جستجوی استاد راهنمای دانشگاهی

• Applied Mathematics
• Probability and Statistics
• Financial Mathematics
• +۲ مورد دیگر

Sergei Balakin is a Research Fellow at Impact Labs, Monash University, with active research output spanning two decades from 2003 to 2025. His work bridges theoretical mathematics with practical applications in finance and computing systems. His research focuses on Applied Mathematics, Probability and Statistics, Financial Mathematics, Operator Theory, and Computer Science. Key contributions include combinatorial analysis of Markov sequences, stochastic modeling for computing systems, and dynamic optimization for energy storage trading, demonstrating strong interdisciplinary connections between pure mathematics and real-world problems. Publication trends show evolution from foundational operator equations (2008-2009) to combinatorial probability (2011) and stochastic computing models (2013), culminating in recent financial mathematics applications (2025). His work consistently applies advanced mathematical techniques to solve complex problems across multiple domains. Based at Impact Labs, Monash University, Balakin contributes to interdisciplinary research initiatives where mathematical modeling intersects with economic and computational challenges.

Wei Liمشاهده پروفایل

پژوهشگر ارشد

• Photovoltaic Engineering
• Materials Science
• Renewable Energy
• +۲ مورد دیگر

Wei Li is a Research Fellow in the Department of Chemical and Biological Engineering at Monash University. Previously, he was an associate researcher in the Cu2ZnSnS4 Group at UNSW's School of Photovoltaic and Renewable Energy Engineering. His research focuses on photovoltaic engineering, materials science, and renewable energy technologies, particularly crystalline silicon thin-film solar cells. Education credentials: PhD in Photovoltaic Engineering from University of New South Wales (2013) Master's in Materials Science from Wuhan University of Technology (2009) Bachelor's in Materials Science from Wuhan University of Technology Research interests center on advanced materials for solar energy conversion, photocatalytic processes, and functional nanomaterials. His work spans fundamental material development to practical applications in energy and environmental technologies. Publications demonstrate expertise in photocatalyst design, material characterization, and process optimization. Recent work focuses on methane photocatalytic conversion, MOF-based adsorption systems, and functional coatings for biomedical applications. Research frequently employs advanced computational modeling alongside experimental validation. Scientific awards: No awards reported in available materials. Laboratory affiliations include Monash's chemical engineering research facilities. Industry collaborations not specified in available documentation.

Sasan Ahdi Rezaeiehمشاهده پروفایل

پژوهشگر ارشد

• Biomedical Engineering
• Antenna Design
• Medical Imaging
• +۳ مورد دیگر

Dr. Sasan Ahdi Rezaeieh is a Research Fellow and Adjunct Research Fellow in the School of Electrical Engineering and Computer Science at The University of Queensland. His work focuses on biomedical engineering, antenna design for medical diagnostics, and microwave imaging systems for detecting conditions like hepatic steatosis and pulmonary edema. He holds a Ph.D. in Engineering from UQ (2016). Position: Research Fellow & Adjunct Research Fellow Affiliation: School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology Research interests include developing antennas and electromagnetic systems for non-invasive medical diagnostics, such as liver health monitoring and torso imaging. Recent work involves HepNet (a deep learning model for liver disease detection) and portable electromagnetic devices using microwave signals. His publications span antennas for medical imaging, microwave-based diagnostic systems, and biomedical applications. Publications highlight advancements in antenna design (e.g., tapered lens antennas, metasurface antennas) and their integration with medical diagnostics. Key contributions include a patent for an apparatus to characterize internal body features using electromagnetic methods and a thesis on wideband microwave imaging for fluid accumulation detection.

