جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Computer Science
• Genomics
• +۱۱ مورد دیگر

Nikolaos Alachiotis is an Associate Professor at the Digital Society Institute, specializing in Artificial Intelligence, Field-Programmable Gate Arrays (FPGA), and Genomics. His work bridges Hardware Acceleration with Bioinformatics , focusing on scalable solutions for genomic analysis and evolutionary biology. Research Themes: AI Hardware, FPGA Optimization, Population Genetics Key Collaborations: Delft University of Technology, Zenodo, Frontiers in High-Performance Computing Research Focus Alachiotis develops deep learning and FPGA-based tools for selective sweep detection and phylogenetic inference , enabling faster genomic analyses. His work spans convolutional neural networks , spiking networks , and adaptive computing for bioinformatics and radio astronomy. Recent articles highlight trends in AMD Versal SoC applications, deep learning for natural selection , and scalable genomic pipelines . Scientific Awards Outstanding Student Paper Award (2023) Supervised Work His supervised projects include Accelerating Selective Sweep Detection and Effective Data Preprocessing Techniques , often in collaboration with institutions like Zenodo and Frontiers journals.

George G. Cabralمشاهده پروفایل

استادیار

• machine learning
• online learning
• concept drift
• +۸ مورد دیگر

George G. Cabral is an Assistant Professor (Lecturer) in the Department of Computing at the Federal Rural University of Pernambuco (UFRPE) in Recife, Brazil. His academic career spans research and teaching in machine learning, with a particular focus on applications to software engineering and other real-world domains. Ph.D. in Computer Science, Federal University of Pernambuco (2014) M.Sc. in Computer Science, Federal University of Pernambuco (2008) B.Sc. in Computer Engineering, University of Pernambuco (2005) PostDoc, University of Birmingham (2019) Dr. Cabral's research primarily focuses on machine learning algorithms applied to real-world problems, with special emphasis on online learning and concept drift in data streams. His work explores class imbalanced learning techniques, ensembles of learning machines , and novelty detection methods. A significant portion of his research applies these techniques to software defect prediction , where he investigates just-in-time approaches that can identify potential bugs as software is being developed. He also applies similar methodologies to intrusion detection systems and public accounting domains, demonstrating the versatility of his research interests across different application areas. Analysis of Dr. Cabral's publication history reveals a consistent trajectory from foundational work on one-class classification techniques toward increasingly applied research in software engineering contexts. His most recent publications (2022-2023) demonstrate expansion into public sector applications, particularly in data auditing for Brazilian municipal governments, while maintaining his core focus on concept drift challenges in software defect prediction. The publications consistently address methodological challenges like verification latency and class imbalance evolution in streaming data environments. Dr. Cabral actively contributes to the academic community as a committee member for the Artifact Evaluation track at major software engineering conferences including ASE (Automated Software Engineering) since 2021 and ICSE (International Conference on Software Engineering). He serves as a reviewer for journals such as Neurocomputing and Applied Soft Computing, and has taught undergraduate courses including Artificial Intelligence, Neural Networks, Computing Theory, and Programming at UFRPE.

• Computational Statistics
• Machine Learning
• Neuroimaging Analysis
• +۳ مورد دیگر

Habib Ganjgahi is a Senior Researcher in Biostatistics focusing on Statistical Machine Learning and Image Analysis. He holds joint affiliations with the Medical Research Council (MRC) at Harwell and the University of Oxford's Department of Statistics. He obtained his PhD in Statistics from the University of Warwick, specializing in statistical methods for genetic analysis of neuroimaging data. His research centers on: Developing computational statistics and machine learning methods for heterogeneous big datasets Creating multivariate statistical models for the International Mouse Phenotyping Consortium (IMPC) Building predictive models for brain imaging data using UK Biobank resources, integrating genomic and environmental factors His recent publications (15 most recent) demonstrate strong emphasis on multiple sclerosis reclassification via AI, Bayesian modeling for clinical trials, MRI harmonization techniques, and genetic epidemiology of obesity. Work consistently features advanced statistical methodologies applied to neuroimaging and large-scale biomedical datasets, with notable focus on longitudinal analysis and disease progression modeling. No scientific awards, student advising, or grant information is documented in available sources.

