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Stanislav JabinskiView profile

Researcher

• Paleoenvironmental Reconstruction
• Stable Isotope Probing (SIP)
• Biomarker Analysis
• +7 more

Stanislav Jabinski is a Postdoc at the Leibniz Institute for Baltic Sea Research (IOW) in Rostock, Germany, where he works in the Research Group Microbial Plankton and Fungal Ecology under Dr. Isabell Klawonn. His research focuses on paleoenvironmental reconstruction and microbial ecology using biomarker and stable isotope analyses. Ph.D. in Ecosystem Biology and Ecology, University of South Bohemia (2024) M.Sc. in International Marine Geosciences, University of Bremen (2019) B.Sc. in Geosciences, University of Bremen (2016) His expertise spans microbial membrane biomarker analysis, stable isotope probing (SIP), and method development for ultra-high-resolution mass spectrometry (GC-FID/MS/IRMS, LC-QToF-MS, FTICR-MS). He investigates carbon turnover mechanisms in soil and aquatic ecosystems, integrating δ¹³C and δ²H isotopes to quantify microbial and fungal activity. Jabinski’s publications highlight applications of SIP in fungal metabolism, paleovegetation reconstruction, and soil organic matter dynamics. His work bridges analytical chemistry, biogeochemistry, and microbial ecology, emphasizing lipid biomarker innovation. He is affiliated with the Microbial Plankton and Fungal Ecology working group at IOW, collaborating on biogeochemical cycles and ecosystem fluxes.

Maria Chiara BrambillaView profile

Associate Professor

• Algebraic Geometry
• Commutative Algebra
• Moduli Spaces
• +3 more

Maria Chiara Brambilla is an Associate Professor in the Department of Industrial Engineering and Mathematical Sciences (DIISM) at Università Politecnica delle Marche, Faculty of Engineering. Her research specializes in algebraic geometry, with emphasis on moduli spaces, vector bundles, and secant varieties. She investigates fundamental structures in birational geometry and interpolation theory, contributing to advancements in the classification of geometric objects and their deformations. Research Focus: Her work explores: Birational properties of Mori dream spaces and blowups Defectivity and non-defectivity in Segre-Veronese varieties Terracini loci and their minimality conditions Geometric invariants of flag manifolds and hypersurfaces Algebraic boundaries in tensor rank theory Publication Trends: Recent articles (2021-2025) demonstrate deepening work on Terracini loci, twistor geometry, and movable divisors, frequently employing combinatorial and deformation-theoretic methods to resolve problems in higher-dimensional algebraic geometry.

Ryan LeducView profile

Associate Professor

• Discrete-Event Systems
• Supervisory Control
• Fault Tolerance
• +5 more

Dr. Ryan Leduc is an Associate Professor at the Department of Computing and Software within McMaster University's Faculty of Engineering . He holds degrees of B.Eng (Victoria) , M.A.Sc. (Toronto) , and Ph.D. (Toronto) . His research focuses on Discrete-Event Systems (DES) , particularly in Supervisory Control , Hierarchical Structures , Concurrency , and Formal Verification of software and hardware. He has developed the DESpot software tool for hierarchical DES research.

Raffaella MulasView profile

Assistant Professor

• Spectral Theory of Graphs and Hypergraphs
• Discrete Laplacians
• Non-backtracking Operators
• +7 more

