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Christiana MavroyiakoumouView profile

Assistant Professor

• Fluid Dynamics
• Fluid-Structure Interactions
• Vortex Dynamics
• +6 more

Christiana Mavroyiakoumou is a Courant Instructor/Assistant Professor at the Courant Institute of Mathematical Sciences, New York University. She specializes in fluid dynamics and fluid-structure interactions, with a focus on vortex dynamics, membrane flutter, and bio-inspired systems. Her research integrates modeling, numerical simulations, and experimental insights to study phenomena such as bird flock formations and fish swimming hydrodynamics. Mavroyiakoumou holds a Ph.D. from the University of Michigan (2022), an M.Sc. from the University of Oxford (2017), and a B.Sc. from Imperial College London (2016). Education: PhD in Applied & Interdisciplinary Mathematics, University of Michigan (2017–2022) MSc in Mathematical Modeling and Scientific Computing, University of Oxford (2016–2017) BSc in Mathematics, Imperial College London (2013–2016) Her research interests span fluid-structure interactions, vortex dynamics, and collective locomotion. She investigates how fluid flows mediate interactions between bodies, such as the aerodynamics of bird formations and the hydrodynamics of flapping foils. Her work bridges theoretical models with experimental observations, contributing to both fundamental science and bio-inspired engineering. Mavroyiakoumou has received prestigious awards including the Joseph B. Keller Fellowship (NYU), Peter Smereka Award (U-M), and ProQuest Distinguished Dissertation (U-M). She actively engages in academic service, organizing conferences and mentoring students. Her teaching experience includes courses on mathematical modeling, differential equations, and algebra at NYU and the University of Michigan. Key Research Themes: Flow-mediated collective behavior and instability mechanisms Vortex wake interactions and their role in locomotion Membrane dynamics in inviscid and viscous flows She collaborates with experimentalists like Leif Ristroph and Jun Zhang at NYU's Applied Math Lab, focusing on experimental validation of theoretical models. Her recent work explores self-amplifying waves in bird formations and the aerodynamic origins of flight coordination.

Melanie WeberView profile

Assistant Professor

• Geometric Machine Learning
• Optimization on Manifolds
• Non-Euclidean Machine Learning
• +3 more

Melanie Weber is an Assistant Professor of Applied Mathematics and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), leading the Geometric Machine Learning Group. Her research focuses on leveraging geometric structures in data for designing efficient machine learning and optimization algorithms with theoretical guarantees. She holds a PhD from Princeton University (2021) and has held fellowships at the Mathematical Institute of Oxford, Brasenose College, and the Simons Institute. Her work bridges geometry, optimization, and machine learning, with funding from NSF, Sloan Foundation, and Harvard initiatives. Education : PhD in Applied Mathematics, Princeton University (2021) BSc/MSc in Mathematics and Physics, University of Leipzig (2016) Research Interests : Dr. Weber's research integrates geometric principles into machine learning and optimization, focusing on non-Euclidean spaces, graph structures, and manifold-based methods. Key areas include optimization on Riemannian manifolds, curvature-based analysis (e.g., Ricci curvature), and developing algorithms resilient to data geometry challenges like over-smoothing in graph neural networks. Her work emphasizes theoretical foundations while addressing practical scalability in high-dimensional data. Awards & Recognition : 2024 Sloan Research Fellowship 2023 Leslie Fox Prize in Numerical Analysis 2023 NSF Grant for Geometric Optimization Grants & Funding : Supported by National Science Foundation (NSF), Alfred P. Sloan Foundation, Aramont Foundation, Harvard Dean’s Fund, and Harvard Data Science Initiative. Labs & Collaborations : Leads the Geometric Machine Learning Group at SEAS, collaborating with institutions like MIT, Max Planck Institute, and industry labs (Facebook, Google, Microsoft). Active in organizing workshops on geometric methods and curvature analysis.