• Building Performance Optimization
• Sustainable Construction Materials
• Building Automation Systems
• +۳ مورد دیگر

Prof. Dr. Jan Mehnert is a Professor specializing in building performance optimization, sustainable construction materials, and data-driven building automation systems. His research bridges the gap between theoretical design and operational performance through innovative quality management frameworks and big data analytics. Current work focuses on algorithmic toolkits for technical monitoring, eco-materials like additive manufactured earth, and carbon-neutral campus initiatives. His primary research domains include building performance verification (addressing the design-operation gap), sustainable material systems (earth-based construction and paper composites), and data-intensive building diagnostics (leveraging big data for energy efficiency). Key methodologies involve functional testing protocols, algorithmic performance monitoring, and lifecycle assessment of novel materials. Recent projects demonstrate practical applications in campus sustainability and neighborhood energy systems. Analysis of his 2017-2023 publications reveals three converging trends: (1) Integration of real-time data analytics into building commissioning processes, (2) Development of eco-material systems with validated thermal and structural properties, and (3) Creation of modular toolkits for scalable performance management. His work consistently targets the operational phase of buildings, emphasizing measurable outcomes in energy conservation, indoor environmental quality, and carbon reduction across both new construction and retrofits.

Adam Bujnowskiمشاهده پروفایل

استادیار

• Biomedical Engineering
• Medical Devices
• Smart Glasses Technology
• +۷ مورد دیگر

Dr. Adam Bujnowski serves as an Assistant Professor at the Department of Biomedical Engineering within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. His academic career spans multiple research domains at the intersection of engineering and medical sciences, with a particular focus on developing innovative biomedical devices and systems for healthcare applications. Dr. Bujnowski's research interests encompass a wide range of biomedical engineering disciplines, with particular emphasis on smart glasses technology , assisted living systems , bioimpedance measurements , and non-invasive medical monitoring . His work demonstrates strong expertise in electroimpedance tomography , image processing algorithms , and human-computer interaction specifically designed for medical applications. He has made significant contributions to the development of devices for sleep apnea detection , bathroom safety monitoring , and intraoperative assessment tools . His recent publications reveal a consistent research trajectory focused on creating practical medical technologies that bridge the gap between engineering innovation and clinical application. Dr. Bujnowski's work shows particular strength in developing non-contact monitoring systems for vital signs, with numerous publications addressing ECG, heart rate, and breathing detection in challenging environments like bathtubs and showers. His research on peripheral artery disease diagnostics demonstrates innovative approaches using thermography and glucose sensors as alternatives to traditional methods. Dr. Bujnowski is actively involved in the Ella4Life project, which develops virtual personal assistants for home and mobile use, specifically targeting elderly care applications. This project, funded through the AAL (Ambient Assisted Living) program, reflects his commitment to translating research into practical solutions for independent living among older populations. As a member of the Biomedical Engineering Team at Gdańsk Tech, Dr. Bujnowski contributes to an interdisciplinary research environment that combines technical expertise with medical knowledge. His work on smart bathroom scales, fall detection systems, and intraoperative assessment devices demonstrates a consistent focus on practical applications that address real-world healthcare challenges, particularly in the domains of elderly care and surgical assistance.

David Perez Del Reyمشاهده پروفایل

دانشیار

• Clinical Research Informatics
• Biomedical Interoperability Standards
• Database Integration
• +۶ مورد دیگر

David Perez Del Rey is an Associate Professor at the Department of Artificial Intelligence, School of Computer Engineering, Polytechnic University of Madrid (UPM). He holds a PhD in Computer Science from UPM and has conducted research visits at Rutgers University (USA), University of Utah (USA), and PHILIPS Research Europe (Netherlands). Since 2003, he has been a key member of the Biomedical Informatics Group. Research Focus: His work centers on clinical research informatics with specialization in: Biomedical data interoperability standards and terminology systems Clinical database integration and preprocessing pipelines Knowledge discovery in biomedical datasets Information retrieval systems for healthcare data Real-world evidence generation from clinical data Major Projects: Significant contributions to EU initiatives including: INTEGRATE (integrative cancer research) EURECA (clinical research-care data linkage) InSite Platform (clinical research acceleration using real-world data) Teaching: Currently instructs courses in: Logic (Computer Engineering) Information Extraction, Retrieval and Integration (Data Science MSc) Biomedical Information Systems (Biomedical Engineering) Health Informatics (Health Management MSc)

