جست‌وجوی استاد و پژوهشگر

Marius Bockمشاهده پروفایل

استاد

• Computer Science
• Machine Learning
• Human Activity Recognition
• +۴ مورد دیگر

Marius Bock is a Professor at the Department of Computer Vision, University of Siegen, Germany. His research focuses on Human Activity Recognition (HAR), wearable computing, and deep learning, with a particular emphasis on sports analytics and dataset creation for real-world applications. Research Interests: Human Activity Recognition (HAR) Wearable and egocentric video technologies Federated learning for fitness tracking Multi-modal sensor fusion Ethical considerations in sensor data collection Open science and reproducibility frameworks Recent work trends include advancements in temporal action localization, weak annotation techniques using vision foundation models, and collaborative repositories to enhance replicability in HAR research. His publications span top venues like ISWC, UbiComp, and Frontiers in Computer Science. Scientific Awards: Best Paper Award at ISWC 2021' He contributes to the Institute for Vision and Graphics, actively developing datasets and educational materials such as tutorials on deep learning for HAR.

Chrisina Jayneمشاهده پروفایل

استاد

• Artificial Neural Networks
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Chrisina Jayne is a Professor and Dean of the School of Computing & Digital Technologies at Teesside University, also serving as Director of the Teesside University London Campus since 2023. She holds a PhD in Applied Mathematics from Sofia University, Bulgaria, and advanced degrees in Computing Science and Management from the University of London. Her research focuses on artificial neural networks, machine learning, and AI applications in engineering, healthcare, and education. She has authored over 70 peer-reviewed publications and serves in leadership roles with the International Neural Network Society (INNS), including President-Elect and conference chair roles. Education: PhD in Applied Mathematics, Sofia University, Bulgaria MSc in Mathematics and Informatics, Sofia University MSc in Computing Science, Birkbeck College, University of London Postgraduate Diploma in Management, Birkbeck College Research interests include developing novel machine learning methods for healthcare diagnostics (e.g., diabetic retinopathy screening), sentiment analysis, and adaptive educational tools. Her work spans interdisciplinary projects like digitization platforms for engineering drawings and AI-driven feedback systems for students. She is an editorial board member for Neural Computing and Applications and associate editor for IEEE Transactions on Neural Networks and Learning Systems . Awards: UK National Teaching Fellowship (2009) Leadership roles include past Headships at Oxford Brookes University, Robert Gordon University, and Coventry University. Her external activities include organizing major conferences like IJCNN and serving as Technical Co-Chair for WCCI 2020. Labs/Teams: Leads the Centre for Digital Innovation and collaborates on projects like AI-based educational platforms and healthcare analytics systems.

Iván Gonzálezمشاهده پروفایل

مدرس ارشد

• Assistive Technologies
• Quantitative Gait Analysis
• Frailty Assessment
• +۸ مورد دیگر

Dr. Iván González is a Senior Lecturer at the University of Castilla-La Mancha (UCLM), affiliated with the Faculty of Social Sciences and Information Technologies. He holds a M.Sc. and Ph.D. in Advanced Computer Technologies from UCLM. As a member of the MAmI Research Group, his work focuses on Assistive Technologies, Smart Health, and IoT-based solutions for healthcare monitoring. He has authored 16 JCR articles and supervised 2 PhD theses with 4 ongoing. Education: M.Sc. in Advanced Computer Technologies, UCLM Ph.D. in Advanced Computer Technologies, UCLM His research interests emphasize Quantitative Gait Analysis, Frailty assessment, and Mild Cognitive Impairment screening using wearable sensors and mobile technologies. Recent projects include the EU-funded TAICare initiative (2021-2023) and Just Move! (2022), focusing on movement analysis and cognitive health monitoring. Key Contributions: - Developed a 3D motion capture system for gait automation - Created emotion recognition algorithms using gait patterns - Designed energy-efficient wearable data storage systems - Pioneered conversational agents for IADL monitoring Academic Service: 2nd Academic Year Coordinator (2020, 2024–present) Computer Engineering Degree Coordinator (2020–2024) Lab Affiliation: MAmI Research Group (specializing in Ambient Intelligence and IoT solutions).

