جست‌وجوی استاد و پژوهشگر

Ian Rennerمشاهده پروفایل

مدرس ارشد

• Ecological Statistics
• Species Distribution Models
• Statistical Theory
• +۳ مورد دیگر

Dr. Ian Renner is a Senior Lecturer in the School of Information and Physical Sciences at the University of Newcastle, specializing in Data Science and Statistics. He holds a PhD in Statistics from the University of New South Wales, complemented by a Master of Statistics from the University of Utah and a Bachelor of Science in Mathematics from Valparaiso University. His research focuses on species distribution models (SDMs), particularly leveraging presence-only data and point process models. He developed the PPM-LASSO approach and maintains the R package 'ppmlasso' for model implementation. Education: PhD (Statistics), University of New South Wales Master of Statistics, University of Utah Bachelor of Science (Mathematics), Valparaiso University Research Interests: Dr. Renner's work bridges statistics and ecology, emphasizing the development of robust SDMs. Key areas include: Unifying MAXENT and Poisson point process models Observer bias correction in ecological data Integration of regularization techniques (e.g., LASSO) for predictive accuracy Application of citizen science data in conservation His methodologies address challenges like taxonomy changes and sampling biases in species distribution studies. Publications: His recent work highlights advancements in SDM stability, citizen science applications, and regularization methods. Key themes include improving model reliability through penalized likelihoods and addressing ecological data complexities. Awards: JB Douglas Award (2011) Runner-up for Best Student Talk (2011) EJG Pitman Prize (2010) Grants & Supervision: He has secured $12,838 in internal grants, including a visiting fellowship at CNRS (France) and conference funding. He currently co-supervises a PhD on deep learning for speech depression recognition and has guided two other students in statistical ecology and methodology. Labs/Teams: Leads the development of the 'ppmlasso' R package, collaborating with researchers like Olivier Gimenez and Eric Beh to advance ecological statistics.

Chris Funkمشاهده پروفایل

استاد پژوهشی

• Climate Science
• Environmental Science
• Climate Change
• +۳ مورد دیگر

Chris Funk is the Director of the Climate Hazards Center (CHC) at the University of California, Santa Barbara (UCSB), within the Department of Geography. His work focuses on developing climate data systems and monitoring tools to support disaster response, particularly in drought and famine-prone regions. He leads an international team of Earth scientists to improve early warning systems for weather and climate extremes. Research Interests: Chris’ research revolves around climate change impacts, drought prediction, and the development of high-resolution climate datasets. His work integrates satellite and station data to create tools like the CHIRPS precipitation dataset and CHIRTS temperature records. He emphasizes bridging scientific research with actionable insights for humanitarian and agricultural decision-making. Labs/Teams: Chris directs the Climate Hazards Center, collaborating with institutions like NASA, NOAA, and the Famine Early Warning Systems Network (FEWS NET). His contributions include advancing methodologies for tracking climate extremes and their societal consequences, such as the 2020 book Drought Early Warning and Forecasting and the upcoming Drought, Flood, Fire (Cambridge Press, 2021).

George Shakerمشاهده پروفایل

دانشیار مدعو

• Wireless Sensor Technologies
• Biomedical Engineering
• Antennas and Propagation
• +۵ مورد دیگر

