جست‌وجوی استاد و پژوهشگر

Stefan Szeiderمشاهده پروفایل

استاد

• Algorithms
• Computational Complexity
• Theoretical Computer Science
• +۴ مورد دیگر

Stefan Szeider is a full professor and chair of the Algorithms and Complexity Group at the Faculty of Informatics, Technische Universität Wien (TU Wien). He also serves as a visiting scientist at UC Berkeley's Simons Institute for the Theory of Computing. His academic journey includes positions at the University of Durham (UK) and the University of Toronto (Canada), and he earned his Mathematics PhD from the University of Vienna in 2001. Dr. Szeider's research focuses on designing efficient algorithms for problems in Artificial Intelligence, automated reasoning, and combinatorial optimization. He leads several initiatives, including the Vienna Center for Logic and Algorithms (VCLA), and has secured funding from the ERC, EPSRC, FWF, and others. His Erdős number is 2, reflecting his collaborative network in mathematics and computer science. Key achievements include the first ERC Starting Grant awarded to an Austrian computer scientist (2009), and awards such as the Highlighted Paper Award at SAT 2023 and Best Paper at CP 2020. He advises numerous PhD students and postdocs, fostering the next generation of researchers in algorithms and complexity. Notable contributions extend beyond academia to public outreach, including initiatives like the 'Algorithms Think Differently' educational program and the 'Algorithms in 60 Seconds' video competition. His work bridges theoretical foundations and practical applications, influencing both academic and real-world computational challenges.

• Theoretical Computer Science
• Proof Complexity
• Automated Reasoning
• +۳ مورد دیگر

Leroy Nicholas Chew is a PostDoc Researcher and FWF Projektassistent at the Vienna University of Technology (TU Wien). He is affiliated with the Department of Algorithms and Complexity within the Faculty of Informatics. His roles include contributing to research projects such as QBFPC (2022–2025), Overcoming Intractability in the Knowledge Compilation Map, and REVEAL-AI (2020–2024). These projects reflect his focus on advancing theoretical computer science and automated reasoning methodologies. While specific educational details are not explicitly provided in the text, Leroy Nicholas Chew holds a PhD, as indicated by his role listing. His current position suggests a strong background in computer science and theoretical foundations, consistent with his research activities. His research interests span several key areas in theoretical computer science, including proof complexity, quantified Boolean formulas (QBF), automated reasoning, and knowledge compilation. He explores the hardness of computational problems in logical frameworks, such as analyzing resolution and CDCL proof systems, developing optimal dual proof systems for answer set programming (ASP), and investigating model counting techniques. His work often bridges foundational theory with practical applications in formal verification and algorithm design. Recent publications (2024) highlight advancements in circuits and proofs, model counting, and ASP-QRAT proof systems. Earlier work (2016–2022) addressed QBF resolution calculi, dependency schemes, and certification challenges. These trends underscore his specialization in formal methods and computational logic. No scientific awards are explicitly mentioned in the provided text. In addition to his research, Chew is involved in multiple funded projects. These include the FWF-supported QBFPC (2022–2025), which examines QBF proofs and certificates, and the REVEAL-AI project (2020–2024), focusing on overcoming intractability in knowledge compilation. While specific grant details beyond project funding are not mentioned, his participation underscores his role in collaborative, grant-funded research initiatives. No formal advisees are listed. Chew is part of the Algorithms and Complexity department at TU Wien, collaborating on projects that emphasize proof systems, formal verification, and algorithmic foundations. His work integrates theoretical insights with practical computational methods.

Aleksei Udovenkoمشاهده پروفایل

پژوهشگر

• Symmetric Cryptography
• Cryptanalysis Techniques
• White-box Cryptography
• +۴ مورد دیگر

Aleksei Udovenko is a post-doctoral researcher at the Laboratory of Algorithmics, Cryptology and Security (LACS) within the Security and Trust (SnT) interdisciplinary center and the Department of Computer Science (CSC) at the University of Luxembourg. His research focuses on symmetric cryptography, cryptanalysis techniques, and white-box cryptography, with notable contributions to projects like Equihash (a memory-hard proof-of-work scheme used in cryptocurrencies) and the SPARX family of lightweight block ciphers. Education: PhD in Cryptology under Prof. Alex Biryukov (2019), MSc from ITMO University (2013). Research Interests: Symmetric-key algorithms, cryptanalysis methodologies, white-box cryptography, blockchain technologies, and lightweight cryptographic primitives. He actively participates in information security competitions (CTFs) and collaborates on projects related to cryptocurrency protocols and hardware-software security. Recent Work: Authored/co-authored papers on cryptanalysis of ARX-based ciphers, LPN attacks in white-box settings, and secure obfuscation techniques. Notable publications include work on Equihash (NDSS 2016), SPARX (ASIACRYPT 2016), and advancements in meet-in-the-filter cryptanalysis techniques (SAC 2022, CHES 2023). Professional Affiliations: Member of the CryptoLUX research group, contributing to interdisciplinary projects at the University of Luxembourg. Involved in conferences such as CRYPTO, CHES, and FSE.

