جست‌وجوی استاد و پژوهشگر

Edward N. Zaltaمشاهده پروفایل

پژوهشگر

• Metaphysics
• Formal Ontology
• Philosophy of Mathematics
• +۷ مورد دیگر

Edward N. Zalta is a Senior Research Scholar at Stanford University's Center for the Study of Language and Information (CSLI) and Director of the Metaphysics Research Lab. He served as Principal Editor of the Stanford Encyclopedia of Philosophy from 1995 to 2022 and now serves as Co-Principal Editor. His academic career spans several decades with significant contributions to philosophical logic and metaphysics. Dr. Zalta's educational background includes an honors B.A. from Rice University (1975) and a Ph.D. in Philosophy from the University of Massachusetts/Amherst (1981). He joined Stanford in 1984 as a Postdoctoral Fellow at CSLI, establishing his long-term affiliation with the institution. His research focuses on metaphysics and formal ontology, philosophy of mathematics, computational metaphysics, and intensional logic. Zalta has developed influential theories in object theory and abstract objects, with applications extending to computational metaphysics. His work bridges traditional philosophical inquiry with modern computational approaches, creating new methodologies for metaphysical investigation. He has made significant contributions to understanding the logical foundations of ontology and the philosophical implications of mathematical structures. Zalta's publication record demonstrates consistent scholarly output across five decades, with recent work showing increasing integration of computational methods in metaphysical research. His articles reveal a trajectory from foundational work in object theory toward more applied computational approaches, reflecting the growing interdisciplinary nature of his research. The recurring themes across his work include formal ontological structures, the nature of abstract objects, and the logical frameworks necessary to model complex philosophical concepts. K. Jon Barwise Prize (2016) Covey Award (2009) 2025 Johan von Spix Visiting Professor at the University of Bamberg As Principal Editor of the Stanford Encyclopedia of Philosophy for over 25 years, Zalta has shaped one of the most influential digital humanities projects in philosophy. His work with the Metaphysics Research Lab has fostered numerous collaborations between philosophers and computer scientists. He has received funding to support the development of computational tools for metaphysical reasoning and has supervised research projects at the intersection of philosophy and computer science. The Metaphysics Research Lab at CSLI serves as the intellectual hub for Zalta's work, supporting research in computational metaphysics and formal ontology. The lab has developed several computational frameworks for representing and reasoning about metaphysical concepts, including implementations of object theory in automated reasoning systems. Zalta's research group has collaborated with computer scientists to create tools that bridge philosophical theory with practical computational applications.

Felix Gilleمشاهده پروفایل

پژوهشگر

• Health Policy
• Governance
• Public Trust in Healthcare Systems
• +۴ مورد دیگر

Felix Gille is a researcher at the University of Zurich , affiliated with the Institute for Implementation Science in Health Care under the Faculty of Medicine. He leads the DSI-funded project "Data handling, security and protection: reaching conceptual equivalence for the concept of public trust in national electronic health records in Switzerland and neighboring countries," collaborating with Prof. von Wyl (IfIS/DSI, UZH) and Prof. Thouvenin (Faculty of Law, UZH). His work bridges health system and policy research, digital health, data law, and data security. Education: BSc in European Public Health, Maastricht University MMedSc in Health Economics, Policy and Management, Karolinska Institute PhD in Public Trust in Healthcare Systems, London School of Hygiene and Tropical Medicine Research Focus: Gille specializes in public trust dynamics within healthcare systems, particularly examining data governance, health policy, and ethical dimensions of digital health innovations. His projects analyze cross-national trust frameworks, biobanking ethics, AI in healthcare, and pandemic data sharing mechanisms. Scientific Contributions: His publications address critical issues such as GDPR impacts on research, trustworthiness of mHealth apps, and interdisciplinary approaches to health data policy. Articles span 2015–2025, emphasizing conceptual trust models, policy harmonization, and stakeholder engagement in health data systems. Awards & Roles: Fellow of the Swiss School of Public Health Associate Editor for Public Health Reviews

Christian Engwerمشاهده پروفایل

استاد

• Partial Differential Equations
• Numerical Analysis
• Finite Element Methods
• +۷ مورد دیگر

