جست‌وجوی استاد و پژوهشگر

Shafi U. Bhuiyanمشاهده پروفایل

دانشیار

• Global Public Health
• Maternal and Child Health
• Health Promotion
• +۴ مورد دیگر

Dr. Shafi U. Bhuiyan is an Associate Professor and SBS Program Coordinator in the Division of Social and Behavioral Sciences at the University of Memphis, TN, USA. He holds a PhD from Osaka University, an MPH from Mahidol University, an MBBS from the University of Dhaka, and an MBA from Toronto Metropolitan University. His expertise lies in global public health, maternal and child health (MCH), and clinical public health education. He has pioneered initiatives such as the Maternal and Child Health Handbook (MCHHB) and co-founded the Clinical Public Health Certificate Program at the University of Toronto. Dr. Bhuiyan has authored over 55 peer-reviewed publications and mentored over 25 graduate students annually since 2015. His research focuses on MCH handbook utilization, global health equity, and digital health innovations. He has managed multi-million-dollar grants for community-based projects and serves on global health boards, including the Canadian Coalition for Global Health Research and the International Committee on MCH Handbook. His awards include the Lions International President’s Distinguished Leadership Award, Melvin Jones Fellowship, and Helen Keller Fellowship for community services. His work emphasizes improving healthcare access in low-resource settings and fostering global health partnerships.

Mags Creanمشاهده پروفایل

استادیار

• Educational inequalities
• Poverty
• Equality
• +۵ مورد دیگر

Mags Crean is an Assistant Professor in Social Policy at Maynooth University, within the Department of Applied Social Studies under the Faculty of Social Sciences. She holds degrees from University College Dublin, including a PhD in Social Justice. Her research focuses on educational inequalities, poverty, equality, social class, care, health, ageing, and critical education. She has extensive experience in community and NGO sectors addressing policy areas like housing, health, and education. Notably, she served as an Independent county councillor, emphasizing participatory policy processes. Crean's research projects include roles as Principal Investigator for 'Dementia Advocacy: A human rights and social justice lens' and co-PI for 'Zero Tolerance Meath community prevention project'. She has led evaluations of social inclusion programs and dementia services. Her 2008 UCD Egalitarian World Initiative scholarship highlights her commitment to civil society roles. Professional service includes board memberships with Purple House Cancer Support, Wicklow Inclusion and Disability Committee, and East Coast Regional Drugs & Alcohol Task Force. Teaching focuses include Advanced Social Policy, Ageing and Social Policy, and Education Inequalities at undergraduate and postgraduate levels.

Saleh Ashkboosمشاهده پروفایل

پژوهشگر

• Deep Learning
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Saleh Ashkboos is a Ph.D. student in the Computer Science Department at ETH Zurich, advised by Professors Torsten Hoefler and Dan Alistarh. He is also a Research Assistant at the Scalable Parallel Computing Lab and an affiliated doctoral student of the ETH AI Center. His research focuses on accelerating deep neural network training and developing systems for large-scale graph processing. Prior to ETH Zurich, he earned his Master's degree in Computer Science from Sharif University of Technology, advised by Professor Amir Daneshgar. His work has led to notable contributions, including the best paper award at SC22 for 'ProbGraph.' Recent research emphasizes efficient LLM training and quantization techniques, with publications on topics like 4-bit inference, quantization-aware training frameworks, and scalable meteorological modeling. He has interned at Apple and Microsoft, and his work is accessible via Google Scholar and GitHub. Key projects include GPTQ (post-training quantization for transformers), SliceGPT (LLM compression), and ProbGraph (high-performance graph mining). His technical contributions span distributed systems, neural network optimization, and climate-related machine learning.

• Power Systems
• Smart Grids
• Synchronized Measurement Technology
• +۶ مورد دیگر

