جستجوی استاد راهنمای دانشگاهی

Jinqiu Yangمشاهده پروفایل

دانشیار

• Software Engineering
• Automated Program Repair
• Software Testing
• +۴ مورد دیگر

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on software reliability, automated program repair, software testing, and quality assurance of machine learning systems, particularly in autonomous vehicles. He holds a PhD and MASc from the University of Waterloo and a B.Eng. from Nanjing University. Yang has been a tenure-track faculty member at Concordia since 2018, following research roles at IBM Watson and IBM CAS. Research Interests: Automated Program Repair Software Testing Machine Learning Systems Text Analytics of Software Artifacts Mining Software Repositories Autonomous Systems Quality Assurance Key Contributions: His recent work includes detecting concept drifts in ML systems (ICSE-25) and investigating social bias in LLM-generated code (AAAI-25). He leads the O-RISA Lab and has authored over 50 peer-reviewed publications, including distinguished papers at MSR-2018. Yang currently holds grants such as the NOVA FRQNT-NSERC Program (2024-2027) and the NSERC Discovery Grant (2019-2025). Service & Awards: Editorial Board Member of the Empirical Software Engineering Journal (EMSE) and active PC member in top venues like ICSE and FSE. Recipient of the IBM CAS Fellowship and ACM SIGSOFT Distinguished Paper Award. Teaching & Students: He mentors students in Master's and PhD programs, with funding available. His lab focuses on cutting-edge topics like secure code generation and autonomous vehicle reliability.

Wendy D. Fisherمشاهده پروفایل

استاد آموزشی

• Machine Learning
• Geophysical Data Analysis
• Structural Health Monitoring
• +۳ مورد دیگر

Wendy D. Fisher is a Teaching Professor and Director of CS Professional Programs at the Department of Computer Science , Colorado School of Mines. She holds a B.S. in Computer Science from Wright State University (1996), and M.S. and Ph.D. degrees from Colorado School of Mines (2014 and 2017, respectively). Her research focuses on applying Machine Learning to geophysical data for anomaly detection in earth dams and levees, with applications in structural health monitoring and infrastructure safety. She teaches courses such as Advanced Data Science, Machine Learning, and Python Computing. Dr. Fisher has industry experience at Ball Aerospace, where she worked on NASA-sponsored flight software projects. Her research combines interdisciplinary approaches, including geophysics and computer science, to address challenges in environmental and civil engineering. She is also passionate about innovative teaching methods, as evidenced by her exploration of online programming tutors versus traditional study guides in education. Her publications span machine learning applications in structural integrity (e.g., dams/levees), geophysical data analysis, and education technology. While no specific awards are listed, her work reflects a strong commitment to both academic excellence and practical engineering solutions. She advises no listed students but maintains active roles in curriculum development and professional programs at the university. Her work is conducted at the Colorado School of Mines campus in Golden, Colorado.

Hamid Alinejad-Roknyمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Bioinformatics
• Genomics
• +۷ مورد دیگر

Dr. Hamid Alinejad-Rokny is a Scientia Senior Lecturer at UNSW Sydney and Adjunct Associate Professor at Concordia University. He leads the UNSW BioMedical Machine Learning (BML) Lab within the Graduate School of Biomedical Engineering. His research focuses on applying machine learning, bioinformatics, and statistical methods to understand genomic mechanisms underlying diseases like cancer and neurodevelopmental disorders. Dr. Rokny holds a PhD in Biostatistical Machine Learning from UNSW and has secured over $13M in grants as a principal or co-investigator. He has published 80+ papers, including 10 as first author and 45 as senior author. Education: Bachelor’s in Software Engineering (2004-2009), Master’s in Artificial Intelligence (2009-2012), PhD in BioMedical Machine Learning (UNSW, 2014-2018), Postdoc at Harry Perkins Institute (2017-2019). Research interests include medical AI, deep learning, genomic data analysis, and systems biology. He actively supervises 20+ PhD/Master’s students and collaborates with industry partners like CSIRO and 23Strands. Awards include the DECRA 2023, NHMRC MERIT, and International Autism Fellowships. He also serves as a keynote speaker at conferences like HUGO and an Honorary Lecturer at Macquarie University. Grants total $2.75M as lead investigator and $10.6M as co-investigator. Industry partnerships include PORSPA Advance ($4.7M) and Australian Digital Domains ($3.6M). His lab develops tools like MaxHiC and DeepGenePrior for genomic analysis. Labs/Teams: Director of UNSW BML Lab, Health Data Theme Leader at UNSW Data Science Hub. Active in mentoring 13 researchers globally and co-supervising international teams.

