Find Academic Supervisors

• Management Information Systems
• Business Analytics
• Decision Support Systems
• +2 more

Wei Huang (Wayne) is a Chair Professor and founding dean of the College of Business at Southern University of Science & Technology (SUSTech) in Shenzhen, China. He holds the prestigious National Yangtze Chair in Management Information Systems and has over 35 years of full-time teaching and research experience across multiple continents. His academic career spans renowned institutions including University of New South Wales (Sydney, Australia), National University of Singapore (NUS), Harvard University, Chinese University of Hong Kong (CUHK), and Xi'an Jiaotong University. His research interests focus on Management Information Systems , specifically on using IS/IT to support decision-making and collaboration, and business analytics. Dr. Huang has published over 200 research papers in top-tier journals including MIS Quarterly (MISQ), Journal of Management Information Systems (JMIS), Journal of the Association for Information Systems (JAIS), and IEEE Transactions, with his work being cited by leading journals such as Management Science and ISR. His recent publications demonstrate a clear trend toward integrating emerging technologies like artificial intelligence, blockchain, and IoT with traditional business analytics to create more robust decision support systems. The research spans multiple subfields including machine learning, data visualization, collaborative technologies, and cross-cultural aspects of information systems adoption, reflecting both the breadth and depth of his scholarly contributions. AIS Fellow (2020) - second professor from a Chinese university to receive this honor Sandra Slaughter Outstanding Services Award (2018) - first mainland Chinese scholar to receive this award National Yangtze Chair in Management Information Systems Outstanding SIG Award for AIS SIG-ISAP (2017) Highest Quality Rating from the British Library Outstanding Professional Service Award of ICIS Dr. Huang has secured significant research funding from diverse sources including the Australian Research Council (ARC), National Science Foundation of China, National University of Singapore, Chinese University of Hong Kong, and Harvard University. His leadership extends beyond research to academic service where he has served as PACIS Council Chair (2019-2022), founding President of AIS SIG-ISAP for over 15 years, and key founding member of China AIS Chapter. He currently serves as Vice Chairman of the Teaching Steering Committee for Management Science and Engineering Programs at Guangdong Universities, Honorary Dean of the China Guoxin Credit Big Data Research Institute, and several other high-level advisory roles with Chinese governmental bodies.

Kenton SenaView profile

Lecturer

• Forest Restoration Ecology
• Environmental Science
• Literature of the Environment
• +3 more

Kenton Sena is a Lecturer at the University of Kentucky's Lewis Honors College, with a focus on forest restoration ecology, environmental science, and ecological literature. He received a B.A. in Biology from Asbury University, an M.S. in Forestry, and a Ph.D. in Integrated Plant and Soil Science (Forest Science emphasis) from the University of Kentucky. His research centers on forest restoration in Appalachia, particularly addressing Phytophthora cinnamomi pathogen dynamics and mine reclamation. Education: B.A. in Biology (Asbury University), M.S. in Forestry (University of Kentucky), Ph.D. in Integrated Plant and Soil Science (University of Kentucky) Sena's scholarly work spans forest ecology, environmental ethics, and invasive species management, with recent publications examining topics like White Oak population dynamics, eDNA monitoring of salamanders, and the environmental legacy in Tolkien's writings. He has contributed to journals including Forests , Science of the Total Environment , and Ecological Restoration . His awards include the 2023 Great Teacher Award. His teaching emphasizes interdisciplinary approaches to environmental challenges, bridging ecological science with literary perspectives on sustainability. While he has not explicitly mentored graduate students in the provided data, his fieldwork and publications demonstrate practical applications for ecosystem management in post-mining landscapes and urban reforestation.