George Shaker is an Adjunct Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada, and Lab Director of the Wireless Sensors and Devices Laboratory at the Schlegel-UW Research Institute for Aging. He is also Chief Scientist at Spark Technology Labs. His research focuses on wireless sensor technologies for healthcare, autonomous systems, and IoT. He earned his bachelor's from Cairo University and master's/PhD from the University of Waterloo. Education: Bachelor’s degree, Cairo University, Egypt Master’s degree, University of Waterloo, Canada PhD, University of Waterloo, Canada Research Interests: Dr. Shaker’s work spans advanced wireless sensor systems for healthcare monitoring, UAVs, and automotive applications. His lab developed the MIRADA initiative for aging populations and pioneered radar-based non-invasive glucose monitoring. Key areas include mm-wave radar, antenna design, bioelectromagnetics, and machine learning integration. He has co-authored over 200 publications and holds 35+ patents, collaborating with companies like Google, Apple, and Toyota. Recent Article Trends: His 2025 work emphasizes AI-driven radar systems for activity recognition, bio-sensing metasurfaces, and UAV classification using digital twins. Projects include 4D radar imaging, low-cost milk quality monitoring, and smart furniture for cardiac health. Awards: IEEE AP-S Best Paper Award IEEE MTT-S Graduate Fellowship arXiv Top Downloaded Medical Article URSI Young Scientist Award Multiple student awards (see full list above) Advising & Grants: He advises graduate students in ECE and has led projects funded by NSERC and industry partners. His students have won Velocity Fund, NASA Tech Briefs, and Canadian Space Agency awards. Collaborates with over 40 companies including Amazon, Microsoft, and Medella Health. Labs & Initiatives: Leads the Wireless Sensors & Devices Lab and co-founded MIRADA, a smart apartment for aging healthcare. Active in Spark Labs for wireless innovation.

• Pulmonary Fibrosis
• Lung Aging
• Single-Cell Profiling
• +۳ مورد دیگر

Taylor Adams is a Researcher at the Yale School of Medicine's Internal Medicine Department, affiliated with the Kaminski Lab. Their work focuses on pulmonary fibrosis mechanisms, lung aging, and single-cell profiling, with a particular emphasis on epigenetic and immunological pathways. Adams collaborates closely with senior investigators like Naftali Kaminski, contributing to studies on fibrogenesis, drug development, and cellular dynamics in lung diseases. Research interests include understanding how epigenetic changes, microRNA regulation, and immune-cell interactions drive fibrotic processes. Recent work explores novel biomarkers for disease progression and non-invasive diagnostic methods. Adams' publications highlight advancements in spatial transcriptomics, somatic mutation analysis in aging lungs, and the role of GPR87 and CD103+ dendritic cells in fibrosis. Key contributions include identifying aberrant basaloid cells in IPF and developing computational tools like SDePER for deconvolving spatial transcriptomic data. Their lab's interdisciplinary approach integrates genomics, bioinformatics, and experimental models to translate findings into therapeutic strategies.

Jean E. Bognerمشاهده پروفایل

استاد پژوهشی

• Environmental Science
• Climate Change
• Waste Management
• +۳ مورد دیگر

Dr. Jean E. Bogner is a Research Professor Emerita in the Department of Earth and Environmental Sciences at the University of Illinois at Chicago (UIC). Her research focuses on landfill methane (CH4) emissions, biodegradation processes in soils, and waste management systems. She has led international collaborations to develop and validate the CALMIM model, a process-based tool for predicting methane emissions from engineered landfills globally. Her work integrates field measurements, laboratory studies, and climate modeling to address climate change mitigation strategies in waste management. Education: B.A. Geology (Augustana College, 1969), M.S. Geology (University of Illinois at Chicago, 1973), Ph.D. Geology (Northern Illinois University, 1996). Research interests include landfill gas dynamics, soil microbiology, climate modeling, and sustainable waste management practices. She has contributed to landmark studies on methane oxidation in landfill cover soils, the impact of operational practices on emissions, and global GHG mitigation strategies. Her collaborative projects span academia, government agencies, and industry across the U.S., Europe, Africa, Australia, and Asia. Key achievements include co-authoring the IPCC’s 2007 Nobel Peace Prize-winning 4th Assessment Report, leading the CALMIM model development, and advancing methodologies for quantifying methane emissions. Her work has informed policy and practice in reducing anthropogenic GHG contributions from waste systems. Awards include the Nobel Peace Prize (2007), the IWWG Lifetime Award (2013), and multiple recognitions for contributions to landfill gas science and engineering.

