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Dharma WijewickremeView profile

Professor

• Geotechnical Engineering
• Earthquake Geotechnical Engineering
• Pipeline Geotechnical Engineering
• +4 more

Dr. Dharma Wijewickreme is a Professor in the Department of Civil Engineering at the University of British Columbia (UBC), part of the Faculty of Applied Science. He joined UBC in 2001 and specializes in geotechnical engineering, with a focus on earthquake geotechnical engineering and pipeline integrity. He holds a Ph.D. and M.A.Sc. in Civil Engineering from UBC (1990 and 1986), following 11 years of industry experience in seismic and pipeline engineering consulting. Dr. Wijewickreme pioneered the Pipeline Integrity Institute (PII) at UBC in 2015, serving as its Co-Director. His research interests include soil-pipeline interaction, liquefaction of silts, advanced laboratory testing, and geotechnical material characterization. He teaches courses such as Soil Mechanics II, Soil Dynamics, and Pipeline Engineering. His awards include the Horst Leipholz Medal (2013), Canadian Geotechnical Journal Editor’s Choice Award (2011), and Fellowships from the Canadian Academy of Engineering and Canadian Society for Civil Engineering. He served as President of the Canadian Geotechnical Society (2017–2018) and Vice President Finance (2013–2017). Recent research trends emphasize advanced imaging techniques (e.g., X-ray μ-CT for silt fabric analysis) and pipeline integrity under seismic and environmental stresses. His work bridges laboratory experimentation with field applications, addressing challenges in geotechnical material behavior and infrastructure resilience.

Alexia ToumpaView profile

Researcher

• Automated Software Engineering
• Model-Based Software Engineering
• Software Repository Mining
• +1 more

Alexia Toumpa is a Research Associate in the Department of Computer Science at the University of York, actively contributing to the Automated Software Engineering research group under Professor Dimitris Kolovos' leadership. Her academic background includes: PhD in Computer Science from the University of Leeds (awarded 2023) MEng from the University of Patras (awarded 2016) Her research centers on Automated Software Engineering methodologies, with emphasis on model-driven approaches and software repository analysis. She investigates techniques for automating software development lifecycles through advanced tooling and empirical studies of software evolution patterns. Within the research group, she collaborates on projects exploring model-based solutions for complex software systems and contributes to developing frameworks that enhance software engineering practices through data-driven insights from repository mining.

• Artificial Intelligence
• Explainable AI (XAI)
• Green AI
• +12 more

Dr Andrew Starkey is a Reader in the School of Engineering at the University of Aberdeen, where he also completed his PhD in 2001. He holds an Honours degree in Applied Mathematics from the University of St Andrews. He is actively involved in research and currently accepting PhD students in Engineering. His work bridges academia and industry, with a focus on AI applications in engineering, bioinformatics, and geosciences. University: University of Aberdeen School: School of Engineering Academic Rank: Reader Email: a.starkey@abdn.ac.uk Phone: +44 (0)1224 272801 Dr Starkey's research centers on Explainable AI (XAI) , Green AI , and Autonomous AI , with applications in robotics, econometrics, bioinformatics, seismic data analysis, and virtual reality. He has developed novel methods for feature selection, autonomous learning, and knowledge abstraction from agent-environment interactions. His work emphasizes low computational cost and transparency in AI systems. The most recent publications reflect a strong trend in applying AI to complex real-world problems, including digital rock technology, robotic grasping, real-time event detection, and medical data analysis. His interdisciplinary research combines machine learning with domain-specific knowledge in engineering and life sciences, often resulting in practical, industry-ready solutions. Millennium Product Award John Logie Baird Award for Innovation Enterprise Fellowship from Royal Society of Edinburgh and Scottish Enterprise Dr Starkey has supervised multiple research projects and secured funding from major bodies including EPSRC, BBSRC, and industry partners. His past work on the GRANIT project led to the development of AI-based condition monitoring for ground anchorages, resulting in commercialization through BlueFlow Ltd. He has collaborated with researchers across disciplines, including Dr Alasdair MacKenzie (bioinformatics), Dr Anne Schwab (seismic analysis), and Dr David Hazlerigg (genomics). He leads research in AI-driven engineering solutions and is the CEO of BlueFlow Ltd, a spinout company commercializing AI technologies developed at the University of Aberdeen. His lab focuses on developing autonomous, explainable, and environmentally sustainable AI systems for real-world deployment.

