جست‌وجوی استاد و پژوهشگر

• Bioinformatics
• Immunogenetics
• HLA Typing
• +۳ مورد دیگر

Fredrick Mobegi serves as an Adjunct Research Fellow in the Department of Microbiology and Immunology within the School of Biomedical Sciences at The University of Western Australia. His research bridges bioinformatics and clinical immunology with a focus on transplant medicine and pathogen genomics. Education: PhD in Bioinformatics and Computational Biology from Radboud University Medical Center (2012-2016), thesis: 'Deciphering the etiology and epidemiology of Streptococcus pneumoniae through bioinformatics' MSc in Bioinformatics from Wageningen University (2009-2011) Mobegi's research centers on immunogenetics and high-resolution HLA typing , leveraging next-generation sequencing for transplant compatibility assessment. His work develops bioinformatic tools like HLA-EP-RESOLVER for rare allele detection and validates Oxford Nanopore-based genotyping systems. Additional expertise spans plant-pathogen interactions in Striga-rice systems and ABO blood group characterization using third-generation sequencing. His fingerprint reveals dominant activity in HLA Typing (66%), Immunogenetics (44%), Next Generation Sequencing (44%), and Bioinformatics (44%) within immunology/microbiology contexts. His five recent publications (2023-2025) demonstrate consistent innovation in transplant genomics, with articles in Transplantation , HLA , and International Journal of Molecular Sciences focusing on clinical implementation challenges, novel tool development, and haplotype analysis. These works collectively advance precision matching in organ transplantation through bioinformatic solutions. Professional Activities: Senior Bioinformatician at PathWest Laboratory Medicine WA (since 2001) ORCID: 0000-0003-0554-9919 Mobegi maintains active external collaborations across international transplant research networks, with recent work involving institutions in Australia and globally. His h-index of 7 reflects growing impact in niche bioinformatics applications for clinical immunology.

Robbert Krebbersمشاهده پروفایل

دانشیار

• Semantics
• Separation logic
• Theorem proving
• +۷ مورد دیگر

Robbert Krebbers is an associate professor at the Department of Software Science at Radboud University Nijmegen, Netherlands. He is a leading researcher in program verification, specializing in separation logic and the Coq proof assistant. Krebbers is a core contributor to the Iris framework, a higher-order concurrent separation logic framework implemented in Coq. His research spans theoretical foundations of programming languages and practical applications to real-world languages including C, Rust, and Scala. His research interests focus on scaling program verification techniques to challenging programming paradigms like concurrency, higher-order functions, and modules. Krebbers' work bridges formal methods with practical language implementation, particularly in verifying memory safety and concurrency properties. He has made significant contributions to Rust verification through the RustBelt project and has pioneered techniques for verifying concurrent data structures and message-passing systems. Krebbers' recent publications demonstrate a strong trend toward verifying complex concurrency patterns, developing automated proof techniques, and applying separation logic to practical programming language features. His work frequently appears in top-tier programming languages conferences including POPL, PLDI, and ICFP, with several papers receiving distinguished paper awards. The research spans from foundational logic development to practical verification tools for real-world programming languages. As a principal investigator in the Iris project, Krebbers has secured significant research funding including the ERC Consolidator Grant for the RustBelt project. His work has influenced both academic research and industrial practice, particularly in the Rust programming language ecosystem. The Iris framework he helped develop has been adopted by numerous verification projects worldwide. Krebbers has advised PhD students including Ike Mulder (focusing on proof automation for concurrent separation logic) and Jules Jacobs (working on guarantees by construction). He has been actively involved in the programming languages research community, serving on program committees for major conferences and organizing workshops on formal methods and verification. He leads research in the Department of Software Science at Radboud University, where his group focuses on developing foundational techniques for program verification. The group collaborates closely with the Logic and Semantics Group and Foundations of Programming Group across institutions, contributing to a vibrant research ecosystem around formal methods and programming languages.

