Find Academic Supervisors

Lea HannolaView profile

Researcher

• Digital Twins
• Sustainable Manufacturing
• Industry 5.0
• +3 more

Lea Hannola is a researcher at the LUT School of Engineering Sciences (Lappeenranta–Lahti University of Technology LUT) focusing on digital twins, sustainable manufacturing, and Industry 5.0. Her work bridges technological innovation with eco-conscious business practices. Academic Rank: Researcher Contact: Lea.Hannola@lut.fi Hannola's research emphasizes digital twin applications for sustainability, including circular manufacturing, smart energy systems, and human-centric industrial design. She explores competencies required for Industry 5.0 adoption, workforce empowerment through digital tools, and integration of environmental and data-driven strategies in manufacturing. Her publications span topics like smart building management , quantum computing challenges , and supply chain sustainability , with a recurring focus on simulation, interoperability, and lifecycle value creation. Key trends include AI-driven sustainability, green HRM, and digital ecosystems. As a scholar, Hannola contributes to advancing digitally extended product-service systems and addressing sociotechnical challenges in industrial digitalization. Her work impacts manufacturing competitiveness, energy efficiency, and organizational transformation.

• Machine Learning
• Educational Data Mining
• Time Series Forecasting
• +5 more

Irena Koprinska is a prominent researcher at the University of Sydney with over 150 publications from 1996 to 2025. Her work spans multiple interdisciplinary domains with significant contributions to machine learning applications in educational technology, time series forecasting, and health informatics. She maintains strong research collaborations, particularly with Kalina Yacef (38 joint publications), Mashud Rana (26 papers), and Bryn Jeffries (22 papers), indicating leadership in her research group. Her research interests focus on practical applications of machine learning across diverse domains. In educational data mining, she has pioneered methods for predicting student performance in programming courses, analyzing syntax errors, and developing automated hint generation systems. Her work in time series forecasting has made significant contributions to solar power prediction using advanced neural network architectures. Additionally, she has applied machine learning techniques to medical domains, particularly in sleep disorder detection and analysis. The analysis of her 15 most recent publications (2022-2025) reveals a continued focus on educational technology and time series analysis, with increasing attention to interpretable methods and health applications. Her work demonstrates a consistent trajectory of applying sophisticated machine learning techniques to solve real-world problems across multiple domains, with particular emphasis on creating practical tools for education and renewable energy management. Notable Research Contributions: Development of the HINTS framework for automated programming hint generation Innovative approaches to multistep-ahead time series forecasting Applications of deep learning to sleep disorder detection Methods for predicting student performance in programming education Her publication record in top venues including Machine Learning journal, AIED, EDM, and IJCNN demonstrates significant impact in both machine learning and educational technology communities. The consistent output of high-quality research over nearly three decades indicates sustained scholarly productivity and leadership in her fields of expertise.

• Business Process Management
• Process Mining
• Complex Event Processing
• +5 more

