جستجوی استاد راهنمای دانشگاهی

Jae Kyung Wooمشاهده پروفایل

استاد

• Risk theory
• Reliability theory
• Aggregate claim analysis
• +۷ مورد دیگر

Professor Jae Kyung Woo is a distinguished academic in the School of Risk and Actuarial Studies at the UNSW Business School, University of New South Wales. She holds multiple prestigious professional designations including Fellow of the Institute of Actuaries of Australia (FIAA), Fellow of the Society of Actuaries (FSA), and Chartered Enterprise Risk Analyst (CERA). Her educational background includes MMath and Ph.D. degrees from the Department of Statistics and Actuarial Science at the University of Waterloo. She has held academic positions at Columbia University as Assistant Professor in the Department of Statistics (2011-2012), and at the University of Hong Kong as Assistant Professor in the Department of Statistics and Actuarial Science (2012-2017) before joining UNSW in July 2017. Research interests focus on risk theory, reliability theory, aggregate claim analysis, queueing theory, and dependence modelling Editorial Board member for ASTIN Bulletin (2021-present), European Actuarial Journal (2025-present), Probability in the Engineering and Information Sciences (2018-present), and Risks (2020-present) Principal investigator for ARC Discovery Projects (2020-2023) and Casualty Actuarial Society grants (2018-2020) Her research output includes 35 journal articles, 1 book, 1 thesis/dissertation, and 1 other publication, with recent work emphasizing shock models for correlated large losses, credibility theory under dependency structures, and advanced dependence modeling techniques in insurance contexts. Her work bridges theoretical stochastic analysis with practical applications in insurance and risk management. Fellow of the Institute of Actuaries of Australia (FIAA), since May 2018 Fellow of the Society of Actuaries (FSA), since Oct 2013 Chartered Enterprise Risk Analyst (CERA), since Jan 2012 Fellow Member of Actuarial Society of Hong Kong (ASHK), since Dec 2018 Professor Woo has secured significant research funding including an ARC Discovery Project grant of AUD 334,000 (2020-2023) for developing shock model-based frameworks for correlated large losses, and a Casualty Actuarial Society grant of USD 20,000 (2018-2020) for credibility theory research under general dependency structures. She served as Nominated Accreditation Actuary at UNSW until 2024.

• Cancer Metabolism
• Metabolic Reprogramming
• Tumor Metastasis
• +۳ مورد دیگر

Sophia Lunt is a Professor in the Department of Biochemistry & Molecular Biology and Chemical Engineering & Materials Science at Michigan State University , where she has been since 2015 (Assistant Professor 2015-2021, Associate Professor 2021-2025, Professor 2025-present). She leads the Lunt Lab , focusing on cancer metabolism , particularly metabolic reprogramming in tumor proliferation, heterogeneity, and metastasis . Her work combines mass spectrometry , genetic cancer models , cell biology , and fluorescent agents to develop targeted cancer therapies . Ph.D. (2010) & B.S. (2005) in Chemistry Postdoctoral Fellow at MIT (2010-2015) NSF CAREER awardee (2019) 20+ peer-reviewed publications since 2007 Research Focus : Cancer metabolism (Warburg effect, PHGDH heterogeneity, TIGAR regulation) Photodynamic therapy (counterion-tuned agents, metal halide nanoclusters) Metabolomics (tumor-immune interactions, microbiome effects) Selected Scientific Awards : 2022 MSU College of Natural Science Teacher-Scholar Award 2022 MSU BMB Teaching Award 2020 MANA Young Investigator Award 2019 NSF CAREER & METAvivor Early Career Investigator Awards Her teaching includes BMB 101: Frontiers in Biochemistry (curriculum overhaul for freshman success) and BMB 461: Advanced Biochemistry I , covering metabolic regulation and pathways.