• Alpine Ecology
• Environmental Chemistry
• Peatland Biogeochemistry
• +4 more

Ewen Silvester is an Associate Professor in the Department of Ecological, Plant and Animal Sciences at La Trobe University's School of Agriculture, Biomedicine and Environment. He serves as Director of Graduate Research overseeing >250 higher degree students and Director of the Research Centre for Applied Alpine Ecology (RCAAE), with strong industry engagement in alpine landscape management. His research group conducts field studies across southeastern Australia's surface water systems including alpine wetlands, headwater streams, and coastal rivers. BSc Hons (Chemistry) - University of Melbourne PhD - University of Melbourne (1992, Physical Chemistry) Professor Silvester's research centers on biogeochemical processes in rivers and wetlands, with three primary focuses: (i) hydrological controls on organic molecule mobility in landscapes, (ii) alpine moss-aqueous environment interactions using synchrotron infrared microspectroscopy, and (iii) peatland biogeochemical function during base-flow and storm events. His work integrates benchtop and synchrotron-based techniques to analyze natural organic matter and environmental contaminants, directly informing wetland management and peatland condition assessment in alpine regions. Key research areas include alpine ecosystem resilience, water quality dynamics, peatland carbon cycling, and legacy mining impacts on river systems. Analysis of his 15 most recent publications (2023-2025) reveals strong thematic consistency in alpine peatland research, with emphasis on carbon cycling, hydrological resilience, and contaminant impacts. His work increasingly incorporates molecular-level analysis (proteomics, amino acid profiling) and remote sensing to develop peatland condition metrics. The publications demonstrate interdisciplinary collaboration across environmental chemistry, ecology, and hydrology, with growing application of synchrotron techniques to understand ecosystem responses to climate change and human disturbance. Professor Silvester leads significant research initiatives including the NESP PeatSense project (2025-2027) improving peatland monitoring, and previously directed the ARC-funded "Rivers of Gold" project examining historical mining impacts. His current grant portfolio includes Australian Government, catchment management, and national park authority funding focused on peatland condition assessment, remote sensing applications, and dissolved oxygen management in river systems. He co-leads a specialized chemical laboratory for natural organic matter and contaminant analysis, utilizing both conventional and synchrotron-based techniques. His research group maintains strong industry partnerships with the Australian Alps National Parks, North East Catchment Management Authority, and Murray-Darling Basin organizations, ensuring practical application of findings to environmental management challenges in alpine and riverine ecosystems.

• Constrained Clustering
• Explainable AI
• Constraint Programming
• +4 more

Thi-Bich-Hanh DIEP-DAO serves as a Lecturer with Habilitation à Diriger des Recherches (HDR) at the University of Orleans, where she is affiliated with the LIFO laboratory (Laboratory of Informatics of Orléans). She holds significant administrative roles including THAT Lab correspondent at the Doctoral School and Member of the Board, demonstrating leadership in academic governance and doctoral education. Her research centers on constrained clustering methodologies, with evolving focus areas including explainable AI, constraint programming applications, and knowledge integration in machine learning systems. Early work established foundational approaches for constraint-based clustering using declarative frameworks, while recent publications demonstrate sophisticated extensions to mineral prospectivity mapping, social media analysis, and knowledge graph-enhanced image classification. Her research consistently bridges theoretical constraint programming with practical applications across diverse domains. Analysis of her publication trends reveals increasing emphasis on explainability and knowledge integration since 2020, with notable contributions to incremental clustering systems and graph-based knowledge representation. Her work maintains strong methodological consistency in constraint programming while expanding into new application areas, particularly evident in her 2023-2025 publications on mineral prospectivity mapping and tweet-based event characterization. As an HDR-qualified researcher, Dr. DIEP-DAO actively supervises doctoral students and contributes to academic training through her roles in the Doctoral School. Her extensive collaboration network, particularly with Christel VRAIN, has produced over 60 publications since 2010, reflecting consistent research productivity. She is integral to the LIFO laboratory's data mining and constraint programming research group, where her work on declarative clustering approaches forms a core research pillar. Current projects focus on knowledge graph integration for explainable image classification systems and constraint-based social media analysis frameworks.