Eugene C. Fitzhughمشاهده پروفایل

دانشیار

• Physical Activity Epidemiology
• Chronic Disease Prevention
• Built Environment and Health
• +۳ مورد دیگر

Eugene C. Fitzhugh is an Associate Professor in the Department of Kinesiology, Recreation, and Sport Studies at the University of Tennessee, Knoxville. He holds a PhD in Health Education from the Universities of Alabama and Alabama at Birmingham, and has held academic and public health roles since 1982, including prior roles as Assistant Professor and chronic disease epidemiologist at the Knox County Health Department. His research focuses on physical activity's dose-response relationship with chronic diseases, leveraging NHANES data to study hypertension, diabetes, and metabolic syndrome. He also investigates how built environments influence physical activity. Education: PhD (UA & UAB), MS (Eastern Kentucky University), BA (Eastern Kentucky University) His work emphasizes public health interventions, including digital support tools for walking programs and policies to increase youth activity. Fitzhugh directs the Center for Physical Activity and Health, promoting campus wellness, and serves on editorial boards for Medicine & Science in Sports & Exercise and American Journal of Health Education . His recent articles (2011–2017) analyze physical activity metrics, metabolic syndrome correlations, and environmental impacts on exercise behaviors. Awards: Jack Davis Professional Achievement Award (2002) His advising and grant activities include collaborations on school health policies and community-based interventions. Fitzhugh’s lab focuses on translating epidemiological findings into actionable public health strategies.

Fan Weiمشاهده پروفایل

استادیار

• Extremal Combinatorics
• Probabilistic Combinatorics
• Theoretical Computer Science
• +۳ مورد دیگر

Fan Wei is an Assistant Professor of Mathematics and Computer Science at Duke University, affiliated with the Trinity College of Arts & Sciences. She holds a PhD in Mathematics from Stanford University (2019), a Master of Advanced Study from Cambridge University (UK), and a Bachelor's degree from MIT. Her research focuses on extremal and probabilistic combinatorics, with applications to computer science, particularly in analyzing large networks using tools from probability, analysis, and algebra. Education: PhD in Mathematics, Stanford University, 2019 Master of Advanced Study, Mathematics, University of Cambridge, UK Bachelor's degree in Mathematics, MIT Research Interests: Extremal combinatorics, probabilistic combinatorics, graph theory, theoretical computer science, and applications to network analysis. Recent work includes studies on undecidability in graph homomorphism densities, independent transversals, and irregularity strength conjectures. Publications: Her work spans over 30 peer-reviewed articles, focusing on graph theory, combinatorial optimization, and algorithmic applications. Notable topics include undecidability in combinatorial problems, spectral graph theory, and phase transitions in graph properties. Awards & Funding: NSF Award DMS-2401414 (2023–2028) NSF Award DMS-1953958 (2020–2023) Simons Foundation Algorithms & Geometry Unit Fellowship (2020) Teaching & Mentoring: Teaches combinatorics courses at Duke, leads a research group, and mentors students in REU programs. Previously taught discrete mathematics and calculus. Labs/Teams: Active in Duke's combinatorics research community, collaborates internationally on projects such as the Barbados Graph Theory Workshop. Hosts a minicourse on regularity methods in combinatorics.

Peter Howardمشاهده پروفایل

استاد

• Stability of nonlinear waves
• Spectral analysis via the Maslov index
• Phase separation and network formation
• +۱ مورد دیگر

Peter Howard is a Professor and Department Head in the Department of Mathematics at Texas A&M University. His research focuses on stability of nonlinear waves, spectral analysis via the Maslov index, phase separation, and viscous conservation laws. He has contributed to foundational work on Hamiltonian systems, Cahn-Hilliard equations, and shock wave dynamics. Research Interests include: Nonlinear wave stability and instability Applications of the Maslov index to spectral analysis Phase separation phenomena in materials science Analysis of viscous conservation laws Publications span mathematical analysis of PDEs, with recent work emphasizing renormalized oscillation theory and spectral properties of Hamiltonian systems. His work often involves collaborations with experts like Y. Latushkin and A. Sukhtayev. No awards or grants are explicitly listed in the provided texts. Advising and Lab Activities: No formal student advising records or lab affiliations are mentioned in the source materials.

