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Frieder LadischView profile

Associate Professor

• Quantum Physics
• Quantum Information
• Photonics
• +2 more

PD Dr. Frieder Ladisch serves as an Associate Professor at the Institute for Mathematics within the Faculty of Mathematics and Natural Sciences at the University of Rostock, Germany. His institutional affiliation includes membership in the Geometry working group (AG Geometry), with office location at Ulmenstraße 69, Haus 3, Room 231, and contact via university email and phone. His research program centers on Quantum Physics and Quantum Information , with specialized expertise in: Non-Hermitian quantum systems and PT-symmetry phenomena Topological photonic implementations including Su-Schrieffer-Heeger lattices Non-Abelian geometric phases and quantum holonomy Quantum random number generation using solid-state photon sources Experimental quantum optics with integrated photonic circuits Analysis of his 15 most recent publications (2019-2025) reveals consistent focus on PT-symmetry breaking experiments, quantum walk implementations in topological photonic structures, and quantum random number generation using hexagonal boron nitride emitters. His work bridges theoretical non-Hermitian quantum mechanics with practical photonic implementations. Dr. Ladisch operates within the Geometry working group at Rostock's Institute for Mathematics, where his research integrates mathematical geometry concepts with quantum information processing through photonic chip technologies and single-photon source applications.

Klas MarkströmView profile

Professor

• Combinatorics
• Graph Theory
• Hypergraphs
• +3 more

Klas Markström is a Professor at the Department of Mathematics and Mathematical Statistics, Umeå University. He holds academic qualifications as a Docent and a Recognised University Teacher in Mathematics. His primary research interests include combinatorics, graph theory, hypergraphs, computational combinatorics, linear algebra, and mathematical physics. He utilizes Swedish supercomputing resources, including those at HPC2N, for his research. Markström has advised PhD students Lan Anh Pham (2019) and Joel Larsson (2018). His postdoctoral collaborators include Jakub Sliacan, Fei Song, and Victor Falgas-Ravry. His work spans theoretical contributions to combinatorics, computational methods, and applications in statistical mechanics and algorithm design. Notable research trends include explorations of Condorcet domains in social choice theory, extremal graph/hypergraph problems, and algorithmic solutions for SAT problems. His software contributions include the Umsat SAT-solver and GrafPack, a Mathematica package for graph theory. He has published extensively on topics such as Turán hypergraphs, covering designs, and computational techniques for graph enumeration. Collaborations involve interdisciplinary projects with physicists and computer scientists, reflecting his engagement with both pure and applied mathematics. His research often bridges combinatorial theory with computational experimentation, yielding impactful results in discrete mathematics and beyond.

Yu ChengView profile

Professor

• Dynamic Treatment Regimes
• SMART (Sequential Multiple Assignment Randomized Trials)
• Causal Inference
• +6 more

Yu Cheng is a Professor and Chair of the Department of Statistics at the University of Pittsburgh, affiliated with the Dietrich School. She holds secondary appointments in Biostatistics and Psychiatry. Her research focuses on dynamic treatment regimes, SMART trials, causal inference, and multiple endpoints analysis. She leads a PCORI-funded method grant on SMART studies and co-investigates projects on cardiovascular disease and maternal/child health outcomes. Her awards include ASA Fellow (2024) and Pittsburgh Chapter Statistician of the Year (2020). Dr. Cheng earned her PhD in Statistics from the University of Wisconsin-Madison (2006), following MS (National University of Singapore, 2001) and BS (University of Science and Technology of China, 1999) degrees. She has held leadership roles including ASA LiDS Section Treasurer (2021–2023), Department Chair (2014–present), and Director of Graduate Studies (2016). Her research integrates methodological innovation with clinical collaboration, addressing topics like HIV, smoking cessation, lupus, depression, and cystic fibrosis. Her 150+ publications span journals like Biometrics , Journal of the American Statistical Association , and Biostatistics . Teaching includes courses in Survival Analysis, Probability, and Statistical Consulting. She actively mentors students through supervised consulting programs.

Javier CabreraView profile

Professor

• Biostatistics
• Data Mining
• Clinical Trial Methodology
• +5 more

Javier Cabrera is a Professor in the Department of Statistics at Rutgers University with a joint affiliation at the Cardiovascular Institute. He holds a Ph.D. from Princeton University and is recognized as a Fulbright Scholar. His office is located at Hill Center 471, 110 Frelinghuysen Road, Piscataway, NJ. His research focuses on: Biostatistics and clinical trial methodology Data mining for functional genomics and DNA/protein arrays Statistical computing, machine vision, and high-dimensional data analysis Cardiovascular health applications using statistical modeling Recent publications (2022-2025) demonstrate strong emphasis on: Novel statistical methods for medical/biological data Machine learning applications in diagnostics and genomics Clinical risk modeling and epidemiological studies Big data reduction techniques and computational efficiency He frequently publishes in interdisciplinary collaborations at the intersection of statistics, biomedicine, and computational science. Awards: Fulbright Scholar He collaborates extensively with the Cardiovascular Institute, contributing statistical expertise to research on cardiovascular outcomes, disease risk modeling, and clinical data analysis.

