جست‌وجوی استاد و پژوهشگر

Kota Saitoمشاهده پروفایل

استاد

• Decision Theory
• Behavioral Economics
• Experimental Economics
• +۳ مورد دیگر

Kota Saito is a Professor of Economics at the California Institute of Technology. He holds a Ph.D. from Northwestern University (2011) and previously served as an Assistant Professor at Caltech from 2011–16 before becoming a full professor in 2017. Education: B.A. (Keio University, 2005), M.A. (University of Tokyo, 2007), Ph.D. (Northwestern University, 2011) His research focuses on decision theory , developing mathematical models that explain behavioral regularities in experimental economics and psychology. Key areas include stochastic intertemporal preferences , random utility models , and discrete choice analysis . Recent work explores non-separable time preferences in dynamic discrete choice models and axiomatizations for random utility with incomplete datasets. The 15 most recent publications span decision theory , discrete choice , and behavioral economics , with particular attention to stochastic intertemporal choice , mixed logit models , and risk-time preference interactions . These studies often involve collaborations with Federico Echenique, Jay Lu, and Taisuke Imai. Scientific Awards NSF Grant SES-1919263 (PI) NSF Grant SES-1558757 (Co-PI) Pension Research Council/TIAA Institute Partnership Grant (2017-18) At Caltech, Saito is affiliated with the Ronald and Maxine Linde Institute of Economic and Management Sciences and the Center for Theoretical and Experimental Social Sciences (CTESS). He teaches graduate courses in economic theory, including Behavioral Decision Theory and Theory of Value .

• Circadian Rhythms
• Molecular Genetics
• Neurobiology
• +۱ مورد دیگر

Professor Ezio Rosato is a Professor of Biology at the University of Leicester's School of Biological Sciences. His research focuses on molecular genetics of circadian rhythms, examining biological clocks across diverse organisms from insects to crustaceans. Research investigates genetic and molecular mechanisms underlying circadian timekeeping, seasonal adaptations, and light-responsive behaviors. Current projects explore cryptochrome-mediated magnetosensitivity, circadian neuronal plasticity, and environmental influences on biological timing. Recent publications analyze ultrastructural changes in clock neurons, genetic regulation of seasonal phenotypes, and evolutionary dynamics of circadian systems. His work employs molecular genetics, behavioral analysis, and comparative genomics across model organisms. Professor Rosato collaborates internationally on chronobiological research and contributes to teaching programs in genetics and molecular biology.

Carlo Pinciroliمشاهده پروفایل

دانشیار

• Swarm Robotics
• Swarm Intelligence
• Multi-Robot Systems
• +۶ مورد دیگر

Carlo Pinciroli is an Associate Professor and Graduate Coordinator in the Robotics Engineering department at Worcester Polytechnic Institute (WPI). He directs the NEST Lab and holds appointments in the Artificial Intelligence Program, Computer Science, and Fire Protection Engineering. His research focuses on swarm robotics, including the development of the Buzz programming language and the ARGoS simulator. He has been funded by NSF, NASA, the US Army, and industry partners like Amazon Science. Education: PhD in Robotics Engineering (Université Libre de Bruxelles, 2014), MS in Computer Science (Politecnico di Milano and University of Illinois at Chicago, 2005), Postdoc at École Polytechnique de Montréal (2015). Research Interests: Multi-robot systems, swarm intelligence, software engineering for robotics, human-swarm interaction, disaster response robotics, and collective perception. He has published over 70 peer-reviewed articles and received awards including the Best Paper Award at AAMAS 2022 and the Romeo L. Moruzzi Young Faculty Award (2020). Editorial Roles: Associate Editor for Swarm Intelligence , ICRA , and IROS . Program committee member for conferences like RSS, ANTS, and AAMAS. Grants and Projects: Includes NASA-funded work on robot swarms in space, NSF grants, and collaborations with companies like Raytheon Technologies and BAE Systems.

Gregor Giebelمشاهده پروفایل

پژوهشگر

• Wind Power Forecasting
• Renewable Energy Integration
• Wind Farm Flow Control
• +۷ مورد دیگر

