جست‌وجوی استاد و پژوهشگر

Thomas Montgomeryمشاهده پروفایل

استادیار

• Organic Chemistry
• Computational Chemistry
• Natural Product Synthesis
• +۳ مورد دیگر

Thomas Montgomery serves as an Assistant Professor in the Department of Chemistry and Biochemistry within Duquesne University's School of Science and Engineering. His academic journey began with a B.A. in Chemistry and Biology from St. Mary's College of Maryland, followed by Ph.D. and M.S. degrees in Organic Chemistry from the University of Chicago where he worked under Professor Viresh Rawal. After completing postdoctoral research at the University of Pennsylvania with Professor Amos B. Smith III focusing on organo-lithium chemistry, he joined Duquesne University in summer 2018. His research spans interdisciplinary domains including synthetic organic chemistry, natural product total synthesis, photophysical materials development, and computational analysis. The Montgomery lab integrates experimental laboratory work with computational methods using DFT on computing clusters, particularly focusing on N-oxide chemistry for nitrogen heterocycle synthesis and photophysical material design. Recent publication trends reveal a strong emphasis on [3+2] cycloadditions using tertiary amine N-oxides, aza-BODIPY synthesis, and computational elucidation of reaction mechanisms. Montgomery has secured significant research funding including NIH grant 1R15GM148917-01 for N-oxide applications in heterocycle synthesis and NSF grant CHE-2244151 for integrating chemical theory with computation and experiments. His educational scholarship examines pandemic-era adaptations in science education, particularly through Universal Design for Learning frameworks. Application of N-Oxides for the Synthesis of Nitrogen Heterocycles (NIH 1R15GM148917-01) Integration of Chemical Theory, Computation and Experiments at Duquesne University (NSF CHE-2244151) The Montgomery lab operates as an interdisciplinary research environment where students engage in both synthetic chemistry projects and computational modeling. Current work focuses on developing novel synthetic methodologies using persistent anions and hydrogen bonding scaffolds, with particular attention to photophysical materials and complex natural product synthesis pathways.

• Economic Theory
• Macroeconomic Management
• Financial Crises
• +۶ مورد دیگر

Professor Marinko Škare is a full professor with permanent position at the University of Pula, Faculty of Economics and Tourism "Dr. Mijo Mirković" in Pula, Croatia. He serves as Vice-Rector for Research, Arts and Collaboration at the institution. Dr. Škare is affiliated with the Department of Economic Theories where he has been actively teaching since 1994. His educational background includes graduation in 1994, Master's degree in 1996, and PhD in 1998. Professor Škare teaches across undergraduate, graduate, and specialist programs, covering diverse subjects from Welfare Economics and History of Economic Thought to Advanced Macroeconomics and AI and Digital Economy. Dr. Škare's research spans multiple domains including Economic Theory, Macroeconomic Management, Financial Crises, Innovation Economics, and Digital Economy. His scholarly work demonstrates a strong focus on contemporary economic challenges, particularly in the context of digital transformation and post-crisis recovery. The 15 most recent publications analyzed show a consistent pattern of addressing pressing economic issues through theoretical frameworks combined with practical applications. His academic leadership extends beyond teaching as evidenced by his role as Vice-Rector for Research, Arts and Collaboration, indicating significant administrative responsibilities within the university structure. His work connects economic theory with practical applications across tourism, finance, and digital transformation domains.

Maria Giniمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Intelligent Agents
• +۶ مورد دیگر

Maria Gini is a distinguished Professor in the Department of Computer Science and Engineering at the University of Minnesota's College of Science & Engineering. She holds the titles of CSE Distinguished Professor and Distinguished University Teaching Professor, reflecting her exceptional contributions to both research and education in computing. Dr. Gini's research focuses on artificial intelligence, robotics, and intelligent agents, with particular expertise in decision making for autonomous agents across various application domains. Her work spans swarm robotics, distributed methods for task allocation, robot exploration of unknown environments, navigation in dense crowds, and conversational agents. She leads the Next Generation Robotics Laboratory and has directed significant projects including MAGNET (Intelligent Agents for Electronic Commerce) and TAC-SCM (Autonomous Agents for Supply-Chain Management). Her recent publications demonstrate a consistent trajectory toward increasingly sophisticated autonomous systems capable of operating in complex, dynamic environments. The research shows strong interdisciplinary connections between robotics, artificial intelligence, and real-world applications in emergency response, environmental monitoring, e-commerce, and social interaction. A notable trend in her recent work is the emphasis on diversity and inclusion in AI research and education. Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring (PAESMEM), 2025 Winner of the IJCAI Donald E. Walker Distinguished Service Award, 2024 ACM/SIGAI Autonomous Agents Research Award, 2022 ACM Fellow, IEEE Fellow, and AAAI Fellow Numerous university-level awards for teaching and service Dr. Gini has advised an impressive 34 PhD students throughout her career, demonstrating her commitment to mentoring the next generation of computer scientists. She has secured significant research funding through grants from NSF and other agencies, supporting projects like the Summer Computing Academy for high-school students and MinneWIC (the ACM-W Celebration of Women in Computing in the Upper Midwest). Her service contributions include leadership roles in major professional organizations including serving as President of the International Foundation on Autonomous Agents and Multi-Agent Systems (IFAAMAS) and General Chair of IJCAI 2021. In addition to her research laboratory, Dr. Gini has been instrumental in establishing several important initiatives including the Summer Computing Academy, MinneWIC, and DREU (Distributed Research Experiences for Undergraduates). Her work extends beyond traditional academic boundaries through collaborations with industry and government agencies focused on applying AI to real-world challenges in emergency response, commerce, and environmental sustainability.

