جست‌وجوی استاد و پژوهشگر

Alexandra Nillesمشاهده پروفایل

استادیار

• Sustainable Robotics
• Robotics for Climate Change
• Planning Algorithms
• +۹ مورد دیگر

Alexandra Nilles is an Assistant Professor of Computer Science at Western Washington University since Fall 2024. Previously, she was a Postdoctoral Associate at Cornell University's Collective Embodied Intelligence (CEI) Lab from 2021-2024, where she worked under Prof. Kirstin Petersen. Her research focuses on sustainable robotics, distributed AI, and embodied intelligence for environmental applications. Education: PhD in Computer Science (2020) from University of Illinois at Urbana-Champaign, advised by Dr. Steven LaValle; BS in Engineering Physics from Colorado School of Mines. Her work develops minimalist mobile robot systems that leverage embodied intelligence and environmental interactions to reduce resource demands (battery, sensors, communication) while maintaining provable performance guarantees. Applications include under-canopy forestry monitoring, micro-scale medical agents, and carbon-capture auditing. She has published in venues such as ICRA, IROS, IJRR, and WAFR, often addressing scalability challenges in chaotic or constrained environments. Key methodologies span formalized planning algorithms, human-in-the-loop control interfaces, and inflatable soft robotics for collectives. Awards: Cyber-Physical Systems Rising Star (2023) Microsoft Future Leader in Robotics and AI (2023) Nilles received NSF support for robotic mouse toy development during her PhD and served as interim PI for Cornell's CEI Lab through the NSF Career-Life Balance Program. She actively participates in interdisciplinary workshops connecting math, art, and robotics.

Saeed Sharifمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Innovative Tele-Health
• Medical Technology
• +۹ مورد دیگر

Dr Saeed Sharif is an Associate Professor in the Computer Science department at the School of Architecture, Computing and Engineering , University of East London . He leads the Intelligent Technologies Research Group and serves as Course Leader for MSc Computer Science programs. With a PhD in artificial intelligence from Brunel University London , his work bridges Artificial Intelligence , Medical Technology , and Smart Infrastructure . His research interests span Medical Image Analysis , Intelligent Diagnosis Systems , Big Data Analysis , and IoT Security . Collaborations with NHS Trusts and global institutions focus on improving healthcare systems through machine learning and biomedical signal processing. 2018-2022: £268,000 KTP project with Innovate UK Multiple Research Internship Schemes at UEL (2017-2021) European Research Centre collaboration (2015) As a technical committee member for conferences like IEEE CIT and a journal Guest Editor , he drives academic discourse. He has supervised numerous PhD students and served on examination panels, while maintaining 53+ publications in venues including IEEE Access , Applied Soft Computing , and Computer Methods and Programs in Biomedicine .

Arun D. Kulkarniمشاهده پروفایل

استاد

• Soft Computing
• Data Mining
• Machine Learning
• +۴ مورد دیگر

Dr. Arun D. Kulkarni is a Professor of Computer Science at the University of Texas at Tyler and Lead Graduate Advisor. He has been with the university since 1986, contributing over seventy refereed papers and two authored books. His research focuses on soft computing, data mining, machine learning, computer vision, and remote sensing. He has successfully completed eight research grants and holds notable awards, including the ONR Senior Summer Faculty Fellowship (2008), President's Scholarly Achievement Award (2005-2006), and Chancellor's Council Outstanding Teaching Award (2001-2002). Education: Ph.D. in Computer Science from the Indian Institute of Technology, Bombay Post-doctoral fellowship at Virginia Tech Research Interests: Dr. Kulkarni's work spans fuzzy neural networks for classification and pattern recognition, machine learning applications in cybersecurity and remote sensing, and computer vision techniques for image analysis. His recent research emphasizes deep learning and convolutional neural networks for tasks like phishing detection and multispectral image processing. Articles Trends: His publications reflect a strong focus on integrating fuzzy logic with neural networks, advancing image classification methods, and applying machine learning to environmental monitoring and security domains. Recent efforts highlight innovations in tabular data classification and hybrid models for complex decision-making. Awards: 2008 ONR Senior Summer Faculty Fellowship 2005–2006 President's Scholarly Achievement Award 2001–2002 Chancellor's Council Outstanding Teaching Award 1997 Piper Award Nominee 1984 Fulbright Fellowship Grants & Advising: Dr. Kulkarni has secured eight completed research grants, though specific grant details are not provided. He currently advises graduate students in computer science, though no named advisees are listed in the provided text.

