Dr. John Haslegrave is a Lecturer in Probability at Lancaster University's School of Mathematical Sciences, where he is an active member of both the Probability and Combinatorics research groups. Previously, he held positions at the University of Oxford (working with Peter Keevash), University of Warwick (with Agelos Georgakopoulos), and University of Sheffield (with Hong Liu and Chris Cannings). He completed his PhD at Trinity College, Cambridge under Béla Bollobás. His research focuses on: Random graphs and evolving network models (especially preferential attachment) Interacting particle systems and random walks on graphs Extremal problems in graph/hypergraph theory Percolation theory and geometric probability Combinatorial optimization and graph invariants Analysis of recent publications reveals strong emphasis on probabilistic combinatorics, with frequent exploration of: structural graph properties, asymptotic behavior of stochastic processes, geometric embeddings, and optimization problems. His work consistently bridges discrete mathematics with statistical physics concepts. Dr. Haslegrave welcomes PhD students interested in discrete probability and graph theory, with current supervision interests including preferential attachment models, interacting particle systems, planar percolation, and extremal problems. He teaches undergraduate courses in Graph Theory (MATH326) and Probability (MATH103), and organizes Lancaster's Pure Mathematics Seminar series.
Marten Brienen is an Associate Professor at Oklahoma State University, affiliated with OSU Global. His career spans multiple disciplines including Political Science, Global Studies, and Fire and Emergency Management Administration at R1 institutions. He is a historian of twentieth-century Latin America with interdisciplinary expertise shaped by diverse teaching experiences. PhD in Social and Political Sciences from University of Amsterdam (2011) Currently teaches Global Crisis Management, Complex Emergencies, and advanced research courses His research focuses on weak institutions and their consequences, such as: Drug trade dynamics in Bolivia, Peru, and Colombia Resource populism and energy security Communal violence and prison overpopulation Corruption and disaster response failures Article trends show evolving expertise from Latin American politics (2015-2019) to pandemic trade analysis (2022-2024) and synthetic opioid studies (2025). Scientific recognition includes: Hargis Fellowship (2023) Two Outstanding Research Awards (2017, 2018) He supervises graduate research and advises on academic integrity, while engaging in media outreach about Latin American political developments.
Gennady Samorodnitsky is a Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University. He holds a B.S. from the Moscow Steel and Alloys Institute (1978), M.S. from Technion – Israel Institute of Technology (1983), and a D.Sc. from Technion (1986). He joined Cornell in 1988 and has held visiting positions at the University of North Carolina at Chapel Hill and Boston University. His research focuses on stochastic processes, particularly heavy-tailed distributions, long-range dependence, and extreme value theory, with applications in finance, teletraffic, and climate modeling. Education: B.S., Moscow Steel and Alloys Institute, USSR, 1978 M.S., Technion – Israel Institute of Technology, 1983 D.Sc., Technion – Israel Institute of Technology, 1986 Samorodnitsky’s research interests span stochastic modeling, including heavy-tailed processes, self-similar processes, and extreme value analysis. He examines the behavior of financial and telecommunication systems under long memory and non-Gaussian conditions. Key areas include the statistical analysis of extremes in climate data and the theoretical foundations of stable and infinitely divisible processes. His work bridges probability theory with applications in risk management, network traffic analysis, and climate science. His publications explore topics such as high-level excursion sets in random fields, tail inference, and the interplay between ergodic theory and stochastic processes. He has contributed to books like Stochastic Processes and Long Range Dependence and authored numerous technical reports on topics like ruin probabilities and multivariate extremes. Samorodnitsky teaches advanced courses, including ORIE 7590: Martingales in Discrete and Continuous Time , and maintains an active role in academic conferences and collaborations. His research group investigates cutting-edge problems in high-dimensional extremes, privacy-aware learning, and topological data analysis.
