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Anthony PurcellView profile

Research Professor

• Immunology
• Proteomics
• Autoimmunity
• +4 more

Anthony Purcell is a Research Professor in Biochemistry & Molecular Biology at Monash University, affiliated with the Monash Biomedicine Discovery Institute. He holds a Visiting Professorship at the University of Southampton (2016–2019) and serves as Councillor of the Human Proteome Organization (2017–2023). His research focuses on quantitative proteomics, immune response targets, and host-pathogen interactions. Key areas include cancer immunology, autoimmunity (e.g., diabetes, psoriasis), antigen processing, vaccine design, and bioinformatics. Purcell has led or contributed to 54 research projects and over 336 publications, including high-impact journals like Nature and Nature Immunology. He is an editorial board member for Molecular & Cellular Proteomics and Molecular Immunology. Notable contributions include developing immunopeptidomics tools and identifying novel therapeutic targets in cancer and autoimmune diseases. His lab collaborates globally and contributes to UN Sustainable Development Goals. Research Interests Quantitative proteomics of complex biological samples Immunopeptidome analysis for antigen discovery Autoimmunity mechanisms (e.g., type 1 diabetes, psoriasis) Cancer immunotherapy targets Vaccine design (influenza, cancer) Recent Research Output Recent studies highlight advances in immunopeptidomics, including identification of tumor antigens, psoriasis triggers, and influenza epitopes. Collaborations span structural biology, bioinformatics, and clinical translation. Grants & Collaborations Active projects include non-canonical influenza immunity, type 1 diabetes epitopes, breast cancer vaccine targets, and psoriasis antigen triggers. Purcell’s work has attracted global attention, including media coverage of superbug research funded by A$9.8 million grants. Labs & Teams Leads the Purcell Lab at Monash, focusing on integrating proteomics with immunology and structural biology to address immune-related questions.

Joris MelkertView profile

Professor

• Aerospace Engineering
• Flight Performance
• Propulsion Systems
• +4 more

Joris Melkert is a Professor at Delft University of Technology in the Faculty of Aerospace Engineering, specifically within the Flight Performance and Propulsion department. With over 104 research outputs spanning more than two decades, he has established himself as a leading expert in aerospace engineering with particular focus on aircraft noise, sustainable aviation, and flight performance optimization. His research interests center on critical challenges facing modern aviation, including noise pollution mitigation around airports like Schiphol, environmental impact assessment of aircraft operations, and pathways toward CO2-neutral flight. Melkert's work bridges theoretical aerospace engineering with practical applications that address real-world aviation challenges, particularly in the Dutch context where airport capacity and environmental concerns intersect. Analysis of his recent publications reveals a strong emphasis on environmental sustainability in aviation, with increasing focus on climate impact assessment, noise modeling, and sustainable fuel alternatives. His research trajectory shows a progression from technical aircraft design topics toward broader systemic challenges in aviation sustainability and environmental compatibility. Education Fellows TU Delft 2016 - Recognizing excellence in academic teaching and educational innovation Melkert is highly active in public engagement, with 77 media appearances in 2024 alone where he provides expert commentary on aviation issues including Boeing safety concerns, Schiphol airport capacity challenges, sustainable aviation fuels, and future aircraft technologies. He regularly participates in public lectures, including the Van Leeuwenhoeklezing on CO2-neutral aviation, demonstrating his commitment to knowledge transfer beyond academic circles. His expertise is frequently sought by national media outlets covering developments in Dutch and European aviation policy, technology, and environmental impact.

Jan JacobsView profile

Associate Professor

• Empirical Macroeconomics
• Health Economics
• Econometric Business Cycle Analysis
• +3 more

