جست‌وجوی استاد و پژوهشگر

Suzan van der Leeمشاهده پروفایل

استاد

• Seismology
• Planetary Geophysics
• Earth's Interior Dynamics
• +۶ مورد دیگر

Suzan van der Lee is the Sarah Rebecca Roland Professor in the Department of Earth, Environmental & Planetary Sciences at Northwestern University , where she has been a faculty member since 2003. She previously served as Visiting Professor at Géoazur (Université Côte d'Azur) and held positions at ETH Zürich and Carnegie Institution of Washington. Education: Ph.D. in Geological and Geophysical Sciences (Princeton University, 1996), M.Sc. in Geophysics (University of Utrecht, 1990) Her research focuses on seismology , planetary geophysics , and data-driven Earth dynamics , particularly through seismic tomography and machine learning applications. She has led major field experiments in Africa, the Americas, and Europe, contributing to understanding Earth's interior structure , plate tectonics , and Mars seismology . Recent work includes the Earthtunes app for public engagement and patent US 2023/0081541 A1 on optimized tomography. Scientific Awards: Member, American Academy of Arts & Sciences (2024-) Fellow, American Geophysical Union (2024-) NSF CAREER Award (2007-2012) Sarah Rebecca Roland Professorship (2022-2027) Her graduate students and postdoctoral researchers have advanced studies in seismic imaging, Marsquake analysis, and computational geophysics. She has served as Principal Director for NU-Geopaths and MCDC , and co-PI for the NSF Research Traineeship program ID3EAS .

Hyunsun Leeمشاهده پروفایل

مدرس ارشد

• Statistical data analysis
• Stochastic branching process in wireless ad-hoc network
• Sound generation mechanism for aircraft engine nozzle
• +۲ مورد دیگر

Hyunsun Lee is a Senior Lecturer at the School of Design , University of Massachusetts Amherst , affiliated with the Department of Mathematics and Statistics . She contributes to the M.S. Option in Statistics and holds a PhD in Mathematics from Stony Brook University, alongside M.S. and B.S. degrees in Mathematics from Yonsei University. Education: PhD in Mathematics, Stony Brook University M.S. in Mathematics, Yonsei University B.S. in Mathematics, Yonsei University Research Interests span statistical data analysis, stochastic branching processes in wireless networks, aircraft engine sound generation mechanisms, homogenization for heterogeneous composites, and Rayleigh-Taylor instabilities. These areas intersect applied mathematics, fluid dynamics, and computational modeling. Scientific Awards include the 2017 Golden Apple Award for Excellence in Mentoring at Hawaii Pacific University and a 2021-22 Distinguished Teaching Award nomination at UMass Amherst.

Yasaman Omidمشاهده پروفایل

پژوهشگر ارشد

• Satellite Communications
• Telecommunications Engineering
• MIMO Systems
• +۴ مورد دیگر

Yasaman Omid is a Research Fellow at Loughborough University, focusing on satellite communication and space-air-ground integrated networks. She holds a PhD in Telecommunications Engineering from Queen Mary University of London, supported by the Queen Mary Principal Studentship. Her postdoctoral research includes roles at the University of Surrey’s 5G Innovation Centre (2022–2023) and her current position at Loughborough since 2023. Her work addresses challenges in direct handheld-to-satellite communication, multi-satellite MIMO systems, and intelligent reflecting surfaces (IRS) for next-generation wireless networks. Research interests span satellite communication systems, MIMO architectures, IRS-enabled networks, and reinforcement learning applications in telecommunications. Her publications analyze topics like delayed channel state information mitigation, beamforming design, and network integration. Key contributions include surveys on multi-satellite MIMO and low-complexity solutions for IRS-aided systems. Awards: Queen Mary Principal Studentship (PhD funding) Her research combines theoretical analysis with practical system design, addressing both academic and industry-relevant challenges in 5G/6G and space-air-ground network integration. Ongoing projects focus on optimizing satellite-ground communication efficiency and resilient network architectures.

