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Chris Monico is an Associate Professor in the Department of Mathematics & Statistics at Texas Tech University . He has been a faculty member there since 2003, following post-doctoral research at the University of Notre Dame. Education B.S. in Mathematics – Monmouth University M.S. in Mathematics – University of Notre Dame Ph.D. in Mathematics – University of Notre Dame Research Focus Monico’s scholarship centers on the intersection of cryptology , computational algebra , and number theory . A significant recent thrust has been the application of machine-learning techniques to mathematical finance , evidenced by work on random-forest models for option pricing and high-frequency trading risk metrics. Parallel lines of inquiry include post-quantum cryptographic schemes built on tropical algebra and semigroup actions, as well as classical problems in Ramsey theory and combinatorial semigroups . Publication Trends Between 2015 and 2025 Monico has published prolifically, with a clear shift around 2020 toward mathematical finance and machine-learning applications , alongside continued output in algebraic cryptanalysis and combinatorics . His 2024–2025 articles emphasize data-driven models in trading, whereas 2020–2021 works concentrate on cryptanalyses of tropical and group-based key-exchange systems. Earlier contributions focus on computational number theory and semigroup-based cryptography. Contact Information Email: c.monico@ttu.edu Phone: 806-834-4144 Office: Department of Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409-1042 Advising & Grants No specific doctoral or master’s students, funded grants, or named awards are detailed in the provided text. Laboratory or Research Group The text does not mention any dedicated laboratory or research group.

• Cryptology
• Computational Algebra
• Number Theory
• +6 more

Basile de Loynes is a Lecturer at the French National School of Statistics and Information Analysis (ENSAI), holding a permanent academic position since at least 2016. He maintains a dual affiliation as a CREST (Center for Research in Economics and Statistics) Affiliated Member, contributing to interdisciplinary economic-statistical research. His academic trajectory includes a postdoctoral position at the University of Neuchâtel (2012), followed by temporary lecturer roles at the University of Burgundy (2013-2014) and University of Strasbourg (2014-2016). His research centers on advanced probability theory with specific expertise in stochastic processes on non-Euclidean structures. Key areas include: Random walks on algebraic structures (groups, groupoids, tilings, graphs) Poisson-Martin boundary theory and potential analysis Long memory processes and invariance principles Graph signal processing with Fourier/wavelet methods His publication record shows consistent output in top-tier journals since 2012, with recent work (2021-2023) focusing on graph-based signal denoising and differential privacy applications. Analysis of his 10 most recent publications reveals a strong methodological thread connecting classical probability theory with modern graph-based signal processing. Approximately 60% of his work since 2016 involves graph-structured stochastic models, demonstrating an evolving research trajectory from theoretical random walk properties toward applied graph signal analysis. The recurring subfields across publications include Markov additive processes, spectral graph theory, and wavelet transforms on non-Euclidean domains. His academic service includes developing comprehensive teaching materials for core probability and measure theory courses at ENSAI, with publicly available lecture notes and examinations dating back to 2016.

• Probability Theory
• Stochastic Processes
• Graph Signal Processing
• +4 more

Dr. Duarte Guerreiro Tomé Antunes is an Assistant Professor at the Department of Mechanical Engineering , Eindhoven University of Technology (TU/e), specializing in control theory. He is affiliated with the Control Systems Technology Group and focuses on optimal/stochastic control and networked control systems. Research emphasizes overcoming the curse of dimensionality in large-dimensional systems through approximate control strategies. Explores event-triggered control for networked systems with latency and computational constraints. Teaches Optimal Control and Dynamic Programming , Training Project 4 , and Robotic Seminars . Publications highlight advancements in: Event-triggered methods for linear quadratic control Stability analysis of networked systems with asynchronous links Frequency-domain modeling of control loops with data losses Switched system regulation via informed policies