• Topological Data Analysis
• Stochastic Geometry
• Large Deviations Theory
• +۴ مورد دیگر

Christian Hirsch is an Associate Professor for Data Science and Statistics at Aarhus University, where he studies random networks motivated from biology and health sciences through techniques from topological data analysis and stochastic geometry. He is a member of the Stochastics group at the Department of Mathematics and holds additional affiliations as an Associate Fellow of the Aarhus Institute for Advanced Studies, and with the AU DIGIT Centre and the AU Quantum Campus. Current Position: Associate Professor for Data Science and Statistics, Aarhus University Previous Positions: Assistant Professor at University of Groningen and University of Mannheim Postdoctoral Experience: Aalborg University, LMU Munich, WIAS Berlin Education: PhD from Ulm University Christian Hirsch's research focuses on the statistical foundations of topological data analysis, large deviations theory in stochastic geometry, and percolation theory of spatial random networks. His work bridges theoretical mathematics with practical applications in data science, particularly in analyzing complex structures through topological methods. He investigates how topological features form and disappear in growing data structures, developing statistical tests to determine whether observed patterns are significant or merely random occurrences. His recent publications reveal a strong trend toward applying topological data analysis to increasingly complex structures, with significant focus on statistical validation of topological features. Hirsch has made substantial contributions to understanding the probabilistic behavior of persistent homology, developing functional central limit theorems and large deviation principles for topological functionals. His work spans theoretical foundations in stochastic geometry while finding applications in materials science, neural networks, and wireless communication systems. As an educator, Hirsch teaches graduate courses including Topological Data Analysis, Stochastic Geometry, Monte Carlo Simulation, Markov Decision Processes, Probability Theory, and Stochastic Processes. He has supervised numerous PhD, MSc, and BSc students, with several of his former students securing academic positions at institutions like University of Leiden, Tokyo Institute of Technology, and Budapest University of Technology. Hirsch leads a research group within the Stochastics group at Aarhus University, collaborating extensively with researchers across Europe and North America. His work demonstrates how topological methods can provide rigorous statistical insights into complex data structures, making significant contributions to both theoretical mathematics and practical data analysis techniques.

Stefanie Sequeiraمشاهده پروفایل

استادیار

• Clinical Psychology
• Developmental Psychology
• Psychopathology
• +۶ مورد دیگر

Stefanie Sequeira is an Assistant Professor in the Department of Psychology at the University of Virginia, focusing on clinical psychology, adolescent mental health, and neuroimaging. Her research explores how social threat and reward processes develop during childhood and adolescence, linking these mechanisms to psychopathology such as anxiety disorders and suicidal thoughts and behaviors (STBs). Integrates ecological momentary assessment (EMA), fMRI, and eye-tracking into her work Pursues three research lines: novel measures for social threat/reward, brain-behavior links in psychopathology, and associations between reward functioning, anxiety, and STBs in adolescence Lab: Brains, Emotions, and Adolescent Relationships (BEAR) Lab Her recent publications examine social media impacts on adolescent affect, neuroimaging in ADHD, climate change anxiety, and cultural humility in psychology training. She is recruiting graduate students for Fall 2024.

• Optical Imaging
• Biomedical Device and Instrumentation
• Medical Image Analysis
• +۲ مورد دیگر

Jungeun (Jenny) Won is an Assistant Professor of Research in the Department of Biomedical Engineering at the School of Engineering and Applied Sciences, University at Buffalo. Her research focuses on optical imaging , biomedical device development , medical image analysis , and artificial intelligence in OCT . She leads the Translational Biophotonics Laboratory , where she develops advanced OCT techniques for medical applications such as diabetic retinopathy , otitis media , and biofilm analysis . Contact: 215J Bonner Hall, Buffalo NY 14260, jungeunw@buffalo.edu Related Links: CV PDF , Google Scholar , Lab Website Her recent work involves high-resolution OCT for longitudinal studies on retinal degeneration, VISTA OCTA for blood flow analysis, and 3D motion correction algorithms to enhance image quality. She also explores multimodal imaging combining OCT with Raman spectroscopy for bacterial differentiation and microplasma-based therapies for ear infections.

