جست‌وجوی استاد و پژوهشگر

• Functional Magnetic Resonance Imaging (fMRI)
• Functional Connectivity
• Resting-state fMRI
• +۵ مورد دیگر

Bharat Biswal is a Distinguished Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology (NJIT), serving as Director of the Center for Brain Imaging. His primary affiliation is with the College of Engineering, where he leads neuroimaging research initiatives. Biswal’s work focuses on functional magnetic resonance imaging (fMRI), brain connectivity analysis, and translational applications in neuropsychiatric disorders. Research Interests: Functional and Resting-State fMRI Methodology Neurovascular Coupling Mechanisms Connectomics and Network Neuroscience Clinical Applications in ADHD, OCD, and Neurodegenerative Diseases Post-COVID Neuroimaging White Matter Function Grant Activity: NIH-funded projects on laminar-specific connectivity (2022–2025) National Science Foundation MRI infrastructure grants (2019–2021) Longitudinal HIV brain studies (2015–2018) Recent Articles Highlight: Biswal’s 2025 work advances understanding of cocaine use disorder neurobiology, obsessive-compulsive disorder network dysregulation, and standardized PET nomenclature. His lab also innovates in AI-driven defect detection and transcriptomic-neuroimaging integrations. Awards: Recipient of NJIT’s 2024 Excellence in Research Award for pioneering contributions to resting-state fMRI and brain connectivity research. Labs/Teams: Leads the Center for Brain Imaging at NJIT, collaborating internationally on neuroimaging standards and translational neuroscience projects.

Graham Rileyمشاهده پروفایل

مدرس

• High Performance Computing
• Scientific Computing
• Parallel Computing
• +۴ مورد دیگر

Graham Riley is a Lecturer in the School of Computer Science at the University of Manchester and holds a part-time position in the Scientific Computing Department (SCD) at STFC, Daresbury. His research focuses on high performance computing (HPC), software engineering for scientific computing, and performance modeling for parallel machines. Key areas include techniques for developing HPC applications, software architectures for coupled modeling, and performance control in distributed systems. His work emphasizes collaboration with computational scientists in domains such as Earth System Modelling (e.g., UK Met Office), computational chemistry, and biology. He has contributed to projects like the EuroExa architecture for exascale computing and the LFRic weather/climate model porting to FPGAs. Riley's research also explores energy efficiency in HPC systems and FPGA acceleration strategies for scientific workloads. Notable contributions include studies on parallelization strategies, FPGA-based acceleration of climate models, and optimizing OpenCL for heterogeneous architectures. His work aligns with UN Sustainable Development Goals, particularly through contributions to climate modeling and sustainable computing practices. Riley collaborates with institutions like the Met Office and the ESM community in Europe/US. His academic profile reflects a balance between theoretical research and practical application, with a strong focus on bridging computational science and engineering challenges.

Georg Grohمشاهده پروفایل

استاد مدعو

• Applied Computer Science
• Social Signal Processing
• Social Network Analysis
• +۴ مورد دیگر

Georg Groh is an Adjunct Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology . His research focuses on modeling social context, social interaction mediated by IT systems, and ML-based natural language processing. He holds a doctorate (2005) and habilitation (2012) from TUM, with prior studies in physics and computer science. Key research areas include social signal processing, network analysis, and bias detection in AI systems. Notable awards include the 2019 Supervisory Award and 2016 Honorary Teaching Certificate. His work bridges computational methods with societal impacts, particularly in health informatics and ethical AI. Recent projects explore LLM hallucination detection, bias profiling, and cross-lingual text classification. Education: PhD in Computer Science (2005), TUM Habilitation in Computer Science (2012), TUM Studies in Physics (University of Kaiserslautern) and Computer Science (Universities of Hamburg, Kaiserslautern, TUM) Research Interests: Groh’s work spans social computing, NLP, and ethical AI . Current projects address bias in language models, hate speech detection, and data-driven health interventions. His methodologies emphasize contextual analysis of social interactions, leveraging ML and graph-based techniques. Awards: 2nd place Supervisory Award (2019) Best Paper Awards (2016, 2008) Advising & Grants: Advised on projects like Nutrilize (nutrition recommender system) and contributed to EU-funded initiatives on mHealth systems. Active in designing AI systems for dietary logging and stress management.