Ann MaharajView profile

Adjunct Associate Professor

• Time Series Classification
• Wavelet Analysis
• Fuzzy Classification
• +9 more

Ann Maharaj is an Adjunct Associate Professor in the Department of Econometrics and Business Statistics at Monash University's Caulfield Campus, within the Faculty of Business and Economics. She is an active researcher and educator with expertise in statistical computing and time series analysis. Department: Econometrics and Business Statistics Role: Adjunct Associate Professor Institution: Monash University Campus: Caulfield Her research focuses on advanced statistical methodologies, particularly in time series classification , wavelet analysis , fuzzy classification , and interval time series analysis . These methods are applied in diverse domains such as finance, environmental science, climatology, and human mobility. She has co-authored a book on time series clustering and classification and has published extensively in top-tier journals. The recent trend in her publications (2020–2024) shows a strong emphasis on clustering and classification of complex time series data using wavelet, cepstral, and fuzzy techniques. Her work integrates statistical theory with practical applications, particularly in financial and environmental datasets, contributing to sustainable development goals through data-driven insights. She has received recognition for her teaching excellence: Monash Business School Award for Teaching Excellence (2017) Ann Maharaj is actively involved in academic service and professional communities. She has supervised research students and contributed to statistical consulting and workshops. Her professional affiliations include: Elected member of the International Statistical Institute (ISI) Member of the International Association of Statistical Computing (IASC), serving on its Council (2013–2017) and Executive (2015–2017) Accredited statistician with the Statistical Society of Australia (SSA) Former Secretary and Academic Vice-President of the Monash Branch of the NTEU (2000–2014) She led a research project funded by the Collier Charitable Fund in 2005 on computational infrastructure, indicating early engagement with data-intensive research. Her ongoing scholarly output demonstrates sustained research activity and collaboration with international scholars in statistics and data science. She is associated with research groups and networks focused on statistical computing and time series analysis, contributing to both methodological advancement and real-world application through interdisciplinary collaboration.

Cameron Joseph MancheView profile

Researcher

• Carbonate Diagenesis
• Carbonate Sedimentology
• Carbonate Petrology
• +3 more

Cameron Joseph Manche is an Assistant Research Scientist at Texas A&M University's Berg-Hughes Center. His research focuses on carbonate diagenesis, integrating tools like X-ray diffraction, scanning electron microscopy, and stable isotope geochemistry. He holds a Ph.D. in Geosciences from Western Michigan University (2021), an M.Sc. in Remote Sensing (2014), and a B.Sc. in Environmental Sciences (2011). His work emphasizes global variations in dolomite stoichiometry and cation ordering, with applications in reservoir characterization and paleoenvironmental reconstruction. Ph.D., Geosciences (Carbonate Petrology), Western Michigan University (2015–2021) M.Sc., Geosciences (Remote Sensing), Western Michigan University (2012–2014) B.Sc., Environmental Sciences & Planning, University of Michigan-Flint (2008–2011) Research interests span carbonate sedimentology, sequence stratigraphy, and the application of geochemical proxies to unravel diagenetic processes. His studies include case studies from the Gulf Coast, Maldives, and Algeria, addressing dolomitization mechanisms and their environmental implications. Scientific achievements include winning the Texas A&M Postdoctoral Research Symposium (2021) and developing an algal bloom early warning system using remote sensing (2016). His publications explore dolomite stoichiometry as a proxy for fluid chemistry and lake-level fluctuations, with contributions to understanding Phanerozoic dolomite abundance trends linked to oceanic anoxia. Manche collaborates internationally, focusing on integrating field data with geochemical analyses to advance carbonate reservoir modeling and paleoenvironmental reconstruction.

