Find Academic Supervisors

• Macroeconomics
• Monetary Policy
• Credit Cycles
• +5 more

Lenno Uusküla is a Chief Economist at Luminor and an Associate Professor of Macroeconomics at the University of Tartu's School of Economics and Business Administration (Faculty of Social Sciences). He previously served as a Senior Economist at the Bank of Estonia (2003–2021) and held research and teaching roles at Tallinn University of Technology and Estonian Business School. His research focuses on macroeconomic policy, inflation dynamics, credit cycles, and financial stability, with emphasis on empirical analysis and policy relevance. Education: PhD in Economics, European University Institute (2011) Research Master's Degrees from European University Institute (2006), University of Tartu (2003), and College of Europe (2002) Research Interests: Macroeconomic policy transmission, inflation expectations, credit gaps, firm dynamics, business cycle analysis, and the Estonian economy. Articles Trends: His recent work explores macro-financial linkages, monetary policy spillovers, and the application of machine learning in forecasting inflation expectations. Key contributions include frameworks for measuring credit risks and analyzing non-performing loans in EU countries. Administrative Roles: Vice Dean of Research and Development at University of Tartu (2021), Head of Research at Foresight Centre (2021–2022). Member of Royal Economic Society, American Economic Association, and other professional bodies. His research has been published in Journal of Economic Dynamics and Control , Journal of Financial Economic Policy , and others. Labs/Teams: Active in the Foresight Centre for economic forecasting and policy analysis. Collaborates with central banks and academic institutions on macroeconomic modeling.

• Business Analytics
• Information Systems
• Data Science
• +6 more

Dr. Ehsan Abedin serves as a Lecturer and coordinator for the Bachelor of Business Analytics, Master of Applied Business Analytics, and Professional Certificate in Data Analytics and Business Intelligence at Flinders University's College of Business, Government and Law. He is a Full Member of both the Digital Trust Research Group and Centre for Social Impact, driving data-driven solutions for societal challenges through interdisciplinary collaboration. His academic foundation includes a PhD in Computing and Information Systems from the University of Melbourne (2023), preceded by research fellowships at The University of Melbourne and Monash University where he contributed to Australian Research Council projects on unstructured data analysis and machine learning applications. Ehsan's research program centers on Business Analytics with specialized expertise in Text Analysis, Social Media Analysis, and Data-driven Decision-making. His work bridges Information Systems theory and practical applications in social commerce, online consumer reviews, and social network dynamics, emphasizing ethical AI deployment and societal impact. Recent publications reveal a trajectory toward applied analytics in education and business contexts, featuring text analysis for business insights, AI-integrated MBA pedagogy, and learning analytics for student engagement. These works demonstrate cross-disciplinary integration of NLP, machine learning, and educational technology to solve real-world problems. His accolades include: Vice-President and Executive Dean's Teaching and Learning Innovation Award (Flinders University, 2025) W. E. and C. H. H. Cook Memorial Award (University of Melbourne, 2020) Google Travel Scholarship (University of Melbourne, 2019) Melbourne Research Scholarship (University of Melbourne, 2018) Chancellor's Letter of Appreciation (University of Tehran, 2018) Educational and Research Festival Winner (University of Tehran, 2016) As academic coordinator for analytics programs, Ehsan oversees curriculum development and delivery while leading research initiatives including Chief Investigator roles for the Transforming Energy Markets Research Centre grant (2023) and ARC projects on unstructured customer data and social virtue epistemology. His professional engagement spans reviewing for top conferences like ICIS and PACIS, and membership in the Association for Information Systems and Australian Computer Society. Through the Digital Trust Research Group and Centre for Social Impact, he collaborates on projects addressing UN Sustainable Development Goals, particularly leveraging analytics for social innovation, community resilience, and ethical technology adoption in complex societal systems.

Ramsey M. WehbeView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +5 more

Dr. Ramsey M. Wehbe, MD, MSAI is a dual-appointed Assistant Professor at the Medical University of South Carolina (MUSC) , holding positions in both the Division of Cardiology and the Biomedical Informatics Center . As a physician scientist , he merges clinical cardiology with artificial intelligence to advance cardiovascular care. Education: Duke University (undergraduate), University of North Carolina at Chapel Hill (MD), Northwestern University (internal medicine residency, cardiovascular disease fellowship, and advanced heart failure fellowship) Specialized Training: Master of Science in Artificial Intelligence from Northwestern McCormick School of Engineering Dr. Wehbe's research interests focus on applying deep learning techniques to multi-modal unstructured clinical data including cardiac imaging, electronic health records, and sensor data. His work aims to uncover pathophysiologic insights , enhance diagnostic accuracy , and improve patient outcomes in heart failure management. The Heart AI Lab (HEAL) under his leadership develops innovative approaches to bridge AI and clinical cardiology . His recent publications demonstrate a strong emphasis on large language models for clinical applications, AI-driven cardiac imaging analysis , and predictive modeling for heart failure patients. The research spans multimodal data fusion , electronic health record mining , and clinical decision support systems . While the text doesn't explicitly list scientific awards , it does mention he has received multiple research grants in the field of clinical artificial intelligence applications. As part of MUSC's advanced heart failure program , he works closely with LVAD patients and explores AI-enhanced phenotyping of heart failure syndromes.

