Find Academic Supervisors

Brian DzwonkowskiView profile

Associate Professor

• Coastal Circulation
• Estuarine Dynamics
• Ocean Observing Systems
• +6 more

Dr. Brian Dzwonkowski is an Associate Professor at the University of South Alabama's Stokes School of Marine & Environmental Sciences and a Senior Marine Scientist at the Dauphin Island Sea Lab. He holds a Ph.D. (2009) and M.S. (2003) in Marine Studies from the University of Delaware, and a B.A. in Mathematics (1999) from The College of New Jersey. Education: Ph.D., Marine Studies (Physical Oceanography), University of Delaware M.S., Marine Studies (Oceanographic Remote Sensing), University of Delaware B.A., Mathematics, The College of New Jersey His research focuses on coastal physical oceanography, examining how tides, winds, and river discharge drive water movement and layering in coastal systems. He investigates interactions between physical processes and biogeochemical cycling, with emphasis on hypoxia, harmful algal blooms, and ecosystem vulnerability to climate change. His work integrates in situ data, satellite observations, and numerical models across spatiotemporal scales. Recent publications highlight his expertise in hurricane-ocean interactions (e.g., J. Phys. Oceanogr. , 2022), shelf hypoxia dynamics (e.g., J. Geophys. Res. Oceans , 2021), and estuarine-shelf exchange processes. Collaborative projects span topics like Vibrio ecology (2024), compound fluvial-marine flooding (2021), and coastal climate resilience. Dr. Dzwonkowski mentors graduate and undergraduate students, including former Ph.D. advisees Steve Dykstra and Jeff Coogan. He contributes to coastal monitoring initiatives via the MyMobileBay.com platform and leads interdisciplinary studies on estuarine restoration (e.g., West Fowl River project).

Wendy MeiringView profile

Professor

• Statistical Methods for Brain Imaging
• Spatial and Temporal Processes
• Computational Statistics
• +4 more

Wendy Meiring is a Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. Her research focuses on statistical methods for analyzing complex data in neuroscience, environmental science, and biomedical applications. She specializes in spatial and temporal processes, computational statistics, machine learning, and uncertainty quantification. Her work integrates advanced statistical techniques with real-world challenges, such as analyzing brain imaging data, pharmacokinetic models, and environmental monitoring. She has contributed to methodologies for functional data analysis, clustering-based correlation estimation, and spatial-temporal modeling. Dr. Meiring has published extensively in top journals, including The Journal of Computational and Graphical Statistics , focusing on topics likeFréchet regression, pharmacokinetic modeling, and environmental phenology. Her research bridges theoretical statistics with practical applications in health, neuroscience, and ecology. She collaborates across disciplines, contributing to programs like the Interdepartmental Graduate Program in Dynamical Neuroscience at UCSB. Her lab develops innovative statistical tools for analyzing high-resolution datasets, emphasizing reproducibility and methodological rigor.

Farshad MoradiView profile

Professor

• Neuromorphic Computing
• Spintronics
• Biomedical Engineering
• +5 more

Farshad Moradi is a Professor at the Department of Electrical and Computer Engineering at Aarhus University, specializing in neuromorphic engineering, spintronics, and biomedical device design. His work focuses on integrating advanced materials and circuits for applications in neural interfaces, energy-efficient computing, and wireless biomedical systems. Research Interests include: Spintronic-based neuromorphic computing architectures Ultra-low power analog/mixed-signal integrated circuits Ultrasonically powered implantable medical devices Neural signal processing and seizure detection systems Wireless energy transfer and structural health monitoring Key Projects (2016-2026): SPICE: Spintronic-Photonic Integrated Circuit Platform PHOTON-NeuroCom: Photonic-assisted Neuromorphic Computing Neuro-Sense: Flexible bioinspired neuroprostheses CorroSense: Self-powered corrosion monitoring HERMES: Hybrid Enhanced Regenerative Medicine Systems Recent innovations include: Ultrasonically powered optogenetic implants Low-power neural amplifiers for deep-brain interfaces Spin-torque nano-oscillator-based neuromorphic hardware Energy harvesting systems for structural monitoring

Xiaohan YuView profile

Lecturer

• Ultra-Fine-Grained Visual Categorization
• Smart Farming
• Automated Crop Cultivar Identification
• +3 more