Burak Galip Aslanمشاهده پروفایل

مدرس

• Decision-support systems
• Expert systems
• Forecasting
• +۷ مورد دیگر

Burak Galip Aslan is a Lecturer in the Computer Engineering Department at Izmir Institute of Technology (IZTECH). He has been affiliated with the institution since 2000. His academic journey includes a B.Sc. in Electrical and Electronics Engineering from Dokuz Eylul University (2000), an M.Sc. in Computer Software from IZTECH (2003), and a Ph.D. in Computer Engineering from Ege University (2010). Education: B.Sc. Electrical and Electronics Engineering, Dokuz Eylul University (2000) M.Sc. Computer Software, Izmir Institute of Technology (2003) Ph.D. Computer Engineering, Ege University (2010) Research Interests: His work focuses on educational technology, adaptive learning systems, and computational methods in education. Key areas include decision-support systems, Bayesian modeling for academic achievement, cloud-based e-learning architectures, and learning style analysis through behavioral data. He also explores hands-on educational approaches in computer hardware instruction for secondary students. Courses Taught: Core courses include Logic Design (CENG 214), Microprocessors (CENG 384), Probability and Statistics (CENG 222), Professional Ethics in Computer Engineering (CENG 411), and Research Methodology (CENG 518). His teaching integrates laboratory work involving digital circuits, microcontroller programming, and ethical considerations in technology. Publications: His research highlights include studies on Bayesian techniques in language education, cloud deployment for e-learning systems, and learning style validation frameworks. Recent work emphasizes practical educational tools like hardware teaching modules for high school students. Laboratory Contributions: Develops hands-on experiments in digital systems design, including BCD-to-7-segment displays, combinational circuits, and microcontroller-based projects. These labs emphasize troubleshooting, ADC interfacing, and embedded systems development.

Leszek Rutkowskiمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Neural Networks
• +۳ مورد دیگر

Leszek Rutkowski is a Professor and Director of the Institute of Computational Intelligence at the Technical University of Czestochowa, Poland. He is also affiliated with the Systems Research Institute of the Polish Academy of Sciences and the AGH University of Science and Technology. His research focuses on machine learning, data mining, neural networks, fuzzy systems, and computational intelligence. He has authored over 200 publications, including influential books on neuro-fuzzy systems and computational intelligence. Rutkowski is an IEEE Fellow, a member of the Polish Academy of Sciences, and a recipient of the Doctor Honoris Causa from AGH University. Education: MSc in Technical Cybernetics (1977), Wrocław University of Science and Technology PhD in Technical Sciences (1980), Wrocław University of Science and Technology Habilitation (1986), Wrocław University of Science and Technology Research Interests: Rutkowski’s work spans data stream mining, neuro-fuzzy systems, adaptive neural networks, and pattern recognition. He has pioneered methods like probabilistic neural networks and contributed to IEEE Transactions publications. His research emphasizes real-time systems and ensemble learning techniques. Professional Activities: He founded the Polish Neural Network Society and organized international conferences on artificial intelligence. Rutkowski has served as editor-in-chief of the Journal of Artificial Intelligence and Soft Computing Research and is on the editorial boards of multiple journals. Awards: IEEE Fellow (2005) Member of the Polish Academy of Sciences (2004–2016) Doctor Honoris Causa, AGH University (2014) IEEE Transactions on Neural Networks 2005 Outstanding Paper Award Grants & Labs: His projects include foundational work on non-stationary environment modeling and collaborations with institutions like the Swiss Government and the National Science Centre. Rutkowski leads the Institute of Computational Intelligence, focusing on cutting-edge AI and soft computing techniques.