• Biomechanics
• Human Balance and Movement
• System Identification
• +7 more

Massimo Cenciarini is a Senior Lecturer in the Department of Mechanical Engineering at the Universitat Politècnica de Catalunya (UPC), affiliated with the School of Industrial Engineering (ETSEIB). He is an active member of the BIOMEC - Biomechanical Engineering Lab, TecSalut - Health Technologies Research Group, and CDEI - Industrial Equipment Design Centre. His work bridges engineering and biomedical applications, focusing on human movement and assistive technologies. Research Interests: Biomechanics and human balance Wearable robotics and exoskeletons System identification and control Assistive mobility technologies Mechatronics and simulation Postural control and motor adaptation His recent publications show a strong trend in wearable robotic devices, particularly knee exoskeletons, with emphasis on human-exoskeleton interaction, misalignment compensation, impedance identification, and gait assistance. His work integrates biomechanical modeling, experimental validation, and translational research for rehabilitation and mobility enhancement. Scientific Awards: 3r Premi UPC al Compromís Social He has participated in competitive R&D projects such as Xartec Salut and technology transfer initiatives, including the development of wearable knee exoskeletons. While no formal students are listed, his collaborative projects suggest mentorship roles within multidisciplinary teams. He is involved in research networks focused on health technologies and Industry 4.0 educational environments. Laboratories and Research Groups: BIOMEC - Biomechanical Engineering Lab (UPC) TecSalut - Health Technologies Research Group CDEI - Centre de Disseny d'Equips Industrials

Wen XinView profile

Assistant Professor

• English Language Studies
• Corpus Linguistics
• Sociolinguistics
• +8 more

Wen Xin serves as an Assistant Teaching Professor in the Department of English at the University of Kansas within the College of Liberal Arts and Sciences. Her academic profile centers on contextual language appropriateness across spoken/written contexts, regional variations, and historical periods, with emphasis on language as both an empowerment tool and mechanism for marginalization. Her research program intersects English Language Studies and Writing Studies through corpus linguistics, sociolinguistics, and text mining methodologies. She investigates pragmatic feature variation in writing classrooms to foster equitable pedagogical environments, with specialization in History of English, Digital Humanities, and Response to Student Writing. This work integrates quantitative analysis with critical language awareness frameworks to address systemic inequities in writing instruction. Publication trends reveal consistent application of diachronic corpus approaches to writing studies, examining linguistic justice in feedback practices, comparative analysis of native/nonnative English-speaking composition instructors, and historical orthographic patterns. Her scholarship demonstrates methodological rigor through mixed-methods designs that bridge historical linguistics with contemporary writing pedagogy. No scientific awards are documented in available materials. While graduate student advising details remain unspecified, her research agenda shows strong emphasis on developing data-driven writing assessment frameworks and equitable feedback systems through corpus-based investigations of classroom language practices.

• Eye tracking
• Online teaching
• Human-computer interaction
• +5 more

Marian Sauter is a researcher at Ulm University's Institute for Psychology in Germany, specializing in the intersection of psychology, human-computer interaction, and educational technology. Her work primarily focuses on eye-tracking methodologies and gaze analysis to improve online teaching and learning environments. She frequently collaborates with colleagues from the Institute of Media Informatics at Ulm University, indicating strong interdisciplinary connections between psychology and computer science departments. Her research interests center on understanding visual attention patterns in educational contexts, particularly how gaze behavior correlates with learning outcomes in digital environments. Dr. Sauter investigates how eye movement data can be visualized and utilized to optimize instructional design, with specific attention to gaze-laser pointer relationships, gaze synchronicity among students, and attention monitoring in virtual classrooms. Her work bridges cognitive psychology theories with practical applications for e-learning platforms and remote education systems. Analysis of her recent publications reveals a strong trajectory in applying eye-tracking technology to solve real-world educational challenges, particularly those emerging during and after the pandemic-driven shift to online learning. Her research spans both theoretical investigations of visual attention mechanisms and practical implementations for educational technology. The consistent focus on webcam-based eye-tracking demonstrates her commitment to developing accessible methods that don't require specialized hardware, making her research particularly relevant for widespread educational applications. Best Paper Award at ETRA '22 Symposium on Eye Tracking Research and Applications Berlin Health Award (2023) Dr. Sauter's collaborative approach is evident in her frequent co-authorship with Tobias Wagner from Ulm University's Institute of Media Informatics and Anke Huckauf from General Psychology. Her research appears to be supported by institutional resources at Ulm University, though specific external grants are not mentioned in the available information. She appears to be actively mentoring students given her involvement in educational psychology research and student-focused studies. Her work suggests involvement with research groups focused on human-computer interaction, educational technology, and cognitive psychology at Ulm University, though specific lab names are not provided in the available information. The interdisciplinary nature of her publications indicates collaboration across psychology and media informatics departments.

