Find Academic Supervisors

• Extreme Value Theory
• Bayesian Methods
• Remote Sensing
• +3 more

Professor Philip Jonathan is Chair in Environmental Statistics and Data Science at the School of Mathematical Sciences, Lancaster University. He specializes in extreme value analysis for oceanographic and offshore engineering applications, Bayesian methods for monitoring large systems, and inverse modeling in remote sensing. His work emphasizes uncertainty quantification in environmental and data science contexts. Key research groups: STOR-i Centre for Doctoral Training , Extreme Value Theory , DSI - Environment , Data Science Institute . Current postgraduate students: Thomas Newman (Bayesian inverse modeling) and Matthew Speers (multivariate extremes for ocean-structure interactions). His recent publications focus on non-stationary extreme value models , directional wave dynamics , and climate change impacts on marine environments, with methodological contributions in penalised piecewise models and Metocean software tools . He collaborates on projects like ARC TIDE 1 (inspection regime optimization) and Modelling Wave Interactions Over Space and Time .

Christine Ann ShoemakerView profile

Professor

• Environmental Engineering
• Optimization Algorithms
• Computational Mathematics
• +4 more

Christine Ann Shoemaker is the Joseph P. Ripley Professor of Engineering Emerita at Cornell University's College of Engineering, holding a position in the Department of Civil and Environmental Engineering. Her academic career spans over five decades, marked by pioneering contributions to optimization algorithms and their environmental applications. She earned her B.S., M.S., and Ph.D. in Mathematics from the University of California, Davis, and the University of Southern California, respectively. Shoemaker's research focuses on developing computationally efficient optimization methods for solving complex environmental challenges, including groundwater remediation, climate modeling, and carbon sequestration. Her work integrates high-performance computing and surrogate models to address large-scale, data-intensive problems. She has held significant leadership roles, including being Cornell Engineering’s first tenured woman faculty member and the first woman department chair. Her awards include membership in the National Academy of Engineering, the INFORMS Harold Hotelling Medal, and the SIAM Geosciences Career Prize. Shoemaker’s international collaborations, such as the UN-sponsored groundwater contamination initiative in developing countries, highlight her commitment to global environmental sustainability. She currently works part-time, continuing her research while mentoring students and contributing to interdisciplinary projects. Education : B.S. Mathematics (UC Davis, 1966), M.S. Mathematics (USC, 1969), Ph.D. Mathematics (USC, 1971) Key Contributions : Development of asynchronous parallel optimization algorithms, climate model parameterization, and green infrastructure optimization Awards : National Academy of Engineering, INFORMS Fellow, AGU Fellow, Distinguished Educator Award (Society of Women Engineers) Her research bridges computational mathematics and environmental science, with applications in hydrology, ecology, and water resource management. She has advised numerous Ph.D. students across disciplines and advocates for women in STEM.

• Isotope Geochemistry
• Analytical Biogeochemistry
• Biogeochemical Reaction Pathways
• +4 more

Marghaleray Amini is a Researcher at the Pacific Centre for Isotopic and Geochemical Research (PCIGR) within the Department of Earth, Ocean and Atmospheric Sciences at the University of British Columbia since 2012. Previously, they served as a Scientific Advisor and Lecturer at Kabul University (2011–2012) and held postdoctoral positions at institutions including the University of Saskatchewan and Johannes-Gutenberg University Mainz. Their expertise lies in isotope and analytical biogeochemistry, with a focus on high-precision analytical tools to study biogeochemical processes. Education includes a PhD from the Helmholtz Centre for Ocean Research (2007), specializing in marine calcium budgets via hydrothermal systems, and a Master’s from the Max-Planck Institute for Chemistry (2003), studying Martian meteorites and submarine glasses. Research interests span fluid-mineral interactions, interphases between organic/inorganic matrices, and large-scale biogeochemical linkages (e.g., bio-geochemistry, social-physical science interfaces). Fieldwork includes Arctic Ocean cruises and Afghanistan’s Band-e-Amir lakes. They lead the laser ablation ICP-MS lab at PCIGR, developing methodologies for students and collaborators. Key contributions include chromium isotope studies for ocean deoxygenation proxies, LA-ICP-MS applications in zircon geochronology, and honey as an environmental biomonitor. Their work bridges geochemical analysis with interdisciplinary applications in archaeology and climate science.

Larry WassermanView profile

Professor

• nonparametric inference
• causal inference
• machine learning
• +6 more

Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.

