems.

Paul Millerمشاهده پروفایل

استاد

• Computational Neuroscience
• Neuroscience
• Dynamical Systems
• +۵ مورد دیگر

Paul Miller is a Professor of Biology at Brandeis University and a member of the Volen National Center for Complex Systems. His research focuses on computational neuroscience, integrating dynamical systems theory with experimental data to understand neural mechanisms underlying decision-making, memory, and sensory processing. He holds a Ph.D. from the University of Bristol (UK) and a B.A. from Cambridge University. Before joining Brandeis in 2000, he was at Georgetown University and conducted postdoctoral work in theoretical physics at Oak Ridge National Laboratory. Education: B.A., Cambridge University (1991) Ph.D., University of Bristol (1994) Research Interests: Computational Neuroscience emphasizes modeling neural circuits for short-term/long-term memory, decision-making processes, and the dynamics of neural ensemble activity. His work bridges theoretical approaches (e.g., hidden Markov modeling, spiking neuron networks) with experimental data from cortical and hippocampal systems. He explores how quasistable attractor states enable robust computation and how homeostatic mechanisms stabilize neural activity. Notable Contributions: Developed models explaining how integral feedback control enables robust sequential decision-making, elucidated synaptic mechanisms for spatiotemporal discrimination, and analyzed the role of calcium/calmodulin-dependent protein kinase II (CaMKII) in long-term memory stability. His textbook An Introductory Course in Computational Neuroscience (MIT Press) is widely used in graduate programs. Labs/Teams: Leads the Computational Neuroscience Lab at Brandeis, collaborating with experimentalists to validate theoretical models. Active in interdisciplinary projects involving neurophysiology, systems biology, and mathematical neuroscience.

Isaac Kofi Ntiمشاهده پروفایل

استادیار

• Applied Machine Learning
• Cybersecurity
• Health Informatics
• +۴ مورد دیگر

Dr. Isaac Kofi Nti is an Assistant Professor in the School of Information Technology at the University of Cincinnati, with over 16 years of interdisciplinary research experience in applied machine learning, health informatics, cybersecurity, and data governance. His work emphasizes responsible AI deployment in high-impact domains including healthcare, finance, and education. He concurrently holds affiliations with Ghanaian institutions (University of Energy and Natural Resources and Sunyani Technical University) and serves as a Visiting Assistant Professor at Cincinnati until 2024. Education PhD in Computer Science, University of Energy and Natural Resources, Ghana (2021) MSc in Information Technology, Kwame Nkrumah University of Science and Technology, Ghana (2016) BSc Computer Science, Catholic University of Ghana (2011) Higher National Diploma in Electrical & Electronic Engineering, Sunyani Technical University, Ghana (2007) Research Focus Dr. Nti develops lightweight, explainable AI systems for cybersecurity, healthcare diagnostics, agricultural optimization, and educational technology. His research integrates secure data analytics, ethical AI frameworks, and cloud-based learning infrastructures. Current projects explore generative AI in education, predictive modeling for disease outbreaks, and blockchain applications for health data verification. Publication Trends His 60+ publications (2022-2024) demonstrate strong emphasis on ensemble learning methods, healthcare AI applications, and cybersecurity innovations. Recent works frequently utilize hybrid models (LSTM/XGBoost/StackNet) for fraud detection, disease prediction, and renewable energy forecasting. Awards & Grants Provost Office Strategic Collaborative Faculty Team Award (2024-2025): $19,200 for AI integration in higher education Faculty Development Grant (2023-2024): $10,038 for emerging technology training Faculty Incentive Awards for Research (CECH, UC 2022-2024) Teaching & Service Teaches graduate/undergraduate courses in machine learning, cybersecurity, and data technologies. Leads multiple university committees including PhD Admissions Review and Applied Research teams. Professional memberships include ACM, IEEE, and AAAI.