TOLGA ERENمشاهده پروفایل

استاد

• Cooperative Localization
• Formation Control
• Wireless Sensor Networks
• +۳ مورد دیگر

Professor Tolga Eren serves as a faculty member in the Department of Electrical and Electronic Engineering at Kirikkale University's Faculty of Engineering and Natural Sciences. Holding a PhD from Yale University with postdoctoral training at Columbia University, he previously held a visiting professorship at KTH Royal Institute of Technology and earned his BSc from Bilkent University under full scholarship. His educational trajectory includes: BSc in Electrical and Electronics Engineering, Bilkent University (full scholarship) MSc from University of Massachusetts and Yale University PhD from Yale University Postdoctoral research at Columbia University Eren's research centers on networked systems with dual focus on cooperative localization in wireless sensor networks and formation control for multi-robot systems. He pioneers applications of optimization algorithms like Particle Swarm and Bat Algorithm for positioning challenges while advancing rigidity theory for network stability. His work bridges theoretical frameworks with practical implementations in constrained environments. Analysis of his 2019-2021 publications reveals three dominant trends: (1) Hybrid optimization approaches for wireless sensor network localization using swarm intelligence variants, (2) Rigidity-theoretic foundations for formation control under input constraints, and (3) Geometric methods for robot path smoothing and shape maintenance. These intersect at the nexus of graph theory, control systems, and distributed computing. Professor Eren has advised at least one graduate student, Gülçin AKTAŞ, who completed her departmental thesis in 2014. While no specific grants are documented in the available records, his research output demonstrates sustained investigation into networked system fundamentals through institutional support at Kirikkale University.

• Electronic Components
• Circuit Design
• Sensor Systems
• +۵ مورد دیگر

Prof. Dr.-Ing. Volker K. S. Feige is a Professor at the Faculty of Electrical Engineering & Information Technology at Düsseldorf University of Applied Sciences (Hochschule Düsseldorf), where he has been teaching since March 2012. He teaches Electronic Components, Circuit Design, Sensor Systems & Signal Processing, and Manufacturing Measurement and Testing Technology. His research focuses on non-destructive testing methods using electromagnetic Terahertz waves, enabling more resource-efficient and sustainable manufacturing processes. Since August 28, 2023, Prof. Feige has also served as a member of the University Council of Düsseldorf University of Applied Sciences. Education: Vocational training as Energieelektroniker - Betriebstechnik (1988-1992) College entrance qualifications and bridge courses (1992-1993) Diploma in Electrical Engineering, Bergische Universität Wuppertal (1993-1998) Doctorate at Bergische Universität Wuppertal (2003) Prof. Feige's research centers on Terahertz technology applications for non-destructive testing and quality control. His work spans electronic components, circuit design, sensor systems, and signal processing with practical applications in corrosion protection of steel bridges and quality control of industrial coatings. He has developed innovative approaches for multilayer thickness measurements using reflection-mode Terahertz time-domain spectroscopy, particularly for challenging industrial environments. His research addresses real-world manufacturing challenges through precise, non-contact measurement techniques that improve sustainability and resource efficiency. Analysis of Prof. Feige's publication history reveals a clear research evolution from fundamental Terahertz measurement techniques toward integrated industrial solutions. His recent work increasingly incorporates robotics and machine learning for automated quality inspection systems, with growing applications in additive manufacturing and 3D printing. The consistent theme across his publications is the development of practical non-destructive testing methods that solve specific industrial challenges, particularly in coating thickness measurement across various substrates and multi-layer systems. Research Leadership: Principal investigator for multiple Terahertz technology research projects Contributor to VDI/VDE guidelines on Terahertz systems standardization Collaborator with industry partners on practical measurement solutions Lead on patents for 3D interferometric position measurement systems Prof. Feige directs research in the Electronics Laboratory at Düsseldorf University of Applied Sciences, where his team develops semi-mobile robotized Terahertz systems for automated quality inspection. His research group maintains strong industry connections, translating fundamental measurement technology into practical industrial applications for non-destructive quality control in manufacturing processes.