• Process Mining
• IoT (Internet of Things)
• Cognitive Science
• +۳ مورد دیگر

Barbara Weber is a Full Professor of Software Systems Programming and Development and Vice President at the University of St. Gallen . She leads research in the Institute of Computer Science (ICS-HSG) , focusing on interdisciplinary applications of process mining, IoT integration, and cognitive aspects of software systems. Her work bridges computer science, business process management, and human-centered design. Education Background: Doctorate in Computer Science (university not specified in text) Research Interests: Process Mining Methodologies & Tool Development IoT-Driven Process Analytics Cognitive Load Analysis in Process Models Human-Computer Interaction in Software Systems Declarative Process Modeling & Modularization Key Contributions: Weber's research emphasizes practical applications of process mining in real-world systems, including IoT integration for smart factories and cognitive workload assessment via eye-tracking. She advocates for user-centric design principles in both software tools and business process frameworks. Labs & Teams: Active in the Institute of Computer Science (ICS-HSG) , where she collaborates on projects such as ProAmbitIon (process conformance checking) and LogView (process mining inquiry validation).

Marta Ariasمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Design and Analysis of Algorithms
• +۳ مورد دیگر

Marta Arias is an Associate Professor in the Computer Science Department at Universitat Politècnica de Catalunya (UPC), Barcelona, and a member of the LARCA research group. She has held academic positions since 2007, including roles at Columbia University and the University of Edinburgh. Her research focuses on Machine Learning, Data Mining, Algorithm Design, and Logic in Computer Science. She teaches advanced courses in Machine Learning, Information Retrieval, and Complex Networks at undergraduate and master's levels, including the Erasmus Mundus program in Data Mining and Knowledge Management. Educations: PhD in Computer Science from Tufts University (2000-2004), Postgraduate Studies at University of Edinburgh (1999-2000), B.Sc. in Computer Science from UPC (1992-1997). Professional experience includes software engineering and research roles in New York and Barcelona. Research highlights include developing algorithms for real-time ranking systems, Twitter-based financial forecasting, and causal network analysis. Notable contributions include the 'Best Paper Award' at AIPESW@ECAI 2020 and a 3rd place award in football performance research. Her work spans theoretical foundations (e.g., Horn clause learning) and applied domains like cybersecurity, healthcare analytics, and energy systems. Teaching responsibilities include labs and lectures on Machine Learning, Information Retrieval, and Programming, with active involvement in curriculum design and pedagogical innovation. Ongoing projects include enterprise risk analysis, synthetic data generation, and network modeling.

Loutfouz Zamanمشاهده پروفایل

دانشیار

• User research in gaming
• Game analytics and data science
• Game evaluation and testing
• +۲ مورد دیگر

