Find Academic Supervisors

Fabian PfefferView profile

Associate Professor

• Wealth Inequality
• Intergenerational Mobility
• Social Stratification
• +5 more

Fabian Pfeffer is Associate Professor in the Department of Sociology and Research Associate Professor in the Institute for Social Research at the University of Michigan. He serves as the founding director of the Center for Inequality Dynamics (CID), leader of the Inequality Lab, and Co-Investigator of the Panel Study of Income Dynamics (PSID). Dr. Pfeffer's research investigates social inequality and its maintenance across time and generations. His current work focuses on wealth inequality and its consequences for the next generation, social mobility processes across multiple generations, and the maintenance of inequality through education. His interdisciplinary approach combines sociological theory with advanced statistical methods to analyze complex patterns of wealth distribution and mobility. Through his extensive publications from 2018-2025, Pfeffer has made significant contributions to understanding how wealth inequality shapes educational opportunities and life outcomes. His research demonstrates how family wealth influences children's educational attainment and how racial disparities in wealth perpetuate across generations. Notable methodological contributions include new approaches to analyzing mobility effects and developing the PSID-SHELF data resource. Dr. Pfeffer has developed innovative visualizations that make complex patterns of wealth inequality accessible to broader audiences, including interactive tools showing intergenerational wealth mobility and racial disparities. His work on the Wealth and Mobility (WAM) Study, conducted with the IRS Statistics of Income unit, gathers comprehensive data on U.S. household wealth. His research has important policy implications regarding wealth taxation and redistribution. Pfeffer's international comparative work reveals that national levels of income and wealth inequality are uncorrelated, challenging conventional wisdom in inequality research. His scholarship connects micro-level processes of social reproduction with macro-level patterns of inequality, providing a comprehensive understanding of how wealth shapes opportunities across generations.

• Civil Engineering
• Soil Sciences
• Expansive Soils
• +5 more

Xi Sun is a Research Fellow at RMIT University's School of Engineering, specializing in civil and geotechnical engineering with a focus on tree-soil interactions, expansive soils, and climate impacts. His work bridges engineering and environmental science, addressing challenges in urban infrastructure and sustainable land management. He holds a postdoctoral position and is affiliated with RMIT’s City Campus in Australia. Research interests include expansive soils behavior, field instrumentation, climate-plant-soil interactions, and tree-induced structural damage assessment. His studies investigate how tree species affect soil moisture dynamics and ground movement in urban settings, with applications to infrastructure resilience and climate adaptation. Key research outputs from 2021 to 2025 explore topics like biochar-enhanced pavement subgrades, empirical modeling of tree-soil mechanisms, and climate change impacts on expansive soil movements. His work often involves interdisciplinary collaboration, combining field monitoring with computational modeling to address real-world engineering challenges. Dr. Sun’s contributions include over 20 peer-reviewed articles, focusing on geotechnical engineering innovations, urban tree management, and sustainable infrastructure solutions. His research aligns with RMIT’s commitment to addressing global environmental and engineering challenges through applied research.

Florian DosterView profile

Professor

• Subsurface Flow Phenomena
• Geological Carbon Storage
• Porous Media Modeling
• +3 more

Florian Doster is a Professor at Heriot-Watt University, affiliated with the School of Energy, Geoscience, Infrastructure and Society and the Institute for GeoEnergy Engineering. He leads the MuPhi Research Group, focusing on multi-scale modeling of subsurface flow phenomena, particularly in porous media and fractured reservoirs. His work addresses challenges in CO2 storage, hydrocarbon production, and groundwater management. He actively supervises PhD students, including Amanzhol Kubeyev, and collaborates with industry and research councils. Research interests include multi-scale simulation, reactive transport, and AI-driven uncertainty quantification, with applications to geological carbon sequestration and reservoir engineering. His group develops open-source tools, such as the Carbonate Reservoir Group HWU code. Notable projects include regional screening of saline aquifers in Malaysia and fracture mechanics studies. Publications highlight advancements in AI for subsurface modeling, CO2 leakage risk assessment, and pore-scale interactions. Funding sources include industry partnerships and research councils. The MuPhi Group’s activities span geomechanics, fracture network analysis, and environmental sustainability.