• Genetics of Obesity
• Body Fat Distribution
• Metabolic Disorders
• +۴ مورد دیگر

Matt Neville serves as the Oxford Biobank Scientific Coordinator within the Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM) at the University of Oxford's Medical Sciences Division. His primary responsibilities include curation of the Oxford Biobank resources, facilitation of translational research studies, and management of biobank operations. As a Lecturer, he supervises DPhil students and contributes to the Experimental and Translational Therapeutics MSc program. His research focuses on the genetic determinants of body fat function and distribution and their impact on obesity-related health consequences. Neville employs genomic, epigenomic, and metabolomic approaches to investigate adipose tissue biology, metabolic disorders, and Type II diabetes pathogenesis. His work leverages large-scale biobank resources including Oxford Biobank and UK Biobank for translational studies. Analysis of his 15 most recent publications reveals a consistent emphasis on genetic architecture of metabolic traits , with particular attention to adipose tissue biology, obesity genetics, and biomarker discovery. His research integrates multi-omic approaches (genomics, epigenomics, proteomics) to understand context-specific molecular mechanisms in metabolic diseases, with strong translational applications for risk prediction and personalized medicine. Neville holds critical operational roles including Person Designated for the OCDEM HTA license and serves as the RDM OCDEM EDI Champion. His academic journey began with a DPhil in human molecular genetics from Oxford in 2000, followed by industry R&D experience, and a postdoctoral position with Prof Fredrik Karpe since 2003 investigating adipose tissue genetics.

Süleyman Meteمشاهده پروفایل

دانشیار

• Operations Research
• Modeling and Optimization
• Multi-criteria Decision Making
• +۴ مورد دیگر

Süleyman Mete is an Associate Professor in the Industrial Engineering Department at Gaziantep University's Faculty of Engineering. He has been serving in this position since 2021, following previous roles as a Doctor Lecturer at Gaziantep University (2019-2021) and Munzur University (2017-2019). His academic career began as a Research Assistant at Gaziantep University (2011-2016) and Tunceli University (2010-2011). Dr. Mete earned his Doctorate in Industrial Engineering from Gaziantep University (2013-2017), followed by a Master's degree in Industrial Engineering from the same institution (2011-2013). He completed his undergraduate studies in Industrial Engineering at Selcuk University (2005-2010). Dr. Mete's research focuses on Operations Research, Modeling and Optimization, and Multi-criteria Decision Making. His work spans several specialized areas including disassembly line balancing, humanitarian logistics, risk assessment, and fuzzy logic applications. His research integrates mathematical programming with practical industrial applications, particularly in disaster response, sustainable manufacturing, and supply chain optimization. He has developed innovative approaches combining various multi-criteria decision-making methods with fuzzy logic to address complex real-world problems in occupational safety, transportation, and humanitarian aid distribution. His extensive publication record demonstrates a clear trajectory toward increasingly complex applications of optimization techniques, particularly in humanitarian contexts. Recent work shows a strong focus on drone-assisted logistics, urban mobility sustainability, and advanced risk assessment methodologies using various fuzzy set approaches. His research consistently bridges theoretical methodological development with practical applications in industrial engineering problems. University 2% Most Influential Scientist (2023) University 2% Most Influential Scientist (2022) Dr. Mete has successfully supervised numerous graduate students through their thesis work, with a particular focus on humanitarian logistics, disassembly line balancing, and risk assessment applications. His research projects include "Sustainable, Digital, and Smart Humanitarian Logistics in Disaster Relief Operations" (EU-funded) and "Data-Driven and Web-Based Decision Support System Development for Integrated Optimization of Vector Control Process" (Tübitak 1505). He has also served as a visiting researcher at Karlsruhe Institute of Technology (KIT) in Germany under the TÜBİTAK 2219 Postdoctoral Research Program. Dr. Mete's work demonstrates strong integration between theoretical methodological development and practical applications, particularly in humanitarian contexts and industrial engineering problems. His research group appears to focus on optimization techniques applied to real-world challenges in disaster response, sustainable manufacturing, and supply chain management.