Dr. Xiaohan Yu is a Lecturer in Artificial Intelligence at Macquarie University's School of Computing, joining in December 2023. Previously, he completed his doctoral studies at Griffith University and served as a Research Fellow at the ARC Research Hub for Driving Farming Productivity. His research focuses on Ultra-Fine-Grained Visual Categorization (Ultra-FGVC), Smart Farming, and Automated Crop Cultivar Identification, with over 70 publications in top-tier venues like ICCV, CVPR, and IEEE Transactions. He holds editorial roles at Pattern Recognition and SN Computer Science , and received the APRS Early Career Award (2022) and ACM MM 2024 Outstanding Area Chair distinction. Education: Completed doctoral studies in Artificial Intelligence at Griffith University, Australia. Research Interests: Ultra-Fine-Grained Visual Categorization (Ultra-FGVC) Smart Farming and Agricultural Robotics Computer Vision Applications in Healthcare (e.g., trachoma detection) Deep Learning, Continual Learning, and Domain Adaptation Key Contributions: Pioneered Ultra-FGVC research, developed frameworks like Mix-ViT and CLE-ViT, and contributed to benchmarking multi-object tracking in farming. His work bridges pattern recognition with real-world applications in agriculture and healthcare. Scientific Awards: Australian Pattern Recognition Society (APRS) Early Career Researcher Award 2022 ACM Multimedia 2024 Outstanding Area Chair Award Advising & Grants: Actively involved in editorial roles (Area Chair for ACM MM, IJCNN) and grant-funded research through ARC hubs. His work is supported by collaborations in agriculture and AI-driven solutions for crop cultivar identification. Labs & Affiliations: Member of Macquarie's Smart Green Cities Research Centre and Frontier AI Research Centre , advancing interdisciplinary AI applications.

Callum AtkinsonView profile

Senior Lecturer

• Turbulent flows
• Heat transfer
• Aerodynamics
• +5 more

Dr. Callum Atkinson is a Senior Lecturer in Mechanical & Aerospace Engineering at Monash University, specializing in turbulent flow dynamics and experimental fluid mechanics. His research focuses on understanding and controlling turbulent shear flows in pipes, boundary layers, jets, and rocket engines, combining high-fidelity numerical simulations with advanced optical diagnostics like holographic PIV and tomographic techniques. He has developed novel methodologies for 3D velocity and density measurements, contributing to drag reduction studies, heat transfer analysis, and flow control in aerospace and mechanical systems. His work addresses UN Sustainable Development Goals related to energy efficiency and sustainable transport. Current roles include leading collaborative projects on adverse pressure gradient boundary layers and flow mixing, and he actively participates in peer review for journals like Journal of Fluid Mechanics and Physics of Fluids . He supervises PhD students in topics such as hybrid rocket engine optimization and turbulence modeling, leveraging Monash's engineering research infrastructure. Notable contributions include one of the world's largest adverse pressure gradient simulations and pioneering volumetric flow visualization techniques. His experimental toolkit includes laser diagnostics, tomographic PIV, and background-oriented schlieren systems. Recent work has explored superhydrophobic surface drag reduction, thermal jet behavior, and the dynamics of high-speed jet flows. He maintains a strong focus on bridging experimental and computational fluid dynamics to advance fundamental understanding and industrial applications.

David J ReinkensmeyerView profile

Professor

• Neuroengineering
• Rehabilitation Robotics
• Motor Control
• +3 more

David J. Reinkensmeyer is a Professor at the University of California, Irvine (UCI), holding appointments in the Department of Mechanical & Aerospace Engineering (The Henry Samueli School of Engineering), Anatomy & Neurobiology (School of Medicine), and Biomedical Engineering. His research focuses on neurorehabilitation engineering, developing robotic and sensor-based technologies to enhance motor recovery after neurological injuries such as stroke and spinal cord injury. He leads interdisciplinary efforts in robotic therapy, sensor design for movement assessment, and computational models of motor learning. Key areas include: Design of robotic devices for upper/lower extremity rehabilitation Development of wearable sensors to monitor home exercise programs Studying motor adaptation and proprioception in clinical populations Optimizing neurorehabilitation interventions through computational modeling His work integrates biomechanics, machine learning, and clinical neuroscience to create practical solutions for disabling movement disorders. Recent projects explore scalable mRehab systems, data-driven diagnostics, and real-time feedback technologies. Funding sources include NIH grants (e.g., R01 HD062744) and industry collaborations. Over 200 peer-reviewed publications and numerous patents reflect his impact in translating engineering innovations into clinical practice.

