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Peter MorseView profile

Assistant Professor

• Computational Physics
• Theoretical Soft Matter
• Condensed Matter Physics
• +3 more

Peter Morse is an Assistant Professor of Physics at Seton Hall University's Department of Physics. He joined the faculty in 2024 after completing postdoctoral research at Syracuse University, Duke University, and Princeton University. His research focuses on computational and theoretical soft matter physics, particularly the interplay between geometry, dynamics, and thermodynamics in disordered systems. Education: PhD in Physics from the University of Oregon (2016), MS in Physics from the University of Oregon (2010), and dual BS degrees in Applied and Computational Mathematical Sciences and Physics from the University of Washington (2008). Research interests include glass and jamming transitions, hyperuniformity, packing problems, and the development of computational tools for high-performance simulations. His lab emphasizes Python and C++ programming, with a focus on optimizing algorithms for studying material memory and rheology. Recent work highlights the dynamics of glass formers, hyperuniform point patterns, and computational modeling of soft matter systems. Articles frequently explore structural dynamics in high dimensions and the interplay between geometry and material properties. Students in his lab gain expertise in HPC cluster usage and algorithm development for analyzing disordered systems. Current projects include optimizing code for stealthy hyperuniform systems and studying jamming transitions in minimally structured glass formers.

Genber KerimliView profile

Professor

• Semiconductor Physics
• Solar Cell Technology
• Electrical Insulation Materials
• +3 more

Genber Kerimli Genber Kerimli is a Professor at Iğdır University's Department of Electrical and Electronics Engineering, Faculty of Engineering. He holds a Doctorate in Physical Electronics from Azerbaijan State Oil Academy (2002), specializing in electrical discharge effects on solid surfaces. His research focuses on semiconductor materials, solar cell efficiency optimization, polymer insulation aging mechanisms, and nanomaterials for energy storage. Education: Doctorate in Physical Electronics (1998-2002), Azerbaijan State Oil Academy Bachelor's Degree in Electrothermic Systems (1983-1990), Azerbaijan State Oil Academy Research Interests: Kerimli's work spans solar cell physics, including bifacial silicon cell performance under magnetic fields and particle irradiation. He investigates thermoelectric effects in quantum-confined semiconductors and develops mathematical models for material synthesis processes. His studies on polymer insulation degrade mechanisms address aging caused by electrical discharges and environmental factors. Recent advancements include self-healing hydrogel electrodes for supercapacitors and TCO layers for dye-sensitized solar cells. Key Contributions: Published extensively in Chinese Journal of Physics , Results in Physics , and Surface Engineering and Applied Electrochemistry . Advised at least one Master's thesis on power system protection coordination in Iğdır's grid. Teaches core courses including Electronics-I and Electrical Engineering Materials. Labs/Teams: Active in semiconductor device fabrication and electrical materials testing laboratories at Iğdır University. Collaborates with international teams on photovoltaic technology and energy storage systems.

• Theory of Machine Learning
• Randomized Algorithms
• Convex Optimization
• +4 more

Nicholas Harvey is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. He holds affiliations as an Associate Member in the Department of Mathematics and serves as Director of the Mathematics of Information, Learning, and Data (MILD) research cluster. His research focuses on randomized algorithms, convex optimization, and machine learning theory, with notable contributions to online learning, submodular functions, and algorithmic aspects of discrepancy theory. Harvey's academic journey includes significant work on problems such as prediction with expert advice, learning mixtures of Gaussians, and graph algorithms. He has supervised numerous graduate students, including notable researchers like Huang Fang and Victor Sanches Portella. His work has been recognized with awards such as the Best Paper Award at NeurIPS 2018 and the Excellence in Teaching Award in 2024. His research outputs span theoretical computer science, optimization, and machine learning, with key contributions in algorithms for submodular functions, online convex optimization, and probabilistic methods. He has also contributed to practical applications, such as algorithms for network coding and efficient failure notification systems in distributed networks. Harvey is actively involved in teaching, including pioneering courses on randomized algorithms and machine learning theory. His research group collaborates on projects related to the MILD cluster, focusing on interdisciplinary applications of data science and mathematics.