Dr. Gregor Giebel is a senior researcher and Head of Section in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU), specifically within the Renewable Plants in Energy Systems group. He is actively engaged in advancing wind energy technologies and their integration into modern power systems. His leadership extends to major international and EU-funded initiatives, including IEA Wind Task 51 and the NEST Facilities research infrastructure. Gregor Giebel's research focuses on short-term wind power forecasting , large-scale integration of wind into electricity grids , wind farm flow control , and condition monitoring of wind turbines . His work is instrumental in improving the reliability and efficiency of renewable energy systems. He has made significant contributions to international standardization through the IEC and is a key figure in global wind energy research coordination. The recent publications highlight a strong trend toward weather-driven energy forecasting , modeling wind farm impacts on weather systems , and developing national research infrastructures for energy transition . These works reflect a deep integration of meteorology, energy systems engineering, and policy-relevant research. His scientific recognition includes: Poster Award at the WindEurope Offshore Conference, Copenhagen 2019 Poster Award on WindEurope Conference, Copenhagen 2021 Dr. Giebel is a Principal Investigator (PI) on multiple high-impact projects such as NEST Facilities , IEA Wind Task 51 , and GreenHyScale . He is renowned for his success in research funding, with a proposal success rate exceeding 50%. He has led the EU Marie Curie Initial Training Network Train 2 Wind and previously headed the FarmConners EU Coordination Action on wind farm control. He leads and is deeply involved in the NEST Facilities —a distributed Danish research infrastructure for energy transition—and contributes significantly to the Weather2X and GreenHyScale projects, which aim to advance green hydrogen production and weather forecasting for energy systems.

İsmail Arıمشاهده پروفایل

استادیار

• Cloud Computing
• Big Data
• Machine Learning
• +۱۲ مورد دیگر

Dr. İsmail Arı is an Assistant Professor in the Computer Science department at Özyeğin University . He holds a PhD from the University of California, Santa Cruz (2004), MS from University of Maryland (2000), and BS in Electrical & Electronics Engineering from Boğaziçi University (1998).

Rolf Wankaمشاهده پروفایل

استاد

• Algorithms
• Combinatorial Optimization
• Swarm Intelligence
• +۳ مورد دیگر

Prof. Dr. Rolf Wanka is a Professor at the Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), specializing in efficient algorithms and combinatorial optimization. His research focuses on swarm intelligence, discrete optimization algorithms, and scheduling problems, particularly in timetabling and robotics applications. Education : Sc.D. (Dr. rer. nat.) in Computer Science His work includes theoretical and experimental analyses of particle swarm optimization (PSO) algorithms, addressing runtime complexity, stagnation behavior, and convergence properties. He has developed novel heuristics for timetabling and sorting problems, with applications in multi-robot systems and medical imaging. Notable collaborations include studies on Markov chain-based PSO and fairness in academic scheduling. Key trends in his recent publications span swarm intelligence , discrete optimization , and scheduling heuristics , with a focus on robust timetabling , runtime analysis , and stochastic algorithm behavior . While no explicit scientific awards are listed, his mentorship in the Max Weber-Programm highlights his advisory role in academia. His publications demonstrate interdisciplinary applications of algorithms in robotics , medical imaging , and parallel computing , leveraging both theoretical rigor and practical experimentation. The full description below provides exhaustive details on his academic contributions and affiliations.

Richard Svejkarمشاهده پروفایل

پژوهشگر ارشد

• Solid-state lasers
• Tm-fibre lasers
• Near-infrared lasers
• +۳ مورد دیگر

Dr. Richard Švejkar is a Research Fellow at the Optoelectronics Research Centre (ORC) at the University of Southampton since 2023. His work focuses on advanced laser technologies, particularly high-power thulium fibre lasers and near/mid-infrared solid-state lasers. Previously, he researched erbium- and iron-based gain media emitting 3–5 µm wavelengths. Education : Master’s in Laser Technique and Electronics (2016), PhD in Physical Engineering (2021) from Czech Technical University in Prague. Research Groups : Advanced Solid-State Sources, Smart Lasers and Special Fibres. His recent publications address challenges in temperature-dependent emission spectra measurement, quantum efficiency optimization, and tunable Tm-doped nested-ring fibre lasers. He has authored/co-authored over 40 papers and is affiliated with Optica and SPIE. Current Research : High-power thulium fibre lasers (2 µm), power scaling of Tm-doped silica fibre lasers, and tunable laser systems.

• Business Process Management
• Declarative Process Modeling
• Hybrid Process Notations
• +۵ مورد دیگر