Dr. Jan Jacobs is an Associate Professor at the University of Groningen's Faculty of Economics and Business, Department of Economics, Econometrics and Finance. He holds ancillary positions as a Research Associate at the Centre for Applied Macroeconomic Analysis (Australian National University) and an Associate Fellow at CIRANO (Montréal). His research focuses on empirical macroeconomics, health economics, and econometric business cycle analysis, with specializations in seasonal adjustment techniques, currency crisis modeling, and financial cycle analysis. Education: M.Sc. in Econometrics (University of Groningen, 1983) and Ph.D. in Econometrics (University of Groningen, 1998). Research Interests: Empirical macroeconomics, health economics, econometric business cycle analysis, seasonal adjustment methodologies, currency crisis modeling, financial cycle dynamics, and multivariate time series analysis. His work addresses topics like output gap coherence in the euro area, global financial cycle measurement, and real-time data challenges in macroeconomic forecasting. Recent Research Trends: Jacobs' publications from 2024 emphasize fiscal impacts of climate anomalies, coherence of euro area business cycles, and multivariate decomposition models. Earlier work (2011–2022) explored currency crises, teleradiology cost-benefit analysis, and data revision methodologies. Honors/Awards: Not explicitly listed in provided materials. Teaching & Grants: Not detailed in provided text, but his long-term affiliation with the University of Groningen (since 1983) suggests active teaching and research leadership roles. Labs/Teams: Affiliated with the CCSO (Centre for Economic Research) and collaborates with international institutions like the Australian National University and CIRANO.

Christos KonaxisView profile

Researcher

• Computational Geometry
• Algebraic Algorithms
• Polynomial Systems
• +5 more

Christos Konaxis is a researcher affiliated with the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens. His academic career spans over a decade, with significant contributions to computational geometry and algebraic algorithms. He serves as Technical Coordinator for major European research projects including GRAPES (Learning, processing and optimising shapes) and previously ARCADES (Algebraic Representations in Computer-Aided Design for complEx Shapes). Dr. Konaxis specializes in computational geometry, algebraic algorithms, and computer-aided design. His research focuses on implicitization techniques, polynomial systems, resultant polytopes, and sparse interpolation methods. He has developed novel algorithms for computing Newton polygons, Minkowski sums, and geometric operations using matrix representations. His work bridges theoretical algebraic geometry with practical applications in geometric modeling and computer-aided design. His publication record shows a consistent trajectory of high-impact research in computational geometry, with recent work emphasizing efficient algorithms for shape processing and optimization. His articles demonstrate expertise in both theoretical foundations and practical implementations, particularly in developing output-sensitive algorithms and software frameworks for algebraic computations. Technical Coordinator, GRAPES Marie Curie Training Network (2019-2024) Technical Coordinator, ARCADES Marie Curie Training Network (2016-2019) Researcher, Thales Collaborative Project (2012-2015) Post-doctoral researcher, ACMAC, University of Crete (2011-2013) Dr. Konaxis has contributed to numerous research projects funded by European programs, demonstrating strong grant acquisition capabilities. His teaching portfolio includes Algorithms in Structural Bioinformatics, Discrete Mathematics, Computational Algebra, and Computational Geometry, reflecting the breadth of his expertise across theoretical and applied domains.

Xianming ChenView profile

Adjunct Professor

• Plant Pathology
• Cereal Rust Diseases
• Stripe Rust Epidemiology
• +5 more

Dr. Xianming Chen is Adjunct Professor at Washington State University’s Department of Plant Pathology, College of Agricultural, Human, and Natural Resource Sciences (CAHNRS). Since 1992 he has led a globally recognized research program on wheat and barley stripe rust, integrating epidemiology, genomics, resistance breeding, and disease management. Education & Training: Extensive post-doctoral and collaborative training in plant pathology, molecular genetics, and cereal breeding (exact degrees not specified in text). Research Interests: Epidemiology and predictive modeling of stripe rust. Population genomics and virulence evolution of Puccinia striiformis . Discovery, mapping, and cloning of rust resistance genes (Yr genes). Marker-assisted and genomic selection for durable resistance in wheat and barley. Integrated disease management including fungicide evaluation. Recent Publication Trends (2018-2025): Chen’s output centers on three major themes—(1) genomic surveillance and race characterization of P. striiformis across continents, (2) high-resolution mapping and deployment of stripe rust resistance loci via GWAS, QTL analysis, and gene cloning, and (3) registration of rust-resistant wheat and barley cultivars adapted to diverse U.S. regions. His cross-cutting work links pathogen virulence dynamics to resistance breeding, delivering cultivars and molecular tools that underpin global food security. Scientific Awards & Honors: USDA-ARS Certificate of Merit (1998) Multiple USDA-ARS Spot, Outstanding, and Superior Performance Awards (1999–2021) Fellow, American Phytopathological Society (2012) Editorial leadership roles: Associate Editor Phytopathology , Senior Editor Plant Disease , Editor-in-Chief Crop Protection , and ongoing board memberships. Grants, Students & Collaborations: Dr. Chen has sustained continuous USDA-ARS and multi-institutional funding, mentoring numerous graduate students, post-docs, and visiting scientists. His laboratory collaborates with breeders, geneticists, and pathologists worldwide, generating integrated datasets and decision-support tools for stripe rust management. Laboratories & Teams: Based in the Information Technology Building at WSU Pullman, he leads a research team embedded within the USDA-ARS Wheat Health, Genetics, and Quality Research Unit, leveraging state-of-the-art genomics, phenotyping, and field trial resources.