• Law and Digital Technology
• Chinese Public Law
• Comparative Administrative Law
• +۴ مورد دیگر

Dr Clement Chen is a Lecturer at the ANU Law School, part of the ANU College of Law, Governance and Policy. He holds affiliations as a Fellow at Sun Yat-sen University’s Center for Public Law and a Visiting Fellow at The University of Hong Kong's Law & Technology Centre. His research focuses on public law, law & technology, comparative administrative law, and algorithmic regulation, with a particular emphasis on China's legal system and governance structures. He obtained his LLB and MPhil from Sun Yat-sen University, a PhD from the University of Hong Kong, and a Postgraduate Diploma from the University of Paris 1 Panthéon-Sorbonne. His work explores intersections between legal theory, technology governance, and Chinese legal practices, including the Social Credit System’s implications and AI-driven legal decision-making. Research trends in his publications highlight critical analysis of China’s regulatory frameworks, digital governance challenges, and algorithmic accountability. His 2024 work on Social Credit-based punishment examines deterrence mechanisms and autonomy erosion, while 2023 collaborations demonstrate AI applications in legal knowledge dissemination. Earlier works (2015–2022) analyze FOI regimes, data privacy, and judicial transparency dynamics. He secured a HK$705,920 General Research Fund grant for algorithm-assisted sanctions research. Teaching includes postgraduate courses on data regulation and Chinese law across Hong Kong, Italy, and Australia. Clement actively advises on legislation related to digitization and freedom of information in China.

Trong Nhan Lyمشاهده پروفایل

پژوهشگر

• Phylogenetics
• Machine Learning
• High Performance Computing
• +۳ مورد دیگر

Trong Nhan Ly is a Researcher at the Australian National University (ANU) School of Computing. He holds a PhD from ANU (2024) and previously earned a Bachelor of Engineering in Computer Networks and Communications (2016) and a Master’s in Computer Science (2019) from the University of Information Technology, Vietnam National University-HoChiMinh City. Since 2024, he has been affiliated with the Computational Phylogenomics Lab at ANU. Education: PhD in Computer Science, Australian National University (2024) Master of Computer Science, University of Information Technology (2019) Bachelor of Engineering (Computer Networks), University of Information Technology (2016) Research Interests: Dr. Ly focuses on advancing phylogenetic methodologies through machine learning and high-performance computing. His work addresses challenges in genomic data analysis, particularly in pandemic-scale phylogenetics, and integrates computational tools like IQ-TREE 3 and AliSim-HPC . He also contributed to IoT network emulation testbeds such as UiTiOt. Publications: His recent work emphasizes phylogenetic software development, pandemic data analysis, and efficient algorithms for genomic datasets. Earlier contributions include container-based IoT testbed frameworks for large-scale network evaluation. Awards: No scientific awards explicitly mentioned in the provided texts. Advising & Grants: No formal advisees or grant details provided. His current role as a Research Associate at ANU focuses on lab-based research and software development. Labs: Active member of the Computational Phylogenomics Lab at ANU, specializing in phylogenetic inference and bioinformatics.

Le Changمشاهده پروفایل

مدرس

• Statistical Modeling
• Actuarial Science
• Robust Penalized Regression
• +۷ مورد دیگر

Le Chang serves as a Lecturer in Statistics at the Research School of Finance, Actuarial Studies & Statistics, Australian National University. His academic profile demonstrates a strong focus on statistical methodology development with direct applications to actuarial science and financial risk assessment. His research interests center on robust penalized regression and model averaging methods , with specific expertise in principal component analysis, clustering techniques, spatio-temporal modeling, graphical lasso approaches, and mortality forecasting. His methodological innovations address real-world challenges in insurance and financial markets, particularly in claims reserving and demographic modeling. Analysis of his recent publications (2020-2024) reveals a consistent trajectory in developing statistically rigorous methods for actuarial applications. His work frequently appears in high-impact journals including the Journal of Computational and Graphical Statistics , Technometrics , Scandinavian Actuarial Journal , and ASTIN Bulletin , demonstrating both methodological sophistication and practical relevance to the insurance industry. Le Chang maintains an active research profile with 17 total publications, including significant contributions to mortality forecasting, housing price modeling in China, and Australian retirement studies. His h-index of 7 reflects growing scholarly impact in the field of statistical actuarial science. As a registered supervisor, Le Chang contributes to academic training in statistics and actuarial science at ANU, though specific student names aren't documented in the available materials. His international research collaborations span multiple countries, with notable work related to France and the United Kingdom.