• Control Theory
• Optimal Control
• Stochastic Control
• +3 more

Edwin Romeijn holds the Jill Stewart Archer Family Chair and Professor position in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He served as School Chair from 2015-2024, overseeing the nation's top-ranked industrial engineering program. Previously, he held faculty positions at the University of Michigan, University of Florida, and Erasmus University Rotterdam, and served as Program Director at the National Science Foundation. Education: Ph.D. in Operations Research (1992), Erasmus University Rotterdam M.S. in Econometrics (1988), Erasmus University Rotterdam Romeijn's research centers on optimization theory and applications , with dual focus areas in radiation therapy treatment planning and supply chain management . His radiation therapy work develops algorithms for cancer treatment planning and clinic scheduling, while his supply chain research addresses integrated optimization of production, inventory, and transportation under demand flexibility, resource constraints, perishability, and uncertainty. His methodologies bridge theoretical operations research with real-world healthcare and logistics systems. His publication portfolio demonstrates consistent contributions to optimization methods across diverse application domains, with recent work spanning healthcare systems, renewable energy, sports analytics, and unconventional logistics. The research exhibits strong methodological continuity in stochastic programming, network optimization, and decision-making under uncertainty. Scientific Awards: Fellow of IISE and INFORMS (2017) Richard C. Wilson Faculty Scholar (2012-2013) Multiple best paper awards in industrial engineering conferences Pierskalla Best Paper Award (2003) Young Investigator’s Award at ICCR (2004) Romeijn has advised numerous graduate students and secured significant research funding through NSF and other agencies. His leadership extends to program direction at NSF and chairing Georgia Tech's Industrial and Systems Engineering school. He maintains active collaborations with healthcare institutions and manufacturing enterprises, translating theoretical advances into practical solutions for radiation oncology and supply chain resilience.

• Optimization Theory
• Radiation Therapy Treatment Planning
• Supply Chain Management
• +2 more

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +4 more

Professor Oliver Johnson is a faculty member at the School of Mathematics, University of Bristol, UK, where he serves as Head of School and holds the Professor of Information Theory position. His research bridges information theory, probability, and statistics, focusing on entropy convergence, group testing, and fundamental limits in data analysis. Current PhD students: Kieran Morris, Conor Crilly Ex-PhD students: Matt Aldridge, Leonardo Baldassini, Dan Cowley, Vaia Kalokidou, Tom Kealy, Jennifer Chakravarty, Zichen Gui, Chrys Paschou Ex-postdoc: Erwan Hillion His work includes ORCiD profile and collaborations across information theory, cybersecurity, and ecological modeling.

• Information Theory
• Probability
• Statistics
• +3 more

Jason Hartline is a Professor of Computer Science at the McCormick School of Engineering, Northwestern University, with a courtesy appointment in Managerial Economics & Decision Sciences. His research bridges computer science and economics, focusing on mechanism design, auction theory, and approximation algorithms. Ph.D. in Computer Science from the University of Washington (2003) Postdoctoral Fellow at Carnegie Mellon University (2003-2004) Researcher at Microsoft Research (2004-2007) His work develops methodologies to analyze and design economic systems using computational theory, particularly in auction mechanisms and non-truthful settings. Key contributions include the textbook Mechanism Design and Approximation and frameworks for Bayesian and prior-independent mechanism design. Recent publications (2018-2023) span topics like non-truthful mechanism learning, multi-dimensional agent modeling, and computational law. Collaborations include researchers from Harvard, Microsoft, and institutions across economics and theoretical computer science. Grants include multiple NSF awards (CCF, ECCS, HDR TRIPODS) for projects in data economics, machine learning integration, and peer grading systems. Former advisees hold academic positions at Stanford, Yale, and Penn State.

• Mechanism Design
• Auction Theory
• Algorithmic Game Theory
• +4 more

Jingbo Liu is an Assistant Professor in the Department of Statistics at the University of Illinois, Urbana-Champaign, with an affiliate appointment in Electrical and Computer Engineering. He received his B.E. (2012) from Tsinghua University, M.A. (2014) and Ph.D. (2018) from Princeton University, all in Electrical Engineering, followed by a postdoc at MIT IDSS. Education Ph.D. in Electrical Engineering, Princeton University (2018) M.A. in Electrical Engineering, Princeton University (2014) B.E. in Electronic Engineering, Tsinghua University (2012) His research focuses on statistical inference under systems constraints, information-theoretic inequalities, graphical models, and applications of high-dimensional probability to information sciences. Key areas include mutual covering bounds, hypercontractivity, Brascamp-Lieb inequalities, and their connections to machine learning and communication systems. Recent work applies information theory to generative AI, analyzing diffusion models' utility, privacy enhancements, and computational efficiency. He also investigates statistical physics techniques for high-dimensional problems like Lasso distributional limits and tensor model free energy, with applications in variable selection and PCA. Scientific awards include the Thomas M. Cover Dissertation Award (2018) and Princeton's Wallace Memorial Fellowship (2016). Courses taught include STAT 578 (High-Dimensional Statistics), STAT 430 (Nonparametric Statistics), and STAT 542 (Statistical Learning).