David Disabatoمشاهده پروفایل

استادیار

• Positive Psychology
• Clinical Psychology
• Health Psychology
• +۳ مورد دیگر

David Disabato serves as an Assistant Professor at Baldwin Wallace University with expertise spanning clinical, health, and positive psychology. His work centers on evidence-based interventions to enhance mental health outcomes through scientific rigor. His academic credentials include: Post-doc in Affective Science, Kent State University Ph.D. in Clinical Psychology, George Mason University M.A. in Clinical Psychology, George Mason University B.A. in Psychology, Villanova University Dr. Disabato specializes in increasing well-being among individuals with psychopathology and chronic illnesses. His research encompasses well-being measurement, personality strengths assessment through self-report instruments, daily positive emotion dynamics via experience sampling methodology, and optimal recovery pathways post-emotional disorders. He emphasizes quantitative approaches with advanced statistical training. A strong advocate for student research involvement, he leverages his statistical expertise through R programming workshops and a proprietary R package for questionnaire data preprocessing. His methodological focus bridges psychological theory with practical health behavior applications.

Qiwei Pengمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Multilingual NLP
• Cross-lingual Understanding
• +۳ مورد دیگر

Qiwei Peng is a Postdoctoral Researcher in the Department of Computer Science at the University of Copenhagen's Faculty of Science, specializing in natural language processing with emphasis on multilingual applications and cross-lingual understanding. His research spans semantic similarity across languages, multilingual concept understanding, and practical implementations in diverse linguistic contexts. Key contributions include advancing multilingual large language model alignment, developing code generation benchmarks, and analyzing tokenization vulnerabilities. Analysis of Peng's 2024 publications reveals concentrated efforts on multilingual NLP challenges, particularly concept space alignment in LLMs, cross-lingual code generation, and subword robustness. His collaborative work extends to multimodal cultural understanding through datasets like FoodieQA for Chinese food culture. Peng actively contributes to the NLP community through publications in premier venues including EMNLP and LREC-COLING, with research networks spanning multiple international institutions as indicated by his co-authorship patterns.

• Molecular Immunomics
• Systems Biology
• Translational Immunology
• +۵ مورد دیگر

Lorenzo Bonaguro is a Research Group Leader in Molecular and Translational Immunomics at the German Center for Neurodegenerative Diseases (DZNE) in Bonn and the University Clinic Bonn (UKB). His research integrates high-resolution, high-throughput technologies like single-cell RNA sequencing with advanced computational methods, including artificial intelligence, to unravel immune system function and dysfunction. Dr. Bonaguro completed his Ph.D. in Molecular Biomedicine at the University of Bonn in 2020 with a dissertation on "Creld1 controls T cell homeostasis in mouse and human," earning summa cum laude distinction. His academic journey began with a B.S. and M.S. in Biotechnologies from the University of Padova, where he consistently graduated with highest honors. His research focuses on understanding how environmental factors like diet and pollution impact immune system aging and response capabilities. By studying model systems such as tonsil organoids and developing innovative computational tools, his work aims to uncover molecular determinants of immune fitness and vaccination efficacy. His group applies a systems biology approach to develop precision medicine strategies that can tailor treatments to individual patients. Analysis of his publication record reveals a strong emphasis on computational immunology and population-scale genomics. His work bridges experimental biology with advanced data science, with particular strength in developing analytical frameworks for complex immune datasets. The huva framework he developed represents a significant contribution to the field of population-scale gene expression analysis. 2023: Renate und Karlheinz Schmidt-Stiftung Award (Early Academic Award) 2020: Best publication award (Bonner Forum Biomedicine) 2018: Best poster award (Bonner Forum Biomedicine) Dr. Bonaguro's research is supported by significant funding, including an ERC Starting Grant (POLIS project 2025-2030) investigating PFAS impact on adaptive immunity, ImmunoSensation2 funding for his Junior Group Leader position (2023-2025), and DFG support for the ImmuDiet project (2023-2026). He has developed several computational tools including huva, cyCONDOR, and Clinical Match. His research group combines biological expertise with advanced technological approaches to address critical questions in immunology and aging. The Bonaguro Lab operates at the intersection of immunology, computational biology, and precision medicine, with active projects investigating environmental impacts on immune function, development of analytical methodologies, and translation of findings to clinical applications.

