جست‌وجوی استاد و پژوهشگر

• Power Systems
• Smart Grids
• Reactive Power Control
• +۳ مورد دیگر

Charu Sharma is an Associate Professor in the Department of Electrical Engineering at UiT The Arctic University of Norway, specializing in power systems and smart grid technologies. Her work focuses on reactive power control, voltage stability, and optimization of renewable energy-integrated networks. Research on cyber-physical co-simulation frameworks for real-time grid management Development of hybrid renewable energy microgrids for rural and industrial applications Expertise in optimization algorithms (e.g., BFOA-PSO, ANFIS) for energy systems Recent publications highlight her contributions to DER-enriched distribution networks, low-inertia system stability, and intelligent load frequency control. She actively collaborates with researchers on projects like Cooperative Isolated Renewable Energy Systems and arcICE , addressing reliability and sustainability challenges.

Shantanu Jadhavمشاهده پروفایل

استاد

• Learning and Memory
• Decision Making
• Neurophysiology
• +۵ مورد دیگر

Shantanu Jadhav is a Professor of Psychology at Brandeis University, with affiliations to the Neuroscience Program, the Volen National Center for Complex Systems, and the Sloan-Swartz Center for Theoretical Neurobiology. He earned his B.Tech. from IIT Bombay, Ph.D. from UC San Diego, and completed postdoctoral training at UCSF and UC Berkeley. Research Focus: Neural mechanisms of learning, memory, and decision-making in rodent models Investigation of hippocampal-prefrontal interactions via multielectrode recordings, optogenetics, and computational analysis Role of neural oscillations (theta, gamma, sharp-wave ripples) in memory consolidation and cognitive flexibility Implications for neurological disorders like Alzheimer’s, autism, and schizophrenia Recent work highlights the coordination of dopamine activity with rule representations in the prefrontal cortex and hippocampus, the role of prefrontal ripples in suppressing hippocampal reactivation during sleep, and geometric transformations in cognitive maps enabling cross-environment generalization. His lab has shown that awake sharp-wave ripples are critical for spatial memory and that cross-region neural synchronization underpins memory-guided decisions. Scientific Recognition: Peter and Patricia Gruber International Research Award (2013) Sloan Research Fellow (2015-2017) NARSAD Young Investigator Award (2015-2018) Whitehall Foundation Award (2016-2020) SFARI Core Member (2022-2025) The Jadhav Lab at Brandeis trains postdoctoral fellows, graduate students (via the Neuroscience and Psychology Graduate Programs), and research assistants. Current studies explore hippocampal-prefrontal network dynamics, dopamine signaling in cognitive flexibility, and geometric representations in memory abstraction.

Gerardo Schneiderمشاهده پروفایل

استاد

• Formal Specification and Analysis of Contracts
• Formalization of Privacy Policies
• Model Checking
• +۵ مورد دیگر

Gerardo Schneider is a Full Professor in Computer Science at the University of Gothenburg, Sweden, and holds a joint appointment at Chalmers University of Technology. He serves as Head of the Data Science and Artificial Intelligence (DSAI) Division and has previously led the Formal Methods Division and acted as Director of Graduate Studies. University of Gothenburg: 2009–present Chalmers University of Technology: 2009–present Uppsala University: 2002–2003 University of Oslo: 2005–2009 His research focuses on formal methods for software engineering, including contract specification and analysis , privacy policy formalization , model checking , and runtime verification . He works on verification of real-time systems, embedded systems (e.g., smart Java cards), and blockchain-based smart contracts. Key projects include: X-LEGAL (2020–2023): Smart Legal Contracts (Swedish Research Council) PolUser (2016–2019): User-Controlled Privacy Policies (Swedish Research Council) ARVI (2014–2018): Runtime Verification Beyond Monitoring (ICT COST Action) ReMU (2013–2017): Reliable Multilingual Digital Communication (Swedish Research Council) He has supervised numerous PhD and Master’s students in formal methods, blockchain security, and privacy compliance. His tools include SPeeDI (Polygonal Hybrid Systems Verification), CLAN (Contract Normative Conflict Detection), and AnaCon (Controlled Natural Language Analysis).