K Siva Kumarمشاهده پروفایل

استاد

• Multi-level Inverters
• Induction Motor Drives
• Micro-grids
• +۳ مورد دیگر

K Siva Kumar is a Professor in the Department of Electrical Engineering at Indian Institute of Technology Hyderabad . His research focuses on multi-level inverters , induction motor drives , and micro-grid systems , with applications in renewable energy and electric vehicles . He holds a Ph.D. in Electrical Engineering from IISc Bengaluru (2010), an M.Tech in Power Electronics from NIT Warangal (2006), and a B.Tech in Electrical and Electronics Engineering from Sri Venkateswara University (2004). He joined IIT Hyderabad in 2011, progressing from Assistant Professor to Professor in 2022. His research interests span power quality and control , Pole Phase Modulation (PPM) , and Open-end winding induction motor drives . His publications (over 20 in 2021-2025) focus on photovoltaic systems , electric vehicle propulsion , and advanced inverter topologies . Scientific recognitions include: Fellow Indian National Academy of Engineers (FNAE) - 2024 Teaching Excellence Award (2021-22), IIT Hyderabad IETE-BIMAL BOSE AWARD - 2018 Senior Member IEEE Best Paper Awards at IEEE ICIAfS (2014), ANSYS Convergence Conference (2014), and IEEE ISIE-2009. He has supervised 15 Ph.D. and M.Tech students , including current scholars Tukaram Alias and Saurabh P Dhavjekar , and alumni like Kiran Kumar N and Madhukar Rao A . His teaching portfolio includes undergraduate courses in Power Electronics and Electromechanical Energy Conversion , and postgraduate courses in Electrical Drives and Multilevel Converters .

K R Guruprasadمشاهده پروفایل

دانشیار

• Robotics and Autonomous Systems
• Motion Planning
• Multi-Robot Systems
• +۷ مورد دیگر

K R Guruprasad is an Associate Professor in the Department of Mechanical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). With a PhD in Aerospace Engineering from the Indian Institute of Science (IISc) Bangalore, he has established himself as a leading researcher in robotics, motion planning, and multi-robot systems. His research interests span a wide range of topics in robotics including Robotics-Mechanics and Control, Motion Planning, Multi Robots Systems, Cooperative Control, Exo-Skeleton Control, and Voronoi Partition applications. His work particularly focuses on UAV/Drones motion planning, mobile manipulation, and artificial intelligence applications in robotics. His research integrates theoretical foundations with practical implementations, evidenced by his numerous publications in high-impact journals. Prof. Guruprasad's scholarly contributions include a book titled 'Robotics: Mechanics and Control' (PHI, 2019) and several edited volumes. His publication record shows consistent output with an h-index of 11, i10 index of 14, and 382 citations according to Scopus data. His recent work demonstrates strong focus on multi-robot coverage algorithms, UAV applications, and distributed control systems. Among his recognitions are a best presentation award at the IISc-IBM Research Workshop (2008) and a best paper award finalist at DARS 2014. His research has practical applications in search and rescue operations, landmine detection, and autonomous exploration. Prof. Guruprasad maintains active collaborations with researchers globally and has contributed significantly to advancing robotics research in India through his work at IIT Kanpur's Mechanical Engineering department.

Elham Shiraziمشاهده پروفایل

استادیار

• Artificial Intelligence
• Energy Management
• Sustainable Energy
• +۴ مورد دیگر

Elham Shirazi is an Assistant Professor specializing in Advanced Manufacturing, Sustainable Products & Energy Systems . Her research aligns with UN Sustainable Development Goals, focusing on Energy Management , Smart Grids , and Artificial Intelligence applications in energy systems. She has an h-index of 10 and 577 citations, primarily in Machine Learning and Power Systems . Her recent publications highlight trends in PV Power Forecasting , Energy Storage Optimization , and Grid Stability . Key methodologies include Long Short-Term Memory (LSTM) , Deep Reinforcement Learning , and Wavelet Packet Decomposition , applied to Building-Integrated Photovoltaics and Multi-agent Systems for grid resilience.