Christian Engwer is a full Professor at the University of Muenster in the Institute for Applied Mathematics, specializing in Analysis and Numerics. He leads the Engwer Group focused on Applications of Partial Differential Equations and is actively involved in the Cells in Motion initiative as a supervisor in the CiM-IMPRS Graduate Programme. His research centers on developing numerical methods for partial differential equations, particularly addressing challenges in complex geometries and multi-physics applications. He specializes in Unfitted Discontinuous Galerkin methods, which allow simulations on complex geometries without requiring domain-fitted meshes. His work spans porous media modeling, biological systems, and bioelectromagnetism applications, with significant contributions to EEG/MEG forward modeling in neuroscience. Analysis of his recent publications reveals a strong focus on model order reduction techniques, stabilized numerical schemes for cut-cell meshes, and applications in bioelectromagnetism. His work demonstrates a consistent trajectory toward developing robust, efficient numerical methods applicable to real-world problems in medical imaging and biological modeling, with increasing emphasis on high-performance computing implementations. Professor Engwer actively supervises doctoral students, with recent completions including Lukas Renelt (2025), Michael Wenske (2021), and Maria Carla Piastra (2019), among others working on topics related to numerical methods and biomedical applications. He leads several major research projects including BrainStorm: Highly Extensible Software for Advanced Electrophysiology and MEG/EEG Imaging (NIH-funded since 2019), multiple EXC 2044 Cluster of Excellence projects through 2025, and the InterKI interdisciplinary teaching program on machine learning and artificial intelligence. His group develops several important software packages including DUNE (Distributed and Unified Numerics Environment), duneuro (for bioelectromagnetism applications), and TPMC (Topology Preserving Marching Cubes). These tools support research in numerical methods and their applications to complex scientific problems.

• Causation
• Artificial Intelligence
• Probabilistic Graphical Models
• +۱ مورد دیگر

Professor Javen Qinfeng Shi is a faculty member at the University of Adelaide, holding the position of Professor in the School of Computer and Mathematical Sciences under the Faculty of Sciences, Engineering and Technology. He serves as Founding Director of the Causal AI Group and as one of the directors at the Australian Institute for Machine Learning (AIML), based at the North Terrace campus location. His research centers on causation, artificial intelligence, mind and metaphysics, with Google Scholar rankings placing him 4th globally in causation and 7th in probabilistic graphical models. Shi develops causal AI methods to identify root causes, discover latent variables, eliminate spurious correlations, enhance cross-domain generalization, model intervention consequences, and solve counterfactual queries. His work focuses on optimizing intervention sequences for desired outcomes under resource constraints, applied to material discovery, agriculture, mining, sports, manufacturing, bushfire prediction, healthcare, and education. Professor Shi's industry impact includes the NOBURN bushfire prediction app (released 2023 with 50+ media coverages), energy material discovery via AI catalysts, and smart manufacturing logistics solutions. His work with the Responsible AI Think Tank (2022-2024) and current AI Industry Forum panellist role (2024 onward) demonstrates active contribution to national and state AI ecosystem development. His scientific awards include: 1st place at Open Catalyst Challenge (NeurIPS AI for Science 2023) Winner of AUS/NZ Bushfire Data Quest 2020 Citizen Science Grant 2021 Finalist in SA Department of Energy and Mining Gawler Challenge 2020 (2k+ participants from 100+ countries), recognized for "The most innovative modelling" 2nd place in Explorer Challenge 2019 (1k+ entries from 62 countries) 1st place at SAIC Volkswagen Logistics Innovation Day 2019 Shi is eligible to supervise Masters and PhD students and has secured research funding including the Citizen Science Grant 2021. His industry collaborations span energy, agriculture, mining, and emergency management, translating theoretical causal AI into practical tools like NOBURN. He leads the Causal AI Group at the University of Adelaide and directs research teams at AIML, focusing on causal inference frameworks for distribution shift resilience and intervention optimization. Current projects emphasize bushfire prediction, material science applications, and AI ethics implementation through the AI Industry Forum.

