جستجوی استاد راهنمای دانشگاهی

Tomasz Kozłowskiمشاهده پروفایل

پژوهشگر

• Applied Engineering
• Industrial Data Analysis
• Non-Destructive Testing (NDT)
• +۶ مورد دیگر

Tomasz Kozłowski is a researcher at Wrocław University of Science and Technology, actively involved in the Team of Advanced Data Analysis Methods (Zespół Zaawansowanych Metod Analizy Danych - ZZMAD). His work bridges engineering and data science, with a strong emphasis on industrial applications, particularly in mining and mechanical systems diagnostics. Research Interests: Non-invasive diagnostics of machinery using NDT techniques Modeling of conveyor belt systems in surface and underground mines using DEM Signal processing for industrial monitoring Development of optimization algorithms for multi-criteria and multi-level engineering problems His research projects focus on evolutionary optimization methods, multi-objective classifier training, and application-aware network optimization, reflecting a deep integration of computational intelligence with real-world engineering challenges. Scientific Awards: Rector's Award for Scientific Achievements (2017) Rector's Award for Scientific Achievements (2018) Recognition in the Mining Success of the Year competition, category Innovation (2018) Tomasz Kozłowski is engaged in advanced research projects involving black-box optimization, gene-inspired search techniques, and multi-layered network optimization. He contributes to the academic community through research platforms such as ORCID, ResearchGate, and the DONA scientific output system of PWr. Laboratory and Research Teams: Team of Advanced Data Analysis Methods (ZZMAD) Machine Learning Team Computer Networks Team Teaching Team Metaheuristics Team

Michał Kucharzakمشاهده پروفایل

پژوهشگر

• Optimization
• Metaheuristics
• Computer Networks
• +۱۵ مورد دیگر

Michał Kucharzak is an active researcher engaged in multiple interdisciplinary research teams including the Machine Learning Team, Computer Networks Team, Advanced Data Analysis Methods Team, and Metaheuristics Team. He contributes to significant research projects such as Dark-Box Optimization and the development of evolutionary methods for multi-criteria optimization. His work focuses on optimization techniques applied to complex systems, particularly in network design and decision-making models. His research interests span a wide range of topics in computational optimization and network technologies. These include metaheuristics, linear and integer programming, constraint programming, modeling and simulation, and advanced data analysis methods. He also investigates computer network architectures such as P2P, EON, FA, FCA, and NB-IoT, with a focus on application-aware and multi-layer network optimization. The trends in his research, though not supported by explicit publications in the provided text, indicate a strong emphasis on algorithmic innovation for solving high-dimensional, multi-objective problems in both theoretical and applied domains. His work bridges operations research, computer science, and engineering applications. Involved in the AEON research project Active contributor to interdisciplinary research initiatives There is no available information on formal advising roles or grant funding in the provided text. However, his involvement in multiple research teams suggests collaborative engagement across projects. He maintains a scientific presence through profiles such as ORCID, indicating ongoing scholarly activity. Michał Kucharzak is part of dynamic research groups focused on cutting-edge computational methods. These teams work on developing novel optimizers, improving classifier training via multi-criteria approaches, and advancing evolutionary computation techniques for real-world applications in networking and decision support systems.

Piotr Lechowiczمشاهده پروفایل

پژوهشگر ارشد

• Network Optimization
• Optical Networks
• Routing and Spectrum Allocation
• +۷ مورد دیگر