Stephanie GilView profile

Assistant Professor

• Artificial Intelligence
• Robotics and Control
• Computer Science
• +4 more

Stephanie Gil is an Assistant Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. Her research focuses on artificial intelligence, robotics, and distributed systems, particularly addressing challenges in multi-agent coordination, resilience to adversarial attacks, and wireless communication for autonomous systems. She leads the REACT Lab, advancing research in resilient multi-robot networks and cyber-physical systems. Her work integrates machine learning, control theory, and wireless sensing to solve problems such as whale tracking via autonomous robots, proactive multi-robot routing, and decentralized exploration without explicit information exchange. She has received prestigious awards, including the DARPA Young Faculty Award (2024) and the Amazon Research Award (2021). Key research areas include resilient distributed optimization, trust-centered coordination in multi-agent systems, and leveraging wireless signals (e.g., WiFi-CSI) for sensing and bearing estimation. Her contributions span both theoretical frameworks and practical implementations, with a focus on real-world applications like autonomous rideshare routing and environmental monitoring. Dr. Gil’s research also explores trust and cybersecurity in dynamic networks, with publications on crowd vetting, malicious robot detection, and adaptive communication strategies. She collaborates on interdisciplinary projects, such as Project CETI, combining AI and robotics for ecological studies.

Clyde KruskalView profile

Associate Professor

• Parallel Computing
• Algorithms
• Computer Architecture
• +3 more

Clyde Kruskal is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park. His research focuses on parallel architectures, models, and algorithms. He earned a Ph.D. from New York University in 1981 and a bachelor’s degree from Brandeis University in 1976. His work includes foundational contributions to parallel computing, such as the read–modify–write concept in distributed systems. Kruskal’s research spans topics like interconnection networks, synchronization mechanisms, and algorithm design for parallel systems. Education: Bachelor’s Degree: Brandeis University, 1976 Master’s Degree: New York University (Courant Institute), 1978 Ph.D.: New York University (Courant Institute), 1981 Research Interests: Parallel computing architectures, parallel algorithms design, multiprocessor synchronization, interconnection networks, and computational geometry problems like graph coloring and visibility analysis. His work emphasizes theoretical foundations and practical implementations in parallel systems. Notable Contributions: Kruskal co-authored the book Problems With A Point: Exploring Math And Computer Science (2019), and his research includes foundational papers on parallel prefix operations, sparse matrix algorithms, and synchronization protocols. His publications span over three decades, reflecting sustained contributions to parallel computing theory and practice. Advising & Outreach: He has mentored students through programs like the Summer Combinatorial Algorithms REU at UMD, fostering undergraduate research in algorithm design and parallel computing.

George YinView profile

Professor

• Stochastic Optimization
• Stochastic Control
• Stochastic Systems Theory
• +4 more

George Yin is a Professor in the Department of Mathematics at the University of Connecticut (since 2020). Previously, he held the position of Distinguished Professor at Wayne State University (2017–2020) and has been a faculty member there since 1988. He earned his Ph.D. in Applied Mathematics from Brown University in 1987, along with M.S. degrees in Applied Mathematics and Electrical Engineering, and a B.S. in Mathematics from the University of Delaware (1983). His research focuses on stochastic optimization, control theory, stochastic systems, and numerical methods, with applications to biology, finance, and engineering. He has held editorial roles at journals such as SIAM Journal on Control and Optimization and has received prestigious awards including SIAM Fellow (2015), IEEE Fellow (2002), and IFAC Fellow (2014–2017). Key funding includes continuous NSF support since 1989, grants from the Air Force Office of Scientific Research, and others. His work spans theoretical advancements in stochastic systems and practical applications in energy systems, control engineering, and data science. He has advised numerous students and maintains active collaborations internationally. Labs/Teams: Goldenson Center for Actuarial Research, Quantitative Learning Center. Grants: NSF, AFOSR, ARO, NSA, and multiple institutional grants.

