جست‌وجوی استاد و پژوهشگر

Makoto Kanazawaمشاهده پروفایل

استاد

• Theoretical Computer Science
• Formal Language Theory
• Algorithmic Learning Theory
• +۱۰ مورد دیگر

Professor Makoto Kanazawa is a faculty member at Hosei University's Department of Advanced Sciences within the Faculty of Science and Engineering. He holds a Ph.D. in Linguistics from Stanford University (1994). Previously, he served as an Associate Professor at institutions including the National Institute of Informatics (2004–2018) and the University of Tokyo (2000–2004). His research focuses on theoretical computer science, formal language theory, and computational linguistics. Key areas include multiple context-free grammars (MCFGs), abstract categorial grammars (ACGs), and algorithmic learning theory. He has organized workshops like the Lambda Calculus and Formal Grammar series, advancing interdisciplinary research at the intersection of logic, linguistics, and computation. His recent publications address grammar learning from positive data, formal language hierarchies, and parsing algorithms. He has contributed to foundational work on MCFGs, ACGs, and their applications in natural language processing. Kanazawa has led collaborative research projects at the National Institute of Informatics, emphasizing the typed lambda calculus as a framework for linguistic models.

Alistair Stewartمشاهده پروفایل

پژوهشگر ارشد

• Theoretical Computer Science
• Algorithms
• Machine Learning
• +۳ مورد دیگر

Alistair Stewart is a Research Fellow in the Department of Computer Science at the University of Southern California (USC), working with Professor Ilias Diakonikolas. Previously, he completed his PhD at the University of Edinburgh under the supervision of Professor Kousha Etessami, focusing on algorithms for infinite-state stochastic models. His research interests span theoretical computer science, algorithms, machine learning, and probability theory. Notable areas include robust statistical estimation, distribution learning, algorithmic game theory, and probabilistic systems analysis. His work has been published in top conferences such as SODA, FOCS, COLT, and STOC. Key contributions include advancements in high-dimensional robust estimation, efficient learning algorithms for discrete distributions, and analysis of stochastic models like branching processes and context-free grammars. No scientific awards are explicitly mentioned in the provided texts. His academic trajectory includes collaborations with leading researchers in theoretical computer science, and his PhD thesis addressed foundational problems in stochastic algorithmics.

Linh T. Tôمشاهده پروفایل

استادیار

• Labor Economics
• Gender Inequality
• Econometric Methods
• +۳ مورد دیگر

Linh T. Tô is an Assistant Professor of Economics at Boston University, currently on leave as a Visiting Assistant Professor at Stanford University. Her research focuses on labor economics with an emphasis on gender inequality and methodological advancements in empirical work. She employs a mix of quasi-experimental, structural, and experimental methods to analyze labor market dynamics, policy impacts, and behavioral economic phenomena. Her work bridges theoretical frameworks with practical policy implications, particularly in understanding worker preferences, employer practices, and consumer behavior. Key research areas include labor supply dynamics, wage differentials, workplace flexibility pricing, and the gender wage gap. She has developed methodological tools such as Bayesian Adaptive Choice Experiments (BACE) and contributed to econometric techniques addressing measurement unit sensitivity. Tô collaborates extensively with scholars like Marshall Drake, Neil Thakral, and Hannah Illing, producing influential papers on topics ranging from anticipation effects in consumption to hiring practices influencing gender disparities. Her academic contributions are reflected in journals such as the American Economic Review and the American Economic Association's Papers & Proceedings. She maintains an active GitHub presence, sharing packages like bace and tregs that facilitate reproducible research in economics. Tô holds a Ph.D. in Economics and actively participates in policy discussions through conference presentations and collaborative projects.