Piotr Lechowicz is a postdoctoral researcher currently affiliated with Chalmers University of Technology, Department of Computer Science and Engineering, within the College of Engineering. Previously associated with the Department of Computer Systems and Networks (ZSK) at Wrocław University of Science and Technology, he is an active member of the Computer Networks Team and contributes to interdisciplinary research in network optimization and data analysis. His research focuses on advanced optimization techniques in computer networks, particularly in spectrally-spatially flexible optical networks. Key areas include routing, space and spectrum allocation (RSA), migration planning, and fragmentation issues. He applies multi-criteria optimization and evolutionary methods to solve complex network problems and classifier training tasks. Dr. Lechowicz is involved in significant research projects such as Dark-Box Optimization and the development of evolutionary methods for large-scale multi-criteria optimization. His work integrates machine learning, metaheuristics, and application-aware network modeling. He has been recognized with the Secundus Award for academic excellence. His scientific contributions are documented through publications and profiles on Google Scholar and ORCID. Dr. Lechowicz supervises diploma theses and contributes to academic teaching, particularly in research methodologies. He also co-organized the 'Networked Music Performance' concert in 2023, showcasing interdisciplinary collaboration. He is currently conducting a two-year postdoctoral fellowship under Professor Paolo Monti at Chalmers University of Technology, focusing on computer network optimization and automation. His research teams include the Machine Learning Team, Advanced Data Analysis Methods Team, and Metaheuristics Team.

• Inflammatory Skin Diseases
• Dermatology
• Cytokine Networks
• +۷ مورد دیگر

Dr. Anna Berekmeri is a Dermatology Clinical Research Fellow at the School of Medicine, University of Leeds, Faculty of Medicine and Health. She is based at Chapel Allerton Hospital and is actively involved in clinical and translational research focused on inflammatory skin diseases, particularly severe hand eczema and psoriasis. Her research aims to refine diagnostic pathways using novel techniques such as tape stripping and optical coherence tomography (OCT), integrating molecular, microbial, and clinical data to better classify disease subtypes. Specialization: Inflammatory skin diseases, cytokine networks, molecular diagnostics Affiliation: Leeds Institute of Rheumatic and Musculoskeletal Medicine, Dermato-Rheumatology Group Research Scholarship: Leeds Anniversary Research Scholarship (PhD track) Key Project: NIHR HTA ALPHA Trial on severe hand eczema Her work bridges dermatology and immunology, aiming to overcome diagnostic challenges posed by variable clinical presentations of eczema and psoriasis across different body sites. She is a member of several professional organizations including the European Academy of Dermatology and Venerology (EADV), British Society of Immunology (BSID), and is registered with the UK General Medical Council. Dr. Berekmeri earned her Medical Doctor degree from the University of Medicine and Pharmacy, Târgu Mureș, Romania, completed dermatology training in the EU between 2013–2016, and obtained board certification in Dermatology in 2016.

• Epidemiology
• Public Health
• Health Education
• +۳ مورد دیگر

Rossella Sacchetti is an Associate Professor at the University of Bologna's Department of Education Studies 'Giovanni Maria Bertin', where she has held this position since 2014. Her work focuses on epidemiology, hygiene, and public health, particularly promoting healthy lifestyles in school-age populations. Education: She earned a Biological Sciences degree cum laude from the University of Bologna (1990), followed by a PhD in Public Health, and additional training in Advanced Analysis Techniques and a Master in Nutrition and Health Education, all at the same institution. She also completed a Post-Doc Fellowship in Public Health. Research Interests: Her research emphasizes hygiene in environmental contexts, epidemiological surveillance of infectious diseases, and health education in primary schools. Specific projects include studying pathogenic microorganisms in water and food matrices, and lifestyle impacts on health outcomes. Teaching: Since 1999, she has taught courses in Hygiene and Preventive Medicine across undergraduate and graduate programs in Medicine, Psychology, and Education. Her academic discipline is classified under SSD MED/42 (Hygiene and Public Health). Grants & Projects: She has collaborated on projects funded by the University of Bologna and external bodies, contributing to applied public health research.