Stefano GalelliView profile

Associate Professor

• Environmental and Water Resources Systems Engineering
• Water Systems
• Energy and the Environment
• +6 more

Stefano Galelli is a tenured Associate Professor in the School of Civil and Environmental Engineering at Cornell University, where he leads the Critical Infrastructure Systems Lab. He also holds an adjunct position as a Research Scientist at the Lamont-Doherty Earth Observatory, Columbia University. His career spans roles in Singapore, including a Postdoctoral Research Fellow at NUS (2011–2013) and faculty at the Singapore University of Technology and Design (2013–2023). Dr. Galelli earned his B.Sc. (2004), M.Sc. (2007), and Ph.D. (2011) in Environmental and Land Planning Engineering and Information Technology from Politecnico di Milano, Italy. His research focuses on the interactions between critical infrastructure systems and natural environments, emphasizing adaptive management solutions for water-energy systems. Techniques include process-based modeling, climatology, statistical learning, control theory, and optimization. He explores topics like hydro-climatic variability impacts, dam re-operation for environmental flows, and cyber-physical security in infrastructure. His contributions to journals such as Nature Sustainability, Earth’s Future, and Environmental Modelling & Software have earned him multiple awards, including the Early Career Research Excellence Award (2014) and SUTD Excellence in Research Award (2017). He has served as an editor for several journals and is recognized for advancing interdisciplinary approaches to water-energy nexus challenges. Teaching highlights include foundational mathematics courses and advanced topics in data analytics, optimization, and water-energy management. He is developing new courses on data-driven control of coupled human-natural systems and risk management for interconnected systems.

Alan HammondView profile

Professor

• Statistical Mechanics
• Mathematical Probability
• Probability Theory
• +5 more

Alan Hammond is a Professor in the Department of Statistics at the University of California, Berkeley. His research focuses on rigorous mathematical probability techniques applied to problems in statistical mechanics, including percolation theory, polymer models, and random growth processes. He has contributed to understanding critical phenomena, phase transitions, and universality classes in stochastic systems. Hammond's work spans topics such as KPZ universality, Brownian motion, and the geometry of random media. He has investigated models like last passage percolation, self-avoiding walks, and tug-of-war games, often uncovering deep connections between stochastic processes and nonlinear PDEs. His teaching includes courses on stochastic processes and statistical theory at both graduate and undergraduate levels. Notable research highlights include studies on fractal properties of Airy processes, stability in dynamical last passage percolation, and the behavior of geodesics in random environments. His contributions bridge probability theory with applications in physics and combinatorics.

Michael GastparView profile

Professor

• Information Theory
• Signal Processing
• Communications
• +3 more

Michael Gastpar is a full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the Laboratory for Information in Networked Systems (LINX). He previously held faculty positions at the University of California, Berkeley (2003-2011, earning tenure in 2008) and Delft University of Technology. His research spans information theory, signal processing, communications, and systems neuroscience. His research interests focus on network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. Recent work demonstrates a strong shift toward exploring the theoretical foundations of modern machine learning, particularly investigating transformer architectures from an information-theoretic perspective. His research group at EPFL explores how information theory principles can provide fundamental limits and novel approaches for contemporary machine learning problems. His recent publications reveal a clear trend toward bridging classical information theory with modern machine learning. The 15 most recent papers show increasing focus on theoretical analysis of transformers, rate-distortion frameworks for language models, universal prediction methods, and applications of information measures to machine learning theory. This represents a strategic evolution from his earlier work on sensor networks and physical-layer network coding toward foundational questions in artificial intelligence. Scientific Awards: IEEE Fellow 2013 Communications Society & Information Theory Society Joint Paper Award Information Theory Society Distinguished Lecturer (2009-2011) ERC Starting Grant (2010) Okawa Foundation Research Grant (2008) NSF CAREER award (2004) 2002 EPFL Best Thesis Award Professor Gastpar has advised over 20 PhD students who have gone on to successful careers in both academia and industry. His research has been generously supported by major grants including an ERC Starting Grant "ComCom" (2011-2016) and ongoing support from the Swiss National Science Foundation. He has served in significant editorial roles, including as Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11) and as Technical Program Committee Co-Chair for the IEEE International Symposium on Information Theory in 2010 and 2021. He leads the Laboratory for Information in Networked Systems (LINX) at EPFL, which brings together researchers working at the intersection of information theory, machine learning, and networked systems. The lab maintains strong connections with both theoretical research communities and practical applications in communications and neuroscience.

