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Izaak NeriView profile

Senior Lecturer

• Statistical Mechanics
• Biological Physics
• Non-Equilibrium Thermodynamics
• +7 more

Dr. Izaak Neri is a Senior Lecturer in Disordered Systems at the Department of Mathematics, King's College London, within the Faculty of Natural, Mathematical & Engineering Sciences. He obtained his PhD in Theoretical Physics from KU Leuven (2010) and held postdoctoral positions at the University of Montpellier, and the Max Planck Institutes for Molecular Cell Biology and Genetics and Physics of Complex Systems. His research focuses on statistical physics, mathematical physics, and biological physics, particularly non-equilibrium thermodynamics, sparse non-Hermitian random matrices, and network dynamics. He explores phase separation in biological systems, spin glass theory, and ecosystem stability using tools from statistical mechanics and stochastic processes. Education: PhD in Theoretical Physics (KU Leuven, 2010), MSc in Physics (Ghent University, 2005) Awards: APS Outstanding Referee (2020), Irwin Oppenheim Award (2024) Key Research Themes: Complex networks, random matrices, biological physics, nonequilibrium systems His work bridges theoretical frameworks and applications, such as modeling cytoskeletal transport and analyzing entropy production in Markov processes. He contributes to understanding how network structure influences dynamical systems and develops methods for studying large sparse systems. Current projects include phase separation in multicomponent biological mixtures and stability analysis of predator-prey networks.

• Population Genetics
• Ecology
• Theoretical Population Genetics
• +4 more

Jesse Taylor is an Associate Professor at Arizona State University (ASU), affiliated with the School of Life Sciences and the School of Mathematical and Statistical Sciences. He is also part of the Biodesign Center for Mechanisms of Evolution and the Mechanisms of Evolution Researchers Team. His research focuses on evolutionary biology, population genetics, and the ecology of soil and myrmecophilous mites in desert ecosystems. Taylor integrates stochastic modeling, statistical analysis, and genetic data to study evolutionary processes in complex environments. Education: Ph.D. in Ecology and Evolutionary Biology from the University of Arizona (2003). Research Interests: Natural history of soil mites, theoretical population genetics (coalescent theory), and the evolution of antigen gene repertoires in parasites like malaria and African sleeping sickness. His lab explores biodiversity and evolutionary dynamics of soil mites in the Sonoran Desert and Madrean sky islands, while also analyzing pseudogene formation in parasite antigens using mathematical models. Taylor has published extensively on topics ranging from phylogeography to epidemiological modeling, with a focus on infectious diseases and genetic diversity. He serves as a referee for journals like PLoS Pathogens and Theoretical Population Biology , and contributes to the ASU undergraduate curriculum in evolutionary biology and mathematics.

Howard BondView profile

Adjunct Professor

• Astrophysics
• Stellar Evolution
• Extragalactic Distance Scale
• +5 more

Howard Bond is an Adjunct Professor of Practice in the Department of Astronomy & Astrophysics at Pennsylvania State University, and an Astronomer Emeritus at the Space Telescope Science Institute (STScI). His career spans over five decades, with key roles including Professor at Louisiana State University (1969-1984), tenured Astronomer at STScI (1984-2008), and co-founder of the Hubble Heritage Program. He holds a B.S. in Physics (University of Illinois, 1964), and M.S. and Ph.D. in Astronomy (University of Michigan, 1965-1969). Education B.S. Physics, University of Illinois, 1964 M.S. Astronomy, University of Michigan, 1965 Ph.D. Astronomy, University of Michigan, 1969 Research Interests Bond's work focuses on stellar evolution, particularly post-asymptotic-giant-branch (post-AGB) stars and light echoes. He pioneered studies of luminous red transients like V838 Monocerotis and demonstrated the utility of post-AGB stars as extragalactic distance indicators. His research also encompasses planetary nebulae, white dwarfs, globular clusters, and variable star monitoring. Notable Contributions Bond co-discovered the optical counterpart to Gamma-Ray Burst 970508, confirming their cosmological origin. He led imaging campaigns with the Hubble Space Telescope, including the iconic Ring Nebula observation featured on U.S. postage stamps (2000). His editorship of the Publications of the Astronomical Society of the Pacific (1991-1997) and the Planets, Stars, and Stellar Systems series (2013) underscores his role in advancing astronomical communication. Awards & Recognition 2003 Klumpke-Roberts Award (ASP) for Hubble Heritage Program contributions 2007 American Astronomical Society Education Prize 2020 Fellow of the American Astronomical Society Grants & Observations Bond secured Hubble observing time in 31 out of 32 cycles (1990-2024) and all James Webb Space Telescope cycles (2021-2024). His work leverages facilities like STScI's WFC3 and ground-based telescopes (e.g., Cerro Tololo). Labs & Collaborations As STScI's HST Telescope Time Review Board Chair, he managed peer review processes. His collaborations include the SMARTS Consortium for variable star monitoring and the Heritage Program's public outreach efforts.