Florian Haselbeckمشاهده پروفایل

استاد

• Machine Learning in Agriculture
• Time Series Forecasting
• Protein Engineering
• +۵ مورد دیگر

Prof. Dr. Florian Haselbeck is a Professor in the Department Sustainable Agriculture and Energy Systems at the University of applied sciences Weihenstephan-Triesdorf (HSWT). His research bridges machine learning, agricultural science, and biological applications with a strong focus on practical implementations. His primary research interests include: Machine Learning applications for time series forecasting in agriculture and energy systems Protein engineering and thermostability prediction using deep learning Computer vision for precision agriculture and weed detection Development of open-source frameworks for scientific computing Smart farming applications with practical industry relevance Dr. Haselbeck's recent publications reveal a strong interdisciplinary approach, combining cutting-edge AI techniques with domain-specific challenges. His work on protein thermostability prediction using graph neural networks (ProtGCN) and protein language models (ProLaTherm) demonstrates innovative applications of AI in biochemistry. He has also developed influential open-source frameworks including ForeTiS for time series forecasting and easyPheno for phenotype prediction, which have gained significant traction in their respective fields. As project lead for the smartBattery initiative, Dr. Haselbeck is applying AI to optimize large-scale battery storage systems for renewable energy integration. His research consistently emphasizes practical applicability, with numerous publications focusing on real-world implementation challenges and solutions in agricultural contexts.

• Machine Learning
• Operations Research
• Computer Science
• +۳ مورد دیگر

Bosung Kim is affiliated with the Ulsan National Institute of Science and Technology (UNIST), Republic of Korea . His research spans Machine Learning , Operations Research , Natural Language Processing , and Autonomous Systems , with a focus on practical applications in technology and data science. Key Research Areas: Machine Learning, Autonomous UAV Systems, Knowledge Graph Completion, Class Imbalance Solutions, Cognitive Radio Networks His work includes 15 recent publications (2021–2025) covering topics like on-device AI generation , zero-shot triplet extraction , and autonomous insect tracking . These contributions highlight interdisciplinary methodologies in robotics , language models , and signal processing .

Sushant Kumar Pandeyمشاهده پروفایل

استادیار

• AI in Software Engineering
• Software Testing
• Autonomous Systems
• +۳ مورد دیگر

Sushant Kumar Pandey is an Assistant Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen. His research focuses on AI-driven software engineering, including software testing, autonomous systems, and data leak detection in computer vision. Previously, he held postdoctoral roles at Chalmers University of Technology and IIT (BHU), Varanasi. Education: PhD in Computer Science (2017–2021) from IIT (BHU), Varanasi, India. Thesis: 'Observations on Software Defect Prediction.' M.Tech in Information Technology (2014) from NIT Patna, India. Thesis: 'Intrusion Detection System using Anomaly Detection in Wireless Sensor Networks.' B.Tech in Information Technology (2008–2012) from United College of Engineering and Research, Greater Noida, India. Research Interests: AI applications in software engineering, defect prediction, deep learning architectures, and industrial collaborations with companies like Volvo Cars. His work bridges theoretical advancements and practical solutions, such as cross-project defect prediction and data handling for autonomous systems. Key Contributions: Over 15 peer-reviewed publications in journals like Knowledge-Based Systems and Expert Systems with Applications, focusing on machine learning techniques for software defect prediction and testing. His recent work explores design pattern recognition using programming language models and input prioritization for deep learning systems. Awards/Grants: Multiple course certifications in machine learning from Coursera and an ethical hacking certification. No specific grants mentioned, but active collaborations with industry partners like Volvo Cars. Labs/Teams: Leads the Software Engineering and Architecture (SEARCH) research group at Groningen under Prof. Paris Avgeriou. Collaborates with teams at Chalmers University and IIT (BHU).