• Pure Mathematics
• Applied Mathematics
• Discrete Combinatorial Mathematics
• +۳ مورد دیگر

Dr. Michael Nyblom is a Lecturer in the School of Science at RMIT University, Australia. His research focuses on Pure and Applied Mathematics, with a particular emphasis on Discrete Combinatorial Mathematics, Number Theory, and their applications to Computer Science. He actively engages in individual and collaborative research, including conference presentations and publications, and explores topics such as Diophantine equations, lattice path counting, and series manipulation. Dr. Nyblom is open to supervising Masters and PhD students in relevant research areas. He holds an ORCID identifier (0000-0001-7903-5705) and can be contacted at michael.nyblom@rmit.edu.au. His teaching interests include Discrete and Combinatorial Mathematics applied to Computer Science. Research highlights include contributions to divergent series analysis, self-avoiding walk enumeration, and number sequence theory. His work spans over two decades, with publications in journals such as the Fibonacci Quarterly, Rocky Mountain Journal of Mathematics, and the Australian Mathematical Society Gazette.

Prasad Tetaliمشاهده پروفایل

استاد

• Discrete Mathematics
• Probability Theory
• Theory of Computing
• +۶ مورد دیگر

Prasad Tetali is the Alexander M. Knaster Professor and Department Head of the Department of Mathematical Sciences at Carnegie Mellon University (CMU), located in Pittsburgh, PA. He also holds adjunct professorships at Emory University (Math/CS) and the Georgia Institute of Technology (Math/CoC). His academic journey includes a Ph.D. from the Courant Institute of Mathematical Sciences, NYU, and postdoctoral research at AT&T Bell Labs. Education: Ph.D. (1991), Courant Institute of Mathematical Sciences, NYU M.S. (1987), Indian Institute of Science, Bangalore, India Postdoctoral Appointments: Mathematical Sciences Research Center, AT&T Bell Labs Research Interests: Dr. Tetali's work focuses on Discrete Mathematics, Probability Theory, and Theoretical Computing. Key areas include Markov chains, isoperimetry, combinatorics, computational number theory, and algorithm design. His contributions span foundational theory and applications in optimization, statistical physics, and network analysis. Publications & Trends: His research has led to influential papers on topics like mixing times of Markov chains, entropy inequalities, and combinatorial optimization. Notable works include foundational studies on the Potts model, the Swendsen-Wang algorithm, and sharp threshold phenomena in number theory. Awards & Recognition: AAAS Fellow SIAM Fellow (Inaugural Class, 2009) Fellow of the American Mathematical Society AMS Fellow (Inaugural Class, 2012) Georgia Tech’s Regents Professor Advising & Grants: Dr. Tetali has mentored 11 PhD students and numerous postdocs. His grants include leadership roles in initiatives such as the SIAM Activity Group on Discrete Mathematics and the ACO PhD Program at Georgia Tech. He has also held editorial roles at top journals like SIAM Journal on Discrete Mathematics and Random Structures & Algorithms. Labs & Collaborations: His work often intersects with interdisciplinary teams, including contributions to distributed algorithms, network science, and computational mathematics. He actively collaborates with researchers in computer science, physics, and applied mathematics.

Judith Lokمشاهده پروفایل

استاد

• Causal Inference
• Survival Analysis
• HIV/AIDS Research
• +۸ مورد دیگر

Dr. Judith Lok is a Professor and Associate Director of Statistics at Boston University's Department of Biostatistics. Her research focuses on causal inference methodologies, survival analysis, and HIV/AIDS-related problems using observational data. She specializes in mediation analysis, clinical trial design (e.g., Learn-As-you-GO trials), and statistical frameworks like Structural Nested Models. Her work addresses challenges in estimating treatment effects, handling censoring, and optimizing multi-component interventions. Dr. Lok collaborates widely on projects involving immune activation, CD4 count trajectories in sub-Saharan HIV care, and personalized medicine. Key collaborations include work with Dr. Victor DeGruttola on coarse structural nested models, Dr. Ron Bosch on long-term HIV outcomes, and Dr. Shu Yang on competing risks models. She has developed software for causal mediation analysis and contributed to foundational statistical theory, including rigorous proofs of estimator properties in continuous and discrete time. Her methodologies have been published in top journals like the Annals of Statistics and Scandinavian Journal of Statistics. Dr. Lok’s research spans theoretical advancements and applied public health challenges, emphasizing rigorous causal frameworks to address real-world medical questions. She advises students and mentors teams on trials optimizing treatment packages while addressing issues like informative censoring and unmeasured confounding.