• Quantum Optics
• Quantum Information
• Solid-State Quantum Emitters
• +2 more

Vikas Remesh is a Researcher in the Department of Experimental Physics at the University of Innsbruck, Austria, actively contributing to quantum optics and quantum information science. His work focuses on quantum emitters, particularly semiconductor quantum dots, for developing advanced quantum light sources and control techniques essential for quantum computing and secure communication. His research centers on manipulating quantum states in solid-state systems, with emphasis on chirped-pulse control, dark-state engineering, and high-dimensional entanglement. Key innovations include the SUPER excitation scheme for collective state preparation and methods for high-purity single-photon generation in 2D materials like WSe 2 . His approaches bridge theoretical models with experimental implementations to overcome coherence and stability challenges in quantum photonics. Analysis of his 15 most recent publications (2022-2025) reveals a dominant focus on quantum dot control mechanisms, particularly using chirped pulses and magnetic fields for adiabatic rapid passage. Recurring themes include single-photon source optimization for quantum cryptography, photon-number-encoded entanglement, and spectral engineering of quantum emitters. His work consistently targets practical quantum technology applications while addressing fundamental coherence limitations. No scientific awards or fellowships were documented in available sources. His research is conducted within the quantum optics group led by Univ.-Prof. Hanns-Christoph Nägerl, likely supported by institutional and national funding frameworks typical for European quantum research initiatives. Remesh operates within the University of Innsbruck's Department of Experimental Physics, part of Austria's leading quantum research ecosystem. His work intersects with the institution's strengths in ultracold atoms and quantum simulation, contributing to experimental platforms for quantum information processing and quantum communication protocols.

Weihua GuanView profile

Professor

• Statistical Genetics
• Genomics
• Chronic Diseases
• +3 more

Weihua Guan, PhD, is a Professor at the University of Minnesota's School of Public Health , specifically in the Division of Biostatistics & Health Data Science. With a career focused on statistical genetics and genomic data analysis , she develops methodologies for high-throughput genetic studies. Education : PhD in Biostatistics (University of Michigan, 2010), MS in Statistics (Texas A&M University), BS in Chemistry (Peking University) Her research centers on genetic determinants of complex diseases , specializing in DNA methylation analysis , proteomic biomarker discovery , and multi-omics integration . Recent work includes proteomic aging clocks and inflammation-related epigenetic signatures. Key publication trends show expertise in cardio-respiratory disease genetics , psychiatric disorder GWAS , and high-dimensional data methods . She contributes to genomic epidemiology through large-scale consortia collaborations. Scientific Honors : Member of Delta Omega Honorary Society in Public Health

• Combinatorics
• Combinatorial Design
• Latin Squares
• +5 more

Ian Wanless is a Research Professor at the School of Mathematics, Monash University. He specializes in combinatorics, with a focus on Latin squares, graph theory, and matrix permanents. His work bridges theoretical mathematics and applications in information technology, such as coding and experimental design. Wanless leads collaborative projects funded by the Australian Research Council, including studies on combinatorial structures and hypergraph matchings. He has authored over 120 peer-reviewed articles, with recent work addressing quantum states, Latin square properties, and quasigroup isomorphisms. Key Awards: Australian Mathematical Society 2009 Medal Collaborations: Global partnerships with institutions in the US, Europe, and Asia His research emphasizes universal solutions to combinatorial puzzles, such as Sudoku and the four-color theorem, while prioritizing theoretical rigor over direct applications.