Zhipeng Caiمشاهده پروفایل

استاد

• Machine Learning
• Cybersecurity
• Privacy Preservation
• +۳ مورد دیگر

Dr. Zhipeng Cai is a Professor in the Department of Computer Science at Georgia State University with joint appointments at the Robinson College of Business. As Director of the INSPIRE Center and leader of the Innovative Computing and Networking group, his research spans machine learning, privacy/security, and large-scale distributed systems. Cai is an IEEE Fellow, ACM Distinguished Scientist, and NSF CAREER awardee ranked among the world's top scientists. His research examines fundamental challenges in federated learning systems, privacy-preserving AI, and efficient computing for next-generation networks. Recent publications demonstrate Cai's work in federated open-world learning, hypergraph processing, and wireless-powered IoT networks. His group develops novel methods for distributed data synthesis, adaptive resource management, and physics-inspired communications. Dr. Cai leads significant initiatives in cybersecurity education through Eureka Labs, providing open-access learning materials used globally. His editorial leadership includes roles at IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Wireless Communications, and as Editor-in-Chief of High-Confidence Computing. IEEE Fellow (2024) ACM Distinguished Scientist (2025) NSF CAREER Award (2013) World's Top 2% Scientist (Stanford University) The INSPIRE Center conducts federally funded research on adaptive learning systems and secure computing architectures. Current projects focus on privacy-enhancing technologies for large language models, robust optimization under adversarial conditions, and efficient federated learning frameworks.

Dhireesha Kudithipudiمشاهده پروفایل

استاد

• Artificial Intelligence
• Neuromorphic Computing
• Energy-Efficient Machine Learning
• +۵ مورد دیگر

Dhireesha Kudithipudi is a Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA) and Director of the MATRIX AI Consortium. Her work focuses on advancing neuromorphic computing, energy-efficient machine learning architectures, and lifelong learning systems. She holds a Ph.D. in Electrical and Computer Engineering from UTSA and an M.S. in Computer Engineering from Wright State University. Her research interests include AI algorithms, neuromorphic hardware design, spiking neural networks, and memristor-based systems. She leads initiatives in neuromorphic benchmarking (NeuroBench), energy-efficient computing roadmaps (EES2), and collaborative frameworks like the Neuromorphic Commons (THOR). Her lab develops neuromorphic chips with on-device learning capabilities, such as the Genesis chip, and explores applications in edge computing. Dr. Kudithipudi has pioneered techniques for continual learning in spiking networks, probabilistic metaplasticity, and low-precision numerical formats (e.g., PositCL). Her work bridges theoretical neuroscience principles with practical hardware implementations, emphasizing sustainability and scalability. She also contributes to NSF-funded projects like EFRI BRAID and NAIAD, advancing interdisciplinary AI research. Her lab’s collaborations include developing the NeuroBench framework for fair benchmarking and exploring neuromorphic systems for tasks like video/activity recognition and time-series forecasting. She advises on hardware-software co-design strategies for efficient neural network deployment on constrained devices.

Yubao Wuمشاهده پروفایل

استادیار

• Big Data Analytics
• Data Mining
• Bioinformatics
• +۳ مورد دیگر

Dr. Yubao (Robert) Wu is an Assistant Professor in the Department of Computer Science at Georgia State University. He holds a Ph.D. from Case Western Reserve University, and B.Eng. and M.Eng. degrees from Dalian University of Technology, China. His research focuses on big data analytics, data mining, and bioinformatics, with emphasis on analyzing large-scale networks in biomedical and social domains. Key areas include graph theory, random walk-based proximity measures, and community detection algorithms. Education: B.Eng. in Electronic and Information Engineering, Dalian University of Technology (2007) M.Eng. in Signal and Information Processing, Dalian University of Technology (2009) Ph.D. in Computer Science, Case Western Reserve University (2016) Dr. Wu’s research emphasizes scalable algorithms for large graphs, including Monte Carlo methods for random-walk-based similarity measures and distributed computing techniques. His work addresses challenges in network analysis such as detecting communities, measuring node similarity, and optimizing search processes in complex networks. He also explores applications in cybercrime analysis (e.g., dark web markets), public health (opioid epidemic prediction via social media), and bioinformatics. His publications span prestigious venues like VLDB, SIGMOD, and ICDE, addressing topics like illicit drug ad detection, dark web certification analysis, and efficient k-vertex connected component discovery. He is based at 1 Park Place, room 638.