Lin Jiangمشاهده پروفایل

استادیار

• Biomechanics
• Robotics
• Medical Assistive Device Development
• +۳ مورد دیگر

Dr. Lin Jiang serves as Assistant Professor in the Department of Mechanical Engineering at San José State University's Charles W. Davidson College of Engineering, where her research bridges biomechanics and robotics to develop medical assistive technologies and human-robot interaction systems. Her educational background includes a Ph.D. and M.Sc. in Mechanical Engineering from the University of Texas at Dallas (2021, 2019), complemented by an M.Sc. in Control Engineering and B.Sc. in Aerospace Engineering from Nanjing University of Aeronautics & Astronautics (2014, 2011) with a minor in Industrial Business Management. Dr. Jiang's research focuses on translating aerospace control systems expertise into medical applications, particularly in rehabilitation robotics and breastfeeding technology. Her work emphasizes human-centered design for devices like the patented SmartLact8 breast pump, with recent publications demonstrating significant contributions to teleoperated rehabilitation systems and lactation biomechanics. Her 15 most recent publications (2020-2025) reveal consistent specialization in medical robotics, with dominant themes in rehabilitation devices (knee braces, upper extremity therapy), breastfeeding technology innovation, and human-robot interaction frameworks for healthcare and driving safety applications. Scientific recognition includes: New Investigator Award from CSUPERB Small Group Project Award from SJSU College of Engineering Exemplary Teaching award from UT Dallas Diversity Award from Summer Biomechanics Conference Best Paper award at ASME IMECE 2018 Research funding includes NSF support for hospital-based human-robot interaction studies. Dr. Jiang actively contributes to IEEE HKN, BMES, ASME, and ISHRML while mentoring students through her Biomechanics and Robotics Lab at SJSU. The Biomechanics and Robotics Lab serves as the primary research hub for developing medical assistive technologies, with current projects focusing on rehabilitation robotics, breastfeeding simulation systems, and human-robot interaction protocols for clinical environments.

• Tumor Microenvironment
• Extracellular Vesicles
• Exosomes
• +۵ مورد دیگر

Nao Nishida serves as Assistant Professor at Waseda University's Institute for Advanced Study since 2022, following appointments at Tokyo Medical University and Fred Hutchinson Cancer Center. Her research bridges cancer biology and cell-cell communication mechanisms within tumor microenvironments. Her educational background includes: PhD in Agriculture (2014) from Kyoto University Master's in Applied Life Sciences (2011) from Kyoto University Bachelor's in Applied Life Sciences (2009) from Kyoto University Nishida's research centers on extracellular vesicle-mediated tumor-stroma crosstalk, with particular focus on exosome-driven metastasis, tumor-associated macrophage reprogramming, and organotypic culture modeling. She investigates how lipid composition and serine metabolism in cancer-derived EVs regulate microenvironmental remodeling and therapeutic resistance. Her work integrates advanced lipidomics, real-time tissue imaging, and spatial EV distribution analysis to uncover metastatic mechanisms. Analysis of her 17 publications reveals dominant themes in exosome biology (71% of works), cancer microenvironment dynamics (63%), and therapeutic targeting strategies (41%). Recent work increasingly incorporates spatial tissue context (2022-2024) and clinical translation potential. Her awards include: ISEV2025 New Parents Scholarship Japan Society for Promotion of Science Outstanding Researcher Candidate (2021) ISEV2017 Junior Member Scholarship Young Researcher Excellent Presentation Award (2015) Nishida directs multiple active grants including JSPS KAKENHI projects on EV secretion mechanisms (2023-2026) and nutritional stress adaptation (2025-2028), plus Mitsubishi Foundation and Uehara Memorial Foundation awards. She mentors through Waseda's bioscience curriculum while developing organotypic slice platforms for drug screening. Her laboratory utilizes advanced tumor slice culture systems and spatial EV mapping techniques to dissect microenvironmental heterogeneity, with current work focusing on stromal contribution to therapeutic resistance and organ-specific metastatic niches.