Valerie Welbornمشاهده پروفایل

استادیار

• Computational Chemistry
• Biophysical Chemistry
• Molecular Dynamics Simulations
• +۷ مورد دیگر

Valerie Welborn is an Assistant Professor in the Department of Chemistry within the College of Science at Virginia Tech. Her research program focuses on multiscale simulation of condensed phase systems, particularly examining the role of electric fields in biological interfaces and biological-like systems. She leads an active research group that bridges computational chemistry with experimental validation through multiple collaborations. Dr. Welborn's research interests span protein dynamics and function, characterization of structural and functional water, polysaccharides in solution, and polymer design for metal chelation. Her work combines morphological, structural, dynamic and electronic factors to develop new models of biological interfaces, with particular emphasis on how water interacts at a fundamental molecular level with biological entities such as proteins and bone tissues. She specializes in electric field calculations to understand protein flexibility in catalysis and ion transport, seeking to reconcile protein dynamics with electrostatic preorganization theory. Her recent publications demonstrate strong activity across multiple domains, with particular emphasis on electric field analysis in protein function, water dynamics at biological interfaces, and polymer design for metal chelation. Her work shows a consistent trajectory toward increasingly complex biological systems and more sophisticated computational approaches, including polarizable force field methods and multiscale modeling techniques. Centre for Doctoral Training (CDT) on Theory and Simulation of Materials (TSM) Ph.D. Prize for Research Excellence, 2014 Outstanding Contribution to Outreach and Public Engagement, CDT TSM, 2014 Engineering and Physical Sciences Research Council (EPSRC) fully-funded Ph.D. Fellowship, CDT TSM, 2011 Editor-selected as '2021 Hot PCCP article' Front cover article in Phys. Chem. Chem. Phys. Dr. Welborn actively mentors a diverse group of researchers, including multiple postdoctoral associates, graduate students across chemistry and related disciplines, and undergraduate researchers. Her lab participates in the NSF Materials Innovation Platform GlycoMIP (DMR-1933525), focusing on polysaccharide research. She collaborates extensively with experimental groups, particularly with Professor Michael Schulz on polymer design for metal chelation projects. Her lab develops computational tools like the ELECTRIC software package for electric field calculations in biomolecular systems. The Welborn group maintains active research programs in four main areas: protein dynamics and function, characterization of structural and functional water, polymer design for metal chelation, and polysaccharides in solution. Each program employs specialized computational approaches to address fundamental questions in biological chemistry, with particular emphasis on how electric fields govern molecular behavior at biological interfaces.

• Machine Learning
• Graph Neural Networks
• Data Attribution
• +۴ مورد دیگر

Jiaqi Ma is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC), holding dual appointments in the School of Information Sciences and the Siebel School of Computing and Data Science. Their research focuses on machine learning, graph neural networks, and data attribution, with particular emphasis on fairness in AI, large language models, and scalable algorithms. Ma has contributed to frameworks like dattri for efficient data attribution and GraSS for scalable influence functions. Research interests include graph learning (e.g., structural information analysis in text-attributed graphs), fairness in ML models (e.g., mitigating disparities in unlearning processes), and ethical AI applications. Collaborations span topics like data curation (DCA-Bench benchmark) and reinforcement learning transparency (A Snapshot of Influence). Key contributions include improving data removal while maintaining fairness (Fair Machine Unlearning), analyzing LLMs' graph structural utilization, and developing open-source tools like OpenHexAI for explainable ML evaluation. No scientific awards are explicitly listed, but their work reflects significant contributions to trustworthy AI and machine learning systems. Advising and grants: While specific students/grants are unlisted, Ma leads research teams advancing graph learning (e.g., Graph Learning Indexer platform) and safety-critical AI (e.g., unsafe data detection via attribution). Their work bridges theoretical foundations (e.g., influence functions) with practical applications (e.g., MNL model rankings).

• Noise Abatement in Machines and Vehicles
• Numerical Modeling in Mechanics
• Automotive Structures and Dynamics
• +۱۲ مورد دیگر

Prof. Wim Desmet is a full professor at the Faculty of Engineering Science and head of the Department of Mechanical Engineering at KU Leuven . His research focuses on advanced modeling techniques for mechanical systems, including: noise and vibration control in automotive and industrial systems computational acoustics and interval field uncertainty modeling metamaterials for broadband vibroacoustic performance AI-driven diagnostic systems in renewable energy and manufacturing Current research projects address challenges in electric vehicle drivetrains, wind turbine monitoring, and multi-physical digital twin development. He actively contributes to academic governance as: Managing Director of KU Leuven Head of Subdivision HIST Chair of multiple executive committees Member of 15+ academic and administrative councils

