جست‌وجوی استاد و پژوهشگر

Gintautas Cibulskasمشاهده پروفایل

مدرس

• Educational Leadership
• Organizational Management
• Leadership Development
• +۲ مورد دیگر

Gintautas Cibulskas is a Lecturer at the Faculty of Social Sciences, Arts and Humanities at Kaunas University of Technology (KTU). He bridges academic and private-sector experiences, focusing on education system management, leadership development, and organizational dynamics. His work extends beyond the classroom, as he has served as vice-president of the men’s football club Žalgiris since 2018 and acted as a scientific consultant for Kaunas Žalgiris basketball since 2009-2010, mentoring players in leadership qualities. His research interests center on leadership as a learned skill, emphasizing the role of self-improvement and environmental factors in nurturing leadership traits. He explores synergies in team dynamics, drawing parallels between basketball and organizational teamwork, and advocates for collaborative environments over competitive ones. Gintautas engages students in extracurricular mentorship, addressing career and life challenges. He believes leadership is not innate but cultivated through persistence, goal-setting, and humanity. His philosophy highlights competing with oneself to achieve incremental growth.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۶ مورد دیگر

Hamid Bouchachia is a Professor of Data Science and Intelligent Systems at Bournemouth University, leading the Machine Intelligence Research Group within the Department of Computing and Informatics, Faculty of Science and Technology. He holds an Engineering degree, Master, PhD, and habilitation from reputable institutions, with a Post-Doc at the University of Alberta, Canada. Education: Engineering Degree Master’s Degree PhD in Data Science Habilitation Degree Postdoctoral Research at University of Alberta (Computer & Electrical Engineering) Research Interests: Bouchachia focuses on scalable machine learning for data streams, medical imaging with ML, smart environments (e.g., smart homes, industrial monitoring), and assistive technologies. His work emphasizes online learning, active learning, and distributed AI applications in healthcare and energy sectors. He develops algorithms for real-time pattern recognition, high-performance computing, and adaptive systems. Key Contributions: His research spans incremental learning frameworks, evolving fuzzy classifiers, and energy-efficient neural networks. Recent work includes lightweight RNN models for forecasting and deep learning for abnormal behavior detection in dementia care. Advising & Students: Supervises PhD students on topics like deep learning for healthcare, cloud migration, and smart home systems. Notable advisees include Damla Arifoglu (abnormal behavior detection) and Saad Mohamad (active learning). Labs & Teams: Heads the Machine Intelligence Research Group, collaborating on projects involving AI-driven medical imaging, smart energy systems, and ambient intelligence.

Sven Helmerمشاهده پروفایل

استاد

• Database Systems
• Big Data Analytics
• Semantic Computing
• +۲ مورد دیگر

Prof. Dr. Sven Helmer is a Professor in the Department of Informatics at the University of Zurich. Previously held positions include Associate Professor at the Free University of Bozen-Bolzano and Senior Lecturer at Birkbeck, University of London. He obtained his PhD from the University of Mannheim. Research Focus: Database technology, big data analytics, query optimization, and distributed systems. Key interests include scalable indexing, time series analysis, and semantic computing. Recent Publications: Work spans query optimization, graph data structures, and educational methodologies in data science. Dominant themes include scalability in database systems and algorithmic innovations for dynamic data. Awards: Distinguished Reviewer Award, ICDE'20 Best Paper Award, ADBIS'17 Best Teacher Award 2013 Contact: Binzmühlestrasse 14, CH-8050 Zürich. Office BIN 2.E.09. Email: helmer@ifi.uzh.ch. Phone: +41 44 635 67 28.