• Ocean Renewable Energy
• Robotics
• Biomechanics
• +۲ مورد دیگر

Dr. Boyin Ding is an Associate Professor at the University of Adelaide , serving as Academic Director at Haide College and researcher in the Mechanical Engineering department within the Faculty of Sciences, Engineering and Technology. He leads the Wave Energy Research initiative established in 2014, while also contributing to Robotics and Biomechanics through his work with the Flinders Medical Device Research Institute. Research Areas: Ocean Wave Energy Harvesting Control Systems for Renewable Energy 6DOF Robotic Testing Spine Biomechanics Transnational Education Programs Key Collaborations: Australia-China Joint Research Centre for Offshore Wind & Wave Energy Acoustics, Vibration and Control Research Group Scientific Awards: Australian Endeavour Fellowship Malcolm Kinnaird Engineering Excellence Award (2012) His recent publications focus on hybrid offshore energy systems, nonlinear hydrodynamics in wave energy converters, and biomechanical testing technologies. He has developed control algorithms for floating offshore wind-wave systems and pioneered 6DOF robotic platforms for medical applications. As an eligible PhD supervisor, he actively collaborates with global industries and academic institutions.

Lianne Lefsrudمشاهده پروفایل

دانشیار

• Risk Management
• Sustainability
• Organizational Theory
• +۵ مورد دیگر

Lianne Lefsrud serves as Associate Professor and Risk, Innovation, and Sustainability Chair (RISC) in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering. Her interdisciplinary research bridges engineering, social sciences, and policy to transform risk management practices across energy, mining, construction, and railroading industries, directly influencing regulations, building codes, and industry operations for sustainable development. Her academic credentials include: BSc in Civil Engineering (Cooperative Program), University of Alberta (1994) MSc in Interdisciplinary Civil & Environmental Engineering and Sociology, University of Alberta (1996) PhD in Strategic Management and Organization, Alberta School of Business (2014) Dr. Lefsrud's research centers on risk management frameworks for sustainability challenges. She examines hazard identification, social license to operate, and technology adoption drivers in high-hazard industries, with emphasis on prospective risk assessment (e.g., hydrogen infrastructure design) and retrospective analysis (e.g., microplastic pollution impacts). Her work integrates circular economy principles into energy systems while addressing unintended consequences across UN Sustainable Development Goals. Recent publications (2024-2025) demonstrate heavy focus on machine learning applications for rail and construction safety, hydrogen infrastructure risk analysis, and science denial mitigation. Key patterns show cross-industry adaptation of AI for incident prediction, regulatory gap analysis for emerging energy systems, and socio-technical approaches to reconcile sustainability goals with operational realities. Scientific recognition includes: Erb Post-Doctoral Fellowship (University of Michigan) Dow Sustainability Research Fellowship (Ross School of Business) Dr. Lefsrud mentors graduate students through industry-integrated projects like her Sustainable Design course where teams generated patents and city solutions. Her research secures Alberta Innovates funding with 1:4 industrial-to-federal matching, collaborating with Suncor, Transport Canada, and Canadian Standards Association. Grants target practical implementations including railcar inspection systems and hydrogen safety protocols. She co-founded Insight Risk Systems and leads the Lefsrud Lab, prioritizing inclusive teams with under-represented groups (women, Indigenous, LGBTQ2S+, neurodiverse) to tackle 'wicked problems' in sustainability. The lab leverages interdisciplinary partnerships across engineering, computer science, psychology, and environmental sociology for real-world risk management solutions.

Zukui Liمشاهده پروفایل

استاد

• Mathematical Optimization
• Machine Learning
• Process Systems Engineering
• +۴ مورد دیگر

Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group

Daniel Fremontمشاهده پروفایل

دانشیار

• Formal Methods
• Automated Reasoning
• Cyber-Physical Systems
• +۶ مورد دیگر

Daniel Fremont is an Associate Professor of Computer Science and Engineering at the University of California, Santa Cruz, where he conducts research at the intersection of formal methods and autonomous systems. His work focuses on developing mathematical techniques to improve the reliability of software, hardware, and cyber-physical systems through precise specification, formal verification, automatic synthesis, and principled testing approaches. Dr. Fremont's research interests center on applications of logic in computer science, particularly using automated reasoning to enhance system reliability. His work spans formal methods for cyber-physical systems (CPS), especially autonomous systems that incorporate machine learning. Key research areas include algorithmic improvisation for creating systems with controlled randomness, probabilistic programming through the Scenic language for environment modeling, and formal verification techniques applicable to safety-critical autonomous systems. His group has successfully applied these methods to autonomous vehicles, aircraft systems, and robotics, with significant contributions to both theoretical foundations and practical implementations. The publication record reveals a strong trajectory from theoretical foundations of control improvisation toward practical applications in autonomous systems verification. Early work established the theoretical framework of control improvisation, while recent publications focus on applying these techniques to real-world challenges in autonomous driving, aircraft systems, and AI-based autonomy. A consistent theme across his research is the integration of formal methods with machine learning to address the verification challenges posed by complex, learning-based systems operating in uncertain environments. Best Paper Award at IoTDI 2016 for 'Control Improvisation with Probabilistic Temporal Specifications' Dr. Fremont leads a research group focused on formal methods for autonomous systems, with significant contributions to the development of tools like Scenic (a probabilistic programming language for scenario specification) and VerifAI (a toolkit for formal design and analysis of AI-based systems). His work bridges theoretical computer science with practical engineering challenges in safety-critical autonomous systems, receiving funding from various sources supporting research at the intersection of formal methods and artificial intelligence. The group's approach combines theoretical algorithm development with practical implementation and testing, often collaborating with industry partners working on autonomous vehicle technology. The research group maintains active development of several open-source tools, including the Scenic language for scenario specification and VerifAI for formal analysis of AI systems. They have demonstrated applications across multiple domains including autonomous vehicles, aircraft systems, and robotics, with particular emphasis on simulation-based testing and verification approaches that can provide formal guarantees about system behavior.

Qi Yuمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Qi Yu is a Professor in the School of Information at the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the Graduate Program Director and directs the Machine Learning and Data Intensive Computing Lab. His research focuses on machine learning, deep learning, and data-driven knowledge discovery, particularly in knowledge-rich domains like medicine and bioinformatics. He holds a B.E. from Zhejiang University, an M.E. from the National University of Singapore, and a Ph.D. from Virginia Tech. His work emphasizes interpretable models, multimodal data fusion, and human-in-the-loop learning. He has secured significant grants, including a $500K NSF award and a $1.6M ONR grant, supporting projects on Bayesian learning frameworks and decision-making under uncertainty. His lab actively explores active learning, few-shot learning, and uncertainty quantification. He advises a vibrant group of Ph.D. and MS students and teaches courses such as Data-Driven Knowledge Discovery and Thesis/Project Capstones. Education: B.E., Electrical Engineering, Zhejiang University (2001) M.E., Computer Engineering, National University of Singapore (2003) Ph.D., Computer Science, Virginia Tech (2008) Research Interests: Machine Learning, Deep Learning, Vision-Language Models, Uncertainty Quantification, Active Learning, Multimodal Data Fusion, Bayesian Methods, and Applications in Healthcare and Cybersecurity. Recent Work Trends: His articles emphasize label-efficient learning, interactive systems, and applying ML to complex domains like medical imaging and anomaly detection. Notable projects include Bayesian learning for dynamic decision-making and evidential optimization for robust models. Awards/Grants: NSF IIS Award ($500K, 2018–2023); DoD/ONR Award ($1.6M, 2018–2023); multiple conference recognitions (NeurIPS, ICML, CVPR). Advising spans over 20 students, many securing roles at Amazon, Samsung, and academia. Labs/Teams: Leads the Mining Lab, collaborating on interdisciplinary projects with domain experts in medicine, cybersecurity, and material science.

• Intellectual Property Law
• Copyright and Artificial Intelligence
• IP and Right to Repair
• +۵ مورد دیگر