Darko StefanovicView profile

Professor

• Programming Languages
• Molecular Computing
• Scientific Simulations
• +3 more

Darko Stefanovic is a Professor of Computer Science (and courtesy faculty in Chemical & Biological Engineering) at the University of New Mexico. He has been continuously active in teaching and research since at least 2000, with a focus on programming languages, molecular computing, and scientific simulations. University: University of New Mexico Department: Department of Computer Science Academic Rank: Professor His research interests span molecular computing , DNA nanotechnology , and synthetic biology , with recent publications exploring applications in artificial β-cells , heterochiral translators , and reservoir computing with chemical systems . Many publications involve collaborations with researchers in biochemistry, nanotechnology, and systems biology. Teaching activities include numerous offerings of courses such as Compiler Construction , Programming Paradigms , Software Foundations , and Algorithms and Data Structures at both undergraduate and graduate levels. Course materials often emphasize functional programming (e.g., Standard ML), λ-calculus, and logic programming implementations. Recent publications (2025–2019) demonstrate ongoing work in molecular robotics , DNA-based computation , and synthetic cell engineering . These works frequently appear in venues related to biomolecular computing , nanotechnology , and synthetic biology . Teaching Career spans multiple decades at UNM, covering topics from foundational programming to advanced compiler construction. Course materials show emphasis on formal methods, programming language theory, and computational models, including implementations of λ-calculus interpreters and Prolog systems.

Dietmar WeinmannView profile

Researcher

• Theoretical Solid State Physics
• Quantum Effects in Electronic Properties
• Mesoscopic Physics
• +1 more

Dietmar Weinmann is a Senior Researcher at the CNRS (Centre National de la Recherche Scientifique) affiliated with the IPCMS (Institut de Physique et Chimie des Matériaux de Strasbourg) and the University of Strasbourg. His work focuses on theoretical solid-state physics, particularly quantum effects in electronic properties, mesoscopic physics, and quantum transport phenomena. His research explores non-local heating in quantum thermoelectrics, scanning gate microscopy applications in graphene and semiconductor heterostructures, power dissipation asymmetry in quantum point contacts, and orbital magnetization mechanisms in mesoscopic systems. Key themes include electron correlations, spin-orbit interactions, and disorder effects in nanoscale devices. Scientific awards include the Marie Curie Fellowship during his postdoctoral work at SPEC Saclay. He teaches an elective course on Electronics for Quantum Science and Technology at the University of Strasbourg. As a member of the Mesoscopic Quantum Physics team, his research combines theoretical modeling with experimental collaborations on quantum transport imaging and inverse problem solving via machine learning.

Anton KlimovskyView profile

Senior Lecturer

• Probability Theory
• Stochastic Processes
• Statistical Mechanics of Disordered Systems
• +7 more

Anton Klimovsky is a Senior Lecturer at the University of Würzburg since 2022, affiliated with the Applied Stochastics department. His career includes visiting professorships at the University of Stuttgart (2022) and the University of Bonn (2014), along with lecturing at the University of Duisburg-Essen (2013-2022). Education : Habilitation (2019), Ph.D. in Mathematics (2008, TU Berlin, advisor: Anton Bovier), M.Sc. in Applied Mathematics (2002, Kharkiv National University, advisor: Mariya Shcherbina). Klimovsky’s research focuses on stochastic models of complex systems , including statistical mechanics of disordered systems , range-free spin glasses , and stochastic processes on evolving networks . His work bridges high-level heuristic ideas from statistical physics (e.g., cavity method, renormalization group) with rigorous probabilistic techniques. The 15 most recent articles highlight his expertise in disordered systems , hierarchical structures , population genetics , and network dynamics . Key trends include analysis of phase transitions in branching Brownian motion, renormalization of Lambda-Cannings processes, and Gaussian fluctuation theory in random energy models. Scientific awards include the Marie Curie Intra-European Fellowship (2010-2012) DFG Ph.D. Fellowship (2003-2005) National Academy of Sciences of Ukraine Ph.D. Fellowship (2002-2003) He has held visiting positions at Leiden University , EURANDOM , and Technische Universität Berlin , and has taught courses on Stochastic Processes , Markov Chains , and Spin Glasses . His grants include DFG funding for stochastic processes on evolving networks (2019-2024) and collaborative projects on hierarchical self-organization (2020-2026).

