جست‌وجوی استاد و پژوهشگر

Ignacy Misztalمشاهده پروفایل

استاد

• Genetic evaluation methodology
• Programming algorithms related to animal breeding
• Genetics of heat tolerance
• +۵ مورد دیگر

Ignacy Misztal serves as a Distinguished Research Professor and D.W. Brooks Professor in the Department of Animal & Dairy Science at the University of Georgia's College of Agricultural & Environmental Sciences. He joined UGA as an Associate Professor in 1996 after academic positions at Warsaw Agriculture University, Hohenheim University, University of Guelph, and University of Illinois at Urbana-Champaign. Dr. Misztal earned his M.S. in Computer Engineering from Warsaw Technical University in 1978 and his Ph.D. in Technical Sciences from the Polish Academy of Sciences in 1985. His research has established him as a leading figure in animal breeding and genetics methodology. His pioneering work focuses on genetic evaluation methodology, particularly the development of single-step GBLUP (genomic best linear unbiased prediction), which has become the standard approach in the field. He also pioneered methods to evaluate animals for heat tolerance using data from public weather stations. His research addresses critical challenges in livestock production sustainability through computational strategies that improve genetic evaluation systems. His publication record demonstrates consistent focus on genomic evaluation methods, with recent work exploring estimation of heritabilities using predictivity in large genomic models, genotype-by-environment interactions, and technical implementations of all-breed single-step GBLUP. His research group has developed software tools that have become the backbone for genetic evaluation programs used by major animal genetics companies worldwide. J. Lush Award (ADSA) Rockefeller Prentice Award (ASAS) Research Award (NAAB) Pioneer Award (BIF) D.W. Brooks Distinguished Professorship Dr. Misztal has delivered over 150 invited talks across 34 countries and taught 30 short courses on 6 continents. His research has attracted more than $12 million in extramural funding and has significant impact on dairy, beef, pig, and poultry industries globally. He leads a research group focused on developing tools for large-scale genetic evaluation of animals, with a philosophy of testing ideas quickly using real-world data from industry partners. His laboratory, accessible through http://nce.ads.uga.edu, develops practical computational solutions that address the growing complexity of genomic data in animal breeding programs while maintaining computational feasibility for national evaluation systems.

Eric Andriantianaمشاهده پروفایل

دانشیار

• Graph Theory
• Combinatorics
• Algebraic Graph Theory
• +۱ مورد دیگر

Eric Andriantiana serves as Associate Professor and Head of the Department of Mathematics at Rhodes University, South Africa, where he earned recognition for his expertise in discrete mathematics. His academic foundation includes an MSc and PhD from Stellenbosch University, establishing his trajectory in theoretical and applied graph theory. Education: MSc, Stellenbosch University PhD, Stellenbosch University His research spans Graph Theory and Combinatorics with emphases on extremal graph theory, algebraic methods, and combinatorial enumeration. Specific contributions include structural analyses of trees, unicyclic graphs, and spectral properties, alongside investigations into independent sets, matchings, and graph invariants. His work bridges theoretical rigor with algorithmic applications, particularly in optimization contexts. Recent publications reveal a concentrated focus on extremal characterizations—especially regarding matching numbers and independence parameters—with increasing sophistication in handling nearly independent structures and group-theoretic graph constructions. This evolution underscores his leadership in advancing combinatorial frameworks for complex graph problems. Scientific Awards: NRF Rating (C, 2021) Claude Leon Merit Award (2017) NRF Rating (Y, 2015) Professor Andriantiana has mentored 11 postgraduate students across Rhodes University and AIMS, including PhD, MSc, and Honours candidates. His professional impact extends through coordination of the CoE-MaSS Graph Theory Focus Area, regional leadership in Siyanqoba and SATMO olympiads, and editorial service for MATCH Communications in Mathematical and in Computer Chemistry, alongside extensive peer review for top combinatorics journals.