David Liben-NowellView profile

Professor

• Computational Social Sciences
• Social Networks
• Theoretical Computer Science
• +5 more

David Liben-Nowell is a Professor in the Department of Computer Science at Carleton College, with research spanning computational social sciences, algorithmic modeling, and interdisciplinary applications of theoretical computer science. He contributes to ACM Technical Symposia on Computer Science Education (SIGCSE) and maintains active roles in academic leadership as an Associate Provost since 2022. AISTATS'25: Machine Learning & Social Choice SIGCSE'24: Student Enrollment Systems SIGCSE'22: Intro CS Pedagogy His research explores social network structures , spoken-word recognition , and computational biology through algorithmic frameworks, with applications in peer-to-peer systems and game theory. Recent work on AISTATS'25 addresses probabilistic ranking models, while SIGCSE publications focus on educational algorithms.

Ján SvoreňView profile

Associate Professor

• Astronomy
• Astrophysics
• Comet Research
• +3 more

Ján Svoreň is an Associate Professor and senior scientist at the Astronomical Institute of the Slovak Academy of Sciences in Tatranská Lomnica, specializing in interplanetary matter, comets, and meteors. With over 280 publications, he has significantly contributed to understanding solar system dynamics and celestial phenomena. Education: Studied astronomy and geophysics at Comenius University (1967–1972) Leadership: Chairman of the Scientific College of the Slovak Academy of Sciences for Earth and Space Sciences (since 2002) Affiliations: Member of the International Astronomical Union and Slovak Astronomical Society Research Interests center on comet and meteor studies, planetary science, and astrometry. His work explores asteroid composition, near-Earth objects, and solar system formation mechanisms, blending observational data with theoretical models. Scientific Contributions include the 2016 Science Popularizer Award and the 2002 asteroid naming Svoreň (1999 TE6) . His recent articles highlight trends in asteroid mining, comet X-ray production, and deep-space missions like OSIRIS-REx. Public Engagement is evident through collaborations with media and initiatives like the European Science Night . He actively participates in workshops and lectures, bridging scientific research with societal outreach.

Jia RongView profile

Researcher

• Data Mining
• Pattern Recognition
• Big Data Analysis
• +5 more

Dr Jia Rong is a Researcher in the Department of Data Science & AI at the Faculty of Information Technology, Monash University, Australia. His work focuses on data mining, pattern recognition, and AI applications in bioinformatics, digital health, behavioral analysis, and social media analysis. Research Interests: Data Mining, Pattern Recognition, Complex Data Analysis, Bioinformatics, Digital Health, Behavioral Analysis, Social Media Analysis Current Role: Researcher, Department of Data Science & AI, Faculty of Information Technology, Monash University Dr Rong’s recent publications highlight the integration of deep learning and fuzzy cognitive maps in diverse domains. Key areas include pavement patch detection , traffic network prediction , medical imaging , educational technology , and debiased predictive analytics . His work also explores distributed stream computing , emotion recognition , and privacy-preserving algorithms , demonstrating a strong emphasis on both theoretical and applied data science across engineering, healthcare, and social sciences.

• Intelligent Data Analysis
• Connected Health Applications
• Activity Recognition
• +7 more

