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John Peter Merryman BoncoriView profile

Professor

• Remote Sensing
• Synthetic Aperture Radar (SAR)
• Glaciology
• +4 more

John Peter Merryman Boncori serves as Head of Microwaves and Remote Sensing at the Department of Space Research and Technology, Technical University of Denmark (DTU). His research directly contributes to UN Sustainable Development Goals through advanced Earth observation techniques. His research spans Remote Sensing , Synthetic Aperture Radar , and Glaciology , with specific expertise in InSAR processing for tectonic monitoring and ice sheet dynamics. Key focus areas include: Greenland Ice Sheet velocity measurement using SAR Multi-scale analysis of active fault systems in Italy Validation of ground motion services using GNSS Subglacial hydrology modeling in polar regions Dr. Boncori actively supervises PhD research in critical areas: Crowd counting via remote sensing (CroCo project) Field-scale soil moisture mapping InSAR error characterization Ship tracking with iceberg discrimination Ice velocity retrieval using TOPS-mode SAR He leads the Microwaves and Remote Sensing group at DTU Space, developing advanced methodologies for radar interferometry and satellite-based deformation monitoring. His team maintains strong international collaborations across Europe for Earth observation projects, with particular emphasis on cryosphere and tectonic applications.

Zheng ZhangView profile

Professor

• Quantum Computing
• Compiler Design
• Parallel Computing
• +3 more

Zheng Zhang is a Professor in the Department of Computer Science at Rutgers University, School of Arts and Sciences. He holds academic expertise in compiler design, quantum computing, and parallel computing. Previously, he earned a Ph.D. in Computer Science from the College of William and Mary, an M.S. in Computational Operations Research from the same institution, and a B.S. in Electronic Engineering from Shanghai Jiaotong University. His research focuses on advancing compilation techniques for emerging architectures, including quantum computers, GPUs, and multi-core processors. Key projects include optimizing quantum circuit compilation for NISQ devices and enhancing qubit reuse through dynamic circuits. He has received NSF grants and contributed to the ISCA Distinguished Artifact Award (2024). Teaching: Currently instructs CS314 (Principles of Programming Languages). Research groups include Computer and Networked Systems and Programming Languages & Compilers. Recent activities include publications on VQE compilation and leadership in ASPLOS, PLDI, and MICRO conferences. Grants: NSF Grant, NSF XPS Grant Labs/Teams: Quantum Compilation Research Group Notable Achievement: Erdős Number 2

• Biostatistics
• Public Health
• Epidemiology
• +3 more

Klea Panayidou is an Assistant Professor in the Department of Health Sciences at the European University Cyprus (EUC), School of Sciences. Her academic journey includes a D.Phil. in Statistics (2011) from the University of Oxford, followed by M.Sc. (2004) and B.Sc. (2003) degrees from the University of Oxford and the University of Warwick, respectively. Her research focuses on biostatistical methodologies applied to clinical and public health challenges, including meta-analysis of clinical trials, HIV/AIDS treatment trends, and molecular prognostication in oncology. Notable work includes validating a molecular panel for clival chordoma prognosis and analyzing global CD4 count trends in antiretroviral therapy. Prior to her current role, she held postdoctoral positions at Carnegie Mellon University (USA), the University of Pittsburgh (USA), and the University of Bern (Switzerland). Her publications span neuro-oncology, global health epidemiology, and statistical modeling innovations, emphasizing real-world drug effectiveness assessment and collaborative treatment program evaluation. Dr. Panayidou’s expertise bridges theoretical statistics with practical applications in healthcare, contributing to both methodological advancements and clinical decision-making frameworks.

Levi StorksView profile

Assistant Professor

• Neuroethology
• Animal Cognition
• Comparative Neuroanatomy
• +2 more

Dr. Levi Storks is an Assistant Professor in the Department of Biology at the University of Detroit Mercy's College of Engineering and Science, specializing in neuroethology and the evolution of cognition in ectothermic vertebrates. His research integrates neuroanatomical, molecular, and behavioral approaches to study brain evolution across biological scales. Education: B.S. Zoology, Michigan State University (2014) Ph.D. Biological Sciences, University of Missouri (2021) Research Focus: His work examines how ecological factors shape neural architecture and cognitive abilities in lizards, fish, and other ectotherms, with particular emphasis on: Evolutionary neuroanatomy and neuron quantification Behavioral plasticity and learning mechanisms Ecological influences on brain development Publication Trends: Recent articles demonstrate multidisciplinary approaches combining field ecology with laboratory neuroscience, frequently using Anolis lizards and zebrafish models to investigate: Cognitive performance correlates with neuroanatomical features Environmental complexity effects on neural development Personality-mediated learning differences Spatial navigation strategies Community Engagement: Founded 'Science on Scales' outreach program and chaired 'Science on Wheels' initiative at the University of Missouri, mentoring undergraduate researchers and promoting science communication.