Muhammad AslamView profile

Lecturer

• Wireless Sensor Networks
• Computer Networks
• Data Communication
• +5 more

Dr. Muhammad Aslam is a Lecturer in the Department of Computer Science at Aberystwyth University. His research focuses on wireless sensor networks, machine learning applications, and smart city technologies. He holds a PhD in Computer Science and has demonstrated expertise in antenna design, image processing, and environmental modeling. Aslam’s work contributes to UN Sustainable Development Goals, particularly in disaster response and smart city infrastructure. He has collaborated with researchers on projects involving landside detection, urban expansion analysis, and secure medical data sharing. His publications span peer-reviewed journals and conferences, with notable contributions to Sensors , Applied Sciences , and Environmental Research . Education: PhD in Computer Science (2017), supervised by Prof. Hu Master of Engineering (2012), supervised by Prof. Javaid Research interests include routing protocols, network security, and machine learning-driven solutions for environmental and healthcare challenges. His recent work highlights advancements in antenna arrays for 6G networks and ensemble learning for disease prediction. Collaborations involve experts in telecommunications, environmental science, and medical informatics. Notable achievements include a press feature on sensor research in Sensors and over 80 research outputs since 2011. His work bridges theoretical computer science with practical applications in sustainable urban development and healthcare systems.

• Statistics
• Econometrics
• Biostatistics
• +7 more

Professor Alessio Farcomeni is a distinguished statistician at the University of Rome Tor Vergata, affiliated with the Department of Economics and Finance. His work spans statistical methodology, biostatistics, and applied econometrics. He teaches advanced courses such as Statistical Learning and Big Data Analysis for Economics and Finance , reflecting his expertise in modern data science. His research focuses on latent Markov models, quantile regression, and population size estimation, with applications in healthcare, epidemiology, and policy analysis. Farcomeni has contributed significantly to statistical methodology through publications in high-impact journals. Notable projects include modeling the Italian response to the Covid-19 pandemic, analyzing atrial fibrillation risk prediction, and developing Bayesian approaches for complex datasets. His work often bridges theoretical statistics with real-world challenges, such as optimizing treatment protocols for cancer patients and evaluating macroprudential policies. He maintains active collaborations with institutions like the Italian National Health Service and has advised on international studies, including the CALIPSO transplant infection project. His research has addressed critical issues like material deprivation measurement in Europe and the prognosis of metastatic prostate cancer. Farcomeni is recognized for pioneering methods in latent variable modeling and robust statistical inference. His teaching and mentorship are complemented by software development contributions, such as the Qtools package for quantile regression analysis. Despite prolific output, he emphasizes interdisciplinary collaboration, evidenced by his work in cardiology, oncology, and public health.