Soon Lay Kiمشاهده پروفایل

دانشیار

• Applied Natural Language Processing
• Data Management
• Aspect-Based Sentiment Analysis
• +۹ مورد دیگر

Soon Lay Ki is an Associate Professor at the School of Information Technology, Monash University Malaysia, where she also serves as Associate Head (Graduate Research) since November 2018. Her academic journey began with roles at Multimedia University (MMU), where she was a Senior Lecturer and Deputy Dean (Research and Innovation) from 2016 to 2018. PhD in Web Engineering, Soongsil University, Korea Master of Science in Database, Universiti Putra Malaysia Bachelor of Computer Science, Universiti Putra Malaysia Her research centers on applied natural language processing and data management , with a focus on analyzing domain-specific and social media content. Her work spans aspect-based sentiment analysis , cyberbullying detection , misinformation detection , and relation extraction from conversational texts. Recently, her research has expanded into digital health , particularly emotion-aware mental health chatbots and emotion detection via video data. The most recent articles highlight a strong trend in AI for social good , including legal reasoning, mental health, accessibility, and public health. Her publications appear in high-impact journals and conferences such as Artificial Intelligence and Law , IEEE Transactions on Dependable and Secure Computing , and ACL-affiliated workshops. She has received notable scientific awards, including: ITEX'24 Silver Award for 'MOBOT' mental health chatbot (2024) Silver Medal at Malaysia Technology Expo 2023 for the same innovation The Incubator Grant: Bolster Category (2023) Dr. Soon has graduated seven PhD and three Master’s students, one of whom received the MMU Best Master Thesis Award in 2015. She leads multiple research grants, including FRGS-funded projects and industry collaborations with Telekom Malaysia and Intel . She is currently a Chief Investigator or Primary Chief Investigator on six active projects, including WHinc, WAge, and Epsilon, often in collaboration with Monash Australia and SEACO. She is part of key research teams such as the Action Lab at Monash University Australia and the South East Asia Community Observatory (SEACO) , contributing to inclusive research infrastructure and public health data access initiatives.

Joe Haleyمشاهده پروفایل

استاد

• Experimental High Energy Physics
• Vector-like Quarks
• Standard Model Extensions
• +۸ مورد دیگر

Joe Haley is a Professor in the Department of Physics at Oklahoma State University, where he has been a faculty member since 2013 (tenured 2018, full professor 2023). His research focuses on experimental high energy physics through the ATLAS experiment at CERN, particularly searching for vector-like quarks and new fundamental particles that could address Standard Model limitations. Dr. Haley earned a B.S. in Physics and Astronomy from the University of Washington (2003), followed by a Ph.D. in Physics from Princeton University (2009) for DZero experiment research at Fermilab. He conducted postdoctoral work at Northeastern University (2009-2013) with the CMS experiment at CERN. His research spans collider phenomenology, including Higgs boson studies, B meson lifetime measurements, and lepton universality tests in W boson decays. He employs neural simulation-based inference techniques and advanced τ-lepton reconstruction methods to analyze data from the Large Hadron Collider. Dr. Haley actively contributes to educational initiatives through multiple grants, including the US ATLAS Summer Undergraduate Program (2012-2025) and QuarkNet programs (2012-2024). He teaches core physics courses such as University Physics I-III and specialized topics in General Relativity and Particle Physics. As a strong advocate for equity in academia, Dr. Haley engages in inclusion programs and public outreach. His work intersects with Sustainable Development Goals in quality education, reduced inequalities, and clean energy research.