Zhenyu CaiView profile

Research Fellow

• Quantum Error Correction
• Quantum Error Mitigation
• Quantum Computing
• +3 more

Dr. Zhenyu Cai is an EPSRC Quantum Technologies Fellow and Principal Investigator in the Department of Engineering Science at the University of Oxford. He previously held a Junior Research Fellowship in Physics at St John’s College (2020–2024). His research focuses on quantum error correction, error mitigation, and their hardware-adapted implementation for scalable quantum computing. He leads a £2.2M EPSRC Fellowship (with £500k industrial support) and contributes to £1.3M+ in additional grants. He is a lead inventor on four patents and actively collaborates with industry partners. Education: DPhil in Materials (Lincoln College, Oxford, 2017–2020), MSci & BA in Physics (Trinity College, Cambridge, 2013–2017). Teaching roles include Lecturer for 'Magnetic Properties of Materials' and Guest Lecturer for 'Introduction to Quantum Information' at Oxford's Mathematical Institute. Research Interests: Quantum Error Correction, Quantum Error Mitigation, Hardware Implementation, and Quantum Algorithms. He organizes the £3M RoaRQ Programme and the SEEQA conference, advancing UK quantum technologies. His work bridges theoretical innovation with practical hardware solutions, emphasizing noise-robust and resource-efficient pathways. Awards include the EPSRC Fellowship, Junior Research Fellowship, and scholarships from Singapore's Ministry of Education, Cambridge Overseas Trust, and Trinity College. His grants total over £4M, combining public and industrial funding. Patents and collaborations highlight his translational impact in quantum computing.

Pietro AmentaView profile

Associate Professor

• Statistics
• Multivariate Analysis
• Transport Systems
• +2 more

Pietro Amenta is an Associate Professor at the University of Sannio, affiliated with the Department of Law, Economics, Management and Quantitative Methods (DEMM). His academic work focuses on statistical methods, multivariate analysis, and transport systems optimization. Position: Associate Professor Department: DEMM Research: Statistics & Transport Systems Contact: amenta@unisannio.it Dr. Amenta's research interests center on Co-Inertia Analysis , Ordinal Data Handling , and Dimensionality Reduction Techniques . He has developed novel scaling methods for categorical variables and explored applications in transportation planning and social data analysis. His publications reveal trends in Transport Systems (2018-2024), Statistical Methodology (1991-2020), and Data Analysis Techniques (1994-2015). Recent work emphasizes non-negotiable group decisions in transport policy and sustainable mobility frameworks.

Frank NoéView profile

Associate Professor

• Machine Learning
• Computational Biology
• Molecular Dynamics
• +3 more

Frank Noé is a W2 Professor (Associate Professor equivalent) of Mathematical Modeling in the Life Sciences at the Free University of Berlin, with promotion to W3 pending. He concurrently serves as an Adjunct Professor in the Department of Chemistry at Rice University. His research develops computational frameworks for molecular kinetics, integrating machine learning with biophysical simulation to decode complex biomolecular processes. Academic qualifications: Dr. rer. nat. in Informatics and Biophysics, Universität Heidelberg (2006, summa cum laude) Master of Science in Computing, Cork Institute of Technology, Ireland (2000-2002) Diplom Ingenieur (BA) in Electrical Engineering, Berufsakademie Stuttgart (1996-1999) Noé pioneers the application of deep learning to molecular simulation, notably through Boltzmann generators and VAMPnets. His work bridges statistical mechanics, biophysics, and data science to model protein dynamics, ligand binding, and cellular signal transmission at unprecedented timescales. This interdisciplinary approach has established new paradigms for analyzing molecular kinetics. Recent publications demonstrate a decisive shift toward AI-driven methodologies, where neural networks extract kinetic features from molecular trajectories. This enables atomistic exploration of processes previously inaccessible to simulation, such as millisecond-scale protein folding and membrane receptor dynamics. Major honors: Simons Fellow at IPAM (2019) ACS Early-Career Award in Theoretical Chemistry (2019) ERC Consolidator Grant (2017) ERC Starting Grant (2012) Eliteprogramm für Postdoktoranden fellowship (2007) Research leadership: Principal Investigator, DFG Collaborative Research Center 958 "Scaffolding of Membranes" (2011-present) Principal Investigator, DFG Collaborative Research Center 740 "From Molecules to Modules" (2009-present) Faculty member, International Max-Planck Graduate School for Computational Biology Organizer, International Workshop on Molecular Kinetics He directs theoretical research within the SFB/TRR 186 consortium on molecular switches, developing open-source software for kinetic analysis while mentoring the next generation of computational biophysicists through DFG-funded graduate programs.