Youjin DengView profile

Adjunct Professor

• Quantum Monte Carlo Methods
• Phase Transitions
• Critical Phenomena
• +5 more

Professor Youjin Deng holds concurrent roles as a physics professor at the University of Science and Technology of China (USTC) since 2009, and an adjunct professor at the University of Massachusetts Amherst (UMass Amherst) since 2016. He has extensive academic experience including a von Humboldt Research Fellowship at Heidelberg University (2007-2008) and postdoctoral roles at New York University and Delft University of Technology. Education : B.Sc. in Physics, Beijing Normal University (1997) M.Sc. in Physics, Beijing Normal University (2000) Ph.D. in Applied Science, Delft University of Technology (2004) Research Interests : Dedicated to advancing computational and theoretical studies in statistical mechanics, including quantum Monte Carlo methods, phase transitions, and quantum simulations. His work bridges classical and quantum systems, with notable contributions to percolation theory, critical phenomena, and ultracold atom dynamics. Recent projects explore machine learning applications in percolation and topological band structures in spin-orbit coupled systems. Awards : 2016 National Science Fund for Outstanding Young Scholars 2008 China Ministry of Education 'New Century Excellent Talent' 2005 Chinese Government Award for Outstanding Overseas Self-Financed Students Advising & Collaboration : Has guided over 30 graduate students and postdocs, many of whom pursue academic roles globally. Active in international collaborations, including work with the Hefei National Laboratory for Physical Sciences and institutions in the U.S., Australia, and Europe. Research outputs span top journals like Physical Review Letters , Nature , and Science . Labs & Teams : Leads theoretical physics research groups at USTC, focusing on computational methods and quantum many-body systems. Collaborates with experimentalists on cold atoms, optical lattices, and quantum simulations.

Asad MalikView profile

Lecturer

• Multimedia Forensics and Security
• Image Processing
• Deep Learning
• +4 more

Asad Malik is a Lecturer at the School of Information Technology, Monash University, Malaysia, since December 2023. Previously, he served as an Assistant Professor in the Department of Computer Science at Aligarh Muslim University, India, for two years. He holds a Ph.D. in Information Security from Southwest Jiaotong University, China (2020), an MCA from Jamia Millia Islamia (2015), and a BSc (Hons.) in Computer Application from Aligarh Muslim University (2012). His research focuses on Multimedia Forensics and Security, Deep Learning for Cyber Security, and DeepFake detection. He is a technical member of the Asia-Pacific Signal and Information Processing Association (APSIPA) and actively reviews for top journals and conferences. His work aligns with UN Sustainable Development Goals, particularly in advancing Education and Innovation. Malik’s publications (34+ outputs) emphasize robust watermarking techniques, reversible data hiding in encrypted media, and AI-driven solutions for security and environmental challenges. Recent trends highlight innovations in deepfake detection, transformer-based audio classification, and AI applications in renewable energy modeling. He is currently accepting PhD students and collaborates on projects combining cybersecurity, digital forensics, and machine learning. No notable awards are listed, but his research contributions reflect significant academic engagement in information security domains.

Bert de GrootView profile

Adjunct Professor

• Computational Biophysics
• Molecular Dynamics Simulations
• Protein Dynamics
• +13 more

Bert de Groot is an Adjunct Professor at the Physics Faculty of Georg-August University, Göttingen, and leads the Computational Biomolecular Dynamics Group at the Max Planck Institute for Multidisciplinary Sciences. His research integrates computational biophysics, molecular dynamics, and method development to study structure-dynamics-function relationships in proteins, particularly ion and water channels. PhD in Biophysical Chemistry, University of Groningen (1998) Postdoctoral Fellow, Max-Planck Institute for Biophysical Chemistry (1998–2003) Head of Research Group since 2004 Adjunct Professor since 2009 His research focuses on protein dynamics , ion channel mechanisms , alchemical free energy calculations , and computational electrophysiology . He develops and applies advanced simulation techniques to study permeation, gating, and selectivity in potassium and aquaporin channels. His group also investigates ubiquitin allostery, amyloid aggregation, and protein-ligand binding for drug design. The 15 most recent publications highlight a strong trend in computational biophysics , with emphasis on ion channel function , free energy methods , and allostery . His work bridges simulation and experiment, often validating findings with structural and electrophysiological data. Scientific leadership includes: Editorial Board Member, Biophysical Journal (2011–2017) Editorial Board Member, PLOS Computational Biology (since 2013) Bert de Groot advises a team of postdocs, PhD, and Master’s students. His group has received no explicitly mentioned grants in the text, but the sustained research output and infrastructure imply significant funding. He actively contributes to teaching through graduate courses in biomolecular simulations and practicals on free energy calculations and Markov modeling. The group maintains close collaborations with experimentalists in NMR, EPR, and crystallography, and develops software such as the pmx toolkit for alchemical simulations.