Raffaella Mulas is an Assistant Professor in the Department of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. She previously served as a Group Leader and Minerva Fast Track Fellow at the Max Planck Institute for Mathematics in the Sciences, where she maintains an ongoing affiliation. Her research lies at the intersection of spectral graph theory, discrete mathematics, and network science. Research Interests: Her work focuses on the spectral theory of graphs and hypergraphs, particularly the properties of discrete Laplacians and non-backtracking operators. She investigates extremal combinatorics problems such as graph coloring and the Turán problem, often applying spectral methods to derive sharp bounds. Her research has strong applications in modeling and analyzing complex networks. Recent Research Trends: Analysis of the 15 most recent publications reveals a consistent focus on spectral characterizations of graphs and hypergraphs, including signed and complex unit hypergraphs. She frequently studies the normalized Laplacian and its extremal eigenvalues, develops non-backtracking operators, and explores measure-theoretic and geometric representations of networks. A strong thread connects spectral bounds to combinatorial invariants like chromatic number. VU Startpremie Grant Elected Member, European Mathematical Society Young Academy (EMYA) Elected Member, Elisabeth-Schiemann-Kolleg, Max Planck Society Minerva Fast Track Fellow, Max Planck Institute Advising and Grants: While no formal students are listed, she is an active researcher with significant grant funding, notably the VU Startpremie Grant. She collaborates internationally and supervises research projects in spectral graph theory and network analysis. Her affiliation with both VU Amsterdam and MPI-MiS enables broad academic mentorship and collaborative supervision. Labs and Research Groups: Raffaella Mulas leads research within the Mathematics Department at VU Amsterdam and is affiliated with the research group at the Max Planck Institute for Mathematics in the Sciences. Her work contributes to advancing theoretical foundations in discrete mathematics with applications in data science and network modeling.

Martin Skovgaard AndersenView profile

Associate Professor

• Optimization
• Signal Processing
• Machine Learning
• +12 more

Martin Skovgaard Andersen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he specializes in Scientific Computing. His research integrates numerical methods, optimization, and machine learning to solve complex computational problems in engineering and data science. Education: Ph.D., University of California, Los Angeles (2006–2011) M.Sc., Aalborg Universitet (2001–2006) Postdoc, Linköping University, Sweden (2011–2012) His research interests include optimization (particularly conic and stochastic programming), numerical algorithms, signal processing, system identification, and fast solvers for integral equations. He works extensively on matrix analysis, regularization, and low-rank approximations, contributing to both theoretical and applied advancements in computational mathematics. The recent publications highlight a strong trend in developing efficient numerical algorithms for large-scale optimization and solving integral equations. His work emphasizes preconditioning, matrix truncation, and Bayesian inversion, with applications in electromagnetics, structural health monitoring, and network identification. There is a consistent focus on improving computational efficiency and scalability of solvers. Scientific Awards: No specific awards mentioned in the provided text. Andersen actively supervises PhD students and leads multiple research projects, including those on non-symmetric conic optimization, data-sparse models, and fast direct solvers. He is the Principal Investigator (PI) on projects related to physics-informed structural health assessment. His collaborative network spans Denmark and international institutions, particularly in computational mathematics and engineering. Labs and Research Teams: He is affiliated with the Scientific Computing section at DTU, which focuses on high-performance computing, numerical algorithms, and mathematical modeling. His work is embedded in a collaborative environment involving researchers in optimization, control theory, and computational electromagnetics.

Thorsten KochView profile

Professor

• Mathematical Optimization
• High-Performance Computing
• Artificial Intelligence
• +4 more

Thorsten Koch is a Professor for Software and Algorithms for Discrete Optimization at Technische Universität Berlin , with multiple leadership roles including Head of the Applied Algorithmic Intelligence Methods (A²IM) , Digital Data and Information for Society, Science, and Culture (D²IS²C) , Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) , and Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) . Based at Zuse Institute Berlin and affiliated with TU Berlin's Institute for Mathematics, he focuses on integrating mathematical optimization with high-performance computing and artificial intelligence to solve complex real-world problems. Research Pillars : Mathematical optimization algorithms Quantum computing applications AI/ML integration in decision systems Energy systems optimization Scientific software development Leadership Roles : Head of Applied Algorithmic Intelligence Methods (A²IM) Head of Digital Data & Information for Society, Science, and Culture (D²IS²C) Head of Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) Head of Forschungs- und Kompetenzzentrum Digitalisierung Berlin (digiS) Key Collaborations : Working with IBM Quantum on quantum optimization Collaborating across institutions for energy system modeling Developing open-source optimization tools like SCIP Contributing to digital library infrastructure Recent Research Trends : Quantum optimization benchmarking Machine learning-aided optimization Multi-objective decision frameworks Energy infrastructure optimization Adaptive algorithm design CO2 network modeling Impact : Advancing hybrid optimization methods Developing open-source tools for scientific computing Building digital infrastructures for libraries and research Exploring quantum-classical algorithm synergies