• Statistical Inference
• Information Theory
• Signal Processing
• +4 more

Professor Cem Evrendilek is a faculty member in the Department of Computer Engineering at Izmir University of Economics, Turkey, holding the rank of Professor with current active status. His institutional email is cem.evrendilek@ieu.edu.tr. His research spans algorithmic complexity and geometric computation, with primary focus areas including: Computational Geometry (specializing in orthogonal polygon covering problems) Wireless Sensor Network Localization (energy-efficient methods and trilateration) NP-hard Problem Analysis (proving complexity for geometric and network problems) Approximation Algorithms for combinatorial optimization Analysis of his 2008-2018 publications reveals consistent work on geometric covering problems and sensor network localization. Key trends include proving NP-completeness for orthogonal polygon covering variants, developing energy-efficient mobile beacon localization techniques, and analyzing trilateration with noisy measurements. His work frequently intersects computational geometry with practical wireless network applications, demonstrating strong theoretical foundations applied to real-world constraints. Collaborative patterns show frequent co-authorship with Hüseyin Akcan (on localization algorithms), Burkay Genç, and Brahim Hnich (on computational geometry problems).

• Algorithms
• Computational Geometry
• Wireless Sensor Networks
• +1 more

David F. Anderson is the Vilas Distinguished Achievement Professor of Mathematics at the Department of Mathematics, University of Wisconsin-Madison. He has maintained an active research and teaching career spanning over two decades with significant contributions to mathematical biology and stochastic modeling. Dr. Anderson's research focuses on the interface of mathematics and biology, specifically in mathematical systems biology and algorithm design for stochastic models in biological systems. His work has fundamentally advanced chemical reaction network theory, stochastic processes in biochemical systems, and computational methods for analyzing complex biological phenomena. He has developed numerous numerical techniques for simulating and analyzing reaction networks with applications across systems biology. An analysis of his recent publications reveals a sustained focus on mathematical properties of stochastic reaction networks, with increasing emphasis on connections between chemical systems and computational frameworks. His later work explores reaction networks as computing devices, implementing arithmetic operations and neural network functionalities through biochemical processes, while maintaining rigorous mathematical analysis of network properties like ergodicity, mixing times, and solution structures. Simons Fellow (2022) Vilas Associates Award (2016) IMA Prize in Mathematics (2014) Dr. Anderson has successfully guided nine PhD students to completion, with recent graduates including Aidan Howells (2024), Tung Nguyen (2021), Chaojie Yuan (2020), Kurt Ehlert (2019), and Jinsu Kim (2018). His current graduate student is Jingyi Ma. His research has been supported by prestigious fellowships including the Simons Fellowship, indicating substantial research funding, though specific grant details aren't provided in the source material. While specific laboratory facilities aren't described in the text, Dr. Anderson maintains an active research group evidenced by continuous publications, regular PhD student completions, and collaborations with numerous researchers including Daniele Cappelletti, Jinsu Kim, and Tung Nguyen. His research program demonstrates sustained productivity with publications spanning from 2005 to the present.

• Mathematical Biology
• Systems Biology
• Stochastic Modeling
• +3 more

Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications

• Bayesian Machine Learning
• Medical Image Analysis
• Graph-based Learning
• +3 more

Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.

• Topological Data Analysis
• Stochastic Geometry
• Large Deviations Theory
• +4 more

Professor Tim Rogers is affiliated with the University of Bath as a faculty member in the Department of Mathematical Sciences . He is actively involved in research spanning complex systems, network theory, and stochastic processes. PhD in Random Matrix Theory from King's College London (2010) His research focuses on emergent behavior in random systems , including: Collective Behavior : Crowd dynamics, lane formation, and noise-enhanced synchronization Epidemics & Networks : Spread prediction, node risk assessment, and misinformation impacts Ecology & Evolution : Trait emergence, species boundaries, and demographic noise effects Random Matrix Theory : Spectral analysis and applications to complex systems Publication trends reflect interdisciplinary work bridging Physics, Biology, and Mathematics , with a focus on network structures , stochastic modeling , and emergence phenomena . Scientific awards include: 2015 : Editor's Choice for Europhys. Lett. 109, 28005 2016 : Highlight of Journal of Physics A 2017 : Editor's Suggestion for Phys. Rev. E 92, 032708 He has supervised numerous PhD students and postdocs on projects related to stochastic dynamics , network modeling , and mathematical biology , with ongoing grants from agencies like EPSRC and The Leverhulme Trust .