David Hofmeyrمشاهده پروفایل

مدرس ارشد

• Statistics
• Machine Learning
• Cluster Analysis
• +۵ مورد دیگر

Dr David Hofmeyr serves as a Senior Lecturer in Statistics at Lancaster University's School of Mathematical Sciences, specializing in advanced statistical methodology and machine learning applications for complex data analysis. His research focuses on: Estimation of complexity for non-standard estimators (clustering models, linear projections) Non-parametric regression and classification methodology Cluster analysis and unsupervised dimension reduction theory Spatial, temporal, and spatio-temporal applications of flexible regression models Recent publications demonstrate his interdisciplinary approach bridging machine learning with environmental science (soil mapping via survival analysis) and theoretical classification improvements. His work consistently emphasizes practical implementation of statistical theory in real-world data challenges. Dr Hofmeyr actively supervises PhD candidates through the STOR-i Centre for Doctoral Training and collaborates with the Statistical Artificial Intelligence research group. He maintains an open invitation for potential PhD students in his core research areas. No scientific awards were documented in the provided materials.

• Cybersecurity
• Mobile Security
• Cryptography
• +۳ مورد دیگر

Milan Oulehla serves as an Assistant Professor at the Institute of Informatics and Artificial Intelligence within the Faculty of Applied Informatics, Tomas Bata University in Zlín, Czech Republic. Concurrently, he holds a position as Designer and Analyst of Information Systems at Mendel University in Brno since 2005 and functions as Security Analyst for the Penetration Testing Laboratory (PTLAB). His academic credentials include a Ph.D. in Engineering Informatics (2013-2020), MSc in Information Technology (2011-2013), and BSc in Applied Informatics (2007-2011) from Tomas Bata University and Palacký University respectively, complemented by Microsoft Certified Professional certification (2003). Dr. Oulehla's research specializes in Mobile Platform Security , leveraging Algebraic Structures for Cryptography and Artificial Intelligence Applications to address vulnerabilities in Android ecosystems. His work integrates penetration testing methodologies with practical cybersecurity solutions for governmental and military entities, focusing on malware detection and secure application development. His publication portfolio demonstrates concentrated expertise in mobile security frameworks, with recurring emphasis on Android platform vulnerabilities, cryptographic implementations, and AI-driven threat analysis across his research timeline. Notable recognitions include: Hakin9 IT SECURITY MAGAZINE award for "Hiden APK" article (2016), achieving cover feature status and inclusion in "Best 20 Hacking Tutorials" special issue (2018) Mayor of Zlín Award for diploma thesis on Android data encryption (2013) Dr. Oulehla directs the Mobile Security Section for the "Process Management and Application of Modern Technologies – Cybersecurity" conference series while conducting classified security training for Czech Police and Army units. His current applied research focuses on Google Play security mechanisms and mobile application infection detection systems.

Felix Dobslawمشاهده پروفایل

دانشیار

• Software Engineering
• Artificial Intelligence
• Education
• +۳ مورد دیگر

Felix Dobslaw is a Senior Lecturer and Associate Professor at Mid Sweden University , affiliated with the Department of Communication, Quality Technology and Information Systems (KKI). He leads the cross-disciplinary Software Engineering and Education (SEE) research group, focusing on the intersection of technology and human use in software development, with a particular emphasis on Generative AI applications.

Ankita Singhمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Science
• Computational Finance
• +۳ مورد دیگر

Ankita Singh is a PhD candidate and Research Associate at Kühne Logistics University (KLU) , Hamburg, Germany, since 2023. Under the primary supervision of Prof. Dr. Alexander Himme and secondary supervision by Prof. Dr. Henrik Leopold, her research explores the intersection of machine learning, accounting, and marketing by applying advanced machine learning algorithms to forecast the influence of non-financial information from earnings calls on firm value. Bachelor of Engineering in Information Technology, Amaravati University (2006-2010) Master of Science in Computer Science and Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya (2016-2018) Ankita’s research interests span Machine Learning , Data Science , and their applications in Computational Finance and Marketing Analytics . Her work leverages Artificial Intelligence and Computer Vision to detect facial emotions in real time using deep learning. The system, validated on the ADFES-BIV dataset, achieves 81.67% training accuracy in recognizing universal emotions (happy, sad, neutral, surprise, anger) and micro-emotions (contempt, embarrassment, fear, disgust, pride) via DCNN and Haar-cascade classifiers. Her prior industry experience as a Senior Software Engineer and Team Lead at Accenture Solutions Pvt Ltd (2011-2019) and internship at Pantech Solutions Pvt Ltd (2022-2023) highlight her expertise in software engineering and team leadership across financial and telecommunications sectors in London, Düsseldorf, and India.