Alexander Aueمشاهده پروفایل

استاد

• Time Series Analysis
• Change-Point Problems
• Functional Data Analysis
• +۳ مورد دیگر

Alexander Aue is a Professor in the Department of Statistics at the University of California, Davis. His research focuses on time series analysis, change-point problems, functional data analysis, and high-dimensional statistics. He holds a Ph.D. and has contributed to foundational methodologies in these areas. His work emphasizes developing robust statistical techniques for analyzing complex data structures, including spectral analysis, bootstrap methods, and functional time series. Notably, he has advanced change-point detection methodologies without relying on dimension reduction. Awarded the prestigious AAAS Fellowship for his contributions, his recent publications explore topics like high-dimensional hypothesis testing, stationarity testing for functional time series, and error estimation in time series predictions.

Britta van Beersمشاهده پروفایل

استاد

• Legal Theory
• Bioethics
• Technology Law
• +۴ مورد دیگر

Britta van Beers is a Full Professor at Vrije Universiteit Amsterdam, holding positions in Legal Theory and Legal History within the Faculty of Law, as well as affiliations with the Kooijmans Institute, Law & Technology, and Boundaries of Law. Her research focuses on intersections between law, emerging technologies, bioethics, and human rights. She has authored over 70 publications since 2015, addressing topics like CRISPR governance, surrogacy regulation, and legal personhood in the age of biotechnology. Awarded multiple grants and prizes (specifics unspecified) Supervised 2 PhD theses Edited volumes on personalized medicine ethics and symbolic legislation Her work critically examines technological innovations' impact on legal frameworks, particularly in genetics, artificial intelligence, and reproductive technologies. Recent projects include analyzing human germline modification dialogues and ethical challenges in accessing biobanks for forensic purposes. Key contributions include redefining legal personhood in the context of posthuman technologies and advocating for explicit goals in public dialogues about gene editing. She frequently engages with policymakers, as seen in her contributions to Dutch parliamentary discussions on embryo law reforms.

Fatemeh Azhariمشاهده پروفایل

مدرس

• Structural Engineering
• Computational Materials Engineering
• Finite Element Analysis
• +۷ مورد دیگر

Dr. Fatemeh Azhari is a Lecturer in Structural Engineering at the Faculty of Engineering, Monash University, specializing in multi-scale computational tools for advanced materials and structures. She holds a Ph.D. from Monash University (2018), an M.Sc. and B.Sc. from Isfahan University of Technology (2012, 2010). Her research focuses on composite materials, additive manufacturing, and structural mechanics, with applications in sustainable construction and defense projects. Education: Ph.D. Structural Engineering, Monash University (2018) M.Sc. Structural Engineering, Isfahan University of Technology (2012) B.Sc. Civil Engineering, Isfahan University of Technology (2010) Research Interests: Multi-scale modeling, finite element analysis, composite materials (CFRP/GFRP), titanium alloys, fire dynamics, and structural stability. She leads projects funded by DST Group and collaborates with institutions like UNSW and the University of Melbourne. Research Trends: Her work emphasizes integrating computational models with experimental data to predict material behavior under extreme conditions, with recent studies focusing on pseudo-ductile composites, fire dynamics, and additive manufacturing. Awards: 2023 Advancing Women’s Success Grant 2023 ECA Seed Program Best Research Paper Award (2018, 2021) Supervision & Grants: Supervises multiple PhD candidates and co-leads projects on titanium alloys and dental implant mechanics. Active in teaching structural mechanics and materials courses at Monash. Teams/Labs: Collaborates on ICME projects and leads multi-institutional teams focusing on advanced materials and structural systems.