Saurabh Prasadمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Remote Sensing
• +۳ مورد دیگر

Dr. Saurabh Prasad is a Professor in the Department of Electrical and Computer Engineering at the University of Houston's Cullen College of Engineering. He directs the Machine Learning and Signal Processing Laboratory and serves as Associate Editor for IEEE Signal Processing Letters and IEEE Transactions on Geoscience and Remote Sensing. Education: Ph.D., Mississippi State University M.S., Old Dominion University B.Tech, Jamia Millia Islamia Research Focus: His work bridges machine learning, signal processing, and remote sensing with applications in hyperspectral image analysis, GeoAI, and biomedical imaging. Key methodologies include adversarial learning, domain adaptation, and transformer architectures for geospatial data interpretation. Publication Trends: Recent articles demonstrate strong emphasis on foundation models for semantic segmentation, domain generalization techniques for satellite imagery, and adversarial robustness in geospatial AI, with applications spanning flood mapping, anomaly detection, and biomedical microscopy. Awards & Honors: State Pride Faculty Award (2010) Graduate Student Research Award (2008) Best Student Paper Award, IGARSS (2008) Graduate Research Assistant of the Year (2007) Laboratory Leadership: Directs the Machine Learning and Signal Processing Laboratory, focusing on hyperspectral imaging algorithms and geospatial intelligence systems.

Scott Stollerمشاهده پروفایل

استاد

• Cybersecurity
• Cyber-physical systems
• Distributed systems
• +۲ مورد دیگر

Scott Stoller is a Professor in the Department of Computer Science at Stony Brook University. His research spans cybersecurity, cyber-physical systems, distributed systems, and programming languages, with a specialty in Security Policy Mining. He co-leads the Reliable Systems Lab and is actively involved in academic leadership. Dr. Stoller received his Ph.D. in Computer Science from Cornell University in 1997 and his B.A. in Physics, summa cum laude, from Princeton University in 1990. His cybersecurity research focuses on access control policies, including learning, analysis, and enforcement techniques. His work in cyber-physical systems addresses safety assurance and AI-based control for multi-agent systems, while his programming languages research explores semantics and optimizations based on incremental computation. Dr. Stoller has received numerous prestigious awards including the NSF CAREER Award (1999), Office of Naval Research Young Investigator Award (2002), NASA Turning Goals Into Reality Award (2003), and multiple Best Paper Awards at major conferences. He serves on editorial boards for Foundations and Trends in Programming Languages and Frontiers in Big Data. As an educator, he teaches courses including Software Engineering (CSE 416) and Distributed Systems (CSE 535), with teaching assignments scheduled through 2025. He is General Chair for SACMAT 2025 and has organized numerous academic events, demonstrating ongoing active engagement in his field.

• Virtual Reality
• Serious Games
• Machine Learning
• +۲ مورد دیگر

Mohd Fairuz Shiratuddin is Associate Professor in the School of Information Technology at Murdoch University's College of Science, Technology, Engineering and Mathematics. He holds a BEng in Electrical & Electronics (Northumbria University), MSc in IT (Universiti Utara Malaysia), MSc in Architecture and PhD in Environmental Design & Planning (Virginia Tech). His research spans virtual reality applications for rehabilitation, education, and transportation; machine learning techniques for data imbalance; and human-computer interaction. Key projects include VR-based stroke rehabilitation systems, traffic visualization platforms, and educational tools for health literacy. Recent publications (2019-2025) demonstrate strong focus on VR therapeutic interventions, data augmentation methods, and user-centered evaluation frameworks. Research integrates technical innovation with practical applications in healthcare, urban planning, and e-commerce.

Teseo Schneiderمشاهده پروفایل

استادیار

• Computer Graphics
• Geometry Processing
• Numerical Simulations
• +۴ مورد دیگر

Teseo Schneider is an Assistant Professor in Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. He holds a PhD from the University of Lugano (USI), where his thesis focused on bijective barycentric mappings. He completed a postdoctoral fellowship at New York University (NYU) under an SNF Early post-doc mobility grant. His research interests include geometry processing, computer graphics, numerical simulations, and differential geometry. Education: PhD in Informatics (2017, USI), MSc in Computer Science (2012, USI), BSc in Computer Science (2010, USI). Professional roles include lecturer for courses like Geometric Modeling and Computer Graphics , and co-supervisor for projects involving mesh generation and simulation. Research highlights include contributions to polyhedral finite elements, contact dynamics, and open-source libraries like PolyFEM and LibHip. His work bridges computational geometry with practical applications in biomechanics and engineering. Awards: SNF Early post-doc mobility fellowship (2017). Publications span high-impact venues such as SIGGRAPH, TOG, and Nano Letters, reflecting his expertise in simulation, meshing, and geometric algorithms.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۲ مورد دیگر