Kanchana Kariyawasam serves as Associate Professor in the Department of Accounting, Finance and Economics within Griffith University's Business School. Holding a PhD in IP Law from Griffith University, an LL.M (Advanced) in IP Law from The University of Queensland, and an LL.B (Hons) from the University of Colombo, she maintains active research affiliations with the Law Futures Centre (2009-2024) and Griffith Asia Institute (2019-present). Her research spans Intellectual Property Law with specialized focus on Copyright and Artificial Intelligence, IP and Right to Repair, Patent Law in Biotechnology, and Gender Equality in IP systems. Current projects examine AI-generated works, digital exhaustion doctrine, and NFT legality under Australian copyright frameworks, reflecting strong alignment with Sustainable Development Goals 4 (Quality Education) and 9 (Industry Innovation). Professor Kariyawasam's funded research portfolio includes significant projects such as the WIPO Study on Illegal Retransmission of Live Broadcasts (2023), Queensland Government Ewaste initiatives (2022), and Griffith University's Right to Repair Workshop (2020). Her publications demonstrate consistent output in high-impact journals including European Intellectual Property Review and International Journal of Law and Information Technology , with recent works analyzing AI copyright challenges, spatial data protection, and medical device repair rights. Deputy Vice-Chancellor's 2024 Student Experience of Teaching Survey Commendation Green Impact Gold Award Recipient (2023, Griffith Repair Cafe) Highly Commended Griffith Award for Excellence in Teaching (Large Classes) Two GBS teaching citations She currently supervises five doctoral candidates researching AI data privacy, patent law intersections, and agricultural IP issues, while having successfully completed supervision of four doctoral theses including works on female entrepreneurship and agribusiness marketing. As Coordinator of Griffith Repair Cafe and Steering Committee Member of Australian Repair Network, she bridges academic research with community engagement on repair rights and e-waste reduction.

Paul Moritz Wiegmannمشاهده پروفایل

استادیار

• Standardization
• Innovation Ecosystems
• Sustainable Development
• +۳ مورد دیگر

Paul Moritz Wiegmann is an Assistant Professor in the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e), where he is part of the ITEM group. He currently serves as President of the European Academy of Standardisation (EURAS). His academic career includes visiting research positions at TU Berlin (2014 & 2015), Yonsei University, Seoul (2018), and the University of California, Davis (2023). Paul holds a PhD from Rotterdam School of Management, Erasmus University, alongside additional degrees from Erasmus University and the University of Warwick. His educational background emphasizes interdisciplinary training, reflected in his research and teaching. His research focuses on the role of standardization in innovation ecosystems, particularly how explicit and implicit rules enable value creation and capture. He employs mixed-methods approaches, including case studies, grounded theory, choice experiments, and conjoint analysis. His work aligns with UN Sustainable Development Goals, addressing societal challenges like sustainability and smart technologies through collaborative frameworks. Notable awards include the Industria Education Award 2022-2023 for Best Teacher Talent (2023) and the ISPIM Dissertation Award 2020 (2020). These recognize his contributions to both education and research in standardization and innovation. In advising and grants, Paul has supervised 30 works (names not listed) and leads the Measuring societal impact of standards project (2021–2022). He also coordinates the FIQCS: FIeldlab Quantum Cryptography Solutions for Safe Society initiative (2023–2028). His activities include organizing conferences like EU-SPRI 2025 and delivering invited talks on topics ranging from standardization governance to quantum technologies. Paul teaches courses such as Methodology for IE research and Patents design rights & standards exploration course , reflecting his expertise in standardization and research methods. He contributed to the Standardization literature database (2024), an interdisciplinary review, and engages in media discussions on topics like AI adoption in SMEs and quantum threat mitigation in public safety.

• Neural control of movement
• Infant development
• Motor control
• +۵ مورد دیگر

Beth Smith, PT, DPT, PhD is an Associate Professor of Pediatrics and Biokinesiology & Physical Therapy at the University of Southern California. She directs the Infant Neuromotor Control Laboratory, where she leads research on neural control of movement during infancy and develops interventions for infants with or at risk for developmental delay. Dr. Smith's research focuses on several critical areas in pediatric development: Neural mechanisms underlying infant motor development Application of wearable sensor technology for objective movement measurement Early identification of developmental delays through quantitative analysis Evaluation of intervention effectiveness for at-risk infants Cross-cultural studies of infant development in diverse settings including rural Guatemala Her work demonstrates a strong integration of technology and clinical practice, with recent publications highlighting innovative approaches to measuring infant movement in natural environments. Dr. Smith's research on algorithmic detection of developmental disabilities using wearable sensors represents a significant advancement in early identification methods. Her studies on telehealth administration of developmental assessments have gained particular relevance following the COVID-19 pandemic, potentially expanding access to early intervention services globally. As director of the Infant Neuromotor Control Laboratory, Dr. Smith oversees a research program that bridges neuroscience, engineering, and clinical practice to improve outcomes for infants with developmental challenges. Her work has important implications for developing evidence-based interventions that can be implemented across diverse healthcare settings.