Marcelo H. KobayashiView profile

Professor

• Computational Fluid Dynamics
• Fluid Mechanics
• Multidisciplinary Design Optimization

Marcelo H. Kobayashi is a Professor in the Department of Mechanical Engineering at the University of Hawaii at Manoa . He holds dual PhDs in Mechanical Engineering (1994) and Mathematics (2003) from the Technical University of Lisbon/IST, Portugal. His research spans Computational Fluid Dynamics (CFD) , Fluid Mechanics , and Multidisciplinary Design Optimization (MDO) , integrating advanced numerical methods and genetic algorithms to solve complex engineering problems in aerospace, biomechanics, and defense applications. CFD: Stream function methods, finite volume schemes for Navier-Stokes equations, and Padé approximations. Fluid Mechanics: Stokes flows, particle dynamics in rotating systems, and acoustic wave propagation in shear layers. MDO: Hybrid genetic programming for metamaterials, cellular division optimization for control surfaces, and bio-inspired designs for micro aerial vehicles. His recent publications reflect the application of evolutionary algorithms , topological optimization , and aeroelastic modeling to challenges in air vehicle design , thermal management , and biological systems . He has led projects funded by AFOSR , NSF , and US Army , including grants for tsunami debris impact analysis and nanosat thermal control systems. As Director of the Hawaii Open Supercomputing Center , he contributes to computational research infrastructure. His teaching includes graduate courses in Computational Fluid Dynamics , Numerical Methods , and Advanced Aerodynamics .

Leibo LiuView profile

Research Fellow

• health data science
• artificial intelligence
• machine learning
• +3 more

Leibo Liu is a Research Fellow at the Centre for Big Data Research in Health (CBDRH) at the University of New South Wales (UNSW) Sydney and a Data Scientist at The George Institute for Global Health (TGI). Education: PhD in Big Data Research in Health from Centre for Big Data Research in Health (CBDRH), University of New South Wales (UNSW), Australia Research Interests: Liu specializes in applying natural language processing (NLP) and deep learning to extract information from unstructured health data, particularly clinical notes. His key research areas include de-identification and clinical coding of free-text data from electronic medical records (EMRs), and predictive modeling using brain imaging data with convolutional neural networks and transformer-based architectures. He developed an end-to-end deep learning framework for removing personally identifiable information (PII) from health data, implemented in the Cardiac Analytics and Innovation (CardiacAI) project. Current Projects: Next-generation Clinical Registry project at CBDRH Outcome PredicTion in IntraCerebral Hemorrhage Study (OPTICS) at TGI (leads machine learning model development) Professional Background: With expertise spanning machine learning, software engineering, and big data analysis, Liu brings a unique combination of skills to health data science. His publication record includes 1 Book Chapter, 9 Journal articles, 1 Conference Paper, 1 Conference Abstract, and 6 Preprints.

Andreas Miroslaus WichertView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +7 more

Andreas Miroslaus Wichert is an Associate Professor at the University of Lisbon, affiliated with INESC-ID (GAIPS research group). His academic journey includes studies in computer science at the University of Saarland and a PhD in computer science from the University of Ulm (2000). He has taught courses like Artificial Intelligence, Machine Learning, Deep Learning, and Quantum Artificial Intelligence at institutions including University of Lisbon and Technical University Munich. Born in Wrocław, Poland, with a grandfather who was a Polish officer (Katyn massacre memorialized) Current affiliations: University of Lisbon, INESC-ID, GAIPS research group Research Interests Andreas specializes in Artificial Intelligence , Machine Learning , Quantum Artificial Intelligence , and Neural Networks . His work explores intersections between quantum computing and AI, as detailed in his 2024 book Quantum Artificial Intelligence with Qiskit . He also investigates cognitive systems and high-dimensional data indexing through the HEIDI project. Scientific Awards Recipient of multiple teaching excellence awards for courses in: Logic for Programming (24/25) Machine Learning (21/22, 23/24, 24/25) Decision Support Systems (14/15) Intelligent Multimedia Databases (14/15) Advising Currently supervises PhD students Maria Osorio, Jose Miguel Penedo Ramos, and Joaquim Domingos Mussandi. Past advisees include Dr. Luis Tarrataca, Dr. Angelo Cardoso, Dr. Joao Sacramento, Dr. Catarina Pinto Moreira, and Dr. Luis Sa-Couto. Projects & Labs Lead developer of the HEIDI project (High-Dimensional Indexing), and active member of the GAIPS research group since 2009.