Dongjie Wang is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas, School of Engineering. His research focuses on artificial intelligence, machine learning, and data science applications in bioinformatics, transportation planning, and data-centric AI. Wang's research interests span several key areas: Development of reinforcement learning frameworks for biological pathway analysis and gene selection Generative AI applications in urban transportation systems Multi-modal representation learning for cross-domain retrieval Neuro-symbolic approaches for feature engineering His recent publications demonstrate a consistent focus on developing novel AI methodologies for real-world applications, particularly in transportation systems optimization, biological data analysis, and tabular data processing. The articles collectively show strong emphasis on reinforcement learning architectures, graph neural networks, and multimodal integration techniques. Wang advises graduate students in computer science and leads research in data-centric AI applications. His laboratory focuses on developing scalable machine learning solutions for complex systems.

Konrad Abramowiczمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Statistical Methods in Biomechanics
• Climate Research
• +۴ مورد دیگر

Konrad Abramowicz is an Associate Professor and Head of the Department of Mathematics and Mathematical Statistics at Umeå University. His research focuses on functional data analysis, classification, and local inference procedures in the presence of misalignment, with applications in biomechanics, climate research, and forest growth analysis. He teaches stochastic simulations and parametric modeling methods, emphasizing student development and curiosity-driven learning. Education: University of Wrocław (Poland), with Erasmus exchange to Umeå University Research: Functional data analysis, interdisciplinary collaborations in biomechanics and climate science Awards: Teaching awards for dedication to student mentorship Internationalization: Postdoctoral work in Australia; collaborations with Italy and India; faculty-level international contact Konrad's research spans functional data analysis applied to knee kinematics after ACL injuries, climate reconstruction using sediment data, and statistical methods for forest growth analysis. He developed computational tools like the fiberLD R package for fiber length determination in forestry studies. His recent publications highlight interdisciplinary applications of statistical methods, with a focus on movement pattern analysis, climate modeling, and non-destructive forest assessment. Konrad advocates for international collaboration and adaptive academic practices to address global challenges. Konrad has received teaching awards and is recognized for his supportive approach to students. He actively participates in university initiatives for international student integration and served as international coordinator for academic exchange programs. As a key member of the IceLab interdisciplinary research hub, Konrad bridges statistical methods with applications in biomechanics and environmental science. His work emphasizes the importance of mathematical abstraction in solving real-world problems.

Cemal Yilmazمشاهده پروفایل

دانشیار

• Software Engineering
• Combinatorial Interaction Testing
• Software Security Testing
• +۴ مورد دیگر

Cemal Yilmaz is an Associate Professor in the Computer Science and Engineering Program at Sabanci University's Faculty of Engineering and Natural Sciences in Istanbul, Turkey. His office is located in FENS G019, and he can be contacted via email at cyilmaz@sabanciuniv.edu or by phone at +90 (216) 483 9532. He directs the SUSOFT (Software Engineering Research Group), focusing on enhancing programmer productivity through practical tools and techniques. Education: Ph.D. in Computer Science, University of Maryland, 2005 M.S. in Computer Science, University of Maryland, 2002 M.S. in Computer Engineering and Information Science, Bilkent University, 1999 B.S. in Computer Engineering and Information Science, Bilkent University, 1997 Research Interests: Yilmaz's research centers on software engineering innovations, particularly combinatorial interaction testing, software security, and runtime failure prediction. He emphasizes experimental approaches with incremental prototyping and empirical analysis. Key domains include: Developing cost-aware testing frameworks for complex systems Hybrid hardware/software instrumentation for security vulnerability detection Systematic testing methodologies for multithreaded applications Real-time side-channel attack detection using hardware counters Publication Trends: His recent articles (2023-2025) demonstrate a strong focus on AI-driven testing automation, cybersecurity vulnerability analysis, and industrial case studies. Predominant themes include model-based test adaptation, privacy compliance in web systems, and deep learning for anomaly detection in telemetry data. Cross-cutting interests in combinatorial optimization and hardware security vulnerabilities are consistently evident. Advising and Grants: Yilmaz actively mentors 6 PhD and 1 MS students while having supervised 9 alumni. Major grants include: TÜBİTAK 1003: Privacy-Preserving Security Operation Center (2017) TÜBİTAK 1001: Cost- and Test Case-Aware Combinatorial Testing (2014) Marie Curie International Reintegration Grant (2009) Laboratory Leadership: As director of SUSOFT, he oversees projects in combinatorial testing, security profiling, and distributed quality assurance. The group collaborates with industry partners like Netas and prioritizes solutions for scalable software reliability.