Laura Wallمشاهده پروفایل

مدرس

• Cognitive Psychology
• Computational Psychology
• Decision Making
• +۳ مورد دیگر

Dr Laura Wall is a Lecturer (0.5 FTE) in Business Psychology and Research Associate (0.5 FTE) at the University of Newcastle's School of Psychological Sciences within the College of Engineering, Science and Environment. She holds a PhD from the University of Newcastle and has extensive experience spanning casual academic roles (2015-2019), post-doctoral research in Health Economics (2019-2021), and current dual roles since 2023. Education: Doctor of Philosophy, University of Newcastle Bachelor of Psychology, University of Newcastle Research Interests: Dr Wall's work focuses on cognitive and computational psychology, decision-making frameworks, and health economics. She employs advanced statistical techniques to improve measurement of cognitive processes and decision-making in clinical populations (e.g., schizophrenia). Her research also examines patient preferences in healthcare settings through discrete choice experiments, addressing topics such as cancer treatment decisions, end-of-life care, and telehealth delivery. Recent projects include studies on placebo surgery trials and pandemic response planning. Grants: Telehealth Research Initiative, Part A: Telehealth Evidence Synthesis Studies ($103,351, 2021-2022) Supervision: Currently co-supervising a PhD student exploring perceptual inference anomalies in schizophrenia. Affiliations: Active collaborations include studies on oncology patient preferences, surgical trial design, and mental health service delivery. She contributes to interdisciplinary teams addressing translational research from basic cognitive models to applied healthcare interventions.

• Complex systems
• Complex networks
• Information theory
• +۳ مورد دیگر

Associate Professor Joseph Lizier is a leading academic at the School of Computer Science, The University of Sydney, with a focus on complex systems and information theory. He has held this position since 2019, following a DECRA Fellowship (2016-19) and Senior Lecturer role (2015-18). Previously, he worked at CSIRO ICT Centre (2012-14) and the Max Planck Institute (2010-12), and has industry experience in telecommunications. His research spans computational neuroscience, complex networks, and information theory, examining how biological and artificial systems process information through transfer entropy and distributed computation. Key themes include neural information flows, small-world network properties, and applications to brain imaging, financial markets, and epidemic modeling. Current projects involve ARC Discovery grants and international collaborations with German institutions. Recent publications analyze multiscale brain dynamics, cholinergic stabilization of neural networks, and information processing in neuromorphic nanowires. His work reveals patterns in neuronal bursts, epidemic modeling, and swarm intelligence through information-theoretic approaches. Scientific awards include the Entropy Best Paper (2019), Sydney Accelerator Fellowship (SOAR), and multiple conference and thesis prizes. He supervises PhD students working on topics like self-organization, neural synchrony, and information flow in biological systems.

• Wireless Communications
• Digital Communication Theory
• Error Control Coding
• +۷ مورد دیگر

Branka Vucetic is a Laureate Professor and ARC Laureate Fellow at The University of Sydney's School of Electrical and Computer Engineering. She leads the Centre for IoT and Telecommunications, focusing on advancing wireless communication systems, including mmWave, URLLC, and IoT. Her research spans coding theory, multi-user detection, and networked control systems. With over 300 publications and four books, she has received prestigious awards such as the Chinese Government Friendship Award and the CSIRO Eureka Prize. She advises numerous PhD and Master’s students on topics like RIS-assisted wireless communications and AI-driven clinical decision support. Education: MSc and PhD from the University of Belgrade. Research interests include 6G technologies, reconfigurable intelligent surfaces, and mission-critical IoT applications. Her work addresses latency, reliability, and energy efficiency challenges in modern communication networks. Current roles include Director of the Centre for IoT and Telecommunications and Fellowships in multiple academies. Awards highlight her contributions to innovation and leadership in engineering and science. She collaborates on projects funded by ARC and industry partners, focusing on practical applications like smart grids and autonomous systems.