Tijs Slaats is an Associate Professor in the Software, Data, People & Society section at the Department of Computer Science, University of Copenhagen. His research focuses on Business Process Management with particular emphasis on declarative and hybrid process notations to provide flexible workflow support for knowledge workers, funded by the Danish Council for Independent Research. His educational background includes: M.Sc. in Information Technology from IT University of Copenhagen Ph.D. in Computer Science from IT University of Copenhagen (supervised by Thomas Hildebrandt) Dr. Slaats specializes in declarative process modeling, particularly Dynamic Condition Response (DCR) graphs which enable flexible workflow systems. His work bridges theoretical foundations with practical applications in cross-organizational settings. Recent research extends into blockchain technologies, smart contracts, and applications in sensitive domains like asylum processing and refugee law. He combines formal methods with empirical evaluation to ensure both correctness and usability of workflow systems. His publication pattern shows increasing application of process mining techniques to blockchain technologies and socially impactful domains, while maintaining core research in Business Process Management. Notable trends include integration of DCR graphs with smart contracts, privacy-preserving techniques for asylum data, and object-centric approaches to process discovery. Research funding includes: Hybrid Business Process Management Technologies project (Danish Council for Independent Research) Technologies for Flexible Cross-organizational Case Management Systems (FLExCMS) industrial Ph.D. project Alongside his academic work, Dr. Slaats maintains strong industry connections through Exformatics A/S where he developed the DCR Graphs workflow solution (www.dcrgraphs.net), and previously worked as a software engineer in the Dutch e-commerce sector. His dual expertise in academia and industry ensures his research addresses real-world workflow challenges while maintaining theoretical rigor.

Maria Laura CARRANZAمشاهده پروفایل

استاد

• Ecology
• Remote Sensing
• Plant Invasion Dynamics
• +۵ مورد دیگر

Prof. Maria Laura CARRANZA is a Full Professor at the University of Molise, Department of Biosciences and Territory. Her work focuses on ecology, remote sensing, and environmental conservation, particularly in Mediterranean coastal ecosystems. She leads the Envix Lab, emphasizing vegetation mapping, invasive species dynamics, and climate change impacts. Her research integrates multi-source remote sensing (e.g., Sentinel, UAVs) with field data to assess biodiversity, dune ecosystems, and human-environment interactions. Key projects include monitoring plant invasions (e.g., Acacia saligna), coastal resilience, and citizen science contributions to ecological data collection. Education background and formal qualifications are not explicitly detailed in the provided text. Her research spans over 15 years, with recent emphasis on Mediterranean dunes, urban biodiversity, and global change effects on alpine grasslands. She collaborates internationally, notably in Argentina (Gran Chaco forests) and Pakistan (yak herding dynamics). Research interests include functional traits of plant communities, spectral diversity metrics, and conservation strategies for threatened ecosystems. Her work highlights the role of protected areas in mitigating litter accumulation and preserving cultural services. She has contributed to policy frameworks like Natura 2000 and the EU’s Invasive Species Directive through applied research. Publications emphasize the synergy between remote sensing and field ecology, with over 30 peer-reviewed articles since 2016. Her work on Acacia saligna invasion models and dune eco-geomorphology via UAVs has advanced management practices. She also explores educational innovations, such as online course efficacy for field subjects like bird recognition.

• Business Process Management
• Process Mining
• Process Analytics
• +۴ مورد دیگر

Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.

Ty Partridgeمشاهده پروفایل

دانشیار

• Urban poverty and child development
• Adolescent risk behaviors
• Developmental psychobiology
• +۵ مورد دیگر

Ty Partridge is an Associate Professor in the Department of Psychology at Wayne State University and serves as the Director of the Research Design and Analysis Unit . He is based at 5057 Woodward Ave, Room 8406, Detroit, MI 48202, and can be reached at tpartrid@wayne.edu. Education Ph.D. in Psychology (1999), Wichita State University Post-Doctoral Fellowship (1999-2001), University of Illinois at Chicago Research Interests focus on the intersection of urban poverty and early childhood development, adolescent risk behaviors, developmental psychobiology, emotion regulation, early childhood literacy, longitudinal methods, complex adaptive systems, and relational developmental systems theory. His work emphasizes community-based research, public health interventions, and evaluation in urban, low-income settings. Teaching Responsibilities include courses such as Introduction to Life-Span Developmental Psychology , Developmental Psychology , and advanced statistical methods like Structural Equation Modeling and Statistical Analysis of Longitudinal and Nested Data . Current Projects include evaluations of community-based school readiness interventions, family check-up models, and health outcomes linked to school-based health centers. These are supported by partnerships with United Way of Southeast Michigan, JFM Consulting, and the Michigan Department of Health and Human Services. Lab Affiliation : ADSLabWSU , with social media presence at @RdaWsu and @wsu_adslab on X.

