جستجوی استاد راهنمای دانشگاهی

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.

Konstantinos Vergidisمشاهده پروفایل

استاد

• Business Process Management
• Enterprise Architecture
• BPMN Modeling
• +۷ مورد دیگر

Konstantinos Vergidis is a Professor at the Department of Applied Informatics, School of Information Sciences, University of Macedonia. With a PhD in Business Process Optimisation from Cranfield University (2008), he has established himself as a leading expert in business process management, enterprise architecture, and process modeling. His academic career spans over a decade at the University of Macedonia, where he has held various administrative roles including Director of the Doctoral Studies Coordination Committee and member of the University's Research Committee. PhD in Business Process Optimisation (2008), Cranfield University MSc in IT for Product Realisation (2005), Cranfield University Degree in Applied Informatics (2003), University of Macedonia Professor Vergidis' research focuses on Business Process Intelligence (BPI), with particular expertise in BPMN modeling, process optimization, and decision modeling with DMN. His work bridges theoretical frameworks with practical applications in both public and private sectors, examining process quality metrics, resilience, and the integration of emerging technologies like low-code platforms and AI. He has pioneered research on process modeling in the public sector and the impact of Large Language Models on BPMN-based process modeling. His publication record includes 21 scientific journal articles, 22 conference papers, and 8 books, demonstrating consistent scholarly output across multiple venues. Recent work shows a clear trajectory toward integrating AI technologies with traditional business process management, particularly examining how LLMs can transform process modeling practices and exploring low-code development platforms for process-driven application development. Teaching Excellence Award (2022-2023) Research Award 2022 (14th cycle) Research Award 2021 (13th cycle) Teaching Excellence Award (2020-2021) Thatcher Bros Prize (2008/09) Professor Vergidis has secured multiple research grants, including projects with Expertise France/European Commission on digital public services and with the National Research Foundation on process modeling for public administration. He leads the Business Process Intelligence research group and has coordinated significant projects including MODA (Multi-Omics Data Analysis) and Science Data and Analytics Platform. His administrative contributions include directing doctoral studies and serving on numerous university committees related to research, curriculum development, and digital transformation initiatives. His work with the National Process Registry (mitos.gov.gr) demonstrates his commitment to applying academic research to real-world public sector challenges, developing tools and methodologies that enhance process transparency and service delivery for Greek citizens.

Stefano Cirilloمشاهده پروفایل

استادیار

• Data Mining
• Artificial Intelligence
• Data Privacy
• +۴ مورد دیگر

Stefano Cirillo serves as an Assistant Professor at the Department of Computer Science, University of Salerno, Italy. His academic career spans roles including Research Fellow (2022-2023) and Adjunct Professor for Databases courses. He maintains significant editorial responsibilities as Associate Editor for Journal of Visual Language and Computing and Multimedia Tools and Applications , while serving on program committees for major conferences including EDBT/ICDT 2024. His research focuses on data-intensive domains with emphasis on Data Profiling, Mining, and Privacy . Core interests include Social Network analysis, AI-driven database optimization, and anomaly detection in data streams. His work bridges theoretical algorithms with practical applications in e-procurement, cybersecurity, and healthcare systems, particularly through projects like Profiling Data Streams for Anomaly Detection and Security and Rights in the CyberSpace (SERICS) . Analysis of his recent publications reveals strong trends in applied AI for societal challenges – including pandemic mental health detection, perinatal depression prediction, and smart city security. His work consistently integrates novel neural architectures (YOLO variants, LSTM hybrids, Transformer networks) with domain-specific constraints, demonstrating expertise in both algorithmic innovation and real-world implementation across transportation, healthcare, and public administration sectors. Professional service highlights include Program Co-Chair for International DMS Conferences (2021-2022), Local Arrangements Chair for EDBT/ICDT 2024, and editorial board membership for journals including Data Science and Management . His research has been supported through PON-funded PhD studies and the SERICS project, with active collaborations spanning Hasso Plattner Institute (Germany) and Italian research centers like CeRICT.