Dr. Shuai Zhang serves as a Senior Lecturer in Computing Science at Ulster University's School of Computing . She joined the School of Computing and Mathematics and the Computer Science Research Institute in 2013 as an Associate Member and has since established herself as a prominent researcher in intelligent data analysis for connected health applications. Dr. Zhang earned her Bachelor's degree in Computer Science with first-class honors from Heilongjiang University in Northeast China, followed by an MPhil in Visual Arts Data Mining from the University of Bradford. She completed her PhD in Intelligent Data Analysis at Ulster University under the Vice-Chancellor's Research Scholarship. Her research focuses on intelligent data analysis applied to connected health, with particular expertise in activity recognition , assistive technology , and behavioral pattern modeling using sensor data. She develops sophisticated algorithms to interpret data from wearable and environmental sensors to support healthcare applications, especially for elderly care and dementia support. Her work bridges computer science with practical healthcare solutions, emphasizing user-centered design and real-world applicability. Analysis of Dr. Zhang's publication record reveals a strong trajectory in healthcare technology research, with increasing focus on machine learning applications for activity recognition, fall detection, and health monitoring systems. Her work demonstrates a progression from foundational data analysis techniques to sophisticated implementations in real-world healthcare settings, with recent publications emphasizing deep learning approaches, privacy-preserving technologies, and multi-sensor fusion techniques. Dr. Zhang actively contributes to significant research projects including the BT-Ireland Innovation Centre (BTIIC), Privacy Computing and Protection Research for IoT-enabled Smart Healthcare Systems, and The Gray Matters study focused on reducing Alzheimer's disease risk. She has served as Principal Investigator for the Gray Matters study and as Co-Investigator on numerous other healthcare technology initiatives. As a dedicated academic, Dr. Zhang supervises research students and collaborates extensively with interdisciplinary teams to advance connected health technologies. Her laboratory work focuses on developing and testing sensor-based systems in realistic environments, with strong connections to healthcare providers and technology developers. She is particularly interested in translating research findings into practical tools that can improve quality of life for individuals requiring healthcare support.

Diane M. McKnightView profile

Professor

• Limnology
• Biogeochemistry
• Environmental Engineering
• +4 more

Diane M. McKnight is a Distinguished Professor at the University of Colorado Boulder , affiliated with the Civil, Environmental and Architectural Engineering and Environmental Studies programs. She co-edits the journal Arctic, Antarctic and Alpine Research and leads fieldwork in the McMurdo Dry Valleys and Niwot Ridge Long-Term Ecological Research (LTER) sites. Her work bridges hydrology, biogeochemistry, and microbial ecology in extreme environments. Education : PhD in Environmental Engineering from Massachusetts Institute of Technology (1979) . McKnight’s research explores interactions between freshwater biota, trace metals, and natural organic matter in lakes and streams, with focus areas in the Colorado Rocky Mountains and Antarctica . She investigates climate-driven changes in stream metal concentrations, microbial mat dynamics in polar deserts, and wildfire impacts on water quality. Her work integrates field experiments, modeling, and laboratory analysis. Recent publications analyze silicon cycling , nitrogen fixation , and diatom dispersal in alpine and Antarctic ecosystems, with keywords spanning biogeochemistry , hydrology , and climate change . Her accolades include the Robert E. Horton Medal and Distinguished Professor title from the University of Colorado System. Scientific Awards : Distinguished Professor, University of Colorado System (2021) Robert E. Horton Medal, American Geophysical Union (2021) AEESP Distinguished Lecturer (2019) John Dalton Medal, European Geosciences Union (2015) Distinguished Research Lectureship, CU Boulder (2015) Hydrology Section Award, American Geophysical Union (2014) Member, National Academy of Engineering (2012) Fellow, American Association for the Advancement of Science (2009) Fellow, American Geophysical Union (2004) Meritorious Service Award, U.S. Geological Survey (1995) McKnight mentors PhD students Lane Allen and Jared Collins and contributes to public outreach through initiatives like the “My Water Comes from the Mountains” and “77 Degrees South: Welcome to the Dry Valleys, Antarctica” projects. She advocates for diversity in LTER and interdisciplinary collaborations between science and art.

Unil YunView profile

Professor

• Data Mining
• Pattern Mining
• Stream Mining
• +10 more

Professor Unil Yun serves as a faculty member in the Department of Computer Engineering at Sejong University, leading the Data Mining Lab (DM Lab) located in Chung 406A. His contact details include email (yunei@sejong.ac.kr) and phone (02-3408-2902), with lab operations centered at DM Lab Homepage . His research spans critical domains in computational intelligence and data systems: Core data methodologies: Data Mining (Pattern/Stream/Rule Mining), Pattern Analysis, and Big Data Processing Web and infrastructure systems: Web Search and Mining, Internet-based Systems, and Database Applications Emerging applications: Machine Learning, Vehicle Data Analysis in Telematics, and cross-domain Data Analytics The DM Lab drives innovation in real-time data processing frameworks, with particular emphasis on streaming data architectures and scalable mining techniques for telematics and web-scale applications.

