Find Academic Supervisors

• Cosmology
• Data-Intensive Computing
• Artificial Intelligence in Astronomy
• +5 more

Alex Szalay is a Bloomberg Distinguished Professor of Physics and Astronomy at the Krieger School of Arts and Sciences and Computer Science at the Whiting School of Engineering, Johns Hopkins University. He leads the Institute for Data Intensive Engineering and Science (IDIES) and contributes to projects like the Sloan Digital Sky Survey (SDSS) and SciServer. His research spans cosmology, data-intensive computing, and AI-driven astronomy. Education: B.Sc. Physics (University of Debrecen, 1969), M.Sc. Theoretical Physics (Eötvös Loránd University, 1972), Ph.D. Astrophysics (Eötvös Loránd University, 1975). Affiliations: Department of Physics and Astronomy, Department of Computer Science, IDIES, Oncology/Dermatology (JHMI). Research Interests Focuses on cosmology (dark matter, galaxy formation), data-intensive systems (SciServer, GrayWulf clusters), AI applications in astronomy, and interdisciplinary projects like AstroPath (cancer immunotherapy via spatial data analysis). Has pioneered big data frameworks for astrophysics and environmental sensor networks. Awards Széchenyi Prize (1991) Jim Gray eScience Award (2007) Sidney Fernbach Award (2015) Member of National Academy of Sciences (2023) Labs & Collaborations Founded IDIES (2009) and co-developed tools like SkyServer, Data-Scope, and the turbulence database JHTDB. Collaborates on genomics alignment (Arioc), soil sensor networks, and oncology spatial profiling (AstroPath).

Jacob FreemanView profile

Professor

• Archaeology
• North American Archaeology
• Southwest Archaeology
• +11 more

Jacob Freeman is a Professor and Program Director in the Department of Sociology, Social Work and Anthropology at Utah State University, affiliated with the School of Social Sciences. He holds a PhD in Anthropology from Arizona State University (2014) and is actively engaged in research, teaching, and graduate mentorship. His research lies at the intersection of archaeology, human ecology, and socioecological theory, focusing on hunter-gatherer societies, population dynamics, territoriality, and the transition to agriculture. His work integrates theoretical models with large-scale empirical data, particularly radiocarbon records, to explore long-term human-environment interactions and sustainability. The 12 peer-reviewed articles and one book chapter in his publication record, spanning from 2015 to 2024, reflect a consistent focus on socioecological modeling, cultural evolution, and archaeological methodology. His recent work in Journal of Archaeological Method and Theory and PLoS One demonstrates a strong emphasis on population expansion, intensification dynamics, and nonlinear transitions in prehistoric societies. Jacob Freeman has mentored multiple graduate students in Sociology, Anthropology, and Environment & Society, guiding research in archaeology and socioecology. He teaches courses such as North American Archaeology, History and Theories of Anthropology, and seminars on Southwest Indian cultures. He has also taught specialized methods courses in GIS and quantitative research design. He is part of an active interdisciplinary research network, collaborating with scholars in archaeology, ecology, and complex systems science. His lab or research group appears to focus on data-driven archaeological inquiry, particularly using radiocarbon datasets to model long-term cultural and demographic trends.

Priya George MartinView profile

Research Fellow

• Clinical Supervision
• Interprofessional Education and Collaborative Practice (IPECP)
• Rural Health Workforce
• +4 more

Priya George Martin is an Affiliate Senior Research Fellow at the School of Health and Rehabilitation Sciences, University of Queensland. Her research focuses on clinical supervision, interprofessional education and collaborative practice (IPECP), rural health workforce dynamics, and telehealth innovations. She has extensively studied the impact of clinical supervision on healthcare outcomes, particularly in rural and remote settings, and has contributed to policy development in these areas. Her work emphasizes bridging gaps between education and practice, with a strong interest in how interprofessional collaboration can enhance healthcare delivery. Recent studies have explored the effects of the pandemic on medical student well-being and training, workforce retention strategies, and the role of technology in clinical supervision. Key research themes include rural health workforce distribution, competency development in allied health professions, and the design of effective telesupervision frameworks. She collaborates widely with health services and educators to translate research into practice, particularly in addressing challenges faced by rural healthcare providers. Her publications span over 100 articles, book chapters, and conference presentations, reflecting her interdisciplinary approach to healthcare education and service improvement. She actively engages with policy discussions to inform curriculum development and workforce strategies at both state and national levels.