Iren Valovaمشاهده پروفایل

استاد

• Artificial Intelligence
• Brain Modeling
• Data Mining
• +۴ مورد دیگر

Iren Valova, PhD, is a Professor and Associate Dean of the College of Engineering at the University of Massachusetts Dartmouth. She holds a PhD in Computer Science from Tokyo Institute of Technology (1997), an MS in Applied Mathematics from Sofia Technical University (1993), and a BS in Computer Science from Sofia Technical University (1991). Her research focuses on artificial intelligence, brain modeling, data mining, expert systems, and image processing. She leads the Neural and Adaptive Research Laboratory (NASLAB), exploring neural networks, medical imaging, and machine learning applications. Dr. Valova has secured over $1.2 million in NSF grants for cybersecurity education and has been recognized as an ELATES Fellow in 2023 for leadership in national programs. Teaching highlights include courses in artificial intelligence, neural computing, algorithms, and game design. She advises students on MS projects and emphasizes academic integrity in coursework. Research contributions span neural network architectures, image segmentation, and olfactory system modeling, with over 50 peer-reviewed publications. Her lab collaborates on interdisciplinary projects involving medical imaging, ecological data analysis, and sensor networks. Current work includes developing adaptive learning algorithms for imbalanced datasets and optimizing CNNs for medical diagnostics. Dr. Valova advocates for innovation in STEM education and cybersecurity training through her administrative and research roles.

• Computational Mechanics
• Finite Element Method (FEM)
• Material Forming Processes
• +۵ مورد دیگر

Robertt Ângelo Fontes Valente is an Associate Professor in the Department of Mechanical Engineering at the University of Aveiro. He specializes in computational mechanics, material forming processes, and engineering education. His research focuses on numerical simulation techniques like the Finite Element Method (FEM), Isogeometric Analysis (IgA), and Finite Cell Method (FCM), with applications in automotive and aerospace industries. He also investigates engineering education innovation, including curriculum design and entrepreneurial mindset development in engineering curricula. Valente holds a PhD in Mechanical Engineering from the University of Porto (2004) and has held academic positions at the University of Aveiro since 2005. He has led multiple research projects funded by entities like the Portuguese Foundation for Science and Technology (FCT), European Commission, and EIT Urban Mobility, focusing on topics such as advanced material modeling, virtual forming processes, and university-business collaboration. His work includes over 50 peer-reviewed publications and active roles in academic administration, including directing the Department of Mechanical Engineering at the University of Aveiro. Key research areas include computational plasticity, material forming simulations, and integrating engineering education with industry needs. Valente collaborates internationally, with sabbatical research at RWTH Aachen University and the University of Pavia. Advising and grants: He has coordinated 20+ research projects and contributed to initiatives like the HEInnovate 2.0 program. His labs and teams are part of the TEMA Research Center and the Laboratório Associado de Sistemas Inteligentes (LASI), focusing on smart systems and advanced manufacturing.