Midya Partoمشاهده پروفایل

استادیار

• Nonlinear Optics
• Photonic Resonator Networks
• Optical Computing
• +۳ مورد دیگر

Dr. Midya Parto is an Assistant Professor at CREOL, The College of Optics and Photonics, University of Central Florida. His research focuses on nonlinear optics, photonic resonator networks, and their applications in sensing, computing, and information processing. He leads the Parto Lab, which explores ultrafast optical nonlinear processes for neuromorphic computing and photonic integrated circuits (PICs). Education: PhD in Optics from CREOL, UCF (2019); Postdoctoral Scholar at California Institute of Technology (2019–2021), specializing in photonic time-multiplexed resonator networks and integrated optical parametric oscillators. Research Interests: Leveraging topological and non-Hermitian physics in photonic systems, developing novel optical sources/sensors, and advancing classical/quantum photonic computing architectures. The lab's work spans table-top setups to chip-scale implementations, emphasizing scalability and real-world applications. Lab Activities: Open positions for students/postdocs. Recent highlights include undergraduate researcher Michael Rusinko's NSF IRES award for nanotechnology research in South Korea and SURF-funded projects on neuromorphic photonic systems. Advising & Grants: Mentors PhD students Mukund Balasubramanian and Deepak Sasi Prakash. Research supported by grants exploring neuromorphic computing, topological photonics, and photonic integrated circuits. Labs/Teams: Parto Lab at UCF CREOL, collaborating on topics like nanophotonic OPOs, time-multiplexed networks, and topological optical systems.

Kristen Schellمشاهده پروفایل

پژوهشگر

• Large scale optimization
• Applied statistics
• Machine learning
• +۶ مورد دیگر

Kristen Schell is a Research Scientist in the Industrial and Systems Engineering Department at Rensselaer Polytechnic Institute, part of the School of Engineering. Her work focuses on mathematical models for energy transition, combining wind energy forecasting, microgrid design, and spatio-temporal statistical methods. She holds dual doctoral degrees in Engineering and Public Policy from Carnegie Mellon University and the University of Porto, alongside a Master's in Environmental Engineering from Johns Hopkins University and a Bachelor's in Chemical Engineering from Carnegie Mellon University. Education: PhD in Engineering and Public Policy, Carnegie Mellon University PhD in Faculty of Engineering, University of Porto MS in Environmental Engineering, Johns Hopkins University BS in Chemical Engineering & Engineering and Public Policy, Carnegie Mellon University Her research interests span large-scale optimization, applied statistics, machine learning, renewable energy systems, and energy policy. She develops models informed by empirical data from utilities, weather predictions, and energy markets to address climate resilience and energy infrastructure challenges. Dr. Schell is actively mentoring PhD students in optimization modeling, statistical methods, and machine learning applications in energy systems. Her work bridges technical and policy dimensions of sustainable energy transitions.

Xiangyi Mengمشاهده پروفایل

استادیار

• Quantum Networks
• Network Science
• Quantum Information
• +۱۱ مورد دیگر

Prof. Xiangyi "X" Meng is an Assistant Professor at Rensselaer Polytechnic Institute's Department of Physics, Applied Physics, and Astronomy. His academic journey includes a B.S. in microelectronics from Peking University and a Ph.D. in physics from Boston University under Prof. Eugene Stanley. He held postdoctoral positions at Northeastern University's Center for Complex Network Research with Prof. Albert-László Barabási and as a Research Associate at Northwestern University with Prof. István Kovács. RPI (2025-present): Assistant Professor Northeastern University (2021-2023): Postdoc Northwestern University (2023-2024): Research Associate His research spans quantum networks, network science, and interdisciplinary applications, with key contributions in: Quantum communication protocols Concurrence percolation theory Quantum field theory applications Computational social science Power grid resilience modeling Multi-community epidemic spreading Prof. Meng has published over 20 papers in top journals like Physical Review Letters, PNAS, and Nature Communications, focusing on quantum advantage in network connectivity, entanglement distribution, and nonlinear network dynamics. His work has been featured in Physics World and Phys.org media outlets. As an educator, he teaches Physics II and Introductory Quantum Mechanics . His team develops quantum-inspired machine learning architectures like entanglement-structured LSTM networks. Current research explores nonconvex entanglement measures and hypergraph immersion techniques for quantum network optimization.

