Find Academic Supervisors

Kanas AngelosView profile

Professor

• Finance
• International Finance
• Financial Markets
• +4 more

Professor Kanas Angelos serves as a Professor of Finance in the Department of Economics at the University of Piraeus' School of Economics, Business and International Studies. His academic leadership spans editorial roles, extensive publication in top finance journals, and significant contributions to economic policy through European Commission and parliamentary engagements. Education: Degree (EXCELLENT) in Economics from ASOEE with annual Chalkiopoulos Foundation scholarships M.Sc in Finance from University of Strathclyde (Bodossakis Foundation full scholarship) Ph.D. in Finance from University of Aston (Hellenic State Scholarships Foundation full scholarship) Research Focus: His work centers on Finance with specialized expertise in International Finance , Systemic Risk , and Data Envelopment Analysis . Recent research explores environmental finance intersections (CO2 emissions and financial stability) and causal networks in US industry portfolios, demonstrating methodological innovation through Bayesian and directional distance function approaches. Publication Trends: His 2019-2025 publications reveal evolving focus from traditional banking risk and dividend policy toward systemic risk modeling, environmental finance, and advanced efficiency analysis. Key journals include Journal of Financial Stability, Journal of Operational Research Society, and Journal of Banking and Finance, with increasing emphasis on sustainability and computational methods. Scientific Recognition: NATO Postdoctoral Fellowship Professional Impact: As Associate Editor for Applied Financial Economics and Empirical Economics, and Board Member for European Journal of Finance, he shapes academic discourse. His refereeing for 25+ journals and contributions to European Economic Forecasts demonstrate policy-relevant scholarship. Teaching includes core undergraduate finance courses with active office hours maintained through 2024-2025.

Mike IrvineView profile

Adjunct Professor

• Mathematical Modeling
• Biostatistics
• Machine Learning
• +4 more

Dr. Mike Irvine serves as a senior scientist in Data & Analytics Services at the BC Centre for Disease Control (BCCDC) and holds adjunct professor appointments in the Faculty of Science at the University of British Columbia and the Faculty of Health Sciences at Simon Fraser University. His interdisciplinary work bridges mathematical modeling, biostatistics, and machine learning to address critical public health challenges across multiple disease domains. Dr. Irvine earned his MSci in Mathematics from University College London, followed by a PhD in Mathematical Ecology and Complexity Science and an MSc in Complexity Sciences from the University of Warwick. His postdoctoral training included evaluating neglected tropical disease elimination campaigns with the Bill & Melinda Gates Foundation modeling consortium. He received a CIHR Health Systems Impact Fellowship to collaborate with BCCDC and the BC Ministry of Mental Health & Addictions on overdose response evaluation. His research focuses on Bayesian evidence synthesis methods that integrate diverse data sources for disease epidemiology and public health applications. This methodological expertise supports intervention evaluations across vector-borne diseases, sexually transmitted and blood-borne infections, respiratory infections, and opioid-related overdoses. His publications demonstrate consistent methodological innovation applied to pressing public health issues. Recent publication trends reveal strong emphasis on pandemic response (particularly COVID-19 transmission dynamics and vaccine safety) and the opioid crisis (overdose surveillance and naloxone distribution modeling), reflecting his responsiveness to evolving public health emergencies through quantitative approaches. CIHR Health Systems Impact Fellowship Dr. Irvine's collaborations with BCCDC and provincial health authorities translate complex modeling insights into actionable public health strategies. His work with the BC Ministry of Mental Health & Addictions on overdose response evaluation exemplifies his commitment to evidence-based policy development through rigorous quantitative analysis. At BCCDC, he leads analytical efforts in Data & Analytics Services, applying advanced statistical methods to public health surveillance data across multiple disease programs, strengthening British Columbia's capacity for data-driven decision-making in public health practice.

