جست‌وجوی استاد و پژوهشگر

Simone Frenchمشاهده پروفایل

استاد

• Obesity Prevention
• Public Health
• Epidemiology
• +۵ مورد دیگر

Simone French is a Professor at the Division of Epidemiology & Community Health within the School of Public Health at the University of Minnesota. She is also a member of both the Healthy Weight Research Center and the Masonic Cancer Center (MCC). PhD in Psychology from University of Minnesota BA in Psychology from Macalester College Her research focuses on obesity prevention interventions targeting children and adults in natural settings like homes, schools, and neighborhoods. She investigates environmental influences on eating behaviors , food policy impacts, and equity in food access . Her work combines individual behavior change with structural environmental modifications . Recent publications emphasize community-based pediatric obesity trials , weather's impact on treatment adherence , and neurobehavioral models for temptation management. Articles span 2002-2023, with methodological diversity from randomized trials to statistical reviews . Member, Healthy Weight Research Center Member, Masonic Cancer Center (MCC) She employs mixed-method approaches for environmental health research , with grant-funded projects exploring low-income food accessibility and policy-level nutritional interventions . Her lab teams focus on community health equity through environmental modification strategies.

Darko Stefanovicمشاهده پروفایل

استاد

• Programming Languages
• Molecular Computing
• Scientific Simulations
• +۳ مورد دیگر

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.

Dominik Lieblمشاهده پروفایل

استاد

• Functional Data Analysis
• Nonparametric Statistics
• Econometrics
• +۳ مورد دیگر

Dominik Liebl is a Professor of Statistics at the University of Bonn's Department of Economics and a member of the Hausdorff Center for Mathematics (HCM), a Cluster of Excellence funded by the German Science Foundation (DFG). He also holds a visiting associate position at Colorado State University's Department of Statistics. His research spans Functional Data Analysis , Nonparametric Statistics , and Longitudinal Data Analysis , with applications in energy economics, finance, e-commerce, emotion psychology, and biomechanics. Recent methodological work focuses on simultaneous inference and statistical fairness . The articles in his profile demonstrate a strong emphasis on functional data methodologies applied to diverse domains like COVID-19 seroprevalence , electricity markets , and human movement science . Key subfields include confidence band design , biomechanical hypothesis testing , and high-dimensional econometric modeling . He actively contributes to open science through R-packages at CRAN/GitHub and serves as Associate Editor for the Journal of the Royal Statistical Society: Series C.

• Algorithms on strings
• Small-space algorithms
• Data structures
• +۴ مورد دیگر

Tatiana Starikovskaya is an Assistant Professor (Maître de Conférences) in the Computer Science Department at École normale supérieure (ENS), Paris, France. She leads the PARSe project (ANR-20-CE48-0001) focusing on approximation and randomized string processing, and participates in AlgoriDAM (ANR-19-CE48-0016). Co-supervises PhD students T. El Ghazi and Gabriel Bathie Co-chaired CPM 2021; served on program committees for STACS, ESA, ICALP, and others Organized CPM summer school (2023) and 'New Horizons of Stringology' workshop (2024) Research Focus Her work centers on algorithms on strings , small-space algorithms , and streaming models , with applications in bioinformatics and data security. Recent research explores trade-offs between time/space complexity in pattern matching, wildcards, and error-tolerant string analysis. Publications Trends Her recent work spans streaming algorithms , compressed data processing , and approximate pattern matching , with collaborations on challenges like k-mismatch problems, Dyck edit distances, and language distance estimation. Education PhD in Mathematics, Lomonosov Moscow State University (2013) M.Sc. in Data Science (Moscow Institute of Physics and Technology/Yandex, 2009) M.Sc. in Mathematics, Lomonosov Moscow State University (2009)