Nadine Andrewمشاهده پروفایل

دانشیار

• Data linkage
• Stroke
• Chronic Disease Prevention and Management
• +۵ مورد دیگر

Associate Professor Nadine Andrew is an Associate Professor (Research) in the Department of Medicine at Monash University's School of Translational Medicine. She leads the National Centre for Healthy Ageing Data Platform and chairs the Monash HELIX data linkage committee. With expertise in data linkage and health services research, her work focuses on chronic diseases of aging, stroke, and dementia. She has secured over $12M in grants and leads projects like the NHMRC-funded PRECISE stroke study and a MRFF digital health initiative for aged care transfers. Her team includes experts in epidemiology, data science, and bioinformatics. Education: PhD in Epidemiology, Master’s in Public Health, clinical background in Physiotherapy. Research Interests: Data linkage methodologies, stroke epidemiology, chronic disease management, digital health solutions for aging populations. She pioneers national data platforms and co-designs patient-centered care systems. Grants & Projects: $2M MRFF grant for digital health in aged care, NHMRC PRECISE project, boosting dementia grant. Active in 20+ collaborative initiatives including national stroke data integration and vulnerable population cohort studies. Awards: 2024 Australian Epidemiological Association Award, 2021 American Heart Association Reviewer Award, NHMRC Early Career Fellowship (2014). Labs/Teams: Directs the National Centre for Healthy Ageing Data Platform and oversees multidisciplinary research teams advancing EHR data utilization.

FOONG Sew Bunمشاهده پروفایل

دانشیار مدعو

• IT Architecture Patterns
• Service-Oriented Architecture (SOA)
• Software Engineering Methodologies
• +۴ مورد دیگر

Professor FOONG Sew Bun is an Adjunct Associate Professor at the Department of Information Systems and Analytics, School of Computing, National University of Singapore. He holds dual roles as Chief Technology Officer (CTO) for IBM Singapore and IBM ASEAN Software Group, and previously served as Managing Director at DBS Bank, Deputy Chief Executive at GovTech, and Global Head of Digital Transformation at Standard Chartered Bank. His career spans over three decades in IT leadership, technical architecture, and national-level advisory roles. Education: BSc and MSc in Computer Science from the University of Texas at Austin. Research interests focus on IT architecture patterns, service-orientation, software reuse, enterprise architecture, and fuzzy logic. He has authored/co-authored multiple IBM Redbooks and peer-reviewed publications in AI and software engineering. Awards include the 2016 IT Professional of the Year (SCS), IBM Distinguished Engineer distinction, and leadership accolades from IBM. He chairs the National Infocomm Competency Framework Steering Committee and has held key roles in Singapore's IT policy-making bodies. Teaching includes courses on digital transformation in finance (FT5002) and AI-driven business innovations (IS4261). His professional activities span technical councils, industry certifications, and academic collaborations.

• Wireless Networks
• Signal Processing
• 5G/6G Communications
• +۳ مورد دیگر

Dr. Wibowo Hardjawana is a Senior Lecturer in Telecommunications Engineering at the School of Electrical & Computer Engineering , University of Sydney. He holds a PhD from the University of Sydney and serves as an ARC DECRA Research Fellow. His research focuses on wireless network softwarisation, enabling programmable radio interfaces to address traffic elasticity in 5G/6G systems. Education : PhD (University of Sydney) Grants : ARC DP210100744 (2021), ARC DECRA DE140101114 (2014) His work spans 5G/6G network architectures , machine learning for wireless systems , and open radio interfaces . Key contributions include graph representation learning for interference management, Bayesian neural network detectors for OTFS modulation, and NOMA decoding techniques . Recent publications analyze ultra-reliable low-latency communications , UAV-enabled networks , and stochastic geometry in wireless systems . He has collaborated with institutions in China, Indonesia, and UAE, and engaged with industry partners like Telstra and Ausgrid.