Magdalini EirinakiView profile

Professor

• Recommender Systems
• Machine Learning
• Data Mining
• +5 more

Magdalini Eirinaki is a Professor and Academic Program Coordinator for the MS in Artificial Intelligence at San José State University's Charles W. Davidson College of Engineering. With a career spanning two decades, her work bridges recommender systems , machine learning , and smart city applications . PhD in Computer Science (2006), Athens University of Economics and Business MSc in Advanced Computing (2000), Imperial College London BSc in Computer Science (1998), University of Piraeus Her research focuses on machine learning and recommender systems with extensions to generative AI , privacy-sensitive algorithms , and social network analysis . Recent publications explore federated learning , multi-resolution diffusion models , and autonomous network defense using reinforcement learning. Current projects include NSF-funded CollaborAIte (2024) EU Horizon/Marie Sklodowska-Curie's MUSIT (2024) IBM SkillsBuild Cloud Credits for Sustainability (2024) She has received multiple teaching and mentorship awards including: Newnan Brothers Award (2019) Applied Materials Award (2017) 5-time SJSU Distinguished Faculty Mentor Award Dr. Eirinaki advises students in AI , ML , and smart city projects, with recent graduates presenting at IEEE CAI (2025) and CSU Conference (2025).

• Machine Learning
• Time Series Analysis
• Space Weather Prediction
• +4 more

Soukaina Filali Boubrahimi serves as an Assistant Professor in the Computer Science Department within the College of Engineering at Utah State University. Her academic appointment is based in the SER 332 building located at 4205 Old Main Hill, Logan, UT 84322-0001. She maintains a research-active position with a focus on computational methods for complex temporal data analysis. Dr. Filali Boubrahimi's research program centers on time series analysis , machine learning , and space weather prediction , with particular emphasis on solar flare forecasting and counterfactual explanation systems. Her work bridges theoretical machine learning advancements with practical applications in heliophysics, hydrology, and social media analysis. The research portfolio demonstrates significant expertise in handling imbalanced temporal datasets, developing novel data augmentation techniques, and creating interpretable AI systems for critical prediction tasks. Analysis of her recent publication trajectory reveals consistent contributions to counterfactual explanation frameworks for time series data (Info-CELS, M-cels, ACTS), space weather prediction systems (solar flare and energetic particle event forecasting), and generative modeling approaches (AVATAR, ChronoGAN). Her work frequently addresses the challenges of severely imbalanced datasets through contrastive learning and sophisticated preprocessing techniques, demonstrating methodological innovation in handling rare but critical space weather events. While no specific awards are documented in the available information, her research program appears substantial based on the volume and quality of recent publications spanning multiple high-impact domains. The research demonstrates strong interdisciplinary connections between computer science, space physics, and environmental science. Her laboratory activities focus on developing machine learning frameworks for temporal data analysis, with particular attention to space weather prediction systems. The research group appears to specialize in creating robust models for rare event prediction, explainable AI systems for time series classification, and novel data augmentation techniques for imbalanced temporal datasets. Current projects likely include the development of multimodal fusion approaches for solar energetic particle prediction and spatio-temporal modeling for hydrological applications.