• Statistics
• Randomized Response Techniques
• Survey Sampling
• +۵ مورد دیگر

Pier Francesco Perri is a Full Professor of Statistics (SSD STAT-01/A) at the Department of Economics, Statistics, and Finance 'Giovanni Anania' (DESF) of the University of Calabria (Unical). He has held academic positions since 2002 and coordinates institutional activities such as the National Plan for Scientific Degrees (L-41, Statistics) and editorial roles in journals like Metron and Model Assisted Statistics and Applications . His research focuses on survey methodology, sensitive data estimation, and statistical modeling. Education: Ph.D. in Methodological Statistics (University of Trento, 2002), Honors Degree in Statistical and Actuarial Sciences (University of Calabria, 1997) Perri’s research spans randomized response techniques for sensitive populations, copula modeling for financial data, and inequality index linearization. He explores indirect questioning methods to detect truthfulness and improve survey accuracy, particularly for stigmatized behaviors like abortion, migration, and non-heterosexual identities. His work integrates auxiliary information and complex sampling designs to enhance estimation efficiency. Recent publications emphasize innovations in sample survey traditions, including the 2025 editorial on methodological advancements and 2024 studies on adjusting self-protective behaviors in prevalence estimation. His 2023–2021 research includes logistic regression extensions for randomized response models, prevalence estimates for health behaviors, and crossed model applications for sensitive topics. Earlier work (2020–2000) covers item sum techniques, financial copula modeling, and environmental sensitivity analysis. He serves on editorial boards and institutional committees, including the Italian Statistical Society’s Council and the Ph.D. Teaching Board at Unical. The Laboratorio Statistico Informatico supports his research in statistical computing and survey design.

Asaf Nachmiasمشاهده پروفایل

استاد

• Probability theory
• Statistical physics
• Random walks
• +۳ مورد دیگر

Asaf Nachmias is a Professor at the Department of Theoretical Mathematics within the Tel Aviv University 's Faculty of Exact Sciences . His research focuses on Probability theory and Statistical physics , particularly on problems involving random walks , percolation , uniform spanning trees , and planar graphs . Scientific contributions: Studied critical percolation and scaling limits in high-dimensional graphs and hypercubes Explored circle packing theorem applications to random walks and graph theory Investigated unimodular random maps , electrical resistance in branching random walks Co-authored foundational works on noise sensitivity , cover time analysis, and random graph components Editorial roles: Associate editor, Annales de l'Institut Henri Poincaré (B) (2016–present) Associate editor, Annals of Probability (2021–present)

• Concrete Durability
• Sustainable Concrete
• Recycled Aggregate Concrete
• +۳ مورد دیگر

Rui Duarte Neves serves as a Professor in the Department of Civil Engineering at the School of Technology of Barreiro, Polytechnic Institute of Setúbal, Portugal. Previously, he conducted research at the National Laboratory for Civil Engineering (LNEC-Portugal), establishing his expertise in concrete technology and structural assessment before transitioning to his current academic role. His research specializes in concrete durability and sustainable construction materials, with primary focus on service life prediction of reinforced concrete structures and development of eco-friendly alternatives like recycled aggregate concrete. He pioneers non-destructive testing methodologies for on-site evaluation of carbonation and chloride penetration, directly addressing infrastructure longevity challenges in aggressive environments. Analysis of his publication portfolio reveals consistent innovation in sustainable concrete technologies, particularly CO 2 sequestration in recycled concrete and AI-driven predictive modeling for carbonation coefficients. His work bridges experimental validation with computational approaches, including genetic programming and tree-based algorithms, while advancing circular economy principles through industrial waste reuse in cement production. Dr. Neves actively contributes to the Resilience R&D Unit, where his research integrates field testing, statistical modeling, and materials innovation to enhance concrete performance under environmental stressors like carbonation and chloride exposure.

