جستجوی استاد راهنمای دانشگاهی

Peter Micalosمشاهده پروفایل

مدرس ارشد

• Anatomy
• Physiology
• Pain Research
• +۷ مورد دیگر

Dr. Peter Micalos is a Senior Lecturer in Anatomy and Physiology at Charles Sturt University's School of Dentistry and Medical Sciences, based at the Port Macquarie campus. With extensive experience in teaching anatomy, pathophysiology, and exercise science, he applies engaging pedagogical approaches across various learning levels. As the School Honours Advisor for the B.Sci (Honours) program, he plays a significant role in academic guidance at the institution. Dr. Micalos's educational background includes a PhD in Neurosensation, Pain, and Exercise, completed between 2005-2012, with research focused on "The effect of aerobic exercise on pain and neurosensory modulation in healthy participants and with chronic pain disorder." His teaching philosophy emphasizes analytical reasoning through observation and interpretation, utilizing scaffolded and sequential learning strategies that foster lifelong learning. His research spans diverse areas of human health including pain mechanisms, exercise science, cancer, nutrition, the human microbiome, and Parkinson's disease. Dr. Micalos has secured over $80,000 in external research funding and has published more than 20 scientific articles, literature reviews, an edited book, and a book chapter, accumulating over 500 citations in Google Scholar with an h-index of 9. His recent publications demonstrate a strong focus on rice bran arabinoxylan compounds in cancer treatment, Parkinson's disease in regional Australia, and prehospital emergency care. Dr. Micalos has received multiple awards including the CRGT Completion Award (2012) and School of Biomedical Sciences Teaching Awards (2017, 2019). His significant research contributions include collaborations with Westmead Hospital's Department of Radiology, the Brain Dynamics Centre at CSU, the University of Aalborg in Denmark, and the University of Technology Sydney. His work on pain measurement employs sophisticated techniques including nociceptive withdrawal reflex, sensory perceptual ratings, somatosensory cortical evoked potentials, and functional magnetic resonance imaging.

Karen Azariمشاهده پروفایل

استادیار

• Cryptography
• Security Proofs
• Secure Group Messaging
• +۴ مورد دیگر

Karen Azari is an Assistant Professor at the University of Vienna, where she is a member of the Research Group Theory and Applications of Algorithms within the Faculty of Computer Science. Her office is located at Währinger Straße 29, 1090 Wien, and she can be reached at karen.azari@univie.ac.at. Dr. Azari's academic background includes: PhD studies at the Institute of Science and Technology Austria (ISTA), advised by Krzysztof Pietrzak Postdoctoral work at ETH Zurich under the guidance of Dennis Hofheinz Her primary research area is cryptography, specializing in formal security definitions and security proofs of cryptographic protocols. She focuses on secure group messaging, proof systems for decentralized systems, and the foundations of resource-restricted cryptography. Her work combines mathematical rigor with practical implementation considerations, developing protocols with provable security guarantees that address real-world security challenges. Dr. Azari approaches cryptographic problems with precision, ensuring her contributions advance both theoretical foundations and practical applications of cryptographic protocols. Analysis of Dr. Azari's recent publications reveals a consistent focus on strengthening the theoretical underpinnings of cryptographic protocols while addressing practical implementation challenges. Her work spans multiple subfields including secure multi-party computation, group key agreement protocols, and cryptographic primitives based on lattice problems. A distinctive trend in her research is the emphasis on adaptive security models, which provide stronger guarantees against sophisticated adversaries who can adapt their attacks based on observed protocol executions. Her scientific contributions have been recognized with notable honors: Best Student Paper award at ACNS 2025 for "Tighter provable security for TreeKEM" As an educator and mentor, Dr. Azari currently supervises PhD student Maximilian von Consbruch (since 2025). She teaches core computer science courses including "Algorithms and Data Structures" and "Foundations of Cryptography" in the Spring semester of 2025. Her service to the academic community includes participation in prestigious program committees for major cryptography conferences including CFAIL 2025, ASIACRYPT 2024, CFAIL 2024, CRYPTO 2024, EUROCRYPT 2023, and TCC 2022. Dr. Azari is a principal investigator within the Theory and Applications of Algorithms research group at the University of Vienna's Faculty of Computer Science, where she leads a research team focused on developing theoretically sound yet practically implementable cryptographic solutions for emerging security challenges in communication systems and decentralized applications.