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

• Autism Spectrum Disorder
• Biostatistical Methods
• Chronic disease
• +4 more

Xiaoyu Che is an Assistant Professor of Biostatistics at Columbia University's Mailman School of Public Health, where he serves as the principal biostatistician in the Center for Infection and Immunity (CII) at Columbia University Irving Medical Center. His work bridges statistical methodology with biomedical research, focusing on complex disease mechanisms through advanced data analysis approaches. Dr. Che received his academic training at prestigious institutions: BS in Mathematics from Zhejiang University (2006) PhD in Mathematics from Claremont Graduate University (2013) Dr. Che's research program centers on the development and application of statistical methods for multi-omics analyses, with particular focus on understanding the pathogenesis of chronic and neurodevelopmental conditions. His work spans multiple domains including Autism Spectrum Disorder (ASD), Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), and Gulf War Illness (GWI). He employs sophisticated biostatistical approaches to integrate diverse biological data types, revealing novel insights into disease mechanisms. His methodological expertise includes Bayesian statistics, metabolomic analysis, immune signature identification, and microbiome characterization, all aimed at translating complex biological data into meaningful clinical insights. Analysis of Dr. Che's publication record reveals a strong thematic focus on applying advanced statistical methods to understand complex disease mechanisms. His work consistently bridges biostatistical innovation with biomedical discovery, particularly in the areas of neurodevelopmental disorders and chronic fatigue conditions. The publications demonstrate progression from foundational methodological work to increasingly sophisticated multi-omics integration approaches, reflecting both technical growth and expanding research impact. His collaborative approach is evident through numerous high-impact publications with interdisciplinary teams across Columbia University and beyond. Dr. Che teaches BIST P8104: Probability in the Biostatistics MS degree program at Columbia, demonstrating his commitment to training the next generation of biostatisticians. While specific grant information isn't detailed in the provided materials, his extensive publication record across multiple high-impact journals suggests successful grant funding supporting his research program. As principal biostatistician in the Center for Infection and Immunity, Dr. Che plays a critical role in the analytical framework of the center's research initiatives. His work supports the center's mission to understand the relationship between infectious agents and human health through rigorous quantitative analysis. The collaborative nature of his research is evident in the diverse range of co-authors spanning immunology, virology, microbiology, and clinical medicine.

• Distributed Computing
• Applied Economics
• Policy and Administration Law
• +2 more

Naranker Dulay is a Professor in the Department of Computing at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the Centre for Cryptocurrency Research and Engineering, Centre for Smart Connected Futures, and the Distributed Software Engineering group. His research focuses on Distributed Computing, Applied Economics, Policy and Administration Law, Computer Software, and Information Systems. His work explores blockchain technologies, smart contracts, consensus algorithms, and distributed systems. Recent research includes optimizing post-trade processing using distributed ledgers and developing adaptive protocols for dispute resolution in smart contracts. He is also involved in cybersecurity and privacy-preserving technologies for data management. Key contributions include frameworks like Chainlog for logic-based smart contracts and FADE for self-destructing data. His articles span over a decade, emphasizing blockchain scalability, energy-efficient neural networks, and decentralized macro-programming in wireless sensor networks. Dr. Dulay collaborates across interdisciplinary domains, blending technical innovation with socio-technical challenges. His affiliations reflect a commitment to advancing smart connected futures through cutting-edge research.

• Sustainable Development
• Disruptive Innovation
• Entrepreneurship Education
• +3 more

Dr. Carla Riverola is a Senior Lecturer in the Department of Management at Griffith University's Griffith Business School. Her research focuses on disruptive innovation for sustainable development, particularly in plant-based foods and AI-driven educational strategies. She holds a Ph.D. in Information Technologies and Management from Ramon Llull University, Barcelona. Professional roles include Deputy Director of the Griffith Business School (2023-2024) and International Lead (2022-2023). She is affiliated with the Centre for Work, Organisation and Wellbeing and Social Marketing@Griffith. Education: Ph.D., Information Technologies and Management, Ramon Llull University M.Sc., IT Management, Ramon Llull University B.Eng., Computer Science, Ramon Llull University Affiliations: Centre for Work, Organisation and Wellbeing Social Marketing@Griffith Climate Action Network International Educators Her research bridges entrepreneurship, sustainable innovation, and AI applications. Recent work examines plant-based food adoption and AI's role in organizational decision-making. She has received the Group Award for Excellence in Teaching (2024) and the Knut Holt Best Paper Award (2017). Her articles span innovation diffusion, food sustainability, and technology networks. Dr. Riverola advises on policy and industry partnerships, contributing to initiatives like the Catapult Program Roadmap (Logan City Council). She actively collaborates with international stakeholders to advance sustainable development goals, particularly Climate Action (SDG 13) and Responsible Consumption (SDG 12).