Thamara Laredoمشاهده پروفایل

دانشیار

• Environmental Chemistry
• Food Science
• Spectroscopy
• +۳ مورد دیگر

Dr. Thamara Laredo is an Associate Professor in the Department of Chemistry at Lakehead University, affiliated with the Centre of Excellence for Sustainable Mining & Exploration. She joined the university in August 2010 after completing her HBSc at Simon Bolivar University (2003) and a PhD at the University of Guelph (2009). Her research focuses on environmental chemistry, food science, spectroscopy, surface science, and electrochemistry, with particular emphasis on solvent separation technologies, phase behavior analysis, and sustainable material development. Her work addresses critical challenges in pollution remediation, including the design of amphiphilic materials for solvent demixing and the development of plant-based composites for food applications. Recent studies explore hydrogen bonding mechanisms in ternary mixtures and the role of bacterial surfactants in pollutant phase separation. She actively contributes to interdisciplinary projects at the interface of chemistry, materials science, and environmental engineering. Dr. Laredo’s publications span over two decades, with notable contributions to electrochemistry (e.g., studies on PbO2 electrodes), lipid bilayer dynamics, and food chemistry innovations. Her research often integrates experimental methods with computational modeling to advance understanding of molecular interactions in complex systems.

Henderik Properمشاهده پروفایل

استاد

• Enterprise Architecture
• Business Process Modeling
• Conceptual Modeling
• +۵ مورد دیگر

Henderik Proper is a Full Professor of Business Informatics at TU Wien's Faculty of Informatics, leading the Research Unit in Business Informatics. He holds roles as Head of Research Unit, Faculty Council Substitute Member, and Curriculum Commission Principal Member. His expertise spans enterprise architecture, digital transformation, and conceptual modeling. Education: Holds a PhD and has held academic positions at TU Wien and previously at the University of Nijmegen. Research focuses on enterprise modeling, AI-driven systems, and governance frameworks. He leads projects like INTEND (2024–2026) and MFP 4.2 (2022–2023), emphasizing digital twin integration and low-code applications. Publications: Over 50 peer-reviewed articles in top venues like Springer and IEEE, focusing on enterprise engineering, process mining, and AI applications. His work bridges theory and practice, addressing challenges in digital transformation and model-driven architectures. Grants: Active in EU-funded projects (INTEND) and national initiatives (MFP 4.2). Advises on doctoral theses and teaches courses like 'Enterprise & Process Engineering' and 'Ontology-Driven Conceptual Modeling'. Labs/Teams: Leads research groups on enterprise architecture and digital systems within TU Wien's Informatics department, collaborating internationally on standards like ArchiMate extensions.

