جست‌وجوی استاد و پژوهشگر

Tricia Wachtendorfمشاهده پروفایل

استاد

• Disaster sociology
• Multi-organizational coordination in disasters
• Social vulnerability to disasters
• +۴ مورد دیگر

Tricia Wachtendorf is a Professor in the Department of Sociology & Criminal Justice with a joint appointment at the Joseph R. Biden, Jr. School of Public Policy and Administration at the University of Delaware. She directs the Disaster Research Center (DRC)—the world's oldest center dedicated to the social scientific study of disasters—and coordinates the Emergency & Environmental Management Concentration. Her research examines multi-organizational coordination across disaster phases, transnational crises, and social vulnerability. With over two decades of fieldwork after events including 9/11, the 2004/2011 tsunamis, Hurricanes Katrina/Sandy, earthquakes in China/Haiti, and the 2020 Covid-19 pandemic, she investigates improvisation in disasters and convergence phenomena. Current projects funded by the National Science Foundation analyze hurricane evacuation decision-making, humanitarian logistics, and stigma in public health emergencies. Analysis of her 2022-2025 publications reveals dominant themes: evacuation behavior (particularly for pets/vulnerable populations), humanitarian logistics optimization, and water injustice in island communities. Her work increasingly addresses climate adaptation while maintaining core focus on social vulnerability and organizational coordination during crises. Scientific Awards: No specific awards were mentioned in source materials. Advising and Grants: Dr. Wachtendorf has secured multiple National Science Foundation grants for disaster research. Current projects investigate temporal aspects of household decision-making during hurricanes, humanitarian logistics in response operations, and stigma/triage mechanisms in public health emergencies. No advisees were listed in provided materials. Labs and Teams: As Director of the Disaster Research Center, she leads rapid-response field teams conducting global post-disaster assessments. The DRC's methodology—deploying within 24-72 hours of events—has shaped federal disaster policy and established foundational frameworks for studying organizational behavior in crises.

Jiří Šímaمشاهده پروفایل

استاد پژوهشی

• Theoretical Computer Science
• Neural Networks
• Computational Complexity
• +۷ مورد دیگر

Jiří Šíma is a senior scientist at the Department of Theoretical Computer Science, Institute of Computer Science, Czech Academy of Sciences. He holds the academic title of Research Professor (DrSc.) and has been a key researcher at ICS CAS since 1994. He has also served as head of the department (2010–2012, 2021–2023) and has held external lecturing positions at Charles University, Masaryk University, and Czech Technical University. His educational achievements include a CSc. (Ph.D.) in 1993, an Associate Professor qualification (doc.) and RNDr. in 2000, and a DrSc. in 2009 from the Slovak University of Technology. These qualifications reflect his deep expertise in theoretical computer science and neural networks. Šíma's research focuses on the theoretical foundations of neural computation, including the computational power of analog and spiking neural networks, energy complexity in deep learning models, formal language recognition by neural automata, and complexity theory. His work bridges theoretical computer science and artificial intelligence, with a strong emphasis on mathematical rigor and computational models. The 15 most recent publications highlight a consistent trend in analyzing the computational capabilities and energy efficiency of neural networks. His recent work (2020–2024) centers on energy complexity in fully-connected and convolutional networks, while earlier work explores analog neuron hierarchies, hitting sets for branching programs, and the limitations of spiking neurons. The research spans subfields such as formal languages, computational complexity, dynamical systems, and neurocomputing, demonstrating a cohesive and long-term research trajectory in theoretical machine learning. Best ICS Paper Award (2024) Best ICS Paper Award (2021) Second/Third Best ICS Paper Award (2019) Best ICS Paper Award (2018) Otto Wichterle Award (2003) Award of the CAS for young scientists (1998) Šíma has been principal investigator on multiple Czech Science Foundation grants, including LEDNeCo (2025–2027), AppNeCo (2022–2024), and FoNeCo (2019–2021). He has also served on grant evaluation panels and scientific councils, including at the Czech Science Foundation and Charles University. Although no formal students are listed, he has collaborated extensively with researchers such as J. Cabessa, P. Vidnerová, S. Žák, and P. Orponen. He is actively involved in the academic community, serving on program committees for major conferences such as ICANN, ICONIP, SOFSEM, and MFCS. His work is primarily conducted within the Department of Theoretical Computer Science at ICS CAS, a leading research group in theoretical computer science in the Czech Republic.