Rasmus Tangsgaard VarneskovView profile

Professor

• Econometrics
• High-Dimensional Statistics
• Asset Pricing
• +3 more

Rasmus Tangsgaard Varneskov is a Professor of Statistics and Financial Econometrics at the Department of Finance, Copenhagen Business School (CBS). He is also employed by Alphadyne Asset Management. His research focuses on econometrics, high-dimensional statistics, asset pricing, and financial economics, with a strong emphasis on time series analysis and financial econometrics methodologies. Before joining CBS, he was a postdoctoral researcher in Finance at Northwestern University's Kellogg School of Management. He holds affiliations with the Center for Big Data in Finance (BIGFI) and the Center for Statistics at CBS. His educational background includes advanced studies in statistics and econometrics, though specific degrees are not detailed in the provided text. Rasmus' research interests span advanced statistical methodologies for financial data, including volatility estimation, bootstrap techniques, predictive regressions, and structural change analysis. His work has been published in top journals such as Journal of Econometrics , Journal of Financial Economics , and Quantitative Economics . A key achievement is his 2023 Econometric Theory Multa Scripsit award for prolific and impactful contributions. His publications address topics like Laplace transforms of volatility, consistent inference in predictive regressions, and dynamic hedging strategies. While no formal advisees are listed, his industry collaborations (e.g., with Nordea and Alphadyne) suggest applied research engagement. He contributes to CBS's research initiatives through participation in interdisciplinary centers focused on big data and financial statistics.

• Stochastic Analysis
• Partial Differential Equations
• Stochastic Filtering
• +7 more

Andrea Pascucci is Full Professor of Probability and Statistics at the University of Bologna, affiliated with the Department of Mathematics. His research focuses on stochastic analysis for diffusions and jump processes, partial differential equations, and applications in quantitative finance. He has authored 8 books and over 80 publications on Kolmogorov-Fokker-Plank equations, transition densities estimation, and financial mathematics. His research interests span stochastic partial differential equations, asymptotic analysis of multi-dimensional diffusion processes, free boundary problems, and volatility modeling in financial mathematics. He integrates mathematical rigor with practical applications in derivative pricing and risk management. Pascucci serves as Associate Editor for the Journal of Computational Finance and SeMA Journal. He directs the university's post-graduate programme in Mathematical Finance, developing advanced curricula that blend theoretical stochastics with computational finance techniques.

• Neuroscience
• Cognitive Sciences
• Neurobiology
• +5 more

Massimo De Agrò is a Research Fellow at the Interdepartmental Center for Mind/Brain Sciences (CIMEC) within the University of Trento, Italy. He holds a PhD in Psychological Sciences (2019) and a Master’s in Cognitive Neuroscience and Clinical Neuropsychology (2016), both from the University of Padua. His research focuses on brain simplification strategies in invertebrates, particularly jumping spiders, exploring how limited neural resources enable functional efficiency. This work aims to bridge biological insights with technological innovation. He has held postdoctoral positions at institutions including Harvard University (2020), Regensburg University (2021-2022), and the University of Florence (2022). His expertise spans neurobiology, cognitive sciences, and ethology, with a focus on visual systems, behavioral ecology, and animal cognition. Key projects include studying animacy perception in arthropods, REM-like sleep states in spiders, and developing tools like the SPiDbox for spider behavior analysis. His interdisciplinary approach integrates experimental biology, computational modeling, and robotics.

• Computer Science
• Information Engineering
• Deep Learning
• +3 more

Di Zhang is affiliated with Guangdong Medical College's School of Information Engineering and holds a PhD in Synthetic Aperture Radar Image Interpretation from the University of Hamburg (2022). Their research focuses on interdisciplinary fields such as deep learning, remote sensing, optimization algorithms, and their applications in medical imaging, environmental science, and education technology. They have published extensively in top-tier journals like IEEE Access, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Remote Sensing. Key affiliations: University of Hamburg (PhD), Guangdong Medical College, and others listed in disambiguation entries. Research interests include AI-driven medical diagnostics, SAR image analysis, IoT data management, and educational assessment systems. Recent work emphasizes deep learning frameworks for image processing, algorithm optimization, and multimodal data fusion. Publications span diverse topics such as migraine diagnosis via radiomics, social support in online learning, and robust visual SLAM systems. Their work bridges theoretical advancements with practical applications in healthcare, robotics, and environmental monitoring.