Francesc J. Ferriمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۷ مورد دیگر

Francesc J. Ferri is a Professor in the Department of Computer Science at the University of Valencia since 2008. He holds a Licenciado in Physics (1987) and a Ph.D. in Pattern Recognition (1993) from the same university. His research focuses on Computer Vision, Pattern Recognition, and Machine Learning, with contributions to subspace learning, metric learning, and kernel methods. He has authored/co-authored ~140 papers (h-index=11 as of 2011), with notable work on feature selection and large-scale classification. Education: Licenciado in Physics (1987), Ph.D. in Pattern Recognition (1993), both from University of Valencia. Affiliations: Member of AERFAI, IAPR, ACM, IEEE. Teaching includes courses on Automata Theory, Algorithms, Computer Vision, and Pattern Recognition at undergraduate and graduate levels. He has advised multiple Ph.D. and MSc students, including Adrian Perez-Suay (metric learning) and Katerine Diaz-Chito (subspace-based image recognition). Research projects include sabbaticals at Surrey University (1993) and Michigan State (2005), and collaborations on audio event detection, bioinformatics, and e-learning systems. His recent work addresses explainable AI, edge computing for safety systems, and multidataset HRTF analysis.

Ryan Layerمشاهده پروفایل

استادیار

• computational genomics
• structural variation analysis
• algorithm development for genomic datasets
• +۲ مورد دیگر

Ryan Layer is an Assistant Professor in the Department of Computer Science at the University of Colorado, affiliated with both the BioFrontiers Institute and the Division of Biomedical Informatics and Personalized Medicine at CU Anschutz Medical Campus. His work bridges computational methods with genomic data analysis to address biological and medical challenges. PhD in Computer Science from University of Virginia Postdoctoral work at University of Utah Human Genetics Department Developed critical tools: LUMPY for structural variation detection, GQT for genotype queries, GIGGLE as genomic search engine Dr. Layer's research focuses on creating scalable algorithms for genomic dataset interpretation, particularly structural variation analysis. His lab designs tools like STIX for population-scale variant classification and samplot for visualization. He aims to unify GQT and GIGGLE into a comprehensive platform for large-scale genomic analysis. His software contributions include: BITS : Scalable interval intersection counting LUMPY : Structural variant discovery framework GIGGLE : Genomic interval search engine STIX : Structural variant indexing system svtools : Population-scale SV analysis SV-plaudit : Cloud curation framework Dr. Layer's team actively recruits postdoctoral researchers and develops solutions for: Genomic interval indexing Genotype-phenotype correlation Biomedical informatics Human disease mutation identification Large-scale data analysis Cancer genomics

• Electronic Design Automation
• Computer-Aided Design
• Analog/Mixed-Signal Circuit Design
• +۷ مورد دیگر

David Z. Pan is a Professor at the University of Texas at Austin, where he leads a prominent research group specializing in Electronic Design Automation (EDA) and Computer-Aided Design for Integrated Circuits. His extensive publication record spanning from 1997 to the present demonstrates his leadership in advancing the field of electronic design. Dr. Pan's research focuses on solving fundamental challenges in analog/mixed-signal circuit design automation, physical design methodologies, and the integration of machine learning techniques with traditional EDA problems. His work bridges theoretical advances with practical applications in semiconductor design, with particular emphasis on photonic computing, quantum circuit design, and FPGA optimization. His research has evolved from traditional layout and placement algorithms to incorporate cutting-edge AI and machine learning approaches for next-generation design automation. Analysis of his recent publications reveals a strong trend toward integrating artificial intelligence with EDA, including the use of large language models for circuit design automation, reinforcement learning for placement optimization, and deep learning for various aspects of the design flow. His work consistently addresses critical industry challenges while pushing the boundaries of what's possible in electronic design. Dr. Pan has advised numerous graduate students who have become significant contributors to the field, with many continuing their research careers in academia and industry. His research group has developed several influential tools and methodologies that have been adopted by both academic and industrial researchers. He actively contributes to major conferences in the field including ICCAD, DAC, ASP-DAC, and ISPD, often presenting invited talks that shape the future direction of EDA research. His work on open-source EDA tools has been particularly impactful, promoting accessibility and reproducibility in electronic design research.