Kevin Liuمشاهده پروفایل

دانشیار

• Phylogenetics
• Computational Biology
• Genomics
• +۳ مورد دیگر

Kevin Liu is an Associate Professor at Michigan State University, affiliated with the Genetics & Genome Sciences Program and the Ecology, Evolution & Behavior Program . His research focuses on computational biology, phylogenetics, and genomics, particularly developing statistical methods for evolutionary analysis. Institution: Michigan State University Programs: Genetics & Genome Sciences Program, Ecology, Evolution & Behavior Program Kevin Liu’s work centers on advancing phylogenetic reconstruction techniques, addressing challenges in species tree estimation, multiple sequence alignment, and handling non-tree-like evolutionary histories. He employs coalescent-based models, hidden Markov models, and resampling methods to improve accuracy in genomic data analysis. Recent trends in his research include developing scalable algorithms for phylogenetic network inference, integrating statistical resampling techniques, and exploring the impact of alignment and tree estimation errors on evolutionary studies. His publications highlight collaborations in computational method development for large-scale genomic datasets.

Philip Clemsonمشاهده پروفایل

پژوهشگر ارشد

• Time Series Analysis
• Complex Systems
• Chaotic Systems
• +۷ مورد دیگر

Dr. Philip Clemson is a Senior Research Associate in the Department of Physics at Lancaster University, specializing in the time series analysis of complex systems. His work focuses on chaotic and stochastic data, particularly from non-autonomous (time-dependent) systems, with significant applications in biomedical physics and living systems. He has extensive experience in industry, having developed algorithms for medical devices and wearable technology. His research also encompasses numerical Bayesian statistical techniques using Markov chain Monte Carlo and sequential Monte Carlo methods. Current projects include phase coherence analysis for autonomic neuropathy diagnosis and network dynamics-based standards for endothelial health, reflecting his interdisciplinary approach between physics and biomedical applications. 2024: Phase coherence in autonomic neuropathy diagnosis 2023-2024: Network dynamics for endothelial health standards His recent publications demonstrate expertise in time-localized analysis, cardiovascular dynamics, and chronotaxic systems. Dr. Clemson is part of the Nonlinear and Biomedical Physics research group within the Physics Department.

Michael N. Shadlenمشاهده پروفایل

استاد

• Neuroscience
• Decision Making
• Computational Neuroscience
• +۷ مورد دیگر

Michael N. Shadlen, MD, PhD , is a Professor of Neuroscience at Columbia University’s Vagelos College of Physicians and Surgeons , a Principal Investigator at the Zuckerman Institute, and an HHMI Investigator since 2000. His work bridges neurophysiology and computational modeling to study decision-making mechanisms in the parietal cortex. Key Affiliations: Vagelos College of Physicians and Surgeons, Columbia University Mortimer B. Zuckerman Mind Brain Behavior Institute Howard Hughes Medical Institute (HHMI) The Kavli Institute for Brain Science Grossman Center for the Statistics of Mind Research Focus: Dr. Shadlen investigates how neural circuits integrate sensory evidence , compute costs/benefits , and enable temporal anticipation . His studies link higher cognition (e.g., language, consciousness) to fundamental neural building blocks, aiming to address disorders like schizophrenia , autism , and dementia . Scientific Contributions: 2023: National Academy of Sciences Fellow 2017: Karl Spencer Lashley Award (American Philosophical Society) 2015: AAAS Fellow 2014: National Academy of Medicine Member 2012: Golden Brain Award (Minerva Foundation) Recent Work Trends: His 2025 Nature Methods paper introduces DREDge , a tool for motion correction in neural recordings . Earlier studies (2016–2023) explore evidence integration , confidence dynamics , and time-sensitive neural processes , emphasizing computational parallels in cognition.