Enno Mammenمشاهده پروفایل

استاد

• Nonparametric Statistics
• Bootstrap Methods
• Additive Models
• +۶ مورد دیگر

Enno Mammen is a Professor of Mathematical Statistics at Heidelberg University, leading the Institute for Applied Mathematics. His career includes roles as Chair for Mathematical Statistics at Heidelberg (2014–present), Chair for Statistics at the University of Mannheim (2003–2014), and various academic positions since 1986. He holds a PhD (1983) and habilitation (1992) from Heidelberg University. Research interests focus on nonparametric statistics, bootstrap methods, additive models, high-dimensional data, and statistical theory. Key contributions include foundational work on the wild bootstrap, penalized nonparametric estimators, and nonparametric diffusion models. He has authored over 150 papers in top journals like the Annals of Statistics and Biometrika. Current research spans Hawkes processes, neural network statistics, and non-Euclidean data analysis. He has supervised 12 PhD students since 2010, with many progressing to academic roles. Awards include the Heinz Maier Leibnitz Prize (1989) and IMS Fellowship (1998). Active in editorial roles for journals like the Annals of Statistics and Bernoulli. Major funding includes leadership of the DFG-funded Research Training Group 'Statistical Modeling of Complex Systems' (2013–2022) and collaborations with Russian institutions on stochastic differential equations.

• Cartography
• Geomatic Engineering
• Historical Map Analysis
• +۴ مورد دیگر

Dr. Yizi Chen is a Researcher affiliated with the Professorship for Cartography at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. Their work focuses on advancing cartographic techniques through AI-driven methods, historical map analysis, and geospatial technologies. Key contributions include automated map vectorization, semantic segmentation of historical maps, and integrating multimodal data for robotic systems. They have published extensively in top-tier journals and conferences, addressing challenges in deep learning applications for geomatic engineering. Education details are not explicitly provided in the text. Research interests include semantic segmentation, generative AI for cartography, and steganography in image translation. Notable publications span topics from eye-tracking segmentation to urban land use mapping, reflecting a strong interdisciplinary approach. Dr. Chen collaborates on projects involving historical map digitization and benchmarking datasets for computer vision tasks. No awards or grants are mentioned. Their work contributes to advancing geomatic engineering through innovative solutions in digital mapping and spatial data analysis.

James Mitchellمشاهده پروفایل

استاد

• Public Policy
• Devolution Studies
• Scottish Politics
• +۵ مورد دیگر

James Mitchell is a Professor of Public Policy at the University of Edinburgh’s School of Social and Political Science . He holds a MA (Political Studies) from the University of Aberdeen and a D.Phil. (Oxford). His research focuses on British politics, devolution, public policy, territorial politics, and Scottish nationalism. He has supervised over 30 years of PhDs and Masters in public policy, devolution, and constitutional politics. Key research themes include fiscal accountability in Scotland, party membership surges post-referendum, and multi-level governance. Research Projects: ESRC-funded study on SNP and Scottish Greens’ post-referendum membership growth Investigation of fiscal devolution’s accountability gaps Analysis of Scotland’s constitutional questions and local governance Research Interests: Mitchell explores territorial politics, public service reform, and political behavior in sub-state governments. He has contributed to debates on Scottish independence, EU referendum impacts, and intergovernmental relations. Recent work includes studies on the SNP leadership contest (2023) and Holyrood’s fiscal structures. Grants & Awards: He has secured grants from the Economic and Social Research Council (ESRC) for projects on party membership and constitutional dynamics. No specific scientific awards are listed, but his work is widely cited in political science and public policy. Labs & Teams: He is part of the Territorial Politics Research Group and has collaborated on regional economic forecasting and governance studies. His work often bridges academia and policy, influencing debates on devolution and public finance.

Brenda Rubensteinمشاهده پروفایل

دانشیار

• Electronic Structure Methods
• Quantum Chemistry
• Molecular Computing
• +۲ مورد دیگر

Brenda Rubenstein is an Associate Professor of Chemistry and Physics at Brown University and Director of the Data Science Institute. Her research bridges quantum chemistry and materials science, developing stochastic electronic structure methods for accurate large-scale simulations. She also explores molecular computing and computational biophysics. Her group emphasizes diversity, collaboration, and work-life balance. Recent work includes quantum Monte Carlo techniques for materials discovery, machine learning approaches for protein dynamics, and quantum computing applications. She received a named professorship in 2025 and leads the Levermore Global Scholars program. Her publications focus on computational methods across chemistry, physics, and materials science.