Mario BožićView profile

Professor

• Forest Measurement
• Forest Growth Modelling
• Dendrochronology
• +3 more

Mario Božić is a Full Professor at the University of Zagreb's Faculty of Forestry and Wood Technology since 2017. His career spans over 25 years with prior roles including Associate Professor (2009-2017), Assistant Professor (2006-2009), and various research positions since 1995. He specializes in forest measurement techniques, growth modeling, and silvicultural practices. His research emphasizes stand dynamics, ecological impacts on tree growth, and innovative data collection methods using technology. Prof. Božić's work combines field methodologies with computational tools, exemplified by developing the SIMPLAG software for even-aged stand projections. His studies on silver fir and pedunculate oak address climate change effects, pest impacts, and historical growth trends. He actively researches private forest management challenges and promotes precision forestry through smartphone applications like MOTI for tree height measurement. His professional experience includes leading research in Croatian Dinaric forests, focusing on mixed fir-beech ecosystems and their management under varying intensities. He contributes to national forest inventories and has advised on protected forest units like Belevine. Teaching responsibilities include dendrochronology, with courses taught in Croatian, German, and English.

• Grid Computing
• Distributed Systems
• Cloud Computing
• +7 more

João Nuno De Oliveira e Silva is an Assistant Professor at the Department of Informatics at Instituto Superior Técnico, University of Lisbon. He teaches Concurrent Programming, Systems Programming, and related courses during the 2024/2025 academic semester. His research focuses on distributed systems, grid computing, cloud computing, and cybersecurity, with notable contributions to secure dataflow programming for Android applications, blockchain-based agent collaboration, and distributed computing frameworks like NuBOINC. His work emphasizes parallel execution environments, resource efficiency in dataflow models, and secure middleware solutions for bags-of-tasks. Key research themes include: Grid and distributed computing architectures Secure distributed applications (e.g., SGX enclave migration, anti-repackaging systems) Cloud infrastructure optimization (A2Cloud, geo-replication QoS) Autonomous agent systems for internet routing domains Biometric authentication for legacy systems Automated biological monitoring systems Notable projects include Flowverine (privacy-sensitive Android apps), TEEnder (SGX enclave migration), and Mercury (automatic parallelization middleware). His work bridges theoretical computer science with practical implementations in distributed and cloud environments. He has collaborated on over 30 publications since 2017, focusing on both foundational research and applied systems development. His teaching and research emphasize hands-on experience with modern distributed computing challenges.

• Machine Learning
• Computer Vision
• Medical Imaging
• +3 more

Catarina Barata is an Assistant Professor at the Department of Electrical and Computer Engineering , Instituto Superior Técnico , Universidade de Lisboa. Her research focuses on machine learning, computer vision, and medical imaging, particularly in dermatology and healthcare applications. She teaches courses such as Machine Learning, Signals and Systems, and Big Data Processing. Her work emphasizes skin lesion analysis and automated diagnosis systems , with contributions to domain generalization, explainable AI, and clinical decision support. She has pioneered techniques like artifact-based domain adaptation for skin cancer models and sparse motion fields for trajectory prediction. Barata's research spans interdisciplinary collaborations, including robotic therapy for autism spectrum disorder, real-time attention classifiers, and fire/smoke detection using weak supervision. Her studies on biomarkers in melanoma and BRAF mutation detection highlight her impact in precision medicine. She actively contributes to international initiatives like the ISIC conference and the International Skin Imaging Collaboration (ISIC), promoting ethical AI standards and consensus guidelines for dermatological applications. Her work balances technical innovation with clinical relevance, addressing challenges in dataset bias, counterfactual explanations, and multimodal medical data integration.