• Plankton Ecology
• Biogeochemistry
• Southern Ocean Studies
• +3 more

Dr. Christine Klaas is a Senior Scientist at the Alfred Wegener Institute (AWI) in Bremerhaven, affiliated with the Biosciences school and the Marine Biogeosciences department. Her research focuses on plankton ecology, biogeochemistry, and the Southern Ocean. She leads studies on phytoplankton dynamics, iron fertilization effects, and carbon cycling in polar and subpolar regions. Her work integrates field expeditions aboard the R/V Polarstern with laboratory experiments, emphasizing the interplay between environmental factors and marine ecosystems. Key projects include analyzing carbon export processes, atmospheric deposition impacts, and diatom growth rates using advanced techniques like the PDMPO fluorescent probe. Dr. Klaas contributes to large-scale initiatives such as the MOSAiC expedition and maintains a strong commitment to open science through data archiving on platforms like PANGAEA . Her research has advanced understanding of Southern Ocean biogeochemical cycles, with recent publications addressing uncertainties in Antarctic sea-ice extent and the role of iron in phytoplankton productivity. She collaborates closely with international teams to address climate change impacts on marine systems.

• Artificial Intelligence
• Smart Grids
• Machine Learning
• +5 more

Carlos Ramos is a researcher affiliated with the University of Porto's Faculty of Engineering and the Polytechnic Institute of Porto's GECAD Research Group. He holds a PhD in Electrical Engineering and Computers from the University of Porto (1993). His work focuses on AI applications in energy systems, ambient intelligence, and agent-based systems. Ramos has collaborated extensively with researchers like Zita A. Vale and Sabah Mohammed on projects involving smart grids, manufacturing optimization, and cyber-physical systems. Research Interests : His primary areas include artificial intelligence, smart grids, machine learning, agent-based systems, and ambient intelligence. Recent work emphasizes explainable AI models in building energy management and genetic algorithms for industrial scheduling. Publications : Ramos has over 138 publications spanning journals like Engineering Applications of Artificial Intelligence and IEEE Transactions on Intelligent Systems . Key themes include AI-driven energy optimization, IoT applications in smart cities, and multi-agent systems for market simulation. His work often bridges theoretical AI with practical industrial and healthcare applications. Labs/Teams : Active in the GECAD Research Group, focusing on intelligent engineering solutions. Collaborates with international partners on projects like the Mascem electricity market simulator and the ISEM agent-based marketplace.

Esty KelmanView profile

Research Fellow

• Theoretical Computer Science
• Computational Complexity
• Probabilistically Checkable Proofs
• +2 more

Esty Kelman is a Research Fellow jointly affiliated with the Massachusetts Institute of Technology (CSAIL) and Boston University, working in theoretical computer science. Her research focuses on computational complexity, sublinear algorithms, property testing, and Boolean function analysis. She was previously a PhD student at Tel Aviv University advised by Muli Safra and participated in the Simons Institute's Causality program at UC Berkeley. Her work has been recognized through the Simons Research Fellowship. Research interests span probabilistically checkable proofs, combinatorics, and adversarial models in algorithm design. Publications demonstrate consistent focus on theoretical computer science foundations, with recent advances in property testing methods and combinatorial mathematics. Collaborative work frequently appears in top venues including FOCS, ITCS, and SODA.

• Biomedical Engineering
• Medical Imaging
• Optical Coherence Tomography
• +3 more

Andrea Curatolo is an Adjunct Research Fellow at the School of Engineering, University of Western Australia, specializing in Electrical, Electronic and Computer Engineering. He holds a PhD from the same university, focusing on improving optical coherence tomography (OCT) and elastography through optical beam shaping and simulations. His research emphasizes biomedical applications, particularly in ophthalmology and cancer diagnostics, contributing to UN Sustainable Development Goals in health and innovation. Key areas include corneal biomechanics, retinal imaging, and telehealth technologies like smartphone-based optical palpation for skin and breast tissue analysis. Collaborations span global institutions, advancing non-invasive diagnostic methods. Education: PhD in Optical Imaging Technologies (UWA, 2017); BSc and MSc from Polytechnic of Milan. Research interests integrate OCT, elastography, and machine learning to address challenges in ophthalmic surgery, cancer margin assessment, and telemedicine. His work bridges engineering and clinical needs, with over 46 publications and 1503 citations, reflecting contributions to high-resolution imaging and biomechanical analysis. Recent articles highlight advancements in spatio-temporal OCT (STOC-T) for retinal hemodynamics, chirped flicker optoretinography, and cross-meridian air-puff OCT for keratoconus detection. Innovations in low-cost imaging systems and robotic tool tracking aim to democratize medical diagnostics. His lab develops handheld elastography probes and smartphone-integrated palpation tools, advancing point-of-care solutions. Awards: While no specific honors are listed, his high citation count and h-index (21) underscore peer recognition. Supervised work includes mentoring students on biomechanical and imaging projects.

