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Richard J. BoucherieView profile

Professor

• Operations Research
• Probability
• Healthcare Logistics
• +5 more

Richard J. Boucherie is a Full Professor at the University of Twente, affiliated with the Digital Society Institute, TechMed Centre, and the Department of Mathematics of Operations Research. His work integrates operations research, stochastic modeling, and queueing theory to optimize complex systems in healthcare, transportation, and logistics. Research Interests: Prof. Boucherie's expertise spans: Healthcare Logistics : Bed census forecasting, patient flow optimization, and clinical scheduling Stochastic Systems : Queueing networks, Markov decision processes, and distributionally robust optimization Applied AI : Reinforcement learning, data mining, and predictive analytics for service systems Publication Trends: His recent articles (2023-2025) demonstrate a strong focus on pandemic-responsive healthcare optimization, including dynamic bed allocation, laboratory workflow design, and fairness in resource distribution during crises. Methodologically, they advance stochastic programming, queueing theory, and Bayesian inference for clinical applications. Awards & Honors: Canadian OR Society Practice Prize (2012) ORTEC Excellence in Advanced Planning Award (2012) Academic Leadership: He has supervised 37 research projects and contributes to institutional initiatives through the TechMed Centre (health innovation) and Digital Society Institute (data-driven solutions).

Guillaume Bastille-RousseauView profile

Assistant Professor

• Animal Movement Ecology
• Behavioral Ecology
• Spatial Ecology
• +5 more

Guillaume Bastille-Rousseau serves as Assistant Professor of Zoology at Southern Illinois University Carbondale, where he investigates spatial behavioral processes including animal movement and habitat selection. His research examines how these behaviors scale to influence population demography and species distributions across multiple sympatric species, with strong applications in conservation biology. B.S. from Université de Sherbrooke M.S. from Université Laval Ph.D. from Trent University Postdoctoral training at SUNY-ESF and Colorado State University Bastille-Rousseau's research program integrates quantitative spatial analysis with wildlife ecology, utilizing GPS telemetry and advanced statistical modeling to address fundamental questions about behavioral responses to environmental change. He specializes in developing innovative analytical frameworks for movement ecology, with particular focus on how resource selection heterogeneity influences population dynamics. His work spans diverse ecosystems from Galapagos Islands to North American forests, examining species including elephants, tortoises, caribou, and elk. Recent publications (2023-2025) reveal three dominant research thrusts: 1) Development of novel methodological approaches for movement and habitat selection analysis; 2) Applications to wildlife disease ecology and conservation management; 3) Investigation of anthropogenic and climate change impacts on behavioral processes. His work consistently bridges theoretical ecology with practical conservation solutions. No scientific awards are documented in available sources. Bastille-Rousseau teaches ZOOL 468 (Wildlife Biology Principles) and likely mentors graduate students through his active research program, though specific student names are not listed. His collaborative publication record indicates participation in multi-institutional research networks including national wildlife disease surveillance initiatives. The researcher leads a spatial ecology group focused on quantitative wildlife conservation, though no formal lab name is specified. His current work emphasizes integration of movement ecology with epidemiological landscapes and climate change resilience planning, particularly for megaherbivores and migratory species.

Christopher DePernoView profile

Professor

• Wildlife Management
• Snake Ecology
• Urban Wildlife Conservation
• +2 more

Christopher DePerno is a Professor in the Department of Applied Ecology at North Carolina State University . His work focuses on wildlife management, conservation biology, and disease ecology in wild mammals, particularly black bears, river otters, and urban wildlife. Recent publications highlight his expertise in: Snake identification and taxonomy in North Carolina Zoonotic disease surveillance (e.g., Trypanosoma cruzi , Toxoplasma gondii ) Population genetics of black bears using UrsaPlex v2.0 Human-wildlife conflict mitigation in urban environments His research intersects with: Fire ecology impacts on avian species Antimicrobial resistance patterns in wildlife Chemical restraint protocols for carnivores Genomic analysis of pathogens in wild populations DePerno's work appears in extension publications and peer-reviewed journals, often collaborating with wildlife veterinarians, ecologists, and conservation practitioners.