Ineke De MoortelView profile

Professor

• Solar Physics
• Magnetohydrodynamics (MHD)
• Coronal Heating
• +4 more

Prof Ineke De Moortel is a Professor in Applied Mathematics at the University of St Andrews, affiliated with the School of Mathematics and Statistics. Her research focuses on magnetohydrodynamic (MHD) waves in the solar corona, coronal seismology, and numerical modeling of solar phenomena. She has led projects funded by organizations like the Leverhulme Trust and STFC, contributing to understanding coronal heating mechanisms and wave dynamics. Prof De Moortel has supervised PhD students including Elisabeth Enerhaug and Anmol Kumar. She holds editorial roles, including at the Monthly Notices of the Royal Astronomical Society, and has received prestigious awards such as the Phillip Leverhulme Prize (2009) and the RAS Fowler Award (2010). Research Highlights: MHD wave propagation, coronal seismology, numerical simulations of solar plasma dynamics. Projects: Includes 'Joining up an Unprecedented View of our Sun' (Leverhulme Trust) and collaborations on the Multi-slit Solar Explorer (MUSE) mission. Awards: Royal Society of Edinburgh Young Academy Co-Chair (2012), Deputy Chair of UK Solar Physics Council (2013).

Moira JardineView profile

Professor

• Stellar Magnetism
• Exoplanet Habitability
• Stellar Winds
• +3 more

Moira Jardine is Professor of Astronomy at the University of St Andrews School of Physics and Astronomy, where she became the first female physics professor in 2010. Education: Ph.D. Applied Mathematics, University of St Andrews B.Sc. Astronomy and Astrophysics, University of St Andrews Research investigates stellar magnetic activity to understand planetary habitability and solar system evolution. Uses magnetic field measurements to model stellar winds, coronal X-ray emissions, and their impact on planetary atmospheres. Work supports exoplanet detection initiatives including JWST, GAIA, and WFIRST. Publications focus on stellar coronae, magnetic confinement processes, star-planet interactions, and coronal rain dynamics. Current projects model magnetic interactions in systems like AB Dor and HD 189733. Collaborates with international consortia including MagIcS and Bcool for stellar magnetic field surveys. Awards: Fellow of the Royal Society of Edinburgh and Suffrage Science Award (2019).

Giorgia RamponiView profile

Assistant Professor

• Artificial Intelligence
• Reinforcement Learning
• Multi-Agent Learning
• +4 more

Giorgia Ramponi is an Assistant Professor (tenure-track) in Artificial Intelligence for Cyber-Physical Systems at the University of Zurich (UZH), leading the Autonomous Learning and Predictive Intelligence Lab. She holds affiliations with ETH Zurich’s AI Center and Chalmers University of Technology. Previously, she was a postdoctoral researcher at ETH AI Center, sponsored by Google Brain. Her research focuses on machine learning and mathematical modeling, particularly Reinforcement Learning (RL), Multi-Agent Learning, and Imitation Learning. Notable contributions include work on non-cooperative Markov Decision Processes, mean-field games, and batch IRL for multiple intentions. Publications highlight advancements in constrained MDPs, policy optimization, and applications of RL in cyber-physical systems. Awards include the IBM Best Student Award (2016) and a Hassler Research Grant (2024). She advises students on topics ranging from RL theory to social network analysis. Her academic journey includes a PhD (Politecnico di Milano, 2021) and MSc/BSc (La Sapienza, Rome) with honors. She has taught courses on AI, data science, and machine learning, and contributed to open-source projects like GAN_Time_Series.