• Databases
• Software Engineering
• Data Warehousing
• +6 more

Breiman Alexander Davidovich is an Associate Professor at the Department of Software Engineering, Faculty of Computer Science at the National Research University Higher School of Economics (HSE) in Moscow. He began working at HSE in 2010 and has 30 years of scientific and teaching experience. Previously, he served as an Associate Professor at the Department of Personal Computers and Networks at the Moscow State University of Instrument Engineering and Informatics from 1994 to 2013. He also works as a Senior Lecturer at the Luxoft Training Center since 2013 and as Head of the IT Department at Titan Meta LLC since 1996. National Research University Higher School of Economics (2010-present): Associate Professor/Invited Lecturer Moscow State University of Instrument Engineering and Informatics (1994-2013): Associate Professor Luxoft Training Center (2013-present): Senior Lecturer Titan Meta LLC (1996-present): Head of IT Department His educational background includes a Candidate of Technical Sciences degree (equivalent to PhD) from Moscow State Academy of Instrument Engineering and Computer Science in 1998, with a dissertation on "Database software for training systems with multimedia elements," and a degree in Computer Science from Moscow Institute of Instrument Engineering in 1994, specializing in "Computers, Complexes, Systems and Networks." Professor Breiman's research focuses on databases, software engineering, data warehousing, information systems, and knowledge engineering. His work spans theoretical database design, practical implementations of database systems, and educational applications of information technology. He has published extensively on database architecture, reliability engineering, and distributed systems, with a particular emphasis on practical applications in educational and business contexts. His teaching covers database systems, distributed databases, data warehouses, and advanced database topics across multiple programming languages and platforms. His publications reveal a consistent focus on database technologies, evolving from foundational database concepts to modern distributed and big data systems. He has explored specialized areas including embedded databases, social network analysis, process mining, and stream data processing systems, demonstrating both theoretical depth and practical application. Gratitude from the Faculty of Computer Science, HSE (July 2025) Best Teacher award (2021-2024) Best Teacher award (2016-2019) Best Teacher award (2013) Best Teacher award (2011) Professor Breiman has supervised numerous student theses at both bachelor's and master's levels, covering diverse topics in database systems, software engineering, and information systems. His students have developed applications ranging from distributed content delivery systems to health monitoring systems for diabetes patients. He teaches a variety of courses including Databases, Advanced Databases, Distributed Databases and Data Warehouses, and Database Construction, in both Russian and English. His teaching spans multiple campuses of HSE including Moscow, Perm, and Nizhny Novgorod. While not explicitly mentioning specific labs or research teams in the provided information, his work with students on practical implementations suggests active engagement with applied research projects, particularly in database technologies and their real-world applications.

Anjin LiuView profile

Researcher

• Concept Drift Detection
• Adaptive Data Stream Learning
• Multi-stream Learning
• +2 more

Anjin Liu is a Researcher at the Centre for Artificial Intelligence , Faculty of Engineering and Information Technology , University of Technology Sydney . His research focuses on Concept Drift , Adaptive Data Stream Learning , Multi-stream Learning , Machine Learning , and Big Data Analytics . His work addresses challenges in nonstationary environments through innovative methods like evolving gradient boosting , ensemble diversity optimization , and dynamic drift detection . He develops real-time systems for applications such as train carriage load prediction in collaboration with Sydney Trains. Scientific Contributions: Proposed EI-kMeans for drift detection via cluster-based histograms Developed Fuzzy Decision Trees to handle uncertainty in stream learning Advanced Multi-stream aggregation techniques for improved generalization He is available for Masters Research or PhD student supervision and has received funding for real-time transportation analytics projects.