Vladimir V. Terzija is a prominent researcher specializing in power systems engineering with a focus on smart grid technologies, synchronized measurement systems, and power system protection. His extensive publication record spans over two decades, demonstrating continuous contributions to the field of electrical power engineering across numerous IEEE journals and conferences. Terzija's research primarily centers on advanced power system monitoring, protection, and control methodologies. His work has significantly contributed to the development of synchronized measurement technology applications, fault analysis algorithms, and state estimation techniques for modern power systems. He has pioneered approaches for wide-area monitoring systems, transmission line fault analysis, and integrating renewable energy resources into power grids while maintaining stability and reliability. His research spans from fundamental power system theory to practical implementations addressing contemporary challenges in grid operation. Analysis of his recent publications reveals a strong focus on integrating artificial intelligence and machine learning techniques into power system applications, particularly for condition monitoring, anomaly detection, and predictive maintenance. His work increasingly addresses challenges posed by the energy transition, including grid stability with high renewable penetration, multi-energy system integration, and advanced control strategies for low-inertia power systems. The interdisciplinary nature of his research connects power engineering with data science, optimization theory, and cybersecurity. Throughout his career, Terzija has collaborated extensively with researchers across Europe and internationally, as evidenced by his numerous co-authored publications with institutions worldwide. His work appears consistently in top-tier IEEE publications, indicating recognition by the power engineering community. While specific awards aren't documented in the available publication records, his sustained research productivity and influence in the field suggest significant professional recognition. Terzija has supervised numerous research projects focused on power system monitoring and control, with particular emphasis on practical implementations that bridge theoretical developments with real-world grid applications. His work on WAMS (Wide Area Monitoring Systems), fault location algorithms, and state estimation techniques has contributed to advancing grid operational capabilities. The research trajectory shows increasing focus on addressing challenges associated with renewable energy integration, grid digitalization, and maintaining stability in modern power systems. His research group appears to focus on developing advanced monitoring and control systems for power networks, with particular expertise in synchrophasor technology applications. The collaborative nature of his work suggests involvement in international research consortia addressing contemporary power system challenges, particularly those related to grid stability in systems with high renewable penetration and the development of intelligent monitoring solutions for power infrastructure.

Vincent DePaulمشاهده پروفایل

استادیار

• Cerebrovascular sciences (including stroke)
• Neurosciences
• Rehabilitation medicine
• +۶ مورد دیگر

Dr. Vincent DePaul is an Assistant Professor in the School of Rehabilitation Therapy at Queen's University, with affiliations including the Faculty of Health Sciences, the Human Mobility Research Centre, and the Centre for Neuroscience Studies. His research focuses on interventions for gait and balance recovery in stroke survivors and older adults, emphasizing motor learning principles and wearable technology applications. He holds cross-appointments as an Associate Scientist in Health Services and Policy Research and Associate Professor in Rehabilitation Therapy. Education: Doctorate of Philosophy (2013), Rehabilitation Science, McMaster University Bachelor of Health Science (1990), Physiotherapy, McMaster University Diploma in Physiotherapy (1989), Mohawk College Research Interests: Cerebrovascular sciences, neurorehabilitation, stroke recovery, aging, motor learning, and community-based interventions. His work explores therapeutic strategies like feedback, guidance, and wearable technology to enhance mobility recovery. Key Contributions: Developed the Oasis Senior Supportive Living model for aging-in-place and led the OSCAIL study to improve stroke care in low-resource settings. Active in global health initiatives and rehabilitation technology development. Affiliations: Member, Human Mobility Research Centre (2017–present) Associate Scientist, Health Services and Policy Research Institute (2018–present) Research Affiliate, Providence Care Hospital (2016–present) Teaching: Teaches foundational clinical skills and motor performance in rehabilitation at Queen's University.

Els Lefeverمشاهده پروفایل

دانشیار

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۵ مورد دیگر

Els Lefever is an Associate Professor at Ghent University, where she works with the LT3 (Language and Translation Technology) research team. Her position focuses on computational linguistics and natural language processing research, with strong ties to both theoretical and applied aspects of language technology. Dr. Lefever earned her PhD in Computer Science from Ghent University in 2012 with her dissertation titled "ParaSense: Parallel Corpora for Word Sense Disambiguation." Her academic journey began as a computational linguist at the R&D department of Lernout & Hauspie Speech Products before transitioning to academia. Els Lefever's research spans multiple areas within computational linguistics with particular expertise in multilingual natural language processing. Her work focuses on computational semantics, cross-lingual word sense disambiguation, and multilingual terminology extraction. Recent research directions include automatic detection of irony in online text, argumentation mining in social media, sentiment analysis of financial news, language modeling for low-resourced languages, and computational approaches to Byzantine Greek epigrams. Her research demonstrates a consistent pattern of bridging theoretical computational linguistics with practical applications across diverse language domains and historical periods. Professor Lefever actively supervises PhD research on several cutting-edge topics including terminology extraction from comparable corpora, event extraction and sentiment mining of financial news, language modeling for low-resourced languages, argumentation mining in social media, and the automatic detection of links between Byzantine Greek epigrams. Her supervision portfolio demonstrates her commitment to advancing multiple frontiers of computational linguistics simultaneously. As an educator, Professor Lefever teaches courses in Terminology and Translation Technology, Language Technology, Localisation, Digital Text Analysis, and Digital Humanities. Her teaching reflects her research interests, providing students with both theoretical foundations and practical skills in language technology applications. The LT3 research group, where Professor Lefever is a key member, maintains strong connections with both academic and industry partners. The group has participated in numerous international conferences and shared tasks including SemEval competitions across multiple years, demonstrating consistent contributions to benchmark datasets and evaluation methodologies in natural language processing.