Ivo J.B.F. AdanView profile

Professor

• Manufacturing Systems
• Warehousing Systems
• Transportation Systems
• +3 more

Prof. Ivo J.B.F. Adan is a Full Professor at Eindhoven University of Technology (TU/e), holding chairs in both Industrial Engineering & Innovation Sciences and Mechanical Engineering. His research focuses on stochastic operations research, queueing models, and manufacturing systems design. He has held visiting positions at the University of North Carolina and was part-time professor at the University of Amsterdam (2008–2011). Education: MSc and PhD in Mathematics from TU/e Affiliations: Eurandom Senior Fellow, Beta Research Director, editorial roles at Queueing Systems and Probability in Engineering and Informational Sciences His work addresses warehouse optimization, transportation logistics, and semiconductor manufacturing. Notable achievements include: Developed analytical models for zone picking systems and conveyor networks Advanced understanding of FCFS infinite bipartite matching systems Recipient of multiple best paper awards including the IE&IS Valorization Prize (2023) Current projects include the DigiTwop digital twin-based warehouse optimization initiative and modular construction research. He teaches courses on stochastic modeling, manufacturing systems, and smart industry applications.

Meng LiView profile

Associate Professor

• Bayesian Analysis
• Machine Learning
• Functional Data Analysis
• +8 more

Meng Li is the Noah Harding Associate Professor of Statistics at Rice University's School of Engineering. He specializes in Bayesian analysis, machine learning, and statistical theory. His research bridges methodological development and applications in biomedical sciences, materials informatics, and neuroimaging. Li holds a Ph.D. from North Carolina State University and a B.S. from Sun Yat-sen University. He has been recognized with awards including the 2020 Rice Engineering Excellence Award and the Ralph E. Powe Junior Faculty Enhancement Award. Li's research focuses on probabilistic modeling of complex data such as images, functional data, and networks. His funded projects include AI frameworks for pancreatic cancer biomarkers and Bayesian spatiotemporal modeling of marine ecosystems. He collaborates with institutions like Houston Methodist and Baylor College of Medicine on medical applications. His teaching includes advanced courses like Bayesian Statistics and Advanced Bayesian Inference. He advises over 30 students, many of whom have pursued academic and industry roles. Li serves as an associate editor for Bayesian Analysis and the new ACM Transactions on Probabilistic Machine Learning.

Nathan ReadingView profile

Professor

• Algebraic Combinatorics
• Geometric Combinatorics
• Coxeter Groups
• +3 more

Nathan Reading is a Professor in the Department of Mathematics at North Carolina State University (NCSU). He holds a Ph.D. in Mathematics from the University of Minnesota (2002) and a B.S. in Physics from Stanford University (1995). His research focuses on algebraic and geometric combinatorics, particularly in Coxeter groups, cluster algebras, and lattice-theoretic approaches. He has been actively involved in organizing the Triangle Lectures in Combinatorics, a biannual research conference. His research interests include noncrossing partitions, cluster scattering diagrams, and the lattice theory of torsion classes. Recent work explores connections between Coxeter groups and combinatorial structures on surfaces. Reading has authored numerous papers on topics such as semidistributive lattices, scattering diagrams, and Cambrian frameworks. He teaches advanced combinatorics courses (e.g., MA 724: Combinatorics II) and has advised graduate students. His work has been supported by grants from the National Science Foundation (NSF), including DMS-1500949. Reading maintains an active presence in the mathematics community through publications, conference organization, and pedagogical contributions.

• Animal Nutrition
• Dairy Cattle
• Methane Emission
• +2 more

Jan Dijkstra is an Associate Professor at Wageningen University specializing in Animal Nutrition . His research focuses on dairy cattle nutrition, methane emissions, and nutrient efficiency. Academic Rank: Associate Professor Department: Animal Nutrition His work emphasizes: Mathematical modelling of methane production Phosphorus and nitrogen balance optimization Feed supplementation strategies for reduced emissions Genotype-environment interactions in dairy systems Recent publications highlight advancements in methane mitigation , nutrient cycling , and rumen microbiome dynamics . He serves as a promotor for multiple PhD projects and contributes to datasets on equine microbiomes and rumen metabolites. Scientific Awards American Feed Industry Association Award (2015) Publicatieprijs ASG (2013) Supervision PhD candidates: Kozorezov, Henry, Koning, Vivares Martinez EngD candidate: Holshof