Shahed Masoudianمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Domain Adaptation
• +۳ مورد دیگر

Shahed Masoudian is a University Assistant at the Institute of Computational Perception, Johannes Kepler University Linz (JKU). His research focuses on machine learning, particularly in transfer learning and domain adaptation. He investigates methods to transfer knowledge from simulated environments to real-world applications, aiming to reduce reliance on large labeled datasets. His work addresses challenges in bias mitigation, audio classification, and neural network optimization. Key research areas include deep domain adaptation, knowledge distillation, and cognitive biases in recommendation systems. His contributions span applications in acoustic scene classification, industrial condition monitoring, and modular neural network architectures. He has authored/co-authored 15+ peer-reviewed articles since 2022, addressing topics from bias reduction in AI systems to efficient model distillation techniques. Shahed's educational background includes a master’s thesis supervised by Prof. Gerhard Widmer, exploring simulation-to-real domain adaptation for neural networks. He actively participates in international challenges like DCASE, demonstrating practical solutions for low-complexity audio processing. His research emphasizes bridging the gap between theoretical advancements and real-world deployment, particularly in computationally constrained environments.

Daehan Wonمشاهده پروفایل

دانشیار

• Large Scale Data Analysis
• Machine Learning Applications
• Manufacturing Process Optimization
• +۳ مورد دیگر

Daehan Won is an Associate Professor at the School of Systems Science and Industrial Engineering, Binghamton University. His research focuses on integrating large-scale data analysis, machine learning, and physics-informed models to address challenges in manufacturing, healthcare, and agriculture. He holds a PhD from the University of Washington and dual MS/BS degrees from KAIST. Education: PhD, University of Washington MS, KAIST BS, KAIST His work spans manufacturing process optimization (e.g., solder joint prediction, reflow oven recipes), healthcare analytics (e.g., autism classification via EEG), and agricultural applications (e.g., leaf disease classification). Recent trends in his research emphasize hybrid models merging domain-specific physics principles with neural networks for precision in electronics manufacturing and medical diagnostics. Publications highlight contributions to defect diagnosis in pick-and-place machines, energy-efficient reflow processes, and explainable AI for imbalanced manufacturing data. His interdisciplinary approach bridges engineering, computer science, and healthcare. No scientific awards are listed, but his extensive publication record reflects active research engagement. He collaborates on projects involving smart manufacturing systems and AI-driven solutions for real-world problems.

Congyu Wuمشاهده پروفایل

استادیار

• Mobile/wearable sensing for health and behavior detection
• Social signal processing and dynamic modeling
• Crowdsensing and social computing
• +۳ مورد دیگر

Congyu Wu is an Assistant Professor at Binghamton University's School of Systems Science and Industrial Engineering. His research focuses on leveraging mobile/wearable sensing, social signal processing, and crowdsensing to advance healthcare, work performance, and community wellbeing. He holds a PhD in Systems Engineering from the University of Virginia and a BS in Industrial Engineering from Tianjin University. His key research areas include: Mobile/wearable sensing for health and behavior detection Social signal processing and dynamic modeling for group interactions Crowdsensing and social computing for population-level informatics He leads a lab exploring computational methods to address human-centric challenges, with affiliations to Google Scholar and ResearchGate. Recent work includes aiding a University of Texas study on Latina women's metabolic health risks. Advising and grant activities are ongoing.

