Find Academic Supervisors

• Organometallic Chemistry
• Crystallography
• Phosphine Ligand Design
• +4 more

Dr Ross Harrington is a Research Fellow in the Department of Chemistry within the School of Natural and Environmental Sciences at Newcastle University. His research focuses on organometallic chemistry, particularly the synthesis and characterization of phosphine-based ligands and their transition metal complexes. He has maintained a long-standing collaborative relationship with Professor William Clegg, resulting in numerous high-impact publications spanning over two decades. His research interests primarily center on organometallic synthesis , crystallography , and ligand design , with significant contributions to phosphine and phosphine-borane chemistry polyoxometalate systems fluorescent organometallic complexes chiral ligand development for asymmetric catalysis structural characterization of novel metal complexes Analysis of his 15 most recent publications reveals a consistent focus on synthetic methodology development, particularly for air-stable phosphine ligands and their applications in catalysis. His work demonstrates expertise in X-ray crystallography for structural characterization and spans applications from fundamental organometallic chemistry to materials science, including fluorescent compounds and molecular conductors. Dr Harrington has contributed to significant advances in several areas including: MOP-phosphonite ligand systems for asymmetric catalysis Structural studies of polyoxometalates with SnW5 and TiW5 frameworks Development of air-stable fluorescent primary phosphine complexes Crystal engineering of radical cation salts for molecular electronics His collaborative approach is evident through extensive co-authorship with Professor William Clegg and other researchers across multiple institutions. While specific grant information isn't detailed in the publications, his sustained research output indicates successful funding acquisition for his projects in synthetic organometallic chemistry. Dr Harrington's work has established him as a significant contributor to the field of synthetic organometallic chemistry, particularly in ligand design and structural characterization of novel metal complexes with applications in catalysis and materials science.

Joshua ReedView profile

Associate Professor

• Stochastic Network Theory
• Queueing Systems
• Operations Research
• +7 more

Joshua Reed is an Associate Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He has been a faculty member since 2007 and currently serves as Deputy Chair of his department. His work bridges operations research, stochastic modeling, and financial applications. Ph.D., Industrial and Systems Engineering, Georgia Institute of Technology, 2007 B.S., Industrial Engineering, University of Illinois at Urbana-Champaign, 2000 Professor Reed's research centers on the performance analysis and control of stochastic networks, with applications in service systems like call centers and internet congestion control. His expertise includes heavy-traffic approximations, diffusion models, and reflected stochastic processes. He has made significant contributions to the theory of queueing systems, particularly in many-server and infinite-server regimes. His recent publications span topics such as limit order books, dynamic pricing, and bandwidth sharing networks. These works often employ advanced probabilistic methods and asymptotic analysis to model complex real-world systems. The dominant themes include stochastic optimization, scaling limits, and financial operations. Optimal Cash Management Using Impulse Control (2023) Reflected Brownian Motion with Drift in a Wedge (2023) Large-scale Bundle Size Pricing: A Theoretical Analysis (2021) Scaling Limits for Limit Order Book Models (2017) High Frequency Asymptotics for the Limit Order Book (2016) Professor Reed has collaborated with leading researchers such as Peter Lakner, Assaf Zeevi, and Benjamin Reed. He has not received any explicitly mentioned scientific awards in the provided text. He advises graduate students and is involved in ongoing research projects related to financial queueing models and dynamic pricing. His work is supported by theoretical grants in operations research and applied probability, though specific grant details are not listed. He is associated with research groups focused on stochastic modeling and financial engineering at NYU Stern. His team explores high-frequency trading dynamics, limit order book modeling, and large-scale service system optimization. Current preprints indicate active work in regime-dependent pricing and decentralized exchange arbitrage.

Jean-Christophe PesquetView profile

Professor

• Optimization
• Signal Processing
• Image Processing
• +4 more

Jean-Christophe Pesquet is a Professor affiliated with the Laboratory Digital Vision Center. His work spans optimization, inverse problems, artificial intelligence, signal and image processing . He actively collaborates on advanced algorithm design and computational methods for imaging and signal recovery. Key research areas: Optimization, Wavelet Analysis, Blind Source Separation, Deep Learning Recent publications focus on nonconvex optimization, primal-dual splitting, and deep unfolding techniques. His methodological contributions include proximal algorithms, stochastic subspace approaches, and Bregman divergences. Applications range from biomedical imaging (e.g., PET, CT scans) to seismic data analysis and aerospace defect detection. Notable collaborations include researchers like Emilie Chouzenoux , Audrey Repetti , and Caroline Chaux . Current projects integrate neural networks with classical signal processing frameworks (e.g., lifting schemes, MCMC algorithms).