Dr Nic Freeman is a Lecturer in Applied Probability and Programme Leader for Financial Mathematics at the School of Mathematical and Physical Sciences, University of Sheffield. He holds an academic position within the Department of Mathematics and is a member of the Probability research group. His research focuses on probability theory, stochastic processes, random geometry, fractals, and applications to population genetics and financial mathematics. Dr Freeman teaches courses such as MAS350 Measure and Probability, MAS352 Stochastic Processes and Finance, MAS451 Measure and Probability, and MAS452 Stochastic Processes and Finance. His work integrates theoretical probability with practical applications, including the study of preferential attachment models, coalescent processes, and spatial stochastic systems. He is affiliated with the Hicks Building, located in Room I20. Research interests span topics such as tightness criteria for random processes, Brownian web/net structures, and cluster dynamics in network models. His recent publications explore foundational aspects of stochastic processes and their convergence properties, contributing to both pure and applied probability theory. Dr Freeman collaborates within interdisciplinary teams and maintains an active research profile in mathematical probability.
Suryanarayana Sankagiri is a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, affiliated with the Information and Network Dynamics (INDY1) group under Professor Matthias Grossglauser. Previously, he earned his Ph.D. in Electrical & Computer Engineering (2018-2022) from the University of Illinois at Urbana-Champaign , where he was supervised by Bruce Hajek and participated in the Coordinated Science Lab . He also holds an M.S. in Electrical & Computer Engineering from the University of Illinois (2016-2018) and a B.Tech. in Electrical Engineering from the Indian Institute of Technology Bombay (2012-2016). Education : Ph.D., Electrical & Computer Engineering, University of Illinois (2018-2022) M.S., Electrical & Computer Engineering, University of Illinois (2016-2018) B.Tech., Electrical Engineering, IIT Bombay (2012-2016) Suryanarayana's research focuses on discrete choice models and their application to recommendation systems , with a particular emphasis on learning from choice data and developing novel models for human decision-making. His broader interests include blockchain security under adverse network conditions, network dynamics , probabilistic modeling , and algorithm design . Recent work explores nonconvex matrix factorization and contextual dueling bandits for recommendation systems. His publications span theoretical and applied domains, including high-impact venues like ICML , Stochastic Systems , and IEEE Transactions on Networking . Themes include blockchain efficiency , hidden community detection in preferential attachment graphs, and temporal analysis of Indian classical music. Current projects involve refining recommendation systems through sparse comparison data and designing protocols for resilient blockchain networks. Scientific Awards : zkCapital Paper of the Week (2021) Rambus Fellowship (2021) Mavis Future Faculty Fellowship (2019) List of Teachers Ranked as Excellent (2019) Nomination for IIT Bombay Undergraduate Colloquium (2016) Best Poster Award, IIT Bombay Undergraduate Research Symposium (2013) Suryanarayana has advised no students listed in the provided materials. His work has been supported by fellowships such as the Mavis Future Faculty Fellowship and Rambus Fellowship . He contributes to the INDY1 group at EPFL, which investigates information and network dynamics through interdisciplinary approaches combining probability , network theory , and algorithmic design .
Steven L. Johnson serves as Associate Professor of Commerce and Area Coordinator for Information Technology & Innovation at the University of Virginia School of Commerce. He also holds significant leadership positions as Faculty Lead of the Digital Technology for Democracy Lab at the University of Virginia Karsh Institute of Democracy and Faculty Affiliate for the Thriving Youth in a Digital Environment research initiative. His academic journey includes a Ph.D. in Information Systems from the University of Maryland, an MBA from The College of William & Mary, and a B.S. in Computer Science from the same institution. Ph.D., Information Systems, University of Maryland M.B.A., The College of William & Mary B.S., Computer Science, The College of William & Mary Professor Johnson's research adopts a sociotechnical systems perspective examining how digital technology intersects with people, processes, and data to impact individuals, organizations, and society. His work spans online communities, social media, open innovation, and the application of social network analysis and computational linguistics to study team dynamics. He investigates critical societal issues including content moderation, algorithmic bias, echo chambers, filter bubbles, and the ethical implications of AI. His ongoing collaborations explore how digital technology supports democracy through information distribution and discovery, its role in healthy youth development, and methods to minimize bias while promoting fairness in AI deployments. His research has appeared in top journals including MIS Quarterly, Organization Science, Information Systems Research, Information, Communication & Society, and Information & Organization. Analysis of his publication trends reveals a consistent focus on the societal implications of digital technology, with increasing attention