• Neuroscience
• Clinical Neurology
• Cognitive Neuroscience
• +7 more

Bryan Strange is a Professor at the Department of Photonic Technology and Bioengineering, Universidad Politécnica de Madrid (UPM), and a member of the Center for Biomedical Technology (CTB) since 2011. His research focuses on neuroscience, particularly in memory mechanisms, neurodegenerative diseases, and neuroimaging techniques. Neuroscience Clinical Neurology Cognitive Neuroscience Neuroimaging Neurodegenerative Diseases Medical Imaging His work explores hippocampal-amygdala interactions in emotional memory, deep brain stimulation (DBS) for psychiatric disorders, and neuroimaging biomarkers for Alzheimer's disease and aging. Recent studies address superagers' resistance to age-related brain changes, genetic factors in hippocampal atrophy, and advanced MEG/CT/MRI methodologies. Analysis of his 15 most recent publications reveals trends in neuroimaging (MRI, MEG, CT), DBS applications, and computational psychiatry, with subfields spanning dementia risk prediction, emotional memory modulation, and cortical connectivity modeling. His contact email is bryan.strange@upm.es . He has not received explicit awards or honors in the provided data and has no listed advisees.

Murat AkcacayaView profile

Associate Professor

• Machine Learning
• Statistical Signal Processing
• Optimization
• +5 more

Murat Akcacaya is an Associate Professor in the Electrical and Computer Engineering Department at the University of Pittsburgh and serves as the director of the Signal Processing and Statistical Learning (SPSL) Laboratory. He is also a Senior Member of IEEE. His research spans multiple disciplines with significant contributions to both theoretical and applied signal processing. PhD in Electrical and Systems Engineering, Washington University, 2010 MSc in Electrical and Systems Engineering, Washington University, 2010 BSc in Electrical and Electronics Engineering, Middle East Technical University, 2005 Dr. Akcacaya's research focuses on machine learning, statistical signal processing, and optimization with diverse applications. His work develops machine learning methods for human-computer interaction, human-in-the-loop systems, and assistive technologies, as well as techniques to analyze ambulatory and multimodal physiological data. He also creates cognitive systems for nonstationary environments and probabilistic models for complex physical systems. His space research aligns with these interests, particularly in physiological data analysis and cognitive systems for nonstationary environments. Analysis of his recent publications reveals a strong trend toward medical applications of signal processing and machine learning, particularly in cardiology (ECG analysis), neurorehabilitation (stroke recovery assessment), and autism research (EEG analysis). His work consistently integrates multimodal data fusion approaches, combining techniques from radar signal processing with biomedical applications. There's also a notable emphasis on practical implementation of theoretical concepts in real-world healthcare settings. Dr. Akcacaya has secured significant research funding from multiple federal agencies including the National Science Foundation (NSF), National Institutes of Health (NIH), Air Force Office of Scientific Research (AFOSR), and Department of Energy (DOE), supporting his diverse research portfolio. As director of the SPSL Laboratory, he mentors students working on cutting-edge projects at the intersection of signal processing, machine learning, and various application domains. The Signal Processing and Statistical Learning Laboratory serves as the hub for Dr. Akcacaya's research activities, providing a collaborative environment for developing innovative signal processing techniques and their applications across multiple domains including healthcare, aerospace, and human-computer interaction systems.

Marko Robnik ŠikonjaView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +7 more

Prof. Marko Robnik Šikonja is a Full Professor at the Faculty of Computer and Information Science , University of Ljubljana . As head of the Machine Learning and Language Technologies Laboratory , he leads research in artificial intelligence, machine learning, data mining, natural language processing, and network analytics. He has authored over 150 publications with more than 5000 citations on Google Scholar. Research Focus: Deep neural networks, model explanation, embeddings, ensemble learning, and interdisciplinary applications in healthcare (e.g., STRATIFYHF for heart failure detection) and digital humanities (e.g., Language Resources for Slovene ). Scientific Awards: ECML/PKDD 2019 Journal Track Reviewer Award 2018 Outstanding Research Achievement at University of Ljubljana 2015 Golden Medal for contributions to the university Key Projects: STRATIFYHF (2023-2027): AI for heart failure risk stratification EMBEDDIA (2019-2021): Cross-lingual embeddings for European news KAUČ (2016-2022): Slovene textbook quality improvement

Behtash BabadiView profile

Associate Professor

• Statistical and Adaptive Signal Processing
• Neural Signal Processing
• Systems Neuroscience
• +3 more