Libo LiView profile

Senior Lecturer

• Mathematical Finance
• Probability Theory
• Stochastic Processes
• +3 more

Dr. Libo Li is a Senior Lecturer at the School of Mathematics & Statistics, University of New South Wales. His research focuses on probability theory, stochastic processes, and mathematical finance. He holds a PhD from the University of Sydney and has undertaken postdoctoral positions at Ritsumeikan University and Universite d’Evry Val d’Essonne. Education: PhD, University of Sydney, Australia (2011) Postdoc, Ritsumeikan University, Japan (2013-2014) Postdoc, Universite d’Evry Val d’Essonne, France (2012) Research Interests: Dr. Li's work investigates mathematical finance applications, including backward stochastic differential equations (BSDEs), optimal stopping problems, default times, and numerical schemes for Lévy-driven stochastic differential equations. His research emphasizes theoretical rigor and computational methods. Recent Trends: His 2024-2025 publications analyze BSDEs with generalized drivers, positivity-preserving numerical methods for CEV processes, and defaultable American options. Earlier works (2022-2019) focus on honest times, parametrix methods, and Lévy-driven SDEs. Contact: Email: libo.li@unsw.edu.au

Laurent DecreusefondView profile

Professor

• Probability and Stochastic Modeling
• Malliavin Calculus
• Telecommunications Network Analysis
• +3 more

Laurent Decreusefond is a Professor of Mathematics at Télécom Paris , affiliated with the Information Processing and Communication Laboratory (LTCI) and the Network, Mobility and Services (RMS) research team. His work bridges probability theory and telecommunications, focusing on stochastic modeling, Malliavin calculus, and applications to wireless network performance. Research Interests: Probability, stochastic processes, determinantal point processes, simplicial homology for network coverage, and Malliavin calculus in telecommunications. Publications: Recent articles address UAV swarm deployment, Stein's method for stable processes, and optimal transport in wireless simulation. His work connects abstract mathematics to practical network optimization. Labs & Teams: Active in LTCI and RMS, he contributes to stochastic geometry and network modeling. No explicit awards or student advisement details provided in the data.

• Blind Signal Processing
• Bayesian Estimation
• Particle Filters
• +3 more

Ayşın Baytan Ertüzün is a Professor at Boğaziçi University, specializing in signal processing and statistical methodologies. Their research focuses on blind signal processing, Bayesian estimation techniques, particle filters, and seismological applications. They have contributed extensively to defect detection in textiles using advanced signal processing methods like wavelet transforms and independent component analysis, as well as geophysical signal analysis through Bayesian deconvolution approaches. Over 25 years of academic work includes pioneering work in non-Gaussian processes, time-varying autoregressive models, and particle filtering algorithms for complex signal environments. Their research bridges theoretical advancements with practical applications in manufacturing quality control and seismic data analysis. Key technical contributions include hybrid deconvolution methods for Bernoulli-Gaussian processes, subband domain subspace analysis for texture defects, and entropy-based 2D lattice filter structures. Their work often emphasizes robust statistical frameworks for non-stationary and non-Gaussian signal environments, with applications spanning from environmental forecasting (wind speed modeling) to medical signal analysis (respiratory sound coherence). Publications span from foundational 1990s lattice filter innovations to modern Bayesian approaches. Their methodologies are characterized by interdisciplinary fusion of signal processing theory with real-world industrial and geophysical challenges. No specific awards are listed in the provided materials, though their extensive publication record indicates sustained academic impact.

Tomi RyynänenView profile

Researcher

• Biomedical Engineering
• Microelectrode Technology
• Stem Cell Applications
• +3 more

Professional Overview Tomi Ryynänen is a Researcher in Biomedical Technology at Tampere University, focusing on advanced microelectrode array (MEA) design and in vitro cell culture systems. His work bridges engineering and biology, particularly in neuroengineering and cardiac cell analysis. Education PhD in Biomedical Engineering, Tampere University (2019) MSc in Physics, University of Jyväskylä (2000) Research Interests Ryynänen specializes in: - Development of transparent and durable MEA materials (e.g., titanium nitride coatings) - 3D cell culture systems with integrated sensors - Neurological disease modeling (Parkinson’s, epilepsy) using tripartite networks - Real-time electrophysiological monitoring of cardiac and neuronal cells under pathophysiological conditions Grant Funding Recipient of grants from Suomen Kulttuurirahasto (2016–2017) supporting his interdisciplinary research initiatives. Key Contributions Pioneered inkjet-printed 3D MEAs for complex cell models Developed temperature-controlled microfluidic platforms for stable in vitro experiments Advanced understanding of electrode material degradation in biomedical applications Labs & Collaborations Co-developed the Portable Microscale Cell Culture System with indirect temperature control, enabling precise environmental regulation in lab settings.