• Wildlife Ecology
• Conservation Biology
• Forest Management
• +4 more

Torfinn Jahren is an Associate Professor at the University of Inland Norway , affiliated with the Faculty of Applied Ecology, Agricultural Sciences and Biotechnology. His research focuses on Wildlife Ecology and Conservation Biology , particularly examining predator-prey dynamics in boreal forest ecosystems. Faculty: Applied Ecology, Agricultural Sciences and Biotechnology Department: Forestry and Outback Studies Email: torfinn.jahren@inn.no His work primarily investigates nest predation in forest birds like capercaillie and black grouse, analyzing interactions between mesopredators (pine marten and red fox). He employs snow tracking and camera surveillance methodologies across multiple Norwegian regions. Key research themes include: Predator competition dynamics Impact of landscape parameters on nesting success Evaluation of hand-reared gamebird releases Human land use effects on predator populations Publications span 2010–2024, with recent studies focusing on capercaillie nest predation factors (2024), microtine rodent influence on pine marten populations (2024), and long-term comparative analyses across 16 countries (2016).

Cyrille ArthoView profile

Associate Professor

• Software Verification
• Software Engineering
• Concurrent Software
• +5 more

Cyrille Artho is an Associate Professor at KTH Royal Institute of Technology in the School of Computer Science and Communication, Department of EECS/TCS (Theoretical Computer Science). He joined KTH in August 2016 after working as a Senior Researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Tokyo and Osaka from April 2007 to July 2016. Prior to that, he was a Postdoctoral Researcher at the National Institute of Informatics in Tokyo. He completed his Ph.D. at ETH Zurich, where his thesis focused on finding multi-threading faults beyond data races. Dr. Artho's research focuses on software verification and engineering, particularly in concurrent and networked software systems. His work spans model-based testing, software model checking, and the development of verification tools. He has made significant contributions to the Java PathFinder ecosystem, developing the net-iocache extension for networked software and the Modbat model-based testing framework. His recent work has expanded into smart contracts, blockchain technology, and formal verification of distributed systems. Dr. Artho has published over 100 papers in top software engineering conferences and journals. His most recent publications demonstrate a continued focus on verification techniques, with increasing attention to smart contracts and formal methods for distributed systems. His work shows a consistent trajectory from foundational research on concurrency verification to practical applications in modern distributed systems. ACM SIGSOFT Distinguished Paper Award (2015) QRS 2015 best paper award Best tool (competition winner) at SBST 2015 Most influential ASWEC paper award (awarded in 2013) Dr. Artho is actively involved in the academic community as a committee member, session chair, and workshop organizer for major conferences including ASE, ICSE, ISSTA, and FM. He is also a member of the management group for the KTH Center for Cyber Defense and Information Security, reflecting his contributions to security-related research.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +4 more

Caio Corro is a Maître de Conférences (Associate Professor) at INSA Rennes, conducting research in the LinkMedia team of the IRISA laboratory. His work bridges theoretical computer science with practical applications in natural language understanding, with particular expertise in structured prediction problems for NLP systems. Dr. Corro's research program spans multiple interconnected domains: Natural Language Processing, with special focus on named entity recognition (including nested and discontinuous entities) Machine Learning algorithms for linguistic structure modeling Combinatorial optimization approaches to NLP challenges Application of AI techniques to linguistic research (AI for science) Development of efficient algorithms balancing theoretical soundness with practical performance His recent publication trajectory shows increasing emphasis on domain adaptation and multilingual capabilities while maintaining rigorous theoretical foundations. Corro has pioneered approaches that reduce computational complexity in structured prediction tasks without sacrificing accuracy, with significant contributions to named entity recognition methodologies. His work on EuroBERT demonstrates commitment to European language technologies, while his insurance domain research shows practical application focus. Dr. Corro maintains an active supervision portfolio: Current PhD students: Ayoub Hammal (co-supervised with Pierre Zweigenbaum and Miguel Couceiro), Benno Uthayasooriyar (co-supervised with Franck Vermet and Antoine Ly) Former students include Alban Petit (PhD graduate), Nicolas Devatine (now PhD student at IRIT), and several master's students who have contributed to his research program As a core member of the LinkMedia team at IRISA, Dr. Corro collaborates across institutional boundaries on cutting-edge NLP challenges, with particular emphasis on adapting techniques to specialized domains where data constraints present unique challenges.