• Assistive Technologies
• Health Informatics
• Biomedical Engineering
• +۷ مورد دیگر

Dr. Andreu Català Mallofré is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Systems, Automatic and Industrial Informatics Engineering. He serves as the principal investigator at the CETpD - Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma (Technical Research Center for Dependency Care and Autonomous Living) and leads the TOC - Tecnologia Orientada a la Comunitat (Community-Oriented Technology) research group. With an academic career spanning over 30 years, he specializes in assistive technologies, human-computer interaction, and biomedical data analysis. His research interests focus on Wearable health monitoring systems Parkinson's disease management solutions Frailty assessment algorithms Emotion-driven computing Community-embedded technology Gerontechnology applications These areas have produced 367 academic outputs including 59 indexed journal articles, 148 conference papers, and 66 competitive R&D projects. Recent publications demonstrate a clear trend toward multimodal health monitoring combining inertial sensors with biometric data for aging populations . Key application areas include freezing-of-gait detection in Parkinson's patients and distress estimation during assisted mobility. His 2024 work explores agricultural sustainability through data-driven fertilizer optimization. Scientific recognition includes UPC Social Council Award (2013) 20+ years as conference committee member Multiple competitive R&D grants 23391773000 Scopus ID Orcid 0000-0001-8775-1955 As academic advisor, he has mentored 3 PhD candidates including Daniel Manuel Rodriguez-Martin (Human Movement Analysis, 2020) Leonid Ivonin (Physiological Signal Processing, 2014) John Neal Abram Brown (Calm Computing, 2014) His teams operate within Vilanova i la Geltrú's School of Engineering, with strong industry and healthcare sector collaborations.

Aleksey Polunchenkoمشاهده پروفایل

دانشیار

• Mathematical Statistics
• Financial Mathematics
• Statistical Surveillance Methods
• +۲ مورد دیگر

Aleksey Polunchenko is an Associate Professor in the Department of Mathematics and Statistics at Binghamton University . His research focuses on mathematical statistics , particularly sequential change-point detection with applications in financial surveillance, anomaly detection, and statistical process control. He has made significant contributions to the analysis of the Shiryaev-Roberts procedure and related methods, including their asymptotic properties, robustness, and performance evaluation. Education : PhD, University of Southern California His recent publications explore the quasi-stationary distributions , first exit times , and asymptotic optimality of change-point detection algorithms, with applications in real-time financial monitoring and cybersecurity. While no scientific awards are listed, his work has been cited in multi-sensor systems and distributed detection frameworks. Email : aleksey@binghamton.edu

Salvatore Ivan Trapassoمشاهده پروفایل

استادیار

• Applied harmonic analysis
• Fourier analysis
• Machine learning
• +۵ مورد دیگر

Salvatore Ivan Trapasso is a Fixed-term Assistant Professor at the Department of Mathematical Sciences "GL Lagrange" (DISMA) , Polytechnic University of Turin , and a member of the SmartData@PoliTO - Big Data and Data Science Laboratory . His research focuses on Applied Harmonic Analysis , Fourier Analysis , Machine Learning , and Quantum Theory , with expertise in Mathematical Analysis (MATH-03/A) and Theoretical PDEs (PE1_11). Education : Implied PhD in Mathematics. Research Areas : Phase Space Analysis, Time-Frequency Methods, and Applications to Quantum Mechanics. His recent publications investigate phase space techniques for Feynman Path Integrals , Twisted Laplacian , Compressed Sensing , and Stability of Scattering Transforms . His work bridges Harmonic Analysis with Machine Learning and Quantum Dynamics . Notable scientific awards include the Axioms Young Investigator Award (2022) , Best Paper Award (ICGF 2020) , and the Quality Award 2019 from Polytechnic University of Turin. He serves on the Editorial Board of Advances in Operator Theory and as Associate Editor for University Texts in the Mathematical Sciences . Teaching roles include Lecturer for Mathematical Principles in the College of Architecture and Design , Collaborator for Mathematical Analysis I/II in Biomedical and Aerospace Engineering, and Contributor to advanced mathematical methods in Computer Science Engineering.