Jie Luمشاهده پروفایل

استاد

• Artificial intelligence
• Machine learning
• Computational intelligence
• +۴ مورد دیگر

Distinguished Professor Jie Lu AO is an internationally renowned scientist in computational intelligence at the University of Technology Sydney, where she serves as Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology and Director of the Australian Artificial Intelligence Institute (AAII), the largest AI hub in Australia with 35 researchers and 230 PhD students. She has been a Professor at UTS since 2007 after serving as Associate Professor from 2004-2006. Professor Lu earned her PhD from Curtin University, Perth, Australia. Her research focuses on computational intelligence with significant contributions to fuzzy transfer learning, concept drift, data-driven decision support systems, and recommender systems. She has developed machine learning models, intelligent recommender systems, and AI-driven decision support systems through collaborations with industry partners including Optus, Sydney Trains, Domain Holdings Australia Ltd, and Workforce Health Assessors Transport NSW. Her recent publications demonstrate a strong focus on addressing challenges in non-stationary environments, out-of-distribution detection, multi-stream concept drift, and applying AI to healthcare applications such as stroke risk prediction and cancer risk assessment. She has pioneered approaches combining traditional AI techniques with large language models for more robust and explainable systems, particularly in legal case recommendation and women's health applications. Officer of the Order of Australia (AO) IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow Australian Laureate Fellow in AI and Industry Laureate Fellow in AI-for-Health UTS Chancellor's Research Medal for Research Excellence (2019) IEEE Transactions on Fuzzy Systems Outstanding Paper award (2019, 2022) Australian Most Innovative Engineer award (2019) NeurIPS 2022 Paper Award Australasian AI Distinguished Research Contribution Award (2022) Australian NSW Premier Prize on Excellence in Engineering (2023) Professor Lu has supervised 60 PhD students to graduation and serves as Editor-In-Chief for Knowledge-Based Systems journal. She has secured 47 ARC grants and over 110 industry projects since 2017, with funding from ARC Discovery projects, ARC Laureate Fellowships, and industry partners. Her leadership has established UTS as a leading center for AI research in Australia, with significant impact across multiple sectors including transportation, telecommunications, healthcare, and education. As Director of the Australian Artificial Intelligence Institute, Professor Lu has built a thriving research ecosystem that bridges academic research with practical industry applications. Her work on concept drift and transfer learning addresses fundamental challenges in adapting machine learning models to changing environments, with direct applications to real-world problems requiring continuous learning and adaptation.

Joe Bradleyمشاهده پروفایل

عضو هیئت علمی

• Engineering Education
• Entrepreneurship
• Biomedical Engineering
• +۵ مورد دیگر

Joe Bradley is a Teaching Assistant Professor at the Carle Illinois College of Medicine within the University of Illinois at Urbana-Champaign system. He also holds a Clinical Assistant Professor position at the Technology Entrepreneur Center and serves as Director of Engineering Education & Entrepreneurship . His academic roles span multiple departments and schools including the Grainger College of Engineering and Gies College of Business . PhD in Systems Engineering from University of Illinois, Urbana MBA from Gies College of Business, University of Illinois, Urbana MS in Mechanical Engineering (minor Electrical Engineering) from Iowa State University, Ames BS in Aerospace Engineering from University of Michigan, Ann Arbor Bradley's research focuses on engineering education and entrepreneurship with specific expertise in innovation management , technology transfer , and socio-technical systems . He has contributed to curricular innovations in biomedical engineering education through publications like "Enhancing Biomedical Engineering Education Through Curricular Innovation: Developing Industry-Ready Engineers" (2025). His work examines complex engineered systems design and interdisciplinary team dynamics in capstone projects. Key awards include being named Entrepreneurial Mindset Fellow by the Academy for Excellence in Engineering Education (AE3) in 2022. Bradley has taught numerous courses across engineering domains including BADM 551 - Managing Intellectual Property and BIOE 570 - Seminar Series . He maintains active collaborations with institutions like Vanderbilt University 's EDEFI Laboratory and participates in professional societies including the BME Council of Chairs and American Society for Engineering Education (ASEE).