Qianqian TongView profile

Assistant Professor

• Stochastic Optimization
• Sparse Learning
• Federated Learning
• +4 more

Qianqian Tong is an Assistant Professor in the Computer Science department at the University of North Carolina at Greensboro. Her research focuses on stochastic optimizations, sparse learning, federated learning, and privacy-preserving machine learning algorithms. She has developed novel methods for efficient optimization in deep learning and federated learning frameworks, including communication-efficient distributed algorithms and decentralized systems. Education: Ph.D. Computer Science and Engineering, University of Connecticut M.S. Computational Mathematics, Zhengzhou University B.S. Mathematics, Zhengzhou University Research interests include designing algorithms for sparse learning, federated learning with privacy guarantees, and applying deep graph learning to drug discovery. Recent projects involve tensor-based models for multidimensional data analysis and improving convergence in ADAM optimization. Her publications span optimization theory, federated learning systems, and molecular modeling applications. Though no awards are listed, her work demonstrates significant contributions to efficient machine learning methodologies. Teaching includes advanced courses on data science and computer science foundations. No lab affiliations or grant details are explicitly mentioned in the provided text.

Dimitris MaroulisView profile

Professor

• Data Acquisition
• Real-Time Systems
• Pattern Recognition
• +6 more

Dimitris Maroulis is a Professor at the Department of Informatics and Telecommunications, University of Athens, leading the Real-Time Systems and Image Analysis Lab (RTS-image). With over 20 years of experience in data acquisition and real-time systems, and 15 years in image/signal analysis, he collaborates extensively with Greek and European hospitals in biomedical informatics. He has led 5 R&D projects and authored 150+ papers with 1400+ citations. University of Athens: Professor (2000–present) Meudon Observatory: Research Fellow (3 years) & Long-term Collaborator (10+ years) Research Interests focus on real-time systems , image/signal processing , and biomedical applications . Key areas include automated segmentation of proteomic images, noise removal methods, and stereo image coding. His 15 most recent publications (2003–2012) span 3D imaging , medical image analysis , and biomedical informatics , with sub-fields like autostereoscopic displays, wavelet-based coding, and computer-aided diagnosis. Awards : Best Paper Award (2012: Integral Image Analysis) Projects include European and national R&D initiatives in image analysis and real-time systems. Labs : RTS-image Lab develops methodologies for biomedical and proteomic applications.

Andrej BogdanovView profile

Professor

• Cryptography
• Computational Complexity
• Pseudorandomness
• +6 more

Andrej Bogdanov is a Professor at the University of Ottawa in the School of Electrical Engineering and Computer Science . He earned his B.S. and M.Eng. from MIT and Ph.D. from UC Berkeley . Before joining Ottawa, he held positions at the Chinese University of Hong Kong , ITCS (Tsinghua) , DIMACS (Rutgers) , and the Institute for Advanced Study . He has served as a Visiting Professor at the Tokyo Institute of Technology (2013) and the Simons Institute (2017, 2021). Research Interests : Computational complexity, cryptography foundations, pseudorandomness, one-way functions, property testing, quantum algorithms, and sublinear-time algorithms. Teaching : Courses on Discrete Mathematics, Great Algorithms, Computational Complexity, and Cryptography at University of Ottawa, Chinese University of Hong Kong, and Rutgers University. Publications : 15+ recent works in TCC , CRYPTO , ICALP , RANDOM , and journals like Journal of Cryptology and Theory of Computing . Service : Program co-chair for SAC 2026 , and committee member for major conferences including CRYPTO , TCC , Eurocrypt , and FOCS . Advising : 12 current and former Ph.D./M.Phil. students, with postdoctoral advisees at institutions like IIT Palakkad and Academia Sinica . His work bridges theoretical computer science with applications in cryptography, quantum computing, and network security.