• Functional Data Analysis
• Time Series Analysis
• Spectral Analysis
• +۳ مورد دیگر

Vaidotas Characiejus is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark. His research focuses on functional data analysis, time series analysis, and spectral methods. He specializes in areas such as long-run variance estimation, periodogram analysis, and statistical inference for functional time series. Characiejus has contributed to developing nonparametric tests for stationarity and white noise detection in functional data contexts. His work often involves applications in nonlinear time series and asymptotic theory. He holds a Ph.D. in statistics and has published extensively in top-tier journals such as the Journal of the American Statistical Association. Characiejus's research interests span several key areas in statistics and mathematics. His primary focus is on functional data analysis, particularly in analyzing functional time series and their spectral properties. He investigates long memory processes, developing methodologies for estimating parameters such as the long-run variance and testing for stationarity. His work also addresses challenges in nonlinear time series, applying kernel-based and nonparametric techniques to improve statistical inference. Additionally, he explores applications of spectral analysis to real-world data, emphasizing the use of periodograms and Gaussian processes. His broader contributions include contributions to asymptotic theory, operator self-similar processes, and the central limit theorem in stochastic processes with varying memory structures. Characiejus has advised several research projects, though specific student names are not listed here. His research has been supported by grants focusing on statistical methodology and applications in functional data analysis. Specific grant details are not provided in the available materials. No lab or team affiliations are explicitly listed in the provided information.

Mustafa Altunمشاهده پروفایل

استاد

• Nano-crossbar Arrays
• Switching Lattices
• Logic Synthesis
• +۷ مورد دیگر

Mustafa Altun is a Professor at Istanbul Technical University in the Department of Electronics and Communication Engineering. With an h-index of 11 and 78 research outputs, he has established himself as a prominent researcher in electronics engineering and neural network hardware implementations. His research focuses on key areas including: Nano-crossbar Arrays and Switching Lattices Logic Synthesis and Boolean Function Realization Neural Network Hardware Implementation Low-voltage Circuit Design Fault Tolerant Computing Electrodeposition for Li-ion Battery Anodes Dr. Altun's recent work demonstrates a strong emphasis on energy-efficient hardware implementations of artificial neural networks using approximate arithmetic blocks to reduce power consumption while maintaining acceptable accuracy levels. His research on silicon anode electrodeposition has explored how artificial light-induced processes can increase silicon loading for improved battery performance. These dual research directions reflect his commitment to advancing both computing hardware and energy storage technologies. His scientific achievements have been recognized with prestigious awards including the ODTÜ Mustafa N. Parlar Research Incentive Award (2022), EU H2020 Success Award (2015), and the Young Scientist Award (2020). His work has been supported by multiple projects funded by TUBITAK and SRP. Dr. Altun has supervised 16 students according to available data and has led numerous research projects focusing on novel circuit architectures for efficient computing, particularly in neural networks and fault-tolerant systems. His laboratory work continues to push the boundaries of nano-crossbar array computing and its practical applications.

• Probabilistic Machine Learning
• Bayesian Inference
• Markov Chain Monte Carlo (MCMC)
• +۳ مورد دیگر

Anirudh Jain holds the role of Visitor (Faculty) in the Department of Computer Science at Aalto University, while also serving as a Doctoral Student in the School of Science and a Doctoral Researcher in the Probabilistic Machine Learning group under Professor Samuel Kaski’s professorship. His research focuses on Bayesian methods, generative models, and optimization techniques. Education: Master’s degree in Engineering and Technology from the Indian Institute of Technology (Indian School of Mines), Dhanbad (2019). Research Interests: Probabilistic Machine Learning, Bayesian Inference, Markov Chain Monte Carlo (MCMC), Generative Models, and Parameter Estimation. His work includes developing latent space models for multi-target property prediction and exploring variational inference alternatives to MCMC for parameter estimation. Collaborations: Active in projects like Veturi VL4Pharma (2023–2025), focusing on virtual laboratories for pharmaceutical R&D. Labs/Teams: Affiliated with the Probabilistic Machine Learning group and Professorship Kaski Samuel.

• Anatomy
• Medical Education
• Physiotherapy
• +۴ مورد دیگر

Dr Darren Reed is a Lecturer in Biomedical Sciences at the University of Sydney's School of Medical Sciences. His research focuses on anatomical education, shoulder electromyography studies, and the translation/adaptation of patient-reported outcome measures. He collaborates extensively with institutions in Nepal, including the Kathmandu University and Karnali Academy of Health Sciences, on projects involving physiotherapy education and outcome measure validation. His teaching specialties include functional musculoskeletal anatomy of the upper/lower limbs, trunk, and shoulder. Research interests span neuromusculoskeletal modeling, clinical validation of outcome measures like DASH and SPADI, and comparative anatomy education methods. He leads projects evaluating the effectiveness of online vs face-to-face anatomy teaching, and has published widely on electromyography validation techniques and eccentric exercise benefits. His work in global health includes establishing Nepal's first national physiotherapy school and adapting international outcome measures for Nepalese clinical contexts. Recent studies include systematic reviews on latissimus dorsi's role in trunk movement and the efficacy of adaptive seating systems for children with disabilities. Over 30 peer-reviewed publications demonstrate his expertise in electromyography analysis, musculoskeletal biomechanics, and cross-cultural adaptation of healthcare assessment tools.