Jason Cantarellaمشاهده پروفایل

استاد

• Geometry
• Topology
• Geometric Knot Theory
• +۳ مورد دیگر

Jason Cantarella is a Professor in the Mathematics Department at the University of Georgia. His research focuses on geometric knot theory, particularly the shapes of random curves and polygons. He teaches Differential Geometry of Curves and Surfaces (MATH 4250) every spring and specializes in the Multivariable Mathematics (MATH 3500-3510) course sequence. His research interests center on geometric properties of random polygons and knots, using differential, algebraic, and symplectic geometry of polygon spaces. Dr. Cantarella is particularly interested in questions like how likely it is that a random curve is knotted or what can be expected from a random diagram of a random space curve. His work bridges pure mathematics with applications in polymer physics and molecular biology. His recent publications show a strong focus on random polygons, topological polymers, geometric probability, and computational methods for analyzing knotting phenomena. There's a clear progression from theoretical foundations to algorithm development and practical applications, particularly in understanding the statistical properties of knotted structures in physical systems. Dr. Cantarella leads a research group that includes graduate students Tom Needham, Michael Berglund, and Harrison Chapman, as well as postdoctoral fellow Clayton Shonkwiler. He collaborates with faculty members Jason Parsley and Matt Mastin at UGA, and internationally with researchers including Tetsuo Deguchi at Ochanomizu University, the Uehara group at Kyoto University, and Clayton Shonkwiler of Colorado State University. He has developed several computational tools including Octrope (for polygonal tube radius calculation), plCurve (polygon library), Ridgerunner (knot tightening software), and Tsnnls (sparse non-negative least squares solver). These tools have enabled significant advances in the computational study of geometric knot theory and random polygon spaces.

Jörn Ostermannمشاهده پروفایل

استاد

• Video Coding
• Computer Vision
• Machine Learning
• +۹ مورد دیگر

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.

Sepehr Assadiمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithm Design
• Complexity Theory
• +۷ مورد دیگر

Sepehr Assadi is an Associate Professor and Faculty of Mathematics Research Chair at the Cheriton School of Computer Science , University of Waterloo, since 2023. His research focuses on theoretical computer science , particularly algorithm design and complexity theory for modern computation models like streaming , distributed , and sublinear time algorithms. Recognized with the 2025 Presburger Award for groundbreaking work in multi-pass streaming lower bounds Developed hierarchical embedding technique for optimal lower bounds in graph streaming Key contributions to maximal independent set , maximum matching , and minimum spanning tree problems His work has been cited over 2,500 times (h-index: 30). He has served on program committees for major conferences (FOCS, STOC, COLT) and mentors students in graph algorithms and complexity theory . Awards include Sloan Research Fellowship , NSERC Discovery Grant , and Google Research Scholar Award .

• Machine Learning
• Computer Vision
• Data Compression
• +۴ مورد دیگر

Donald Adjeroh is a Professor and Associate Chair of Computer Science at the Lane Department of Computer Science and Electrical Engineering, within the Benjamin M. Statler College of Engineering and Mineral Resources at West Virginia University. He also serves as the Program Director for Computer Science Graduate Studies and is the Project Lead PI for the WVAR-CRESH (West Virginia Arkansas Research Collaboration in Engineering, Science, and Health) initiative. Dr. Adjeroh received his Ph.D. from the Chinese University of Hong Kong in 1997. His academic journey has led him to become a prominent researcher in multiple interdisciplinary fields, with a focus on developing computational methods for biomedical applications. Dr. Adjeroh's research spans several interconnected domains: Machine Learning approaches for biomedical data analysis Computer Vision techniques for medical image processing Data Compression algorithms with applications in bioinformatics Search Algorithms & Data Structures for large-scale biological data Bioinformatics and Computational Biology methods for genomic analysis Digital Health applications for healthcare cost reduction and improved outcomes His publication record demonstrates consistent contributions across these fields, with recent work focusing on parameterized string algorithms, probabilistic suffix arrays for protein family prediction, and robust texture analysis techniques. His research shows a clear trajectory toward increasingly sophisticated applications of computational methods to biomedical problems. Dr. Adjeroh has received significant research funding from various sources including the National Science Foundation (NSF), Department of Energy (DOE), NASA, Department of Defense (DOD), Department of Homeland Security (DHS), and others. His current projects include "NSF NRT: Bridges in Digital Health" and "RII Track 2 FEC: Multi-Scale Integrative Approach to Digital Health," among others. He has mentored numerous students through research projects and serves as an advisor for graduate students interested in pursuing PhD research in areas related to search data structures, computational biology, bioinformatics, biometrics, machine learning, and digital health. Dr. Adjeroh is actively involved in organizing academic events, including the Summer School on AI & Smart Health, and serves as a key collaborator in multi-institutional research efforts focused on leveraging artificial intelligence to improve healthcare outcomes.