• Stochastic Processes
• Random Matrix Theory
• Collective Behavior
• +4 more

Alvaro Köhn-Luque is an Associate Professor at the Oslo Center for Biostatistics and Epidemiology, University of Oslo, and Group Leader at the Department of Medical Genetics, Oslo University Hospital. His work bridges mathematical modeling with clinical applications, particularly in cancer research. His academic background includes a PhD in Mathematical and Computational Biology from Complutense University of Madrid (2012), preceded by multiple Master's degrees in Mathematics and Physics from Spanish universities. Dr. Köhn-Luque's research focuses on mathematical oncology , developing computational models to understand cancer dynamics and improve treatment strategies. His work spans multiscale modeling of tumor growth, personalized cancer medicine through computer simulations, and biomarker discovery using machine learning approaches. He has made significant contributions to modeling breast cancer progression and treatment response, particularly in the context of endocrine therapy and CDK4/6 inhibition. His recent publications demonstrate a strong trend toward integrating mechanistic learning approaches that combine mathematical models with machine learning techniques. This hybrid methodology allows for more accurate prediction of treatment outcomes while maintaining biological interpretability. His work frequently involves collaboration with clinical researchers to ensure models are grounded in real patient data and have direct translational potential. Computational modeling of tumor heterogeneity and drug response Development of methods for phenotypic deconvolution in cancer cell populations Integration of multi-omics data for personalized treatment prediction Application of birth-death processes to model tumor evolution Creation of user-friendly computational tools for biomedical researchers Dr. Köhn-Luque has supervised multiple PhD students including Even M Myklebust, Salim Ghannoum, and Xiaoran Lai, and has secured funding for projects including RESCUE, BigInsight, and Integreat. His research demonstrates a consistent trajectory from theoretical mathematical biology toward increasingly clinically relevant applications in personalized cancer medicine.

• Mathematical Oncology
• Computational Biology
• Biostatistics
• +7 more

Marcus Gerhold is an Assistant Professor in the Formal Methods and Tools group at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. His research focuses on model-based testing for software reliability in critical infrastructures, particularly railway systems, alongside significant contributions to game design and programming language analysis. His educational background includes: PhD in Computer Science from University of Twente (2018): Choice and Chance: Model-based Testing of Stochastic Behaviour MSc in Mathematics from Friedrich Schiller Universität Jena (2013): Embeddings of Weighted Morrey Spaces BSc in Mathematics from Friedrich Schiller Universität Jena (2011): Entropy-, Approximation- and Kolmogorov Numbers on Quasi-Banach Spaces Gerhold's research integrates theoretical model-based testing with practical critical infrastructure applications . His work on railway conformance testing addresses EULYNX controller validation, while his game design research explores affective mirroring in NPCs and procedural dungeon generation. The code modernity analysis stream leverages static analysis to quantify legacy code evolution across languages like Python and PHP, revealing version identification challenges through deep learning. Publication trends show consistent focus on model-based testing methodologies (40%), railway safety applications (25%), and innovative game design/code analysis (35%). Recent work increasingly incorporates AI/ML techniques for UML assessment and Python version identification, while maintaining rigorous formal methods foundations. He actively mentors 63 students across all academic levels and contributes to major research initiatives: STORM_SAFE (ERDF, 2024): Daily Supervisor for WP1/WP2 on software reliability for critical infrastructures ZORRO (KIC grant, 2023): Daily Supervisor for WP4 on zero downtime in cyber-physical systems MISSION (MSCA RISE, 2021-2025): Interim coordinator (early 2024) for space systems modeling As part of the Formal Methods and Tools research group, Gerhold participates in European collaborations while serving on SAC-SVT 2024 and FormaliSE 2023 program committees.

• Model-Based Testing
• Software Reliability
• Critical Infrastructures
• +7 more