Robert WoodView profile

Professor

• Robotics
• Materials Science
• Mechanical Engineering
• +7 more

Robert Wood is the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences at Harvard University, affiliated with the School of Engineering & Applied Sciences. He holds a PhD in Electrical Engineering and Computer Sciences from UC Berkeley. His research focuses on robotics, particularly bio-inspired systems and soft robotics applications in natural environments and biomedicine. He emphasizes interdisciplinary approaches, combining engineering with biological insights to advance robotic design and functionality. Wood teaches courses such as 'How Music Works: Engineering the Acoustical World,' 'Microrobotics and Bio-inspired Autonomous Robotic Systems,' and 'Advanced Topics in Robotics Research.' He advocates for hands-on learning and the value of iterative failure in education, stating, 'Making mistakes and learning from them is an absolutely essential part of the learning process.' His lab, the Microrobotics Lab, explores topics like compliant actuators, bioinspired locomotion, and soft robotic systems. While no formal scientific awards are listed, Wood’s contributions to robotics have been highlighted through his work on projects like RoboBee and the Cetacean Translation Initiative (Project CETI). He directs graduate studies in his department and has advised students across his research and teaching activities. His lab’s current and future work includes advancements in soft wearable sensors, modular robotic systems, and biomimetic mechanisms for medical and environmental applications.

Shuping ShiView profile

Professor

• Financial Econometrics
• Applied Economics
• Bubble Detection
• +2 more

Shuping Shi is a Professor in the Department of Economics at Macquarie University, affiliated with the Centre for Risk Analytics and Housing and Urban Research Centre. Her research focuses on financial econometrics with applications to real estate markets and bubble detection. Key research areas include: Development of PSY bubble monitoring technique used by central banks High-frequency data analysis for financial markets Risk assessment in global housing markets Causal change detection in economic systems Publications demonstrate strong focus on empirical finance methodologies, particularly volatility modeling, unit root testing, and bubble detection algorithms. Recent work advances statistical methods for real-time market monitoring. She has secured over $1 million in external funding including Australian Research Council grants: Discovery Early Career Researcher Award (2019-2021) and Discovery Project Awards (2015-2018, 2019-2021). Her bubble detection techniques have been adopted by institutions including Federal Reserve Bank of Dallas and Hong Kong Monetary Authority.

Paul BartonView profile

Professor

• Energy Systems Engineering
• Energy Efficiency
• Renewable Energy
• +3 more

Paul Barton is a Professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), affiliated with the MIT Energy Initiative. His research focuses on energy systems engineering, particularly in energy efficiency, conventional and renewable energy technologies, and process optimization. He leads projects on liquid air energy storage (LAES), natural gas liquefaction processes, and hybrid polygeneration systems. His work integrates advanced mathematical modeling, nonsmooth analysis, and optimization techniques to address challenges in energy systems and chemical engineering. Recent research highlights include developing novel frameworks for energy storage feasibility analysis, optimizing LNG production processes, and advancing nonsmooth differential-algebraic equation (DAE) simulation tools. His projects often involve interdisciplinary collaborations with MIT’s Future Energy Systems Center and the U.S. Department of Energy. Barton has contributed to over 30 publications in 2021–2025 alone, covering topics from stochastic chemical systems to dynamic optimization of pharmaceutical manufacturing. His research portfolio demonstrates a strong emphasis on bridging theoretical advancements with practical industrial applications, particularly in decarbonization and sustainable energy infrastructure. Current work includes analyzing LAES economics in future electricity markets and improving decision-making under uncertainty for energy infrastructure investments.

• System Identification
• Stochastic Signal Processing
• Dynamic System Modelling
• +4 more

Professor Brett Ninness is a faculty member at the University of Newcastle, Australia, holding the position of Professor in the School of Electrical Engineering and Computing. He has served in various administrative roles, including Assistant Dean (Research Training), Deputy Head of Faculty, and Acting Pro Vice Chancellor. His research focuses on dynamic system modeling, system identification, stochastic signal processing, and wireless communications. He has authored over 100 papers and held editorial roles in journals like Automatica and IEEE Transactions on Automatic Control. Education: Brett Ninness earned his BE, ME, and PhD in Electrical Engineering from the University of Newcastle. His teaching spans systems theory, signal processing, digital systems, and communications. Research Interests: He specializes in system identification, noise-robust signal processing, and applications in wireless communications. His work includes developing algorithms for state estimation, Kalman filtering, and nonlinear model identification. Recent projects involve collaborations with industry partners like Bell Labs and Agere Systems. Publications: His most recent works address smoothed state estimation, nonlinear system identification, and distributed Kalman filtering. Articles highlight advancements in stochastic processes, control systems, and Bayesian methods. Awards: Recipient of the 2001 Tall Poppy Award for outstanding scientific contribution. He is a member of editorial boards and international committees, including the IEEE Technical Committee on System Identification and IFAC. Grants & Supervision: Extensive grants in system identification and control theory. Supervised numerous PhD/Master’s students, though specific names are not listed here. Collaborations: Partnerships with institutions like Royal Institute of Technology (Sweden), Linköping University, and Vrije Universiteit Brussel. Active in global research networks through IEEE and IFAC committees.