Yih-Choung YuView profile

Associate Professor

• Bioengineering
• Control Systems
• Signal Processing
• +5 more

Yih-Choung Yu is an Associate Professor and Acting Department Head of Electrical and Computer Engineering at Lafayette College. He holds a Ph.D. in Electrical Engineering from the University of Pittsburgh, an M.S. from SUNY Binghamton, and a B.S. from Chinese Culture University. His research focuses on interdisciplinary applications of control systems in bioengineering, including cardiovascular modeling, brain-computer interfaces (BCI), and medical device development. He has pioneered work in dyslexia classification using machine learning and BCI-based robotic navigation. Dr. Yu’s teaching philosophy emphasizes mentorship and student-driven projects, reflecting his third-generation teaching heritage. Notable honors include the B. Vincent Viscomi Engineering Prize for mentoring excellence. Education: Ph.D., Electrical Engineering, University of Pittsburgh M.S., Electrical Engineering, SUNY Binghamton B.S., Electrical Engineering, Chinese Culture University His work bridges electrical engineering with healthcare, addressing challenges in cardiac function monitoring, assistive robotics, and neurodevelopmental diagnostics. Recent projects include developing affordable BCI systems for robotic navigation and non-invasive cardiac monitoring algorithms. Dr. Yu collaborates with Easton Area High School to promote engineering education and mentors students in Lafayette’s robotics initiatives. Publications highlight innovations in cardiovascular modeling (e.g., rotary blood pump interactions), BCI applications (quadcopter control, dyslexia classification), and biomedical signal processing. His research trends emphasize interdisciplinary problem-solving, leveraging control theory and machine learning to advance healthcare technologies. Awards: B. Vincent Viscomi Engineering Prize for Excellence in Mentoring and Teaching Dr. Yu’s advising style emphasizes building student confidence and fostering creativity, with many projects arising from independent studies and collaborations. Grants and funding support his work in assistive technology and bioengineering. He leads Lafayette’s efforts in regional STEM outreach, connecting undergraduates with high school students through robotics teams and educational partnerships. Active in Lafayette’s Engineering Division, he oversees facilities and curriculum development, maintaining ABET accreditation. His lab focuses on cardiovascular system modeling, BCI development, and educational robotics, with ongoing projects in dyslexia biomarkers and low-cost medical devices.

Wei-Min HuangView profile

Professor

• Statistics
• Machine Learning
• Data Science
• +6 more

Wei-Min Huang is a Professor in the Department of Mathematics at Lehigh University. He joined Lehigh in 1982 as an Assistant Professor, was promoted to Full Professor in 1995, and served as Chair of the Mathematics Department from 2007 to 2016, with an interim role in 2018. His research focuses on statistical theory and applications, including semiparametric modeling, nonparametric methods, and interdisciplinary collaborations in bioinformatics, engineering, and environmental science. Huang has published extensively in top-tier journals, addressing topics like GARCH models, plasma processes, and epidemiological forecasting. Education: Ph.D. in Statistics, University of Rochester (1982). Research Interests: Huang’s work bridges foundational statistics and applied domains. Key areas include semiparametric efficiency, kernel density estimation, and statistical applications in wireless networks and bioinformatics. Recent research extends to sustainable chemistry processes and optimal sampling strategies in ecology. His methods emphasize robustness and adaptability, with tools like the Bickel-Rosenblatt test and approximate entropy. Articles Trends: Recent publications highlight interdisciplinary applications, such as cold plasma reforming for hydrogen production and ensemble algorithms for influenza prediction. Earlier contributions include foundational work on semiparametric models and bootstrap methods for financial time series. Advising & Grants: Huang has supervised numerous Ph.D. students, fostering collaboration across disciplines. His grants and partnerships span statistical theory, environmental engineering, and materials science. Labs/Teams: Engaged in collaborative projects with engineering and chemistry departments, particularly on plasma-catalyzed processes and material characterization through XPS spectroscopy.