• Mathematical Statistics
• Data Science
• Algebra
• +۳ مورد دیگر

Jukka Mikael Kohonen is a University Lecturer in the Department of Mathematics and Systems Analysis at Aalto University, Finland. He is affiliated with the Mathematical Statistics and Data Science, as well as Algebra and Discrete Mathematics research groups. His research interests span lattice theory and combinatorics, with recent work focusing on modular lattice reduction and enumeration techniques. He has contributed to algorithmic optimization and outlier correlation detection, collaborating across disciplines such as biomedical signal processing and computational mathematics. In his publications since 2017, Kohonen has explored topics ranging from symmetry reduction algorithms to additive number theory, with a notable emphasis on computational methods in discrete mathematics. Recent work (2025) advances techniques for simplifying modular lattices through elimination of irreducible elements. Research groups: Mathematical Statistics and Data Science, Algebra and Discrete Mathematics Email: jukka.kohonen@aalto.fi

Sándor Kisfaludi-Bakمشاهده پروفایل

استادیار

• Algorithms
• Computational Geometry
• Geometric Optimization
• +۴ مورد دیگر

Sándor Kisfaludi-Bak is an Assistant Professor in the Department of Computer Science at Aalto University, specializing in theoretical computer science with a focus on computational geometry. He develops algorithms for geometric problems involving points, curves, shapes, and spatial networks. His research interests include Algorithm design for geometric optimization Computational geometry fundamentals Spatial network analysis Hyperbolic and planar graph algorithms Parameterized complexity in geometric contexts Recent publications demonstrate expertise in Traveling Salesman Problem optimization, Steiner network construction, and hyperbolic graph analysis. Key research trends span computational geometry, graph theory, and algorithmic complexity in spatial domains. He has no listed scientific awards in the provided materials. No information about student advising or organizational affiliations beyond Aalto University was found.

• Geometric Group Theory
• Low Dimensional Topology
• Combinatorial Group Theory
• +۶ مورد دیگر

Matthew B. Day is an Associate Professor in the Department of Mathematical Sciences at the University of Arkansas. His research focuses on geometric group theory, low-dimensional topology, and combinatorial group theory, with a particular emphasis on automorphism groups of right-angled Artin groups (RAAGs), free groups, and mapping class groups. Research Interests: Automorphism groups, RAAGs, free groups, Torelli subgroups, Johnson homomorphisms, symplectic structures on groups. Collaborators: Richard D. Wade, Andrew Putman, Benson Farb (PhD supervisor). Education: PhD from the University of Chicago (2008), supervised by Benson Farb. Key Contributions: Studied virtual cohomological dimensions, solvable subgroups, and exact sequences related to automorphism groups.

• Signal Processing
• Wireless Communication Systems
• Power Amplifier Modeling
• +۴ مورد دیگر

María José Madero Ayora is a Professor at the Department of Signal Theory and Communications , Universidad de Sevilla , specializing in nonlinear system modeling and digital predistortion for wireless communication systems. Her research focuses on Volterra series applications in power amplifier linearization, microwave measurements , and machine learning techniques for signal processing. Principal Investigator for projects like Statistical Signal Modeling for Brain-Computer Interfaces (PID2021-123090NB-I00) Recipient of the Arftg Roger Pollard Student Fellowship in microwave measurement Her work spans 5G waveform linearization , I/Q modulator impairments , and thermal memory effects in RF amplifiers. Recent publications combine sparse Bayesian methods with Volterra models to address nonlinear distortion in OFDM and visible light communication systems. She has supervised doctoral theses and participated in international conferences across the U.S., Europe, and Asia.