Xue Linمشاهده پروفایل

دانشیار

• Robust and Secure Machine Learning
• Deep Learning on the Edge
• Cyber-Physical Systems
• +۳ مورد دیگر

Xue Lin is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in Khoury College of Computer Science. She joined Northeastern in 2017 and holds a PhD from the University of Southern California (2016) and a bachelor’s from Tsinghua University. Her research focuses on robust and secure machine learning, deep learning on edge devices, and cyber-physical systems. She leads the High Energy-Efficiency & Performance System Lab, which develops efficient algorithms and systems for applications like autonomous vehicles and medical AI. Dr. Lin’s work is supported by NSF, DARPA, and the U.S. Department of Transportation, among others. Notable achievements include a $1M DARPA grant for adversarial diagnosis systems, a 1st Place ISLPED 2020 Design Contest win, and multiple best paper awards. She has advised students such as Kaidi Xu (PhD’21), Mengshu, and Siyue, who have contributed to impactful projects like adversarial T-shirt attacks and FPGA-based DNN accelerators. Her research also addresses security in autonomous systems and inclusive design challenges for older and visually impaired passengers. Key grants include NSF CPS Small Awards, SaTC Medium Awards, and collaborations with institutions like the University of Maine and Michigan State University. Awards include the 2024 Faculty Fellow Award and recognition in Stanford’s top 2% cited scientists. Her lab’s projects span secure autonomous systems, energy-efficient inference frameworks (e.g., GRIM), and robust neural network verification techniques.

Weiwei Leiمشاهده پروفایل

استاد

• Nanomaterial Synthesis
• Sustainable Water Solutions
• Energy Harvesting
• +۴ مورد دیگر

Weiwei Lei is a full Professor and Australian Research Council Future Fellow at RMIT University's School of Science (STEM College). His research focuses on nanomaterials for sustainable water and energy solutions, with over 230 publications and $20M in grants. He leads interdisciplinary collaborations globally, mentoring 29 students and 6 postdocs. Research & Impact: Specializes in functional nanomaterials for energy conversion/storage and water purification. His work has garnered 500+ media mentions (BBC, ABC) and industry partnerships (e.g., UK Highways, AquaPure). Key contributions include nanomaterial synthesis routes and energy-water nexus solutions. Awards: ARC Future Fellowship (2022), DECRA (2014), TechConnect Innovation Award (2015). Leadership roles include Research Theme Leader and ERA Engineering Deputy Cluster Leader. Grants: Secured $12.2M as lead CI across ARC grants, IMCRC, and federal funding. Active in industry partnerships for applied research. Mentoring: Supervised 17 primary PhD/Master’s students, many now professors/industry leaders. Postdoc mentorship includes 2 ARC DECRAs. Labs/Teams: Leads initiatives like 'Plasma coating for sustainable future' and 'Advanced nanomaterials for energy harvesting'. Collaborates with global researchers (Asia/Europe/Americas) on 75% of publications.

Sang-Woo Junمشاهده پروفایل

استاد

• Big Data Analytics
• Hardware Acceleration
• Non-Volatile Memory
• +۳ مورد دیگر

Professor Sang-Woo Jun is a leading researcher in systems and software for big data analytics, focusing on FPGA-based hardware acceleration and non-volatile memory (NVM) storage. His work spans applications such as graph analytics and bioinformatics, with a strong emphasis on cost-effective, high-performance computing architectures. He advises PhD students like Shengquan Ni and Yicong Huang, both of whom have achieved notable milestones (e.g., thesis defense, fellowship awards). Research Interests: Hardware Acceleration for Big Data FPGA-Based System Architectures Non-Volatile Memory Systems Graph Analytics and Bioinformatics Edge Computing and Low-Power Systems Recent Contributions: His articles highlight innovations in edge accelerators (e.g., IceSpy, Eciton), genomics acceleration (Bancroft), and scalable graph processing (Durin, Sting). These works emphasize reconfigurable systems, privacy-preserving techniques, and energy-efficient designs. Lab & Team: As part of the Intelligent Systems Group (ISG), he collaborates on events like the Southern California Database Day. His research bridges hardware-software co-design with real-world applications in IoT, environmental monitoring, and genomics.