• Embedded Systems
• Wearable Computing
• Biomedical Signal Processing
• +۲ مورد دیگر

Oliver Amft serves as Professor of Intelligent Embedded Systems at the University of Freiburg and executive board member of the Hahn-Schickard research center, focusing on the convergence of embedded systems and healthcare applications. His academic foundation includes a Dipl.-Ing. (M.Sc.) in Electrical Engineering and Information Technology from Chemnitz Technical University (1999) and a Dr. sc. ETH (Ph.D.) from ETH Zurich (2008). Professor Amft's research pioneers intelligent wearable systems for continuous health monitoring, with core expertise in biomedical signal processing and AI-driven sensor analytics . His work addresses critical healthcare challenges through innovations in gastrointestinal monitoring via bowel sound analysis, dietary assessment using smart eyewear, and rehabilitation-focused motion tracking with inertial sensors. Methodologically, he specializes in unsupervised machine learning for real-time data stream processing in resource-constrained embedded environments. Analysis of his 2021-2023 publications reveals a strategic emphasis on edge AI for medical diagnostics , particularly in developing context-aware wearable systems that operate independently of cloud infrastructure. Key thematic clusters include gastrointestinal health monitoring (bowel sound analysis), behavioral nutrition science (eating detection), and biomechanical rehabilitation (joint angle estimation), consistently leveraging multi-sensor fusion and lightweight deep learning models. As an active academic contributor, Amft serves as Associate Editor for Frontiers in Computer Science specializing in Mobile and Ubiquitous Computing, shaping discourse in wearable/IoT systems design. His research ecosystem integrates the University of Freiburg's academic resources with Hahn-Schickard's engineering capabilities, facilitating translational development from laboratory prototypes to clinically viable embedded health technologies through cross-institutional collaboration.

• Galactic Astronomy
• Stellar Populations
• Chemical Abundances
• +۳ مورد دیگر

Puragra Guhathakurta is a Professor of Astronomy and Astrophysics at the University of California Santa Cruz, where he has held a continuous faculty position since November 1994. His research focuses on galactic structure and evolution through detailed studies of stellar populations in the Milky Way, Andromeda (M31), and Triangulum (M33) galaxies using multi-wavelength surveys. His educational background includes: PhD and MS in Astrophysical Sciences, Princeton University (1985-1989) Physics, University of Calcutta, Science College, Rajabazar (1984-1985) BSc in Physics, Saint Xavier's College, Kolkata (1980-1983) La Martiniere for Boys, Kolkata (1970-1980) GuhaThakurta's primary research interests encompass: Galactic and Extragalactic Astronomy Stellar Populations and Chemical Abundances Galactic Structure and Kinematics Chemical Evolution of Galaxies Stellar Halos and Tidal Streams Dwarf Galaxy Dynamics Analysis of his 15 most recent publications (2023-2025) reveals a consistent focus on chemical abundances and kinematics in nearby galaxies. His work integrates Hubble Space Telescope imaging, ground-based spectroscopy, and Gaia astrometry to investigate weak CN absorption features in evolved stars, stellar halo structures, and the evolutionary histories of dwarf galaxies. A significant theme involves using resolved stellar populations to constrain galaxy formation models in the local universe. His major professional recognitions include: Hubble Fellowship (Princeton University, 1992-1994) Three-year Membership at the Institute for Advanced Study (1989-1992) While specific details of his advising and grant history are not documented in the source material, his extensive publication record demonstrates sustained research leadership. He is a principal investigator or key collaborator in major observational programs including SPLASH, PHATTER, HALO7D, and TREX, which have produced foundational datasets for studies of M31 and M33.

• Data and Knowledge Engineering
• Intelligent Systems
• Business Intelligence
• +۸ مورد دیگر

Professor Panagiotis Chountas is a distinguished academic at the University of Westminster with expertise in Data and Knowledge Engineering and Intelligent Systems. His research focuses on applying advanced computational methods to business and public organization challenges, with particular emphasis on Business Intelligence, Big Data Analytics, Health Informatics, and Large Databases. His research interests span multiple cutting-edge domains including Intuitionistic Fuzzy Sets, Fractional Brownian Motion, Concept Drift Analysis, and Predictive Big Data Quality. Professor Chountas has developed innovative approaches for volatility clustering in financial markets, time series prediction, and business process data quality assessment. His recent publications reveal a strong trend toward integrating fuzzy logic with stochastic processes, particularly in financial applications and concept drift analysis in machine learning. The research shows increasing sophistication in modeling uncertainty and complexity in real-world data streams. Royal Society research support Technology Strategy Board research support Professor Chountas actively supervises doctoral students in areas including concept drift complexity, time series prediction, and predictive big data quality. His research group, the Distributed and Intelligent Systems Research Group, focuses on developing theoretical foundations with practical business applications to maximize profitability and improve process management efficiency.