Behtash Babadi is an Associate Professor in the Department of Electrical & Computer Engineering and a faculty member at the Institute for Systems Research and the Brain and Behavior Institute at the University of Maryland, College Park. He also holds affiliate appointments in the Program in Neuroscience & Cognitive Science and the Applied Mathematics & Statistics program. Education: Ph.D. in Engineering Sciences, Harvard University (2011) M.Sc. in Engineering Sciences, Harvard University (2008) B.Sc. in Electrical Engineering, Sharif University of Technology (2006) Research Interests: Dr. Babadi’s work focuses on statistical and adaptive signal processing frameworks for understanding neural systems. Key areas include: Neural signal processing and systems neuroscience Granger causality and functional connectivity analysis Dynamic modeling of neuronal assemblies Applications to auditory processing and cognitive recovery Scientific Contributions: His recent publications address cortical network dynamics, MEG source analysis, and robust causal inference. Notable methods include Network Localized Granger Causality (NLGC) for direct connectivity estimation and multitaper spectral analysis for neuronal spiking data. Awards: NSF CAREER Award (2016) E. Robert Kent Teaching Award (2019) GSAS Merit Fellowship (Harvard, 2010) Collaborations: Dr. Babadi collaborates with institutions like MIT, Harvard, and Massachusetts General Hospital, and participates in interdisciplinary initiatives such as the Brain and Behavior Initiative (BBI) and NIH BRAIN grants.

Nicola NerettiView profile

Associate Professor

• Computational Biology
• Genomics
• Biology of Aging
• +5 more

Nicola Neretti is an Associate Professor of Molecular Biology, Cell Biology, and Biochemistry at Brown University and serves as Associate Director for the Center on the Biology of Aging. He is affiliated with the Center for Computational Molecular Biology (CCMB) and leads the Neretti Lab, which combines genomics and computational biology to study the biology of aging and age-associated diseases. His educational background includes: PhD in Computational Biology from Brown University (2001) MS from Brown University (1999) Dr. Neretti's research focuses on the application of high throughput techniques such as RNA-seq to study changes in transcriptional networks caused by genetic and environmental interventions that extend lifespan. His work integrates computational methods with experimental biology to detect age-associated chromatin changes and activation of transposable elements. He develops computational tools to analyze 3D chromosome structure and has made significant contributions to understanding the role of genomic instability in aging. Analysis of Dr. Neretti's recent publications reveals a strong focus on cellular senescence, chromatin remodeling, and transposable element activation in aging. His work spans multiple model organisms and human tissues, with an increasing emphasis on single-cell and spatial transcriptomics approaches. A consistent theme across his publications is the connection between genomic instability, particularly through transposable element activation, and age-related inflammation. Dr. Neretti teaches several courses at Brown University: BIOL 2010 - Quantitative Approaches to Biology BIOL 2010B - Introduction to Data Science in Molecular Biology BIOL 2350 - The Biology of Aging The Neretti Lab has developed several important software tools for genomic analysis: SIMBA3D: Statistical method for inferring 3D chromosome structure from single-cell Hi-C data GINOM: Statistical framework for assessing interval overlap of multiple genomic features RepEnrich: Method to estimate repetitive element enrichment using high-throughput sequencing data CORaL: Software for comparing expression profiles across experiments FISH-MDS: Multidimensional scaling for chromosome structure inference with FISH constraints

Nina JanasikView profile

Lecturer

• Social Policy
• Environmental Risk Management
• Sustainability Science
• +3 more

Nina Janasik is a University Lecturer at the University of Helsinki within the Faculty of Biological and Environmental Sciences , affiliated with the Ecosystems and Environment Research Programme and the Helsinki Institute of Sustainability Science . Her work bridges Social Policy with Environmental Risk Management , focusing on strategic decision-making in complex socio-environmental contexts. Research Interests: Environmental governance, sustainability transitions, participatory modeling, and policy innovation. She specializes in addressing wicked problems through Bayesian networks and scenario planning , emphasizing the integration of facts and values in expert deliberation. Projects: Current projects include WISE (2021–2023) on socio-environmental disruptions and LONGRISK (2020–2023) for managing urban multi-hazard risks. Past work involved the RegEx project (2010–2011) on Baltic Sea environmental monitoring. Scientific Activities: Active in conferences like ESEE and 4S/EASST , she has supervised 3 doctoral/thesis projects and contributed to 39 activities, including organizing workshops on endocrine disruptors and ecosystem services.

Tarik KelestemurView profile

Researcher

• Robotics
• Artificial Intelligence
• Machine Learning
• +3 more

Tarik Kelestemur is a roboticist specializing in autonomous systems, with affiliations including Boston Dynamics AI Institute and Northeastern University. His work bridges robotics, artificial intelligence, and computer engineering, focusing on tactile manipulation, 3D semantic understanding, and policy learning frameworks. His research interests include: Robotics Artificial Intelligence Machine Learning Computer Engineering Autonomous Systems Human-Robot Interaction Recent publications highlight advancements in diffusion policies, vision foundation models, and 3D relational object graphs. Tarik received an Outstanding Paper Award Finalist at CoRL 2024 and contributes to open-source robotics projects like point_cloud_proc and icub_arm_imitator .