Chia-Yi Chiuمشاهده پروفایل

دانشیار

• Educational Measurement
• Nonparametric Cognitive Diagnosis
• STEM Assessments
• +۳ مورد دیگر

Chia-Yi Chiu is an Associate Professor in Applied Statistics at Teachers College, Columbia University. She specializes in educational measurement and psychometrics, focusing on nonparametric cognitive diagnosis and small data applications in STEM assessments. Her work emphasizes real-time classroom monitoring and innovative algorithms for diagnostic testing. PhD: Quantitative Methodology, Measurement, and Evaluation (University of Illinois at Urbana-Champaign) MS: Statistics (University of Illinois at Urbana-Champaign) MA: Mathematics in Teaching (University of Northern Colorado) BS: Mathematics (National Taiwan Normal University) Her research advances classroom-level assessments through methods like the restricted DINA model and nonparametric CD-CAT, addressing challenges in small-sample settings. She collaborates widely, with notable contributions to Q-matrix theory and cognitive diagnosis algorithms. Recent publications explore method selections in cognitive diagnosis and Q-matrix optimization. Awards: John P. Yackel Professorship (2022-2024), Chancellor’s Scholar Award (2017-2022), and NCME Dissertation Award (2010). Dr. Chiu advises students on topics like diagnostic testing algorithms and adaptive assessment systems. Her grants include innovations in education technology and small-data psychometric methods.

Charles Gillanمشاهده پروفایل

مدرس ارشد

• High Performance Computing (HPC)
• Artificial Intelligence (AI) in Healthcare
• Quantum Computing
• +۴ مورد دیگر

Charles Gillan is a Senior Lecturer at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science, affiliated with the High Performance and Distributed Computing department and the Institute of Electronics, Communications & Information Technology. His research bridges HPC systems, AI applications in healthcare, and computational physics. Key projects include managing ICU patient care via neural networks, exascale-ready mathematical packages, and edge computing architectures. Research interests focus on high-performance computing (HPC), quantum computing, real-time data analytics, and electron-molecule scattering simulations. Notable contributions include developing microserver architectures for edge analytics and advancing AI-driven clinical decision support systems. Gillan has collaborated on interdisciplinary projects like food authenticity testing using spectroscopy and improving ventilator management in intensive care units. Publications span AI in healthcare, HPC system design, and computational methods for physics problems. He has secured funding for initiatives such as the KTP partnership with Foods Connected Ltd and the HANDHELD olfactory detection project. Gillan's work emphasizes practical applications of advanced computing across healthcare, engineering, and cybersecurity domains.

Shivam Saxenaمشاهده پروفایل

استادیار

• Smart Grids
• Energy Systems
• Distributed Control

Shivam Saxena is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick, located in Head Hall D65, Fredericton. His research focuses on smart grid technologies, including distributed energy resource integration, blockchain applications for energy trading, electric vehicle-grid interactions, and resilient microgrid design. This work addresses decarbonization challenges through technological and market innovations. Publications demonstrate a strong emphasis on real-world implementation, with field-tested solutions for V2X integration, blockchain-based transactive energy, and distributed control systems. Recent work explores novel applications in agricultural energy management and trust mechanisms for decentralized systems.