Karin Boreviمشاهده پروفایل

مدرس ارشد

• Immigration Policy
• Citizenship Studies
• Multiculturalism
• +۵ مورد دیگر

Karin Borevi is a Senior Lecturer in Political Science at Södertörn University's Department of Social Sciences since September 2014. She maintains dual affiliations as an affiliated researcher at Uppsala University's Department of Government and guest researcher at the Centre for Multidisciplinary Research on Religion and Society. Her research centers on three interconnected domains: Swedish integration policy history, European comparative analysis of the 'civic integrationist turn', and political theory examining multiculturalism, justice, and equality. Key methodological approaches include historical institutionalism, comparative policy analysis, and normative political theory, primarily focused on Scandinavian welfare states. Publication trends reveal sustained examination of civic integration policies since 2010, with increasing focus on state-religion relations since 2023. Her work consistently challenges exceptionalist narratives about Scandinavian models while documenting policy convergence toward conditionality in family migration, language requirements, and welfare access. Current research examines institutionalization of state-Islam relations through mosque financing debates and the double insider/outsider status of intra-EU migrants facing begging restrictions. Her scholarship demonstrates strong engagement with European migration governance frameworks while maintaining distinct Scandinavian policy perspectives. Her advisory roles include contributions to Swedish government reports (SOU) and edited volumes on integration policy, with notable leadership in multi-author projects examining categorization frameworks in Swedish society. She participates in international collaborative projects including RESPOND and CONNEX.

Gokberk Cinbisمشاهده پروفایل

دانشیار

• data-efficient machine learning
• few-shot learning
• zero-shot learning
• +۶ مورد دیگر

Gökberk Cinbiş is an Associate Professor at the Department of Computer Engineering, Middle East Technical University (METU), leading research in data-efficient machine learning with minimal supervision. His work spans zero-shot, few-shot, weakly-supervised, and self-supervised learning, with applications in vision-language integration and large-scale image/video understanding. PhD from Université de Grenoble (2014) M.A. from Boston University (2010) His research focuses on generative models, meta-learning, and robust computer vision systems. Recent work explores sparsity in parameter-efficient fine-tuning, token embeddings for vocabulary extension, and domain-aware LoRA components for personalization. Notable trends in his publications include: Advancements in zero-shot and few-shot learning Applications in remote sensing (SAR2ET for evapotranspiration) Meta-learning and hybrid augmentation techniques Sign language recognition and industrial vision benchmarks Scientific honors include: Science Academy - Young Scientist Award (BAGEP 2024) Google Faculty Research Award (2019-2020) Best PhD Thesis Prize by AFRIF (2015) Alper Atalay Best Student Paper Award (2016) He has supervised MSc students Gencer Sumbul and Berkan Demirel, secured TUBITAK grants on meta-learning and any-shot learning, and organized workshops on industrial inspection at CVPR and ECCV. Cinbiş leads METU ImageLab, focusing on computer vision and machine learning.

Michael Crimminsمشاهده پروفایل

استاد

• Climate Science
• Drought Monitoring
• Wildfire Risk Assessment
• +۲ مورد دیگر

Michael Crimmins is a Professor and Climate Science Extension Specialist at the Department of Environmental Science, University of Arizona. He bridges climate science with practical applications for resource managers in Arizona, focusing on drought preparedness, wildfire risk, and water resource management. His work spans rangelands, forests, and policy domains. His research interests include climate change impacts , drought monitoring , and wildfire risk assessment , with a focus on developing metrics like 'Burn Period' for fire risk tracking and integrating citizen science data for regional climate services. He emphasizes socio-ecological resilience and practical tools for ranchers and policymakers. His recent publications highlight trends in fire-climate dynamics monsoon flood risk data systems rancher drought adaptation soil moisture drought controls climate gamification for public engagement transdisciplinary wildfire decision support . These works reflect his commitment to applied climate science. As an extension specialist, he collaborates with Arizona Cooperative Extension, contributes to drought policy via the Arizona Governor’s Drought Task Force, and develops educational resources like climate summaries and participatory monitoring frameworks.