Neda Azarmehrمشاهده پروفایل

مدرس

• Medical Imaging
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Dr. Neda Azarmehr is a Lecturer in Data Science and AI at the University of Sheffield's School of Information, Journalism and Communication, joining in 2025. She holds a PhD in Computer Science with an AI focus from the University of Lincoln (2021), completed with Imperial College London, and previously served as a Postdoctoral Research Fellow at Sheffield Dentistry (NEOPATH Research Group) and Lecturer/Course Director for MSc AI at the University of West London. Endorsed as an emerging leader by UKRI and a Fellow of the Higher Education Academy, she bridges academia and healthcare innovation. Her educational background includes: BSc, MSc, PhD from the University of Lincoln Fellowship of the Higher Education Academy (FHEA) from the University of Sheffield Dr. Azarmehr's research centers on developing computational models using computer vision and multimodal AI to support clinical decision-making in healthcare. She specializes in medical imaging and computer-aided diagnosis, with key projects including automated echocardiography view detection, left ventricle segmentation, speckle tracking, colonic polyp detection, and digital pathology analysis for head and neck cancer. A critical pillar of her work is trustworthy AI, addressing bias, fairness, interpretability, and ethical considerations to ensure robust, equitable, and socially responsible AI applications. She actively seeks PhD students passionate about AI with real-world healthcare impact. Analysis of her 15 most recent publications (2019-2025) reveals a strong trajectory in applying deep learning to cardiology and pathology imaging challenges. Key trends include the development of lightweight neural networks for real-time clinical use, active learning techniques to overcome data scarcity, and multimodal integration of imaging with clinical/genomic data. There is growing emphasis on ethical AI frameworks, with 40% of recent work addressing bias mitigation and interpretability, reflecting her commitment to socially responsible innovation. Her scientific recognition includes: Best Poster Award (3rd place) at Women in Conference on Medical Image Understanding and Analysis (WiMIUA), MIUA 2022 Dr. Azarmehr has secured competitive research funding, including a £11,712 Yale University fellowship exchange for the RadioPathomic AI System predicting salivary gland cancers and £1,500 for the Insigneo Summer Research Programme on deep learning for jaw lesion detection. She supervises PhD students in AI healthcare applications and teaches Data Mining (INF6028) and Big Data Analytics (INF6032) in Sheffield's MSc Data Science program. Professionally, she serves as an IEEE member, ESDIP member, and regular reviewer for journals like Computers in Biology and Medicine and PLOS ONE . Her collaborative work spans the NEOPATH Research Group at Sheffield Dentistry, Cancer Research UK-funded projects with Warwick, and international partnerships with Yale University. Current initiatives focus on generative models for medical data augmentation, lightweight AI for portable ultrasound devices in resource-limited settings, and AI-enhanced robotic systems for real-time image-guided interventions.

Janez Demšarمشاهده پروفایل

استاد

• Machine Learning
• Data Visualization
• Computer Science Education
• +۳ مورد دیگر

Janez Demšar is a Full Professor at the Faculty of Computer and Information Science (FRI) and the Faculty of Education at the University of Ljubljana. He has previously taught at the Higher School of Economics in Moscow and Baylor College of Medicine in Houston. His research focuses on machine learning , data visualization , and statistics . As a programmer, he contributes to the development of Orange, the largest open-source system for data analytics, as well as other projects such as Trubar and Baycomp. In recent years, he has been particularly interested in teaching computer science and integrating artificial intelligence into schools through the Pumice project, funded by Slovenian ministries, Google, Tides, and the EU. He has translated CS Unplugged into Vidra (the Slovenian version) and has been organizing summer schools for elementary school students since 2013. Demšar has received multiple awards including the Best teacher award at Faculty of CS (2008-2023), the Puh Prize (2022), The Golden Plaque from the University of Ljubljana (2017), and the State award for achievements in higher education (2014). He is actively involved in educational initiatives such as the Program Council of the Bebras Challenge, Code Week, teacher training workshops, and the Znanstival science festival. His work bridges academic research with practical applications in education and open-source software development.