Charlotte LanglaisView profile

Associate Professor

• Signal Processing
• Digital Communications
• Wireless Communications
• +2 more

Charlotte Langlais is a Study Director and Researcher at IMT Atlantique in the Mathematical and Electrical Engineering department. She obtained her PhD in Electronics from INSA de Rennes in 2002, with research focused on turbo-equalization for wireless HF ionospheric transmission. Her current research explores signal processing and digital communications, particularly wireless communications, coding/modulation, MIMO systems, and multi-user transmission. Dr. Langlais teaches digital electronics using innovative flipped classroom approaches and coordinates project-based learning initiatives. She leads the coordination of project courses and industry partnerships involving over 50 economic partners. Her publications demonstrate expertise in mMIMO systems, NOMA techniques, and reconfigurable intelligent surfaces for 5G/6G applications. Recent work shows strong focus on railway communications, channel modeling, and efficient beamforming strategies for mmWave networks.

Natasha T. DimovaView profile

Associate Professor

• Environmental Geochemistry
• Biogeochemistry
• Hydrogeology
• +6 more

Natasha T. Dimova is an Associate Professor in the Department of Geological Sciences at the University of Alabama. Her research focuses on marine and environmental geochemistry, hydrogeology, and application of radioisotopes as tracers in hydrological studies. She leads the Coastal Hydrogeology and Hydrology Research Group, advising over 10 graduate students since 2013. Dr. Dimova's work integrates field measurements, geophysical methods (e.g., electrical resistivity), and isotope geochemistry to study groundwater-surface water interactions in diverse environments including karst aquifers, Arctic regions, and coastal zones. Education: PhD in Chemical Oceanography (Florida State University), MS in Inorganic and Analytical Chemistry (University of Sofia, Bulgaria). Research interests include: Applications of uranium/thorium isotope series in hydrological studies Electrical resistivity imaging of coastal subsurface processes Radioprotection assessment of natural/artificial radioactivity Key projects involve studying SGD nutrient fluxes in Mobile Bay, Arctic methane dynamics, and arsenic transport in Bangladesh river systems. Her lab operates advanced equipment including alpha spectrometers and radon monitors. Recent work highlights global subterranean estuary impacts on coastal nutrient budgets and development of new radioisotope methodologies. Advising: Current 2 PhD students and over 8 former advisees (2013–2022) now in positions at USGS, Geosyntec Consultants, and other environmental agencies. Research group collaborates internationally with institutions in China, Indonesia, and Alaska. Labs: Director of Alabama Stable Isotope Laboratory (ASIL) focusing on radiometric and isotopic analysis.

• Experimental Nuclear Astrophysics
• Big Bang Nucleosynthesis
• Solar Fusion Reactions
• +4 more

Dr. Konrad Schmidt is a staff scientist and HZDR High-Potential Fellow at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , embedded in the Nuclear Astrophysics group within the Division of Nuclear Physics of the Institute of Radiation Physics . He also lectures nuclear-astrophysics courses at TU Dresden and serves as coordinator for Transnational Access within the EU ChETEC-INFRA consortium, organising monthly online schools ( SNAQs ) and leading experiments at the underground Dresden Felsenkeller accelerator . Education & Career Path Doctoral degree (Dr.) in experimental nuclear astrophysics. Staff scientist, Nuclear Astrophysics group, HZDR Institute of Radiation Physics. HZDR High-Potential Fellow (competitive three-year programme with optional extension). Lecturer & exercise instructor, TU Dresden – Nuclear Astrophysics (2020-2023). Seminar facilitator & laboratory course supervisor, TU Dresden (2007-2023). Research Interests Dr. Schmidt’s experimental programme centres on low-energy nuclear reactions that forged the chemical elements . His work spans three astrophysical eras: Big Bang Nucleosynthesis: Measuring the 3 He(α,γ) 7 Be reaction underground to test predictions of primordial lithium abundances against cosmic-microwave-background data. Solar Fusion: Quantifying CNO-cycle reactions and neutrino fluxes that underpin helioseismology and modern solar-model tests. Explosive Nucleosynthesis: Determining the 40 Ca(α,γ) 44 Ti reaction rate to resolve the long-standing discrepancy between supernova 44 Ti γ-ray observations and theoretical yields, and investigating rp- and αp-processes during X-ray bursts using the JENSA gas-jet target. Experimental Facilities & Collaborations He utilises an international network of accelerators and detectors: Dresden Felsenkeller – 5 MV underground accelerator for ultra-low-background studies. HZDR Ion Beam Center – 3 MV Tandetron for high-intensity stable beams. JENSA at NSCL/MSU – Gas-jet target coupled to silicon arrays and recoil separators. CENBG (France) , GSI Target Lab , ATOMKI (Hungary) – Target fabrication and complementary measurements. JINA-CEE (USA) – Joint Institute for Nuclear Astrophysics collaboration. Scientific Awards & Distinctions HZDR High-Potential Fellow (competitive fellowship with 100 k€ annual funding). Coordinator, ChETEC-INFRA Transnational Access (13 partner infrastructures, EU). Founder & organiser, SNAQs – monthly online Schools on Nuclear Astrophysics Questions. Teaching & Mentoring Dr. Schmidt has taught nuclear-astrophysics lecture series every summer term since 2019, co-taught with Prof. Dr. K. Zuber, and has supervised numerous advanced laboratory courses and seminars for second-year through graduate students at TU Dresden. Laboratory & Group Leadership He leads the experimental programme at the Dresden Felsenkeller underground accelerator, coordinates target development across European labs, and heads data-taking campaigns that combine γ-ray spectroscopy, accelerator mass spectrometry, and recoil-separator techniques to deliver high-precision cross-section data for astrophysical reaction networks.