• Biomedical Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +4 more

Hoifung Poon is General Manager at Microsoft Health Futures and affiliated faculty at University of Washington Medical School. He leads Real-World Evidence (RWE) research focusing on AI applications for precision health. Poon earned a B.S. with Distinction in Computer Science from Sun Yat-Sen University and a Ph.D. in Computer Science and Engineering from University of Washington. Specializes in biomedical AI research Focuses on structuring unstructured medical data Co-PI for DARPA Big Mechanisms projects Research strength lies in biomedical multimodal learning (text, radiology, pathology, genomics) and causal learning for real-world evidence generation. His team develops methods for LLM self-verification , multi-modal fusion , and biases correction in observational data. Publications show expertise in Nature , Nature Methods , and NEJM AI , covering topics from digital pathology to clinical text analysis. Scientific recognition includes: Best Paper Awards at NAACL, EMNLP, and UAI Winner of ACM Health Best Paper Award Named Technology Champion 2022 by Puget Sound Business Journal

• Data Mining
• Artificial Intelligence & Machine Learning
• Bioinformatics & Computational Biology
• +6 more

Professor Raymond Tak-Yan Ng is a distinguished faculty member in the Department of Computer Science at the University of British Columbia's Faculty of Science. His research spans data mining, bioinformatics, health informatics, and natural language processing. He serves as the Director of the Data Science Institute at UBC and holds the Canadian Research Chair on Data Science and Analytics. Additionally, he works part-time as the Chief Informatics Officer at the PROOF Centre of Excellence for the Prevention of organ failures based at St. Paul's Hospital. Professor Ng received his academic training at prestigious institutions: B.Sc. (Hons) in Computer Science from the University of British Columbia (1986) M.Math. in Computer Science from the University of Waterloo (1988) Ph.D. in Computer Science from the University of Maryland, College Park (1992) Professor Ng's research focuses on developing data mining tools that place the human user front and center in the discovery process. His work emphasizes constraint-based mining, unified models for analysis and mining, performance optimization, and new data mining capabilities like outlier detection. In bioinformatics, he applies data mining techniques to link clinical and genomic data, particularly for cancer analysis. His health informatics research develops biomarker panels for conditions related to organ failures in hearts, lungs, and kidneys. His natural language processing work focuses on creating metadata from unstructured conversations to facilitate access to raw data. Professor Ng has received numerous prestigious awards recognizing his contributions: Outstanding Paper Award Genome BC Award Academic Data Leader List Killam Research Prize Fellow of the Royal Society of Canada Bio-IT Best Practices Award CASCON Best Exhibit Award Best Paper Award from ACM SIGMOD Professor Ng has supervised numerous graduate students, with doctoral dissertations completed from 2009-2021 and master's theses from 2013-2022 covering diverse topics from RNA-binding proteins to financial knowledge graphs. His research has been supported by significant grants enabling work at the intersection of computer science, healthcare, and data science. His collaborative approach has led to partnerships with medical researchers, clinicians, and industry partners to translate computational methods into practical healthcare applications. Professor Ng leads the Data Systems and Mining Laboratory at UBC and is deeply involved with multiple research centers including the CAIDA (Centre for Artificial Intelligence Decision-making and Action), the Data Science Institute, the Blockchain at UBC initiative, and the Language Sciences Institute. He collaborates extensively with the PROOF Centre of Excellence and various health research institutes including the Centre for Heart Lung Innovation and the Providence Health Care Research Institute. His interdisciplinary work bridges computer science with clinical applications through partnerships with medical researchers and healthcare institutions.