Michael A. Matthewsمشاهده پروفایل

استاد

• Thermodynamics
• Biomedical Engineering
• Hydrogen Storage
• +۴ مورد دیگر

Michael A. Matthews serves as Professor in Biomedical Engineering and Chemical Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing, concurrently holding leadership positions as Vice Dean of Engineering & Computing and Associate Vice President for Research and National Laboratory Relations since 2011 and 2021 respectively. His academic credentials include: B.S. in Chemical Engineering, Texas A&M University, 1979 M.S. in Chemical Engineering, Texas A&M University, 1984 Ph.D. in Chemical Engineering, Texas A&M University, 1986 Matthews' research program centers on thermodynamics with three interconnected pillars: hydrogen storage in chemical hydrides, biomedical applications of supercritical carbon dioxide (particularly for tissue decellularization and sterilization), and green chemistry innovations. His work integrates fundamental chemical engineering principles with practical solutions for biomedical challenges and sustainable processes. Analysis of his recent publications reveals a dual trajectory: cutting-edge biomedical engineering using supercritical CO 2 for scaffold processing and sterilization (2021-2023), alongside transformative educational research focusing on professional development curricula, critical thinking pedagogy, and diversity initiatives in engineering pathways (2018-2020). This synergy between technical innovation and educational reform defines his scholarly identity. His distinguished recognition includes: Fellow of the American Chemical Society (2011) Fellow of the American Institute of Chemical Engineers (2014) Uof SC Mungo Graduate Teaching Award (2020) Joseph Gibbons Teaching Award (2018-2020) ASEE William Corcoran Award for Chemical Engineering Education (2007) Matthews has secured over $10 million in research funding while developing specialized professional development courses for graduate students and biomedical engineering ethics curricula. His advising philosophy emphasizes critical thinking and publishing ethics, reflected in courses like Graduate Student as Scholar and Engineering Research Methods . Current administrative responsibilities encompass research strategy for the college and university-wide research initiatives. Though specific laboratory facilities aren't detailed, his supercritical CO 2 research suggests dedicated high-pressure processing equipment, with applications spanning tissue engineering scaffolds to food safety systems.

Lukasz Laniewski-Wollkمشاهده پروفایل

پژوهشگر ارشد

• Computational Fluid Dynamics
• Lattice Boltzmann Methods
• Aerodynamic Optimization
• +۳ مورد دیگر

Dr. Lukasz Laniewski-Wollk serves as an Honorary Fellow at the School of Mechanical and Mining Engineering, The University of Queensland, where he conducts advanced research in computational fluid dynamics and optimization methodologies. His work bridges theoretical development with practical applications in energy systems and resource extraction. His primary research domains encompass: Computational Fluid Dynamics Lattice Boltzmann Methods Aerodynamic Optimization Multiphase Flow Fracture Mechanics Particle Transport Analysis of his 2020-2025 publications reveals a concentrated effort on developing high-fidelity computational frameworks for complex fluid phenomena, particularly in multiphase systems and fractured media. His methodologies demonstrate strong interdisciplinary integration between fluid mechanics, materials science, and petroleum engineering, with significant contributions to lattice Boltzmann variants for non-Newtonian fluids and fracture permeability quantification. Collaborative patterns indicate sustained partnerships with UQ's computational mechanics group and international energy research consortia.

Giovanna Castellanoمشاهده پروفایل

دانشیار

• Computational Intelligence
• Computer Vision
• Deep Learning
• +۷ مورد دیگر

Giovanna Castellano is a Full Professor at the University of Bari Aldo Moro, Italy, where she serves in the Department of Computer Science. She coordinates the Computational Intelligence Laboratory (CILab) and has been active in academia since at least May 2015. Her extensive publication record with 354 publications demonstrates her significant contributions to the fields of Computational Intelligence, Computer Vision, and Explainable AI. Dr. Castellano earned her PhD in Computer Science from the University of Bari Aldo Moro between September 1988 and March 1993. Her academic journey has led her to become a prominent researcher in artificial intelligence applications across various domains including healthcare, education, and digital art analysis. Her research interests span a wide range of computational intelligence topics, with current focus on Computer Vision, Deep Learning, Explainable AI (XAI), and Fuzzy Systems. Dr. Castellano's work demonstrates a strong interdisciplinary approach, applying AI techniques to solve real-world problems in healthcare diagnostics, precision agriculture, educational analytics, and cultural heritage preservation. Her research consistently emphasizes model interpretability, recognizing the importance of transparent AI systems, particularly in critical domains like healthcare where trust in AI decisions is paramount. Analysis of her recent publications reveals a consistent trajectory toward more interpretable and explainable AI systems across multiple application domains. Her work spans medical imaging analysis, stress prediction in educational contexts, art analysis, and precision agriculture. A notable trend is the integration of large language models with computer vision techniques to enhance both performance and explainability of AI systems, particularly in medical and artistic contexts. Excellent Presentation Award at 1st International Conference on Soft Computing and Intelligent Systems 2002 Dr. Castellano leads the Computational Intelligence Laboratory (CILab) at the University of Bari, where she supervises research projects and collaborates with numerous researchers including Gabriella Casalino, Gennaro Vessio, and Gianluca Zaza. Her research group has secured various grants for projects in healthcare AI, educational data mining, and digital art analysis. The CILab research team works on diverse projects including medical diagnostics using AI, analysis of digital art collections, educational data mining, and precision agriculture applications. Their work often involves collaboration with healthcare institutions, cultural organizations, and agricultural technology companies, demonstrating the practical impact of their theoretical research.