• Fluid Dynamics
• Hydrogen Engines
• Combustion
• +4 more

Elia Distaso is an Assistant Professor at the Department of Mechanics, Mathematics & Management, Politecnico di Bari, Italy. His research focuses on fluid dynamics, hydrogen combustion, and computational modeling. University: Politecnico di Bari Department: Mechanics, Mathematics & Management Academic Rank: Assistant Professor Email: elia.distaso@poliba.it His work spans hydrogen engines , CFD simulations , and hydraulic systems , with recent publications addressing auto-ignition mechanisms, cavitation phenomena, and sustainable aviation technologies. He specializes in leveraging numerical methods for combustion and fluid flow analysis. His 15 most recent publications highlight trends in computational fluid dynamics, including boundary condition modeling, pressure-velocity coupling, and OpenFOAM® applications. Key subfields include hydrogen combustion dynamics , lubricant oil reactivity , cryogenic heat exchanger design , and piezohydraulic pump analysis .

• Ecology
• Evolutionary Biology
• Population Genetics
• +3 more

Kalle Matias Leppälä is a postdoctoral researcher at the University of Helsinki's Faculty of Biological and Environmental Sciences, affiliated with the Organismal and Evolutionary Biology Research Programme. His work focuses on population genetics, statistical modeling, and evolutionary biology through computational approaches. Recent research includes Bayesian Gaussian process models for variance component estimation, generalized D-statistic methods for admixture analysis, and mathematical formulations for odds ratio calculations. His computational work intersects with ecology and genomics. He participates in the TreeBio Center of Excellence (2024–2026), funded by the Research Council of Finland, exploring tree biology and population dynamics. Current projects emphasize genetic admixture patterns in Arctic populations and statistical methodology development.

• Description Logics
• Ontologies
• Automated Reasoning
• +7 more

Professor Boris Konev is a faculty member at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He holds the academic rank of Professor in Computer Science. Description Logics Ontologies Automated Reasoning Temporal Logic Formal Verification Encrypted Database Applications His recent research focuses on temporal queries mediated by ontologies, knowledge evaluation agents using large language models, and semantic modularity in description logics. Key sub-fields include LLM applications, encrypted databases, and formal verification techniques. He has contributed to software development projects and industry partnerships, including design of equine simulators and online services with Racewood Limited. Current teaching includes the Foundations of Computer Science module (COMP109). Professional roles include guest editorships for AI Communications and program committee membership for the European Conference on Logics for Artificial Intelligence (JELIA).

• Animal Behavior
• Behavioral Neuroscience
• Big Data
• +23 more

Dan Cox serves as Dean of Natural Sciences in The College of Liberal Arts and Sciences and Professor in the School of Life Sciences at Arizona State University. He holds a B.S. in Biology from Wake Forest University and a Ph.D. in Cell Biology from Duke University, where he was an NIH pre-doctoral fellow. His postdoctoral training included a Jane Coffin Childs Fellowship and HHMI Research Associate position at UCSF. His educational trajectory includes: B.S. magna cum laude with honors in Biology, Wake Forest University (1992) Ph.D. in Cell Biology, Duke University Medical Center (1999) Postdoctoral training: Jane Coffin Childs Fellow & HHMI Research Associate, UCSF (2000-2004) Cox leads an internationally recognized neuroscience research program focused on neuronal diversity, circuit construction, and neural mechanisms of behavior—particularly pain perception and sensory neuropathies. His expertise spans neurobiology, cell biology, genetics, and computational modeling, with emphasis on multimodal sensory processing and brain plasticity. His lab has maintained continuous NIH funding for 20 years. His recent publications reveal dual research thrusts: fluid dynamics studies on drag reduction in pipe flows (examining vortex dynamics, Reynolds number effects, and wall oscillation techniques) and genetic engineering work including CRISPR-Cas9 delivery systems for immune system gene screening. This combination reflects interdisciplinary collaboration across engineering and biological domains. Key recognitions include: NIH pre-doctoral fellowship Jane Coffin Childs Fellowship for Medical Research HHMI Research Associate appointment Cox demonstrates exceptional commitment to mentoring across career stages and advancing student success through innovative pedagogies. His leadership extends to directing research centers, imaging facilities, and graduate programs—including roles as Director of the Center for Neuromics and Neuroscience Institute at Georgia State University. His NIH-funded research supports extensive interdisciplinary collaboration and training initiatives. Throughout his career, Cox has cultivated collaborative research environments through leadership in core facilities and interdisciplinary clusters, notably directing the 2CI Neurogenomics Fellowship and Brains & Behavior initiatives. His current deanship emphasizes cross-college research integration within ASU's liberal arts framework.