• Signal Processing
• Machine Learning
• Acoustics
• +5 more

Roland Badeau is a Full Professor at Télécom Paris (part of Institut Polytechnique de Paris) within the Signal, Statistics and Learning (S2A) team of the Image, Data, Signal (IDS) Department. His work focuses on statistical modeling of non-stationary signals, particularly in audio and music processing, with applications including source separation, denoising, dereverberation, and automatic music transcription. He has authored over 30 journal papers, 130 conference papers, 4 patents, and a book chapter. Education : Habilitation à Diriger des Recherches (2010), PhD in Signal Processing (2005) from Télécom Paris Research Themes : Stochastic reverberation models, time-frequency analysis, probabilistic latent variable models, audio coding, and biomedical data applications Teaching : Applied Mathematics and Signal Processing at Télécom Paris; Music Signal Processing at Sorbonne Université; Audio-Frequency Analysis at ENS Paris-Saclay Awards : Featured in Stanford's Top 2% Researchers (2024); contributed to a best student paper award at ICASSP 2020 Responsibilities : Supervision of doctoral theses, teaching units, and academic programs like TSIA and Master ATIAM

Florian BrueckView profile

Researcher

• Statistical Model Comparison
• Non-Parametric Bayesian Analysis
• Lévy Processes
• +3 more

Florian Brueck is a Postdoctoral Researcher at the Research Center for Statistics at the University of Geneva, under Prof. Sebastian Engelke. He earned his PhD in Mathematics (Dr. rer. nat.) from the Technical University of Munich (TUM) in 2023, with distinction, under Prof. Matthias Scherer. His research focuses on statistical model comparison, Bayesian non-parametric survival analysis, Lévy processes, extreme value theory, and the integration of machine learning into classical statistics. Education: PhD in Mathematics, TUM (2023) MSc in Financial Mathematics and Actuarial Sciences, TUM (2019) BSc in Business Mathematics, Ludwig Maximilians University Munich (2017) Research Interests: Statistical model selection via Maximum Mean Discrepancy (MMD) Exchangeability and non-parametric Bayesian methods in survival analysis Inference for Lévy and stable processes Extreme value theory applications in risk modeling Machine learning techniques for statistical inference Key Contributions: Developed distribution-free MMD tests for model selection with estimated parameters Advanced infinitely divisible priors for multivariate survival functions Applied generative neural networks to characteristic function estimation Awards & Recognition: None explicitly listed. Advising & Grants: Supervised 5 Master’s theses on topics including MMD-based model selection and clustering-based portfolio optimization Co-led industry project with WWK Versicherungen on risk assessment tools Labs/Teams: Research Center for Statistics at University of Geneva.

Boris BuchmannView profile

Associate Professor

• Probability and Statistics
• Lévy Processes
• Fractional Brownian Motion
• +3 more

Boris Buchmann is an Associate Professor at The Australian National University (ANU), affiliated with the Research School of Finance, Actuarial Studies & Statistics. His academic role focuses on research and supervision in probability theory, mathematical statistics, and mathematical finance. He holds a Dr. rer. nat. (PhD) from Leibniz Universität Hannover. Education: Dr. rer. nat. (PhD) in Mathematics, Leibniz Universität Hannover Research Interests: Boris’ research centers on Probability and Statistics, with a focus on Lévy processes, fractional Brownian motions, empirical processes, and their applications in mathematical finance. His work explores extremal processes, local behavior of Lévy processes, and stochastic differential equations driven by fractional Brownian motion. Key contributions include the discovery of weak subordination for improved financial model dependence, statistical convolution logarithm (decompounding), and analysis of GARCH models’ Le Cam deficiency limits. Scientific Awards and Funding: Australian Research Council Discovery Project Grant (2016–2019): "Frontiers of Risk Modelling: Dependence and Extremes of Levy Processes" Advising and Grants: Boris supervises research students and has led projects such as the ARC-funded initiative on risk modelling. His research has been supported by major grants, reflecting the significance of his contributions to theoretical and applied statistics.