Silke Göbelمشاهده پروفایل

استاد

• Numerical Cognition
• Developmental Cognitive Neuroscience
• Mathematical Learning Difficulties
• +۳ مورد دیگر

Professor Silke Göbel is a leading academic in Numerical Cognition and Developmental Cognitive Neuroscience at the University of York, where she leads the Numerical Cognition Lab. She holds a Visiting Professorship at the University of Oslo (since 2019) and is a Co-director of the ESRC Centre for Early Mathematics Learning at Loughborough University (since 2022). Her academic journey includes a Diplom from Goethe University Frankfurt, a DPhil from Oxford, and postdoctoral research in Oxford before joining York. Her research focuses on numerical processing in typical/atypical development, cross-cultural differences, dyscalculia, and the interplay between language and mathematics. Key methodologies include longitudinal studies, EEG/fMRI, and eye-tracking. She has secured significant grants, including £347k (2023-2026) for studying conceptual spaces and £454k (2022-2025) on rational numbers understanding. Teaching roles include directing the MSc in Developmental Cognitive Neuroscience and teaching modules on mathematical cognition. She has supervised students like Eleanor Redfern (PhD) and contributes to textbooks such as *An Introduction to Mathematical Cognition*. Her work bridges cognitive science, education, and clinical neuroscience, addressing fundamental questions about how children acquire numerical skills and why some struggle. Collaborators include leading researchers in Germany, Austria, and France. Her lab’s work has implications for educational interventions targeting math learning difficulties and cross-cultural disparities in mathematics performance.

Marinho Bertanhaمشاهده پروفایل

دانشیار

• Econometrics
• Causal Inference
• Applied Microeconomics
• +۵ مورد دیگر

Marinho Bertanha is a tenured Associate Professor of Economics at the University of Notre Dame, serving as concurrent faculty in Statistics and a faculty fellow at the Kellogg Institute. His academic work bridges econometrics and applied microeconomics with significant contributions to causal inference methodologies. His educational background includes a Ph.D. in Economics from Stanford University (2015), an M.A. from Fundação Getúlio Vargas in Rio de Janeiro (2009), and a B.A. from Universidade de São Paulo (2006). Prior to his current position, he completed postdoctoral work at the University of Louvain and served as a visiting professor at the University of Chicago. Bertanha's research focuses on causal inference , resampling methods , and policy evaluation , with particular expertise in regression discontinuity designs, bunching estimation, and permutation testing. His methodological innovations address critical challenges in econometric identification and statistical inference, especially in contexts with strategic reporting and imperfect compliance. His publication record shows consistent high-impact output in top journals including the Journal of the American Statistical Association , Journal of Econometrics , and Review of Economics and Statistics . Recent work demonstrates increasing focus on practical implementation through software development (Stata and MATLAB packages) that has become standard in empirical economics research. Through his concurrent appointment in Statistics and faculty fellowship at the Kellogg Institute, Bertanha contributes to interdisciplinary research initiatives focusing on economic policy evaluation and statistical methodology development. His current work-in-progress examines causal effects in matching mechanisms, returns to education with strategic reporting, and tax elasticity estimation across multiple countries.

Eric Vigodaمشاهده پروفایل

استاد

• Markov Chain Monte Carlo (MCMC) algorithms
• Statistical Physics Phase Transitions
• Randomized Algorithms
• +۳ مورد دیگر