• Analysis
• Geometry
• Probability
• +۳ مورد دیگر

Nicholas Marshall is an Assistant Professor in the Department of Mathematics at Oregon State University. His work bridges analysis, geometry, and probability with strong applications in data science. Current faculty: Oregon State University Postdoctoral training: Princeton University (NSF Fellowship) Doctoral training: Yale University Undergraduate education: Clarkson University His research focuses on: Interplay between geometric structures and probabilistic models Development of numerical methods for high-dimensional data Applications in cryo-electron microscopy and hyperdimensional computing Analysis of stochastic algorithms and convergence properties Recent publications indicate significant contributions to: Harmonic expansion techniques Equivariant function learning Momentum-accelerated optimization methods Binary hyperdimensional geometry NSF Postdoctoral Fellowship (Princeton) He actively mentors graduate and undergraduate students, including Wyatt Whiting, Peter Cowal, Heather Fogarty, and Seth Alderman. Teaching appointments include advanced courses in probability theory, numerical linear algebra, and mathematics of data science.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۲ مورد دیگر

Slawomir Nowaczyk is a Professor at the School of Information Technology , Halmstad University. His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Knowledge Representation with Weakly-Supervised Models . Practical applications: Predictive maintenance, healthcare informatics, smart industry, and energy systems Developing interestingness metrics for distributed data analysis and self-organization in AI systems Publication Trends : Recent work spans Explainable AI , spatiotemporal forecasting , feature selection , and smart city applications. Key areas include healthcare diagnostics , transportation optimization , and industrial fault detection . Academic Leadership : Serves as Research Leader for the School of Information Technology. Supervises six PhD students and co-supervises one additional student across academic and industrial domains.

Bo Huiمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Graph Neural Networks
• +۶ مورد دیگر

Bo Hui is an Assistant Professor of Computer Science at The University of Tulsa's College of Engineering & Computer Science. He received his Ph.D. in Computer Science and Software Engineering from Auburn University in 2023 and earned his B.S. in Computer Science from Xi'an Jiaotong University in 2013. Prior to his Ph.D., he worked as a senior software engineer in the industry. Education: Ph.D., Computer Science and Technology, Auburn University, 2023 B.S., Computer Science and Technology, Xi'an Jiaotong University, 2013 Dr. Hui's research focuses on data mining and machine learning, particularly in designing machine learning methods for complex real-world data while addressing social concerns such as privacy in AI. His work spans multiple application domains including traffic prediction, social recommendation systems, and biological data analysis. He has made significant contributions to graph neural networks, knowledge graph unlearning, and federated learning systems. Dr. Hui's publication record demonstrates a strong focus on machine unlearning (the 'right to be forgotten' in AI models), graph neural networks, and traffic prediction systems. His work bridges theoretical machine learning with practical applications, with a growing emphasis on privacy-preserving AI techniques. He frequently publishes in top-tier venues including ICCV, ICLR, KDD, and AAAI. Awards and Honors: NSF award #2348177 for machine unlearning research AAAI student scholarship award Dr. Hui currently advises two Ph.D. students (Ruimeng Ye and Yang Xiao) who began their studies in Fall 2024. His research is supported by an NSF grant (#2348177) focused on machine unlearning. He has served as a reviewer for major conferences including ICLR, AAAI, KDD, CVPR, ACL, and EMNLP. His teaching includes Data Mining (2024 Spring) and Interaction Design (2023 Fall). His research group appears to focus on data mining and machine learning with applications to real-world problems requiring privacy-aware solutions.

Dongyi Wangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics
• Machine Vision
• +۳ مورد دیگر