to AI ethics, content moderation, and democratic processes in recent years. His methodological approach combines computational social science with network analysis to examine large-scale digital interactions. Professor Johnson has received numerous prestigious awards recognizing his scholarly contributions: Association for Information Systems Distinguished Member, Cum Laude (2021) University of Virginia Research Achievement Award (2021, 2022) MISQ Best Paper Award (2020) AOM OCIS Division Best Conference Paper Award (2018) Prix académique de la recherche en management (2015) As an educator, Johnson has taught numerous undergraduate and graduate courses covering systems and strategy, business analytics, information technology management, and specialized topics including Managerial View of AI and Race in Commerce. He has provided significant service to the academic community as Associate Editor at Information Systems Research, previously at MIS Quarterly, and as Senior Editor at Information & Organization. He serves on the executive committee of the Communication, Digital Technology, and Organizations division of the Academy of Management and was Program-Chair Elect for the 2025 Annual Meeting. His community engagement extends beyond academia through roles as Commissioner on the Charlottesville Economic Development Authority and involvement with local organizations including the Fifeville Neighborhood Art Gallery. Professor Johnson leads the Digital Technology for Democracy Lab at the Karsh Institute of Democracy, which examines how digital technology supports democratic processes through information distribution and discovery. He is also affiliated with the Thriving Youth in a Digital Environment research initiative, investigating how youth benefit from or are harmed by digital environments. His technical infrastructure contributions include operating the cville.online Mastodon server and managing the Fifeville air quality monitor.
Richard A. Davis is a Professor of Statistics at Columbia University and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). He holds the Howard Levene Professorship in Statistics. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory. Davis has contributed to nonlinear time series models, spatial modeling, and financial econometrics. He has held academic positions at Colorado State University and MIT, and has been recognized with awards including the Koopmans Prize, IBM Faculty Award, and fellowships from the Institute of Mathematical Statistics and American Statistical Association. His education includes a B.A. and Ph.D. in Mathematics from the University of California, San Diego. He co-authored influential textbooks like Time Series: Theory and Methods and has advised numerous papers on topics ranging from count time series to spatial extremes. Current research explores machine learning applications in time series and environmental risk modeling through collaborations like the STARMAP program.
Arne Grauer is a researcher in probability theory at the Department of Mathematics, University of Cologne. His work focuses on geometric random graphs, percolation, and stochastic processes in random environments. He completed his PhD in 2022 under Prof. Peter Mörters, exploring ultrasmallness and chemical distance in scale-free geometric random graphs. Education: PhD in Mathematics (2022, University of Cologne), Master’s in Mathematics (2017, University of Münster), Bachelor’s in Mathematics (2015, University of Münster). Research interests include understanding network structures and dynamics, particularly in scale-free and spatially embedded networks. His studies analyze ultrasmall graph distances, infection spread via contact processes, and preferential attachment models. Publications focus on mathematical physics and network science, with contributions to Communications in Mathematical Physics and Journal of Statistical Physics . He co-organized workshops on random geometric graphs and spatial networks.
Aad van der Vaart is a distinguished Professor of Statistics at Delft University of Technology (since 2021). Previously, he held Full Professorships at Leiden University (2012–2021) and Vrije Universiteit Amsterdam (1996–2012). His research focuses on foundational statistical theory and applications, including high-dimensional statistics, Bayesian methods, inverse problems, and genomics. He has made seminal contributions to nonparametric Bayesian inference, empirical processes, and semiparametric theory. Van der Vaart has authored influential textbooks such as Asymptotic Statistics (1998) and Fundamentals of Nonparametric Bayesian Inference (2017, with S. Ghosal). His work bridges theoretical rigor and practical applications, with over 34,830 citations (Google Scholar, 2023) and an H-index of 60. Key honors include the Spinoza Prize (2015, Netherlands’ highest science award), DeGroot Prize (2020), and membership in the Royal Netherlands Academy of Sciences. His academic journey includes roles such as Miller Fellow at UC Berkeley (2000), visiting positions at leading universities, and leadership in statistical societies. His research group actively explores modern challenges in statistical theory and methodology, including causal inference, adaptive estimation, and large-scale data analysis. Notable grants include an ERC Advanced Grant (2012) for Bayesian inverse problems. Collaborations span academia and industry, emphasizing interdisciplinary impact. While specific lab affiliations are not explicitly stated, his work is rooted in foundational mathematical statistics with broad applicability.