Dimosthenis KifokerisView profile

Associate Professor

• Construction Management
• Construction Informatics
• Machine Learning
• +7 more

Dimosthenis Kifokeris is an Associate Professor and Docent in Construction Production and Management of Construction Projects at Chalmers University of Technology's Department of Architecture and Civil Engineering, where he also serves as the Coordinator for Artificial Intelligence. His research bridges construction engineering with digital technologies and organizational theory, focusing on how emerging technologies can transform traditional construction practices. His research interests span construction informatics (particularly machine learning, blockchain/DLT, IoT, and BIM), lean construction, constructability, and sustainable, circular, and regenerative production processes. He approaches these topics through interdisciplinary lenses including labor process theory, labor theory of value, organizational theory, and data science. His PhD thesis utilized supervised and unsupervised machine learning to appraise project constructability through risk analysis. Dr. Kifokeris has led or participated in Swedish and international research projects totaling over 57 million SEK, funded by Formas, SBUF, Vinnova, CMB, and Horizon Europe. His work demonstrates how digital technologies can address longstanding challenges in construction information management, safety analysis, and production processes. He holds significant leadership positions in the international construction research community, serving as Secretary of the Board of the European Council on Computing in Construction (EC3), a Board member of Construction Researchers on Economics and Organisation in the Nordic Region (CREON), and leader of the thematic group Byggprocess och Förvaltning in Sveriges Bygguniversitet (SBU). He chaired the EC3 2022 PhD Summer School in Rhodes and will chair the Thesis-in-Three competition at the EC3 2025 Conference in Porto. His recent publications reveal a strong trajectory toward integrating artificial intelligence with construction management, particularly focusing on how large language models can transform accident analysis and how blockchain can create more transparent and trustworthy information environments throughout the built asset lifecycle. His work represents a unique synthesis of technical innovation and critical organizational perspectives.

Eleazar LealView profile

Associate Professor

• Spatial Databases
• Stream Databases
• Database Algorithms
• +3 more

Eleazar Leal serves as an Associate Professor in the Swenson College of Science and Engineering at the University of Minnesota Duluth, where he advances research and education in database systems and high-performance computing. His academic work bridges theoretical computer science with practical data-intensive applications. His research program focuses on optimizing database technologies for modern hardware architectures, with particular emphasis on: Spatial database systems for geographic information processing Real-time analytics in stream database environments Parallel algorithm design for multicore processors GPU acceleration techniques for database operations Scalable data mining methodologies Hardware-aware query optimization These interconnected research areas address critical challenges in big data processing, enabling efficient handling of complex spatial and temporal datasets through innovative hardware-software co-design approaches. Dr. Leal maintains active collaborations with industry partners to translate theoretical advances into practical database solutions, with ongoing work exploring the integration of machine learning techniques into next-generation database engines. Professional correspondence should be directed to eleal@d.umn.edu or via telephone at +1 218 726 8452, with appointments scheduled exclusively through email consultation.

Mizuho IwaiharaView profile

Professor

• Database Systems
• Web Information Systems
• Text Mining
• +3 more

Professor Mizuho Iwaihara is affiliated with Waseda University's Faculty of Science and Engineering and Graduate School of Information, Production, and Systems. Her research focuses on database systems, web information retrieval, text mining, security/privacy, and social media analysis. She has led significant projects on Wikipedia edit history analysis, knowledge graph construction, and privacy-preserving frameworks. Key research areas: Database Query Processing, Web Information Systems, Text Mining, Knowledge Management, Social Media Her recent publications address semantic analysis of collaborative content, topic evolution tracking, and privacy behavior modeling. Over 85 papers with 349 citations reflect her impact in database and social media research. Scientific achievements include: Best Demo Award (2014) for WikiReviz Best Paper Award (2008) at IFIP e-Business Conference EC-Web2006 recognition Grants from Japan Society for the Promotion of Science (JSPS) span multiple projects on knowledge graph development, social content analysis, and privacy-preserving systems. She supervises numerous graduate students and leads the Data Engineering Laboratory at Waseda University.