Dan ObenourView profile

Associate Professor

• Water Quality Dynamics
• Environmental Forecasting
• Geospatial Analytics
• +4 more

Dan Obenour is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at North Carolina State University. His research focuses on geospatial analytics for studying water quality dynamics in streams, lakes, and coastal areas, with an emphasis on hypoxia, harmful algal blooms, and environmental forecasting. He develops spatial and spatiotemporal geostatistical models to quantify uncertainties in environmental outcomes, aiding policymakers in decision-making under future scenarios. His work integrates field monitoring data, satellite imagery, and experimental results to reduce model uncertainty. Key research themes include Bayesian hierarchical modeling of phosphorus export, nutrient management strategies in agriculturally intensive regions, and the ecological impacts of hypoxia on fisheries. His team collaborates with NOAA and produces annual hypoxia forecasts for the Gulf of Mexico, emphasizing the interplay between climate change and water quality. Publications highlight trends in Gulf hypoxia drivers, phosphorus dynamics across the U.S., and the ecological trade-offs of water quality improvements. While no formal awards are listed here, his contributions to environmental modeling and policy-relevant forecasting are substantial. His lab’s experimental shrimp distribution forecasts for the Louisiana-Texas shelf demonstrate applied impacts of his research. Dan’s work bridges mechanistic and empirical approaches, prioritizing rigorous uncertainty quantification to enhance environmental management practices.

• Power Engineering
• Smart Grids
• Distribution Networks
• +7 more

Dr. Ian Elders is a Senior Research Fellow in the Department of Electronic and Electrical Engineering at the University of Strathclyde, part of the Faculty of Engineering. His research focuses on smart grids, distribution networks, and the integration of low-carbon technologies such as electric vehicles and heat pumps. His work emphasizes geospatial modeling, network impact assessment, and the socio-technical dimensions of energy system transformation. Key research themes include: Low-voltage distribution network modeling Hosting capacity for decentralized energy resources Flexibility aggregation and congestion management Geospatial and data-driven methodologies DSO-TSO coordination and market design Recent publications demonstrate a strong trend toward high-resolution, data-intensive modeling of energy networks, combining engineering analysis with real-world data from sources like smartphone location tracking and electricity network measurements. His research supports the transition to sustainable, resilient power systems. Scientific contributions include participation in two major EPSRC-funded projects: EPSRC Centre for Doctoral Training in Future Power Networks and Smart Grids (2018–2024) HiDEF: Supergen 3 HDPS Renewal Core and Pluses (2009–2019) Dr. Elders has supervised student research and contributed to over 40 research outputs. He collaborates with leading academics such as Stuart Galloway and Connor McGarry, and his work contributes to UN Sustainable Development Goal 7 (Affordable and Clean Energy). He maintains an active research profile with datasets publicly shared, including SP Energy Networks 'Flexible Networks' measurements.

Danae Hernández-CortésView profile

Assistant Professor

• Environmental Economics
• Environmental Justice
• Renewable Energy Policy
• +4 more

Danae Hernández-Cortés is an Assistant Professor at Arizona State University with dual appointments in the School for the Future of Innovation in Society and the School of Sustainability . She is also a Faculty Research Fellow at the National Bureau of Economic Research (NBER) and contributes to interdisciplinary research at the intersection of environmental economics, policy analysis, and environmental justice. Her research focuses on the distributional impacts of environmental policies , environmental inequality , and heterogeneous responses to environmental risks . Methodologically, she integrates causal inference techniques with remote sensing and pollution transport models to quantify spatial and demographic disparities in pollution exposure. Recent publications analyze renewable energy employment effects in Brazil, cap-and-trade equity in California, and agricultural water diversions' impact on particulate pollution. Her work has been featured in American Journal of Agricultural Economics , Nature Energy , Journal of Public Economics , and other leading journals. She has collaborated with institutions like Resources for the Future and contributed to policy blogs in English and Spanish. As an intern at Resources for the Future in 2018, she explored disaster assistance policies. She actively engages in public communication through her blog at Resources for the Future and media appearances, including the Radio Ambulante podcast discussing environmental justice implications for agave-based spirits. Her teaching includes Sustainable Energy Analytics at ASU, covering quantitative methods and environmental justice frameworks.