Hugo Alves Akitayaمشاهده پروفایل

استادیار

• Algorithms
• Computational Geometry
• Graph Drawing
• +۵ مورد دیگر

Hugo Alves Akitaya is an Assistant Professor at the University of Massachusetts Lowell in the Miner School of Computer & Information Sciences, which is part of the Kennedy College of Sciences. He holds a Ph.D. in Computer Science from Tufts University (2018), an M.Eng. from University of Tsukuba (2014), and a B.Eng. from University of Brasília (2011). His educational background includes: Ph.D. in Computer Science from Tufts University (2018) M.Eng. in Computer Science from University of Tsukuba (2014) B.Eng. in Control and Automation from University of Brasília (2011) Hugo's research primarily focuses on computational geometry and algorithms, with special emphasis on reconfiguration algorithms, discrete geometry, graph drawing, and origami-related folding problems. His work bridges theoretical computer science with practical geometric applications, often exploring the mathematical foundations of folding and unfolding processes. His research has significant implications for robotics, computer graphics, and network design. Hugo's publications demonstrate consistent contributions to major computational geometry conferences including SODA, SoCG, GD, and FWCG. His work shows a clear trajectory from theoretical foundations in geometric algorithms to practical applications in areas such as robot swarm coordination (Freeze Tag problem), paper folding mathematics, and geometric network optimization. The recurring themes across his publications include NP-hardness proofs, geometric optimization techniques, and novel algorithmic approaches to longstanding problems in computational geometry. Hugo has received several prestigious awards including: Loevner Fellowship (2016-2017) LASPAU-administrated scholarship (Science without Borders) (2014-2018) Outstanding Presentation Award at JSST2013 1st Place in ACM Student Research Competition at SIGGRAPH 2013 MEXT scholarship from Japanese Government (2011-2014) Outstanding Student Award from University of Brasília (2011) Hugo teaches courses including COMP 5030 (Algorithms) and COMP 5800 (Computational Geometry) at UMass Lowell, and previously taught Comp 160 (Algorithms) at Tufts University. His research is conducted in collaboration with prominent figures in computational geometry including Erik Demaine, Diane Souvaine, and Csaba Tóth. His work often involves interdisciplinary approaches, connecting computer science with mathematical theory and practical applications. Hugo maintains an active research laboratory focused on computational geometry problems, with particular emphasis on folding algorithms and geometric reconfiguration. His team explores both theoretical foundations and practical implementations of geometric algorithms, often drawing inspiration from origami mathematics. The group maintains strong collaborations with researchers at institutions including Tufts University, Carleton University, and various international partners.

Pavel S. Ruzankinمشاهده پروفایل

دانشیار

• Medical Statistics
• Image Reconstruction in Emission Tomography
• Nonparametric Statistics
• +۴ مورد دیگر

Pavel S. Ruzankin is an Associate Professor at Novosibirsk State University's Department of Probability Theory and Mathematical Statistics. He also serves as Head of the Laboratory of Applied Inverse Problems at the Sobolev Institute of Mathematics. His email is ruzankin@math.nsc.ru . Education: 1993-1999: Novosibirsk State University (Mathematics), 1999-2001: Postgraduate at Sobolev Institute of Mathematics, 2001: Candidate of Science (PhD) from Sobolev Institute of Mathematics. His research spans Medical Statistics , Image Reconstruction in Emission Tomography , Nonparametric Statistics , and Poisson Approximation . He has contributed to Monge-Kantorovich Problem solutions and developed advanced Statistical Regression Models with uniform consistency guarantees. His work bridges theoretical probability with practical applications in biomedical research. Key publication trends include computational algorithms, cardiovascular medicine applications, and nonparametric estimation methods. He has co-authored multidisciplinary studies combining Cardiac surgery trials Genomic diagnostics Mathematical statistics Algorithm design As course leader, he designed the Random Processes curriculum for master's students at Novosibirsk State University and provides guidance through editorial roles at journals like Heart and Vessels and Journal of Cardiothoracic and Vascular Anesthesia . His leadership in the Laboratory of Applied Inverse Problems demonstrates expertise in solving complex mathematical challenges, particularly in Statistical inverse problems Poisson process approximations Medical data analysis

