جستجوی استاد راهنمای دانشگاهی

Po-Yen Tungمشاهده پروفایل

دانشگاهی

• Materials Science
• Machine Learning in Materials Discovery
• Electron Microscopy
• +۵ مورد دیگر

Dr. Po-Yen Tung is a Research Associate at the University of Cambridge's Department of Earth Sciences, jointly appointed with Materials Science and Metallurgy. He holds a PhD from the Max Planck Institute under Prof. Dirk Raabe and was a postdoc at Cambridge from 2021–2024, affiliated with Peterhouse College. His work bridges materials science and machine learning, focusing on accelerating materials discovery and microscopy analysis. Research Interests: Machine learning in materials characterization and alloy design High-entropy Invar alloy discovery Automated electron microscopy data analysis 3D microstructure segmentation Key Contributions: Developed SIGMA, an open-source tool for SEM-EDS analysis Discovered high-entropy Invar alloys using active learning frameworks Pioneered few-shot learning for 3D tomographic segmentation Publications highlight data-efficient material design, planetary science insights, and environmental magnetism studies. His work supports sustainable materials development and net-zero energy applications through AI-driven methods.

Sri Krishnanمشاهده پروفایل

استاد

• Biomedical Signal Analysis
• Wearables/Textile Computing
• Assistive Technology
• +۵ مورد دیگر

Dr. Sri Krishnan is a Professor and Dean at the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. With a focus on interdisciplinary research, he bridges electrical engineering, computer science, and biomedical applications. His work emphasizes mathematical signal analysis for healthcare solutions. PhD, University of Calgary (1999) MSc, University of Calgary (1996) BE, College of Engineering, Guindy, Anna University (1993) Research Interests: Dr. Krishnan specializes in biomedical signal analysis, affective computing, and compressive sensing. He develops wearable technologies and machine learning algorithms for applications in mental health, cardiovascular diagnostics, and neurological disorders. Scientific Contributions: His recent publications highlight advancements in fetal ECG separation, seizure detection via EMD dictionaries, acoustic scene classification using local binary patterns, compressive sensing in gait signals, and wavelet-based gait diagnostics. Fellow, Canadian Academy of Engineering Outstanding Canadian Biomedical Engineer Award Canada Research Chair in Biomedical Signal Analysis Sarwan Sahota Distinguished Scholar Award Laboratory: He leads the Signal Analysis Research Group, fostering innovation in time-frequency theory and non-stationary signal processing.

Asger Hobolthمشاهده پروفایل

استاد

• Stochastic Processes
• Statistical Methods
• Molecular Evolution
• +۱ مورد دیگر

Asger Hobolth is a Professor at the Department of Mathematics, Aarhus University. His primary research focus lies in stochastic processes and statistical methods, with applications in molecular evolution and population genetics. His expertise spans computational biology, mutational signature analysis, and phylogenetic modeling. Recent collaborations include work on phase-type distributions in population genetics and non-negative matrix factorization for mutational signature extraction. Contact Information: Phone: +45 87 15 58 01 Email: asger@math.au.dk

• Machine Learning
• Data Mining
• Graph Theory
• +۳ مورد دیگر

Sebastian Dalleiger is an Assistant Professor at the Division of Theoretical Computer Science, Kungliga Tekniska Högskolan (KTH Royal Institute of Technology). His research focuses on theoretical foundations of machine learning, data mining, and graph theory, with particular expertise in matrix factorization, pattern discovery, and hypergraph analysis. Current affiliation: KTH Royal Institute of Technology Department: Theoretical Computer Science Email: sdall@kth.se His recent work explores federated learning architectures, non-negative matrix factorization, and structural analysis of stochastic block models across multiple graphs. He develops algorithms combining proximal optimization with privacy-preserving techniques, addressing challenges in distributed data analysis. Publications demonstrate interdisciplinary applications in network science, information theory, and computational geometry. Key contributions include novel frameworks for Ollivier-Ricci curvature in hypergraphs and sequential false discovery control for pattern mining.

Behtash Babadiمشاهده پروفایل

دانشیار

• Statistical and Adaptive Signal Processing
• Neural Signal Processing
• Systems Neuroscience
• +۳ مورد دیگر

Behtash Babadi is an Associate Professor in the Department of Electrical & Computer Engineering and a faculty member at the Institute for Systems Research and the Brain and Behavior Institute at the University of Maryland, College Park. He also holds affiliate appointments in the Program in Neuroscience & Cognitive Science and the Applied Mathematics & Statistics program. Education: Ph.D. in Engineering Sciences, Harvard University (2011) M.Sc. in Engineering Sciences, Harvard University (2008) B.Sc. in Electrical Engineering, Sharif University of Technology (2006) Research Interests: Dr. Babadi’s work focuses on statistical and adaptive signal processing frameworks for understanding neural systems. Key areas include: Neural signal processing and systems neuroscience Granger causality and functional connectivity analysis Dynamic modeling of neuronal assemblies Applications to auditory processing and cognitive recovery Scientific Contributions: His recent publications address cortical network dynamics, MEG source analysis, and robust causal inference. Notable methods include Network Localized Granger Causality (NLGC) for direct connectivity estimation and multitaper spectral analysis for neuronal spiking data. Awards: NSF CAREER Award (2016) E. Robert Kent Teaching Award (2019) GSAS Merit Fellowship (Harvard, 2010) Collaborations: Dr. Babadi collaborates with institutions like MIT, Harvard, and Massachusetts General Hospital, and participates in interdisciplinary initiatives such as the Brain and Behavior Initiative (BBI) and NIH BRAIN grants.

Peter George Martinمشاهده پروفایل

پژوهشگر ارشد

• Radiation Detection
• Nuclear Threat Reduction
• Nuclear Forensics
• +۵ مورد دیگر

Dr. Peter George Martin is a Royal Academy of Engineering Research Fellow in the School of Physics at the University of Bristol, specializing in radiation detection systems and nuclear threat reduction. His work develops responsive, high-resolution platforms for monitoring radioactive materials and enhancing nuclear security infrastructure. He earned his BSc and PhD from the University of Bristol, with doctoral research analyzing the 2011 Fukushima Daiichi Nuclear Power Plant accident across 10 orders of magnitude. His educational foundation combines physics with nuclear engineering expertise. Dr. Martin's research spans critical nuclear domains: Radiation Detection: Innovating gamma spectroscopy and sensor networks for real-time monitoring Nuclear Threat Reduction: Creating systems to prevent illicit radioactive material trafficking Nuclear Forensics: Investigating accident scenarios and radiological release incidents AI Integration: Developing machine learning algorithms for radiation data analysis Decommissioning Technologies: Pioneering robotics for nuclear site characterization His recent publications demonstrate converging trends in autonomous radiation mapping, AI-driven spectral analysis, and fundamental nuclear materials research, particularly focused on Fukushima and Chernobyl applications. Scientific recognition includes: Faculty of Science Thesis Commendation (2019) Innovus Nuclear Institute Technology Supply Chain Academic Abstract Prize (2016) Roy G. Post Scholarship (2016) Dr. Martin leads significant grant-funded initiatives including the RAIN at Chernobyl project and international collaborations with Japan, Ukraine, and the USA. His research partnerships span UK Atomic Weapons Establishment, Sellafield, IAEA, and global academic institutions, with emphasis on fieldwork at Fukushima and Chernobyl sites. He directs a research group developing open-source radiation analysis tools and autonomous mapping systems, maintaining active field deployments at nuclear facilities worldwide while advancing nuclear waste characterization methodologies.