Find Academic Supervisors

Peter BraunsteinsView profile

Lecturer

• Probability Theory
• Stochastic Processes
• Branching Processes
• +3 more

Dr Peter Braunsteins serves as a Lecturer in Statistics within the School of Mathematics and Statistics at the University of New South Wales (UNSW Sydney), operating under the Faculty of Science. His academic appointment focuses on advancing theoretical and applied probability through rigorous mathematical research. He completed his PhD at the University of Melbourne in 2018, followed by postdoctoral positions at the University of Amsterdam and King Abdullah University of Science and Technology (KAUST) before joining UNSW. His scholarly background bridges European and Middle Eastern research institutions with Australian academia. Braunsteins' research centers on stochastic processes , with pioneering contributions in three interconnected domains: branching processes (modeling population dynamics and extinction events), random graphs (analyzing network evolution and structural properties), and spatial extremes (studying rare events in geographical contexts). His work combines deep theoretical insights with applications in epidemiology, insurance risk modeling, and network science, often employing large deviation principles and parameter estimation techniques for complex systems. Analysis of his 15 most recent publications reveals a sustained focus on branching process theory, particularly population-size-dependent models and extinction probabilities, while simultaneously developing novel frameworks for dynamic random graphs. His research demonstrates increasing interdisciplinary reach, connecting probability theory with actuarial science through adaptations of the Cramér-Lundberg model and with network science through graphon analysis. No scientific awards or major honors are documented in the available materials. His collaborative network includes prominent researchers such as Sophie Hautphenne, Frank den Hollander, and Michel Mandjes across multiple continents. Professional activities include manuscript review for leading probability journals and participation in academic seminars at UNSW, though specific advising roles or grant funding details remain undisclosed in the source material. His office is located in Room 2056 of the Anita B. Lawrence Centre at UNSW Sydney.

Thomas JordanView profile

Lecturer

• Dimension theory of dynamical systems
• Self-similar sets
• Self-affine sets
• +4 more

Thomas Jordan is a Lecturer in the ergodic theory group within the Department of Mathematics at the University of Bristol. Previously, he worked as a research assistant at Warwick University and completed his PhD under Mark Pollicott. His research focuses on dimension theory of dynamical systems, particularly examining self-similar and self-affine sets, multifractal analysis, thermodynamic formalism, and Fourier transforms of measures. His primary research interests include: Dimension theory of dynamical systems Self-similar sets and fractals Self-affine sets Multifractal analysis Thermodynamic formalism Fourier transforms of measures Ergodic theory Thomas Jordan's publications reveal a consistent focus on geometric and measure-theoretic aspects of dynamical systems, with particular emphasis on dimensional properties of fractal sets. His work demonstrates strong collaborative relationships with researchers including Mark Pollicott, Michał Rams, Jonathan Fraser, and Henna Koivusalo across numerous publications spanning two decades. He has organized significant academic events including: Projection and Slicing Theorems in Fractal Geometry conference (Bristol, June 2014) Special session of the 2016 BMC on ergodic theory (Bristol, April 2016) Probability, Analysis and Dynamics workshops (2018, 2022) Thermodynamic formalism workshop at ICMS (June 2018) Workshop on affine and overlapping iterated function systems (Bristol, May 2022) Thomas Jordan serves as a key contact for the One Day Ergodic Theory Meetings series, a collaborative effort between multiple UK universities funded by the London Mathematical Society. He teaches first year analysis (Analysis A) and dynamical systems courses at the University of Bristol.