Eric Cancèsمشاهده پروفایل

استاد

• Mathematical analysis of electronic structure models for quantum chemistry and materials science
• Algorithms for electronic structure calculation
• Numerical analysis of linear and nonlinear eigenvalue problems
• +۶ مورد دیگر

Eric Cancès is a Professor at Ecole des Ponts ParisTech and affiliated with INRIA Paris as part of the Molecular and Multiscale Modeling team. His research focuses on mathematical analysis of electronic structure models for quantum chemistry and materials science, algorithms for electronic structure calculations, numerical analysis of eigenvalue problems, implicit solvent models, and molecular dynamics. He has made significant contributions to the development of continuum solvation models, multiscale methods, and computational frameworks for quantum chemistry. Research Interests Mathematical foundations of quantum chemistry Algorithmic development for electronic structure Implicit solvent models and continuum electrostatics Multiscale and domain decomposition techniques Greedy algorithms for high-dimensional problems Scientific Awards Le Rivot prize (French Academy of Sciences, 1992) Best PhD award (Ecole des Ponts, 1998) Blaise Pascal prize (SMAI and French Academy of Sciences, 2009) Ordway visiting professor (University of Minnesota, 2013-2014) Invited lecturer at ICM (2014) Publications span over two decades, addressing Hartree-Fock and Kohn-Sham models, quantum Monte Carlo methods, domain decomposition for solvation models, and multiscale approaches. His work emphasizes mathematical rigor combined with computational efficiency, with recent studies on perturbation methods, polarization energy calculations, and embedded corrector problems for homogenization.

Marko Đukanovićمشاهده پروفایل

استادیار

• Combinatorial Optimization
• Graph Theory
• Bioinformatics
• +۴ مورد دیگر

Marko Đukanović serves as an Assistant Professor at the Department of Computer and Information Sciences within the Faculty of Natural Sciences and Mathematics at the University of Banja Luka. His academic career spans combinatorial optimization, graph theory, and bioinformatics applications, with significant contributions to algorithm design for complex computational problems. His research focuses on combinatorial optimization , particularly in graph domination problems and longest common subsequence variants . His work bridges theoretical computer science with practical applications in bioinformatics, social network analysis, and computational biology. Recent publications demonstrate expertise in developing advanced algorithms including biased random-key genetic algorithms, variable neighborhood search, and neural network-guided optimization techniques. Key publication trends show his specialization in solving constrained optimization problems on graphs, with growing emphasis on integrating machine learning components into traditional combinatorial algorithms. His 2024 publications in IEEE Transactions on Evolutionary Computation and Applied Mathematics and Computation represent significant contributions to the field. Best Paper Award at EvoStar Conference 2024 for 'A Neural Network Based Guidance for a BRKGA: An Application to the Longest Common Square Subsequence Problem' Dr. Đukanović actively participates in international academic collaboration, including recent panel discussions on knowledge transfer between Bosnia and Herzegovina and global expert communities. His work demonstrates strong international collaboration, particularly with researchers from TU Wien and institutions across Europe.

Gilles Blanchardمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Statistical Learning Theory
• +۵ مورد دیگر

Gilles Blanchard is a Professor at Paris-Saclay University , affiliated with the Institute of Mathematics in Orsay. His research focuses on statistical learning theory , multiple testing , kernel methods , and high-dimensional statistics . Key Contributions: Decontamination of mutually contaminated models Novelty detection with semi-supervised learning Non-Gaussian component analysis (NGCA) for dimension reduction Scientific Awards: PhD thesis award from University of Paris 6 (2004) Google Scholar h-index of 49 with over 8,000 citations Recent Research Trends: False Discovery Rate (FDR) control in structured hypothesis testing Conformal prediction for link analysis Adaptive sampling in restless bandits Statistical learning on measure spaces His work bridges theoretical statistics with practical machine learning applications, including genome-wide association studies , persistent homology in topological data analysis , and random feature moments for compressive learning . He is involved in open-source software development like μTOSS for multiple testing standardization and has advised on projects related to flow cytometry and Hadrontherapy applications.