Eric Vigoda is a Professor of Computer Science at the University of California, Santa Barbara (UCSB). Previously, he held positions at Georgia Institute of Technology, including Director of the Algorithms and Randomness Center (2016–2019). He earned his PhD in Computer Science from UC Berkeley (1999) and BS/MS from Johns Hopkins University (1994). His research focuses on Markov Chain Monte Carlo (MCMC) algorithms, phase transitions in statistical physics, and randomized algorithms for approximate counting/sampling problems. He has authored over 100 papers and has been recognized with awards such as the Fulkerson Prize (2006) and the Machtey Award (1999). Education: PhD (UC Berkeley, 1999), MS/BS (Johns Hopkins, 1994). Research interests include MCMC methods, randomized algorithms, and applications in evolutionary biology. His work bridges theoretical computer science and statistical physics, particularly in analyzing mixing times of Markov chains. Awards: American Mathematical Society Fellow (2019), Fulkerson Prize (2006), Machtey Award (1999), William A. Baird Teaching Award (Georgia Tech, 2019). Supervised 6 PhD students, including Zongchen Chen (MIT Postdoc), Andreas Galanis (Oxford), and Linji Yang (Entrepreneur). Teaching: Current courses include CS 130A (Data Structures and Graph Algorithms) and CS 190A (Randomized Algorithms). Former courses include Markov Chains in Evolutionary Biology and a MOOC on Graduate Algorithms for Georgia Tech's OMSCS program. Contributions: Organized the 2022 Summer School on 'Spectral Independence and Entropy Decay' at UCSB. Collaborated with researchers on topics like high-dimensional expanders and phase transitions in sampling algorithms.

Tom Bohmanمشاهده پروفایل

استاد

• Extremal Combinatorics
• Probabilistic Combinatorics
• Discrete Mathematics
• +۳ مورد دیگر

Tom Bohman is a Professor of Mathematical Sciences at Carnegie Mellon University, affiliated with the Mellon College of Science. His research focuses on extremal and probabilistic combinatorics, exploring discrete structures inspired by mathematics, information theory, statistical physics, and computer science. He holds a Ph.D. from Rutgers University and has held postdoctoral positions at MIT and the Mathematical Sciences Research Institute (MSRI). Bohman's work includes studies on random graph processes, such as the triangle-free process and Hamilton cycles in random graphs, as well as hypergraph coloring and Ramsey numbers. His publications span over three decades, addressing topics like coprime matchings, lonely runner conjectures, and dynamic concentration phenomena in combinatorial systems. He teaches advanced courses in combinatorics and discrete mathematics, including Graph Theory and Random Structures & Algorithms. His research has contributed to understanding phase transitions in random processes and the interplay between combinatorial structures and probabilistic methods. While no awards are explicitly listed, his extensive publication record and editorial roles (e.g., with Random Structures & Algorithms ) highlight his academic impact. Bohman's work often bridges theoretical foundations with algorithmic applications, influencing both pure and applied combinatorics.

• Bayesian statistics
• Spatial statistics
• Shape analysis
• +۳ مورد دیگر

Qiwei Li is an Assistant Professor of Statistics in the Department of Mathematical Sciences at The University of Texas at Dallas. He holds a Ph.D. in Statistics from Rice University (2016), an M.A. in Statistics from Rice University (2015), an M.Phil. in Information Engineering from The Chinese University of Hong Kong (2010), a B.Sc. in Economics from Peking University (2009), and a B.Eng. in Electronic Engineering from Tsinghua University (2008). His research focuses on developing AI-powered Bayesian statistical methodologies and machine learning approaches to analyze high-dimensional, spatial, and shape data. Key areas include Bayesian clustering, spatial transcriptomics, microbiome community detection, and integrative omics analysis. He is actively involved in advancing methodologies for spatial molecular profiling and disease-relevant gene discovery. Recent publications highlight contributions to spatial transcriptomics integration, microbiome analysis, and health outcome studies. His work bridges statistical theory with biomedical applications, addressing challenges in genomics, imaging, and clinical data interpretation. Dr. Li is seeking highly motivated Ph.D. and undergraduate students to join his research group, offering financial support. He emphasizes interdisciplinary collaboration and innovation in statistical methodologies for solving complex biomedical problems.

Roman Sauerمشاهده پروفایل

استاد

• Topology
• Geometric Group Theory
• Geometric Topology
• +۳ مورد دیگر

Prof. Dr. Roman Sauer is a Professor of Mathematics at Karlsruhe Institute of Technology (KIT), leading the Topology and Geometric Group Theory Group within the Institute of Algebra and Geometry. His research focuses on geometric topology, geometric group theory, and L²-invariants, with particular emphasis on manifolds, cohomology, and group actions. He has held roles such as Head of the Topology Group and contributed to foundational work on simplicial volume, bounded cohomology, and Kazhdan properties. Key research areas include the interplay between geometry and topology in manifolds, the study of arithmetic groups through profinite invariants, and applications of measure-theoretic methods to group theory. His work often bridges algebraic topology with geometric analysis, addressing questions about rigidity, stability, and quantitative invariants. Publications highlight contributions to L²-Betti numbers, geometric group actions, and the interplay between group properties and topological structures. Notable collaborations involve Uri Bader, Clara Löh, and others in advancing the field of geometric topology and related cohomological theories.