Jeff LupkerView profile

Assistant Professor

• Artificial Intelligence and Music
• Machine Learning for Music Composition
• Electroacoustic Music
• +3 more

Jeff Lupker serves as an Assistant Professor at Western University's Don Wright Faculty of Music, specializing in the intersection of artificial intelligence and musical creativity. His work develops computational tools that augment human composition through deep learning algorithms and interactive systems, positioning him at the forefront of AI-driven music innovation. Lupker completed his entire academic training at Western University: PhD in Composition (2021) Master of Music in Composition (2016) Bachelor of Music in Theory and Composition (2014) His research program focuses on artificial intelligence applications for musical creation, including deep learning models for algorithmic composition, sentiment analysis of social media as compositional input, and mobile-based spatial audio systems. Lupker investigates how transformer architectures generate musical structures and how real-time web applications enable collaborative performance, emphasizing practical tools that combat writer's block while expanding composers' stylistic range through AI-assisted creativity in electroacoustic and popular music contexts. Analysis of his 2021 publications reveals a cohesive research trajectory leveraging cutting-edge AI methodologies to solve creative challenges in music. The works demonstrate how deep learning transforms composition through systems like Score-Transformer and explore mood-pattern recognition using machine learning, collectively establishing foundational work for human-AI creative collaboration that bridges music theory with big data analytics. As founder of Staccato, Lupker leads the development of an AI co-writing platform that functions as an intelligent creative partner. The system generates lyrical content from keywords and suggests musical continuations, helping composers overcome creative blocks while expanding artistic possibilities across diverse genres through accessible, user-friendly interfaces.

Ilie SarpeView profile

Researcher

• Graph Mining
• Temporal Networks
• Algorithm Design
• +5 more

Ilie Sarpe is a postdoctoral researcher in the Division of Theoretical Computer Science at KTH Royal Institute of Technology, mentored by Prof. Aristides Gionis. He is an active member of both VandinLab and AIDA Lab, focusing on developing rigorous algorithms for temporal network analysis. His educational background includes: PhD in Computer Engineering from the University of Padova (2019-2023), with thesis on 'Efficient and Rigorous Techniques for the Analysis of Large Temporal Networks' Master's Degree in Computer Engineering (summa cum laude) from the University of Padova (2017-2019) Bachelor's Degree in Computer Engineering from the University of Padova (2014-2017) Sarpe's research centers on the design of scalable algorithms for data-mining problems, particularly in graph-mining and clustering. He specializes in probabilistic algorithms with rigorous theoretical guarantees, with a strong focus on temporal networks. His work integrates tools from sampling theory, probability, concentration inequalities, and statistical learning theory to develop efficient solutions for complex network analysis problems. His publications demonstrate a consistent focus on temporal network analysis, with recent work accepted at top venues including KDD 2024, WWW 2022, CIKM 2021, and SIAM SDM 2021. The research trajectory shows increasing sophistication in handling temporal motifs, dense subnetwork discovery, and centrality measures in evolving networks. His scientific recognition includes: SoBigData TNA Fellowship (2022) 3 years Ph.D. Fellowship (2019) Award for Scientific Degrees (2017) Two 'Mille e una lode' awards (2016, 2017) Sarpe actively supervises master's thesis projects in data mining at KTH and has secured research funding through the SoBigData TNA Fellowship. His research group affiliation with VandinLab and AIDA Lab provides collaborative opportunities across multiple institutions. He maintains active laboratory work focused on developing practical implementations of his theoretical algorithms, as evidenced by his GitHub repositories containing C++ implementations of his published methods.