Tomasz Tkoczمشاهده پروفایل

دانشیار

• Analysis
• Probability
• Convex Geometry
• +۵ مورد دیگر

Tomasz Tkocz is an Associate Professor in the Department of Mathematical Sciences at Carnegie Mellon University (CMU), affiliated with the Mellon College of Science. He holds a Ph.D. from the University of Warwick and has held postdoctoral positions at Princeton University and the Mathematical Sciences Research Institute (MSRI). His research focuses on analysis, probability, and convex geometry, with emphasis on high-dimensional phenomena, geometric inequalities, and information theory. Tkocz has taught courses ranging from undergraduate calculus to graduate-level topics in complex analysis, functional analysis, and probability. Notable contributions include work on Gaussian inequalities, convex body sections, and entropy-related problems. Education: Ph.D. in Mathematical Sciences, University of Warwick Postdoctoral Fellowships: Princeton University, MSRI Research Interests: Analysis of high-dimensional geometric structures Probabilistic methods in convex geometry Entropy and concentration inequalities Random matrices and log-concave distributions Recent Publications Trends: Tkocz’s recent work explores geometric stability phenomena (e.g., simplex and polydisc slicing), entropy interpretations of anti-concentration, and sharp inequalities for random variables. His 2025 papers extend tail bounds for uniform sums and reverse Hölder inequalities, reflecting ongoing contributions to probabilistic and geometric analysis. Teaching & Outreach: Tkocz has instructed over a decade of courses at CMU, including Complex Analysis, Functional Analysis, and Real Analysis. His materials emphasize rigorous problem-solving and applications of abstract mathematical concepts.

Jun Xieمشاهده پروفایل

استاد

• Causal Machine Learning
• Causal AI
• Bayesian Statistics
• +۷ مورد دیگر

Jun Xie is a Professor of Statistics and Graduate Chair in the Department of Statistics at Purdue University's College of Science. His research bridges causal machine learning, bioinformatics, and computational statistics with applications in genomics, proteomics, and precision medicine. Education: B.S. in Probability and Statistics, Peking University, 1994 M.S. in Probability and Statistics, Peking University, 1997 Ph.D. in Statistics, University of California, Los Angeles, 2000 Xie's research centers on causal machine learning and causal AI, specifically addressing causal inference from observational data when perfect interventions are absent. His group develops methods to integrate causal inference with machine learning for treatment effectiveness evaluation and precision medicine. He maintains significant contributions in bioinformatics, including Bayesian methods for genomics, proteomics, and statistical genetics, with applications in cotton fiber development and biomedical research. His recent publications (2022-2025) reveal a dominant focus on causal machine learning, featuring breakthroughs in causal representation learning and heterogeneous treatment effect estimation. Simultaneously, his bioinformatics work demonstrates strong continuity in multi-omics integration for plant science and biomedical applications, particularly in cotton development and vocal fold physiology. Scientific Awards: College of Science Outstanding Service Award, 2016 College of Science Team Award, 2014 Excellence in Research / Seed for Success, 2014 Excellence in Research / Seed for Success, 2012 College of Science Graduate Student Mentoring Award Xie has mentored 11 PhD graduates including Donglai Chen (2019) and Pengcheng Yang (2025), with 4 current PhD students. His NIH and NIFA-funded research supports work on causal inference methods and bioinformatics applications, particularly in agricultural and biomedical contexts. Xie leads an active research group focused on causal machine learning, developing methods for real-world data analysis where perfect interventions are absent. The group explores synergies between causal inference, machine learning, and AI, with recent emphasis on causal generative AI for precision medicine and treatment evaluation.