Marco Donatoمشاهده پروفایل

استادیار

• Hardware for Machine Learning
• Non-Volatile Memories
• SoC Design
• +۵ مورد دیگر

Marco Donato is an Assistant Professor in both the Department of Electrical and Computer Engineering and the Department of Computer Science at Tufts University. He leads the TECS Lab (Testchip, Embedded Computing Systems) focused on hardware design for emerging applications. Prior to joining Tufts, he was a postdoctoral fellow at Harvard University's John A. Paulson School of Engineering and Applied Sciences. Dr. Donato received his academic training from prestigious institutions: Ph.D. in Electrical Sciences and Computer Engineering from Brown University (2016) M.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2010) B.Sc. in Electrical Engineering from Università di Roma La Sapienza, Rome, Italy (2008) Dr. Donato's research primarily focuses on designing reliable and energy-efficient hardware systems leveraging emerging technologies. His work centers on co-design methodologies for building specialized architectures for machine learning applications that utilize dense, fault-prone embedded non-volatile memories. He investigates noise modeling and reliability aspects of next-generation memory technologies, with particular emphasis on how these can be effectively integrated into system-on-chip (SoC) designs for edge computing and IoT applications. His research bridges the gap between circuit-level design and system-level architecture to create holistic solutions for hardware acceleration of machine learning workloads. Analysis of Dr. Donato's publication record reveals a strong focus on hardware acceleration for machine learning, particularly through innovative memory system designs. His work spans multiple domains including non-volatile memory technologies, energy-efficient circuit design, and flexible SoC architectures. A notable trend is his exploration of how emerging memory technologies can be leveraged to create more efficient implementations of deep neural networks, with particular attention to the trade-offs between reliability, density, and energy consumption. His research often involves full-stack approaches that consider everything from device physics to system architecture. Dr. Donato is actively involved in mentoring and has indicated he is "looking for Ph.D. students." His work has been supported by significant research grants that have enabled the fabrication of multiple test chips, as evidenced by his extensive publication record in top-tier venues including IEEE Journal of Solid-State Circuits, ISSCC, and MICRO. He leads the TECS Lab at Tufts University, which focuses on testchip development, embedded computing systems, and hardware acceleration. The lab appears to maintain connections with researchers at Harvard University and other institutions, reflecting Dr. Donato's collaborative approach to research. The lab's work emphasizes practical, real-world implementations of novel hardware concepts through actual silicon fabrication, which is relatively rare in academic settings.

Teng Wuمشاهده پروفایل

استاد

• Wind Engineering
• Hurricane Engineering
• Structural Engineering
• +۸ مورد دیگر

Teng Wu is a Professor and Director of Graduate Studies in the Department of Civil, Structural and Environmental Engineering at the School of Engineering and Applied Sciences, University at Buffalo . His research focuses on wind engineering, hurricane risk assessment, and climate change adaptation in infrastructure systems. PhD, Civil Engineering, University of Notre Dame (2013) MS, Civil Engineering, University of Notre Dame (2012) MS, Bridge Engineering, Tongji University (2010) BS, Civil Engineering, Tongji University (2007) Minor, Financial Engineering, Fudan University (2006) Teng Wu's research spans multiple domains including Wind Engineering , Hurricane Engineering , Structural Engineering , and Climate Change Adaptation . He specializes in nonlinear aerodynamics, performance-based wind design, and computational fluid dynamics applications to infrastructure resilience. His recent publications focus on machine learning applications for storm surge prediction, wind-induced structural response analysis, and climate change-informed infrastructure recovery frameworks. Key methodologies include hierarchical deep neural networks, knowledge-enhanced models, and reinforcement learning-based control systems. Contact: tengwu@buffalo.edu | Office: 226 Ketter Hall, Buffalo, NY 14260

Karl Gregoryمشاهده پروفایل

دانشیار

• Bootstrap Methods
• High-Dimensional Regression
• Nonparametric Regression
• +۳ مورد دیگر