• Computational Complexity
• Boolean Functions
• Communication Complexity
• +5 more

Shachar Lovett is a researcher at the University of California, San Diego (UCSD), specializing in computational complexity, combinatorics, and theoretical computer science. His work spans advanced topics in communication complexity, pseudorandomness, and coding theory, often intersecting with problems in additive combinatorics and Boolean function analysis. Education : Not explicitly detailed in the provided text. Research Interests : Lovett's research focuses on computational complexity, particularly in communication and circuit complexity, combinatorial structures like sunflowers and high-dimensional expanders, and the analysis of Boolean functions through Fourier and Gowers norms. His work explores the limits of deterministic vs. randomized computation, the structure of codes over finite fields, and the interplay between additive combinatorics and theoretical computer science. Article Trends : His recent publications address exact vs. approximate representations of Boolean functions, quasipolynomial bounds in combinatorics, hypercontractivity in high-dimensional expanders, and advancements in the log-rank conjecture. These works emphasize connections between computational complexity, discrete mathematics, and pseudorandomness, often yielding improved bounds or novel frameworks for understanding Boolean function behavior. Scientific Awards : No specific awards or honors were mentioned in the provided text. Advising and Collaborations : Lovett collaborates extensively with researchers like Hamed Hatami, Kaave Hosseini, and Jiapeng Zhang, contributing to fields such as non-malleable codes, matrix multiplication algorithms, and communication complexity. No formal student advising details were provided.

• Microelectronics
• Analog/RF/Mixed-Signal Circuits
• Digital Integrated Circuits
• +3 more

Dr. Vishnu Unnikrishnan is an Assistant Professor at the Department of Electrical Engineering, Tampere University, within the Faculty of Information Technology and Communication Sciences. His research focuses on energy-efficient high-performance analog/digital/RF integrated circuits and systems in nanometer-scale CMOS technologies. Key areas include time-based data conversion, high-speed serial links, and 5G/6G wireless transceivers. He leads research on analog interfaces using digital/switch components and collaborates with the SoC Hub ecosystem to bridge academic and industrial interests in system-on-chip design. He has secured significant funding, including an EU Marie Curie ITN grant (SMArT) worth €818k and an Academy of Finland Project (2021) of €821k. His work spans over 40 peer-reviewed publications, emphasizing innovations in time-based ADCs, beamforming receivers, and RF system design. Dr. Unnikrishnan actively supervises doctoral and postdoctoral researchers, offers paid master's theses and summer jobs in IC design, and collaborates with industry through the SoC Hub. His research aims to advance cross-technology portable analog interfaces and high-performance mixed-signal systems.

• Supply Chain Risk Management
• Blockchain Technology
• Sustainability & Environmental Initiatives
• +6 more

Srinivas (Sri) Talluri is the John Hoagland - Richard Metzler Professor in Purchasing and Supply Management at the Department of Supply Chain Management, Michigan State University (Broad College of Business). He holds the title of DSI Fellow and served as the faculty director of the Axia Institute, focusing on advancing supply chain research and education. His work emphasizes supply chain resilience, risk management, and sustainability through innovative applications of blockchain, generative AI, and quantum computing. Dr. Talluri's research spans demand-supply uncertainty modeling, sustainable supply chain design, and strategic sourcing. Notable contributions include frameworks for blockchain integration in logistics, eco-efficient vaccine distribution during pandemics, and data-driven optimization for remanufacturing systems. His work frequently addresses macroeconomic disruptions, geopolitical risks, and environmental compliance under carbon tax schemes. His scholarly recognition includes the Fulbright-Hanken Distinguished Chair (2016-2017) and top 100 Operations Management scholar rankings. His research has been published in leading journals such as International Journal of Production Research and IEEE Transactions on Engineering Management . Key Professional Contributions: Over 140 peer-reviewed articles, leadership in supply chain education, and industry collaborations addressing global supply chain challenges. Labs/Initiatives: Active in the Axia Institute, focusing on supply chain innovation and policy.

Justin J. WestView profile

Assistant Professor

• Music Education
• Teacher Professional Development
• Arts Education Policy
• +3 more