• Error Control Coding
• Information Theory
• 6G Wireless Networks
• +۴ مورد دیگر

Chentao Yue is a Research Fellow at the School of Electrical and Computer Engineering, University of Sydney. He holds a BSc in Information Engineering (Honors) from Southeast University (2017) and a PhD in Electrical Engineering from the University of Sydney (2021). His research focuses on developing foundational theories and AI-driven techniques for 6G wireless networks, specifically targeting ultra-reliable low-latency communication through advanced error control coding. Research Interests: Dr. Yue's work bridges information theory, machine learning, and telecommunications. Key areas include intelligent channel coding/decoding for 6G, sparse vector transmission, NOMA systems, and distributed consensus protocols. His DECRA-funded project aims to create self-configurable coding frameworks adaptable to dynamic 6G environments. Publication Trends: Recent articles emphasize optimizing short block-length codes for 6G URLLC, integrating Bayesian neural networks with modulation techniques, and refining ordered-statistics decoding (OSD) algorithms. Machine learning applications in signal prediction and distributed ledger technologies are emerging themes. Awards: ARC Discovery Early Career Researcher Award (DECRA, 2024) Postgraduate Scholarship in Wireless Engineering Grants: Leads the ARC DECRA project (2024): "Intelligent self-configurable coding and decoding for 6G wireless networks," focusing on real-time adaptive error correction for industrial automation and telemedicine.

• Operations Research
• Energy Economics
• Stochastic Optimization
• +۳ مورد دیگر

James Anderson is an Associate Lecturer at the University of Sydney, specializing in Operations Research and Energy Economics. His research focuses on stochastic optimization, energy transition modeling, supply chain coordination, and game-theoretic market design. He has contributed to understanding strategic behavior in electricity markets, supply function equilibria, and risk management in uncertain environments. Anderson’s work spans theoretical and applied domains, addressing challenges in energy policy, auction mechanisms, and supply chain dynamics. His recent studies explore long-term decarbonization pathways, robust decision-making under uncertainty, and the integration of stochastic models into real-world systems. He is affiliated with the University of Sydney’s academic staff, though specific departmental or school affiliations are not explicitly stated in available records. His publications frequently address topics such as capacity procurement efficiency, minimax decision rules, and the performance of stochastic algorithms in complex systems. Anderson’s research also extends to auction theory, including multi-unit auctions with uncertain supply, and the design of contracts that mitigate risks in agriculture and retail supply chains. His work emphasizes practical applications of mathematical modeling to enhance decision-making in competitive markets and resource allocation contexts.

• Bayesian Statistics
• Financial Econometrics
• Computational Statistics
• +۳ مورد دیگر

Wilson Chen is a Senior Lecturer at the University of Sydney. He holds a PhD in Financial Econometrics from the same institution. Prior roles include a post-doctoral fellowship at the University of Technology Sydney and an Assistant Professorship at the Institute of Statistical Mathematics in Japan. His research focuses on advancing computational Bayesian methods and statistical tools for financial time series analysis. Education: PhD in Financial Econometrics, University of Sydney Research Interests: Wilson develops efficient computational techniques for Bayesian inference, with applications to financial data analysis. His work emphasizes MCMC optimization, quantile function models, and the integration of machine learning with statistical methodologies. Recent efforts include improving sampling efficiency in Bayesian frameworks and exploring variational approaches for complex posterior distributions. Publications: His work spans themes in Bayesian computation, financial econometrics, and machine learning. Notable contributions include optimizing MCMC thinning, semiparametric GARCH models, and Stein-based sampling techniques for probabilistic inference. Awards: No scientific awards explicitly mentioned. Advising & Grants: Currently supervising three PhD students: Peiwen JIANG (Modelling Complex Posteriors in Bayesian Inference), Wen PENG (Bayesian Neural Networks for Volatility Dynamics), and Yuning ZHANG (Stochastic Loss Reserving). Grant details are not specified in the provided texts. Labs/Teams: No specific lab or collaborative team affiliations mentioned.