Stefan WoltranView profile

Professor

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Abstract Argumentation
• +3 more

Stefan Woltran is a Professor at TU Wien, affiliated with the Research Group Databases and Artificial Intelligence. His work focuses on formal methods in AI, particularly in abstract argumentation frameworks, knowledge representation, and computational logic. He leads research initiatives exploring semantics analysis, preference handling in argumentation, and algorithmic optimizations for complex reasoning tasks. His contributions bridge theoretical foundations with practical applications in logic programming and automated reasoning. Research Interests: Abstract Argumentation (semantics, expressiveness, complexity) Answer Set Programming (grounding optimization, formal properties) Knowledge Representation (formal systems, computational complexity) Algorithm Design (tree decompositions, dynamic programming) Publications reflect advancements in argumentation theory, logic-based AI, and algorithmic problem-solving. He supervises PhD students in these areas and collaborates on projects addressing bottlenecks in automated reasoning systems.

Vinay K. IngleView profile

Associate Professor

• Signal Processing
• Hyperspectral Imaging
• Image Processing
• +2 more

Vinay K. Ingle is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, where he has held a faculty position since 1981. His research focuses on multidimensional signal processing and hyperspectral imaging with applications in defense and security systems. Education: PhD in Electrical and Computer Engineering, Rensselaer Polytechnic Institute, 1981 MSEE, Illinois Institute of Technology, 1977 Bachelor of Technology in Electrical Engineering, Indian Institute of Technology Professor Ingle maintains broad expertise in signal and image processing, stochastic processes, and estimation theory. His current research emphasizes hyperspectral imaging techniques, particularly in long-wave infrared (LWIR) spectrum analysis for threat detection and anomaly identification in sequential imagery. Collaborations with MIT Lincoln Laboratory drive his applied research in temperature-emissivity separation and atmospheric error modeling. Recent publications (2017-2019) reveal concentrated research in hyperspectral data analysis, specifically addressing calibration challenges, atmospheric interference, and time-series modeling for defense applications. These works demonstrate innovative approaches to temperature-emissivity separation and wavelength calibration in LWIR systems while advancing statistical signal processing methodologies. Professor Ingle serves as Principal Investigator for multiple MIT Lincoln Laboratory-funded projects including Algorithms for Threat Detection, Anomaly Detection using Low-Rank Modeling, and Hyperspectral Target Detection research. His extensive graduate teaching portfolio indicates significant student mentorship in signal processing disciplines despite no specific advisees being listed in available records. Research operations are conducted within Northeastern's Electrical and Computer Engineering Department through sustained collaboration with MIT Lincoln Laboratory, focusing on developing next-generation signal processing algorithms for hyperspectral imaging systems in defense contexts.

Fabian HartmannView profile

Researcher

• Semiconductor Physics
• Nanoelectronics
• Quantum Devices
• +3 more

Dr. Fabian Hartmann is a Group Leader of the 'Nanoelectronics' group at the Chair of Technical Physics, University of Würzburg. His research focuses on III-V semiconductor systems, particularly InAs, GaSb, and AlSb, for optoelectronic devices including resonant tunneling diodes (RTDs) and mid-infrared detectors. He leads a team exploring quantum mechanical effects in nanoscale devices, with applications in quantum communication, gas sensing, and topological insulators. His group develops advanced heterostructures and superlattices to enhance device performance in high-speed electronics and photon detection. Research interests include resonant tunneling phenomena, mid-infrared light emitters/detectors, and topological quantum wells. Key projects involve designing GaSb-based RTD photodetectors for single-photon counting and developing type-II superlattices for enhanced detectivity. The team also investigates dual-gated InAs/GaInSb quantum wells for tunable bandgap energies. Awards: None explicitly listed. His work is supported by projects such as the Cluster of Excellence ct.qmat and the Würzburg-Wrocław Nanophotonics Center. He advises PhD and master’s students in technical physics and collaborates on EU/DFG-funded initiatives. His laboratory is located in Building P1 (Physics), Room A026, at Am Hubland in Würzburg.

Casey DunnView profile

Professor

• Animal Evolution
• Genomics
• Phylogenetics
• +5 more

Casey Dunn is a Professor in the Department of Ecology & Evolutionary Biology at Yale University, with a secondary appointment in Genetics. His research focuses on animal evolution, particularly using computational methods to analyze evolutionary relationships and genomic data. Dunn's lab studies siphonophores, exploring their structure, development, and ecological roles. He co-authored Practical Computing for Biologists to bridge computational skills with biological research. Education: PhD in Ecology and Evolutionary Biology from Yale University (2005). Key research interests include phylogenetics, functional specialization in colonial organisms, and the integration of genomics with ecological studies. His work spans field, laboratory, and computational approaches, with contributions to understanding HIV transmission networks through molecular epidemiology. Publications highlight advancements in phylogenetic analysis, siphonophore biology, and HIV cluster dynamics. Collaborations include public health partnerships to apply molecular data in infectious disease control. Dunn's lab members and students engage in diverse projects, from genomic studies to marine ecology.