• Earth Observation
• Remote Sensing
• Data-centric AI
• +۹ مورد دیگر

Mariana Belgiu is an Associate Professor at the Department of Earth Observation Science (EOS) within the Faculty of Geo-Information Science and Earth Observation (ITC) at the University of Twente. Her work bridges Earth Observation (EO), data-centric artificial intelligence (AI), and food security, with a focus on addressing environmental and societal challenges through innovative geospatial solutions. PhD in Remote Sensing, University of Salzburg MSc in Applied Geoinformatics, University of Salzburg Her research develops AI methods for analyzing multi-temporal EO data, particularly in hidden hunger (micronutrient deficiencies) and slum mapping. Key themes include: Data-centric AI in scarce-label environments Transferability of EO-driven models Imaging spectroscopy for crop nutrient estimation Citizen science integration for climate vulnerability assessments The 69 research outputs span EO applications for: Global crop nutrient prediction Urban poverty mapping Climate resilience in Sub-Saharan Africa AI fairness and explainability in geospatial contexts Earth observation education frameworks Scientific Recognition Copernicus Masters 2015, T-Systems Big Data Challenge Esri Young Scholar Award (2013) Best Master Thesis in Geoinformatics (2010) As a supervisor of 7 PhD students , she mentors work on deep learning for cloud removal, global crop monitoring, and transferable slum mapping. She also leads the EO4all working group, promoting gender equity in EO science, and serves as Associate Editor for the ISPRS Journal (Impact Factor 12.7). Major grants include the SPACE4ALL project (NWO, 2023–2027) and EO4Nutri (ESA, 2023–2025), alongside contributions to Horizon Europe's ASTRAIOS initiative.

Chi Wangمشاهده پروفایل

استاد

• Cancer Biostatistics
• Bioinformatics
• Survival Analysis
• +۱۰ مورد دیگر

Chi Wang, Ph.D., is Professor of Internal Medicine and Statistics at the University of Kentucky, Associate Director of the Biostatistics & Bioinformatics Shared Resource, and a lead computational scientist within the Markey Cancer Center. With an h-index of 39 and >5,300 citations, he develops cutting-edge survival-analysis and high-dimensional-omics methods that are translated into prostate, breast, lung and neuroblastoma trials. Education: Ph.D. Biostatistics, Johns Hopkins University, 2009 M.S. Statistics, Peking University, 2003 B.S. Mathematics, Peking University, 2001 Research Focus: Wang’s group integrates Bayesian statistics, machine-learning and single-cell sequencing to model tumor evolution, drug resistance and biomarker-driven adaptive trials. Major themes include enzalutamide resistance in prostate cancer, PLK1-mediated immune evasion in lung adenocarcinoma, obesity-adipose drivers of breast cancer, and epigenetic reprogramming of tumor-infiltrating lymphocytes. Recent Article Trends (2023-2025): Fifteen showcased papers reveal a dual trajectory—(1) mechanistic dissection of kinases (PLK1, DDR1, ABL1/2) and metabolic enzymes (FASN, NNMT, HSP47) that orchestrate resistance and metastasis, and (2) probabilistic algorithms for temporal mutation ordering, biomarker evaluation and single-cell deconvolution. Overarching keywords are precision oncology, drug resistance, tumor micro-environment, and computational systems biology. Funding & Awards: He is PI/co-I on 152 grants (32 active) from NCI, ACS, V Foundation, etc., totaling >$30 M direct costs. Highlights include NCI U01 for predictive biomarkers, American Cancer Society award on collagen lysyl hydroxylase metastasis drivers, and NCI R01s targeting NNMT in triple-negative breast cancer and TIL epigenetic reprogramming in NSCLC. Student & Team Mentorship: Wang trains a large multi-disciplinary cohort of Ph.D. students, post-docs and clinical fellows who routinely lead first-author papers and are funded on his grants. His lab sits within the Markey Cancer Center’s Molecular & Cellular Oncology program and the Center for Computational Sciences, fostering daily bench-to-bedside collaboration.

Amine Mhedhbiمشاهده پروفایل

استادیار

• Database Management
• Graph-Structured Databases
• Query Optimization
• +۴ مورد دیگر

Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal in the Department of Computer Engineering and Software Engineering. He is affiliated with the Institute for Data Valorization (IVADO) and the Software Engineering for Machine Learning Applications (SEMLA) group. His research focuses on data management systems, particularly graph-structured databases, multimodal data engineering, and AI-driven query optimization. Ph.D. in Computer Science from University of Waterloo Former technical advisor to enterprise companies Prior applied research leadership at Distyl AI and internships at Microsoft Research His recent work explores integrating large language models (LLMs) into database systems, optimizing SQL generation, and advancing graph database architectures. Key projects include GraphflowDB and FlockMTL , addressing scalability and declarative semantic applications. Scientific awards include: NSERC Discovery Grant with Discovery Launch Supplement (2025) Cheriton School Distinguished Dissertation Award (2024) Microsoft Research Ph.D. Fellowship (2020) VLDB Best Paper Award (2018) He supervises graduate students in database systems and machine learning applications and serves on program committees for top-tier conferences like VLDB and SIGMOD.