• Machine Learning
• Computer Vision
• Medical Imaging
• +5 more

Yi Cheng is an active academic researcher with a prolific publication record spanning multiple disciplines in computer science and engineering. Their work demonstrates strong affiliations with research institutions in China, frequently collaborating with researchers from Chinese Academy of Sciences and other Chinese universities. The publication pattern shows consistent high-quality output across top venues in machine learning, computer vision, and control systems. Yi Cheng's research interests center around artificial intelligence applications across diverse domains. Their work bridges theoretical advances in machine learning with practical applications in medical imaging, robotics, control systems, and natural language processing. Recent publications show increasing focus on multimodal learning approaches, domain adaptation techniques, and interpretable AI systems. The research demonstrates both theoretical depth in algorithm development and practical implementation in real-world scenarios. Analysis of recent publications reveals a strong trend toward developing robust, efficient AI systems that can operate across different domains with minimal adaptation. The work spans from fundamental control theory to applied medical imaging, showing versatility across the AI spectrum. Key methodological contributions include novel transformer architectures, multi-view learning approaches, and stability analysis for complex systems. Yi Cheng has received recognition through publications in top-tier venues including IEEE transactions, ACL, AAAI, and other prestigious conferences and journals. While specific awards aren't detailed in the publication record, the consistent acceptance in high-impact venues indicates peer recognition of research quality. The research trajectory shows increasing collaboration with interdisciplinary teams, particularly in medical applications where AI techniques are applied to healthcare challenges. Current work emphasizes practical deployment considerations including computational efficiency, robustness to domain shifts, and interpretability of models - addressing key challenges in real-world AI deployment.

Allon PercusView profile

Professor

• Discrete Optimization
• Statistical Physics
• Network Models
• +5 more

Allon Percus is the Joseph H. Pengilly Professor of Mathematics at the Institute of Mathematical Sciences at Claremont Graduate University, part of the Claremont Colleges. He holds a B.A. in Physics from Harvard University (1992) and a Ph.D. in Theoretical Physics from Université Paris-Sud (1997). His career includes roles as Associate Director of UCLA's Institute for Pure and Applied Mathematics (IPAM, 2003–2006) and technical staff member at Los Alamos National Laboratory (LANL). Percus specializes in discrete optimization, statistical physics, and network models, with applications to quantum algorithms, fracture networks, and computational complexity. He co-developed the Extremal Optimization method, widely applied in interdisciplinary fields like social networks and protein folding. His research has been funded by NSF, DOE, and others. Percus organizes conferences on combinatorics and computational complexity and teaches courses in scientific computing, discrete mathematical modeling, and statistical mechanics. Education: B.A. Physics, Harvard University, 1992 Ph.D. Theoretical Physics, Université Paris-Sud, Orsay, 1997 Research Interests: Discrete optimization and algorithm performance analysis Statistical physics models for combinatorial problems Random network structures and real-world data modeling Interdisciplinary applications in quantum computing and geology Recent work emphasizes machine learning for fracture network analysis and affective polarization dynamics in social networks. His publications span computational materials science, graph theory, and epidemic diffusion modeling. Awards and Grants include major funding from the Air Force Office of Scientific Research, National Science Foundation, and Department of Energy. He has led interdisciplinary teams at LANL and organized IPAM programs fostering cross-disciplinary collaboration between mathematicians and scientists. Labs/Teams: Part of Claremont’s Institute of Mathematical Sciences and collaborates with LANL teams on computational projects. Active in organizing conferences bridging combinatorics and computational complexity fields.