• Child Development
• Sensory-Motor Development
• Emotion Comprehension
• +7 more

Edouard Gentaz is a Professor in the Psychology Section of the Faculty of Psychology and Educational Sciences at the University of Geneva (UNIGE). He leads the Sensory-Motor, Affective and Social Development Laboratory (SMAS), where he conducts research on child development with particular focus on sensory-motor, emotional, and social aspects across typical and atypical developmental trajectories. His research interests span multiple domains of developmental psychology, with emphasis on emotion comprehension in children , tactile exploration skills , social-emotional development , and intervention strategies for children with developmental challenges including autism spectrum disorders, preterm infants, and children with intellectual disabilities. His methodological approach frequently employs eye-tracking technology to assess socio-emotional capabilities in populations with communication limitations. Gentaz's publication record shows consistent productivity with 78 publications spanning two decades, with recent work focusing on screen time effects on young children, emotion training interventions, and sensory processing in neurodevelopmental conditions. His collaborative approach is evident in publications addressing practical applications in educational settings and healthcare contexts. His scientific contributions include research on: Early social-emotional abilities in infants and toddlers Development of emotion discrimination across childhood Interventions for preterm children's emotional and executive competences Multisensory perception in neurodevelopmental disorders Impact of digital media on young children's development Gentaz has supervised 120 academic works, indicating significant mentorship activity. His research demonstrates strong translational potential with applications in educational settings, clinical interventions, and parenting guidance, particularly regarding screen exposure for very young children.

Andrew ElliottView profile

Senior Lecturer

• Statistics
• Data Analytics
• Modeling in Space and Time
• +8 more

Dr Andrew Elliott is a Senior Lecturer in the School of Statistics at the University of Glasgow. His research bridges Statistics , Computational Statistics , and Machine Learning & AI , with applications in Social & Urban Studies and Imaging, Image Processing & Image Analysis . Education: Not explicitly detailed in the text. Dr Elliott's work focuses on modeling in space and time , synthetic data generation , and network analysis . His recent publications explore fairness constraints in AI , agent swarms , and temporal network modeling , reflecting interdisciplinary applications in urban analytics and environmental science. His publications from 2014–2024 span network science , machine learning , image analysis , and geospatial studies . Key trends include privacy auditing , counterfactual explanations , and deep learning for network time series . Dr Elliott supervises doctoral students and has advised Zhengduo Zhao and Weiyue Zheng . He actively contributes to research groups in Statistics & Data Analytics and Machine Learning . Notable collaborations include work with Mihai Cucuringu and Gesine Reinert .

• Inventory Management
• Behavioural Operations Management
• Forecasting
• +3 more

Anna-Lena Sachs is a Senior Lecturer in Predictive Analytics at Lancaster University's Management Science department. Her research bridges inventory management, behavioural operations, and forecasting, with applications in retail, healthcare, automotive, and logistics sectors. She develops quantitative models and leverages industry datasets to solve practical supply chain challenges. Research Interests : Inventory management for spare parts, data-driven decision support systems, multi-echelon optimization, markdown pricing strategies, and human decision behavior in operational contexts. She emphasizes translating academic insights into industry practice through field experiments and lab studies. Scientific Recognition : Dean’s Award for Academic Excellence Fellow of the Higher Education Academy (ATLAS) Research Impact Award for Centre for Marketing Analytics and Forecasting PhD Supervision : Actively mentors students in Operations Research, Management Science, and supply chain analytics. Current PhD candidates include Ritika Arora, Benjamin Lowery, Adam Page, Carlos Rodriguez Calderon, and Joe Rutherford. Collaborative Networks : Affiliated with Lancaster’s STOR-i Centre for Doctoral Training, Centre for Marketing Analytics & Forecasting, and Data Science Institute. She has led projects with Royal Mail, Jaguar Land Rover, and GlaxoSmithKline.

• Information Theory
• Quantum Communication
• Wireless Networks
• +3 more

Pau Colomer is a Researcher at the Technical University of Munich , affiliated with the Theoretical Information Technology department. His work focuses on quantum communication, information theory, and wireless networks, contributing to foundational research in quantum channel identification and network information processing. His research interests span quantum information theory , neural networks , and signal processing , with recent publications addressing deterministic identification over quantum channels, coherence theory in quantum systems, and multi-user quantum communication. These works highlight advancements in quantum channel coding , decentralized processing , and security in wireless networks . Publications demonstrate a trend toward quantum communication and network information theory , emphasizing challenges in interference modeling , entanglement transmission , and information-theoretic security . Key subfields include superlinear rates in finite output channels , decoupling mechanisms , and quantum-enhanced sensing .