Theodor Misiakiewiczمشاهده پروفایل

استادیار

• Theory of Deep Learning
• Kernel and random feature methods in high-dimension
• Non-convex optimization, implicit regularization, landscape analysis
• +۲ مورد دیگر

Theodor Misiakiewicz serves as an Assistant Professor in the Department of Statistics and Data Science at Yale University, based in Room 1049 of Kline Tower, New Haven, Connecticut. His research bridges statistics, machine learning, probability theory, and computer science with a core focus on developing theoretical foundations for deep learning algorithms. His academic background includes: PhD in Statistics from Stanford University under Prof. Andrea Montanari M.Sc. in Theoretical Physics from Ecole Normale Superieure de Paris B.Sc. in Mathematics from Ecole Normale Superieure de Paris Dr. Misiakiewicz's work centers on fundamental theoretical questions in machine learning, particularly examining deep learning through the lenses of kernel methods, random feature models, non-convex optimization landscapes, and high-dimensional probability. His approach integrates rigorous mathematical analysis with practical machine learning challenges to establish provable guarantees for modern algorithms. His publication record (2019-2025) demonstrates consistent contributions to understanding generalization in kernel methods, SGD dynamics in neural networks, and dimension-free bounds for deep learning models. These works collectively advance the mathematical framework for analyzing learning algorithms in high-dimensional regimes using tools from random matrix theory and statistical physics. He actively recruits PhD students through Yale's program and plans to hire postdoctoral scholars starting Summer 2025, while also welcoming undergraduate and graduate visitors to his research group. His mentorship focuses on training the next generation of theoretical machine learning researchers. Dr. Misiakiewicz leads a dedicated research team within Yale's Statistics and Data Science department that investigates the mathematical principles underlying artificial intelligence, maintaining an active seminar series and collaborative projects with international researchers.

• Remote Sensing
• Forest Ecology
• Terrestrial Laser Scanning
• +۳ مورد دیگر

Mark Danson is a Professor at the University of Salford's School of Science, Engineering & Environment, specializing in remote sensing and forest ecology. His research focuses on developing terrestrial laser scanning technologies for vegetation analysis and ecological monitoring. He leads work on the Salford Advanced Laser Canopy Analyser (SALCA) system and contributes to global environmental databases like Globe-LFMC. His research explores vegetation structure measurement, lidar technology development, and ecological applications of remote sensing. Key interests include forest canopy analysis, plant moisture estimation, wildfire risk assessment, and climate change impacts on ecosystems. Recent work advances 3D forest modeling and validation of satellite-derived ecological parameters. The publication record demonstrates consistent focus on terrestrial laser scanning methodologies and forest applications. Research trends show progression from instrument development (dual-wavelength lidar systems) toward large-scale ecological validation studies and global dataset creation for climate monitoring. Danson leads technology development initiatives including the SALCA instrument design and calibration. Collaborative work appears through international projects like the Global Ecosystem Dynamics Investigation validation studies.

Islam S.M. Khalilمشاهده پروفایل

دانشیار

• Micro Robotics
• Healthcare Robotics
• Biomedical Engineering
• +۳ مورد دیگر

Islam S.M. Khalil is an Associate Professor in Robotics and Mechatronics at the TechMed Centre. His research focuses on micro-robotics applications in healthcare, particularly developing millirobots for medical interventions like thrombus fragmentation and intravascular navigation. With an h-index of 24 and 2150 citations, he has contributed to 131 peer-reviewed publications. His work aligns with UN Sustainable Development Goals related to healthcare and well-being. Key Research Areas: Medical robotics, magnetic actuation, biomedical engineering Notable Projects: Wireless clot-busting millirobots, X-ray guided robotic clusters Media highlights include breakthroughs in navigating blood vessels using untethered robots. He actively participates in editorial roles for journals like IEEE Transactions on Robotics and Applied Physics Reviews.