Dr. Loutfouz Zaman is an Associate Professor in the Game Development and Interactive Media department at Ontario Tech University , part of the Faculty of Business and Information Technology . He holds a PhD in Computer Science with a focus on Human-Computer Interaction from York University. His research explores visual programming interfaces, game analytics, and extended reality technologies, with a focus on practical applications such as automated bug detection and user experience optimization. Dr. Zaman has collaborated on projects funded by MITACS, NSERC, and industry partners, addressing challenges in healthcare incident management, language learning gamification, and air traffic control simulation training. He teaches courses ranging from introductory game math to graduate-level topics in human-computer interaction and machine learning for game analytics. Education: Bachelor of Science in Computer Science (Software Systems) – Concordia University Master of Science in Computer Science (Human-Computer Interaction) – York University PhD in Computer Science (Human-Computer Interaction) – York University Research Interests: Dr. Zaman’s work spans user research in gaming, game evaluation methodologies, and emerging technologies like AR/VR. His team focuses on developing tools for visual game analytics and automated testing, with recent projects including mixed reality fitness gaming interfaces and deep learning-based bug detection systems. Grants & Collaborations: His industry partnerships include projects on healthcare incident management during pandemics, CRM gamification, and pet identification systems using computer vision. He actively seeks doctoral candidates to expand research in visual analytics, XR technologies, and automated bug detection. Labs & Affiliations: Dr. Zaman is affiliated with the Software and Informatics Research Centre (SIRC) and contributes to the university’s efforts in bridging academic research with real-world applications.

Christopher Turnerمشاهده پروفایل

مدرس ارشد

• Business Analytics
• Digital Manufacturing
• Industry 4.0
• +۵ مورد دیگر

Dr. Christopher Turner is a Senior Lecturer in Business Analytics with a focus on research in human-centric manufacturing, Industry 5.0 automation, and explainable AI. He is affiliated with the cross-University People-centred AI Institute and the IEEE Task Force on Process Mining. His work bridges advanced technologies like the Metaverse and machine learning with practical applications in logistics and maintenance systems. Education: MSc, MSc, PhD Research interests include business analytics, data mining, machine learning, digital manufacturing, and sustainable distributed manufacturing. Recent publications emphasize the integration of extended reality (XR) in production systems and frameworks for explainable AI. With over 100 peer-reviewed publications and 5,600 citations (H-index 30), his research has significant impact. Notable projects include the AUTONOM initiative (collaborating with Network Rail) and a Knowledge Transfer Partnership (KTP) with Basemap, developing logistics management software. Teaching responsibilities include the MSc module Operations Management and Digital Services within the Operations and Supply Chain in the Digital Era and Entrepreneurship programs.

Jayanth Raghothamaمشاهده پروفایل

دانشیار

• Healthcare Informatics
• Complex Systems Modeling
• Simulation and Gaming in Policy Design
• +۳ مورد دیگر

Jayanth Raghothama is an Associate Professor at KTH Royal Institute of Technology, affiliated with the School of Computer Science and Communication (CBH) and the Digital Futures Faculty. He is part of the Division of Health Informatics and Logistics, focusing on healthcare systems, complex systems modeling, and simulation-based decision-making. His academic role includes co-leading the Digitalization Platform and serving as a Vice-Director. Education: PhD in Complex Systems from KTH (2017). Prior experience includes software industry roles and work with a think tank in Bangalore. Research Interests: Healthcare logistics, complex systems analysis, participatory simulations, AI/ML ethics, and the philosophy of games and simulations. His work bridges computational models with real-world applications in healthcare, urban planning, and disaster management. Recent Projects: Co-Principal Investigator of the 'Data-driven Improvement of Work-Flows at the Karolinska University Hospital' project. Active in AI-based colorectal cancer detection in primary care (AIDCCIP project). Teaching: Courses on Applied Machine Learning, Simulation Methods, and Healthcare Logistics at Bachelor, Master, and PhD levels. Modules include AI/ML ethics and Agent-Based Modeling. Publications: Over 20 peer-reviewed articles focusing on healthcare simulation models, real-world data analysis in oncology, and hybrid modeling techniques. Notable works include patient flow optimization algorithms and frameworks for risk stratification in lung cancer studies.