• Artificial Intelligence
• Bioinformatics
• Natural Language Processing
• +۳ مورد دیگر

Dr. Ahmad Aghaebrahimian is a researcher at the ZHAW School of Life Sciences and Facility Management, affiliated with the Institute of Computational Life Sciences. He specializes in computational methods applied to healthcare, natural language processing (NLP), and bioinformatics. His work integrates deep learning, ontology-based systems, and signal processing to address challenges in healthcare informatics, biomedical research, and security systems. Research Projects: Project Leader: Advancing Information Accessibility in Hospitals (LLMs) Project Leader: Multi-document Patient Records Summarization Project Leader: Plant Cell Cultures with Deep Learning Deputy Leader: Automatic Supply Chain Monitoring Research Interests: His research focuses on AI-driven solutions for healthcare, including ontology-aware relation extraction, medical text mining, and robust signal processing systems. He also explores NLP applications in question answering, entity disambiguation, and parallel corpus creation. Recent work includes drone detection using CNNs in low SNR environments and computational methods for natural products discovery. Publications Trends: Over the past decade, his publications emphasize interdisciplinary approaches combining machine learning with bioinformatics and medical informatics. Key themes include deep learning model optimization, biomedical knowledge graph construction, and practical applications of NLP in healthcare systems. Grants & Collaboration: Leads research initiatives on AI in colorectal cancer classification and supply chain monitoring, demonstrating expertise in securing project leadership roles within academic-industry collaborations.

Mato Baotićمشاهده پروفایل

استاد

• Control Systems
• Model Predictive Control
• Renewable Energy Systems
• +۵ مورد دیگر

Prof. Mato Baotić is a Full Professor and Head of the Department of Control and Computer Engineering at the University of Zagreb's Faculty of Electrical Engineering and Computing (FER). His research focuses on advanced control systems, model predictive control (MPC), renewable energy systems, and power engineering. He leads projects in distributed power systems, microgrid control, wind turbine optimization, and mechatronics. His work integrates theoretical advancements with practical applications in energy efficiency, robotics, and smart grids. Key research areas include MPC for wind turbines, grid reconfiguration, and omnidirectional mechatronic systems. He has contributed to the LARES laboratory for renewable energy systems and collaborates on international projects like DYMASOS and MICROGRID. His publications span control algorithms, grid optimization, and energy storage solutions. Prof. Baotić teaches courses on control systems synthesis, predictive control, and automatic control. He advises numerous research initiatives and oversees labs developing systems for energy and robotics applications.

Linda Larson-Priorمشاهده پروفایل

استاد

• Neurobiology
• Developmental Sciences
• Neuroimaging
• +۳ مورد دیگر

Linda Larson-Prior is a Professor of Neurobiology and Developmental Sciences, Neurology, Pediatrics, Psychiatry, and Biomedical Informatics at the University of Arkansas for Medical Sciences (UAMS), College of Medicine. She directs the Budding Brain Lab and contributes to the HEALthy Brain and Child Development (HBCD) Study. Her research integrates neuroimaging (EEG, fMRI) to study brain-environment interactions across lifespan, focusing on nutrition, sleep, and Parkinson’s disease. Education: B.A. in Arabic/Anthropology from Ohio State University; M.A. in Physical Anthropology from Case Western Reserve University; Ph.D. in Neurobiology from Kent State University. Research interests emphasize cognitive development in children and adults, with projects addressing how environmental factors like diet and sleep impact brain function. Her work employs 'connectomics' and multidisciplinary teams to study neural networks in health and disease. Key areas include Parkinson’s motor/cognitive decline, pediatric neurodevelopmental norms, and rural healthcare access challenges. Her articles explore neuroimaging tools for disease diagnosis (e.g., voice-based Parkinson’s detection via machine learning), telemedicine for underserved populations, and large-scale brain network analysis. Ongoing projects include the HBCD Study’s EEG protocol development and longitudinal studies on infant neurodevelopment. Labs/Teams: Director of the ACNC Budding Brain Lab; collaborator in Neurocognitive Development Laboratory. Her work bridges clinical research and technology innovation, aiming to improve diagnostic tools and patient care in neurology and pediatric medicine.