Melina FreitagView profile

Professor

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +6 more

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

Felix VoigtlaenderView profile

Professor

• Mathematics of Machine Learning
• Neural Networks
• Approximation Theory
• +11 more

Felix Voigtlaender is a Professor for Mathematics with a focus on Reliable Machine Learning at the Mathematical Institute for Machine Learning and Data Science, KU Eichstätt-Ingolstadt. He has held previous academic positions at TU Munich as an Emmy Noether Group Leader, at the University of Vienna as a Senior Scientist, and at KU Eichstätt and TU Berlin in research and postdoctoral roles. PhD: RWTH Aachen University, 2015 Master: RWTH Aachen University, 2013 Bachelor: RWTH Aachen University His research lies at the intersection of mathematics and machine learning, with a strong emphasis on the theoretical underpinnings of neural networks. He investigates approximation properties, expressiveness, and the existence of adversarial examples. His work also extends into harmonic analysis, functional analysis, and multiscale systems such as wavelets and shearlets. He is deeply interested in the mathematical foundations of data science, including sampling theory and information-based complexity. The recent publications highlight a consistent focus on the mathematical analysis of neural networks, particularly approximation capabilities and universal approximation theorems in both real and complex domains. His work often provides sharp theoretical bounds and deep insights into the behavior of deep learning models in high-dimensional settings. Friedrich-Wilhelm-Award 2016 for his PhD thesis Felix Voigtlaender has supervised students and is actively involved in teaching and academic development, notably contributing to the launch of a new BSc program in Data Science at KU Eichstätt-Ingolstadt. He has collaborated with prominent researchers such as Götz Pfander, Gitta Kutyniok, and Hartmut Führ, and has been funded through competitive grants like the Emmy Noether program. He leads a research group focused on rigorous mathematical approaches to machine learning challenges. He is affiliated with the Mathematical Institute for Machine Learning and Data Science, a newly founded institute at KU Eichstätt-Ingolstadt, which will be based in Ingolstadt. His team is involved in both theoretical exploration and practical applications of data science methods.

Anestis TouloumisView profile

Senior Lecturer

• Biostatistics
• Categorical Data Analysis
• High-Dimensional Statistics
• +13 more

Anestis Touloumis is a Principal Lecturer in the School of Architecture, Technology and Engineering at the University of Brighton, affiliated with the Computing and Mathematical Sciences Research Excellence Group. He holds a PhD in Statistics from the University of Florida and has held postdoctoral positions at the European Bioinformatics Institute and the University of Cambridge before joining Brighton in 2015. Education: PhD in Statistics, University of Florida (2006–2011) MSc in Applied Statistics, University of Piraeus (2003–2006) BSc in Mathematics, Aristotle University of Thessaloniki (1998–2003) His research centers on biostatistics, high-dimensional statistics, and statistical genetics, with a focus on developing methods for correlated responses, covariance matrix estimation, and hypothesis testing in high-dimensional settings. His work is applied in omics, health studies, and social sciences, and he actively disseminates methods through R packages on CRAN and Bioconductor. He teaches a range of statistical modules using R and SAS, emphasizing conceptual understanding and critical thinking. The recent research articles reflect strong trends in biostatistical methodology, particularly in high-dimensional inference, longitudinal and correlated data analysis, and statistical computing. There is a consistent emphasis on developing robust, open-source tools for real-world applications in healthcare and data science. Scientific Awards and Professional Service: Treasurer, British and Irish Region of the International Biometric Society (2020–present) Committee Member, British and Irish Region of the International Biometric Society (2019–present) External Examiner, MSc in Applied Statistics, Birkbeck University of London (2020–present) Member, International Biometric Society He has been active in research supervision and mentoring, with a stated interest in supervising postgraduate students in machine learning, categorical data, and high-dimensional statistics, often involving software development. He has contributed to research grants, notably as a Co-Investigator on the ERDF-funded Digital Research & Innovation Value Accelerator (DRIVA) project (2018–2021). He is involved in multiple research collaborations and has delivered invited talks and peer reviews for leading journals. His work bridges theoretical statistics and practical applications across disciplines.