Ada Kedingمشاهده پروفایل

مدرس

• Clinical Trials
• Orthopaedic Statistics
• Medical Statistics
• +۳ مورد دیگر

Ada Keding is a Lecturer and Researcher at the York Trials Unit , University of York, since 2012. She specializes in statistical analysis for orthopaedic clinical trials and supports trial design across diverse health domains, including surgical interventions and public health. Her educational background includes a BSc in Psychology (York), MSc in Human-Computer Interaction (UCL), and MSc in Statistics (Sheffield). Research Roles: Deputy Chair (Dissertation Development Committee), Deputy Chair (Library Committee), Member (Departmental Research Governance Committee) Key Collaborations: NIHR Health Technology Assessment Programme, TB & Tobacco Consortium, DiaDeM Global Health Research Group Research Focus: Ada’s work centers on randomized controlled trials (RCTs), particularly longitudinal studies in orthopaedics. She has contributed to trials like ACTIVE , PROFHER , UK FROST , and DiaDeM , addressing clinical effectiveness, cost-utility, and biases in trial design. Her methodological expertise includes Bayesian sequential models , patient-reported outcomes , and health economics . Publication Trends: Her articles span orthopaedic interventions , diabetes-depression comorbidity , tobacco cessation , and public health strategies . Recurring themes include comparative effectiveness, economic evaluations, and statistical methodology for pragmatic trials.

• Evolutionary Algorithms
• Optimization
• Multi-Objective Optimization
• +۳ مورد دیگر

Ofer M. Shir is a faculty member affiliated with Tel-Hai College and MIGAL - Galilee Research Institute in Israel. His primary research focuses on evolutionary algorithms, multi-objective optimization, and their applications in quantum control, machine learning, and computational science. He has collaborated extensively with institutions and researchers globally, contributing to advancements in optimization theory and practical problem-solving through evolutionary computation. Shir's work spans theoretical foundations, such as covariance-Hessian relations in evolution strategies, to applied domains like quantum control experiments and algorithmic-guided discovery of viral epitopes. He has developed and benchmarked algorithms like the CMA-ES and SMS-EMOA, emphasizing their performance in complex, real-world scenarios. His contributions also include methodologies for sequential experimentation and improving model accuracy through techniques like batch normalization. Shir has published extensively in top-tier venues such as IEEE Transactions on Evolutionary Computation, Genetic Programming and Evolvable Machines, and the GECCO conference series. His research bridges theoretical computer science with practical applications, impacting fields from bioinformatics to engineering.

• Cognitive Psychology
• Human Memory
• Reasoning
• +۴ مورد دیگر

John Dunn is an Adjunct Professor at the School of Psychological Science, The University of Western Australia (UWA). He holds a PhD and BA (Hons) in Psychology from UWA (1985 and 1976, respectively). Previously, he served as a Professor at the University of Adelaide (2005–2016) and held roles including Interim Dean at Murdoch University (2017–2018) and a part-time Professorship at Edith Cowan University (2018–2021). His research focuses on cognitive psychology, human memory, reasoning, decision making, and methodological issues in experimental psychology. He has led or co-led nine grants, including projects on reasoning models and eyewitness identification. His recent work emphasizes statistical methodologies and interdisciplinary applications. **Research Interests**: His expertise spans cognitive processes such as recognition memory, decision-making under uncertainty, and statistical modeling. He has contributed to debates on measurement, Bayesian analysis, and experimental design. His studies often bridge theoretical frameworks with practical applications, such as improving forensic procedures. **Grants & Collaborations**: Dunn has secured funding for projects like 'State-trace analysis: Theory and application' (2013–2019) and 'Uncovering the processes underlying human reasoning' (2017–2018). His collaborations span institutions globally, addressing topics like eyewitness identification and dual-process theories. **Recognition**: While no explicit awards are listed, his sustained contributions to cognitive psychology and interdisciplinary research reflect significant scholarly impact.

Fredrik Lundvall Wollbraatenمشاهده پروفایل

پژوهشگر ارشد

• Bayesian Statistics
• Computational Statistics
• Data Science
• +۳ مورد دیگر

Fredrik Lundvall Wollbraaten is a Research Fellow at the University of Oslo, affiliated with the Department of Mathematics within the Faculty of Mathematics and Natural Sciences. His PhD research, supervised by Professor Geir Olve Storvik and in collaboration with Professor Ingrid Glad and Associate Professor Morten Stakkeland (ABB), focuses on sequential learning and decision-making from maritime sensor data, including failure detection and maintenance rule optimization. His work combines methodological development in model building, inferential tools, and computational methods. Education: Master's in Statistics from the University of Oslo (2020), Bachelor's in Mathematics from NTNU (2018). Prior to his PhD, he worked at RagnaRock Geo (AI startup), SpareBank 1 Markets (equity research analyst), and Nordea Markets (Data Scientist). His academic interests span applied Bayesian methods, parallelized MCMC simulations, and statistical learning techniques. He is part of the Big Insight group and contributes to projects on statistical and machine learning methods for sensor data analysis. Fredrik is also actively involved in the Oslo Sports Student Association (OSI).