• optimal transport
• probability theory
• mathematical statistics
• +2 more

Aleksey Vladimirovich Kroshnin is a Senior Research Fellow at the Faculty of Computer Science, National Research University Higher School of Economics (HSE), working within the Institute of Artificial Intelligence and Digital Sciences and the Laboratory of Theoretical Foundations of Artificial Intelligence Models. He joined HSE in 2018 and maintains a dual affiliation as Junior/Senior Researcher at the Dobrushin Mathematical Laboratory, IITP RAS since 2016. His educational background includes a PhD from University of Lyon (2022), a Master's degree in Applied Mathematics and Physics from Moscow Institute of Physics and Mathematics (2018), and a Bachelor's degree in the same field (2016). Dr. Kroshnin's research focuses on optimal transport theory , probability and mathematical statistics , functional analysis , and convex optimization . His work bridges theoretical mathematics with practical applications in machine learning and data science, particularly in the area of Wasserstein geometry and barycenter computation. His publication record from 2018-2024 demonstrates consistent contributions to top-tier journals in mathematics, statistics, and machine learning. The research shows a clear trajectory toward increasingly complex applications of optimal transport in machine learning, with significant contributions to computational methods for Wasserstein barycenters and statistical inference in metric spaces. While specific awards aren't listed in the provided information, his publications in prestigious journals like Annals of Statistics, SIAM Journal on Mathematical Analysis, and Journal of Convex Analysis indicate recognition within the mathematical community. Dr. Kroshnin collaborates extensively with researchers at HSE and internationally, particularly with Gasnikov Alexander and other members of the optimization and machine learning community in Russia. His work combines deep theoretical insights with practical computational approaches to challenging problems in data science.

• convex optimization
• mathematical modeling of traffic flows
• Markov processes
• +3 more

Alexander Vladimirovich Gasnikov is a Leading Researcher at the National Research University Higher School of Economics (HSE), specifically within the Faculty of Computer Science, the Institute of Artificial Intelligence and Digital Sciences, and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis. He has been working at HSE since 2015 and has accumulated 17 years of scientific and teaching experience. Gasnikov received his academic credentials from the Moscow Institute of Physics and Technology (State University). He earned his Bachelor's degree in Applied Mathematics and Physics in 2004, followed by a Master's degree in the same specialty in 2006. He completed postgraduate studies at MIPT from 2006-2007, earned his Candidate of Physical and Mathematical Sciences degree in 2007, was awarded the academic title of Associate Professor in 2011, and ultimately received his Doctor of Physical and Mathematical Sciences degree in 2016 with a dissertation on 'Efficient Numerical Methods for Finding Equilibria in Large Transport Networks.' Gasnikov's research focuses on convex optimization, mathematical modeling of traffic flows, and Markov processes. His work bridges theoretical mathematics with practical applications in optimization algorithms and transportation science. He has made significant contributions to developing efficient numerical methods for optimization problems, particularly in the context of large-scale networks and stochastic settings. His research has evolved from traditional optimization methods to more advanced techniques involving stochasticity, decentralization, and applications to machine learning problems. His publications demonstrate expertise in both theoretical analysis (establishing convergence rates, lower bounds) and practical algorithm development across multiple prestigious venues including Journal of Optimization Theory and Applications, SIAM Journal on Optimization, and top machine learning conferences like NeurIPS. Gasnikov has received recognition including a bonus for publication in international peer-reviewed journals (2019-2021) and has served as a member of the editorial board of the 'Siberian Journal of Computational Mathematics' since 2017. As a supervisor, Gasnikov has guided multiple doctoral candidates, including Alexander Ogaltsov, Titov A. A. (2023), and Tyurin A. I. (2020) for Candidate of Sciences degrees, and Dvurechensky P. E. (2020) for a Doctor of Science degree. His research has been supported by grants from the President of Russia (MD-1320.2018.1) and the Russian Foundation for Basic Research (18-31-20005 mol_a_ved). Gasnikov's work contributes significantly to the theoretical foundations of optimization algorithms that power modern artificial intelligence systems. His leadership in the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis positions him at the forefront of research connecting mathematical optimization with practical applications in machine learning and data science.

• Solid-state NMR methodology
• Protein structure and dynamics
• Biomolecular assemblies
• +5 more

Józef R. Lewandowski is a Professor of Physical Chemistry at the University of Warwick, leading the Lewandowski Group within the Department of Chemistry. His research focuses on solid-state NMR methodology and its application to studying structure-dynamics-function relationships in biomolecular systems , particularly membrane proteins , amyloid fibrils , and trans-AT polyketide synthases . He has held his position since 2019, following promotions from Assistant to Associate Professor between 2011 and 2019. His group specializes in solid-state NMR for insoluble systems atomic resolution dynamics characterization antibiotic biosynthesis mechanisms protein complex structural analysis