Wolfgang GreinerView profile

Professor

• Health Economics
• Health Technology Assessment (HTA)
• Quality of Life Research
• +5 more

Prof. Dr. Wolfgang Greiner, born in 1965, is the Chair of Health Economics and Health Management at Bielefeld University's Faculty of Health Sciences. He has held this position since April 2005 and leads Working Group 5 (Health Economics and Health Management). Current roles: Dean of Faculty, Chairman of Structure and Budget Commission, Habilitation Committee member. Affiliated with Bielefeld Center for Data Science (BiCDaS). His research focuses on health technology assessment , economic evaluation of health services , quality of life metrics , and disease management frameworks . Recent projects include AI-driven clinical decision support systems for sepsis, financial burden analysis for cancer patients, and smart home emergency call system effectiveness. Key awards include the Austrian Prize for Health Economics, Hannover University Science Prize, and Medvantis Research Prize. He serves as Managing Editor of the European Journal of Health Economics and sits on scientific advisory boards for health insurers. His 2025 articles address dengue burden, AI in critical care, and dyslipidemia epidemiology. Earlier works span meningococcal vaccines, HPV disease costs, and chronic disease management in Germany.

Urshila SriramView profile

Assistant Professor

• Nutrition
• Health Equity
• Community-Based Health Interventions
• +2 more

Urshila Sriram is an Assistant Professor in the Department of Nutrition at Simmons University, specializing in community-based programs for nutrition and health equity. She holds a PhD in Nutritional Sciences and an MSPH in Human Nutrition from Cornell University and Johns Hopkins School of Public Health, respectively. Education: PhD in Nutritional Sciences (Cornell), MSPH in Human Nutrition (Johns Hopkins), BS in Biochemistry (McGill) Her research focuses on nutrition and health equity , community-based interventions , and food security . She emphasizes cultural humility and structural inequities in dietetics practice. Her work includes evaluating civic engagement programs for rural health promotion and developing NIH-funded health intervention measures. Dr. Sriram’s teaching philosophy prioritizes experiential learning and student-centered growth . Courses taught include NUTR 452 (Program Planning), NUTR 490 (Nutrition Research), and LCIS 201 (Health Promotion). She mentors students in nutrition equity and policy. Community engagement highlights: Co-Chair for Food Is Medicine Massachusetts , co-advisor for Simmons Nutrition Diversity Mentorship Program, and member of Diversify Dietetics Mentorship Program . She advocates for health equity through civic action and curriculum innovation.

Michele LoboView profile

Associate Professor

• Child Development
• Rehabilitation Technologies
• Early Intervention
• +5 more

Michele Lobo is an Associate Professor in the Department of Physical Therapy at the University of Delaware. Her research focuses on child development, rehabilitation technologies, and early intervention strategies for infants and children with neuromotor disorders. She leads the Move2Learn Innovation Lab and co-founded the GoDesign Group, emphasizing interdisciplinary collaboration in wearable technology and assistive device design. Education: PhD in Biomechanics from the University of Delaware (2006), Postdoctoral research at Vrije Universiteit (2001–2002), MPT in Pediatric Rehabilitation from Drexel University (1997), and BS in Biology from The College of New Jersey (1994). Research Interests: Investigating perceptual-motor experiences in learning, designing interventions to enhance development in infants with delays, and developing assistive devices such as exoskeletons (e.g., Playskin LiftTM). Key projects include the START-Play intervention, which improves motor and cognitive outcomes in infants with cerebral palsy. Awards: Recipient of the prestigious Gayle G. Arnold Award (2022), Chattanooga Research Award (2022), and Toby Long Award (2021). Her work has been supported by grants from the NIH, IES, and the American Physical Therapy Association. Labs/Teams: Co-leader of the Move2Learn Innovation Lab, focusing on wearable tech and functional fashion for pediatric rehabilitation. Affiliated with the Center for Autonomous and Robotic Systems and the Fashion & Apparel Studies Department.