Berthold Lausenمشاهده پروفایل

استاد

• Machine Learning
• Medical Statistics
• Data Science
• +۳ مورد دیگر

Berthold Lausen is a Professor in Data Science & Statistics at the School of Mathematics, Statistics and Actuarial Science (SMSAS) at the University of Essex, and concurrently holds a professorship in Biometry and Epidemiology at the University of Erlangen-Nuremberg. He has led the SMSAS as Head of Department (2016–2021) and significantly expanded its academic and student body. His research focuses on machine learning, medical statistics, and data science applications in healthcare and industry. He is a Fellow of the Royal Statistical Society (since 2009) and has been nominated for the KTP Academic of the Year award (2022). Education: Diplom-Statistiker (1987), Dr. rer. nat. (PhD, 1990), and Privatdozent (habilitation in biometry and epidemiology, 2003), all from German institutions. Research Interests: Development of nonparametric methods, classification algorithms, survival analysis, and machine learning applications in medicine. He has pioneered techniques like Maximally Selected Rank Statistics and p-Value Adjusted Classification Trees. His work bridges theoretical statistics with practical data science challenges in healthcare and industry. Grants & Partnerships: Principal Investigator for Innovate UK-funded KTPs with Prophasis, Mondaq, and Rolls-Royce Motor Cars/BMW. These projects focus on data-driven solutions for industry and legal sectors. His leadership in academic-industry collaborations has been recognized nationally. Professional Roles: President of the British Data Science Society (since 2022), Vice-President of the European Association for Data Science (since 2023), and former President of the International Federation of Classification Societies (IFCS, 2018–2022). He serves on editorial boards of journals like Advances in Data Analysis and Classification and Archives of Data Science .

Jane Barnetteمشاهده پروفایل

استاد

• Adaptational Dramaturgy
• Theatre History
• Performance Studies
• +۳ مورد دیگر

Jane Barnette is a Professor of Dramaturgy and Theatre History at The University of Kansas, focusing on adaptation practices, witches in theatrical representation, and the intersection of historical and contemporary performance. Her academic work spans dramaturgical theory, classical reception, and innovative pedagogical approaches in theatre education. Position: Faculty Fellow Professor Institution: The University of Kansas Contact: jane@ku.edu Her research interests include: Adaptational dramaturgy across stage and screen Witchcraft narratives and their cultural implications Reconstructing historical theatre practices through modern lenses Pedagogical innovations in ungraded classrooms Jane's publications demonstrate a recurring focus on palimpsestic storytelling (e.g., WandaVision's witch archetypes), consent-based staging ethics, and the impact of technological changes on theatrical traditions (railroads' influence on Chicago-area theatre). Her methodological approaches include research-creation and public scholarship models like Chautauqua/Colloquia. While no formal scientific awards are documented here, her extensive publication record highlights significant contributions to dramaturgy and theatre historiography.

Patrick Dennyمشاهده پروفایل

دانشیار

• Computer Vision
• Automotive Systems
• Medical Imaging
• +۳ مورد دیگر

Patrick Denny is an Associate Professor at the University of Limerick, affiliated with the Department of Computer Science & Information Systems, the Centre for Sustainable Digital (Re)Manufacturing, and Lero – the Research Ireland Centre for Software. His research focuses on computer vision, automotive systems, and medical imaging, with notable contributions to object detection, instance segmentation, and image processing in autonomous vehicles and healthcare. He holds patents in automotive camera systems and has authored over 38 research papers. His work addresses challenges such as rain impact on automated vehicle perception, medical image classification using graph neural networks, and optimizing camera exposure for automotive applications. He collaborates extensively on projects involving V2X communications and intelligent transportation systems. Denny’s research also extends to waste management through computer vision and medical imaging innovations. Patents: Over 10 patents in automotive imaging, including systems for calibrating image-capturing devices and thermal infrared sensors. Key Research Themes: Automotive perception, computer vision algorithms, medical image analysis, and sensor optimization. He actively engages in interdisciplinary projects, combining machine learning with real-world applications in transportation and healthcare.

Kari Adamsمشاهده پروفایل

استادیار

• Music Teacher Education
• Teacher Identity Construction
• Aesthetic Response to Music Performance
• +۳ مورد دیگر

Kari Adams is an Assistant Professor of Choral Music Education and conductor of the Concert Chorale at the University of Missouri. She holds a PhD in Music Education from the University of North Texas, alongside degrees from the University of Central Missouri and Freed-Hardeman University. Prior to her position at Mizzou, she served as Assistant Professor and director of Levana at Florida State University, where she won the 2023 Outstanding Teaching in the Major (Undergraduate) award. Her career spans middle school music teaching, including a Teacher of the Year award in 2015, and extensive guest conducting and adjudicating roles across multiple states. Dr. Adams’s research focuses on music teacher education, identity construction, aesthetic responses to performance, and implicit theories of musical ability. Her work has been published in leading journals such as Journal of Research in Music Education and Choral Journal . She actively presents at national and international conferences, including ACDA and NAfME, and has contributed articles to practitioner-oriented publications like Music Educators Journal . Her professional engagements include conducting All-State and regional honor choirs in states like Florida, Kansas, and Missouri, alongside clinician roles at practitioner conferences. She also explores innovative teaching methods for online learning environments and has developed strategies to enhance community-building in virtual educational settings. Adams’s research emphasizes practical applications for music educators, bridging theoretical insights with classroom and rehearsal techniques. Her contributions to music education span K-12 through higher education, reflecting a commitment to advancing pedagogical practices across all levels.