Zhaoyan Fanمشاهده پروفایل

استادیار

• Advanced Manufacturing
• Sensor Design
• Energy-Efficient Networks
• +۱ مورد دیگر

Zhaoyan Fan is an Assistant Professor in the College of Engineering at Oregon State University, specializing in Mechanical, Industrial and Manufacturing Engineering since 2016. His research focuses on model-enhanced sensor design for manufacturing processes, energy-efficient sensor networks, and MEMS-enabled sensor miniaturization. Research interests include: In-situ monitoring of manufacturing processes Sensor characterization and optimization Miniaturization of MEMS sensors Energy efficiency in industrial systems Publications primarily focus on advanced manufacturing technologies, particularly developing novel sensing methods for additive manufacturing processes, quality control systems, and industrial automation solutions.

Jiaying Wengمشاهده پروفایل

استادیار

• Sufficient Dimension Reduction
• Statistical Learning
• Non-parametric Analysis
• +۲ مورد دیگر

Dr. Jiaying Weng is Assistant Professor of Mathematical Sciences at Bentley University. Her teaching interests include Introductory Statistics, Regression, Statistical Learning, and Business Analytics. Her research focuses on developing novel statistical methods for sufficient dimension reduction and variable selection, particularly in high-dimensional data contexts. She has contributed to advancements in Fourier transform approaches for inverse dimension reduction and sparse estimation techniques. Professor Weng has published in statistical and interdisciplinary journals, applying her methodologies to diverse domains including environmental health monitoring, computational statistics, and decision support systems. Her work often addresses challenges in analyzing complex, large-scale datasets through innovative mathematical frameworks.

Cyrus P. Olsen IIIمشاهده پروفایل

دانشیار

• Religion and Society (1750-Present)
• AI and Faith Ethics
• Health Humanities
• +۶ مورد دیگر

Cyrus P. Olsen III serves as Associate Professor of Theology/Religious Studies at The University of Scranton and holds Affiliate Faculty status in Health Humanities. His academic appointments include Research Fellow for AI&Faith and Affiliate Research Fellow through a Templeton World Charity Foundation Grant at the Human Network Initiative (Harvard Medical School's Dhand Lab). Previously, he held the Lo Schiavo Chair in Catholic Social Thought at the University of San Francisco during Academic Year 2022-2023. Dr. Olsen's research spans three primary domains: the intersection of artificial intelligence and theological ethics, health humanities with focus on African contexts, and public health theology. His work examines AI alignment through virtue ethics frameworks, analyzes Ugandan community health systems through religious lenses, and develops theological anthropologies for ecological sustainability. Current projects include Templeton-funded brain health research in Uganda and collaborative NEH-supported Health Humanities curriculum development. His scholarship demonstrates consistent interdisciplinary integration across theology, medical ethics, and technology studies. Recent publications reveal growing emphasis on AI ethics (40% of recent output), African health systems (35%), and critical care philosophy (25%), with increasing collaboration between neurology researchers and theologians. Lo Schiavo Chair in Catholic Social Thought (University of San Francisco, 2022-2023) Templeton World Charity Foundation Grant #20714 (2022-2025) for Ugandan brain health research National Endowment for the Humanities Grant for Health Humanities Concentration development Multiple invited presentations at UN Science Summit, African Studies Association, and Harvard-affiliated forums Dr. Olsen directs collaborative health humanities initiatives involving Makerere University, Bwindi Community Hospital, and Brigham and Women's Hospital. His Templeton-funded work with the Human Network Initiative examines neurological impacts of environmental stressors in Ugandan communities, while his Health Humanities Concentration development (with Holy Cross and Geisinger Commonwealth) creates new interdisciplinary pathways. Current advising focuses on graduate students researching AI ethics and global health theology, with emerging projects on Batwa indigenous medical knowledge preservation. His laboratory affiliations include the Dhand Lab at Harvard Medical School (neurology focus) and the Bannakroli Health Clinics research network in Uganda. The Human Network Initiative team integrates neurologists, theologians, and Ugandan community health workers to study brain health determinants in resource-limited settings, while his US-based work connects with the Lane Center for Catholic Social Thought for policy applications.