Marco A.R. FerreiraView profile

Associate Professor

• Bayesian Statistics
• Multi-Scale Modeling
• Spatial-Temporal Models
• +4 more

Marco A.R. Ferreira is an Associate Professor in the Department of Statistics at Virginia Polytechnic Institute and State University (Virginia Tech), affiliated with the College of Science. He holds a Ph.D. in Statistics from Duke University (2002), with a dissertation on Bayesian multi-scale modeling under M. West. He also earned an M.Sc. (1994) and B.Sc. (1993) in Statistics from the Federal University of Rio de Janeiro. Research Interests: Ferreira specializes in Bayesian statistics, multi-scale modeling, spatial-temporal models, computational methods (e.g., MCMC), and applications in environmental science, genomics, and epidemiology. His work emphasizes hierarchical models, inverse problems, and high-dimensional data analysis. Publications Trends: His research spans advanced statistical methodologies for environmental monitoring, civil unrest modeling, and genomic data analysis. Key themes include Bayesian hierarchical models, spatiotemporal fusion, and computational algorithms for optimal experimental design. Awards & Honors: OBAYES Poster Prize (2009) CNPq Fellowship (2003–2006) WNAR/COBAL 2 Award (2005) Springer Poster Prize (2003) Finalist, Savage Award (2003) Best Contributed Paper (JSM 2000) Professional Activities: He serves as an Associate Editor for Bayesian Analysis and is a member of the American Statistical Association and the International Society for Bayesian Analysis.

Andrew TörökView profile

Professor

• Dynamical Systems
• Ergodic Theory
• Statistical Mechanics
• +4 more

Andrew Török is a Professor in the Department of Mathematics at the University of Houston. His research focuses on dynamical systems, ergodic theory, and their applications to statistical mechanics and probability. He has contributed to studies on random dynamical systems, extreme value theory, and stability properties of hyperbolic flows. Teaching responsibilities include courses like MATH 3364: Introduction to Complex Analysis. His work spans topics such as cohomology of dynamical systems, transitivity of group extensions, and statistical limit theorems for non-uniformly hyperbolic systems. Key publications address Birkhoff sum convergence, stable laws for Gibbs-Markov systems, and transitivity properties of Heisenberg group extensions. Research extends to applications in financial mathematics and market dynamics through studies on stock price fluctuations. Professional activities include organizing the Dynamics Colloquium and participating in interdisciplinary projects like gene network modeling. His address is at PGH Building, University of Houston, with contact via torok@math.uh.edu.

Soumya BeraView profile

Associate Professor

• Theoretical Condensed Matter Physics
• Quantum Simulation
• Topological Phases
• +4 more

Soumya Bera is an Associate Professor in the Department of Physics at the Indian Institute of Technology Bombay (IIT Bombay). His research focuses on theoretical condensed matter physics, integrating computational approaches to study quantum systems. Key research interests include quantum simulation of novel phases of matter, topological phases, many-body localization, and non-equilibrium dynamics. Current research topics emphasize quantum phase transitions, quantum criticality, and the interplay of topology, disorder, and interactions. His work often explores these themes through quantum information theory and Floquet systems. Notable contributions include studies on disorder-induced delocalization, Chern-Hopf insulators, and Floquet thermalization. Publications span topics like quantum Hall criticality, graphene disorder effects, and many-body localization dynamics. His work frequently appears in high-impact journals like Phys. Rev. B and SciPost Phys., with a focus on disordered systems and quantum critical phenomena. While no explicit awards or grants are listed here, his prolific publication record underscores sustained contributions to condensed matter theory. His research group actively investigates quantum matter under non-equilibrium conditions and topological material properties.