Matt Wrightمشاهده پروفایل

مدرس

• Computer Music
• Acoustics
• Music Technology
• +۷ مورد دیگر

Matt Wright is a Lecturer at Stanford University's Center for Computer Research in Music and Acoustics (CCRMA), specializing in media systems design and computer music research. He previously served as Musical Systems Designer at UC Berkeley's CNMAT (1993-2008) and Research Director at UCSB's CREATE center for eight years. His work focuses on real-time music synthesis, sound standards (SDIF/OSC), and computational ethnomusicology. He holds a PhD from Stanford CCRMA on musical rhythm perception, and contributed to the AlloSphere immersive audiovisual instrument. Education: PhD in Computer-Based Music Theory from Stanford University (completed at CCRMA). Earlier studies include training in traditional lutes and Afro-Brazilian percussion. Research interests bridge technology and music: developing tools for musical gesture analysis, sound synthesis, and cross-cultural music analysis. His recent work also involves genomic data platforms (ClinGen) for clinical variant curation, reflecting a dual focus on computational music and bioinformatics. Publications emphasize both music technology (e.g., EEG studies of piano duets) and genomics (e.g., ClinGen variant curation tools). He has advised students in electronic arts and contributed to genetic disease research through collaborative frameworks like ClinVar. Labs/Teams: Lead the CREATE Ensemble (technology-driven live performance) and contributed to the AlloSphere research instrument at UCSB's CREATE center.

Timothy Jonesمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Computing
• Compilers
• +۴ مورد دیگر

Timothy Jones is a Professor of Computer Architecture and Compilation at the University of Cambridge Computer Laboratory, with a research focus on extracting parallelism (thread-level, data-level, memory-level) through compilers, binary translators, and microarchitectures to enhance performance, energy efficiency, and reliability. He is also a Fellow and 1956 College Lecturer at Gonville and Caius College, serving as joint Director of Studies for Computer Science. Teaching: Advanced Topics in Computer Architecture (MPhil ACS, Part III), Introduction to Computer Architecture (Part IB), Optimising Compilers (Part II) Research Themes: Computer Architecture, Security, Programming Languages, Parallel Computing Contact: Room SC03, Department of Computer Science and Technology, William Gates Building, Cambridge CB3 0FD His recent publications emphasize heterogeneous parallelism, fault tolerance, speculative execution, and security implications in compiler and microarchitecture design. Notable trends include integrating fine-grained security analysis into superscalar cores and optimizing GPU architectures for scalarization and memory bypassing. He is active on Twitter ( @TJonesCL ) and maintains an Erdős Number of 4.

• Scientific Visualization
• Data Visualization
• Data Science
• +۵ مورد دیگر

Min Chen is a Professor of Scientific Visualization at the University of Oxford, affiliated with the Department of Engineering Science and Pembroke College. He holds fellowships from the British Computer Society, European Computer Graphics Association, and Learned Society of Wales. His career spans over three decades, with previous roles at Swansea University (1984–2011) and current leadership in visualization research. His research focuses on visualization theory, video visualization, visual analytics, and interdisciplinary applications in fields like epidemiology and cybersecurity. He has authored over 200 publications and led projects such as RAMPVIS during the COVID-19 pandemic. Key roles include editor-in-chief of Computer Graphics Forum and associate editor of IEEE Transactions on Visualization and Computer Graphics. Education: BSc and PhD in relevant fields (details not explicitly stated in texts). Awards include the VGTC Visualization Lifetime Achievement Award (2024). His work emphasizes the theoretical underpinnings of visualization and practical tools for data intelligence.