Cui Yuمشاهده پروفایل

دانشیار

• Database Management
• Information Retrieval
• High-Dimensional Indexing
• +۲ مورد دیگر

Dr. Cui Yu serves as an Associate Professor in the Department of Computer Science and Software Engineering at Monmouth University, where she maintains an active research program focused on database systems and information retrieval. With over two decades of scholarly contributions, her work bridges theoretical foundations and practical applications in data management. Her academic foundation includes a Ph.D. from the National University of Singapore, establishing her expertise in advanced database methodologies. This background enabled her pioneering work in high-dimensional indexing techniques that address the 'curse of dimensionality' in modern data systems. Dr. Yu's research program centers on innovative solutions for database indexing, query optimization, and information retrieval. She developed the influential iDistance method and FB+-tree indexing structures that transform high-dimensional data into manageable single-dimensional spaces. Her recent work demonstrates significant evolution from core database theory toward applied domains including privacy-preserving social networks, smart city infrastructure (notably CNN-based parking systems), and mobile applications for skill development. This trajectory reflects her ability to adapt database principles to emerging technological challenges while maintaining rigorous theoretical grounding. Analysis of her publication history reveals consistent leadership in database indexing innovation, with foundational work in the early 2000s maturing into diverse applications by the late 2010s. Her research maintains strong connections between theoretical indexing advances and real-world implementations across domains including multimedia retrieval, geospatial systems, and security applications. Her scientific recognition includes two significant U.S. patents: Apparatus and method for performing transformation-based indexing of high-dimensional data (US patent: 7117217, 2006) Transformation-based method for indexing high-dimensional data for nearest neighbor queries (US patent: 6,834,278, 2004) As an educator, Dr. Yu actively mentors students through research projects and application development initiatives. She teaches core curriculum courses including Computer Architecture, Database Systems at multiple levels (undergraduate CS 432, graduate CS 533), and Senior Capstone Projects. Her teaching load demonstrates substantial commitment to both foundational computer science education and advanced specialized topics, with consistent course offerings through 2025. She maintains active professional engagement through IEEE membership and continuous scholarly output.

• Distributed Database Design
• Query Optimization
• Evolutionary Algorithms
• +۸ مورد دیگر

Prof. Ahmet Cosar is a faculty member in the Department of Computer Engineering at the Middle East Technical University (METU), Ankara, Turkey. He holds a PhD in Computer Science from the University of Minnesota (1996), an MS in Computer Engineering from Bilkent University (1988), and a BS in Computer Engineering from METU (1986). His research focuses on distributed database design, query optimization, evolutionary algorithms, computer networks, and cloud computing. He leads the Intelligent Data Analysis Group (IDAG) and teaches courses such as CENG 240, CMPE 275, and CENG 280. Education: PhD, Computer Science, University of Minnesota, 1996 MS, Computer Engineering, Bilkent University, 1988 BS, Computer Engineering, METU, 1986 Research Interests: Query optimization in distributed databases Evolutionary algorithms (e.g., genetic algorithms, particle swarm optimization) Cloud computing and data warehouse design Machine learning applications in cybersecurity Wireless sensor networks and data fusion Publications: Over 50 peer-reviewed articles in journals like Computers & Industrial Engineering and Neurocomputing , focusing on optimization algorithms, cloud resource allocation, and machine learning techniques. Recent work includes island-parallel metaheuristics for graph coloring and reinforcement learning for adaptive interventions. Advising & Grants: Supervised over 30 graduate students (PhD and MS) in areas like evolutionary algorithms, cloud databases, and sensor networks. Active in research grants related to distributed systems and big data. Labs/Teams: Leads the Intelligent Data Analysis Group (IDAG), collaborating on projects in optimization, machine learning, and cloud computing.

• Sustainable Orchestration in Edge-Cloud Environments
• Multi-Cloud Workload Distribution
• Large Language Models (LLMs) Deployment
• +۳ مورد دیگر

Dr. Hadi Tabatabaee Malazi is an Assistant Professor at the University of College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). He holds an Orcid identifier (0000-0002-2960-6896) and has extensive academic experience across institutions like Maynooth University, Shahid Beheshti University, and Trinity College Dublin. His research focuses on sustainable computing, edge-cloud continuum orchestration, AI/ML deployment (e.g., LLMs), and energy efficiency. He is a Senior Member of IEEE, serves as an Associate Editor for IEEE Access, and chairs the COST Action CA22151 (CYPHER) on decarbonizing energy-intensive industries. Education: PhD (2012): University of Isfahan (Computer Engineering) MSc (prior to 2012): University of Isfahan Research Visit (2010-2011): Delft University of Technology Research Interests: Sustainable edge-cloud systems, AI-driven workload optimization, carbon-aware resource management, distributed LLMs, and IoT interoperability. Key contributions include over 20 journal articles in IEEE, Elsevier, and Springer venues, with topics ranging from vehicular network security to smart city event processing. Professional Activities: Committee member of COST Action CA22151, IEEE Access Associate Editor, and Technical Program Committee member for IEEE conferences. Authored certifications on research leadership and research integrity from Epigeum. Labs/Teams: SOC² Lab (UCD), leading projects on edge-cloud sustainability and AI application deployment.