Daniel ConusView profile

Associate Professor

• Probability Theory
• Stochastic Modeling
• Financial Mathematics
• +3 more

Daniel Conus is an Associate Professor of Mathematics and Associate Chair in the Department of Mathematics at Lehigh University. He co-directs the MS in Financial Engineering program, bridging academic research with practical financial applications. His research focuses on probability theory, stochastic modeling, and their applications in finance, economics, and catastrophe modeling. Dr. Conus has contributed to advancing methodologies in stochastic partial differential equations (SPDEs), quantization algorithms, and risk assessment for extreme events. Key research areas include: Probability Theory and Stochastic Processes Financial Mathematics and Derivatives Pricing Catastrophe Modeling and Resilience Assessment Intermittency Phenomena in SPDEs His recent work emphasizes the development of stochastic models for seismic hazards, ecological systems (e.g., bat carrying capacity in Africa), and financial derivatives. Collaborations with industry and interdisciplinary teams highlight his commitment to real-world applications of mathematical theory.

Malte KnüppelView profile

Researcher

• Forecasting
• Business Cycle Analysis
• Monetary Policy
• +3 more

Malte Knüppel is a Researcher at the Research Centre of the Deutsche Bundesbank. His primary research interests include forecasting methodologies, business cycle analysis, and monetary policy. He has been affiliated with the Bundesbank since 2003, contributing to a wide range of studies on economic forecasting uncertainty, macroeconomic risk assessment, and the empirical evaluation of forecast horizons. His work often focuses on developing statistical tools to measure and communicate forecast uncertainty, particularly in central banking contexts. Key contributions include analyzing the predictive content of macroeconomic forecasts, testing calibration methods for multi-step density forecasts, and evaluating the reliability of central bank inflation outlooks. Knüppel has presented his research at numerous international conferences, including the International Symposium on Forecasting and the European Central Bank workshops. His publications have appeared in leading journals such as the Journal of Applied Econometrics and the International Journal of Forecasting . His recent work emphasizes the limitations of long-term forecasting horizons and the role of structural assumptions (e.g., interest rate paths) in forecast accuracy. He has also contributed to methodological advancements in path-forecasting techniques and simultaneous prediction regions. While no formal awards or grants are explicitly mentioned, his ongoing research at the Bundesbank underscores his role as a leading expert in macroeconomic forecasting and uncertainty quantification.

Mauro SerpelloniView profile

Associate Professor

• Electrical and Electronic Measurements
• Sensors and Transducers
• Biomedical Applications
• +4 more

Mauro Serpelloni is an Associate Professor in Electrical and Electronic Measurements at the Department of Information Engineering, University of Brescia. He holds a Laurea (cum laude) in Engineering (2003) and a PhD in Electronic Instrumentation (2007), both from the University of Brescia. His research focuses on advanced sensor technologies and instrumentation for biomedical and industrial applications, including additive manufacturing of sensors, wearable devices, and surgical training models. Education: Laurea (cum laude) in Engineering, University of Brescia (2003) PhD in Electronic Instrumentation, University of Brescia (2007) Research Interests: Development of flexible and 3D-printed sensors for biomedical applications (e.g., tissue hardness estimation, surgical training models) Industrial sensors for environmental monitoring and process control Wearable technologies for real-time health and performance analysis Integration of additive manufacturing techniques (e.g., aerosol jet printing) in sensor fabrication Recent Article Trends: His work emphasizes innovative sensor designs using printed electronics, with applications in agriculture (soil pollution monitoring), healthcare (phenylalanine detection, neural surgery training), and Industry 4.0 (smart packaging, IoT-enabled systems). Awards: No scientific awards explicitly mentioned in the provided texts. Advising & Grants: No specific students or grants listed. His research collaborations likely involve interdisciplinary projects at the University of Brescia's Department of Information Engineering. Labs/Teams: Engages with teams focused on biomedical instrumentation, additive manufacturing, and sensor integration, though specific lab names are not provided.

• Forensic Chemistry
• Analytical Chemistry
• Drug Analysis
• +3 more

Dr. Ruben Kranenburg is affiliated with the Van 't Hoff Institute for Molecular Sciences (HIMS) at the University of Amsterdam's Faculty of Science. His research focuses on advanced analytical techniques for forensic drug analysis, particularly the identification of novel psychoactive substances (NPS) and their isomers using spectroscopic methods like GC-MS, NIR, and FTIR. He emphasizes chemometric approaches and portable instrumentation for rapid, field-deployable detection of illicit drugs and explosives. His work bridges fundamental analytical chemistry with applied forensic science, addressing challenges in drug control and forensic investigation. Key research interests include: Development of instrument-independent chemometric models for NPS differentiation Portable NIR spectroscopy for on-scene drug identification Isomer-specific analysis of cathinones and other synthetic drugs Optimization of GC-MS and derivatization techniques for NPS analysis Integration of machine learning with spectroscopic data His recent articles highlight trends in forensic analytics, such as the need for robust methods against drug adulteration and the evasion of legislative controls through isomer mixtures. Kranenburg collaborates on projects involving explosive detection and the impact of water of crystallization on MDMA analysis. While no awards or grants are explicitly listed, his extensive publication record indicates active participation in international forensic chemistry research networks. Laboratory and team involvement is centered within HIMS, leveraging state-of-the-art facilities for mass spectrometry and spectroscopy. His work aims to improve forensic laboratory efficiency and on-site field capabilities through innovative methodological advancements.