• Community Detection
• Graph Clustering
• Graph Representation Learning
• +۴ مورد دیگر

Susanna Pozzoli is a doctoral student at the Division of Software and Computer Systems (SCS) within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology. She also serves as the vice chairperson of the EECS PhD Council, representing doctoral students at the school level. Education: Bachelor of Science in Engineering of Computing Systems from Politecnico di Milano (2017) EIT Digital Master's Programme in Data Science (2017–2019), completed first year at Politecnico di Milano and second year at KTH in Stockholm, Sweden Research Interests: Susanna’s work focuses on advancing graph-based machine learning techniques, including community detection, graph clustering, role discovery, and unsupervised learning. She explores how graph representation learning can enhance performance evaluation in complex networks, with applications ranging from social network analysis to biomedical data science. Publications: Her recent work highlights innovations in graph embedding techniques and diffusion-driven role recognition, alongside interdisciplinary research in domain-agnostic feature selection for breast cancer analysis. These studies reflect her expertise in bridging theoretical computer science with practical data-driven solutions. Awards & Grants: No specific awards or grants mentioned in the provided information. Labs & Teams: While no specific lab affiliations are listed, her research is embedded within the collaborative environment of KTH’s SCS division.

Jyh-Charn Liuمشاهده پروفایل

استاد

• Real-time distributed computing systems
• Behavior modeling and simulation
• Cyber physical systems security
• +۵ مورد دیگر

Jyh-Charn 'Steve' Liu is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on real-time distributed systems, cyber-physical security, and interdisciplinary applications such as mathematical expression analysis and GNSS spoofing mitigation. Education: Ph.D., Electrical & Computer Engineering, University of Michigan (1989) M.S., Electrical Engineering, National Cheng Kung University (1981) B.S., Electrical Engineering, National Cheng Kung University (1979) Research Interests: Real-time distributed computing systems Cyber-physical systems security Behavior modeling and simulation Mathematical expression analysis and tools for STEM education GNSS spoofing detection and mitigation Blockchain-based supply chain management Key Publications Trends: Recent work emphasizes AI-driven solutions for mathematical document processing (e.g., LaTeX conversion from images), cybersecurity in navigation systems, and interdisciplinary applications like STEM education tools. Early-career contributions include real-time scheduling algorithms and embedded systems design. Awards: Senior Member, IEEE Computer Society (2014) Nominated for ACM Eugene L. Lawler Award (2013) Conference leadership roles (RTAS 2006/2007) Lab & Collaborations: Leads the Real Time Distributed Systems Lab, exploring areas such as verifiable credentials, urban navigation systems, and medical image analysis. Collaborates on projects like DIME (mathematical expression tool) and MOP (mathematical PDF labeling).

Yehuda Bockمشاهده پروفایل

مدرس ارشد

• Geodesy
• Lithospheric Deformation
• Seismology
• +۴ مورد دیگر

Yehuda Bock is a Senior Lecturer and Research Geodesist at the University of California San Diego (UCSD), affiliated with the Scripps Institution of Oceanography and its Institute of Geophysics and Planetary Physics (IGPP). He directs the Scripps Orbit and Permanent Array Center (SOPAC), a key facility for geodetic data management and analysis. His roles include advancing seismogeodetic techniques for natural hazard mitigation and improving observational networks. Education: B.A. from New York University, B.S. from Technion-Israel Institute of Technology, M.S. and PhD from Ohio State University. Research focuses on crustal deformation, geodetic instrumentation, and integrating GNSS with seismic data for earthquake and tsunami warning systems. He explores applications of GNSS in meteorology, structural health monitoring, and climate studies. Recent work emphasizes real-time data fusion, machine learning for geophysical analysis, and understanding tectonic processes in regions like the San Andreas Fault and Cascadia Subduction Zone. His publications highlight advancements in deformation modeling, early warning systems, and interdisciplinary approaches to geoscience data.

Marya Bazziمشاهده پروفایل

استادیار

• Network Science
• Time-Dependent Networks
• Multilayer Networks
• +۷ مورد دیگر

Marya Bazzi is an Honorary Assistant Professor at the University of Warwick. Her research focuses on developing methods for analyzing large time-dependent data, with a primary focus on Network Science. She investigates mesoscale structures in networks such as community detection, core-periphery structures, and multilayer networks, applying these tools to domains like online media, biology, finance, and digital humanities. Her work includes contributions to scalable graph representation learning (Local2Global framework), semantic change detection using the UK Web Archive, and computational journalism. She co-organizes academic initiatives like Data Science for Social Good, Data Study Group, and workshops on media in the digital age. Her research bridges algorithmic development with practical applications, emphasizing interdisciplinary collaboration. Key publications span Machine Learning, Network Science, and Computational Biology, reflecting her expertise in both theoretical frameworks and applied problem-solving. While no awards are explicitly mentioned, her active participation in organizing academic activities demonstrates leadership in fostering collaborative research environments.