Aleksandra SarcevicView profile

Professor

• Computer Science
• Human-Computer Interaction
• Healthcare Informatics
• +7 more

Aleksandra Sarcevic is a Professor of Information Science at Drexel University's College of Computing & Informatics (CCI), where she directs the Interactive Systems for Healthcare (IS4H) Research Lab. She earned her PhD (2009) and MLIS (2005) from Rutgers University's School of Communication and Information, and holds a BA in Film and TV Production from the University of Arts, Belgrade. PhD, Communication, Information and Library Studies MLIS, Library and Information Science BA, Film and TV Production Her research focuses on computer-supported cooperative work (CSCW) , human-computer interaction (HCI) , and healthcare informatics , with specializations in: Collaboration in high-risk environments Medical team coordination Context-aware systems for critical care Crisis informatics Socio-technical system design Recent publications examine AI-enabled decision support , PPE compliance monitoring , and real-time clinical alert systems , with funding from NIH , NSF , and AHRQ . She received the NSF CAREER award in 2013 and mentors both current and graduated PhD students in interdisciplinary research. The IS4H lab develops interactive healthcare systems for trauma resuscitation and infection control, employing ethnographic methods and sensor-based activity recognition to improve medical team performance.

BAYRAM ÇETİNView profile

Professor

• Measurement and Evaluation in Education
• Educational Statistics
• Research Methods in Education
• +3 more

Professor BAYRAM ÇETİN is affiliated with Gaziantep University , where he serves in the Faculty of Education within the Department of Educational Sciences , specializing in Measurement and Evaluation in Education . His academic career spans multiple institutions, including Gazi University and Sakarya University. Doctorate in Measurement and Evaluation in Education (2005, Hacettepe University) Master’s in Measurement and Evaluation in Education (2002, Hacettepe University) Licence in Classroom Teaching (1993, Çukurova University) His research focuses on educational measurement , psychometric modeling , statistical analysis in education , and assessment equity . He has developed numerous scales and rubrics for educational evaluation, emphasizing SOLO taxonomy , CHAID analysis , and generalizability theory . Professor Çetin has authored 12 books/chapters, including Research Methods in Education and Measurement and Evaluation in Education , and published over 65 peer-reviewed articles. His recent work explores online formative assessment , ADHD diagnostics , and cross-cultural PISA data . He has supervised 25+ theses, including studies on cyberbullying , metacognition , and test development . He has held administrative roles such as Director of Research and Application Center (2022) and Dean (2020). His projects include international collaborations like Erasmus+ KA201 and national grants on virtual reality in medical education .

Holger SchwarzView profile

Associate Professor

• Data Management
• Database Systems
• Data Lakes and Lakehouses
• +6 more

Holger Schwarz is an Associate Professor (Apl. Professor) at the Institute for Parallel and Distributed Systems (IPVS) within the Faculty of Computer Science, Electrical Engineering and Information Technology at the University of Stuttgart. He serves as Head of the Infrastructure Department and is actively involved in research and teaching in the areas of data management, database systems, and data analytics. Professor Schwarz earned his doctorate (Dr. rer. nat.) from the University of Stuttgart in 2003 with a dissertation on "Integration of Data Mining and Online Analytical Processing." He later completed his habilitation (Dr. rer. nat. habil.), qualifying him as a university professor in Germany. His primary research interests focus on data management systems , particularly in the domains of data lakes, lakehouses, enterprise data platforms, and metadata management. Professor Schwarz investigates how to design efficient and scalable data architectures that support modern analytical workloads while addressing challenges in data integration, governance, and democratization. His work bridges theoretical database concepts with practical industrial applications, as evidenced by numerous collaborations with industry partners. Professor Schwarz's recent publications demonstrate a clear trend toward enterprise data management solutions, particularly focusing on data lakehouse architectures, enterprise data marketplaces, and advanced clustering techniques. His research shows a consistent pattern of addressing real-world data management challenges through innovative architectural patterns and algorithmic improvements, with strong emphasis on practical industrial implementation. Professor Schwarz supervises numerous research projects including MetaMan (metadata management in complex data landscapes), DLArchitecture (design of comprehensive data lake architecture), INTERACT (interactive rapid analytic concepts), and VALID-Partition (improving prediction quality using domain knowledge). He also coordinates the University of Stuttgart's projects within the Software Campus initiative and serves as Managing Director of the Technology Partnership Lab and as a Member of the Board of Directors of the Industrial Data Lab. His teaching portfolio includes courses on Advanced Information Management, Database Systems, and Data Science projects across multiple semesters, demonstrating his commitment to educating the next generation of data management professionals.