Donald Wunsch IIمشاهده پروفایل

استاد

• Neural Networks
• Fuzzy Systems
• Evolutionary Computing
• +۷ مورد دیگر

Donald Wunsch II is the Mary K. Finley Missouri Distinguished Professor and Director of the Kummer Institute Center for Artificial Intelligence and Autonomous Systems at Missouri S&T. He also directs the Applied Computational Intelligence Laboratory. His academic career includes roles at Texas Tech University and industry positions at Boeing, Rockwell International, and others. He holds a Ph.D. in Electrical Engineering from the University of Washington, an Executive MBA from Washington University in St. Louis, and additional certifications in Nonprofit Management. Research interests focus on neural networks, fuzzy systems, evolutionary computing, and applications in AI, robotics, and bioinformatics. Notable contributions include work on unsupervised learning, reinforcement learning architectures, and adaptive resonance theory. Awards include the IEEE Neural Networks Pioneer Award (2023), INNS Gabor Award (2015), and Ada Lovelace Service Award (2019). He has advised numerous Ph.D. students across Computer Engineering, Electrical Engineering, and related fields. Leadership roles include service on the NSF’s Program Director panel, St. Patrick’s School Board, and multiple academic and professional society boards. His labs and initiatives emphasize interdisciplinary AI integration and policy development.

• Data Integration
• Big Data
• Entity Resolution
• +۷ مورد دیگر

Sonia Bergamaschi is a Professor in the Department of Engineering 'Enzo Ferrari' at the University of Modena and Reggio Emilia. She holds dual roles as Contract Professor and Senior Professor, focusing on advanced data management, big data integration, and AI applications in cultural heritage. Her work emphasizes entity resolution, data preparation, and scalable systems for diverse domains such as energy communities and Islamic studies. Her research spans interdisciplinary areas including data science, machine learning, and database systems. Recent publications highlight contributions to entity resolution methodologies, sustainable data practices, and AI-driven analysis of religious texts. She collaborates with international teams on projects like the Digital Maktaba initiative, which digitizes non-Latin cultural heritage materials. Key research themes include: Big Data Platforms for Energy Communities AI in Islamic Studies and Digital Libraries Schema-Aware Entity Resolution Efficient Stream Processing Techniques Data Privacy and Auditable Tokenization Her work bridges theoretical advancements with practical applications in industry and cultural preservation.

Conrad Tuckerمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Robotics
• +۶ مورد دیگر

Conrad Tucker is a Professor of Mechanical Engineering at Carnegie Mellon University (CMU) and Director of CMU-Africa, with courtesy appointments in Machine Learning, Robotics, and Biomedical Engineering. He serves as Associate Dean for International Affairs (Africa) and holds an endowed Trustee Professorship. His research focuses on AI-driven systems design, data mining, cybersecurity, and education innovation, with grants from NSF, DARPA, and the Gates Foundation. Education: Ph.D. in Industrial & Systems Engineering, University of Illinois, 2011 MBA in Business Administration, University of Illinois, 2009 M.S. in Industrial & Systems Engineering, University of Illinois, 2007 B.S. in Mechanical Engineering, Rose-Hulman Institute of Technology, 2004 Research Interests: Dr. Tucker’s work bridges AI, engineering design, and societal impact. Key areas include: AI/ML for security and privacy Optimization of additive manufacturing AI applications in healthcare and education Data-driven product innovation His lab, AiPEX, develops ML methods for personalized design solutions and addresses challenges like deepfake detection in education. Grants & Awards: Endowed Trustee Professorship (CMU, 2023) NAE Frontiers of Engineering Education Advisory Committee (2016–present) Over $10M in grants from NSF, AFOSR, DARPA, and others Labs & Collaborations: Leads the AiPEX Lab, advancing AI in product design and cybersecurity. Collaborates with institutions globally, including the African Engineering and Technology Network (Afretec), to promote STEM education and digital infrastructure in Africa.