• Machine Learning
• Reinforcement Learning
• Causal Machine Learning
• +۷ مورد دیگر

Dr. Bevan Smith is a Senior Lecturer at the School of Mechanical, Industrial & Aeronautical Engineering at the University of the Witwatersrand (Wits), where he also completed his BIng(Mech), MSc(Eng), and PhD. His research bridges machine learning and engineering systems, with a focus on optimization through simulation and AI. His research interests include: Machine Learning and Reinforcement Learning Causal Machine Learning and Causal Discovery Graph Neural Networks Explainable AI (XAI) Digital Twins Application of AI in Education and Industrial Systems His recent publications demonstrate a strong trajectory in applying causal inference and machine learning to both industrial optimization (e.g., inventory management, air quality monitoring) and educational challenges (e.g., at-risk student prediction, online video interventions). He utilizes simulation-based methods and counterfactual reasoning to evaluate treatment effects and improve decision-making. His work spans journals in industrial engineering, artificial intelligence in education, and computational applications. Notable scientific contributions include: Developing AI-driven solutions for low-cost air quality monitoring (AI_r) Evaluating X-Learner performance under confounding and non-linearity Applying Pearl’s counterfactual framework to assess explanation validity Designing personalized interventions for engineering students using model-agnostic explanations Dr. Smith has contributed to curriculum development through the creation of online educational videos and has explored structural design in mechanical engineering, notably in carbon fibre swingarm development. While no formal advising or grant information is available, his work reflects an interdisciplinary approach combining mechanical engineering principles with cutting-edge AI methodologies. He is actively publishing and remains engaged in both technical and educational research domains.

Xubo Yueمشاهده پروفایل

استادیار

• Federated Data Analytics
• Bayesian Optimization
• Continuous Optimization
• +۵ مورد دیگر

Xubo Yue is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Northeastern University. His research focuses on federated data analytics, Bayesian optimization, continuous optimization, Gaussian processes, and deep learning. He holds a PhD in Industrial & Operations Engineering from the University of Michigan, Ann Arbor (2023). His work bridges theoretical advancements with practical applications in advanced manufacturing, predictive maintenance, and sustainable materials discovery. Key affiliations include the Institute of Industrial and Systems Engineers (IISE), INFORMS, and the American Statistical Association (ASA). Recent research emphasizes scalable federated learning frameworks for distributed systems, causal inference in sensor networks, and sharpness-aware optimization techniques to enhance generalization. His methodologies are applied to interdisciplinary domains such as materials science, IoT systems, and renewable energy simulations. Research trends reveal a focus on: Federated learning architectures for privacy-preserving analytics Bayesian optimization for high-dimensional design spaces Integration of causal reasoning with machine learning systems Autonomous experimentation for accelerated materials discovery No scientific awards are explicitly listed in the provided information. His academic advising and grant activities are not detailed in the current data.

Beau Schelbleمشاهده پروفایل

استادیار

• Human-AI Teaming
• Responsible AI
• Trust in Human-AI Systems
• +۱۲ مورد دیگر

Beau Schelble is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He is the founding director of the AI and Robotics for Collaborative Systems (ARCS) Lab, where he leads interdisciplinary research on human-AI teaming, responsible AI, and team cognition. His work bridges human factors, systems engineering, and artificial intelligence, with applications in advanced manufacturing, healthcare, emergency response, and defense. Education: PhD in Human-Centered Computing, Clemson University, 2023 BS in Psychology, Clemson University, 2018 Dr. Schelble's research focuses on optimizing collaborative systems involving humans and AI. Key interests include trust development, situational awareness, shared knowledge, and ethical AI integration. He employs mixed-methods approaches—qualitative, quantitative, and computational—to study human-AI interaction in complex environments. His work has resulted in over 30 peer-reviewed publications in top-tier venues across human factors, HCI, and systems engineering. The 15 most recent articles reflect a strong trend toward ethical AI, explainability, autonomy adaptation, and team cognition in human-AI systems. Topics span healthcare applications, collaborative technologies, training methods, and emotional expression in AI teammates, demonstrating a broad yet cohesive research agenda centered on responsible and effective human-AI collaboration. Scientific Awards and Recognitions: NSF Research Traineeship (THINKER) Scholarship, August 2019 Multiple best paper awards and nominations (5 total) in human factors and systems engineering conferences Dr. Schelble actively mentors students and leads a dynamic research lab. He has secured research support through academic collaborations and professional engagement with IEEE, HFES, ACM, and IISE. His lab, ARCS, fosters innovation in AI-driven collaborative systems. Future work includes expanding frameworks for outpatient care, adaptive autonomy in healthcare, and deeper integration of human teaming principles into AI design. He continues to publish extensively and contribute to shaping the future of human-AI teaming.