• High-Dimensional Statistics
• Statistical Machine Learning
• Data Mining
• +۴ مورد دیگر

Professor Peter Radchenko is a leading academic in Statistics at the University of Sydney Business School. Prior to joining Sydney in 2017, he held positions at the University of Chicago and the Marshall School of Business, University of Southern California. He earned a PhD in Statistics from Yale University and an undergraduate degree in Mathematics from Lomonosov Moscow State University. Education: PhD in Statistics, Yale University Bachelor's in Mathematics/Applied Mathematics, Lomonosov Moscow State University Research Interests: Professor Radchenko specializes in high-dimensional statistics and statistical machine learning, focusing on methods for analyzing complex modern datasets. His work addresses challenges in high-dimensional regression, large-scale clustering, functional data analysis, and optimization-driven statistical techniques. Notable contributions include mixed-integer optimization approaches for sparse models and advancements in variable selection under low-signal conditions. Research Trends: His recent work emphasizes optimization-based solutions for discrete statistical problems, particularly in sparse modeling and subset selection. This aligns with broader themes of leveraging computational efficiency for high-dimensional data challenges. Awards & Honors: Elected Member, International Statistical Institute (ISI) 2023 INFORMS Computing Society Prize Honorable Mention Australian Research Council Grants (2025, 2019) US National Science Foundation Grant (2012) Teaching Excellence Awards (University of Sydney, 2018–2024) Grants & Advising: Current research student: Sanghyun KIM (Forecast Reconciliation). Grants include collaborations with Cornell University and multidisciplinary initiatives at the University of Sydney. His research has been supported by major funding bodies globally. Labs & Teams: His work is embedded within the University of Sydney Business School’s analytics research groups, focusing on statistical methodology and its applications in business and health.

• Decision Support Technologies
• Integrated Modeling
• Behavioral Economics
• +۲ مورد دیگر

Rosa Taghikhah is a Lecturer in Business Analytics at the University of Sydney Business School, specializing in decision support systems for sustainability. With a PhD from UTS, she develops analytics-driven frameworks for socio-environmental challenges, including agri-food supply chains, disaster risk management, and renewable energy transitions. Her interdisciplinary work integrates AI, behavioral economics, and systems modeling. Research Focus: Decision technologies for climate resilience and decarbonization Causal inference in consumer behavior (e.g., organic products adoption) Digital twins for natural resource management Explainable AI in environmental contexts She supervises PhD students on projects like bushfire risk modeling and organizational knowledge transfer, and has led ARC-funded projects on farm resilience modeling. Taghikhah promotes Women in STEM and previously worked with Ernst & Young.

Vanessa Mossمشاهده پروفایل

استاد مدعو

• Radio Astronomy
• Astrophysics
• High-Energy Astrophysics
• +۳ مورد دیگر

Dr. Vanessa Moss is an Adjunct Professor at the School of Electrical Engineering & Computer Science, University of Queensland. Her research focuses on radio astronomy, astrophysics, and collaborative science practices. She is actively involved in large observational projects like ASKAP and Apertif, contributing to surveys of galactic structures, transient phenomena, and HI absorption studies. Key research interests include radio surveys (e.g., RACS), fast radio bursts (FRBs), and advancing accessible conference formats. Her work bridges observational astronomy with data analysis, emphasizing sustainability and inclusivity in global scientific collaboration. Publications highlight her contributions to radio galaxy studies, interstellar medium dynamics, and technological advancements in radio telescopes. She has co-authored over 80 peer-reviewed articles, focusing on cosmological surveys, HI absorption in AGN, and multi-wavelength astronomy. Dr. Moss has pioneered efforts to digitize conferences, such as the virtual ICWIP 2021, and contributed to initiatives like Astronomers for Planet Earth to reduce the carbon footprint of astronomy. She collaborates internationally with institutions like the Westerbork Synthesis Radio Telescope (WSRT) and the Australian Square Kilometre Array Pathfinder (ASKAP).