• Data-Centric Systems
• Distributed Systems
• Big Data Processing
• +3 more

Luo Mai is a Lecturer in Data Centric Systems at the School of Informatics, The University of Edinburgh. They are affiliated with the Institute for Computing Systems Architecture, contributing to research in large-scale and data-intensive computing systems. Their research focuses on data-centric systems, encompassing areas such as distributed computing, big data processing, cloud infrastructure, and scalable database technologies. This work supports modern applications requiring high-performance and efficient data management. Luo Mai has no listed publications or scientific awards in the provided content. There is no information available regarding student supervision or research grants. Their work is associated with the Institute for Computing Systems Architecture, a research institute within the School of Informatics focusing on advanced computing architectures and systems software.

• Artificial Intelligence
• Ethical AI
• Responsible AI
• +5 more

Adam Berry is a Professor and Deputy Director of the Human Technology Institute at the University of Technology Sydney (UTS). He previously served as Deputy Director of the UTS Data Science Institute from 2021-2024. His work focuses on leading inclusive, responsible and innovative artificial intelligence for Australia, with emphasis on translating data into real-world impact through data curation, machine learning, and statistical approaches. Education: PhD in Computer Science, University of Tasmania (2004-2008) BSc (Hons) in Computer Science, University of Tasmania (1999-2003) Professor Berry's research spans ethical AI, energy systems, and accessibility. His work consistently bridges theoretical research with practical implementation, focusing on human-centered AI that delivers value while upholding rights and preventing harm. He has a strong track record in multi-disciplinary collaboration, bringing together expertise from computer science, social science, electrical engineering, and ethics. His recent publications reveal a strategic evolution from foundational work in energy systems and reinforcement learning toward increasing focus on ethical and responsible AI frameworks. While maintaining strong contributions to energy analytics (electricity price forecasting, carbon intensity prediction), his 2021 survey on ethical AI principles marks a significant pivot toward governance and implementation frameworks for trustworthy AI systems. Scientific Awards: CSIRO Collaboration medal (inaugural winner) Professor Berry has secured substantial funding from diverse sources including the Digital Health CRC, Australian Renewable Energy Agency, Department of Home Affairs, and industry partners. He has led multi-million dollar initiatives like the National Energy Analytics Research Program and currently oversees the Human Technology Institute's mission for responsible AI. His industry collaborations include partnerships with Reejig (ethical talent AI), Sydney Trains (delay prediction systems), and numerous energy sector organizations. He actively contributes to the Disability Research Network, applying data-driven approaches to improve outcomes in the disability sector. As part of the Trustworthy Digital Society research center, he advances human-centered AI through cross-disciplinary work that integrates technical expertise with social considerations. His leadership spans research design, capability development, and strategic partnership building across government, industry, and academic sectors.

• High Performance Computing
• Parallel I/O Optimization
• Data Compression
• +4 more

Dr. Julian Kunkel is a Researcher at the University of Reading specializing in High Performance Computing (HPC) with 44 publications spanning 2014-2024. His work focuses on parallel I/O optimization, data compression, and machine learning applications for scientific computing and financial systems. Research interests include: High Performance Computing infrastructure optimization Parallel I/O monitoring and prediction using machine learning Data compression techniques for climate and scientific datasets Energy efficiency in data centers and HPC systems Financial time series forecasting with deep learning Tropical cyclone detection through satellite imagery analysis Recent publications (2020-2024) demonstrate a strong trend toward integrating machine learning with HPC challenges, particularly in I/O optimization and cross-domain applications like financial forecasting and meteorology. His work on the HPC Certification Forum shows leadership in standardizing HPC education globally. Dr. Kunkel collaborates internationally with researchers from Germany, Japan, and the USA on projects like AIMES (Advanced Computation and I/O Methods for Earth-System Simulations). His development of tools like SIOX for I/O monitoring and TrioFlow for financial prediction highlights practical applications of his research.