• Electrical Power Systems
• Analysis, Planning, Operation and Economics of Electrical Systems
• Renewable Energy Integration
• +۱۲ مورد دیگر

Enrique Lobato Miguélez is a Full Professor at the Higher Technical School of Engineering (ICAI) of Comillas Pontifical University in Madrid, Spain, where he has been a faculty member since June 1, 1998. His research is conducted at the Technological Research Institute (IIT), focusing on electrical power systems, with a strong emphasis on renewable integration, energy storage, and island grid stability. His research interests include the analysis, planning, operation, and economics of electrical systems, with specific expertise in frequency-power control, voltage control, electricity markets, and the sustainability of insular systems. He has made significant contributions to the integration of renewable energy sources and the application of storage elements in power systems. The recent publications highlight a consistent focus on optimization, control, and integration challenges in modern power systems, particularly in islanded and renewable-rich environments. Key themes include unit commitment with frequency constraints, robust design of under-frequency load shedding, battery energy storage in wind farms, and data-driven modeling for stability enhancement. His work often combines theoretical modeling with practical applications for industry partners like Red Eléctrica de España, Naturgy, and Endesa. Scientific Awards: Eolo annual Innovation Award in May 2016 awarded by the Wind Energy Business Association AEE Distinción Honorífica a la mejor Tesis Doctoral en Ingeniería «Voltage control design of wind energy harvesting networks» por Elena Sáiz Professor Lobato has supervised several PhD students, including M. Rajabdorri, I. Saboya, and E. Saiz. He has led numerous research and consulting projects funded by the European Commission, Spanish government agencies, and private companies such as Iberdrola, Endesa, and Viesgo, covering topics like grid integration of renewables, energy storage, and market design. He has also organized major scientific events such as the ICREPQ conferences and conducted training courses for industry professionals. He has conducted research stays at Sophia University in Tokyo and University College Dublin, and his work has resulted in a book on island power systems and over 60 journal publications.

Rafael Capillaمشاهده پروفایل

استاد

• Software Architecture
• Architectural Knowledge
• Software Product Line Engineering
• +۵ مورد دیگر

Rafael Capilla is a Professor of Software Engineering at Rey Juan Carlos University of Madrid and an Adjunct Professor at LUT School of Engineering Sciences. His research focuses on software architecture, architectural knowledge, software product line engineering, dynamic variability, technical debt, software sustainability, and Industry 4.0 architectures. He has conducted international collaborations across Europe, Latin America, and Asia, with over 80% of his collaborators from foreign institutions. Research Interests : Software Architecture, Technical Debt, Dynamic Variability, Software Sustainability, Industry 4.0 Quality Evaluation Recent Publications highlight trends in sustainable software practices, Industry 4.0 systems, and AI-assisted architectural decision-making. His work explores intersections between technical debt identification, serverless computing economics, and runtime variability models.

Ahamed Idrisمشاهده پروفایل

استاد

• Emergency Medicine
• Cardiopulmonary Resuscitation
• Cardiac Arrest Treatment
• +۵ مورد دیگر