Karl Gregory is an Associate Professor in the Department of Statistics at the University of South Carolina, part of the McCausland College of Arts and Sciences. He holds a BS from Central Michigan University, and MS and PhD in Statistics from Texas A&M University, followed by a postdoctoral position at the University of Mannheim. His research focuses on bootstrap methods for high-dimensional regression, nonparametric regression, and sparse linear regression models. He currently serves as an Associate Editor for The American Statistician. Dr. Gregory’s work emphasizes methodological advancements in statistical inference, particularly in handling complex data structures such as group testing and high-dimensional settings. His contributions bridge theoretical statistics with practical applications in epidemiology and biomedical research. He is also actively involved in teaching, contributing to courses like STAT 516 and STAT 513. His research trends highlight interdisciplinary collaborations, integrating computational statistics with real-world challenges in health and data science. While no awards are explicitly listed, his editorial role underscores his impact on the statistical community. He maintains a lab focused on developing scalable statistical tools for modern datasets through his website and published works.

Frances O'Donnellمشاهده پروفایل

دانشیار

• Ecohydrology
• Water Resources
• Stormwater Management
• +۴ مورد دیگر

Frances O'Donnell is an Associate Professor in the Department of Civil and Environmental Engineering at Auburn University, where she leads the Auburn Ecohydrology Lab. Her research focuses on ecohydrology, emphasizing vegetation-water cycle interactions, stormwater management, and ecosystem restoration with applications in geographically isolated wetlands and transportation infrastructure resilience. Education: Ph.D. in Civil and Environmental Engineering, Princeton University B.A. in Organismic and Evolutionary Biology, Harvard University Research Interests: Dr. O'Donnell investigates soil moisture dynamics and evapotranspiration processes to develop green infrastructure solutions for stormwater management. Her work integrates field measurements, hydrological modeling, and remote sensing to quantify ecosystem services of wetlands and improve climate resilience in water systems. Key projects examine forest restoration impacts on groundwater and sustainable practices for agricultural pollution mitigation. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: landslide prediction along Alabama highways using soil moisture thresholds, sediment/nutrient dynamics in agricultural wetlands under climate change, and cost-optimization tools for green infrastructure. International collaborations in Haiti address drinking water accessibility, while Arizona watershed studies analyze forest management effects on water balance. Scientific Awards and Grants: NASA Research Grant for Geographically Isolated Wetlands USDA-NIFA Foundational and Applied Science Program Grant ($500,000) Advising and Collaborations: Dr. O'Donnell mentors graduate students through thesis research with real-world applications, including permeable pavement design tools and UAV-based water quality monitoring. She collaborates with NASA, USDA, and NOAA on federal projects, and her students publish in journals like Water Environment Research while securing positions at Virginia Tech and Geosyntec Consultants. Laboratory: The Auburn Ecohydrology Lab conducts field experiments in Alabama watersheds and Haiti, utilizing sensor networks, hydrological models, and drone technology to advance understanding of water-ecosystem interactions for sustainable infrastructure planning.

• Nanoelectronic Circuit Design
• Neuromorphic Computing
• Hardware Security
• +۴ مورد دیگر

Garrett Rose is a Professor and Department Head in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville (UTK). He holds a B.S. in Computer Engineering from Virginia Tech (2001), and M.S. and Ph.D. in Electrical Engineering from the University of Virginia (2003/2006). Prior to UTK, he served as Assistant Professor at NYU Polytechnic (2006–2011) and Senior Electronics Engineer at the Air Force Research Lab (2011–2014). His research focuses on nanoelectronic circuit design, neuromorphic computing, hardware security, and memristor-based systems. He leads the SENECA Research Group and the TENNLab initiative, exploring applications in neuromorphic architectures, hardware security primitives (e.g., PUF devices), and device modeling. Recent work emphasizes memristor-driven neuromorphic systems, secure FPGA designs, and in-memory computing. Grants include projects on neuromorphic target detection and nanotechnology-based security solutions. Rose actively mentors students and collaborates on co-design methodologies for real-world neuromorphic applications. Education: Ph.D. Electrical Engineering, University of Virginia, 2006 M.S. Electrical Engineering, University of Virginia, 2003 B.S. Computer Engineering, Virginia Tech, 2001 Research Interests: Dr. Rose’s work spans neuromorphic hardware design, including memristor-based neural networks and spiking systems. He investigates hardware security through nanoscale devices like memristors for PUFs and side-channel resistant circuits. His team develops novel memristor models and explores applications in reconfigurable computing and energy-efficient architectures. Recent efforts focus on neuromorphic vision systems, robotic navigation, and neuromorphic processors with co-design frameworks. Grants & Projects: "Ground-roaming autonomous neuromorphic targeter" (2020) "Secure Backup and Restore for IoT using Nanotechnology" (2020) "Physically Unclonable Reconfigurable Computing System (PURCS)" (2020)