William J. Reed is a Professor in the Department of Mathematics and Statistics at the University of Victoria, with a distinguished career spanning theoretical and applied statistics. His work bridges mathematical theory with real-world applications across ecology, finance, and biology, focusing on distributional phenomena in complex systems. Dr. Reed earned his Ph.D. from the University of British Columbia and has maintained an active research program for over three decades. His scholarly contributions are characterized by rigorous statistical modeling of natural and socioeconomic patterns. Research centers on probability distributions—particularly power-law, Normal-Laplace, and circular distributions—with applications in forest fire dynamics, income inequality, gene family evolution, and financial markets. His work explains why power-laws emerge universally across disciplines through mechanistic stochastic models. Recent publications emphasize survival analysis with bathtub-shaped hazard rates and directional data modeling. His publication trend (2000-2010) reveals interdisciplinary innovation: developing the double Pareto-lognormal distribution for size phenomena, modeling sexually transmitted disease networks, and creating Brownian-Laplace motion for financial applications. These works consistently connect theoretical distribution theory to empirical patterns in nature and society. Scientific Awards: No specific awards documented in source material Dr. Reed has supervised six graduate students on distribution-focused theses, including Peter Ott (1995) on animal abundance estimation, Tony Ho (1997) on wildfire modeling, and Fan Wu (2008) on Normal-Laplace applications. While grant details aren't specified, his collaborative work with B.D. Hughes demonstrates sustained research productivity across mathematical biology, economics, and environmental science.
Bikramjit Das is an Associate Professor and Associate Head of Pillar (Graduate Programme) at Singapore University of Technology and Design (SUTD). He holds a PhD in Operations Research from Cornell University and prior to SUTD, was a postdoctoral researcher at ETH Zurich’s RiskLab. His research focuses on extreme events analysis using applied probability, optimization, and statistical learning, with applications in finance, telecommunications, federated learning, and climate modeling. He teaches courses in Probability, Stochastic Modeling, and Analytics, and directs the Master of Science in Technology and Design (Data Science) program. Education: PhD in Operations Research (Cornell University), B.Stat & M.Stat (Indian Statistical Institute). Research emphasizes heavy-tailed distributions, risk contagion, and network modeling. Key areas include risk analysis in financial networks, robust optimization under uncertainty, and extreme value theory. His work bridges theoretical probability and real-world applications in data science and public policy. Notable contributions include studies on asymptotic independence in high dimensions, robust newsvendor models, and inference techniques for heavy-tailed data. His articles explore topics ranging from federated learning under noise to climate modeling and congestion phenomena in sparse networks. Collaborations include visiting positions at MIT and the Karlsruhe Institute of Technology. Active in academic leadership, he has contributed to technical reports on healthcare provider choice analysis and probabilistic flood risk assessments for nuclear power plants.
Bahman Gharesifard is a Professor in the Department of Mathematics and Statistics at Queen's University, Canada. He holds a Ph.D. from Queen's University (2009) and advanced degrees from Shiraz University (B.Sc., 2002; M.Sc., 2005). His research focuses on systems and control theory, with emphasis on distributed control, optimization, geometric control, and their intersections with network sciences, machine learning, and game theory. He has been recognized with the First Year Instructor Teaching Award in Engineering & Applied Science (2014 & 2016). His academic journey includes postdoctoral research at the University of California, San Diego (2009–2012) and the University of Illinois, Urbana-Champaign (2012–2013). His work bridges theoretical foundations of control systems with practical applications in distributed optimization, neural networks, and contagion models on networks. Recent research trends include advancing Lyapunov-based methods for reinforcement learning, analyzing structural controllability in sparse systems, and developing models for network dynamics using Pólya urn frameworks. His articles explore topics like averaged controllability, flexible-step MPC, and stability in distributed algorithms. Education: Ph.D., Queen's University (2009) M.Sc., Shiraz University (2005) B.Sc., Shiraz University (2002) Awards: Engineering & Applied Science First Year Instructor Teaching Award (2014) Engineering & Applied Science First Year Instructor Teaching Award (2016) He collaborates on projects involving secure distributed optimization, epidemic modeling via Pólya contagion networks, and neural network approximation guarantees. His lab contributes to theoretical control advancements with practical implications in robotics, energy systems, and AI.