Laxman DhulipalaView profile

Assistant Professor

• Parallel Algorithms
• High-Performance Computing
• Graph Algorithms
• +5 more

Laxman Dhulipala is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, and a research scientist at Google Research. He holds a Ph.D. from Carnegie Mellon University, advised by Guy Blelloch, and was a postdoc at MIT under Julian Shun. His research focuses on scalable parallel algorithms, high-performance graph processing, and models of computation for emerging hardware. Dhulipala has contributed to parallel graph clustering, dynamic graph algorithms, and practical implementations of parallel computational geometry. Affiliations: University of Maryland, College Park (Department of Computer Science) UMIACS (University of Maryland Institute for Advanced Computer Studies) Google Research (Graph Mining team) Education: Ph.D. in Computer Science, Carnegie Mellon University (2020) B.S. in Computer Science, Carnegie Mellon University (2014), with University Honors and Phi Beta Kappa. Research Interests: Efficient parallel algorithms for static, dynamic, and streaming graphs; parallel clustering techniques; models of computation for emerging hardware; scalable static/dynamic/streaming graph algorithms. His work bridges theory and practice, emphasizing algorithm design, system implementation, and performance optimization. Awards and Honors: 2024 ACM Paris Kanellakis Theory and Practice Award 2023 Allen Newell Award for Research Excellence 2022 Best Paper at SPAA 2019 Distinguished Paper at PLDI 2018 Best Paper at SPAA Grants: NSF SHF: REU Site (Co-PI) NSF SHF: Scalable Graph-Based Clustering (PI) NSF SATC: Differential Privacy in Graph Mining (Co-PI) Teaching: Recent courses include CMSC858N (Scalable Parallel Algorithms), CMSC451 (Design and Analysis of Algorithms), and workshops on parallel computing and graph algorithms. Labs and Teams: He leads research in parallel graph algorithms and systems at the University of Maryland, collaborating with Google Research and MIT on projects like the Graph-Based Benchmark Suite (GBBS) and ParGeo library.

Deena FrancisView profile

Assistant Professor

• Digital Twins
• Machine Learning
• Smart Manufacturing
• +3 more

Deena Francis is an Assistant Professor at the Department of Engineering Technology and Didactics within the Energy Technology and Computer Science school at the Technical University of Denmark (DTU). Her research focuses on digital twins, machine learning applications in smart manufacturing and precision livestock farming, and sensor technology for chemical detection. She leads and participates in high-impact projects such as SEAL (Seabed Environmental Analyzer for Methane Leakage) and Machine Learning for Beehives Monitoring , addressing environmental and agricultural sustainability challenges. Key research areas include reinforcement learning for digital twin systems, dimensionality reduction techniques, and data-driven decision support in agriculture. Her work bridges theoretical advancements in kernel methods and explicit feature maps with practical applications in manufacturing optimization and environmental sensing. Francis has collaborated internationally and contributed to over 15 peer-reviewed publications, with a focus on smart systems and sensor-based solutions. Education: Not explicitly stated in the text. Labs/Teams: Involved in the InnoTech TaskForce , focusing on green transition technologies and data-driven innovation. Her advisory role includes supervising PhD student S. Gopalakrishnan on metal-organic framework arrays for liquid-phase sensing. Francis’s projects often emphasize interdisciplinary collaboration, combining engineering, computer science, and environmental science to address global challenges.

Petros ValettasView profile

Associate Professor

• Convex Geometry
• Functional Analysis
• Probability Theory
• +4 more

Petros Valettas is an Associate Professor at the University of Missouri, holding dual appointments in the Department of Mathematics and the Department of Electrical Engineering and Computer Science. His research focuses on Asymptotic Convex Geometry, Geometric Functional Analysis, High-Dimensional Probability, and Probabilistic Methods in Analysis and Geometry. He has taught advanced courses such as High-Dimensional Probability, Analysis of Boolean Functions, and Advanced Calculus I, alongside foundational courses like Discrete Mathematical Structures and Calculus I/II. Dr. Valettas’ work explores the interplay between probability, geometry, and functional analysis, with contributions to topics like concentration inequalities, convex body properties, and stochastic processes. His recent publications address areas such as Gaussian convex bodies, hypercontractivity in normed spaces, and decomposition methods for high-dimensional random arrays. He has authored a monograph, Geometry of Isotropic Convex Bodies , and maintains an active role in academic seminars and curriculum development. His teaching spans undergraduate and graduate levels, emphasizing rigorous mathematical foundations and modern applications. Collaborations with co-authors like Paouris and Giannopoulos highlight his engagement with leading figures in geometric analysis. No scientific awards are explicitly mentioned, though his prolific publication record underscores sustained academic excellence.

Yuri GodinView profile

Associate Professor

• Partial Differential Equations
• Mathematical Physics
• Wave Propagation
• +3 more

Yuri Godin is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. His research focuses on Partial Differential Equations, Mathematical Physics, and Wave Propagation in periodic and random media. He holds an M.Sc. from St. Petersburg State Polytechnic University (1985) and a Ph.D. from Technion – Israel Institute of Technology (1994). His work explores the propagation of acoustic and elastic waves in structured materials, emphasizing dispersion, spectral gaps, and composite material properties. Key interests include analyzing wave behavior in periodic media with soft inclusions and studying the effects of disorder on wave dynamics in optical systems. Godin has published extensively in journals such as Physics Letters A, Proceedings of the Royal Society A, and the SIAM Journal on Applied Mathematics. His research bridges theoretical analysis and applications in materials science and photonics, with a focus on high-contrast media and effective properties of complex structures.

• 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.