Justin J. West is an Assistant Professor of Music and Human Learning at The University of Texas at Austin's Sarah and Ernest Butler School of Music. He previously held positions at LSU, where he received the 2021 Rising Faculty Research Award. His research focuses on music teacher professional development (PD), organizational resilience in music programs, and arts education policy. West teaches courses on children’s musical development, qualitative research, and arts education law. Educations: Ph.D. in Music Education, University of North Texas M.A. in Music Education, Columbia University B.M. in Music Education, Lamar University Research Interests: Music teacher learning trajectories, pandemic-era PD adaptations, policy frameworks for arts education, and organizational strategies for rebuilding music programs. He employs qualitative, quantitative, and mixed-methods approaches to study how PD impacts teacher motivation and institutional sustainability. Articles Trends: His recent work analyzes post-pandemic PD innovations, comparative studies between music and non-music teacher practices, and longitudinal evaluations of certification pathways. Themes emphasize system-level coordination, crisis-driven educational changes, and evidence-based policy design. Awards: 2021 Rising Faculty Research Award (LSU) Sandra E. New Endowed Professorship Fellowship Butler School of Music Endowment Fellowship Advising & Grants: Recipient of UT Austin’s Office of the Vice President for Research grants. Leads PD design initiatives for Louisiana and Texas music teachers. Active in the Society for Music Teacher Education’s professional development working group. Labs/Teams: Collaborates with editorial boards of Journal of Research in Music Education and International Journal of Research in Choral Singing . Coordinates reciprocal peer coaching programs for music educators.

• FinTech
• Machine Learning
• Credit Risk Modeling
• +8 more

Alessandro Bitetto is an Assistant Professor in Statistics at the Department of Economics and Management, University of Pavia. He holds an MSc in Applied Mathematics and has professional experience as a data scientist in banking/insurance sectors, focusing on econometric models and machine learning applications. His research interests span FinTech applications, network theory, credit risk modeling, and deep learning techniques like Graph Neural Networks. He collaborates externally with the University of Milan’s Mathematics and Logic department and teaches courses in Big Data Analysis, Python programming, and Advanced Statistics at the University of Pavia. Education: MSc in Applied Mathematics Research Interests: Alessandro's work bridges financial analytics and machine learning, with a focus on credit risk modeling, cryptocurrency ESG integration, and medical imaging applications. His recent projects include developing data-driven financial indexes using network theory and applying explainable AI to public health risk assessment. Publications Trends: His articles emphasize machine learning applications in credit risk, ESG factors in blockchain finance, and time-series analysis for financial stability. Notable contributions include studies on ICO underpricing mitigation and deep learning in echocardiogram analysis. Awards/Grants: No specific awards listed, but his collaborations suggest active grant-funded projects in interdisciplinary domains. Labs/Teams: Involved in cross-institutional collaborations, including work on pandemic data analysis via GitHub repositories like 'Computer_Vision_Tutorial' for medical imaging research.

Jan van den BrakelView profile

Professor

• Survey Methodology
• Statistical Analysis
• Time Series Modeling
• +7 more

Jan van den Brakel is an Extraordinary Professor of Survey Methodology at Maastricht University and a Senior Statistician at Statistics Netherlands. He holds a position in the Department of Quantitative Economics within the School of Business and Economics. His primary affiliation is with the methodology department at Statistics Netherlands, where he focuses on advancing statistical methods for official statistics. **Research Interests:** Professor van den Brakel specializes in survey methodology, with a focus on mixed-mode surveys, time series modeling, small area estimation, and discontinuity analysis. He explores topics such as sampling techniques, variance estimation, and the integration of non-probability data sources (e.g., social media) into official statistics. His work addresses challenges in labor force surveys, health surveys, and the impact of technological changes like tablet-based data collection. **Key Projects:** Current projects include mixed-frequency time series modeling for short-term statistics, accuracy assessments of non-probability web panels, and modeling discontinuities in official surveys. He has contributed to high-impact studies, such as analyzing mobility patterns during the pandemic and improving unemployment rate estimation using Bayesian models. **Awards:** His 2013 paper on factorial designs in probability samples was honored as the best journal paper by Survey Methodology in 2014. He is a member of the Advisory Committee on Statistical Methods at Statistics Canada, reflecting his international leadership in statistical methodology. **Professional Roles:** In addition to his academic role, he collaborates extensively with national statistical offices, advising on methodological challenges. His work bridges theory and practice, ensuring statistical methods are robust and applicable to real-world data collection and analysis.