Dongyi Wang is an Assistant Professor in the Department of Biological and Agricultural Engineering at the University of Arkansas, where he directs the Smart Agriculture and Food Engineering (SAFE) Lab. His work bridges advanced technologies like artificial intelligence, robotics, and machine vision with agrifood manufacturing to enhance product quality, safety, and worker welfare. Ph.D. in Bioengineering from the University of Maryland, College Park B.S. in Electrical and Computer Engineering from Fudan University Visiting experience at The Chinese University of Hong Kong Research interests span smart agrifood manufacturing , robotics , machine vision , and artificial intelligence , with applications in crop monitoring, food safety, and healthcare. His lab develops solutions like automated defect detection, pathogen sensing, and sustainable processing systems. Article analysis reveals a focus on AI-driven agricultural automation , hyperspectral imaging , robotic manipulation of bio-products , and food safety innovations . Recent works include YOLO-based tomato defect segmentation, E. coli biosensing, and UAV-based blackberry monitoring. Awards & Memberships College of Engineering Dean’s Award of Excellence Rising Star Research Award (UARK) Outstanding Mentor Award (UARK) Professional memberships in ASABE and IEEE As an educator, he teaches instrumentation and artificial intelligence in agrifood manufacturing . The SAFE Lab, funded by USDA NIFA, NSF, and federal/local agencies (> $7M), prioritizes workforce development in AI/robotics for agrifood industries.

• Mathematical Biology
• Dynamical Systems
• Quantitative Developmental Biology
• +۴ مورد دیگر

Roeland Merks is a Professor of Mathematical Biology at Leiden University's Faculty of Science, holding dual appointments in the Mathematical Institute (Analysis and Dynamical Systems group) and the Institute of Biology Leiden (Animal Sciences). His research bridges advanced computational modeling with biological questions, focusing on vascular development, tumor angiogenesis, and quantitative developmental biology. His research interests center on mathematical modeling of biological systems , particularly using agent-based approaches like the Cellular Potts Model to simulate lumen formation, tumor vasculature, and developmental processes. Key areas include computational angiogenesis, vascular network dynamics, endothelial cell behavior, and mathematical oncology. His work integrates dynamical systems theory with biological experimentation to unravel complex morphogenetic processes. Analysis of his recent publications reveals a strong trend toward translational computational biology , with increasing focus on tumor vasculature repair mechanisms, infant gut modeling, and plant sensory systems. His work consistently applies mathematical rigor to biomedical problems, particularly in understanding how physical forces and biochemical signals interact during vascular development. Professor Merks actively mentors doctoral candidates including Tessa Vergroesen and David Mattias Versluis, with former students such as Burak Demirbaş and Lisanne Rens contributing to his research legacy. His laboratory operates at the mathematics-biology interface, developing computational frameworks that generate testable biological hypotheses.

• Numerical Analysis
• Machine Learning
• Computational Mathematics
• +۴ مورد دیگر

Elena Toscano is a researcher in the Department of Mathematics and Computer Science at the University of Palermo. She specializes in numerical analysis, machine learning, and computational mathematics, with a focus on mesh-free methods like Smoothed Particle Hydrodynamics (SPH) and applications to physics, engineering, and interdisciplinary fields. Teaching: Numerical Analysis (Master's in Informatics and Mathematics, 2025/2026) Research Areas: Signal/image processing, SPH consistency restoration, genetic algorithms for tomography, and mathematical-literary collaborations (e.g., Oulipo). Her publications span computational physics, machine learning, and mathematical modeling, emphasizing numerical stability and interdisciplinary innovation.

Michael F. P. O'Boyleمشاهده پروفایل

استاد

• Optimizing compilation
• Machine learning
• Heterogeneous systems
• +۳ مورد دیگر

Michael F. P. O'Boyle is a Professor of Computer Science at the University of Edinburgh's School of Informatics. He is a leading researcher in compiler technology, specializing in optimizing compilation, machine learning for compilation, and heterogeneous systems. His work addresses the critical challenges of compiling software for increasingly diverse hardware architectures in the post-Moore's Law era. Professor O'Boyle's research interests focus on: Optimizing compilation techniques Machine learning applications in compilation Heterogeneous computing systems Program synthesis Neural machine translation for code Hardware/software co-design His recent publications demonstrate a strong focus on tensor optimization, compiler infrastructure for heterogeneous systems, and machine learning applications in program analysis and transformation. O'Boyle's work bridges traditional compiler techniques with modern AI-driven approaches to code optimization, addressing the growing complexity of hardware-software interfaces. Professor O'Boyle has received several notable honors and awards: ACM CGO Test of Time award (2017) Senior EPSRC Research Fellow Fellow of the British Computer Society (BCS) He holds significant leadership roles including Director of the ARM Research Centre of Excellence at Edinburgh and Director of the EPSRC Centre for Doctoral Training in Pervasive Parallelism. O'Boyle is also a founding member of HiPEAC, a European network for high-performance and embedded architecture and compilation, and has delivered keynote addresses at major conferences including PPoPP 2019 where he presented his vision for "Rethinking Compilation in a Heterogeneous World."