• Neuroscience
• Mathematical Biology
• Computational Mathematics
• +۶ مورد دیگر

Amitabha Bose is a Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on mathematical neuroscience, particularly modeling neural networks and circadian rhythms. He has actively contributed to understanding synaptic plasticity, rhythmic timing mechanisms, and oscillatory systems through interdisciplinary approaches combining mathematics and biology. His work spans circadian entrainment dynamics, phase resetting in neural networks, and applications of mathematical models to music perception and sleep disorders. Bose has secured multiple NSF grants, including projects on undergraduate biology-mathematics training programs and the role of synaptic plasticity in neuronal networks. His research outputs include over 50 peer-reviewed articles in journals such as Mathematical Biosciences , Scientific Reports , and Journal of Theoretical Biology . Key Projects : Conference on Frontiers in Applied and Computational Mathematics (2022) UBM Training Program in Biology and Mathematics (2004–2010) Role of short-term synaptic plasticity in feedback neuronal networks (2006–2010) Bose’s grants and collaborations reflect his expertise in bridging mathematical analysis with biological systems, particularly in rhythm generation and neural network behavior. His recent work includes studies on circadian misalignment in shift work and jet lag, as well as biophysical models for rhythmic timing in recurrent neural networks.

• Remote Sensing
• Machine Learning
• Satellite Image Processing
• +۴ مورد دیگر

Masoud Mahdianpari is a Cross-appointed Professor between Memorial University's Department of Electrical and Computer Engineering and C-CORE, where he serves as Remote Sensing Technical Lead. He holds a PhD in Electrical Engineering from Memorial University, complemented by graduate degrees from the University of Tehran. His research specializes in advanced remote sensing techniques, including PolSAR data processing, multi-sensor fusion, and deep learning applications for environmental monitoring. Primary research domains encompass wetland ecosystem mapping, agricultural surveillance, and Arctic landscape analysis using satellite data and geo big data platforms. Mahdianpari's publications demonstrate a consistent focus on machine learning applications in earth observation, with recent work emphasizing wetland biomass quantification, methane emission tracking, and wildfire prediction using convolutional neural networks and transformer architectures. T. David Collett Best Industry Paper Award (2020) ESRI Map Award for 'Big Data for Big Country' (2020) Microsoft AI for Earth Grant (2018-2019) Outstanding Reviewer Award for Remote Sensing of Environment (2018) RDC Ocean Industries Student Research Award (2016-2019) PhD Comprehensive Examination Pass with Distinction (2017) He coordinates research initiatives at C-CORE's remote sensing division and mentors graduate students in the GeoBigData Laboratory. Current projects include developing novel CNN-Transformer hybrid models for wetland classification and satellite-based methane monitoring systems.

Oliver van Kaickمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Computer Games
• +۴ مورد دیگر

Oliver van Kaick is an Associate Professor at the School of Computer Science, Carleton University, Canada. He joined in 2014 after postdoctoral research at Tel Aviv University (Azrieli Fellow) and Simon Fraser University. His research focuses on geometric modeling, shape analysis, and computer graphics, with applications in 3D content creation, medical imaging, and game development. He holds a PhD from Simon Fraser University and degrees from the Federal University of Paraná, Brazil. Education: PhD in Computing Science, Simon Fraser University (2011) M.Sc. and B.Sc. in Computer Science, Federal University of Paraná (2005 and 2003) Research interests include shape segmentation, correspondence, and similarity estimation, with a focus on learning shape properties through geometry, topology, and functionality. His work has been published in top venues like SIGGRAPH and Eurographics. He leads the Graphics, Imaging, and Games Lab (GIGL) and serves on program committees for major conferences. Key Awards: Azrieli Fellowship (Tel Aviv University) CVMJ 2024 Best Paper Award Advising & Grants: Supervised PhD and Master’s students including Akshai Srinivasan and Tianshu Zhao. Active in research grants related to 3D modeling and computer vision. Labs/Teams: Graphics, Imaging, and Games Lab (GIGL), collaborating on projects like procedural modeling and functionality-aware shape evolution.