Gianluca Rizzo is an Adjunct Professor at HES-SO Valais-Wallis, affiliated with the Higher School of Management (Haute Ecole de Gestion) and the Internet of Things (IoT) department linked to EPFL. He holds a Computer Science Bachelor's degree from UC3M University in Madrid. His research focuses on IoT, vehicular communications, AI-driven network optimization, and disaster-resilient systems. Education: Computer Science BSc (UC3M University, Madrid) Affiliations: HES-SO Valais-Wallis, EPFL IoT Group, RECODIS (Post-Disaster Communications Lab) His work spans energy-efficient networking, opportunistic content dissemination (Floating Content), and distributed learning techniques. Key contributions include optimizing multi-agent systems in dynamic environments, developing gossip learning frameworks for urban trajectory prediction, and analyzing SWIPT (Simultaneous Wireless Information and Power Transfer) in vehicular networks. He also explores emergency networks for post-disaster scenarios, leveraging technologies like UAVs and floating content for situational awareness. Recent publications emphasize AI-native vehicular communications, edge computing orchestration, and infrastructure savings via moving base stations. Collaborative projects include V-Edge (virtual edge computing) and the NOSE nomadic sensing ecosystem. His work bridges theoretical models (e.g., stochastic geometry) with practical implementations, addressing challenges in 5G/6G, smart cities, and industrial IoT.
Marcel Ortgiese is a Reader in the Department of Mathematical Sciences at the University of Bath, affiliated with the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) and the Probability Laboratory at Bath. His research focuses on probability theory, including spatial population models, stochastic processes in random environments, and evolving random graphs. He investigates large-scale behaviors of interfaces in evolutionary biology contexts, branching processes in inhomogeneous environments, and dynamic processes on random graph structures. His work integrates theoretical analysis with applications to real-world systems, emphasizing interdisciplinary connections between probability and complex networks. Current projects include studying the interplay between geometry and randomness in fitness landscapes for expanding populations (EPSRC-funded) and advancing methods for cumulants and superconcentration phenomena. Ortgiese has contributed to foundational research on contact processes, voter models, preferential attachment networks, and symbiotic branching systems. His recent studies explore adaptive network dynamics, subcritical random graph properties, and asymptotic behaviors in weighted recursive trees. He holds a PhD from the University of Bath (2009) under Prof. Peter Morters and has supervised numerous graduate students through SAMBa's training programs. His academic contributions are reflected in over 20 peer-reviewed articles in top journals such as Stochastic Processes and their Applications and Annals of Applied Probability . Research themes consistently emphasize stochastic analysis of complex systems, with applications ranging from epidemiological modeling to evolutionary dynamics.
Backhausz Ágnes is a Assistant Professor at Eötvös Loránd University's Faculty of Science , specifically in the Department of Probability Theory and Statistics . She also holds a part-time Researcher position at the Alfréd Rényi Institute of Mathematics . Her academic journey includes habilitation and a PhD in Mathematics, focusing on random graph models and their asymptotic properties. Research Group: Struktúrák limeszei (since 2013, part-time since 2015) Grants: ERC Grant on 'Limits of Discrete Structures' (2014–2019) Ágnes specializes in Probability Theory and Random Graphs , with emphasis on graph limits , factor of i.i.d. processes , and spectral theory . Recent publications analyze epidemic spread on multilayer networks, entropy inequalities, and action convergence in graph operators. Her work bridges theoretical mathematics with applications in network science and stochastic processes. Notable awards include the Grünwald Géza Memorial Medal (2014) from the Bolyai János Matematikai Társulat. She actively contributes to academic service as a Supervisor and Training Lead for the Beyond The Edge Marie Curie Doctoral Network (2024–2027) and serves on program committees for conferences like Eurocomb and the European Girls' Mathematical Olympiad . Her teaching portfolio spans Probability Theory, Stochastic Processes, and Mathematical Statistics at both undergraduate and graduate levels.
![](https://www.queensu.ca/mathstat/people/faculty/profiles/"/mathstat/sites/mastwww/files/styles/staggered_tiles_image/public/2021-01/gharesifard-bahman.jpg?h=10d202d3&itok=2vakrgj4")