Mahroo EftekhariView profile

Professor

• Building Energy Management Systems
• HVAC Systems Optimization
• Thermal Comfort Modeling
• +5 more

Professor Mahroo Eftekhari is a Professor in Building Services Engineering at Loughborough University, leading the Low Energy Building Services Engineering MSc programme. Her research focuses on energy-efficient building systems, thermal comfort, HVAC optimization, and renewable integration. She has pioneered control systems for airports and buildings, including MPC-based strategies to reduce energy use while enhancing occupant well-being. Notable contributions include the development of BISPA (Building & Industrial Services Pipework Academy), a national center for BIM and pipework training. Education: Holds qualifications including CEng (Chartered Engineer), DPhil (Doctor of Philosophy), FCIBSE (Fellow of Chartered Institution of Building Services Engineers), and SFHEA (Senior Fellow of the Higher Education Academy). Her academic career is marked by collaborations with Tata Steel, Mitsubishi R&D, and Vexo, yielding applied research in sustainable building technologies. Research Interests: Indoor Air Quality, Thermal Comfort Modeling, Zero Energy Buildings, Digital Twins, and Advanced Control Systems. She has developed innovative solutions like AI-driven thermal management for Building Energy Management Systems (BEMS) and interfaces to synchronize airport operations with energy systems. Awards & Grants: Secured funding from diverse bodies for projects such as adaptive thermal comfort models, BISPA infrastructure, and energy-efficient HVAC strategies. Her work emphasizes practical applications, including reducing CO₂ emissions via airport terminal optimization and improving renewable energy use in buildings. Lab & Teams: Leads the Building Energy Research Group, managing projects in closed-loop heating systems, IEQ monitoring, and hydronic system efficiency. The Civil Engineering labs house interactive BIM rigs launched with institutional and industry support.

Marco SerafiniView profile

Assistant Professor

• Database Systems
• Distributed Systems
• Data Science
• +3 more

Marco Serafini is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences (CICS). He leads the DREAM Lab (Data systems Research for Exploration, Analytics, and Modeling) and is part of the Center for Data Science. Prior to UMass, Serafini worked as a Senior Scientist at the Qatar Computing Research Institute (QCRI) and held a postdoctoral fellowship at Yahoo! Research in Barcelona. He earned his PhD in Computer Science from TU Darmstadt (Germany), where his thesis was recognized through nominations for best thesis awards across German, Swiss, and Austrian computer science societies. His research focuses on the intersection of database systems, distributed systems, and data science, emphasizing scalable architectures for big data analytics and machine learning. Key areas include computation pushdown in cloud DBMSs, graph neural network training systems, and efficient graph pattern matching. His work addresses challenges in tail latency mitigation, resource optimization, and transparent scaling of ML models. Serafini has contributed to influential systems like Arabesque (for distributed graph mining), E-Store (elastic partitioning), and Aion (event-time stream processing). He has been awarded an NSF CNS Core grant to advance scalable GNN training. His publications span top venues such as ACM SIGOPS, VLDB, and ICDE, reflecting his expertise in both theoretical foundations and practical system implementations. Professional recognition includes thesis nominations from major computer science societies and sustained contributions to open-source projects in distributed computing. Serafini advises students through the DREAM Lab, focusing on preparing the next generation of data systems researchers.

Syeda Amna SohailView profile

Lecturer

• Process Mining
• Data Privacy
• Metadata Management
• +3 more

Syeda Amna Sohail is an Lecturer at the University of Twente , affiliated with the Public Administration school and the Datamanagement & Biometrics department. Her research focuses on process mining, metadata management, and privacy-preserving techniques in healthcare contexts. She actively contributes to interdisciplinary projects addressing ethical challenges in AI and data governance. Key research interests include: Data privacy in healthcare processes Process mining applications for metadata analysis Ontology-based frameworks for FAIR data principles Ethical AI and surveillance technologies Recent work highlights collaborations with institutions like CAiSE and VMBO workshops. She has presented at conferences such as the 33rd International Conference on Advanced Information Systems Engineering and delivered invited talks on privacy value modeling. Her research has been published in peer-reviewed venues including Lecture Notes in Computer Science and MDPI journals. Professional activities include organizing workshops, contributing to conference proceedings, and participating in multidisciplinary initiatives aligning with UN Sustainable Development Goals. Her work emphasizes practical solutions for balancing privacy and utility in healthcare data systems.