Jamal Jokar Arsanjaniمشاهده پروفایل

استاد

• Remote Sensing
• Climate Change
• Land Use Change
• +۵ مورد دیگر

Jamal Jokar Arsanjani is a full professor in Geoinformatics and Earth Observation at Aalborg University (Denmark), leading the Geoinformatics & Earth Observation group. He holds a PhD from the University of Vienna (2011), with postdoctoral research at Heidelberg University supported by an Alexander von Humboldt fellowship (2012–2015). His academic career includes roles as a UN consultant in Vienna and senior scientist at Heidelberg's GIScience group. Research focuses on integrating geospatial data with computational models to address complex environmental challenges, including land use change, climate impacts, and natural hazards. Key methodologies include agent-based modeling, machine learning, and remote sensing. He contributes to UN Sustainable Development Goals, particularly SDGs related to climate action, sustainable cities, and responsible consumption. Recent projects include 'PROMISE' (micro-mobility integration in urban transport) and 'AI4Covid' (AI-driven pandemic monitoring). Awards include the Springer Outstanding PhD Dissertation (2012) and an Earth Observation award (2020). He serves on the European Environment Agency board and edits the journal Land . Research outputs span 105+ publications since 2008, emphasizing urban sustainability, disaster resilience, and geospatial technologies. Active in 7 ongoing projects and 45+ academic activities, he advises on policy and innovation in smart cities and climate adaptation.

• Network Performance
• Cybersecurity
• Congestion Control
• +۴ مورد دیگر

Simone Ferlin is an Adjunct Senior Lecturer at Karlstad University and a Senior Performance Engineer. She holds a PhD in Computer Science from Simula Research Lab and Universitetet i Oslo (2017), focusing on robustness in multipath transport protocols like MPTCP. Her research spans network performance, security, and congestion control in mobile/5G networks and the Internet. She collaborates actively with academia and industry, co-supervising students in areas such as edge computing, container orchestration, and distributed systems. Affiliations: Department of Informatics, Karlstad University; Red Hat Research; Ericsson R&D. Education: PhD (2017), Simula/UiO; Master’s and Undergraduate studies emphasized networking and electronics. Her work includes projects like AIDA (AI-driven edge networking) and DRIVE (latency-sensitive mobile services). She has published over 50 papers on topics like QUIC, eBPF, and containerized microservices. Awards include the Best Paper at IEEE ICIN 2021 and ANRP 2025 Prize. Teaching responsibilities include Future Internet Design and Service Quality . Advising spans 15+ students across institutions like TU Berlin, KTH, and Unifesp. She chairs conferences (e.g., ACM SIGCOMM 2025) and serves on editorial boards (IEEE Communications Magazine). Key interests: network observability, low-latency protocols, and sustainability in networking.

Liping Wangمشاهده پروفایل

استاد

• Building Performance Modeling
• HVAC Fault Detection and Diagnosis
• Energy Efficiency in Indoor Agriculture
• +۹ مورد دیگر

Liping Wang is a Professor in the Department of Civil and Architectural Engineering at the University of Wyoming, part of the College of Engineering and Physical Sciences. She also serves as the Director of the Center for Controlled Environment Agriculture, highlighting her leadership in sustainable building and agricultural systems. Her work bridges engineering, energy efficiency, and environmental resilience. Academic Background: B.S., Xi’an University of Architecture and Technology, China M.S., Xi’an University of Architecture and Technology, China Ph.D., National University of Singapore Her research interests focus on building performance modeling, fault detection and diagnosis for HVAC systems, energy efficiency in indoor agriculture, and the development of resource-efficient and resilient future communities. She applies data-driven and adaptive modeling techniques to enhance building energy systems and indoor environmental quality. The analysis of her recent publications reveals a strong trend toward intelligent building systems, with recurring use of machine learning (e.g., Gaussian mixture models, particle swarm optimization) for HVAC modeling, fault detection, and energy optimization. Her work increasingly integrates controlled environment agriculture, reflecting a shift toward sustainable food-energy-water nexus solutions. Topics span from fundamental thermodynamics to applied field evaluations of ASHRAE standards. Scientific Awards: NSF CAREER Award (1944823), National Science Foundation, 2019 Appreciation Award, Advisor of ASHRAE Student Branch, University of Wyoming, 2019 Liping Wang has secured competitive research funding, notably the NSF CAREER Award, and mentors students through research and professional engagement. She actively contributes to the academic community as an Associate Editor of the International Journal of Sustainable Energy Technologies and Assessments and a guest editor for Energy and Buildings . Her work involves interdisciplinary collaboration, with co-authors from diverse engineering and environmental science backgrounds. She has served as an ad hoc reviewer for top journals including Building and Environment , Energy and Buildings , and Applied Energy . She leads the Center for Controlled Environment Agriculture, which likely involves a research team focused on energy-efficient indoor farming technologies, greenhouse modeling, and sustainable building-agriculture integration. Her lab appears to utilize low-cost sensor systems (e.g., Arduino-based platforms) for real-time monitoring of environmental conditions.