Yuting Zhangمشاهده پروفایل

پژوهشگر ارشد

• Probabilistic Geotechnics
• Reliability-Based Design
• Data-Driven Site Characterisation
• +۳ مورد دیگر

Dr Yuting Zhang serves as a Research Fellow within the Department of Civil, Maritime, and Environmental Engineering at the University of Southampton's Faculty of Engineering and Physical Sciences. He is an integral member of the Royal Academy of Engineering Chair Centre of Excellence for Intelligent & Resilient Ocean Engineering (IROE), focusing on machine learning applications for geotechnical site characterization. His educational background includes a Bachelor's degree and MPhil in Geotechnical Engineering from Wuhan University, China, followed by a 2024 PhD from the University of Newcastle, Australia, specializing in probabilistic calibration of resistance factors for piling designs. Zhang's research centers on probabilistic geotechnics and reliability-based design methodologies, with particular emphasis on data-driven site characterization techniques. His work bridges machine learning algorithms with geotechnical and geophysical data analysis to enhance foundation engineering practices, especially in offshore and marine environments. Current projects investigate spatial soil variability effects on pile group reliability, optimization of resistance factors, and innovative data augmentation approaches for rock fracture prediction. His publication record demonstrates consistent output in high-impact journals since 2022, with recent 2025 publications indicating active research momentum. The articles reveal strong thematic focus on probabilistic methods for pile design, integration of diverse geotechnical data sources, and machine learning applications in subsurface characterization. As part of the Infrastructure Group and Southampton Marine and Maritime Institute, Zhang contributes to ocean energy research initiatives while maintaining active collaborations with international researchers including Jinsong Huang, Jiawei Xie, and Anna Giacomini. His work supports the development of more resilient offshore infrastructure through advanced geotechnical reliability frameworks.

Junlin Yuanمشاهده پروفایل

دانشیار

• Fluid Dynamics
• Turbulence Modeling
• Computational Fluid Dynamics (CFD)
• +۴ مورد دیگر

Dr. Junlin Yuan is an Associate Professor in the Department of Mechanical Engineering at Michigan State University, within the College of Engineering. Her research focuses on large-scale numerical simulations of complex turbulent shear flows, particularly addressing non-equilibrium turbulence, wall roughness, and fluid-structure interaction. Applications span engineering, environmental, and bio-locomotive systems. She has secured funding from NSF, ONR, and industry partners. Education: Ph.D. Mechanical Engineering, Queen's University, Canada (2015) M.S. Mechanical Engineering, Queen's University, Canada (2011) B.Eng. Aerospace Engineering, Northwestern Polytechnical University, China (2009) Research Interests: Dr. Yuan's work emphasizes understanding turbulence in complex geometries through direct numerical simulations (DNS) and developing physics-based models. Key areas include roughness sublayer dynamics, adverse pressure gradient effects, hyporheic exchange modeling, and turbulence-induced noise prediction. Her lab, the Turbulence Simulation & Modeling (TSM) Lab, bridges fundamental turbulence physics with practical engineering challenges. Grants & Advising: She secured a $400k NSF grant (CC* Compute) for high-memory computing infrastructure and a $490k NSF award on biomimetic fluid-structure interaction. Advised PhD students include Guangchen Shen, Saurabh Pargal, and Sai Mangavelli. Students have received fellowships such as the Richard H. Brown Endowed Fellowship and CFD Society of Canada scholarships. Labs & Teams: Directs the TSM Lab, which employs DNS and CFD tools to explore turbulence in environmental and engineered systems. Collaborates on projects involving sediment-water interface dynamics and rough wall aerodynamics.

Konrad Kowalczykمشاهده پروفایل

دانشیار

• Speech and Audio Signal Processing
• Machine Learning
• Deep Neural Networks
• +۶ مورد دیگر

Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.

Laura Toniمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Immersive Communications
• +۵ مورد دیگر

Laura Toni is an Associate Professor in the Department of Electronic & Electrical Engineering at University College London (UCL). She serves as Director of the MSc in Telecommunications and Internet Engineering and the MRes in Telecommunications. Additionally, she is a Turing Fellow at the Alan Turing Institute and a member of ELLIS (European Lab for Learning and Intelligent Systems). Her research focuses on coding, streaming technologies, machine learning for immersive communications, decision-making under uncertainty, and large-scale signal processing. She leads the LASP (Learning And Signal Processing) group at UCL. Education: MSc (2005) and PhD (2009) from the University of Bologna, followed by postdoctoral research at UC San Diego and EPFL under Professors L. Milstein, P. Cosman, and P. Frossard. Key roles include Technical Program Chair at ACM MM 2022, Keynote Co-Chair at ACM MMSys 2022, and leadership in organizing workshops on graph-based machine learning and emerging technologies in performing arts. She is a Senior IEEE Member and holds editorial roles in IEEE Multimedia Magazine and EURASIP Journal on Signal Processing. Her work bridges communication systems and machine learning, with contributions to adaptive streaming, network optimization, and graph signal processing. She actively promotes diversity and inclusion in technical conferences, including roles as Diversity Chair at MMSys 2021 and PIMRC 2020.