Kaidi YangView profile

Assistant Professor

• Intelligent Transportation Systems
• Traffic Flow Theory and Control
• Multimodal Transportation Systems
• +5 more

Kaidi Yang is an Assistant Professor at the National University of Singapore (NUS) in the Department of Civil and Environmental Engineering, specializing in Intelligent Transportation Systems and related fields. He holds a PhD from ETH Zurich (2019), an M.Sc. in Control Science and Engineering from Tsinghua University (2014), and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University (2011). His research focuses on advancing traffic control, connected/automated vehicles, shared mobility systems, and data privacy in transportation. He has contributed to developing algorithms for efficient traffic signal control, platooning coordination, and privacy-preserving data sharing in transportation networks. Education: Ph.D., Civil and Environmental Engineering (Transportation), ETH Zurich, 2019 M.Sc., Control Science and Engineering, Tsinghua University, 2014 B.Sc./B.Eng., Dual Degrees in Pure/Applied Mathematics and Automation, Tsinghua University, 2011 Yang has received prestigious awards including the Swiss National Science Foundation’s Postdoc Mobility Fellowship (2021–2022) and the IEEE ITS Conference Best Student Paper Award (2020). He serves as an Associate Editor for the IEEE Conference on Intelligent Transportation Systems (2024). His work bridges theoretical advancements in operations research, robotics, and machine learning with practical applications in urban mobility systems. Recent efforts emphasize integrating privacy-preserving techniques into traffic management and optimizing mixed-autonomy platoon control.

Yongjie Jessica ZhangView profile

Professor

• Finite Element Method
• Isogeometric Analysis
• Image-Based Geometric Modeling
• +5 more

Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .

• Graph Analytics
• Graph Mining
• Graph Algorithms
• +3 more

Dr. Lijun Chang is an Associate Professor in the School of Computer Science at the University of Sydney. He holds an ARC Future Fellowship (2019–2022) and an ARC DECRA Fellowship (2015–2017). Previously, he was at the University of New South Wales. His research focuses on graph analytics, mining, algorithms, and network science. He teaches courses like INFO5011 (Competitive Programming), COMP5313 (Large Scale Networks), and COMP9120 (Database Management Systems), and coaches the USYD Programming Competition Teams. Education: B.Eng. in Computer Science & Technology from Renmin University of China; PhD from the Chinese University of Hong Kong. Research highlights include scalable graph processing systems (e.g., ScaleG), densest subgraph detection, and graph similarity search. He leads projects funded by ARC grants such as 'Advanced Search of Cohesive Subgraphs in Big Graphs' (2018) and 'Directionality-Aware Cohesive Subgraph Search' (2022). His work emphasizes efficient algorithms for large-scale networks and graph databases. Awards : ARC Future Fellow, ARC DECRA Fellow Students : Yu KONG, Rashmika MATHTHAKA GAMAGE, Mouyi XU Labs/Teams : Focuses on graph algorithms and systems research, contributing to open-source tools and large-scale network analysis.