• Econometrics
• Time Series Analysis
• Financial Econometrics
• +5 more

Prosper Dovonon is Full Professor of Economics at Concordia University, Montréal, Canada, where he holds the Tier 1 Concordia University Research Chair in Econometrics of Large Datasets . He is concurrently Adjunct Professor at the University of Adelaide, Australia, and has previously served as Associate and Assistant Professor at Concordia, Visiting Professor at HEC Montréal, and Assistant Vice-President at Barclays Wealth in London. Education Ph.D. in Economics, Université de Montréal (2007) M.Sc. in Statistics and Economics, ENSEA, Abidjan, Côte d’Ivoire (2000) M.Sc. in Mathematics, Université Nationale du Bénin, Abomey-Calavi, Benin (1996) Research Interests Professor Dovonon’s research lies at the intersection of theoretical econometrics and financial data applications . He focuses on developing robust inferential procedures for moment-condition models, bootstrap techniques for high-frequency data, identification issues in GMM, and volatility modeling with factor structures that accommodate skewness and leverage effects. His work on large-dimensional datasets emphasizes scalable methods for estimation and testing in big-data environments. Scientific Awards & Recognition Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets (2022–present) Collaborations & Affiliations Beyond Concordia and the University of Adelaide, he is affiliated with the Centre Interuniversitaire de Recherche en Économie Quantitative (CIREQ) in Montréal and has collaborated with leading scholars across North America, Europe, and Australia. His research is frequently cited in top econometrics and statistics journals, attesting to its broad impact.

Yeo Howe LimView profile

Professor

• Water Resources Engineering
• Hydrologic Modeling
• Open Channel Hydraulics
• +5 more

Dr. Yeo Howe Lim is a Professor and Department Chair of Civil Engineering at the University of North Dakota, with a focus on water resources engineering. He serves as Graduate Program Director for Civil and Environmental Engineering, teaching courses in fluid mechanics, hydrology, and applied hydraulics. Education: BEng & MEng from University of Canterbury, PhD from Memorial University of Newfoundland Research Areas: Open channel hydraulics, flood frequency analysis, streambank stabilization, urban stream revitalization, cold region hydrodynamics His research explores climate change impacts on flood patterns, hydrodynamic modeling of wetlands, and innovative use of Unmanned Surface Vehicles for aquatic studies. Recent publications emphasize lithium extraction technologies in oilfields, distributed Muskingum flood routing models, and optimization algorithms for cold climate water systems. Scientific awards include the Dean’s Outstanding Faculty Award (2013), ASCE Outstanding Reviewer recognition (2009), and the Institution of Civil Engineers (UK) Overseas Prize (2001). He has supervised numerous graduate students in hydrological modeling, including Mohammed Almousa and Vahid Atashi. Current projects involve HYCAT technology for bridge scour assessment, LiDAR bathymetry modeling, and climate change adaptation tools for cold region water management. His work bridges computational hydrology, hydraulic structure design, and sustainable water resource solutions.

Kirsty HassallView profile

Assistant Professor

• Applied Statistics
• Bayesian Modeling
• Agricultural Statistics
• +5 more

Kirsty Hassall is an Assistant Professor in Applied Statistics at the Department of Statistics, University of Warwick, having joined the department in 2024. She has previously worked for nearly 10 years as an applied statistician in the agri-environment sector, bringing extensive practical experience to her academic role. Dr. Hassall holds a PhD in "The temporal and spatial analysis of single cell gene expression" from the University of Warwick (2015). She is a Chartered Statistician with the Royal Statistical Society (2019) and has served as a committee member (2017-2021) and currently as Secretary (2021-present) of the British and Irish Region of the International Biometric Society (IBS-BIR). Her research focuses on developing robust hybrid models that combine empirical, mechanistic, and stochastic elements. With expertise spanning Bayesian networks, spatially coherent clustering techniques, and farm typology development, Dr. Hassall applies statistical methods to solve complex problems in agricultural and environmental science. Her work bridges traditional statistical consultancy (mixed models, multivariate statistics, experimental design) with methodological developments for real-world applications. Analysis of Dr. Hassall's recent publications reveals a strong interdisciplinary focus at the intersection of statistics, agriculture, and environmental science. Her work demonstrates consistent application of advanced statistical techniques to address pressing challenges in food security, sustainable agriculture, and environmental management. Key trends include the development of Bayesian Belief Networks for soil quality assessment, spatial modeling for crop production estimation, and statistical approaches to address gender dimensions in agricultural research. Chartered Statistician (Royal Statistical Society, 2019) Dr. Hassall is actively involved in research supervision and teaching, currently offering the ST344: Professional Data Science course. She has secured funding for PhD research, including a fully funded EPSRC scholarship project titled "Unravelling the sound underground: Detecting and estimating earthworm abundance using low-frequency vibrations." She also organizes the "Statistical Challenges in the Agri-Environmental Sector" workshop at the University of Warwick. Through her extensive publication record and active participation in professional societies, Dr. Hassall has established herself as a researcher who effectively bridges statistical methodology with practical applications in the agri-environmental sector. Her collaborative approach is evident in her numerous multi-institutional research projects across the UK, Africa, and Europe.