Muhammad Azmatمشاهده پروفایل

مدرس

• Logistics 4.0
• Innovation in Mobility
• Autonomous and Connected Vehicles
• +۴ مورد دیگر

Dr. Muhammad Azmat is a Lecturer and researcher at Aston University's School of Engineering and Applied Science, specializing in Engineering Systems & Supply Chain Management. He holds a PhD from Vienna University of Economics and Business, an MBA from Iqra University, and completed leadership programs at Oxford. His research focuses on disruptive technologies like autonomous vehicles, IoT, and their applications in supply chains and mobility. He has collaborated with organizations such as the Federal Procurement Agency of Austria and the Kühne Foundation, addressing challenges in logistics, urban mobility, and humanitarian aid. Education: PhD in Transport, Logistics & Supply Chain Management (Vienna University of Economics and Business, 2019) MSc in Supply Chain Management (Vienna University of Economics and Business, 2015) MBA in Supply Chain Management (Iqra University, 2013) Research Interests: Dr. Azmat explores innovation in mobility (autonomous/connected vehicles, shared platforms), logistics 4.0, and humanitarian supply chains. He examines how technologies like IoT and big data can enhance efficiency while addressing ethical and operational challenges. Recent work includes drone applications for disaster relief and blockchain for supply chain transparency. Publications Trends: His work spans urban logistics optimization, technological adoption barriers in SMEs, and cross-border humanitarian coordination. A common theme is leveraging emerging tech to solve real-world logistical and infrastructural problems. Awards: Best paper in Health and Environment (2019) PhD Distinction Award (2019) PhD Supervision & Collaboration: Actively supervises PhD candidates in mobility innovation and supply chain resilience. Leads industry-academia projects with highway authorities and tech firms, focusing on pilot testing autonomous vehicle infrastructure and IoT-based logistics solutions. Labs/Teams: Collaborates with Aston's Logistics Research Group and external networks like the Kühne Foundation to advance sustainable logistics systems and smart mobility frameworks.

Md Arifuzzamanمشاهده پروفایل

استادیار

• High-Performance Networking
• Quantum Networking
• Distributed Systems
• +۳ مورد دیگر

Dr. Md Arifuzzaman is an Assistant Professor in the Department of Computer Science at Missouri University of Science and Technology (Missouri S&T). He specializes in High-Performance Systems, Quantum Networking, and Distributed Systems, focusing on optimizing large-scale system performance and scalability. His work addresses challenges in next-generation networks and storage systems, with publications in top venues like IEEE TPDS and ACM Supercomputing. Education: Ph.D. in Computer Science and Engineering, University of Nevada, Reno (2023) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2016) Research Interests: Dr. Arifuzzaman's work spans cutting-edge topics including quantum entanglement routing, reinforcement learning for network optimization, and high-speed file transfer protocols. His research emphasizes practical solutions for emerging technologies like terabit networks and quantum communication systems. Publications: Recent work focuses on quantum network protocols, machine learning-driven network probing, and storage reliability. His articles highlight advancements in both theoretical frameworks and real-world system implementations. Awards: No specific awards mentioned in the provided information. Advising/Grants: Details regarding student advising and grant activities are not explicitly stated in the text.