Mihai NicaView profile

Assistant Professor

• Deep Neural Networks (DNNs)
• Probability Theory
• Stochastic Processes
• +4 more

Prof. Mihai Nica is an Assistant Professor in the Department of Mathematics and Statistics at the University of Guelph, affiliated with the CARE-AI institute and Vector Institute. His research focuses on probability theory, stochastic processes, and their applications to machine learning, particularly deep neural networks (DNNs). He explores scaling limits of DNNs, numerical methods using neural networks, and phase transitions in high-dimensional learning problems. Education: B.Math in Pure & Applied Math with Physics Option, University of Waterloo PhD in Mathematics, Courant Institute of Mathematical Sciences, New York University Postdoctoral Fellow at University of Toronto (supervised by Jeremy Quastel) Research Interests: His work bridges mathematical theory and practical AI applications, emphasizing topics like the neural tangent kernel, KPZ universality class, and stochastic processes in machine learning. Notable contributions include studies on neural network dynamics, random matrices, and directed polymers. Publications: Over 15 peer-reviewed articles in journals like Communications in Pure and Applied Mathematics and Electronic Journal of Probability , with a focus on theoretical foundations of AI and stochastic systems. Recent work explores infinite-width limits of neural networks and their connections to differential equations. Labs/Teams: Affiliated with CARE-AI (bridging mathematics, engineering, and philosophy) and the Vector Institute, fostering interdisciplinary collaborations.

Jayadev S. AthreyaView profile

Associate Professor

• Dynamical Systems
• Geometric Topology
• Number Theory
• +4 more

Jayadev S. Athreya is an Associate Professor in the Department of Mathematics at the University of Washington, where he also serves as Director of the Washington Experimental Mathematics Lab. His primary affiliation is with the College of Liberal Arts & Sciences. He holds dual roles as a Professor of Mathematics and Professor of the Comparative History of Ideas. Athreya co-directs the Pacific Institute for the Mathematical Sciences and is a founder of the Washington Experimental Mathematics Lab, emphasizing interdisciplinary experimental mathematics. His research interests span dynamical systems, geometric topology, number theory, and algebraic geometry. He focuses on translation surfaces, billiards dynamics, geometric flows, and probabilistic methods in geometry. His work often intersects with problems in ergodic theory, Teichmüller theory, and moduli spaces. Athreya has an extensive publication record, with recent work exploring billiard complexity in regular polygons, linear flows on translation prisms, and spectral properties of marked tori. His papers frequently address counting problems, asymptotic distribution of geometric objects, and connections between number theory and dynamical systems. He has taught advanced courses such as Quasiconformal Maps and Teichmüller Theory, Complex Analysis, and Elementary Number Theory. His pedagogical approach emphasizes hands-on exploration through the Washington Experimental Mathematics Lab, fostering collaborative research projects with undergraduates. Athreya is committed to accessible mathematics education, reflecting his alignment with Federico Ardila-Mantilla's axioms on equity and inclusivity in mathematical experiences. His work bridges theoretical research with educational outreach, advocating for mathematics as a universal, adaptable tool.

Suchuan DongView profile

Professor

• Computational and Applied Mathematics
• High-Order Numerical Methods
• Multiphase Flows
• +7 more

Suchuan Dong is a Professor in the Department of Mathematics at Purdue University , affiliated with the Center for Computational and Applied Mathematics . His work bridges Computational Mathematics and Machine Learning , focusing on High-Order Numerical Methods and Multiphase Flows . Academic Background Post-Doc in Applied Mathematics, Brown University (2004) Ph.D. in Mechanical Engineering, SUNY Buffalo (2001) M.S. in Physics, Zhejiang University (1995) B.S. in Aerospace Engineering, National University of Defense Technology (1992) His research centers on Neural Network-Based Numerical Methods and Data-Driven Scientific Computing , with applications to Computational Fluid Dynamics , Contact Line Dynamics , and High-Performance Computing . Publications highlight Physics-Informed Neural Networks , Energy-Stable Schemes , and Extreme Learning Machines for PDEs. The articles reflect trends in Neural Network Applications to Dynamic PDEs , Phase Field Modeling , and High-Dimensional Computing , often combining High-Order Numerical Methods with Interfacial Phenomena . His recent work focuses on Exact Time Integration Algorithms and Hidden-Layer Concatenation for stability and efficiency. He leads research in the Center for Computational and Applied Mathematics , emphasizing Thermodynamically Consistent Modeling and Flow-Structure Interactions . His teaching includes MA-36600: Ordinary Differential Equations (Spring 2025).