• Fluid Dynamics
• Biomedical Engineering
• Cardiovascular Systems
• +4 more

Pavlos Vlachos is a Professor of Mechanical Engineering at Purdue University, affiliated with both the Department of Mechanical Engineering and the Biomedical Engineering program. He holds an office in the MANN building and is part of the Moveable Dream Hire Committee. His research focuses on fluid dynamics, biomedical engineering, and cardiovascular systems, with applications in medical imaging, drug delivery systems, and computational modeling. His work combines experimental and computational methods to study intracardiac flow in congenital heart diseases, subcutaneous drug delivery mechanisms, and novel echocardiographic biomarkers for sepsis and heart failure. He has developed advanced imaging techniques like 4D flow MRI reconstruction and Doppler velocity analysis tools for clinical diagnostics. His projects often bridge engineering and medicine, addressing challenges in pediatric cardiology, nanomedicine, and biomaterials. Key contributions include computational models for autoinjector performance, non-invasive cardiac flow assessment, and machine learning approaches for echocardiogram analysis. He actively engages in educational initiatives integrating virtual and physical labs for fluid mechanics education, emphasizing hands-on learning through open projects and data-driven experiments. His research portfolio spans over 50 peer-reviewed articles between 2022-2025, with a focus on translational biomedical engineering. Collaborations involve multi-disciplinary teams from cardiology, bioengineering, and materials science. Current interests include optimizing subcutaneous injection systems, predicting sepsis outcomes via hemodynamic indices, and improving diagnostic accuracy through automated image segmentation.

Sandeep DattaView profile

Professor

• Neuroscience
• Sensory Systems
• Motor Systems
• +3 more

Sandeep Datta, M.D., Ph.D., is a Professor of Neurobiology at Harvard Medical School and Co-Director of the PiNBAC Program. His research focuses on understanding how the brain integrates sensory input and motor output to generate natural behaviors in unrestrained animals. He leads the Datta Lab, which employs interdisciplinary techniques such as optogenetics, single-cell sequencing, and machine learning to study olfactory systems, neural circuits, and behavior. The lab explores how sensory experiences shape motor actions and vice versa, with a particular emphasis on odor-driven behaviors in mice. Key methodologies include closed-loop experiments, functional imaging, and computational approaches like Motion Sequencing to decompose behavioral patterns into structured components. Dr. Datta’s work bridges neuroethology and systems neuroscience, emphasizing the study of animals in ecologically relevant settings. His research has advanced our understanding of olfactory cortex evolution, the interplay between internal states and sensory processing, and the neural mechanisms underlying exploration and learning. He has developed innovative tools for automated behavioral phenotyping, including algorithms for parsing rodent behavior into syntactical elements. His contributions span from molecular genetics to systems-level analyses, addressing fundamental questions about how brains construct purposeful behaviors. While no formal educational background details are provided, his academic credentials include an M.D. and Ph.D., reflecting his dual expertise in clinical and experimental neuroscience. His lab’s collaborations extend to computational neuroscience and biomedical engineering, as evidenced by patents related to facial analysis systems and 3D reconstruction of animal behavior (ArMo method). The Datta Lab actively investigates post-COVID-19 olfactory dysfunction, dopamine’s role in novelty-driven behavior, and the evolutionary conservation of neural circuits across vertebrate species.

• Nanomanufacturing
• Self-powered Nanosystems
• Renewable Energy
• +3 more

Dr. Wenzhuo Wu is a Professor at Purdue University's Edwardson School of Industrial Engineering. His research focuses on advanced manufacturing techniques, renewable energy systems, and human-integrated technologies. With expertise spanning nanomaterials, sensors, and energy harvesting devices, Dr. Wu develops innovative solutions for sustainable technology and healthcare applications. His research interests center on nanomanufacturing processes, self-powered nanosystems for IoT applications, renewable energy conversion technologies, and intelligent sensor networks for human-machine interfaces. Key focus areas include triboelectric nanogenerators, 2D materials engineering, flexible electronics, and printed wearable devices. Recent publications demonstrate consistent focus on energy harvesting materials (33% of articles), tellurium-based devices (27%), and wearable sensors (20%). The work shows strong interdisciplinary integration of materials science, electrical engineering, and robotics. Emerging themes include sustainable manufacturing (2024), quantum material properties (2023), and multi-functional sensor systems (2023). Dr. Wu maintains active research facilities for nanomaterial synthesis and device prototyping. His team collaborates with multiple departments including Materials Engineering and Electrical Engineering, focusing on translational research with industry partners in energy and healthcare sectors.