Xiangnan Kongمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Biomedical Informatics
• +۵ مورد دیگر

Xiangnan Kong is an Associate Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), serving as the Graduate Coordinator of the Data Science Program. His academic journey includes a BS and MS from Nanjing University (2006, 2009) and a PhD from the University of Illinois at Chicago (2014). He maintains an active research program with significant contributions to data mining and machine learning. Dr. Kong's primary research interests focus on data mining and machine learning, with emphasis on addressing data science problems in biomedical and social applications. His work specifically targets data variety issues across multiple domains including neuroscience, biomedical informatics, social networks, and business intelligence. He has published extensively in top-tier conferences and journals such as KDD, ICDM, SDM, WWW, WSDM, CIKM, and TKDE. His recent publications demonstrate a strong trend toward interdisciplinary applications, with significant work in neuroimaging analysis, healthcare applications, urban traffic estimation, and efficient deep learning architectures. His research bridges theoretical advances in graph mining, heterogeneous information networks, and deep learning with practical applications in biomedical domains and social computing. Dr. Kong has successfully advised numerous PhD and Master's students, with former students now working at prominent organizations including Facebook Research, VISA Research, MIT CSAIL, and Amazon AI. His research is supported by multiple grants including NSF awards, Adobe Gift Grant, Huawei Research Grant, and WPI TRIAD Grant.

Simon Fooمشاهده پروفایل

استاد

• Photovoltaics
• Multi-junction III-V compound Solar Cells
• Organic/Polymer Solar Cells
• +۵ مورد دیگر

Simon Foo is a tenured Professor of Electrical and Computer Engineering at the FAMU-FSU College of Engineering, part of Florida A&M University and Florida State University. He has held roles including Department Chair (2010–2018) and has been a professor since 2005. His research focuses on photovoltaics and machine learning, with over 150 publications and 50+ graduated students. He leads 20+ funded projects from agencies like NASA, DOE, NSF, and industries like Boeing and Airbus. Education: Ph.D. (1988), M.S. (1985), and B.S. (1983) in Electrical Engineering from the University of South Carolina. Research Interests: Photovoltaics (multi-junction solar cells, quantum dots, perovskites) and machine learning applications in energy systems, control, and healthcare. His lab develops AI-driven solutions for renewable energy optimization and battery management systems. Key Awards: Teaching Incentive Award (1995) Teacher of the Year (2001 & 2010) Engineering Research Awards (2004 & 2008) Two Best Paper Awards (2001 & 2004) Advising & Grants: Mentored over 50 graduate students in photovoltaics and AI. Secured grants from NASA, DOE, NSA, NSF, and industry partners. Current projects include battery state-of-charge estimation and AI for aircraft fire detection systems. Labs & Collaborations: Collaborates with Airbus on aircraft safety systems and maintains an active research group focused on renewable energy and AI integration. His lab is currently full and not accepting students until 2027.