Nabil J. Sarhanمشاهده پروفایل

دانشیار

• Computer Systems
• Deep Learning
• Video Streaming
• +۵ مورد دیگر

Dr. Nabil J. Sarhan is an Associate Professor of Electrical and Computer Engineering at Wayne State University , where he directs the Computer Systems and Deep Learning Research Laboratory and the interdisciplinary M.S. Program in Artificial Intelligence (Systems and Hardware Track) . He previously served as Graduate Program Director and College of Engineering Faculty Assembly Chair. Education : Ph.D. and M.S. in Computer Science and Engineering from Pennsylvania State University (GPA 4.0), B.S. in Electrical Engineering from Jordan University of Science and Technology Internationally recognized for expertise in computer systems , deep learning , video streaming , and AI hardware accelerators , Dr. Sarhan's research bridges theoretical and applied domains in automated video surveillance and medical AI systems . His work includes novel approaches to epileptic seizure prediction , energy-efficient neural network accelerators , and mixed-signal circuit design for edge computing. Recent grant highlights include a $418,907 National Science Foundation award for reconfigurable AI accelerator development. His patent (US Patent 9,313,463) on automated video surveillance systems demonstrates practical technology impact. Scientific Awards IEEE SEM Outstanding Professional of the Year Wayne State President's Excellence in Teaching Award College of Engineering Excellence in Teaching Award WSU Academy of Teachers (2022) IEEE Senior Member (2016) As an educational reform leader , he has chaired over 20 ABET accreditation panels in Saudi Arabia and Palestine, evaluating programs in electrical engineering, computer science, and software engineering. His intellectual property expertise spans significant patent infringement cases including ACQIS v. ASUSTek and VideoShare v. Google .

• Machine Learning
• Deep Learning
• Plant Disease Detection
• +۳ مورد دیگر

Geeta Rani is a researcher actively contributing to the intersection of Computer Science and Agricultural Technology , with recent publications in plant disease detection , IoT-based smart farming , and medical imaging applications . She collaborates with researchers like V. Dhaka, Nidhi Kundu, and M. Woźniak. Key Research Areas : Deep Learning, Plant Disease Classification, IoT Frameworks, Agricultural AI, Medical Imaging, Neural Networks Her work includes a 2023 focused review on IoT and Deep Learning for plant disease monitoring , highlighting research gaps and model performance comparisons. In 2022, she contributed to disease severity prediction in maize, achieving significant citation impact. Earlier projects (2021-2019) involved developing frameworks for pearl millet disease prediction, comparative studies of CNN models for plant diseases, and AI-based irrigation systems. She also explored medical image transformation using GANs (2020) and diabetes prediction via neural networks.

Yu Kangمشاهده پروفایل

استاد

• Precision Agriculture
• Remote Sensing
• Plant-Environment Interactions
• +۳ مورد دیگر

Prof. Yu Kang is a Professor of Precision Agriculture at the TUM School of Life Sciences, Technische Universität München (TUM). His research focuses on integrating imaging, sensing, and computational methods to study plant-environment interactions. He aims to enhance resource efficiency and reduce environmental impact through precision crop management. Prior to TUM, he held positions at China Agricultural University (CAU) and conducted postdoctoral research at ETH Zurich and KU Leuven. Prof. Yu's career includes roles such as Associate Professor of Crop Science at CAU and postdoctoral fellowships in physical geography. His educational background includes a doctoral degree from the University of Cologne (2014) and undergraduate studies at China Agricultural University. Key research areas include remote sensing for crop health monitoring, hyperspectral imaging for disease detection, and machine learning applications in agriculture. His work has led to innovations in crop nitrogen management and precision phenotyping. Awards include the Innovation Team Award (2019) from the Crop Science Society of China and the GSGS Fellowship (2014) from the University of Cologne.