Rick SchultingView profile

Professor

• Mesolithic and Neolithic Europe
• North American Northwest Coast and Plateau archaeology
• Siberian hunter-gatherers and pastoralists
• +11 more

Professor Rick Schulting is a faculty member at the School of Archaeology, University of Oxford, affiliated with Wolfson College. His research focuses on the Mesolithic and Neolithic periods of Western Europe, alongside comparative studies of Siberian hunter-gatherers, Japanese prehistory, and the transition to agriculture. Co-Investigator for Cultivating Societies: Agriculture in Neolithic Ireland Member of the Baikal-Hokkaido Archaeological Project His methodological expertise includes stable isotope analysis for dietary reconstruction, strontium isotope provenance studies, and Bayesian modeling of radiocarbon chronologies. His work often addresses skeletal evidence of interpersonal violence and resilience to environmental changes. Recent publications highlight his interdisciplinary approach to reconstructing prehistoric diets, mobility, and conflict across Europe, Siberia, and the Americas. He collaborates on projects involving ancient DNA, reservoir effect corrections, and GIS applications in archaeology. Supervised doctoral students work spans topics like: Neolithic violence in NW Europe Basque uniqueness through isotope analysis Maize adoption in Chile Atacama Desert mobility Medieval Al-Andalus diets His teaching covers bioarchaeology, GIS in archaeology, and European prehistory.

Mei-Cheng WangView profile

Professor

• Survival Analysis
• Longitudinal Data Analysis
• Multivariate Statistics
• +5 more

Mei-Cheng Wang, PhD is a Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, with additional affiliations at the Johns Hopkins Center for Injury Research and Policy. Her educational background includes: PhD in Statistics from University of California at Berkeley (1985) Dr. Wang specializes in Survival, Longitudinal And Multivariate statistics (SLAM), with particular expertise in point processes, recurrent marker processes, and semiparametric models. Her research has significant applications in medical studies, particularly in the areas of biomarker diagnostic testing, risk prediction, and the birth-illness-death process in relation to risk factors. Recently, her work has increasingly focused on Alzheimer's disease research, developing statistical methods to analyze biomarker trajectories and disease progression. Analysis of her recent publications (2023-2024) reveals a strong emphasis on Alzheimer's disease research, particularly in developing statistical methods for analyzing biomarker trajectories during the preclinical phase. Her work combines advanced biostatistical techniques with neuroimaging and cerebrospinal fluid biomarker data to understand disease progression before clinical symptoms appear. This research sits at the intersection of biostatistics, neuroscience, and machine learning, with applications in early detection and intervention strategies for neurodegenerative diseases. Dr. Wang has received significant recognition for her contributions to statistics: Fellow of the American Statistical Association (ASA) Elected member of the International Statistical Institute (ISI) Fellow of Institute of Mathematical Statistics (IMS) Throughout her career, Dr. Wang has advised numerous graduate students and secured research funding for her work in biostatistical methodology development. Her research group, the SLAM (Survival, Longitudinal And Multivariate statistics) group, focuses on developing innovative statistical methods with applications in medical and public health research. Current projects include the BIOCARD Study, a prospective birth cohort study examining pre- and peri-natal determinants of autism spectrum disorders and developmental disabilities, as well as research on Alzheimer's disease biomarkers and progression.

• Physical Education
• Sports Science
• Exercise Physiology
• +4 more

Henryk Król is a multidisciplinary researcher affiliated with the Jerzy Kukuczka Academy of Physical Education in Katowice, with expertise spanning physical education, sports science, public health, and biomedical engineering. His collaborative research bridges exercise physiology, cancer survivorship, and optical engineering domains. Research Interests: Sports Science & Exercise Physiology: Investigating biomechanics of acrobatic movements and jump training effectiveness Cancer Survivorship: Examining obesity, physical fitness, and lifestyle factors in breast cancer survivors Public Health Epidemiology: Studying diabetes, cardiovascular risk factors, and socioeconomic health disparities Optical Engineering: Developing thermal imaging techniques for optical coating characterization His recent work demonstrates a unique integration of physical education principles with biomedical applications, particularly in cancer survivorship studies combining accelerometer-based activity monitoring with self-reported health metrics. The optical engineering contributions showcase expertise in thermal radiation effects on specialized coatings, with applications in laser systems and photonics. Collaborative Research: Dr. Król's extensive co-authorship networks span Polish, Swedish, and French institutions, reflecting international collaboration in both health sciences and engineering disciplines. His research has been continuously funded as evidenced by publications from 2017-2025.