Neil Spencerمشاهده پروفایل

استادیار

• Network Analysis
• Bayesian Modeling
• Forensic Statistics
• +۲ مورد دیگر

Dr. Neil Spencer is an Assistant Professor in the Department of Statistics at the University of Connecticut. His research integrates Bayesian inference, network analysis, and computational statistics, with applications ranging from forensic science to neurological disorders. He earned a PhD in Statistics and Machine Learning from Carnegie Mellon University, MSc from University of British Columbia, and BScH from Acadia University. Research focuses on developing novel methods for network data analysis (latent position models, efficient MCMC), robust Bayesian inference, and forensic statistics. Publications demonstrate consistent innovation in computational techniques for complex data structures and interdisciplinary applications. Teaching includes STAT5410 (Statistical Computing) and STAT3345Q (Probability Models for Engineers). He co-advised PhD candidate Tolani Olarinre and participates in the New England Statistical Society's NextGen committee. Research publications emphasize methodological innovations in network modeling, Bayesian computation, and experimental design, with significant applications in neuroscience and forensic science.

Fraser Hofمشاهده پروفایل

استاد

• Medicinal Chemistry
• Molecular Recognition
• Synthesis
• +۳ مورد دیگر

Fraser Hof is a Professor and Associate Vice-President Research in the Department of Chemistry at the University of Victoria. He holds a BSc from the University of Alberta and a PhD from The Scripps Research Institute, followed by postdoctoral fellowships at ETH Zurich. His research focuses on medicinal chemistry, molecular recognition, and supramolecular systems applied to epigenetic therapies and chemical biology. Key projects include designing host molecules to target epigenetic markers like methyllysines, developing diagnostic tools using supramolecular sensing, and exploring high-throughput methods for host synthesis. His teaching spans organic, supramolecular, and biological chemistry, with active mentorship of undergraduate researchers through Chemistry 498/499 courses and the co-op program. Hof collaborates with biochemistry and cancer research groups to translate fundamental discoveries into clinical applications. His work bridges synthetic chemistry with biological systems, emphasizing the development of innovative chemical tools for biomedical challenges. Research highlights include synthetic hosts that disrupt histone-epigenetic reader interactions, adaptive supramolecular networks for sensing, and antimicrobial peptide design using machine learning. Hof's lab integrates computational, synthetic, and analytical approaches, addressing 'undruggable' targets and societal health challenges.

• Information Processing Systems
• Neural Networks
• Embedded Systems
• +۳ مورد دیگر

Musci Myrtle is a Researcher at the Department of Industrial and Information Engineering, University of Pavia. Their work focuses on information processing systems, neural networks, and embedded systems applications in healthcare and cultural heritage preservation. They have contributed to 3D modeling of historical artifacts and developed neural network architectures for wearable devices. Research interests include interdisciplinary topics such as digital heritage documentation, real-time health monitoring systems, and scalable neural network architectures. Publications span conference proceedings, book chapters, and peer-reviewed journals in neural networks and information systems. Teaching activities are documented but lack specific details in the provided materials. No awards or grants are explicitly mentioned.

• Software Defined Radio
• AI/ML for Wireless Communications
• Cyber Security
• +۳ مورد دیگر

Ruolin Zhou is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Massachusetts Dartmouth, part of the College of Engineering. She joined UMass Dartmouth in 2018 after serving as a tenured Associate Professor at Western New England University from 2012 to 2018. Her research focuses on software-defined radio (SDR), AI/ML-driven wireless communications, spectrum sensing, cyber & electromagnetic spectrum security, and hardware security. Her work has been supported by grants from NSF, ONR, AFRL, ARL, and industry partners like Lockheed Martin. Dr. Zhou holds a PhD in Engineering from Wright State University (2012), an MS in Electrical Engineering (2007), and a BS in Electrical Engineering from Dalian Jiaotong University (2003). She teaches courses including Foundations of Cybersecurity, Digital Logic Design, and Software-Defined Radio. Her research has led to innovations in FPGA-accelerated deep learning architectures, real-time spectrum monitoring, and secure communication systems. Her contributions have been recognized with awards including the 2024 IEEE Region 1 Outstanding Teaching Award, ONR Summer Faculty Research Fellowships (2022-2023), and Best Demo Awards at GLOBECOM 2010. She actively serves in IEEE roles, including Vice President for Technical Activities within the IEEE Reliability Society (2025) and steering committee memberships in IEEE Future Networks and IoT Technical Communities.