• Neuromorphic Computing
• Embodied Artificial Intelligence
• Robotics
• +۶ مورد دیگر

Dr. Yulia Sandamirskaya is the Head of Research Center "Cognitive Computing in Life Sciences" at Zurich University of Applied Sciences (ZHAW), focusing on neuromorphic computing applications for embodied artificial intelligence. Her work bridges computational neuroscience and robotics, emphasizing neural-dynamic architectures for real-time decision-making, learning, and sensorimotor integration in autonomous agents. Key Research Areas: Neuromorphic hardware, dynamic neural fields, spiking neural networks, spatial language modeling, and autonomous sequence generation. Projects: Developed controllers for UAVs and robotic arms using event-based vision sensors, explored on-chip unsupervised learning, and designed models for spatial language interpretation in robots. Scientific Contributions: Her publications span robotics conferences and journals like Science Robotics and Frontiers in Neurorobotics , addressing topics such as path integration, obstacle avoidance, and cognitive architectures. Recent work (2024) includes visual odometry with resonator networks and hyperdimensional scene factorization on neuromorphic chips. Advising: Supervised multiple MSc theses at ETH Zurich and NSC/INI programs, mentoring students on neuromorphic navigation, spiking networks, and tactile learning. Collaborated with institutions like ETH Zurich, University of Queensland, and INI Bochum. Labs & Collaborations: Leads the "Neuromorphic Computing Applications: Embodied AI" group at ZHAW, partnering with INIvation (Zurich) and Jörg Conradt (KTH) on neuromorphic hardware implementations. Projects integrate cognitive models with robotic platforms, emphasizing energy efficiency and low-latency interaction.

• Time Series Analysis
• Deep Learning
• Data-Intensive Systems
• +۶ مورد دیگر

Tomasz Wiktorski is a Professor at the Faculty of Science and Technology , University of Stavanger , where he serves as Study Program Manager for MSc and PhD programs in Computer Science and Data Science. His research integrates conventional time series analysis with deep learning for applications in biomedical data (e.g., wearable devices), oil and gas drilling automation, energy systems prediction, and cloud infrastructure optimization. Education : Not explicitly detailed in the text. Research interests span data-intensive system modeling, focusing on: Biomedical time series analysis (wearables, ECG signal correction) Drilling process optimization via transfer learning and temporal models Energy systems prediction using machine learning Cloud infrastructure monitoring Curriculum development in data science education Scientific trends reveal expertise in recurrent neural networks, support vector machines, and hybrid data modeling for sensor networks across domains like health, petroleum, and cloud computing. Leadership includes designing data science programs and contributing to the EDISON Data Science Framework for global standards.

Cristine Agrestaمشاهده پروفایل

دانشیار

• Sports Biomechanics
• Human Performance Analysis
• Athlete Readiness Assessment
• +۵ مورد دیگر

Cristine Agresta, PT, MPT, PhD, serves as Associate Professor in the Department of Rehabilitation Medicine at the University of Washington School of Medicine. Her academic appointment bridges clinical practice and translational research in sports biomechanics, with affiliations in both the Doctor of Physical Therapy program and Orthopaedic Residency training. Her educational background includes: BS in Movement Science, University of Michigan (2002) Masters of Physical Therapy, Youngstown State University (2006) PhD in Movement Science, Temple University (2015) Postdoctoral Fellowship in Kinesiology, University of Michigan (2017) Dr. Agresta's research focuses on enhancing physical resilience and performance in athletes through advanced biomechanical assessment. She specializes in clinical movement analysis, wearable sensor applications, footwear biomechanics, and athlete readiness monitoring. Her work bridges laboratory findings with real-world sports applications through collaborations with athletic organizations, equipment manufacturers, and clinical teams. Current projects examine baseball pitching mechanics, basketball practice demands, and running injury prevention strategies. Her recent publications demonstrate strong translational focus across sports medicine domains, particularly in baseball workload monitoring (2023-2025), running biomechanics (2022-2024), and basketball movement analysis (2024). Key trends include sensor-based workload quantification, footwear-athlete interaction dynamics, and evidence-based practice development for clinicians. Dr. Agresta actively supervises research within the University of Washington Performance Research Lab, mentoring students in biomechanics methodology and clinical translation. Her teaching responsibilities span the musculoskeletal track of the DPT program and Orthopaedic Residency curriculum. Her laboratory work emphasizes field-deployable assessment methods, particularly inertial measurement units for monitoring athlete performance and injury risk in natural settings. Current projects integrate biomechanical analysis with clinical decision-making protocols for sports medicine practitioners.