• Biomechanics
• Sports Science
• Orthopaedics
• +4 more

Tobias Menzel is a researcher at the Institute of Biomechanics and Orthopaedics, affiliated with German Sport University Cologne. His work focuses on sports biomechanics, particularly in boxing, involving the development and validation of smart wearables for performance analysis, force measurement, and pressure distribution studies. Education: PhD in Sports Science from German Sport University Cologne (completed 2023). Research Interests: Tobias specializes in biomechanical analysis of athletic performance, with emphasis on boxing. His research includes sensor-based monitoring systems, kinematics, and smart sports equipment development. Publications: His 2021–2023 works highlight validated wearable technologies for boxing, centering on center of pressure analysis, punch intensity measurement, and sensor system applications. Collaborations: He collaborates with Wolfgang Potthast (4 shared publications) and Igor Komnik (1 shared publication), focusing on biomechanical parameters in sports. Labs: Active in the Institute of Biomechanics and Orthopaedics, driving innovation in sports technology and athlete performance metrics.

• High-Performance Computing
• Scientific Workflows
• Distributed Computing
• +14 more

Justin M. Wozniak is a computational scientist at Argonne National Laboratory’s Mathematics and Computer Science Division within the Computing, Environment and Life Sciences directorate. He is a key contributor to advanced scientific workflow systems such as Swift/T and Parsl, enabling scalable, distributed, and many-task computing for data-intensive science. His work supports major initiatives in cancer research (CANDLE), epidemiological modeling, and exascale computing (ExaWorks). He collaborates extensively with leading researchers including Ian T. Foster, Michael Wilde, and Kyle Chard. His research focuses on high-performance computing, scientific workflows, distributed systems, and machine learning applications in science. He has pioneered techniques in workflow automation, fault tolerance, in-situ data analysis, and performance optimization. His work enables robust, scalable execution of complex computational pipelines across heterogeneous environments, from supercomputers to cloud platforms. His recent publications (2021–2025) emphasize workflow interoperability, resilience, benchmarking, and applications in cancer and epidemic modeling. Themes include automated model comparison, job management portability (PSI/J), adaptive workflow steering, and exascale-ready workflow toolkits. These works reflect a strong trend toward reproducibility, scalability, and real-world scientific impact. Justin M. Wozniak has no listed scientific awards in the provided text. However, his leadership in major DOE-funded projects and high-impact publications in top venues (SC, HPDC, e-Science) underscores his significant contributions to computational science. He has mentored or collaborated with numerous researchers, though specific advisees are not listed. His work is supported by large-scale computing grants and initiatives such as the ExaWorks project and CANDLE, which aim to accelerate scientific discovery through advanced computing infrastructure. He contributes to open science through tools like Parsl and Swift/T, which are widely used in the scientific community. He is a core developer in the ExaWorks ecosystem and contributes to workflow frameworks that integrate with AI/ML pipelines, containerization, and real-time data analysis. His work on Braid-DB and provenance tracking supports AI-driven science with full reproducibility. These efforts are central to modern computational laboratories aiming for autonomous, data-intensive discovery.

Valeriia HaberlandView profile

Lecturer

• Cancer Genetics
• AI in Healthcare
• Epidemiology
• +3 more

Dr Valeriia Haberland is a Lecturer in Computer Science at Brunel University London within the College of Engineering, Design and Physical Sciences, appointed in 2023. Her expertise bridges computing and biological sciences with a focus on translational health applications. She holds a PhD in Computer Science from King's College London (2015) and was awarded Fellow of the Higher Education Academy (FHEA) in October 2023. Educational background includes advanced training in computational methods and data science. Her research centers on cancer genetics and AI-driven healthcare solutions , specializing in modeling cancer aggressiveness through machine learning analysis of molecular profiles. She pioneers epidemiological causal inference techniques to identify genetically translatable disease factors and develops specialized web infrastructure for biomedical research. Current projects target breast cancer wound monitoring and laminopathy mechanisms. Publication analysis reveals a distinct evolution from early grid computing research (2012-2017) to contemporary health data science leadership. Recent work dominates cancer genomics, proteomics, and osteoarthritis therapeutics using UK Biobank data, with flagship contributions to EpiGraphDB and MR-Base platforms. Her methodology integrates Mendelian randomization, graph databases, and high-throughput sequencing analytics. Dr Haberland secures active research funding including a Brunel Partners-funded project on remote breast cancer wound monitoring (May-July 2024). She supervises MSc dissertations while teaching Modern Data, Logic and Computation, and final-year projects. Her academic service includes placement tutoring and curriculum development in data-intensive healthcare applications.