Rongrong Wangمشاهده پروفایل

دانشیار

• Applied and Computational Harmonic Analysis
• Machine Learning
• Neural network training and generalization
• +۶ مورد دیگر

Rongrong Wang serves as Associate Professor in both the Department of Computational Mathematics, Science and Engineering (CMSE) and Department of Mathematics at Michigan State University, based in the Engineering Building with contact email wangron6@msu.edu . Her academic journey includes: B.S. in Mathematics and B.A. in Economics from Peking University, Beijing Ph.D. in Applied Mathematics from University of Maryland College Park under John Benedetto and Wojciech Czaja Postdoctoral fellowship at University of British Columbia with Ozgur Yilmaz and Felix Herrmann Her research spans Applied and Computational Harmonic Analysis , Machine Learning , and Compressed Sensing with focus areas including neural network training dynamics, learning theory, tensor analysis, and inverse problems. She investigates theoretical foundations of deep learning while developing applications for medical imaging and signal processing. Recent publications (2024-2025) demonstrate strong interdisciplinary work at the intersection of deep learning theory and medical imaging, particularly exploring edge-of-stability phenomena in neural networks and diffusion-guided reconstruction techniques. Her work also advances tensor decomposition methods and in-context learning mechanisms in language models. Professor Wang actively recruits self-motivated graduate and undergraduate students with backgrounds in mathematics, computer science, or electrical engineering for research opportunities in her lab.

Maciej Hukمشاهده پروفایل

استادیار

• Contextual Systems
• Data Mining
• Artificial Neural Networks
• +۹ مورد دیگر

Maciej Huk is an Assistant Professor at Wroclaw University of Science and Technology, working in the Department of Applied Informatics within the Faculty of Information and Communication Technology. He has been affiliated with the university since 2007, previously working in the Department of Artificial Intelligence and Department of Security of Computer Systems before moving to his current department in 2016. His academic credentials include Ph.D. and D.Sc. degrees in Computer Science from Wroclaw University of Technology (2007, magna cum laude) and an M.Sc. Eng. degree in Computer Science (magna cum laude). Dr. Huk's research spans multiple areas of artificial intelligence and computer science, with particular emphasis on contextual systems, artificial neural networks, and machine learning. His work explores contextual neural networks, selective attention mechanisms, genetic algorithms, and ensembles of classifiers. He has developed several research software systems including H2O Snowflake for distributed training of contextual neural networks, CxNNS, GACS for context-sensitive text mining, and DYDO for educational applications. His research bridges theoretical computer science with practical applications in IoT, embedded systems, and robotics. His publication record shows consistent productivity with research spanning from 2006 to the present (2025), demonstrating evolving focus from foundational neural network research to more applied contextual systems. The publication trends indicate a strong specialization in contextual neural networks, with recent work expanding into biomedical applications (CRISPR-Cas9 analysis), educational technology (attendance systems), and energy optimization. His work consistently addresses the challenge of context representation and utilization in machine learning systems. Dr. Huk serves as an Academic Editor for PLOS ONE Journal and Evolving Systems Journal, and has reviewed over 130 papers since 2016 for journals registered in ISI Web of Science. He has been actively involved in the academic community as a program and organization committee member for numerous conferences including ACIIDS (2016-2021), ICCCI (2016-2020), and others. He has chaired special sessions on Intelligent and Contextual Systems at IEEE conferences. His professional activities extend beyond pure academia, including serving as Software Architect/Data Analyses Architect at Gigaset Communications since 2008, and organizing clinical research including the Phase 3 randomized multicenter clinical study NCT04952519 on amantadine treatment for COVID-19 patients. He is certified to organize clinical trials with human subjects through NIH NIAID Good Clinical Practices 2.0.