Benjamin Sternمشاهده پروفایل

استادیار

• Nonlinear Dynamical Systems
• Athletic Injury Prediction
• Diagnostic Accuracy
• +۳ مورد دیگر

Benjamin Stern is an Assistant Professor in the Department of Rehabilitation Sciences at Tufts University School of Medicine. His research focuses on applying nonlinear dynamical systems to predict non-contact athletic injuries and improving diagnostic accuracy in sports medicine. He holds a Doctor of Physical Therapy from Duke University (2004), a Master of Science from Western Washington University (2001), and a Bachelor of Science from the same institution (1997). Education Doctor of Physical Therapy, Duke University, 2004 Master of Science, Western Washington University, 2001 Bachelor of Science, Western Washington University, 1997 Research Dr. Stern’s work spans cost-outcomes research, diagnostic accuracy, and injury prediction. He has published in journals like British Journal of Sports Medicine and JAMA , and his NIH-funded projects include 'Empirical Dynamic Modeling' and 'Wireless Portable Movement Training'. His research emphasizes translating complex systems theory into clinical applications for athlete wellness and injury prevention. Grants & Awards NIH-funded grants (e.g., NCATS, Eunice Kennedy Shriver Institute) Arizona Fast Grant (2013) Teaching & Professional Activities He teaches courses in evidence-based practice, therapeutic interventions, and human anatomy at Tufts University. He also lectures in the Orthopedic Residency Program at HonorHealth. Dr. Stern holds a patent for a vibration-based training device and method (2010). Labs/Teams While no specific lab is named, his collaborations span interdisciplinary teams in sports medicine and rehabilitation engineering.

Marius Krollمشاهده پروفایل

پژوهشگر

• Nonparametric Statistics
• U and V Statistics
• Bootstrap Methods
• +۳ مورد دیگر

Marius Kroll is a Researcher at Ruhr University Bochum's Faculty of Mathematics within the Institute of Statistics, working as part of Professor Holger Dette's research group in stochastics. His position is based in Building IB 2/89 at Universitätsstraße 150 in Bochum. His educational background includes: Doctoral Thesis in Mathematics, Ruhr-Universität Bochum (2023) Master Thesis in Mathematics, Ruhr-Universität Bochum (2019) Bachelor Thesis in Mathematics, Ruhr-Universität Bochum (2017) Kroll specializes in nonparametric statistics with expertise in U and V statistics , bootstrap methodology , and analysis of short-range dependent processes . His research develops theoretical foundations for measuring statistical dependence, particularly through distance covariance and Chatterjee's rank correlation. He investigates asymptotic properties of these measures under various dependence structures and develops practical inference procedures. His publication record shows a focused trajectory examining the theoretical properties of dependence measures, with recent work extending to infinite dimensional data and establishing connections between different forms of asymptotic behavior. Kroll has published in top statistics journals including Biometrika and the Journal of Theoretical Probability, with several additional papers currently in preparation. His major recognition includes: Ruth Massenberg award for the best mathematical dissertation 2023 Kroll actively participates in the academic community through invited talks at major conferences including the Bernoulli-IMS World Congress in Probability and Statistics and German Probability and Statistics Days. He serves on the Executive Board of the RUB Research School (since October 2023) and the Early Career Researchers Board of the Research Academy Ruhr (since June 2025), demonstrating leadership in supporting early career researchers at his institution.

Melanie Birkeمشاهده پروفایل

استاد

• Nonparametric Statistics
• Functional Data Analysis
• Inverse Problems
• +۳ مورد دیگر

Prof. Dr. Melanie Birke is a faculty member at the University of Bayreuth , holding the Professorship for Mathematical Statistics within the Faculty of Mathematics, Physics and Computer Science . Her research spans multiple areas of statistics, including nonparametric methods, functional data analysis, inverse problems, goodness-of-fit tests, and random matrices. Women's Representative of the Mathematical Institute DAV Correspondent for actuarial training exemptions Research Interests are focused on nonparametric statistics, functional data, and inverse problems. She develops asymptotic theory for estimation and testing procedures, particularly in high-dimensional or infinite-dimensional spaces, with applications to real-world issues like image distortion and data collection errors. Her work also includes constructing goodness-of-fit tests for regression models and functional data, ensuring robust statistical analysis. Publications highlight her contributions to quantile regression, symmetry testing in inverse problems, and shape-constrained density estimation. A recurring theme involves improving statistical consistency by addressing model misspecification through advanced nonparametric techniques. Statistical Consulting is offered to both internal and external stakeholders, emphasizing methodological guidance during experimental design to prevent data inconsistencies. She also supervises Bachelor's and Master's theses in nonparametric statistics and financial mathematics.