Cezary Z. Janikowمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Evolutionary Computation
• Genetic Algorithms
• +۹ مورد دیگر

Cezary Z. Janikow is an Associate Professor and former Chair of the Department of Mathematics and Computer Science at the University of Missouri–St. Louis (UMSL), where he conducts research in artificial intelligence, evolutionary computation, and machine learning. He is the developer of FID (Fuzzy Inference Decision tree), a system for generating fuzzy decision trees that handle missing, noisy, and continuous data. His research is supported by NIH, NSF, and NASA/JSC, and he has contributed significantly to the field of genetic programming, particularly in constrained and adaptable representations (ACGP). Ph.D. in Computer Science, University of North Carolina at Chapel Hill, 1991 His research interests center on evolutionary algorithms , fuzzy decision trees , and symbolic machine learning . He explores how genetic programming can adapt representations and incorporate constraints to improve search efficiency and solution quality. His work on FID has advanced fuzzy logic applications in classification and data analysis. The recent publications reflect a sustained focus on genetic programming , particularly Adaptable Constrained Genetic Programming (ACGP) , heuristics in evolutionary search , and fuzzy decision systems . His work spans theoretical development, algorithm design, and practical implementation, with applications in optimization and machine learning. Dr. Janikow has delivered corporate training in software development and served as an expert witness in software-related legal cases. He has also organized academic workshops and presented tutorials on evolutionary algorithms. Co-PI, NIH: Statistical Methods for Recursively Partitioned Trees NSF Support: Fuzzy Decision Trees NASA/JSC Collaboration: Constrained Genetic Programming He leads the FID project, a software system for fuzzy decision tree generation, which includes a GUI and supports cross-validation and noise simulation. This tool reflects his integration of research and software development.

• Combinatorics on Words
• String Algorithms
• Data Mining
• +۳ مورد دیگر

Gabriele Fici is an Associate Professor at the Department of Mathematics and Computer Science, University of Palermo (Unipa). His research focuses on combinatorics on words, string algorithms, and algorithmic pattern matching. He teaches courses in text sequence analysis, data mining, and software development. Research Interests : Combinatorial pattern matching, Abelian repetitions, digital convexity, and theoretical foundations of string processing. Key Contributions : Pioneering work on Lyndon words, de Bruijn sequences, and jumbled pattern matching algorithms. Publications : Extensive publications in top-tier conferences like CPM and journals, with a focus on 2013-2025 trends in algorithmic complexity and computational linguistics.

• Natural Language Processing
• Educational Technology
• Text Mining
• +۴ مورد دیگر

Dr. Thushari Atapattu is a Grant-Funded Researcher (A) and Post-doctoral Research Fellow in the School of Computer and Mathematical Sciences at the University of Adelaide . She leads the Learning Technology for Social Good (LT4SG) research group and contributes to the Computer Science Education Research Group . Her research focuses on text mining , natural language processing (NLP) , and discourse analysis , with applications in educational technology and social good initiatives. Her work addresses challenges such as combating cyberbullying via linguistic fingerprints, modeling mental health through social media data, and analyzing discourse patterns in Massive Open Online Courses (MOOCs). Key projects include: Developing computational models to improve communication processes in education and healthcare. Exploring teachers' academic discourse impact on MOOC video engagement. Creating emotion-annotated mental health corpora and frameworks for detecting learner confusion in online platforms. Dr. Atapattu is eligible to supervise Masters and PhD candidates, focusing on interdisciplinary topics at the intersection of NLP, education, and social impact. Her research group, LT4SG, emphasizes leveraging technology for community-driven solutions.