Josip Žubrinićمشاهده پروفایل

استادیار

• Applied Mathematics
• Homogenization Theory
• Elasticity and Materials Science
• +۳ مورد دیگر

Dr. Josip Žubrinić is an Assistant Professor in the Department of Applied Mathematics at the Faculty of Electrical Engineering and Computing (FER), University of Zagreb. His research focuses on applied mathematics, homogenization theory, elasticity, and interdisciplinary applications in materials science and quantum systems. He is affiliated with FER's applied mathematics group and actively contributes to the Bibliography (CROSBI) research repository. Dr. Žubrinić's work spans theoretical and applied domains, including: Homogenization of PDEs in heterogeneous media Statistical mechanics models (e.g., Rydberg atoms, Flory polymer models) Combinatorial optimization for settlement planning Mathematical analysis of elastic structures and poroelastic plates Recent research highlights include studies on critical-contrast PDEs, quantum lattice systems, and multiscale modeling of composite materials. His articles demonstrate a strong emphasis on interdisciplinary methods bridging pure mathematics and real-world engineering problems. No scientific awards or grants are explicitly listed in the provided materials. His teaching includes specialized topics like complexity functions in jammed systems and combinatorial models.

Xiongjie Chenمشاهده پروفایل

پژوهشگر ارشد

• Statistical Machine Learning
• Bayesian Inference
• Uncertainty Quantification
• +۵ مورد دیگر

Dr. Xiongjie Chen is a Research Fellow at the University of Surrey's School of Computer Science and Electronic Engineering, affiliated with the Computer Science Research Centre. He holds a B.Sc from Fujian University of Technology, an M.Eng from Tongji University, and a PhD in Computer Science from the University of Surrey under Dr. Yunpeng Li's supervision. Chen's research develops uncertainty quantification methods to enhance trustworthiness in machine learning systems. His work spans statistical machine learning, Bayesian inference, Monte Carlo methods, and AI applications in healthcare—particularly dental radiography analysis. Current projects focus on AI-assisted dental disease detection, differentiable particle filtering for complex systems, and efficient optimal transport techniques. His publications predominantly explore differentiable particle filters, uncertainty quantification, and optimal transport, with recent emphasis on integrating these techniques with neural networks for healthcare and dynamical systems. Research demonstrates consistent focus on Bayesian methods, sequential Monte Carlo, and practical applications in computer vision and intelligent systems. Chen has contributed as a demonstrator for courses including Computational Intelligence and Data Science at Surrey. His academic background and research collaborations reflect strong foundations in both theoretical machine learning and applied AI solutions.

• Signal Processing
• Statistical Analysis
• Bayesian Methods
• +۳ مورد دیگر

Dr. Alireza Ahrabian is a Research Fellow in the Department of Electronic Engineering at the University of Surrey. He holds a PhD in Signal Processing and an MEng in Electrical Engineering from Imperial College London (2014 and 2010, respectively). His research focuses on developing algorithms for detecting changes in time-series data, with emphasis on Bayesian approaches and statistical signal processing techniques. He has contributed to IoT data stream analysis, sensor data web services, and multi-sensory accelerometer segmentation. Education: PhD in Signal Processing, Imperial College London, 2014 MEng in Electrical Engineering, Imperial College London, 2010 His work bridges theoretical advancements with practical applications, including bone loading exercise classification using machine learning and parameter labelling in Bayesian change detection models. While no awards are explicitly listed, his publications demonstrate impactful contributions to data analysis methodologies. Dr. Ahrabian's research emphasizes: Sequential and batch processing algorithms IoT-enabled sensor data interpretation Bayesian statistical models for dynamic systems His publications span domains like smart cities, health monitoring, and activity recognition, reflecting a multidisciplinary approach to time-series challenges.