János Kertészمشاهده پروفایل

استاد

• Statistical Physics
• Complex Networks
• Financial Analysis
• +۹ مورد دیگر

János Kertész is a Professor at the Department of Network and Data Science at Central European University (CEU) since 2012, and previously held the position of Professor at the Budapest University of Technology and Economics (1992–2018). He obtained his PhD in Physics from Eötvös University (1980) and DSc from the Hungarian Academy of Sciences (1989). His research spans statistical physics applications, complex networks, and financial analysis. He has authored over 280 papers and served on editorial boards of journals like Journal of Physics A and Physical Review E . His research focuses on interdisciplinary topics including social network dynamics, systemic risk in economic systems, and algorithmic bias in digital environments. Notable awards include the Széchenyi Prize (Hungary’s highest scientific honor) and the Finland Distinguished Professorship. He has led projects such as SAI (Socially Explainable AI) and HUMANE-AI-NET, addressing algorithmic bias and AI ethics. His work bridges physics-based modeling with real-world social and economic systems, emphasizing computational approaches to corruption, opinion formation, and innovation diffusion. Key contributions include modeling cascading failures in interdependent networks and analyzing attention dynamics on platforms like Sina Weibo during the pandemic. He advises on systemic risk mitigation strategies and collaborates internationally, with visiting roles in Germany, the U.S., France, Italy, and Finland.

Duncan Robertsonمشاهده پروفایل

مدرس ارشد

• Simulation
• Agent-Based Modelling
• Strategy Dynamics
• +۳ مورد دیگر

Dr. Duncan Robertson is a Senior Lecturer in Management Sciences at Loughborough University and Coordinator for the MSc Business Analytics program. His research focuses on simulation, agent-based modeling, strategy dynamics, and pandemic policy analysis. Prior to academia, he worked as a Chartered Accountant at KPMG and held visiting roles at institutions like The Wharton School and London Business School. He is a Fellow and Trustee of St Catherine’s College, Oxford. Educations: BSc (University of London), MA & DPhil (University of Oxford), ARCS CSci CPhys MInstP FHEA FCA Professional Background: Former Chartered Accountant at KPMG, Advisory roles in banking sector Robertson’s research emphasizes pandemic response strategies, policy modeling, and the integration of foresight approaches in healthcare. His work on pandemic control models has informed government decisions during the COVID-19 crisis. Key contributions include analyzing the interaction between scientific models and policy implementation, as well as advocating for improved data infrastructure for future health crises. Notable Achievements: Recipient of the Companion of Operational Research Award (2024) Published extensively on pandemic policy, including seminal works on SAGE’s role in scientific dialogue and foresight methodologies Advisor to UK government bodies during the pandemic, with frequent media engagement on epidemiological strategies Advising & Grants: While specific grants are not detailed, his pandemic modeling research aligns with major public health initiatives. No formal advisees listed, but actively contributes to collaborative research networks like the Management Science and Operations Group at Loughborough. Labs/Teams: Leads research within Loughborough’s Management Science and Operations Group, focusing on agent-based simulation and policy impact analysis.