• Data Visualization
• Geographic Information Systems (GIS)
• Energy Consumption Analysis
• +3 more

Sarah Goodwin is an academic affiliated with City, University of London, focusing on visualization techniques, geodemographics, and energy consumption analysis. Her work bridges computer science and geographic information systems (GIS), emphasizing user-centered design and collaborative methodologies. She holds a PhD in Visualisation for Household Energy Analysis from City University London (2015). Her research explores multivariate data visualization, particularly in energy-based geodemographic classifications and the impact of scale and geography on data interpretation. Notable contributions include frameworks for creative visualization workshops and topology-preserving map deformations for dissimilarity data. She has received the VAST 2012 Mini Challenge Award for Efficient Use of Visualization. Publications span journals like IEEE Transactions on Visualization and Computer Graphics, and she has presented at conferences such as IEEE VAST and GIS Research UK. Her work addresses energy consumption patterns, network health monitoring, and spatial data analysis. Current research emphasizes innovative visual methods for complex data exploration across scales and geographic contexts.

• Hardware Design
• VLSI Systems
• Digital Circuit Optimization
• +4 more

Rajkumar Sarma is a Research Fellow at the Department of Computer Science & Information Systems at Lero – the Research Ireland Centre for Software, University of Limerick. His work focuses on hardware design, VLSI systems, and optimization techniques for digital circuits. He specializes in areas such as low-power architectures, floating-point arithmetic, and evolutionary algorithms for automated design. His research emphasizes hardware-software co-design, reliability analysis under PVT (Process, Voltage, Temperature) variations, and efficient implementations of multiply-accumulate (MAC) units critical to digital signal processing and image processing applications. Key technical contributions include the development of the UCM algorithm for delay optimization, grammatical evolution for synthesizable HDL code generation, and novel approaches to reduce power consumption in MAC architectures. His work bridges theoretical algorithmic innovation with practical VLSI implementation challenges, addressing both performance and reliability under extreme operating conditions. Rajkumar’s publications span 2012–2025, with a strong focus on digital circuit design, including hybrid adders, low-power flip-flop implementations, and quantum gate-based reversible circuits. His research also extends to reliability analysis of electronic components like multi-layer ceramic capacitors. He has utilized advanced simulation tools such as Cadence ADE-XL for accelerated PVT analysis, demonstrating expertise in both computational modeling and hardware validation. While no specific awards or grants are listed, his extensive publication record reflects sustained engagement with cutting-edge challenges in computer architecture and VLSI systems design.

Melanie DirksView profile

Professor

• Clinical Psychology
• Developmental Science
• Peer Relationships
• +6 more

Professor Melanie Dirks is the Department Chair of Psychology at McGill University, specializing in Clinical Psychology and Developmental Science . Her research focuses on mapping social and emotional skills across childhood, adolescence, and young adulthood, with emphasis on peer/sibling relationships and their impact on psychological symptoms. Uses multimodal methodologies: observational studies, daily diaries, emotion recognition tasks Current projects: friendship quality maintenance, interpersonal victimization in youth, emotional communication dynamics Key research themes from publications include: Peer Relationships : Dissolution patterns, prosocial behavior, bullying, and dating aggression Emotional Processing : Vocal emotion recognition, neural reward responses, stress-neuroimaging links Contextual Influences : Family dynamics, socioeconomic status, post-pandemic social changes Her lab ( CASC Lab ) trains students in advanced social-emotional assessment techniques, with notable advisees including *Morningstar, *Gilbert, and *Santucci. Google Scholar articles (2014-2025) demonstrate expertise in developmental psychopathology, social neuroscience, and cross-cultural mental health determinants.