Jennifer MacRitchieمشاهده پروفایل

عضو هیئت علمی

• Music Technology
• Health and Wellbeing
• Music Education for Older Adults
• +۳ مورد دیگر

Dr. Jennifer MacRitchie is a Senior Research Fellow at the University of Sheffield's Department of Music, affiliated with the School of Languages, Arts and Societies. She holds an M.Eng in Electronics with Music from the University of Glasgow and a PhD in Science and Music Research from the same institution. Previously, she held roles at the Conservatorio della Svizzera Italiana and Western Sydney University as a Research Lecturer and Senior Research Fellow. Her research focuses on the intersection of music, technology, and health, particularly designing assistive technologies for older adults with dementia and their carers. Key collaborations include work with Prof. Renee Timmers, Prof. Luc de Witte, and Prof. Andrew McPherson. She co-supervises PhD students exploring topics like dementia-friendly musical interfaces and notation systems. Research interests span music performance neuroscience, technology-enhanced music education, and interdisciplinary methods combining engineering, psychology, and healthcare. Notable projects include the UKRI Future Leaders Fellowship-funded initiative to develop new musical technologies for dementia care and the Active Minds Music Ensemble study. Grants include a £1.18M UKRI Fellowship and an Australian Research Council Discovery Project. Her work has appeared in journals like Alzheimer's & Dementia , Music & Science , and Frontiers in Psychology . Awards include prestigious fellowships and grants supporting her innovative research.

Magued Iskanderمشاهده پروفایل

استاد

• Geotechnical Engineering
• Sustainable Construction Materials
• Foundation Engineering
• +۶ مورد دیگر

Magued Iskander is the Department Chair and Professor in the Civil and Urban Engineering Department at the NYU Tandon School of Engineering. With over 25 years of expertise, he focuses on geotechnical engineering, including foundation design, soil-structure interaction, and sustainable materials. His research emphasizes transparent soil modeling, high-strain rate soil behavior, and offshore geotechnology. He leads projects on machine learning applications for geotechnical analysis, pile capacity prediction, and UXO (unexploded ordnance) penetration studies. Research Interests: Transparent soil modeling for soil-structure interaction Sustainable piling using recycled polymers Geotechnical instrumentation and monitoring Offshore/marine foundation design High-strain rate soil mechanics Penetrating dynamics in granular media Publications highlight advancements in machine learning for geotechnical data analysis, projectile penetration mechanics, and tunneling-induced ground settlements. His work bridges experimental, computational, and AI-driven approaches to solve complex geotechnical challenges. He advises NYU Tandon’s Concrete Canoe and Steel Bridge teams, fostering student engagement in competitive engineering projects.

Paula Brancoمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Data Science
• +۸ مورد دیگر

Paula Branco is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa (since 2020). Previously, she was a PostDoctoral Fellow at Dalhousie University's Faculty of Computer Science (2019). She holds a PhD in Computer Science from the University of Porto, alongside a MSc in Data Mining and a Mathematics degree. She is an external researcher at INESC TEC's LIAAD laboratory. Her research focuses on Machine Learning, particularly utility-based learning and imbalanced domains, with applications in cybersecurity, healthcare, and environmental monitoring. Key areas include anomaly detection, fraud detection, rare event prediction, and spatio-temporal data analysis. Recent work emphasizes resampling techniques for imbalanced regression (e.g., SMOGN, ImbalancedLearningRegression Python package), evaluation metrics for imbalanced domains, and cybersecurity applications like intrusion detection and malware analysis. She has supervised seven graduate students and contributed to open-source projects like UBL (R package) and ImbalanceMetrics. Her teaching includes courses on databases and AI for cybersecurity. She actively publishes in top venues and collaborates on tools like SMOGN-LIDTA17 and NeighborhoodBiasResamplingRegression.

• Artificial Intelligence
• Machine Vision
• Distributed Computing
• +۱۱ مورد دیگر

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

Kerstin Bunteمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• XAI (Explainable Artificial Intelligence)
• +۷ مورد دیگر

Kerstin Bunte is a Professor of Machine Learning for interdisciplinary data analysis at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute's Intelligent Systems Group. She holds an Honorary Fellowship at the University of Birmingham and leads the Intelligent Systems Group. Her research focuses on interpretable machine learning, interdisciplinary applications (e.g., astrophysics and biomedical data), and visualization techniques. Research Interests: - Machine Learning - Artificial Intelligence - Explainable AI (XAI) - Interpretable Models - Dimensionality Reduction - Data Visualization - Astrophysical Data Analysis - Medical Imaging Awards & Grants: - DSSC XS funding (2023) - NWO VIDI grant (2020) - Rosalind Franklin Fellowship (2016–present) Advising & Students: Supervised PhD students include Elisa Oostwal, Janis Norden, Matteo Marcantoni, and Petra Awad. Research spans topics like tumor segmentation in medical imaging, astrophysical structure detection, and autonomous navigation systems. Labs & Collaborations: Leads the Intelligent Systems Group, collaborating with institutions like the University of Birmingham and the University of Warwick. Work involves interdisciplinary projects combining machine learning with astronomy, biomedical sciences, and robotics.