Amitabh Mishraمشاهده پروفایل

استاد مدعو

• Computer-Communication Networks
• Wireless Networks
• Network Security
• +۵ مورد دیگر

Amitabh Mishra is an Adjunct Professor at the University of Delaware. His research focuses on three core areas: computer-communication networks (wireless architectures, cross-layer design, mobile cloud computing), network performance analysis (stochastic models, numerical optimizations), and network security (vulnerability assessments, authentication protocols). He has contributed to interdisciplinary fields including IoT security, smart healthcare frameworks, and socio-technical systems analysis. His work spans technical domains like wireless sensor networks, tactical network management, and quantum dot material studies, alongside applied research in tourism economics, healthcare data analytics, and educational technology. Notable contributions include frameworks for energy-efficient physiological monitoring, secure IoT configurations, and machine learning-driven security protocols. Recent research highlights include: Developing secure mobile cloud computing paradigms Modeling Multipath TCP capacity bounds using stochastic theory Investigating AI applications for deepfake ethics and tourism marketing His publications span technical journals in computer networks, medical IoT systems, and interdisciplinary studies in cultural tourism and climate change resilience.

Slim Essidمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۷ مورد دیگر

Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.

• Geotechnics
• Numerical Simulations
• Bayesian Inference
• +۴ مورد دیگر

Stéphane Commend is an Associate HES Professor at the Fribourg School of Engineering and Architecture (HEIA-FR) under HES-SO Valais-Wallis. He also holds a lecturer role at the School of Engineering and Management of the Canton of Vaud. His primary research focuses on geotechnics, numerical simulations, and probabilistic modeling applied to infrastructure projects like tunneling and deep excavations. Education and affiliations include roles across multiple HES-SO institutions, with a strong emphasis on integrating advanced computational methods into geotechnical engineering. Notable projects include the Grand Paris Express tunnel project, Bayesian inference for wood constitutive modeling, and probabilistic risk analysis for urban construction. Research interests span soil-structure interaction, finite element modeling, and uncertainty quantification. Recent work emphasizes Bayesian methods for parameter calibration, machine learning in excavation design, and natural hazard vulnerability assessment. Key contributions include frameworks linking ZSOIL and UQLab for reliability analysis, and prototypes like SLIDE-PM for mudflow impact modeling. Current projects (e.g., iBAG and OptiSoil) focus on optimizing construction methods using AI and data-driven approaches. He leads collaborative teams across HES-SO institutes and academic partners like EPFL and CETU. Key Projects: iBAG Project (2022–2025): Bayesian methods in geotechnics OptiSoil (2019–2025): Machine learning for excavation design TULIP Project: TBM-pile interaction probabilistic analysis

• Real Estate Economics
• Housing Economics
• Urban Economics
• +۸ مورد دیگر

Rainer Schulz is a Senior Lecturer at the Business School of the University of Aberdeen, where he conducts research and teaches in real estate economics, housing markets, and financial economics. He is actively accepting PhD students and supervises research in real estate. His affiliations include the Gillmore Centre for Financial Technology at Warwick Business School and the Berlin Center for AI Research in Economics and Management. Research Interests: Dr. Schulz's research focuses on urban real estate markets using econometric and machine learning methods. Key areas include automated valuation modelling (AVM), real estate investment vehicles, fund management, and location choice. He investigates the impact of teleworking on housing demand and the application of real options in real estate development. His work is interdisciplinary, combining urban economics, financial theory, and data science. Publication Trends: His recent publications (2022–2024) emphasize the effects of teleworking on housing markets, machine learning applications in real estate, and local housing market information systems. Earlier works focus on land value mapping, hurdle rates in investment, and the relationship between house prices and macroeconomic variables. His research consistently applies advanced quantitative techniques to real-world property market problems. Scientific Awards: Poster award for Semiparametric Estimation of Land Values, 2015 Aareal Award of Excellence in Real Estate Research, 2012 ARES Foundation Award, 2006 Deutsche Immobilien Akademie Research Prize for doctoral thesis, 2003 Advising and Grants: Dr. Schulz is currently accepting PhD students in Real Estate. He leads the compilation of the Aberdeen Housing Market Report, a knowledge exchange project with the Aberdeen Street Property Company (ASPC). While specific grants are not listed, his research is supported through institutional collaborations and publications in high-impact journals. Labs and Teams: He is affiliated with the Centre for Real Estate Research at the University of Aberdeen Business School. He collaborates with researchers at Humboldt University (Berlin) and participates in international research networks such as the Berlin Center for AI Research. His work involves interdisciplinary teams focusing on urban data, housing policy, and financial technology.