• Database Systems
• Natural Language Processing
• Knowledge Graphs
• +1 more

Nicolas HIOT is a Post-doctoral fellow at the University of Orleans affiliated with the LIFO laboratory (Laboratoire d'Informatique Fondamentale d'Orléans) and the Pamda project. His research bridges database systems, natural language processing, and medical informatics with a focus on text-to-database integration and consistency maintenance. His research interests center on: Database Systems for medical applications with emphasis on consistency and evolution Natural Language Processing for clinical text analysis and relation extraction Knowledge Graph construction from unstructured textual data Medical Informatics applications for healthcare data management Analysis of his 15 most recent publications (2020-2024) reveals a cohesive research trajectory at the intersection of databases and NLP. Key thematic clusters include automated medical database construction from clinical texts, consistency management in evolving RDF/property graph systems, and clinical entity/relation extraction for knowledge graphs. His work consistently addresses real-world challenges in healthcare data integration through tools like DataFix and ArchiTXT, demonstrating strong translational potential. Nicolas HIOT actively contributes to the LIFO research laboratory at the University of Orleans, collaborating extensively with Jacques CHABIN, Mirian HALFELD-FERRARI, and Dominique LAURENT. His technical output includes multiple software systems for database evolution management and clinical text processing, reflecting both theoretical contributions and practical implementations in semantic data management.

• Software Quality
• Aesthetics in Programming
• Probability and Statistics
• +5 more

Asst. Prof. Lachezar Tomov, PhD is a faculty member at the Department of Informatics , New Bulgarian University (NBU), with 14+ years of experience in software engineering, optimization methods, and web programming. His research spans software quality, programming aesthetics, probability/statistics, and the history/philosophy of science. He collaborates with Medical University-Sofia and BAS Institute of Mathematics and Informatics on epidemiological studies, particularly in infectious diseases and COVID-19 pandemic modeling. Research Interests include: Software Quality and Aesthetic Metrics Epidemiological Modeling and Medical Statistics Risk Management and Statistical Process Control History/Philosophy of Science and Mathematics Crosstalk between Liberal Arts and STEM Scientific Awards : 2021 BAIT Award - First Place in Practical IT Teaching SUPER STEM AMBASSADOR Gold Medal (2021-2023) Grant Collaborations : NBU Long-Term Energy Consumption Forecasting Project (2016) MU-Sofia Hepatitis A Clinical Data Modeling (2020) NBU Faculty Interdisciplinary Conference (2022) Central Development Fund Project #1603 (2022) Labs & Teams : Collaborates with NBU Risk Analysis Laboratory , multidisciplinary teams at Medical University-Sofia, and contributes to open-access science platforms like BG Nauka and Venets journal.

Omar Khadeer HussainView profile

Associate Professor

• Logistics and Supply Chain Management
• Distributed and Grid Systems
• Decision Support
• +3 more

Dr. Omar Khadeer Hussain serves as an Associate Professor and Deputy Head of School (Research) at the School of Business, UNSW Canberra. He has been with the School since February 2014, initially working as a Lecturer and Senior Lecturer before his current appointment. Prior to joining UNSW, he worked as a Senior Research Fellow at Curtin University. Dr. Hussain's educational background includes a Bachelor of Technology in Computer Science from JNTU (2002), a Master of Research in Computer Science from La Trobe University (2004), and a Doctor of Philosophy in Information Management from Curtin University (2008). His research focuses on Logistics and Supply Chain Management, with particular emphasis on Supply Chain Risk Management, Distributed and Grid Systems, Decision Support, and Group Support Systems. Dr. Hussain applies these areas to develop knowledge synthesis from data for business applications such as decision making, risk management, cloud service management, new product development, and milk quality management. His work incorporates predictive analytics to enable informed business decision making, with recent research increasingly integrating artificial intelligence and large language models for supply chain risk identification and management. Analysis of Dr. Hussain's recent scholarly output reveals a strong focus on applying cutting-edge AI techniques to supply chain challenges. His work spans systematic literature reviews on supply chain risk modeling, development of frameworks for SLA violation prevention in Cloud of Things environments, and innovative applications of explainable AI in various domains. There is a clear trend toward leveraging large language models for event identification in supply chain risk management and developing dual-sided decision frameworks that integrate multiple stakeholder perspectives. His research bridges theoretical advances with practical applications in logistics and business engineering. Curtin Business School New Researcher of the Year award for 2012 Prize for Early Career Researcher, Curtin Business School (2013) Chancellor's thesis commendation award, Curtin University (2008) Master Prize – Computer Science, La Trobe University (2004) Dr. Hussain has successfully secured multiple competitive research grants, including ARC Linkage Projects on 'Economically Efficient Green Logistics through Cyber Physical Systems' (2016) and 'Intelligent CRM through Conjoint Data Mining of Heterogeneous Sources' (2015), both with Professor Elizabeth Chang as lead CI. He has also supervised 9 PhD students to completion, serving as both main and joint supervisor. While specific lab or team information isn't explicitly mentioned in the available text, Dr. Hussain's research appears to be conducted within the School of Business at UNSW Canberra, likely collaborating with colleagues across business disciplines and computer science to address complex supply chain and logistics challenges through interdisciplinary approaches.