Carla FerreiraView profile

Researcher

• Distributed Systems
• Concurrency
• Low-code Development
• +5 more

Dr. Carla Ferreira is a Researcher at NOVA University Lisbon, Portugal. She actively contributes to academic communities as a committee member and session chair in conferences like POPL, ECOOP, and SPLASH. Her work bridges distributed systems, low-code development, and program verification. Researcher at NOVA University Lisbon Conference committee roles: POPL, ECOOP, SPLASH, MODELS, PLDI Research interests: distributed systems, low-code development, invariant verification Carla’s work focuses on ensuring correctness in distributed systems through coordination-free designs and replicated data types. She has explored low-code templates for safe application composition and automated scaling of sequential systems. Her recent contributions include consistency models for weakly consistent databases and educational approaches for teaching distributed verification with TLA+. She has authored key works on ECROs, OSTRICH, and VeriFx frameworks. Her publications span from 2016 to 2025, reflecting ongoing contributions to system design and verification. Carla Ferreira can be reached via her personal website at http://ctp.di.fct.unl.pt/~cf/ , though no direct email addresses are publicly listed in the scraped data.

Virginia HuddlestonView profile

Professor

• Conservation Ecology
• Ecotoxicology
• Physiological Ecology
• +3 more

Virginia Huddleston is a Professor in the Department of Biology at Benedictine College, where she maintains an active research program focused on conservation ecology and wildlife management. Her office is located in Westerman Hall 211, and she can be contacted at 913.360.7281. Educational Background: Ph.D. in Marine Biology from University of North Carolina Wilmington (2012) M.S. in Marine Biology from College of Charleston (2008) B.S. in Biology from Benedictine College (2004) Dr. Huddleston's research integrates conservation ecology, ecotoxicology, and physiological ecology to examine human impacts on wildlife. Her work focuses on three primary areas: Effects of energy infrastructure (wind turbines) on prairie bird populations Mercury contamination pathways in wetland ecosystems Long-term recovery monitoring of raptor species like Bald Eagles Habitat management through prescribed fire and grazing practices She emphasizes interdisciplinary approaches that combine field ecology, toxicology, and physiology to address conservation challenges. Her publication record demonstrates consistent focus on anthropogenic impacts on avian species, with recurring themes in grassland bird conservation, ecotoxicology of mercury bioaccumulation, and population responses to habitat management. Recent work shows increased emphasis on genomic applications in conservation biology. Dr. Huddleston actively mentors undergraduate researchers through projects involving: Pheasant translocation studies Pollinator conservation initiatives Long-term bird biodiversity monitoring at Benedictine Bottoms Collaborative field studies with Kansas Wildlife, Parks & Tourism She maintains research partnerships with Kansas State University on prairie-chicken ecology and federal agencies on mercury contamination in North Dakota wetlands.

Andriy SirchenkoView profile

Assistant Professor

• Econometrics
• Applied Statistics
• Data and Business Analytics
• +8 more

Andriy Sirchenko serves as Assistant Professor of Econometrics at Nyenrode Business University, where he is affiliated with the Faculty Research Center for Marketing & Supply Chain Management. Previously, he held academic positions at Maastricht University, University of Amsterdam, and Higher School of Economics in Moscow. His educational background includes a BSc and MSc in Applied Physics and Mathematics/Biophysics from Moscow Institute of Physics & Technology, an MA in Economics from the University of Iowa, and a PhD in Economics from the European University Institute in Florence. His dissertation focused on monetary policy identification and predictability. Sirchenko's research spans advanced econometric methodologies including finite mixture models, discrete-choice analysis, and time-series econometrics. His work demonstrates particular expertise in developing computational approaches for complex statistical problems involving hierarchical structures and zero-inflated data. His publication record shows consistent focus on methodological innovations in econometrics, with recent work addressing challenges in ordered probit modeling, policy interest rate analysis, and specialized techniques for count data with bounded support. These contributions appear in respected journals including Journal of Economic Dynamics & Control and The Stata Journal. Sirchenko has supervised 72 theses and secured eight research and teaching grants from external organizations. His technical contributions include developing five statistical software packages in Stata and R. He has taught 24 different courses across nine countries, with expertise in Econometrics, Statistics, Game Theory, Microeconomics, and Business Analytics. His international collaborations include work with Augsburg University and Helmut-Schmidt University in Germany, and research appointments at institutions including the University of California at San Diego and multiple central banks.