• Bayesian Analysis
• Approximate Bayesian Computation
• Deep Learning Methods
• +3 more

Yuexi Wang is an Assistant Professor in the Department of Statistics at the University of Illinois. Their research focuses on Bayesian methodology, approximate Bayesian computation, and deep learning applications in statistical inference. Key areas include uncertainty quantification, sparse deep learning, and statistical modeling for count data. Education: Not explicitly stated in text. Research interests span Bayesian analysis, with emphasis on developing scalable methods for posterior approximation, adversarial simulation, and generative models. They have contributed to variable selection via Bayesian forests and uncertainty quantification in sparse neural networks. Recent work explores optimal transport-based methods for posterior sampling and Pochhammer priors in count models. Publications emphasize methodological advances in Bayesian deep learning, including data augmentation techniques and adversarial approaches. Articles often bridge theory and application in machine learning and computational statistics. No scientific awards explicitly mentioned. Advising and grants information unavailable in provided text.

• Parameterized Complexity
• Algorithms
• Graph Theory
• +3 more

Saket Saurabh is a Professor at the Department of Informatics, University of Bergen. His research focuses on parameterized complexity, algorithms, graph theory, and combinatorial optimization. He has contributed extensively to theoretical computer science, with a strong emphasis on algorithm design and analysis for NP-hard problems. His work includes studies on graph algorithms, approximation schemes, and fairness in computational problems. Recent publications address topics such as minimum membership dominating sets, hybrid clustering, and fair hitting set problems. Saurabh has collaborated widely, with co-authors like Fedor Fomin, Petr Golovach, and others. Key research interests include parameterized algorithms for graph problems, exponential-time approximation methods, and structural graph theory. His contributions have advanced the understanding of computational complexity and practical algorithmic solutions for challenging problems.

Nidhal JamiaView profile

Lecturer

• Linear and Nonlinear Structural Dynamics
• Turbomachinery Blade Vibrations
• Experimental Modal Analysis
• +5 more

Dr. Nidhal Jamia is a Lecturer in Aerospace Engineering at Swansea University, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds a PhD in Mechanical Engineering and has experience as a Research Officer and Research Assistant at Swansea's Faculty of Science and Engineering (FSE). His expertise lies in Linear and Nonlinear Structural Dynamics, Turbomachinery Blade Vibrations, and Experimental Modal Analysis, with a focus on jointed structures and bolted interfaces. Dr. Jamia's research emphasizes predicting dynamic responses in complex systems, such as bolted joints and turbomachinery blades, with practical applications in vibration control and structural integrity. He has contributed to advancements in blade tip timing techniques, eddy current sensor modeling, and nonlinear system identification. His work bridges theoretical models with experimental validation, addressing challenges in aerospace and mechanical engineering. Selected research highlights include studies on mistuned bladed disks using wavelet transforms, sensor characteristics in blade tip timing, and the development of equivalent models for nonlinear joints. His recent efforts focus on stochastic updating of nonlinear systems, digital twin platforms, and the TRC benchmark system's experimental analysis. Education: PhD in Mechanical Engineering, Swansea University (2019–2022) MSc in Computational Mechanics, Polytechnical School of Tunisia (2015–2019) BEng in Civil Engineering, Engineering School of Gabes, Tunisia (2010–2014) Dr. Jamia is actively involved in teaching modules such as Engineering Mathematics, Experimental Studies for Mechanical and Aerospace Engineers, and Design and Laboratory Classes. He is available for postgraduate supervision and collaborates with industry on research projects involving structural dynamics and vibration analysis.