Abdelmounaam Rezguiمشاهده پروفایل

استادیار

• Big Data Management
• Cloud Computing
• Data Analytics
• +۲ مورد دیگر

Abdelmounaam Rezgui is an Assistant Professor in the Department of Computer Science and Engineering at New Mexico Tech, where he directs the Cloud Computing and Big Data (C2BD) Lab. He holds a Ph.D. from Virginia Tech and specializes in developing efficient cloud computing solutions for big data applications. His research focuses on cloud computing optimization, big data management, and distributed systems design. Key areas include: Efficiency improvements for cloud platforms supporting big data workloads Fault-tolerant architectures for distributed systems Social computing and network analysis techniques Service-oriented computing paradigms Recent publications demonstrate strong emphasis on cloud infrastructure optimization, distributed computing frameworks, and AI-assisted system maintenance. Research consistently addresses practical challenges in large-scale data processing across federated environments. Awards and recognitions include: Best student paper award at IEEE ISCC 2016 Best paper award at ICICS 2016 He has advised multiple graduate students including 9 PhD candidates and 2 MS students, with research often supported through cloud computing infrastructure grants. As director of the C2BD Lab, he leads projects investigating volunteered federated clouds, hardware failure prediction systems, and efficient data processing frameworks. The lab collaborates with industry partners on practical cloud computing implementations.

Andi Wangمشاهده پروفایل

استادیار

• Data Science
• Machine Learning
• Manufacturing Systems
• +۳ مورد دیگر

Andi Wang is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison, joining in August 2024. Previously, he held an Assistant Professor position at Arizona State University's School of Manufacturing Systems and Networks. His research focuses on applying data science and machine learning to address challenges in engineering systems, including predictive analytics, monitoring, and design optimization. He holds a PhD (2021) from Georgia Institute of Technology, a PhD (2016) from Hong Kong University of Science and Technology, and a BS (2012) from Peking University. Research interests include multi-sensor data fusion for manufacturing process prediction, surrogate modeling for multi-stage engineering systems, and distributed/federated learning applications. His work spans domains like nuclear engineering, additive manufacturing, and semiconductor production. Awards: Multiple IISE and INFORMS awards, including the 2023 IISE Transactions Best Application Paper Award and the 2020 QCRE Division Best Student Paper Award finalist. Advising & Grants: Actively involved in research grants and advising students, though no formal advisee list is currently available. Labs/Teams: Engaged in collaborative projects with industry partners, focusing on industrial systems improvement through data-driven solutions.

Madhu Chettyمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۷ مورد دیگر

Professor Madhu Chetty is a distinguished academic in Information Technology at Federation University Australia's Institute of Innovation, Science and Sustainability (IISS). He also serves as the Director of AI and ML Stream within the Health Innovation and Transformation Centre (HITC). With over 35 years of tertiary teaching, research, and leadership experience in Australia and overseas, Professor Chetty has held academic positions at the University of Melbourne, Monash University, and the National Institute of Technology, India. His notable visiting appointments include Indian Institute of Technology Bombay, University of Warwick, Jawaharlal Nehru University, and Delft University of Technology. Amity University, India conferred on him a 'Citation and Lifetime Professorship'. Professor Chetty's research focuses on applying Artificial Intelligence (AI), Machine Learning (ML), Large Language Models, and Blockchain to problems in bioinformatics, health, and energy trading. His interdisciplinary work contributes to FedUni's strategic research centers in Health and IT. Key research areas include modeling genetic networks for cardiovascular and eye disease research, mental health applications using AI techniques for analyzing biopsychosocial data, drug repurposing with IBM collaboration, and blockchain algorithms for energy trading funded by the Qatar government. His publication record shows a consistent focus on computational approaches to biological problems, with recent work emphasizing genetic network modeling, mental health applications of AI, and blockchain technology. The research demonstrates a progression from foundational work in protein structure prediction to current applications of AI in healthcare and energy systems, with a strong emphasis on translating computational methods to real-world problems. 2021 Overall Award for Excellence in Graduate Research Supervision 2024 Dean's award for excellence in PhD thesis (awarded to one of his students) 2021 Vice Chancellor's Certificate of Commendation for Excellence in Community Engagement and Impact Professor Chetty has supervised over 22 PhD students to completion and currently supervises 5 PhD students across diverse topics including dementia prediction, drug repurposing, cancer classification, and mental health. His leadership extends to editorial roles for journals, conference organization, and development of publicly available software tools like GRAMP and GlobalMIT for genetic network analysis. He has secured substantial research funding totaling over $1.1 million as lead investigator, including projects funded by NHMRC, Qatar Research, Development and Innovation, and industry partners. As an academic leader, he has served as Deputy Head of School, member of the School Leadership Team, and HDR Coordinator. His professional service includes roles as General Chair of IEEE International Conference and Vice Chair of the IEEE Victorian/Tasmanian Section, demonstrating significant contribution to the broader academic community.