• Online learning
• Data streams
• Resource-awareness
• +۶ مورد دیگر

Kirsten Köbschall is a Researcher at the Institute of Computer Science, Johannes Gutenberg University Mainz, Germany. She also served as a Guest Research Associate at the Lamarr Institute, TU Dortmund (09/2024-12/2024). Her academic journey includes a B.Sc. (2015–2019) and M.Sc. (2019–2021) in Computer Science from Mainz University, with a semester abroad at Universitat de València (2019). Research Focus: Online learning and resource-aware AI systems Transparency and interpretability of machine learning models Stochastic methods in data stream processing Deep neural networks and their applications Teaching includes supervising Data Mining/Machine Learning seminars, workshops on AI ethics, and tutoring courses in computability, complexity theory, and formal languages. Recent Work: Her 2025 publication introduces the Soft Hoeffding Tree model, advancing transparent and differentiable approaches for data stream analysis. Presentations include Discovery Science 2024 and the Mainz and Friends AI Conference (MAInC 2023). Contributions to open-source projects like CapyMOA reflect her commitment to advancing stream mining tools.

Michael Berryمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Text Mining
• +۴ مورد دیگر

Michael Berry is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. He holds a PhD in Computer Science from the University of Illinois at Urbana-Champaign, an MS in Applied Mathematics from North Carolina State University, and a BS in Mathematics from the University of Georgia. His research focuses on data science, machine learning, text mining, nonnegative matrix factorization, parallel computing, and their applications in biomedical and environmental domains. Notable contributions include work on tensor decomposition for big data analysis, algorithms for text mining, and computational tools like PolyLens and CodeAssessor. Berry's publications emphasize interdisciplinary applications, such as using nonnegative tensor factorization for biomedical literature analysis and developing GPU-accelerated methods for traffic flow analysis. His work spans conferences like the International Conference on Soft Computing in Data Science (SCDS) and journals in computational science. He has contributed to software tools like FutureLens for text visualization and SHEPPACK for interpolation algorithms. His research also addresses environmental challenges via parallel ecosystem modeling and spatial control problems. No scientific awards or grants are explicitly mentioned in the provided text.

Sheela Ramannaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Soft Computing
• +۷ مورد دیگر

Dr. Sheela Ramanna is a Professor and Chair of the Applied Computer Science Graduate Program at the University of Winnipeg , with an adjunct appointment in the Department of Computer Science at the University of Manitoba. She holds a Ph.D. in Computer Science from Kansas State University (2003), an M.S. in Computer Science (1998), and a B.S. in Electrical Engineering (1996) from Osmania University , India. Adjunct Professor, University of Manitoba Professor & Chair, University of Winnipeg Graduate Program Her research focuses on Artificial Intelligence , Machine Learning , and Soft Computing (Rough Sets, Fuzzy-Rough Sets, Tolerance-based Methods) with applications in Multimodal Information Processing , Natural Language Processing , and Topological Data Analysis . She has developed novel tolerance near-set algorithms for sentiment classification, named entity recognition, and community detection in social networks. Recent publications include 2025 work on speech emotion recognition, 2024 studies on diabetic retinopathy detection and plant species recognition, and 2023 research on text summarization and NLP applications. Her work spans 15+ peer-reviewed articles in journals like Scientific Reports , Information Fusion , and Frontiers in Artificial Intelligence . Scientific Awards include the UW Merit Award for Exceptional Performance (multiple years), MITACS Globalink Research Intern (2024), and 3MT People's Choice Award (2018). She has served as Editor for EAAI Journal , Associate Editor for KES Journal , and Organizing Co-Chair for ISCMI 2025 . She supervises 22+ graduate students and postdocs, including Vrushang Patel (President's Scholarship), Habib Ben Abdallah (MITACS Fellow), and Anil Rahate (collaborative PhD with SIT Pune). Her NSERC-funded projects include precipitation forecasting with WeatherLogics Inc., road condition classification, and LULC mapping using satellite imagery.