Dr. Ahamed Idris is a tenured Professor of Surgery and Internal Medicine at the University of Texas Southwestern Medical Center (UTSW) who has been active in clinical practice of emergency medicine at Parkland Memorial Hospital since 2003. He serves as Director of Emergency Medicine Research in the Division of Emergency Medicine and Chair of IRB 4 at UTSW. His professional affiliations include being a Past National Chair of Basic Life Support for the American Heart Association, a member of the National Emergency Cardiovascular Care Committee, and a consultant to the National Institutes of Health Cardiovascular Special Emphasis Panel. Dr. Idris's research focuses on emergency medicine with particular emphasis on cardiopulmonary resuscitation, cardiac arrest treatment, and emergency cardiovascular care. His work explores optimal chest compression techniques, impedance threshold devices, and the physiological mechanisms underlying successful resuscitation. He has made significant contributions to understanding the relationship between chest compression parameters and survival outcomes, as well as innovative approaches to managing cardiac arrest in both prehospital and hospital settings. His most recent publications demonstrate consistent research productivity in resuscitation science, with particular focus on chest compression metrics, airway management during cardiac arrest, and the application of impedance threshold devices. These works represent collaborative efforts within the North American Resuscitation Outcomes Consortium and have shaped current emergency medical protocols. Bronze Star Medal (US Army) American Heart Association National Outstanding Service Award (2003) Inducted into NASA Space Technology Hall of Fame (2008) Fellow of the American Heart Association Fellow of the American College of Emergency Physicians Inaugural Fellow of the American Heart Association's Council on Cardiopulmonary and Critical Care Dr. Idris serves as Director of the Dallas-Fort Worth Center for Resuscitation Research sponsored by the National Institutes of Health, recognized as the most productive organization in the North American Resuscitation Outcomes Consortium. He previously served as Director of the NASA Human Space Flight Rescue Team for the Space Shuttle and continues to consult with NASA. His leadership extends to chairing IRB 4 at UTSW and assisting medical students, fellows, and faculty with research project design and grant proposals. He also serves on the Integrated Research Team Steering Committee for Advanced Capabilities for Combat Medics with the US Army Medical Research and Materiel Command.

• Pharmacoepidemiology
• Cancer Epidemiology
• Population Health
• +۵ مورد دیگر

Dr. Benjamin Daniels is a Senior Research Fellow and Cancer Institute NSW Early Career Fellow at the School of Population Health , University of New South Wales. His work specializes in leveraging linked administrative health data for cancer treatment analysis, policy evaluation, and pharmacoepidemiological research. Pharmacoepidemiology Cancer Outcomes Research Health Policy Analysis Real-World Data Utilization Education PhD in Pharmacoepidemiology, UNSW MA in Sociology & Statistics, McGill University BSc in Psychology, Drexel University Research Focus Daniels' research explores biologic cancer therapies, polypharmacy impacts, opioid prescription policies, and real-world evidence generation. His work bridges population health science with clinical decision-making frameworks. Publication Trends Recent publications demonstrate expertise in 2024 with methodological innovations in instrumental variable application, dispensing data analysis, and opioid policy evaluation, followed by 2023 studies on HER2-targeted therapies, cancer-cardiovascular interactions, and post-pandemic treatment patterns. Awards 2020 UNSW Medicine ECAN Best Paper Award in Public Health Supervision & Grants Currently supervising Jun Ni Ho (PhD candidate on biologic medicine risk management). Holds active grants including: Cancer Institute NSW Early Career Fellowship (2022-2025): $345,000 UNSW Cancer Seed Grants (2022, 2023): $50,000 each Co-investigator on NHMRC Ideas Grant ($480,000)

• Nutrition
• Chronic Kidney Disease
• Dietary Management
• +۳ مورد دیگر

David St-Jules is an Assistant Professor in the Department of Nutrition at the University of Nevada, Reno. His research focuses on dietary interventions for individuals with adiposity-based chronic diseases, particularly chronic kidney disease (CKD) and hemodialysis patients. He investigates potassium and phosphorus homeostasis, protein-energy wasting, and technology-supported dietary counseling for lifestyle modifications. BSc - University of British Columbia, 2008 PhD - University of Hawaii at Manoa, 2013 St-Jules' work delves into the intersection of nutrition and chronic disease management, with a specific emphasis on dietary recommendations for CKD patients. His studies address the challenges of phosphorus and potassium restrictions, the role of whole grains, and the impact of plant-based versus animal-based protein sources. He also explores the feasibility of mobile health (mHealth) interventions in improving dietary adherence among hemodialysis patients. Recent publications highlight his focus on hyperphosphatemia and hyperkalemia management, personalized nutrition , and technology-supported counseling for patients with dual chronic conditions like diabetes and CKD. His research bridges nutritional science with clinical practice , questioning traditional dietary frameworks and advocating for innovative approaches.