Philippe GAGNONView profile

Associate Professor

• Bayesian Statistics
• Robust Statistical Methods
• Actuarial Science
• +5 more

Philippe GAGNON is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal, affiliated with the Centre de recherches mathématiques (CRM). He specializes in robust statistical methods, Bayesian inference, and actuarial science applications. His research focuses on developing robust models and efficient algorithms for data analysis, particularly in high-dimensional and outlier-prone contexts. He teaches courses such as ACT-2284 (Mathematics of Property and Casualty Insurance) and ACT-3261 (Predictive Modeling). Education: PhD in Statistics Master’s in Statistics Bachelor’s in Actuarial Science (fulfilled requirements for Society of Actuaries’ Associate designation) Postdoctoral Research, University of Oxford (with Arnaud Doucet) Research Interests: GAGNON’s work integrates robust Bayesian methods with advanced computational techniques like Markov Chain Monte Carlo (MCMC). Key themes include: Robust generalized linear models for actuarial applications Non-reversible jump algorithms for efficient model selection Automated statistical learning procedures Outlier-resistant parameter estimation Funding and Grants: NSERC Discovery Grant (2020–2027) FRQNT Emerging Researcher Grant (2020–2026) MITACS Accelerate Projects (Synthetic Data in Insurance, Spatial Regression Models) Advising: Current/previous advisees include Yuxi Wang (MSc 2022) and Arghya Datta (PhD in progress). He actively recruits students with backgrounds in theoretical statistics, actuarial science, or computer science. Labs/Teams: Collaborates within Université de Montréal’s research groups on computational statistics and actuarial science applications.

David SivakoffView profile

Associate Professor

• Probability Theory
• Stochastic Processes
• Interacting Particle Systems
• +7 more

David Sivakoff is an Associate Professor at The Ohio State University with a joint appointment in the Department of Statistics and the Department of Mathematics. His research focuses on probability theory, particularly stochastic processes on graphs, including interacting particle systems, cellular automata, and bootstrap percolation models. He explores applications in epidemiology, sociology, and statistical mechanics, with recent work on nucleation phenomena, phase transitions, and network dynamics. Education: PhD in Applied Mathematics from the University of California, Davis (2010). Research Interests: His work examines systems like cyclic cellular automata, threshold growth models, and diffusion-limited annihilating systems. He investigates how local rules lead to global patterns, with notable studies on phase transitions in heterogeneous networks and the impact of obstacles on percolation processes. His interdisciplinary collaborations include analyzing information spread in social networks and elite influence on public opinion. Recent Publications: Key contributions include studies on competing growth models (2024), advantageous mutant spread dynamics (2024), and particle density in annihilating systems (2023). NSF and Simons Foundation funding supports his research. Teaching: Courses include Probability Theory, Mathematical Statistics, and Real Analysis. Recent classes: Stat 6201 and Math 6251 in Autumn 2024. Advising: Supervised 11 graduate students across Statistics and Mathematics, including ongoing advisees in both departments. Notable graduates include Hanbaek Lyu (2018) and Nikolas Henderson (2024). Labs/Teams: Collaborates with interdisciplinary teams on projects like empirical studies of information sources and network change-point detection. Active in conferences like SoCG and IJCAI.

João CarvalhoView profile

Researcher

• Robotics
• Machine Learning
• Reinforcement Learning
• +7 more

João Carvalho is a Postdoctoral Researcher at the Intelligent Autonomous Systems (IAS) group within the Technische Universität Darmstadt . He obtained his PhD in Computer Science from TU Darmstadt in 2025, advised by Jan Peters, following a MSc in Computer Science from Albert-Ludwigs-Universität Freiburg and a Master's in Electrical and Computer Engineering from Instituto Superior Técnico (University of Lisbon). His work focuses on developing machine learning and reinforcement learning algorithms for robot manipulation, particularly in motion planning, grasping, and contact-rich tasks like insertions. Education: PhD in Computer Science, TU Darmstadt (2025) MSc in Computer Science, Albert-Ludwigs-Universität Freiburg Master's in Electrical and Computer Engineering, Instituto Superior Técnico (University of Lisbon) His research integrates generative models for motion planning and grasping, reinforcement learning for contact-rich tasks, and policy gradient methods with variance reduction. Recent publications highlight applications of diffusion models and tensor planning in robotics, with a focus on spatial symmetry and efficient policy generation. Key contributions include work on Motion Planning Diffusion , Grasp Diffusion Networks , and Model Tensor Planning . He actively supervises thesis students in areas related to robot learning , generative models , and residual reinforcement learning .