• Human Activity Recognition
• Process Mining
• Transfer Learning
• +6 more

Dr. Ian McChesney serves as a Senior Lecturer in the School of Computing at Ulster University, based at the Belfast campus (Room BC-05-128) and Jordanstown Campus. His research spans Human Activity Recognition, Process Mining, and Transfer Learning with significant contributions to Autonomic Computing and Open Data initiatives. Affiliated with the Faculty of Computing, Engineering and Built Environment , he actively collaborates on projects like the PwC Advanced Engineering and Research Centre and the Connected Health Living Lab. His research interests focus on Human Activity Recognition (91% fingerprint match), Process Mining (57%), and Transfer Learning (45%), with applications in smart homes, business processes, and healthcare. Key methodologies include semi-Markov models for IoT device management, synthetic data generation for autonomic systems, and semantic enrichment of HAR datasets. His recent work shows increasing emphasis on educational frameworks for competency-based computing education in the UK. Among his 49 research outputs and 3 datasets, notable contributions include the InSync dataset and research on dyslexia in programming. He received the Best Paper Award ICAS 2025 for work on synthetic data generation. His projects include the TRAXX Fusion consultancy (2014-2016) and current involvement in the PwC Advanced Engineering Centre (2021-2026). Supervision: Mentored 4 students through supervised research work Grants: Contributed to KTP Programme with MJM Marine Limited (2022-2025) and Connected Health Living Lab (2018) His work supports UN Sustainable Development Goals through applications in healthcare optimization, educational innovation, and industrial process improvement. Current projects focus on few-shot learning, large language models, and human activity recognition in connected health environments.

• Data Science
• Big Data
• Natural Language Query Processing
• +4 more

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

• Machine Learning
• Artificial Intelligence in Healthcare
• Digital Health & mHealth
• +7 more

Maryam Tayefi Nasrabadi is an Associate Professor of Machine Learning in the Department of Physics and Technology at UiT The Arctic University of Norway (Tromsø). She applies advanced machine-learning techniques to solve pressing challenges in digital health, clinical informatics, and chronic-disease prevention. Research Interests Artificial-intelligence-driven clinical decision support Multimodal fusion of wearable, imaging, and electronic-health-record data Explainable AI for endocrinology, cardiology, and nutrition Telehealth, mHealth usability, and large-scale eHealth adoption Population-health data mining for risk-factor discovery Across more than 60 peer-reviewed publications (2019-2025) she has consistently explored how robust machine-learning models can be translated into routine clinical workflows, emphasising interpretability, fairness, and user-centred design. Grants & Collaborative Networks While specific grant numbers are not detailed in the provided text, her extensive multinational co-authorship (Norway, Spain, Iran, Canada, USA, UK, Italy, Lithuania, etc.) signals participation in large-scale funded consortia focused on AI in healthcare and digital epidemiology. Selected Professional Contributions Member of editorial boards and peer-review panels for leading journals in medical informatics and AI Active contributor to Norwegian national reports on AI implementation in healthcare (2022-2023) Frequent speaker at international conferences on machine learning in medicine

• Data Science
• Artificial Intelligence
• Healthcare Analytics
• +3 more

Dr. Andrew Peng is a Lecturer (Research) at the Australian Artificial Intelligence Institute (AAII) within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS), Australia. With dual PhDs from UTS (2015) and Beijing Institute of Technology (2013), he has published 45 peer-reviewed papers across top venues like IEEE ICDM, COLING, and Frontiers in Molecular Biosciences. Education: Dual PhD (2013-2015) from Beijing Institute of Technology and University of Technology Sydney His research focuses on Data Science , Artificial Intelligence , and Healthcare Analytics , addressing challenges in medical data analysis, unstructured clinical text processing, and federated learning frameworks. Recent publications explore: Deep graph clustering for community detection Privacy-preserving medicine shortage detection via social media Time-aware medication recommendation using dynamic treatment regimes Knowledge tracing enhancements for online education Contrastive learning approaches for ICD coding Hypergraph-based sequential diagnosis prediction Dr. Peng has secured over AUD $1M in external research grants and serves as Subject Coordinator for undergraduate/postgraduate courses. He contributes to professional leadership through roles as Web Chair at AJCAI 2021 and ADMA 2021, PC member for major conferences, and reviewer for journals like NeurIPS and AAAI. His work spans collaborations with universities, industry, and government agencies.