Martin SchmidtView profile

Professor

• Mathematical Optimization
• Energy Systems
• Mixed-Integer Nonlinear Optimization
• +3 more

Martin Schmidt is a Full Professor (W3) for Nonlinear Optimization at the Department of Mathematics, Trier University, since 2019. He has held leadership roles in research training groups and international committees, focusing on mathematical modeling and optimization of energy systems, gas networks, and market equilibria. His work bridges mixed-integer nonlinear optimization , bilevel optimization , and robust methods with applications to real-world energy challenges. Education: PhD in Mathematics (2013), Diplom in Mathematics (2008), both from Leibniz University Hannover. Editorial Roles: Editorial Board member of Journal of Optimization Theory and Applications and Optimization Letters , Associate Editor for OR Spectrum and EURO Journal on Computational Optimization . His research integrates complex physical systems (e.g., gas transmission networks) with game-theoretic models to analyze energy markets. Recent publications emphasize robust optimization , decomposition techniques , and machine learning integration in bilevel frameworks. Awards highlight his contributions to gas market feasibility , linear bilevel optimization , and practical applications in energy systems. Collaborations span institutions like Universidad Zaragoza, Sapienza University, and Forschungszentrum Jülich.

Ioan TodincaView profile

Professor

• Graph Algorithms
• Theoretical Computer Science
• Exact Algorithms
• +6 more

Ioan Todinca is a Professor of Computer Science at the University of Orléans, France, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades, with significant contributions to theoretical computer science, particularly in graph algorithms and distributed computing. Faculty of Science, University of Orléans LIFO Research Laboratory Member of Institut thématique pluridisciplinaire Modélisation, Systèmes, Langages (since 2014) Former director of MIPTIS doctoral school (2012-2014) Former head of Computer Science degree program (2007-2011) Former leader of LIFO Graphs, Algorithms and Computational Models team (2008-2012) Todinca's research focuses primarily on graph algorithms, with expertise in exact algorithms (moderately exponential), parameterized algorithms, and algorithms for specific graph classes. He has made significant contributions to techniques involving tree decompositions, treewidth, minimal separators, and potential maximal cliques. More recently, his work has expanded into distributed algorithms, especially in communication-constrained models like the broadcast congested clique. His research bridges theoretical foundations with practical algorithmic approaches for NP-hard problems. The analysis of his recent publications reveals a strong trend toward distributed computing problems, particularly in congested network models. His work spans from fundamental graph theory problems (cycle detection, graph modification) to applications in quantum computing and model checking. The consistent focus on communication complexity, verification, and efficient algorithms across different computational models demonstrates his ability to adapt theoretical computer science principles to emerging computational paradigms. Todinca has supervised numerous PhD students and has been actively involved in the theoretical computer science community through conference organization and editorial work. His publications appear consistently in top-tier venues including SIAM Journal on Computing, Algorithmica, and proceedings of major conferences like STACS, WG, and DISC. Teaching responsibilities include algorithms, graph theory, and discrete structures for undergraduate and graduate students, with previous experience teaching databases, programming, and software engineering. His educational materials are hosted on the university's Celene platform.