Francis L. Huangمشاهده پروفایل

استاد

• Quantitative Methods
• Multilevel Modeling
• School Climate
• +۴ مورد دیگر

Dr. Francis L. Huang is a Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri-Columbia 's College of Education and Human Development. An applied quantitative methodologist , he teaches courses in program evaluation, multilevel modeling, and data management while researching school climate, bullying prevention, and large-scale educational data analysis. PhD in Research, Statistics, and Evaluation (University of Virginia) MA in Instructional Technology and Media (Teachers College, Columbia University) BS in Legal Management (Ateneo de Manila University) His methodological expertise spans clustered data analysis , plausible values modeling , and robust standard error estimation . He develops tools like the MLMusingR package for multilevel modeling in education research. Recent work focuses on 2025 grant-funded studies about data weighting in international assessments and 2024 open-access replication frameworks for nonexperimental datasets. He advocates for rigorous causal inference and equitable discipline policy analysis through projects like the National Center for Rural School Mental Health (funded by Institute of Education Sciences). Dr. Huang contributes to Missouri Prevention Science Institute as Methodology Co-Director and collaborates with interdisciplinary teams on school violence prevention and behavioral threat assessment systems. His 2023-2025 publications demonstrate technical innovations in three-level cluster-robust errors and missing data handling for large-scale assessments.

Miguel A. Carreira-Perpiñánمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Deep Learning
• +۷ مورد دیگر

Professor Miguel A. Carreira-Perpiñán is a faculty member in the Department of Computer Science & Engineering at the University of California, Merced's School of Engineering. His current research focuses on the intersection of optimization and machine learning, particularly algorithms for deep neural networks and nonlinear embeddings. He has advised multiple PhD students in areas spanning decision trees, clustering, and robotics applications. PhD in Computer Science (2001), University of Sheffield Licenciado en Informática (1995), Technical University of Madrid Research interests include: Machine learning algorithms and representations Optimization for deep learning and nested systems Dimensionality reduction and unsupervised learning Applications in computer vision, speech processing, and robotics His recent publications demonstrate trends in tree-based optimization (TAO algorithm), neural network compression techniques, and interpretable machine learning models. Papers from 2022-2015 highlight extensions of the method of auxiliary coordinates (MAC) to distributed systems, binary autoencoders, and nonlinear embeddings. Scientific awards and grants include: NSF Career Awards (2006-2011) Google Faculty Research Award (2013-2014) NSF Grant IIS #2007147 (2020-2023) for tree alternating optimization Notable Paper Award at AISTATS 2014 Current professional service includes area chair positions at NeurIPS 2025, ICML 2025, and AAAI 2025. He leads the Learning-Compression (LC) algorithm development for neural network optimization and collaborates with researchers at institutions including Meta AI, Google DeepMind, and the University of Iowa.

Virginie Rollandمشاهده پروفایل

استاد

• Population dynamics
• Global change ecology
• Climate change biology
• +۷ مورد دیگر

Dr. Virginie Rolland is a Professor of Quantitative Wildlife Ecology at Arkansas State University , affiliated with the Beck College of Sciences & Mathematics and the Department of Biological Sciences. She holds a PhD in Ecology and Population Dynamics from University Paris VI (2008), an MS in Ecology, Evolution, Biometry from the University of Leicester and University Lyon I (2005), and a BS in Population Biology from University Lyon I (2003). Research Focus : Population dynamics, global change ecology, climate change biology, foraging ecology, and conservation ecology. Key Contributions : Investigates immune responses in reptiles, habitat use in bats and pocket gophers, climate-driven selection pressures in birds, and conservation interventions like predator guards for nest boxes. Collaborative Work : Engages community science for spatiotemporal analysis of host-parasite interactions and songbird nesting parameters. Her recent publications (2022-2025) span wildlife immunology, climate change impacts on avian and bat populations, and habitat conservation strategies. She applies genomic, telemetry, and observational methods across terrestrial and freshwater ecosystems. Contact: vrolland@AState.edu | Office: Lab Science East 314 | Lab Website

Thomas Tscheulinمشاهده پروفایل

استادیار

• Pollinator Ecology
• Biodiversity Conservation
• Ecosystem Services
• +۶ مورد دیگر

Thomas Tscheulin is an Assistant Professor in the Department of Geography at the University of the Aegean, focusing on pollinator ecology, biodiversity conservation, and the impacts of environmental disturbances like climate change and habitat degradation. He teaches courses in Ecology, Biogeography, and Environmental Threats. University of Freiburg (Biology, Germany) PhD in Population Ecology, Imperial College London (UK) His research examines how agricultural practices, invasive species, and climate change affect pollinator communities (wild bees, Syrphidae) across local to regional scales. He has contributed to understanding pollination networks, fire ecology, and thermal tolerance in bees. Recent publications analyze olive fly dynamics, thermal stress responses, and habitat management through AI-driven monitoring. Collaborative projects include POL-AEGIS and LIFE 4 POLLINATORS. While no specific awards are documented, his work appears in journals like Science of the Total Environment and Nature Communications . He mentors PhD and MSc students in pollination studies and collaborates across Europe on biodiversity research.