Florian Schreier-AignerView profile

Research Fellow

• Algebraic Combinatorics
• Enumerative Combinatorics
• Bijective Combinatorics
• +8 more

Florian Schreier-Aigner is a Research Fellow and PostDoc at the University of Vienna's Department of Mathematics, where he leads the FWF Stand-Alone Project Symmetric polynomials and (arrowed) monotone triangles . He is an active member of Christian Krattenthaler's combinatorics group and supervises both PhD and Master's students. His academic background includes: PhD in Mathematics, University of Vienna (2015-2019) Master in Mathematics, University of Vienna (2014-2015) Bachelor in Mathematics, University of Vienna (2010-2014) Schreier-Aigner's research focuses on algebraic, enumerative, and bijective combinatorics, with connections to representation theory, statistical physics, probability theory, and geometry. Specific interests include symmetric functions, alternating sign matrices, plane partitions, Catalan combinatorics, and probabilistic aspects of combinatorial structures. His work frequently explores connections between discrete mathematics and theoretical physics. Recent publications demonstrate a strong emphasis on combinatorial structures like alternating sign matrices, plane partitions, and symmetric functions, often employing probabilistic methods and exploring connections to representation theory. His work consistently appears in top combinatorics journals and features collaborations with international researchers. Awards and fellowships: Erwin Schrödinger Fellowship (2019-2021) He currently advises: PhD student: Julia Hörmayer (since 2024) Master's student: Christian Theiner (since 2024) He leads the FWF-funded project on symmetric polynomials and monotone triangles. Schreier-Aigner is an active member of the combinatorics research group at the University of Vienna and regularly presents at international conferences including FPSAC and Séminaire Lotharingien de Combinatoire.

Matina Donaldson-MatasciView profile

Associate Professor

• Behavioral Ecology
• Social Insects
• Collective Behavior
• +4 more

Matina Donaldson-Matasci is an Associate Professor in the Department of Biology at Harvey Mudd College, Claremont, CA. She holds a B.A. in Mathematics from Reed College and a Ph.D. in Biology from the University of Washington. Her postdoctoral training includes positions at the Max Planck Institute for Evolutionary Anthropology and the University of Arizona. Teaching Portfolio: She leads diverse courses including Introductory Biology, Biostatistics, Experimental Ecology Laboratory, Computational Biology, and specialized topics in Behavioral Ecology and Game Theory. Courses feature hands-on laboratory components and interdisciplinary approaches connecting biology with mathematics. Research Focus: As director of the HMC Bee Lab, she investigates collective behavior in social insects using field experiments (honey bees), lab studies (arboreal ants), mathematical modeling, and computational simulations. Key research themes include: Communication systems in foraging and defense Emergent network formation in ant colonies Adaptive responses to environmental variability Evolutionary optimization of group decision-making Current projects involve drone-based floral mapping and ant transportation network analysis. Awards: Received Honorable Mention for the ASN Student Paper Award (2013) for work on information reliability in ecological systems. Student Advising: Actively mentors undergraduate and high school researchers in experimental design and computational modeling, with several appearing as co-authors on publications. Lab projects emphasize field ecology techniques and theoretical frameworks.

Adam KaminskiView profile

Professor

• Condensed Matter Physics
• Quantum Materials
• Angle Resolved Photoemission Spectroscopy (ARPES)
• +5 more

Adam Kaminski is the Harmon-Ye Professor of Physics at Iowa State University and a Senior Physicist at Ames Laboratory. He holds a M.Sc. from Maria Curie-Sklodowska University (1992) and a Ph.D. from the University of Illinois at Chicago (2001). His research focuses on quantum materials using Angle-Resolved Photoemission Spectroscopy (ARPES), with specialties in unconventional superconductivity, topological insulators, Weyl semimetals, and magnetic materials. He leads the ARPES group, developing advanced instrumentation for material characterization. Key honors include the 2013 American Physical Society Fellowship and the 2024 Harmon-Ye Professorship. Education: M.Sc., Physics, Maria Curie-Sklodowska University (1992) Ph.D., Physics, University of Illinois at Chicago (2001) Research Interests: Electronic structure of novel quantum materials Fermi arcs and topological phases Magnetic ordering effects in superconductors Development of ARPES techniques Scientific Contributions: Published over 100 papers on topological materials and superconductivity Discovered Fermi arcs in antiferromagnetic systems Advanced understanding of Weyl semimetals and Dirac node arcs Awards: Early Career Achievement Award (Iowa State, 2009) APS Fellowship (2013) Harmon-Ye Professorship (2024) Labs/Teams: Leads the ARPES group at Ames Laboratory, collaborating on crystal growth and advanced material synthesis.