Robert L. HicksView profile

Professor

• Environmental Economics
• Natural Resource Economics
• Welfare Economics
• +3 more

Robert L. Hicks is a Professor of Economics and Marine Science at The College of William and Mary, holding joint appointments in the Department of Economics and the School of Marine Science. He is affiliated with the Environmental Science and Policy Program and the Thomas Jefferson Program in Public Policy. With a Ph.D. from the University of Maryland and a B.A. from North Carolina State University, Hicks has been a visiting professor at institutions in Germany and Spain, including the University of Bonn and the University of Hannover. His research focuses on environmental and natural resource economics, welfare economics, and econometrics, with notable contributions to fisheries management, eco-labeling, and recreational resource valuation. He has served on the editorial board of Marine Resource Economics and received grants from the National Science Foundation, U.S. Department of Commerce, and philanthropic organizations like the Bill and Melinda Gates Foundation. His awards include the Alumni Fellowship Award and the Plumeri Award for Faculty Excellence. Education: B.A., North Carolina State University Ph.D., University of Maryland Research Interests: Environmental economics emphasizes sustainable resource management, while his work in econometrics advances quantitative methods for policy analysis. Key areas include recreational fishing valuation, eco-labeling impacts, and spatial modeling in fisheries. His research bridges theoretical frameworks with practical applications in coastal and marine environments. Grants & Awards: Recipient of grants from NSF, U.S. Department of Commerce, William and Flora Hewlett Foundation, and Bill and Melinda Gates Foundation Alumni Fellowship Award Plumeri Award for Faculty Excellence Labs & Affiliations: Active in interdisciplinary collaborations, Hicks contributes to policy boards such as NOAA’s Science Advisory Board and aids in shaping environmental and marine policy through academic partnerships.

Ton DiekerView profile

Associate Professor

• Stochastic Processes
• Operations Research
• Queueing Theory
• +3 more

Ton Dieker is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University's School of Engineering and Applied Science. He is a DSI Member and affiliated with the Center for Financial and Business Analytics. His research focuses on stochastic models, simulation techniques, and high-dimensional stochastic analysis. Dieker holds a Master’s in Operations Research from Vrije Universiteit Amsterdam (2002) and a PhD in Mathematics from the University of Amsterdam (2006). Dieker’s research explores stochastic processes, queueing theory, and rare-event simulation. He has contributed to methodologies like QPLEX for stochastic systems and advanced techniques in sequential analysis and exact simulation. His work bridges theoretical foundations with computational applications, addressing challenges in large-scale networks and high-dimensional problems. Education: PhD in Mathematics, University of Amsterdam, 2006 Master’s in Operations Research, Vrije Universiteit Amsterdam, 2002 Dieker’s articles emphasize computational modeling, stochastic calculus, and optimization, reflecting his focus on bridging theory and practice. His work often addresses efficiency in simulation, exactness in algorithms, and scalability in complex systems. Awards: Goldstine Fellowship (IBM Research) NSF CAREER Award Erlang Prize (INFORMS) Fouts Family Early Career Professorship (Georgia Tech) He serves on editorial boards for Operations Research and Mathematics of Operations Research . His research also addresses capacity management in stochastic networks and applications in cloud computing and commodity sourcing.

Barzan MozafariView profile

Associate Professor

• Database Systems
• Data Management
• Machine Learning
• +3 more

Barzan Mozafari is an Associate Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, and leads a research group focused on scalable database systems and approximate computing. He holds a PhD from UCLA (2011) and was a Postdoctoral Associate at MIT. His research emphasizes data-intensive systems, combining statistical models, optimization, and machine learning to enhance database performance and predictability. Notable projects include BlinkDB (approximate query processing), DBSeer (database diagnosis), and VerdictDB (platform-independent AQP). He co-founded Keebo, advancing data learning technologies, and contributed to SnappyData (acquired by TIBCO). His work spans technical innovations like CATS/VATS scheduling algorithms (adopted in MySQL/MariaDB) and grants such as ConFlux (NSF-funded supercomputing-big data integration) and a smart black-box for autonomous vehicles. Awards include the NSF CAREER Award and Best Paper recognitions at SIGMOD and EuroSys. Teaching includes EECS 484/584 (Database Management Systems) and advanced topics courses. He advises students like Yongjoo Park (SIGMOD Dissertation Runner-Up), Jiamin Huang, and Boyu Tian.