Antar Bandyopadhyayمشاهده پروفایل

استاد

• Probability Theory
• Theoretical Statistics
• Random Graphs
• +۷ مورد دیگر

Antar Bandyopadhyay is a Professor at the Theoretical Statistics and Mathematics Division of the Indian Statistical Institute (ISI), currently serving as Head of the Delhi Centre (May 01, 2023 - present). He has previously served as Professor-in-Charge of the Theoretical Statistics and Mathematics Division (September 18, 2020 - September 17, 2022). His academic journey includes a Ph.D. in Statistics from the University of California, Berkeley (2003), postdoctoral studies at the Institute for Mathematics and Its Applications (University of Minnesota) and Chalmers University of Technology (Sweden). Professor Bandyopadhyay's research focuses on theoretical and applied probability, with emphasis on discrete problems arising from combinatorics, statistical physics, and computer science. His specific interests include random graphs, probability on trees, combinatorial optimization, recursive distributional equations, branching random walks, percolation theory, interacting particle systems, Markov chains, random walks in random environments, and urn models. His work demonstrates a strong connection between theoretical developments and applications in various scientific domains. Analysis of his recent publications reveals a consistent focus on probability theory with particular emphasis on urn models, branching random walks, and random processes on graphs and trees. His research shows a progression from foundational work on recursive distributional equations to more complex applications in network theory and statistical physics. The publications demonstrate significant contributions to theoretical probability with practical implications in fields such as epidemiology, combinatorial optimization, and stochastic geometry. Outstanding Graduate Student Instructor Award from UC Berkeley (2002) Teaching Effectiveness Award from UC Berkeley (2002) Professor Bandyopadhyay has supervised multiple Ph.D. students including Deborshi Das (expected 2026), Partha Pratim Ghosh (2022), Gursharn Kaur (2018), Debleena Thacker (2015), and Farkhondeh Sajadi (2013). He has also guided M.Stat. dissertation students including Somak Laha (2021-2022) and Subhabrata Sen (2012-2013). His collaborative work spans numerous institutions including UC Berkeley, Chalmers University, and various Indian Statistical Institute centers.

• Clinical Trials
• Global Health
• HIV/AIDS
• +۶ مورد دیگر

Tassos C. Kyriakides , PhD, is an Assistant Professor of Biostatistics at the Yale School of Public Health and Director of the Cooperative Studies Program Coordinating Center at the Department of Veterans Affairs. With a PhD in Biostatistics from Yale (1999), he specializes in clinical trial methodology, global health, and infectious disease research. Education: PhD (Yale, 1999), MPhil (Yale, 1996), BS (UCLA, 1993) Key collaborations: James Dziura, Gerald Friedland, Kimberly Hieftje His research spans HIV/AIDS clinical trials , pragmatic trial design , and nonpharmacological pain interventions , with recent work on fecal microbiota transplantation for Clostridioides difficile infections. He actively mentors at International AIDS Conferences and serves as statistical reviewer for Lancet Infectious Diseases and Lancet Gastroenterology & Hepatology . Current projects include analyzing data from multinational HIV therapy trials ( OPTIMA ), abdominal aortic aneurysm repair studies, and developing the Yale institute for olive tree research . His work bridges biostatistics with Mediterranean nutrition and healthcare sustainability , reflecting diverse interests in data science and socio-cultural health dimensions.

Laura Palagiمشاهده پروفایل

استاد

• Nonlinear Optimization
• Mixed Integer Nonlinear Programming
• Semidefinite Programming
• +۷ مورد دیگر

Laura Palagi is a Full Professor at Sapienza University of Rome, Department of Computer, Automation, and Management Engineering "Antonio Ruberti". She has been with the institution since 1996, progressing from Researcher to Associate Professor in 2005, and Full Professor since March 2020. She currently serves as Coordinator of the PhD program in Automatic Control, Bioengineering and Operations Research (ABRO) since February 2024 and is the GEP committee representative at the DIAG Department. Her research focuses on developing and analyzing methods for solving nonlinear optimization problems with continuous and discrete variables. Key research themes include decomposition methods for training Support Vector Machines and Deep Networks, methods for Mixed Integer Nonlinear Programming (MINLP), Semidefinite Programming (SDP), standard quadratic problems (StQP), and convex quadratic constrained problems. She also develops predictive machine learning models for healthcare applications and surrogate models for engineering. Her recent publications span optimization methods, machine learning applications in healthcare, sustainability assessment, and network design. Her work demonstrates a strong integration of theoretical optimization with practical applications across multiple domains. Continuous Optimization Awards 2020 Member Best Paper Award from Journal of Global Optimization Honorable Mention from Marguerite Frank Award jury for Best EJCO Paper 2024 EURO Doctoral Dissertation Award Professor Palagi teaches multiple courses including Optimization and Decision Science, Laboratory of Decision Science, Optimization Methods for Machine Learning, and Optimization for Medical programs. She serves as thesis advisor for numerous Bachelor's and Master's students in Management Engineering, Mechanical Engineering, and Data Science programs.