Enes Ayanمشاهده پروفایل

مدرس

• Artificial Intelligence
• Deep Learning
• Medical Image Analysis
• +۳ مورد دیگر

Enes Ayan serves as a Doctor Lecturer in the Department of Computer Engineering at Kirikkale University's Faculty of Engineering and Natural Sciences, where he has maintained continuous academic affiliation since his 2014 appointment as Research Assistant. His career trajectory includes completing both master's (2015) and doctoral degrees (2019) in Computer Engineering at the same institution following his 2013 bachelor's graduation from Süleyman Demirel University. Education Bachelor's: Computer Engineering, Süleyman Demirel University (Isparta), 2013 Master's: Computer Engineering, Kirikkale University, 2015 Doctorate: Computer Engineering, Kirikkale University, 2019 Research Focus Dr. Ayan's work centers on deep learning applications across three primary domains: medical imaging (specializing in dental diagnostics, radiology, and endoscopy), security systems (weapon detection and traffic monitoring), and agricultural technology (crop pest classification). His research bridges theoretical AI advancements with practical healthcare solutions, particularly through explainable AI frameworks for dental caries detection and pneumonia diagnosis. Publication Trends Analysis of his 15 most recent publications (2020-2025) reveals intensifying specialization in dental AI applications (60% of 2024-2025 output), with significant contributions to caries detection under prostheses and tooth numbering systems. Concurrently, his work maintains strong threads in security-focused computer vision (UAV-based traffic analysis, weapon detection) and agricultural AI, demonstrating methodological versatility through genetic algorithm optimizations and ensemble CNN architectures. Professional Context No information regarding student supervision, research grants, laboratory facilities, or scientific awards appears in the source materials. His academic progression from Research Assistant to Doctor Lecturer indicates standard career advancement within Kirikkale University's engineering faculty without notable interruptions.

• Electrical Energy and Power Systems
• Power System Optimization
• Renewable Energy Integration
• +۴ مورد دیگر

Salih ERMİŞ serves as Assistant Professor and Department Head in the Department of Electrical and Electronics Engineering at Kırşehir Ahi Evran University's Faculty of Engineering and Architecture. With 25+ years of continuous service since 2000, he progressed from Instructor to Doctoral Lecturer in 2018 and assumed departmental leadership in 2022. His academic foundation includes a PhD (2019), MSc (2003), and BSc (2000) in Electrical Education from Gazi University, complemented by English proficiency certification (YDS 55, 2015). His educational journey demonstrates deep institutional commitment: PhD: Electrical Education, Gazi University Institute of Science (2004-2019) MSc: Electrical Education (Thesis), Gazi University Institute of Science (2001-2003) BSc: Electrical Education, Gazi University Faculty of Technical Education (1995-2000) ERMİŞ's research centers on electrical energy systems with emphasis on metaheuristic optimization applications. His work bridges theoretical algorithms and practical power grid challenges, particularly in voltage stability, renewable integration, and smart grid technologies. Recent publications reveal a strategic shift toward photovoltaic forecasting and educational technology, including augmented reality tools for circuit analysis. His methodology consistently applies nature-inspired algorithms (Ant Colony, Grey Wolf, Particle Swarm) to solve multi-objective power flow problems while addressing agricultural energy needs and distribution network efficiency. His publication portfolio shows consistent growth in high-impact domains, with 2023-2025 works focusing on distributed generation optimization, PMU placement, and environmental economic dispatch. The research demonstrates strong industry relevance through industrial control circuit analysis and solar radiation-based output estimation. Award recognition includes: Best Paper Award at 2017 IEEE ICRERA for FACTS devices research As an advisor, he has supervised two Master's theses on photovoltaic forecasting and metaheuristic load flow analysis. His grant leadership spans seven projects including mobile AR applications for engineering education (2025-2027), short-circuit fault analysis software (2023-2024), and solar panel efficiency testing (2021-2023). Collaborative work with researchers like Ramazan Bayındır and Mehmet Yeşilbudak over 2016-2023 demonstrates sustained interdisciplinary teamwork. Current initiatives focus on visual programming tools for power system education and hybrid PV/wind optimization.