James Stovoldمشاهده پروفایل

استادیار

• Emergent Behavior
• Cognitive Robotics
• Unconventional Computing
• +۴ مورد دیگر

Dr. James Stovold is an Assistant Professor at the Data Science Institute, Lancaster University. His research focuses on emergent behavior, cognitive robotics, and unconventional computing methods, particularly leveraging neural cellular automata and swarm robotics for innovative applications in AI and computational systems. His recent work explores topics such as human-AI symbiosis, reaction-diffusion chemistry for neural networks, and mixed-initiative design tools. Publications span disciplines including artificial intelligence, computational biology, and human-AI interaction. His research has been featured in numerous conference contributions and journal articles, emphasizing interdisciplinary approaches to solving complex computational and robotic challenges. He can be reached via email at j.stovold@lancaster.ac.uk.

• Software
• Intelligent informatics
• Control and system engineering
• +۴ مورد دیگر

Professor Shigeru Fujimura is affiliated with Waseda University as a faculty member of the Faculty of Science and Engineering , specifically in the Graduate School of Information, Production, and Systems . With a PhD in Engineering from Waseda University, his research focuses on intelligent informatics and system engineering , particularly in scheduling, optimization, and human-robot collaboration. His research spans multiple domains including: Deep Reinforcement Learning for combinatorial optimization Energy-efficient manufacturing systems Multi-agent collaboration frameworks Augmented Reality interfaces IoT business modeling Recent publications demonstrate expertise in graph neural networks , particle swarm optimization , and generative adversarial networks applied to industrial problems. He has received multiple awards including the Invention Encouragement Prize (2003) and IEEJ Paper Presentation Award (1994) .

• Algorithms
• Graph Theory
• Dynamic Algorithms
• +۴ مورد دیگر

Kathrin Hanauer is an Assistant Professor at the University of Vienna, where she is affiliated with the Research Group Theory and Applications of Algorithms and the Research Network Data Science. She conducts research in the design, analysis, and experimental evaluation of fast algorithms, with a focus on Algorithm Engineering connecting theoretical foundations with practical implementations. Her research interests include: Algorithm Engineering for practical algorithm implementation Dynamic algorithms for efficiently handling changing data Graph algorithms and network analysis Reachability problems on directed graphs Ranking problems, particularly the NP-hard Feedback Arc Set problem Network analysis, motif search, and subgraph counting Dr. Hanauer's recent publications demonstrate a strong focus on dynamic graph algorithms, with significant contributions to reachability queries, subgraph counting, and datacenter network optimization. Her work spans both theoretical algorithm design and practical implementation, often with C++ software projects. Notable contributions include the O'Reach algorithm for faster reachability queries in large graphs and several dynamic algorithms for subgraph counting and network analysis. Her scientific contributions: O'Reach: A novel approach to reachability queries in large graphs that outperforms previous methods Dynamic algorithms for four-vertex subgraph counting with efficient update operations Work on demand-aware link scheduling for reconfigurable datacenters Interdisciplinary research on normative reasoning with Aristotelian diagrams Dr. Hanauer actively supervises student research, with numerous completed theses focusing on dynamic graph algorithms, geometric algorithms, and reachability problems. Her lab maintains several software projects related to graph algorithms, including a modular algorithms library for dynamic graphs written in C++ and specialized implementations for reachability queries and subgraph counting.