Ching-yun WangView profile

Academic

• Survival Analysis
• Measurement Error
• Missing Data
• +5 more

Ching-Yun Wang is an Affiliate Professor in Biostatistics at the University of Washington and a Professor in the Biostatistics Program at Fred Hutchinson Cancer Research Center (Fred Hutch). He holds roles in the Public Health Sciences Division and the Translational Data Science Integrated Research Center (TDS IRC). His research focuses on methodological advancements in measurement error, missing data, survival analysis, and clinical trials, with applications in nutritional epidemiology and cancer prevention. He collaborates extensively with researchers on trials involving diet, physical activity, and colorectal cancer screening strategies. Education: PhD in Statistics from Texas A&M University (1993) and MS from National Tsing-Hua University (1985). Research interests include semi-parametric models, joint modeling of survival and longitudinal data, and high-dimensional biomarker analysis. Key research contributions include developing robust statistical methods for handling covariate errors in survival models and analyzing data from large-scale clinical trials. His work has been applied in studies like the Women’s Health Initiative and colorectal cancer screening interventions. Publications highlight methodological innovations in regression calibration, longitudinal data analysis, and missing data frameworks. Collaborations span multiple disciplines, including oncology, epidemiology, and public health. His lab (C-Y Wang Group) actively engages in translational data science projects.

Alina PremrovView profile

Research Fellow

• Biogeochemical Processes
• Ecosystem Modelling
• Climate Change Mitigation
• +3 more

Dr. Alina Premrov is a Research Fellow at the Discipline of Botany, Trinity College Dublin, the University of Dublin. Her research focuses on biogeochemical processes in terrestrial ecosystems, particularly peatlands, with an emphasis on greenhouse gas emissions, soil organic carbon dynamics, and climate change mitigation. She leads the EPA-funded CO 2 PEAT project, aiming to refine methodologies for reporting CO 2 removals and emissions from Irish peatlands. Prior roles include lecturing in Geography at Maynooth University and postdoctoral research on soil carbon and pesticide risk modelling. Education: PhD in Geology from Trinity College Dublin (2011), focused on nitrate leaching reduction measures in tillage farming. Postdoctoral work included contributions to the CForRep, AUGER, and SOLUM projects, advancing soil carbon stock estimation and biogeochemical modelling. Research Interests: Peatland restoration, GHG flux modelling, pesticide risk assessment, and policy-driven environmental science. Her work integrates advanced models like ECOSSE with observational data to address land use and climate impacts on ecosystems. Recent efforts focus on enhancing IPCC Tier methodologies for peatland reporting and developing tools like the miniRECgap package for CO 2 flux data gap-filling. Grants & Roles: Principal Investigator of the CO 2 PEAT project (EPA grant 2022-CE-1100). Collaborated on DAFM's PROTECTS project and contributed to ECOSSE model enhancements for drained peatlands. Her research has been published in journals like Science of the Total Environment and Geoderma. Labs & Teams: CO 2 PEAT project team at Trinity, ICARUS Department at Maynooth University. Active in interdisciplinary collaborations on ecosystem modelling and climate policy.