• Human-Computer Interaction
• Usability Research
• Eye Tracking
• +۲ مورد دیگر

Kasper Hornbæk is a Professor at the Department of Computer Science, University of Copenhagen, specializing in Human-Centred Computing. He conducts research in human-computer interaction (HCI), usability, eye tracking, visualization, and software engineering. Primary Fields: Human-Computer Interaction, Usability Research, Eye Tracking, Visualization, Software Engineering Recent Collaborations: International (country/territory-level) partnerships in HCI and AI Research Outputs: 228 publications focusing on multimodal interaction, VR, and causal modeling His work explores audio-tactile integration, theoretical frameworks in HCI, and principles for user interface design through empirical studies and meta-analyses. Recent projects include heartbeat resonance interfaces and critiques of experimental methodology. Contact: kash@di.ku.dk | Research Website

Alan J. Michaelsمشاهده پروفایل

استاد

• National Security & Restricted Research
• RF Spectrum Dominance
• Spread Spectrum Satellite Communications
• +۳ مورد دیگر

Alan J. Michaels is a Professor and Director of the NSI Spectrum Dominance Division at Virginia Tech, leading research in digital communications, electronic warfare, and quantum algorithms. He holds affiliations with the Bradley Department of Electrical and Computer Engineering and the Department of Mathematics. Previously, he served at Harris Corporation in engineering leadership roles. Education includes multiple degrees from Georgia Institute of Technology (B.S., M.S., Ph.D. in ECE; B.S., M.S. in Applied Mathematics; M.S. in Operations Research) and an MBA from Carnegie Mellon University. Research focuses on RF spectrum dominance, applied mathematics, and cybersecurity. Notable contributions include 44 U.S. patents, over 80 peer-reviewed publications, and leadership in $171M+ research projects. He pioneered initiatives like the Vertically Integrated Projects (VIP) model for undergrad research and the Southwest VA node of the Commonwealth Cyber Initiative (CCI). Awarded Fellow of the National Academy of Inventors for his inventive impact. His work bridges academia and industry, emphasizing practical applications in restricted research domains.

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

Charles L. Isbell Jr. is a Professor in the College of Computing at Georgia Institute of Technology. His work spans foundational research in artificial intelligence, machine learning, and robotics, with a focus on human-robot interaction, reinforcement learning, and educational technology. He leads efforts in scalable online education programs and contributes to policy discussions on AI's societal impact. Research Interests: Isbell's research emphasizes practical and theoretical advancements in AI systems, including Bayesian methods, multi-agent systems, and the ethical implications of AI. He explores how machines can learn from human interaction and adapt to complex environments. His recent work addresses challenges in robotics collaboration, scalable educational platforms, and the development of robust reinforcement learning algorithms. Publications Trends: His articles reflect a commitment to advancing AI through interdisciplinary approaches, with contributions to robotics collaboration (e.g., Nash equilibrium frameworks), scalable education systems, and foundational work in reinforcement learning and imitation learning. Recent collaborations include studies on machine learning systems engineering and long-term AI policy. Awards: No awards explicitly listed in the provided text. Advising & Grants: While specific grants are not detailed here, his extensive publication record indicates sustained research funding and mentorship of students in AI and robotics. His work on online education at scale highlights contributions to pedagogical innovation.

• Predictive Maintenance
• Machine Learning
• Fault Detection
• +۵ مورد دیگر

Dr. Lilach Goren Huber is a Senior Lecturer and R&D Projects Leader at the ZHAW School of Engineering, specializing in predictive maintenance and data-driven solutions for industrial systems. She leads multiple projects focusing on AI applications in anomaly detection (e.g., wind turbines, solar power plants), physics-informed machine learning for sensor error correction, and fault prognostics under data scarcity. Her work bridges academic research with industrial implementation, emphasizing scalable deep learning frameworks for commercial fleets and energy infrastructure. Research interests include predictive maintenance strategies, machine learning for industrial IoT, and hybrid prognostics combining domain knowledge with AI. She has published extensively in journals like International Journal of Prognostics and Health Management and Journal of Big Data , with a focus on renewable energy systems, SCADA data analysis, and cross-domain transfer learning. Current Projects: Physics-informed ML for elastomer sensors, end-to-end fault prognostics for power grids, and hybrid prognostics research. Past Contributions: Developed decision support systems for laser cutting machines, optimized hydroelectric maintenance schedules, and created risk-based frameworks for Swiss national road safety equipment. She actively contributes to the Expert Group Smart Maintenance and serves as a project leader in the ZHAW-PARC initiative. Her work addresses technical challenges like data contamination, sensor calibration errors, and real-world implementation barriers in industrial AI adoption.