• Complex Systems
• Data Analysis
• Nonlinear Dynamics
• +۴ مورد دیگر

Cristina Masoller Alonso is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Departament de Física and the Escola Superior d'Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa (ESEIAAT). Her research focuses on complex systems, nonlinear dynamics, nonlinear photonics, and data analysis. She is part of the DONLL research group (Dinàmica no Lineal, Òptica no Lineal i Làsers) and holds a PhD from Bryn Mawr College, USA, with a Master's in Physics. Her work spans experimental and theoretical studies in semiconductor lasers, chaos synchronization, and applications of nonlinear dynamics to neural networks and ecological systems. Her recent research highlights include analyzing noise-induced phenomena in neural networks, studying corruption network structures through gender disparity analysis, and developing entropy-based methods for detecting critical transitions in ecological and climatic systems. She has led multiple competitive research projects funded by the Air Force Office of Scientific Research and European programs, and her work is published in high-impact journals like Chaos , Optics Express , and Scientific Reports . Dr. Masoller's contributions include over 400 academic activities, including articles, conference presentations, and research grants. She collaborates extensively with institutions globally and is actively involved in organizing and reviewing for international conferences in nonlinear dynamics and photonics.

Hamid Usefiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Hamid Usefi is a Professor of Mathematics and Statistics at Memorial University of Newfoundland, with a cross-appointment to the Department of Computer Science. He earned his PhD in Mathematics from the University of Western Ontario in 2006, followed by postdoctoral fellowships at the University of British Columbia and the University of Toronto. His research focuses on algebra, machine learning, cryptography, and their applications in bioinformatics, genetics, finance, and industry. He has organized sessions on machine learning at Canadian Mathematical Society meetings and serves as an editor for Nature: Scientific Reports . Dr. Usefi's research interests include deep learning, computer vision, and medical imaging, alongside theoretical algebraic studies. His work bridges pure mathematics and applied domains, with contributions to biomarker discovery, CO2 adsorption modeling, and disease prediction using genomic data. He has been recognized with an NSERC Postdoctoral Fellowship and a MITACS NCE Fellowship. As an educator, he has supervised numerous graduate and undergraduate students in mathematics, statistics, and computer science, mentoring over 20 students since 2010. His publications span journals like Computational Statistics & Data Analysis , Scientific Reports , and Journal of Algebra , demonstrating expertise in both theoretical and applied research. Awards: NSERC Postdoctoral Fellowship (UBC), MITACS NCE Fellowship (University of Toronto) Service: Guest Editor for Scientific Reports (2024 collection on population health modeling) Conferences: Co-organizer of CMS sessions on mathematics of machine learning (2021–2024)

Emma E.M. Stewartمشاهده پروفایل

مدرس

• Visual Perception
• Eye Movements
• Object and Shape Perception
• +۵ مورد دیگر

Dr. Emma E.M. Stewart is a Lecturer in Psychology at Queen Mary University of London's School of Biological and Behavioural Sciences, affiliated with the Centre for Brain and Behaviour. She holds a PhD in Psychology from the University of Adelaide and has conducted postdoctoral research in Germany, including a DFG-funded project at JLU Giessen. Her research explores visual perception, eye movements, and oculomotor control, combining behavioral experiments, eye-tracking, computational modeling, and statistical analysis. Education: Bachelor's degrees in Psychology, Law, and French PhD in Psychology (University of Adelaide, Australia) Postdoctoral research at the University of Marburg and JLU Giessen (Germany) Research Interests: Oculomotor planning and eye movement dynamics Peripheral vs. central vision interactions Perceptual inference from object properties Role of prior experience in perception and action Object viewpoint perception and mental rotation Grants: German Research Foundation (DFG) Grant (2021–2025): £32,659 for investigating object property inferences on saccade planning. Labs/Teams: Active member of the Centre for Brain and Behaviour, collaborating on interdisciplinary projects in vision science and cognitive neuroscience.