• Control Engineering
• System Identification
• Bayesian Estimation
• +5 more

Adrian Wills is an Associate Professor in the School of Engineering (Mechatronics) at the University of Newcastle, Australia. He leads the Mechatronics Engineering program and holds academic appointments since July 2015. His research focuses on Bayesian estimation, system identification, and control engineering, with applications in robotics and mechatronics. Wills has collaborated with institutions globally, including Linköping University, Uppsala University, and the University of British Columbia. He earned his B.E. (Elec.) and Ph.D. from the University of Newcastle in 1999 and 2003, respectively. Teaching expertise includes delivering advanced courses in estimation and optimization within the Mechatronics Engineering program. Administrative roles include program convenor for Mechatronics Engineering. Research highlights include contributions to state-space models, nonlinear system identification, and model predictive control. His work bridges theoretical advancements with practical applications in engineering systems and healthcare. Key collaborations involve Professors Lennart Ljung, Thomas Schön, and Bhushan Gopaluni, among others. His technical contributions span MATLAB toolboxes (e.g., UNIT), FPGA/ASIC implementations for control systems, and interdisciplinary projects in strain measurement using neutron diffraction.

Şule ŞahinView profile

Senior Lecturer

• Actuarial Science
• Stochastic Investment Models
• Mortality Modelling
• +3 more

Dr. Şule Şahin is a Senior Lecturer in Actuarial Science at the University of York's School for Business and Society and Director of the Centre for Actuarial Compensation and Valuation of Life (CAVOL). She holds a PhD in Actuarial Mathematics from Heriot-Watt University and is a Fellow of the Institute and Faculty of Actuaries. Her affiliations include membership in the Ogden Working Party (developing injury compensation tables) and the AFIR-ERM board of the International Actuarial Association. Her research integrates actuarial mathematics, statistics, economics, and sociology, focusing on: Stochastic investment models for long-term applications Mortality modelling with transitory jumps (e.g., pandemics) Actuarial compensation systems for loss of earnings Longevity risk hedging and economic scenario generators Recent publications center on pandemic-influenced mortality modelling, actuarial compensation frameworks, and insurance pricing innovations, reflecting interdisciplinary applications across legal and socioeconomic contexts. Awards: SCOR Actuarial Award, Spain and Portugal (2023) Current Projects: Ogden Tables work-life expectancy estimation (UK government injury compensation) COVID-19 impact on public pension systems International actuarial compensation table development She leads CAVOL and collaborates globally, including research visits to Columbia University and the University of Witwatersrand funded by actuarial societies.

Robert AzencottView profile

Professor

• Genomics
• Proteomics
• Medical Imaging
• +9 more

Robert Azencott is a Professor of Mathematics at the University of Houston and holds the title of Emeritus Professor at École Normale Supérieure in France. He specializes in interdisciplinary research at the intersection of mathematics, biosciences, and imaging. His work spans stochastic processes, data mining, and medical imaging applications such as 3D-echocardiography analysis and deformable shape matching. Azencott has contributed to probabilistic methods for bacterial genetic evolution models and financial market microstructure analysis. His research also addresses texture classification, kernel-based learning, and stochastic differential equations (SDEs) in finance and biology. Research Interests Genomics & Proteomics : Sparse modeling of gene interactions, microRNA impact on cancer survival, proteomic mass spectra analysis. Bacterial Evolution : Stochastic models for genetic evolution with periodic selection and large deviations theory. Medical Imaging : 3D-echocardiography strain analysis, deformable shape dynamics via diffeomorphic matching, and ROI reconstruction in X-ray tomography. Probability & Statistics : SDE parameter estimation, kernel-based clustering, and applications to algorithmic trading and option pricing. Labs & Collaborations Azencott collaborates on projects such as digital stains for live-cell microscopy and intra-cardiac echography analysis to assess myocardium deformations. His work intersects with teams in cardiology, computational biology, and financial engineering.

Xuemiao HaoView profile

Associate Professor

• Risk Theory
• Financial Risk Management
• Stochastic Processes

Xuemiao (Samuel) Hao is an Associate Professor at the Warren Centre for Actuarial Studies and Research within the Asper School of Business at the University of Manitoba. He holds dual bachelor's degrees in mathematics and economics from Peking University, and PhD in statistics from University of Iowa. Research focuses on: Mathematical modeling of insurance/financial risks Applications of stochastic processes Credit derivative pricing Ruin theory with heavy-tailed distributions His methodological work employs advanced techniques including Lévy processes, Fourier transforms, and asymptotic analysis for risk quantification.