• Wildlife Ecology
• Arctic Biology
• Conservation Biology
• +1 more

Leif Egil Loe is a Professor in the Department of Ecology and Natural Resource Management at the Norwegian University of Life Sciences. His research specializes in Arctic wildlife ecology and conservation, particularly focusing on ungulate behavior and climate change impacts. Research encompasses: Reindeer ecology in Arctic ecosystems Climate change adaptation in ungulates Wildlife health and parasitology Conservation genetics Publications feature advanced methodologies: Long-term population monitoring GPS tracking and movement analysis Gut microbiome sequencing Experimental stress physiology Genomic approaches

• Machine Learning
• Automated Machine Learning (AutoML)
• Hyperparameter Optimization
• +3 more

Janek Thomas is a researcher actively contributing to the fields of Machine Learning and Automated Machine Learning (AutoML). His work focuses on hyperparameter optimization, multi-objective algorithms, and improving the interpretability of machine learning models. Collaborating with institutions like TU Munich and LMU Munich, he has authored over 35 publications since 2016. Key contributions include the AMLB benchmark suite for AutoML systems and foundational research on multi-objective hyperparameter tuning. His research bridges theoretical advancements with industrial applications, emphasizing robust model verification and scalable optimization techniques.

Manuela ZucknickView profile

Professor

• Statistical learning for translational cancer research
• Personalized cancer therapies
• Multi-omics data integration
• +7 more

Manuela Zucknick is Professor and Director of the Oslo Centre for Biostatistics and Epidemiology at the University of Oslo's Faculty of Medicine, Department of Biostatistics. Her research integrates statistical learning with translational cancer research to advance personalized medicine through multi-omics data integration. PhD Biostatistics, Imperial College London (2008) MSc Bioinformatics, Imperial College London (2004) Diplom Statistik, University of Dortmund (2003) Her research focuses on Bayesian methods for integrating heterogeneous data sources in cancer research, particularly for drug response prediction in pharmacogenomic screens and patient prognosis. She develops structured high-dimensional regression models for 'large p, small n' problems in molecular medicine, with emphasis on incorporating prior biological knowledge into risk prediction frameworks. Her work bridges statistical methodology with clinical applications in personalized cancer therapies. Her recent publications demonstrate consistent contributions to multi-omics integration and survival modeling across diverse clinical contexts including cancer, pregnancy complications, and rheumatoid arthritis. The research shows strong methodological innovation in handling high-dimensional biological data while maintaining clinical relevance. Through the Oslo Centre for Biostatistics and Epidemiology, she leads collaborative projects spanning oncology, obstetrics, and rheumatology. Her work frequently involves designing statistical frameworks for pharmacogenomic screens and developing tools for biomarker discovery in complex disease settings.

• Hydrology
• Soil Erosion and Conservation
• Sediment and Water Interaction
• +7 more

Dr. Fahimeh Mirchooli is a postdoctoral researcher in the Department of Geography at the University of Bonn, Germany, working within the AG Klaus research group. Her work focuses on hydrology, soil erosion, and sustainability of catchment systems. She holds a PhD in Natural Resource Engineering from Tarbiat Modares University, Iran, and has held academic research roles at Sari Agricultural Sciences and Natural Resources University and Hakim Sabzevari University. PhD, Natural Resource Engineering (Watershed Science and Engineering), Tarbiat Modares University, Iran, 2020 Visiting Researcher, University of Salzburg, Austria, 2019 MSc, Natural Resource Engineering (Watershed Management), Isfahan University of Technology, Iran, 2013 BSc, Natural Resource Engineering (Rangeland and Watershed Management), University of Tehran, Iran, 2011 Dr. Mirchooli's research centers on soil erosion and conservation , sediment-water interactions , spatial modeling using machine learning , and catchment health and sustainability . She integrates geospatial analysis, environmental modeling, and data science to assess land degradation, ecosystem services, and environmental risk. Her work often involves remote sensing, GIS, and hybrid modeling approaches combining empirical and machine learning techniques. Her recent publications (2018–2024) reflect a strong trend in environmental risk modeling , land degradation assessment , and machine learning applications in hydrology and soil science . Topics include gully erosion susceptibility, dust emission risk, flood modeling, and watershed sustainability. The interdisciplinary nature of her research spans ecology, geoscience, agricultural science, and public health . Scientific awards and honors include: Ranked 1st among PhD students in Watershed Management Sciences and Engineering (2020) 6th rank in national PhD entrance exam for Watershed Management (2015) George Forster Fellowship (2025) Sabbatical Scholarship from Ministry of Science, Research and Technology, Iran (2019–2020) Dr. Mirchooli has received research grants including the George Forster Fellowship and a Sabbatical Scholarship. While no formal advisees are listed, her collaborative publication record indicates active mentorship and team-based research. She is involved in hydrology and sustainability research within the AG Klaus group, contributing to projects on catchment health, erosion modeling, and environmental monitoring.