• Medical Imaging
• Deep Learning
• Artificial Intelligence in Healthcare
• +3 more

Menghua Xia is a Research Fellow in the Department of Radiology & Biomedical Imaging at Yale School of Medicine, Yale University. Their research focuses on advancing medical imaging techniques through deep learning and artificial intelligence, particularly in PET, SPECT, and CT imaging. Key areas include low-dose imaging, image denoising, and federated learning applications for clinical settings. Research interests emphasize improving diagnostic accuracy through computational methods, such as cross-tracer generalizability in PET attenuation correction, dynamic cardiac imaging optimization, and anatomically informed diffusion models. Collaborations involve multi-institutional validation and real-world low-dose data analysis to enhance clinical workflows. Prominent contributions include frameworks like Fed-NDIF for federated low-count PET denoising and anatomically/metabolically informed diffusion models. Their work bridges machine learning innovation with practical medical imaging challenges, aiming to reduce radiation exposure while maintaining diagnostic quality.

• Cardiovascular Disease
• Atherosclerosis
• Immunology
• +4 more

Dr Helen Williams is an Adjunct Lecturer in the Department of Surgery at the University of Sydney's Westmead Clinical School. She leads cardiovascular research focusing on monocyte-driven mechanisms in atherosclerosis, with a particular interest in how inflammatory pathways contribute to heart attack and stroke risk. Her work combines clinical observations with translational research in animal models. Williams completed her PhD at the University of Sydney in 2011, investigating metabolic profiles in genetically modified mice. Since 2011, she has conducted postdoctoral research at Westmead Hospital, advancing understanding of cardiovascular disease pathogenesis through studies of monocyte behavior in human patients and preclinical models. Her research interests include: Monocyte heterogeneity and polarization Role of inflammatory pathways in atherosclerosis Interactions between lipid metabolism and immune function Monocyte contributions to hypertrophic scarring and burn injury Recent publications highlight breakthroughs in DNA vaccine development for atherosclerosis (2018), discovery of monocyte-ApoA1 interactions (2021), and characterization of fetal monocyte profiles in pregnancy complications. Her work bridges basic science with clinical applications, emphasizing translational potential. Awards include the 2023 Cardiovascular Research Network Travel Award and 2021 Best Oral Presentation at the Westmead Symposium. She holds memberships in major cardiovascular research societies including the Australian Atherosclerosis Society (director 2015-2017, 2020-2022) and International Atherosclerosis Society. Current projects include establishing the NSW Familial Hypercholesterolaemia Biobank to address knowledge gaps in this genetic disorder. This initiative combines clinical sample banking with advanced lipid profiling techniques.

Amy HinsleyView profile

Academic

• Wildlife Conservation
• Machine Learning Applications
• Conservation Policy
• +3 more

Dr. Amy Hinsley is a Senior Research Fellow and Schmidt AI in Science Fellow at the Oxford Martin Programme on the Illegal Wildlife Trade, based at the University of Oxford. Her work bridges cutting-edge machine learning methods with social science to address wildlife trade sustainability, particularly focusing on interventions to mitigate threats from illegal and unsustainable practices. She leads projects on predicting wildlife trade trends using AI, Bayesian Belief Network models for medicinal plant markets, and analyzing patents to identify commercial shifts in wildlife products. Her research emphasizes collaboration with NGOs, policymakers, and international bodies like the IUCN (as Chair of the Orchid Specialist Group) to ensure evidence-based conservation policy. Prior to Oxford (since 2017), she worked at the UNEP World Conservation Monitoring Centre on CITES-related policy support. She holds a PhD investigating online orchid trade dynamics and has published extensively on topics ranging from bear bile markets to jaguar trade monitoring in Bolivia. Key achievements include co-designing stakeholder interventions to reduce wildlife consumption and pioneering methodologies like the Unmatched Count Technique to study sensitive behaviors. Her work on orchids spans domestic trade in Vietnam, CITES compliance in Nepal, and the global ornamental plant trade’s environmental risks. She advocates for inclusive conservation approaches that address both commercial and non-commercial drivers of biodiversity loss. Dr. Hinsley’s awards include the Schmidt AI in Science Fellowship, recognizing her innovative application of AI to conservation challenges. Her research portfolio spans over 50 peer-reviewed articles and spans disciplines such as policy analysis, behavioral science, and computational ecology.