• Data Science
• IoT
• Blockchain
• +5 more

Zheng Wen is an Associate Professor (non-tenure-track) at Waseda University, affiliated with the Faculty of Science and Engineering and the Global Center for Science and Engineering. His research spans multiple interdisciplinary domains at the intersection of information technology, security systems, and artificial intelligence applications. Dr. Wen received his Ph.D. from Waseda University between 2013 and 2019, following undergraduate studies at Wuhan University from 2005 to 2009. Dr. Wen's research interests focus on the convergence of emerging technologies for practical applications. His primary areas include Data Science , Internet of Things (IoT) , Blockchain , and Artificial Intelligence , with specific applications in communication networks, disaster management, and content-oriented networking. His work demonstrates a strong emphasis on solving real-world problems through technological innovation, particularly in security-critical domains. Analysis of Dr. Wen's publication record reveals a consistent research trajectory focused on applying machine learning and AI techniques to security and communication challenges. His recent work shows increasing emphasis on blockchain applications for IoT security, GNSS spoofing detection for drone systems, and millimeter-wave imaging for security applications. The interdisciplinary nature of his research connects computer science, electrical engineering, and practical security implementations. Dr. Wen is an active member of professional organizations including IEEE and IEICE, reflecting his engagement with the broader academic community in his fields of expertise. While specific details about his advising and grant activities are not provided in the available information, his extensive publication record across multiple domains suggests active research supervision and likely participation in collaborative research projects. His work on drone security, blockchain applications, and millimeter-wave imaging indicates potential industry partnerships and practical implementations of his research. Dr. Wen's research appears to be conducted within collaborative teams focusing on security systems, wireless communications, and AI applications, with frequent co-authorship patterns suggesting established research groups working on related projects in these domains.

Aleida BraaksmaView profile

Lecturer

• Artificial Intelligence
• Health and Well-being
• Reinforcement Learning
• +7 more

Aleida Braaksma is a Lecturer at the University of Twente, affiliated with the TechMed Centre and Mathematics of Operations Research department. Her work bridges Artificial Intelligence with Health and Well-being , focusing on optimizing healthcare systems through Operations Research methodologies. Key Affiliations: Digital Society Institute, TechMed Centre, Mathematics of Operations Research department Research Themes: Reinforcement Learning, Data Mining, Process Mining, and Queueing Theory applications in healthcare logistics Her recent publications highlight advancements in medical diagnostic scheduling , bed allocation , and adaptive clinical trial designs . She has pioneered dynamic robust optimization frameworks for time-sensitive pharmaceutical workflows and developed sampling-based methods for Gittins index approximation in stochastic environments. Scientific contributions include: Optimization of rheumatology outpatient clinics via patient classification algorithms Response-adaptive procedures in clinical trials using constrained Markov decision processes Real-time forecasting systems for pandemic-related hospital capacity planning Computerized decision support for nurse-to-patient assignment

• Extrasolar Planet Physics
• Protoplanetary Disk Physics
• Machine Learning
• +5 more

Dr. Michiel Min is a researcher at the Netherlands Institute for Space Research and affiliated with the University of Groningen (RUG). His work focuses on astrophysical phenomena related to protoplanetary disks, exoplanet atmospheres, and machine learning applications in astronomy. Research Themes: Protoplanetary Disk Physics, Extrasolar Planet Analysis, Dust Mineralogy, and Atmospheric Retrieval Instrumentation: Expertise in James Webb Space Telescope (JWST) data analysis and mid-infrared spectrography Michiel’s recent publications emphasize dust-gas separation in disks, exoplanet atmospheric modeling using machine learning, and hydrocarbon chemistry in planet-forming regions. Collaborations span institutions like CDS, ESO, and NASA, with datasets shared on platforms like Mendeley. His research contributes to UN Sustainable Development Goals, particularly those related to planetary protection and scientific innovation.