• Statistical Quality Control
• Machine Learning
• Data Science
• +4 more

Dr. Inez Zwetsloot is a Visiting Professor in the Business Analytics section at the Faculty of Economics and Business , University of Amsterdam. Her work bridges statistical quality control, machine learning, and data-driven decision-making across diverse domains. Focuses on adaptive monitoring techniques for complex systems Integrates AI into traditional quality control frameworks Explores social media dynamics in financial markets Her recent research emphasizes real-time anomaly detection in high-dimensional data, dynamic social network analysis , and robust control chart methodologies . She also contributes to open data practices and ethical AI implementation in quality control systems. Dr. Zwetsloot's work demonstrates cross-disciplinary impact through applications in manufacturing, healthcare monitoring, and sustainable infrastructure systems. Her articles highlight methodological innovations in LSTM predictive modeling , EWMA dispersion control , and energy-efficient predictive maintenance .

• Probabilistic Combinatorics
• Extremal Combinatorics
• Random Combinatorial Structures
• +2 more

Guillem Perarnau Llobet is an Associate Professor at the Department of Mathematics, Universitat Politècnica de Catalunya (UPC), affiliated with CRM (Centre de Recerca Matemàtica), IMTech (Institut de Matemàtiques de la UPC), and BGSMath (Barcelona Graduate School of Mathematics). His academic journey includes a PhD at UPC under Oriol Serra, followed by a CARP Postdoc Fellowship at McGill University with Bruce Reed and Louigi Addario-Berry (2013–2015), and a Lecturer position at the University of Birmingham (2016–2019). Current Role: Associate Professor, UPC Past Roles: Lecturer, University of Birmingham; Postdoc, McGill University Affiliations: CRM, IMTech, BGSMath Research Focus: Probabilistic and Extremal Combinatorics, Random Combinatorial Structures, Discrete Stochastic Processes, and Analysis of Randomized Algorithms. His work bridges theoretical rigor with practical algorithmic insights, particularly in graph theory and random graph dynamics. Article Trends: Recent publications emphasize algorithmic combinatorics, probabilistic methods in random graphs, and hypergraph Hamiltonicity. Key themes include mixing times, synchronization, and percolation phenomena across diverse graph models. Scientific Awards: CARP Postdoc Fellowship Grants & Collaborations: COCOA Grant (coPI) RandNET MSCA Exchange Programme Participant Spanish Discrete and Algorithmic Mathematics Network Coordinator

Marc G. BellemareView profile

Adjunct Professor

• Reinforcement Learning
• Deep Reinforcement Learning
• Generative Modeling
• +5 more

Marc G. Bellemare is a Canada CIFAR AI Chair at Mila, an Adjunct Professor at the School of Computer Science at McGill University and Université de Montréal, and the Chief Scientific Officer at Reliant AI, a Montréal-Berlin startup. His research focuses on reinforcement learning and generative modeling, with significant contributions to distributional reinforcement learning, exploration in high-dimensional spaces, and the development of the Arcade Learning Environment (ALE). He has held previous roles at the Google Brain team and DeepMind. Marc’s work includes both theoretical and applied advancements, such as distributional reinforcement learning theory, exploration strategies, and the commercial application of RL for Loon’s stratospheric balloons. His publications span journals like Nature and top conferences including NeurIPS, ICML, and AAAI. He has received multiple best paper awards, including at NeurIPS 2021 and ICML 2019 Workshop. Current Students: Adrien Ali Taiga, Jacob Buckman, Harley Wiltzer, Pierluca D'Oro, Nathan U. Rahn, Jesse Farebrother Graduated Students: Rishabh Agarwal, Charline Le Lan, Max Schwarzer, Johan Obando Céron, Philip Amortila, Vishal Jain His recent articles emphasize distributional RL, exploration strategies, and large-scale applications. Scientific awards include best papers at NeurIPS, ICML, and RLDM. He is also a co-founder of Reliant AI and developed the ALE benchmark, which underpins deep RL research.