Mingbin FengView profile

Associate Professor

• Monte Carlo simulation
• Nested simulation
• Derivative pricing
• +7 more

Mingbin (Ben) Feng is an Associate Professor of Actuarial Science at the University of Waterloo's Department of Statistics and Actuarial Science. His research focuses on Monte Carlo simulation design, nested simulation, and applications in risk management, financial engineering, and stochastic optimization. He holds an ASA designation from the Society of Actuaries and a Certified Analytics Professional (CAP) credential. Education: Ph.D. in Industrial Engineering and Management Sciences (Northwestern University, 2016), M.Math in Actuarial Science (University of Waterloo, 2011), and B.Math with Distinction (University of Waterloo, 2010). His academic journey includes internships at AXIS Capital Holdings and The Hong Kong University of Science and Technology. Research Interests: Machine learning, green simulation, derivative pricing, systemic risk, and simulation analytics. Awards: Royal E. Cabell Fellowship, Hickman Scholarship, and Arthur P. Hurter Award. Teaching: Courses include Corporate Finance, Financial Mathematics, and Portfolio Optimization. He advises a diverse group of graduate and undergraduate students, with notable contributions to developing the vamc R package for variable annuity modeling. Key Research Contributions: Innovations in nested simulation efficiency, green simulation methodologies, and applications of machine learning in actuarial science.

Bo HanView profile

Visiting Professor

• Catalysis
• Artificial Photosynthesis
• CO2 Reduction
• +5 more

Dr. Bo Han is a Visiting Professor affiliated with the Department of Zoology at the University of Cambridge, specifically associated with the Conservation Science Group under the supervision of Prof. Andrew Balmford. Despite the zoological departmental affiliation, Dr. Han's research bridges conservation science with advanced materials chemistry, focusing on sustainable energy solutions that have direct environmental applications. Dr. Han's research interests center on catalysis, artificial photosynthesis, CO2 reduction, and water oxidation . Their work uniquely combines environmental conservation goals with cutting-edge materials science, developing chemical solutions for sustainability challenges. The research integrates in situ characterization techniques with novel catalyst design to create systems that mimic natural photosynthetic processes while addressing energy and environmental challenges. This interdisciplinary approach connects conservation biology with chemical engineering to develop practical environmental technologies. The publication record demonstrates a clear trajectory toward increasingly sophisticated systems for artificial photosynthesis and CO2 conversion. Recent work (2024-2025) shows integration of machine learning with multi-variable optimization of photocatalytic systems, indicating a move toward data-driven materials design. The research consistently focuses on understanding fundamental mechanisms (degradation pathways, electron transfer processes, structural dynamics) to develop more efficient and stable systems for solar fuel production and carbon management. Key themes include self-assembly strategies, biomimetic approaches, and the development of stable catalysts for acidic water oxidation. Dr. Han collaborates extensively within Cambridge's interdisciplinary research environment, particularly with conservation scientists, to ensure their chemical innovations have practical environmental applications. While specific grant information isn't provided in the available materials, the research clearly aligns with sustainability and conservation technology development, likely supported by both chemistry-focused and environmental research funding streams. Based at the Department of Zoology, Dr. Han appears to be part of research teams focused on the chemical dimensions of conservation science, particularly those developing technological solutions for environmental challenges. The work connects fundamental chemical research with practical applications for biodiversity conservation and sustainable resource management.