Sebastiaan Mathotمشاهده پروفایل

دانشیار

• Eye Movements
• Pupillometry
• Visual Perception
• +۳ مورد دیگر

Dr. Sebastiaan Mathôt is an Associate Professor in the Department of Experimental Psychology at the University of Groningen, Netherlands. His primary research focuses on eye movements, pupillometry, and visual perception, with an emphasis on how pupil size influences visual processing. He leads the Experimental Psychology group, develops scientific software (e.g., OpenSesame, DataMatrix), and maintains CogSci.nl, a platform for cognitive science resources. Mathôt's work explores the functional role of pupil dynamics, including light responses, near responses, and psychosensory modulation. Key areas include pupil-size effects on retinal responses, time perception, and neural activity. He has published extensively in journals like NeuroImage , Psychophysiology , and Journal of Cognitive Neuroscience , with over 150 peer-reviewed articles and preprints. He has received grants such as the VIDI and VENI awards for research on pupil-sensation feedback loops and cognitive pupillometry methods. His software contributions, including OSWeb and MindProbe, support online experimentation and open science practices. Collaborations span cognitive psychology, neuroscience, and applied research on topics like visual working memory and attention-guided perception. Awards include the Dutch Psychonomic Society’s Best PhD Thesis (2013) and a finalist in the Best Illusion of the Year Contest (2014). His lab actively engages in interdisciplinary projects, integrating EEG, eye-tracking, and computational modeling to understand perception and cognition.

Daniel RANCمشاهده پروفایل

مدرس ارشد

• Big Data
• Cloud Computing
• IP Multimedia Subsystem
• +۵ مورد دیگر

Daniel RANC is a Senior Lecturer at Telecom SudParis, affiliated with the NeSS research group within the SAMOVAR laboratory. His academic work focuses on Big Data processing, cloud computing, and IP Multimedia Subsystem (IMS) networks, with particular expertise in data modeling, NoSQL databases, and quality of service management. His research interests span multiple domains including Big Data analytics, where he has developed preprocessing and modeling tools; cloud computing with predicate-based resource management; and telecommunications with a focus on IMS networks and QoS management. His work demonstrates strong interdisciplinary connections between data science, network engineering, and educational technology. RANC's publication record shows consistent contributions to his fields from 2007 through 2016, with a particular concentration on Big Data processing frameworks and network management systems. His research demonstrates a progression from eLearning systems interoperability toward more complex Big Data and cloud computing challenges. Daniel RANC collaborates extensively with researchers including Hadi Hashem and Brahim Raouyane, with whom he has co-authored numerous publications across multiple conferences and journals. His work appears in reputable venues including Foundations of Computing and Decision Sciences, International Journal of Computer Science and Applications, and various IEEE conferences.

Sullivan Blairمشاهده پروفایل

استاد

• Graph Algorithms
• Computer Science
• Algorithm Design
• +۳ مورد دیگر

Blair Sullivan is a Professor at the Kahlert School of Computing, University of Utah. Her research focuses on graph algorithms, parameterized complexity, and network analysis. She has contributed to theoretical advancements in clustering, graph decomposition, and algorithmic efficiency with applications in robotics, bioinformatics, and social networks. Education details are not explicitly stated in the provided text, but her affiliation indicates a terminal degree in Computer Science or a related field. Her work frequently intersects with interdisciplinary domains such as computational biology and quantum computing. Research interests emphasize algorithm design for large-scale networks, with a focus on graph-based problems such as clustering, coloring, and structural optimization. Recent publications explore hypergraph clustering, robotic motion planning, and fairness in network information access. Her work often bridges theoretical computer science with practical applications, including biomedical data analysis and quantum program compilation. Publications since 2023 reflect a sustained focus on graph-theoretic challenges such as parameterized complexity, edge augmentation for fairness, and decomposition techniques. Notable themes include algorithmic approaches to gerrymandering, genetic association analysis, and hyperbolicity in networks. No scientific awards or grants are listed in the provided text. She is affiliated with the University of Utah’s Kahlert School of Computing, where she contributes to research and education in computational theory and applications.