Ebroul Izquierdoمشاهده پروفایل

استاد

• Multimedia Signal Processing
• Machine Vision
• Image Processing
• +۷ مورد دیگر

Professor Ebroul Izquierdo holds a position at Queen Mary University of London's School of Electronic Engineering and Computer Science. His academic background includes a BSc, MSc, and PhD. He specializes in Multimedia Signal Processing, Machine Vision, and Mathematical models in image processing. His research focuses on video coding, computer vision, AI applications in agriculture, and security systems. He leads major grants such as the £305k Smart Farm project and the £370k SHIFT initiative. His work includes innovations in camera calibration for sports videos, self-supervised learning models, and federated learning architectures. He is affiliated with the Centre for Multimodal AI and collaborates with organizations like BBC and Innovate UK. His email is ebroul.izquierdo@qmul.ac.uk . Research Grants : Smart Farm and Agri-environmental Big Data Space (£305,116 EPSRC) SHIFT Project (£370,577 EPSRC) KTP: Machine Learning for Sport Broadcast Graphics (£214,061 Innovate UK) Labs & Teams : Centre for Multimodal AI, collaborating with BBC and EU Horizon 2020 initiatives.

Fabio Caraffiniمشاهده پروفایل

دانشیار

• Evolutionary Computing
• Heuristic & Hyperheuristic Optimization
• Algorithm Behaviour
• +۵ مورد دیگر

Dr. Fabio Caraffini is an Associate Professor in Computer Science at Swansea University's School of Mathematics and Computer Science. He holds dual PhDs in Mathematical Information Technology (University of Jyväskylä, 2016) and Computer Science (De Montfort University, 2014), along with BSc and MSc degrees in Engineering from the University of Perugia. His research focuses on computational intelligence, particularly heuristic optimization methods like evolutionary algorithms and differential evolution. He also holds an honorary position as Senior Research Fellow at De Montfort University (2022–2024). Education History: BSc in Electronics Engineering (University of Perugia, 2008) MSc in Telecommunications Engineering (University of Perugia, 2011) PhD in Mathematical Information Technology (University of Jyväskylä, 2016) PhD in Computer Science (De Montfort University, 2014) Research Interests: Dr. Caraffini's work bridges theoretical optimization and practical applications. Key areas include evolutionary computing, structural bias analysis in algorithms, and interdisciplinary AI applications such as medical imaging, climate risk modeling, and robotics. His SOS Platform and BIAS toolbox are notable contributions to algorithm benchmarking and bias detection. Recent projects include AI-driven solutions for crop mapping, medical record analysis, and rail scheduling optimization. Publications & Trends: His 150+ publications span journals like Information Sciences , IEEE Transactions , and Applied Soft Computing . Themes include algorithmic robustness, constraint handling, and real-world optimization challenges. Notable works address differential evolution improvements, climate transition risk prediction, and medical decision support systems. Awards & Grants: Fellow of the Higher Education Academy (FHEA) Recipient of multiple research grants for projects in optimization and AI applications Advising & Collaboration: Actively supervises PhD students in AI-driven optimization and interdisciplinary applications. Collaborates with institutions globally on topics like microgrid energy management and pandemic prediction through self-organizing maps. Labs & Teams: Engaged in Swansea's Computational Foundry and the Morgan Advanced Studies Institute (MASI), contributing to cross-disciplinary research initiatives in AI and computational science.