• Knowledge Representation and Reasoning
• Quantum Computation
• Artificial Intelligence
• +۳ مورد دیگر

Professor Sanjiang Li is affiliated with the University of Technology, Sydney (UTS) as a Professor in the Faculty of Engineering and Information Technology , specifically within the Centre for Quantum Software and Information . With a PhD in Mathematics from Sichuan University and a BSc from Shaanxi Normal University, his research spans quantum computation , knowledge representation and reasoning , and formal verification of quantum systems . His recent work focuses on quantum circuit transformation , including novel methods like adaptive divide-and-conquer and Monte Carlo Tree Search frameworks. He has advanced symbolic verification techniques using tensor decision diagrams and explored classical-quantum hybrid algorithms for resource-efficient training. Dr. Li has received prestigious awards such as the ARC Future Fellowship and Alexander von Humboldt Research Fellowship . He supervises PhD students including Calum Holker and Guangxi Li , and contributes to quantum software development through funded projects like the Sydney Quantum Academy and ARC Discovery Projects .

Igor Ballaمشاهده پروفایل

پژوهشگر ارشد

• Extremal Combinatorics
• Combinatorics
• Linear Algebra
• +۱۰ مورد دیگر

Igor Balla is a Strauch Postdoctoral Fellow at the Simons Laufer Mathematical Sciences Institute (SLMath) in Berkeley, where he participates in a research program on extremal combinatorics. He holds a PhD in Mathematics from ETH Zürich, advised by Benny Sudakov, and earned prior degrees from New York University (Master’s) and Carnegie Mellon University (Bachelor’s). He has held postdoctoral positions at Tel Aviv University, the Hebrew University of Jerusalem, and Masaryk University. PhD : Mathematics, ETH Zürich Master’s : New York University Bachelor’s : Carnegie Mellon University His research lies at the intersection of combinatorics and linear algebra, focusing on extremal problems with connections to geometry, theoretical computer science, probability, and quantum physics. Key themes include equiangular lines, orthonormal representations, spectral graph theory, and extremal set systems such as union-closed families. His work often combines algebraic methods with combinatorial reasoning to solve long-standing open problems. The recent articles highlight a strong trend in equiangular lines, spectral extremal combinatorics, and the application of linear algebra to graph theory and coding. His work on extension complexity and the MaxCut problem bridges discrete mathematics and optimization. The recurring use of eigenvalues, matrix projections, and vector representations underscores a unified methodological framework across his publications. Strauch Postdoctoral Fellow Igor Balla has taught as a lecturer in Graph Theory and Advanced Combinatorics at Masaryk University and served as a teaching assistant at ETH Zürich and Carnegie Mellon University across courses in graph theory, algebra, and number theory. He has not been mentioned in connection with any specific grants, but his postdoctoral fellowship indicates competitive research support. His frequent invited talks at institutions like Princeton, MIT, Harvard, and ICERM reflect active engagement in the global combinatorics community. He is affiliated with the research program in extremal combinatorics at SLMath, a leading institute in mathematical sciences. His collaborations span institutions including ETH Zürich, Tel Aviv University, and the Hebrew University, indicating a strong, collaborative research network. His recent work with prominent mathematicians like Benny Sudakov, Po-Shen Loh, and Noga Alon further emphasizes his integration into elite research circles.