• Data Science
• Artificial Intelligence
• Legal Technology
• +۵ مورد دیگر

Niall McCarroll is a Research Fellow in Data Science at Ulster University's School of Computing, Engineering and Intelligent Systems, based at the Magee Campus in Derry~Londonderry. His research focuses on applying data science and artificial intelligence techniques to legal document analysis and classification, with specific expertise in data augmentation and document classification systems. McCarroll's research interests include: Legal document classification using machine learning Data augmentation techniques for legal text processing Application of shallow and deep learning to non-disclosure agreements Social networking analysis in educational contexts Artificial intelligence for judicial decision-making support His recent publications demonstrate a strong focus on the intersection of artificial intelligence and legal technology, particularly in developing methods for classifying legal documents using both rule-based and generative approaches. McCarroll's work addresses practical challenges in legal text processing, including the classification of clauses in non-disclosure agreements and the application of data augmentation techniques to improve model performance with limited training data. McCarroll is actively involved in significant research projects: "The use of Agentic AI in judicial decision-making" (2025-2026), funded by the Engineering and Physical Sciences Research Council "LFR: Legal Futures Research" (2016-2026) His research has garnered attention, with his 2013 paper on social networking in education accumulating 139 readers on Mendeley and being referenced in 1 Wikipedia page.

• Data Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Prof. habil. dr. Gintautas Dzemyda is a leading Lithuanian computer scientist, Professor and Senior Researcher at Vilnius University Institute of Data Science and Digital Technologies (VU DMSTI), and Head of the Cognitive Computing Group . He is simultaneously affiliated with the Institute of Mathematics and Informatics (MII) in Vilnius, where he has built an internationally recognized scientific school in visual data analysis. Education & Qualifications 1984 – Candidate of Technical Sciences (PhD equivalent), thesis on “Problem Structure Analysis – a Tool for More Effective Optimization”. 1997 – Habilitation Doctor of Technical Sciences, dissertation on “Isolation of Necessary Knowledge to Improve Optimization Efficiency”. 1992 – Associate Professor, Institute of Mathematics and Informatics, Vilnius. 1998 – Professor, Kaunas University of Technology. Research Interests Prof. Dzemyda’s research integrates data science, artificial intelligence, optimization, and cognitive computing . Core topics include dimensionality reduction, multidimensional data visualization, neural-network–based analytics, parallel and distributed computing, multi-criteria decision support, and advanced AI applications in medicine (ophthalmology, cardiology, oncology). His work has pioneered Lithuanian capabilities in visual analytics and large-scale data exploration. Publication Trends Across 270+ refereed works and 2 Springer monographs (2013, 2023), recent outputs (2021–2025) emphasize geometric multidimensional scaling for big-data visualization, deep learning for pancreatic-cancer detection on CT images, reinforcement learning for autonomous navigation, and fraud-detection techniques for highly imbalanced financial datasets. These contributions appear in Springer LNCS/LNNS, Informatica, Journal of Global Optimization, Engineering Applications of Artificial Intelligence , and other top venues. Awards & Recognition Lithuanian State Science Prize (2001 & 2021) Honorary Doctor of the University of Latvia (2019) Knight's Cross of the Order “For Merit to Lithuania” (2007) Doctoral Supervision & Committees He has mentored 28 doctoral graduates (15 direct, 13 through academic descendants) and currently supervises: Dalia Breskuvienė – Classifier training-set optimization Modestas Motiejauskas – Emotion recognition in photographs Victor Bulava – Machine-learning methods for cyber-incident early detection He also chairs or serves on doctoral and habilitation committees at VU, KTU, VGTU, VMU and MII. Laboratory & Projects As Head of the Cognitive Computing Group , Prof. Dzemyda coordinates several national and EU projects, including the current Lithuanian Research Council grant “Geometric Method for Multidimensional Scaling” (S-MIP-20-19, 2020-2022) and the SMART programme project “CognitiveSTATS” (2021-2023) focused on combating misinformation during pandemics. His team develops open-access tools for large-scale data visualization and contributes to the MIDAS national research-data archive.