Geneviève GauthierView profile

Professor

• Financial Engineering
• Quantitative Finance
• Risk Management
• +5 more

Geneviève Gauthier is a Full Professor in the Department of Decision Sciences at HEC Montréal, a leading business school in Canada. She holds a Ph.D. in Mathematics from Carleton University and an M.Sc. in Mathematics from UQAM. As a member of the Study and Research Group on Decision Analysis (GERAD) and a researcher at the Interuniversity Center for Research in Organizational Analysis (CIRANO), she bridges academic research with practical applications in quantitative finance. Her educational background includes: Ph.D. in Mathematics, Carleton University, Ottawa M.Sc. in Mathematics, Université du Québec à Montréal (UQAM) Professor Gauthier specializes in financial engineering with expertise spanning stochastic calculus, probability and statistics, mathematical modeling, pricing, risk management, and credit risk. Her research focuses on developing advanced mathematical models for financial markets, with particular emphasis on option pricing, volatility modeling, and credit risk analysis. She integrates machine learning techniques with traditional quantitative methods to address complex financial problems, contributing significantly to both academic literature and industry practice. Her recent publications demonstrate a strong trend toward integrating deep learning with traditional quantitative finance methods, particularly in hedging strategies and volatility surface modeling. She has made significant contributions to understanding the informational content of high-frequency option data and developing semi-closed-form solutions for complex derivatives. Her work spans energy markets, credit risk modeling, and the intersection of machine learning with traditional financial engineering approaches. Professor Gauthier has received numerous prestigious awards recognizing her contributions to finance and statistics: The Canadian Derivatives Institute CDI Conference Best Discussion Award (2022) Research Professorship in Financial Engineering at HEC Montréal (2019-2028) SSC Award for Impact of Applied and Collaborative Work (2018) Best Paper Award on Derivatives from the Northern Finance Association (2017) Best paper award in the Accounting and Finance section at the World Business and Economics Research Conference (2012) As an academic advisor, Professor Gauthier has supervised 69 students throughout her career, including 2 PhD candidates, 10 Master's theses, and 7 supervised projects in the last five years alone. Her PhD students have worked on advanced topics like multivariate stochastic volatility models and extreme events in financial markets, while her Master's students have explored areas ranging from VIX option pricing to electricity price forecasting. She teaches graduate courses in numerical methods, stochastic calculus, and machine learning applied to financial data. Professor Gauthier is actively involved with research groups including GERAD (Group for Research on Decision Analysis) and CIRANO (Centre for Interuniversity Research and Analysis on Organizations), where she collaborates with interdisciplinary teams to advance quantitative methods in finance and risk management. Her work often bridges theoretical developments with practical applications in the financial industry.

• Marine Geology
• Paleoceanography
• Biogeochemistry
• +3 more

Andres Rüggeberg is a Lecturer in the Department of Geosciences at the University of Fribourg and a Senior Researcher affiliated with the Interfacultary Institute of Environmental Sciences and Humanities. His research focuses on marine geology, cold-water coral ecosystems, evaporite deposits, and paleoceanographic reconstructions. Key areas of study include the environmental dynamics of coral mounds in the Mediterranean and North Atlantic, the geochemistry of seep carbonates, and the climatic influences on evaporite formation in extreme environments like the Danakil Depression. Dr. Rüggeberg employs advanced analytical techniques such as XRF core scanning, stable isotope analysis, and sedimentological methods to investigate past climate changes and their impacts on marine ecosystems. His work integrates interdisciplinary approaches, combining field observations with laboratory analyses to address questions about benthic habitat resilience, oceanographic processes, and the interplay between geological and biological systems. Recent studies highlight contributions to understanding the formation mechanisms of cold-water coral mounds, the role of hydrodynamic conditions in benthic faunal distributions, and the application of novel proxies for reconstructing seawater density and pH in past oceans. Dr. Rüggeberg’s research has produced extensive datasets from cores in the Mediterranean Sea, Norwegian margin, and Maldives, emphasizing the importance of long-term environmental monitoring and the preservation of deep-sea carbonate ecosystems. His publications span over two decades, showcasing a sustained focus on both fundamental and applied aspects of marine geoscience.

Maryamsadat TahavoriView profile

Associate Professor

• Sustainable Energy Systems
• Asset Management
• Model Reduction
• +5 more

Maryamsadat Tahavori is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the Department of Engineering Technology and Didactics Energy Technology and Computer Science. Her research focuses on sustainable energy systems, asset management, control systems, and fault diagnosis in robotics and district heating networks. She leads projects addressing district heating network optimization, prescriptive analytics, and soil sustainability through sensor technologies. Her academic contributions include advancements in model reduction techniques for bilinear systems and multi-objective optimization algorithms. She supervises PhD candidates like Jens Grønborg and collaborates internationally on initiatives like the IEA DHC Annex for district heating asset management. Key awards include IEEE Senior Member (2021). Her work bridges theoretical control systems with practical applications in energy infrastructure, agriculture, and autonomous systems. She actively participates in conferences, organizes academic events, and serves as an editor for the Sensors journal.