Daniel Gillenمشاهده پروفایل

استاد

• Survival Analysis
• Longitudinal Data Analysis
• Clinical Trials Methodology
• +۵ مورد دیگر

Daniel Gillen is Chancellor’s Professor and Chair of the Department of Statistics at the Donald Bren School of Information and Computer Sciences, University of California, Irvine. A leading biostatistician, his research spans survival analysis, longitudinal data methods, clinical trials design, and spatial risk estimation, with applications in Alzheimer’s disease, cancer, diabetes, and cardiovascular research. He has developed robust statistical tools for censored data and adaptive clinical trial frameworks. Ph.D. in Biostatistics (University of Washington, 2003) M.S. in Biostatistics (University of Washington, 2001) B.S. in Mathematics (Cal Poly State University, 1997) Gillen’s work addresses health disparities through innovative recruitment strategies in Alzheimer’s studies, biomarker validation under biased sampling, and regulatory guidance for pharmaceutical trials. He has served on over 80 Independent Data Monitoring Committees (IDMCs) and as a Special Government Employee (SGE) to the FDA for 20 years, influencing drug approval processes and trial ethics. His recent grants and accolades include: AAAS Fellowship (2024) for contributions to statistical methods Chancellor’s Professor title (2022) recognizing scholarly excellence $3.7M NIH grant to improve diversity in Alzheimer’s trials $1.8M NIH grant for biomarker research under biased sampling

David Bromanمشاهده پروفایل

استاد

• Programming Languages and Compilers
• Real-Time Systems and Cyber-Physical Systems
• Probabilistic Machine Learning
• +۲ مورد دیگر

David Broman is a Professor at KTH Royal Institute of Technology in Sweden, leading the Division of Software and Computer Systems (SCS) and serving as Associate Director Faculty for Digital Futures. He holds a Ph.D. in Computer Science from Linköping University (2010), with visiting roles at UC Berkeley (2012–2016) and Stanford University (2023–2024). His research spans programming languages, real-time systems, and probabilistic machine learning, emphasizing secure and efficient compilation techniques. Education: Ph.D. in Computer Science (Linköping University, 2010) Visiting Roles: UC Berkeley (2012–2014 part-time), Stanford University (2023–2024) Research Interests: David focuses on advancing programming language theory, compilers, and probabilistic methods. Key areas include transformer-based compilation, cyber-physical systems, and formal verification. His work bridges theory and practice, addressing challenges in software reliability and efficiency. Awards: Notable recognitions include the Best ETAPS Paper Award (2023), Distinguished Artifact Award (ESOP 2022), and Teacher of the Year (KTH 2017). His contributions also include grants from the Swedish Foundation for Strategic Research and Wallenberg AI, Autonomous Systems and Software Program (WASP). Advising & Grants: David mentors graduate students (e.g., Gizem Caylak, Viktor Palmkvist) and leads projects funded by WASP, focusing on next-generation compilation techniques. He also contributed to tools like Miking, Timed C, and FlexPRET. Labs & Teams: His team develops frameworks for probabilistic programming and real-time systems, emphasizing collaboration with industry and academia through initiatives like Digital Futures at KTH.

Kevin J. Liuمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Biology
• Comparative Genomics
• +۳ مورد دیگر

Kevin J. Liu is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University, with affiliations in the Ecology, Evolution, and Behavior (EEB) Program and the Genetics and Genome Sciences (GGS) Program. His research focuses on developing computational methodologies for comparative genomics, particularly under complex evolutionary scenarios, to connect genomic insights to biological function and phenotype. The lab employs big-data-driven approaches to generate hypotheses for biological and biomedical discoveries. Research interests include phylogenetic analysis, evolutionary genomics, computational methods for handling non-tree-like evolutionary histories, and statistical resampling techniques. Liu emphasizes interdisciplinary work at the intersection of bioinformatics and computational biology, addressing challenges in alignment-free phylogenetics and species network inference. Publications reflect a strong focus on statistical methods for phylogenetic reconstruction, error estimation in sequence alignment, and scalable genomic analysis tools like Fastnet and Coal-Miner. His work bridges computational innovation with biological interpretation, aiming to advance our understanding of evolutionary processes and genomic diversity. Prospective students and researchers interested in interdisciplinary bioinformatics are encouraged to contact him via email at kjl@msu.edu .