Sayan Banerjeeمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Interacting Particle Systems
• +۹ مورد دیگر

Sayan Banerjee is an Associate Professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill, within the College of Arts and Sciences. His research lies at the intersection of probability theory, stochastic processes, and network science, with applications in operations research and statistical physics. Research Interests: His work focuses on interacting particle systems, ergodicity of diffusions, stochastic networks, probabilistic couplings, and dynamic random graphs. He also investigates random walks in random environments, random matrices, and network sampling and ranking algorithms. These areas reflect a deep engagement with both theoretical probability and its real-world applications in complex systems. Recent Publication Trends: His recent articles (2023–2025) reveal a strong focus on the analysis of infinite particle systems (e.g., Atlas models), dynamic network evolution under various delay regimes, convergence properties of machine learning algorithms like Stein Variational Gradient Descent, and structural properties of random graphs. The keywords span probability, operations research, network science, and computational statistics, indicating interdisciplinary impact. Subfields include rank-based diffusions, local weak convergence, load balancing, flocking dynamics, and network centrality. Scientific Awards: NSF CAREER Award (DMS-2141621) Grants and Advising: He is the principal investigator on the NSF CAREER award DMS-2141621 and a co-PI on the NSF RTG grant DMS-2134107, supporting research training in probability and stochastic processes. While no formal list of students is provided, his role as a Ph.D.-granting faculty member in a top statistics department implies active graduate student mentorship. His prior postdoctoral fellowship at the University of Warwick under Prof. Wilfrid Kendall highlights strong international collaboration. Laboratories and Research Teams: Though no named lab is mentioned, his extensive collaborative network—including researchers like A. Budhiraja, S. Bhamidi, P. Dey, and M. Olvera-Cravioto—indicates leadership in a vibrant research group focused on stochastic modeling and network analysis at UNC.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Sebastian Thrun is a Professor at Stanford University, USA, with a prolific research career spanning robotics, artificial intelligence, and machine learning. His work has significantly impacted autonomous vehicle technology, computer vision, and medical AI applications. Thrun's research interests focus on robotics, particularly simultaneous localization and mapping (SLAM), autonomous driving systems, and probabilistic state estimation techniques. His work extends to deep learning applications in medical imaging, notably achieving dermatologist-level skin cancer classification. He has pioneered approaches in meta-learning, vector quantization, and efficient clustering algorithms using multi-armed bandits. His recent publications demonstrate a strong trend toward improving efficiency in machine learning algorithms, particularly in areas like decision trees, vector quantization, and nearest neighbor search. Thrun has also made significant contributions to medical AI, applying deep learning to skin cancer detection and molecular property prediction. Max Planck Research Award (2011) Thrun has advised numerous PhD students who have become prominent researchers in robotics and AI, including David Stavens, Anna Petrovskaya, and Jesse Levinson. His research has been supported by significant grants, particularly for autonomous vehicle development, including Stanford's entry in the DARPA Urban Challenge (Junior). His work bridges theoretical advances with practical applications across multiple domains. Thrun has led research teams focused on autonomous driving systems, 3D reconstruction, and medical AI applications. His work on the DARPA Urban Challenge demonstrated advanced capabilities in urban autonomous navigation, while his more recent research explores the intersection of deep learning and efficiency optimization in machine learning algorithms.

• Combinatorics
• Discrete Mathematics
• Graph Theory
• +۳ مورد دیگر

Professor Daniel Kleitman is a faculty member in the Massachusetts Institute of Technology (MIT) Department of Mathematics , where he has taught courses such as 18.310 Principles of Discrete Applied Mathematics , 18.01 Calculus , and 18.022 Calculus . His academic rank is Professor, with a career spanning decades in combinatorics, discrete mathematics, and interdisciplinary applications. Teaches 18.310 (Discrete Applied Mathematics) Teaches 18.01 (Calculus) Teaches 18.022 (Multivariable Calculus) Research Interests Professor Kleitman’s work lies at the intersection of combinatorics , graph theory , and discrete applied mathematics . His research explores: Theoretical foundations of discrete mathematics Error-correcting codes and cryptographic algorithms Applications to computer science, biology, and physics Geometric and probabilistic methods in combinatorics Publications Trends His publications since 1980 emphasize combinatorics , graph theory , and algorithm design . Key areas include: Combinatorial geometry (convex sets, planarity) Network optimization (spanning trees, linear programming) Coding theory (Shannon bounds, BCH codes) Interdisciplinary applications (gene recognition, urban homicide analysis)