جست‌وجوی استاد و پژوهشگر

Pierre Lafaye de Micheauxمشاهده پروفایل

دانشیار

• Applied Statistics
• Biostatistics
• Statistical Theory
• +۱۱ مورد دیگر

Associate Professor Pierre Lafaye de Micheaux is a statistician based at the School of Mathematics and Statistics, University of New South Wales , where he has worked since 2020. He previously held academic roles at Université Paul Valéry (2020, Associate Professor), ENSAI (2015–2017, Professor), Université de Montréal (2011–2016, Associate Professor), and Grenoble Alps University (2003–present, Assistant Professor). His research spans theoretical and applied statistics , focusing on complex random vectors , neuroimaging genetics , and data science for IoT . Education: PhD in Statistics (2003, Université de Montréal & Montpellier) MSc in Biostatistics (1998, Montpellier) BSc in Mathematics and Physics (1996, Montpellier) MSc in Cognitive Neuroscience (2007, Grenoble Institute of Technology) Research interests include: Dependence Measures : Leveraging complex analysis for big data dependence testing under 3V's (Volume, Variety, Velocity). Neuroimaging Genetics : Developing statistical tools for fMRI/EEG/DTI phenotyping of genetic variation with institutions like CHeBA and INSERM. IoT Data Science : Creating Raspberry Pi-based statistical computing tools for real-time sensor data streams. Complex-Valued Inference : Building a unified framework for complex random vectors in neuroimaging and nuclear engineering. Recent publications demonstrate expertise in circular data analysis , nonparametric testing , and central limit theorem counterexamples , with applications in medical imaging and finance. He has supervised numerous PhD, MSc, and honors students on topics ranging from deep learning to stochastic processes. Scientific achievements include: Université de Montréal Provost Honor List (2003) Editor of the Journal of Statistical Software (2017–present) Co-leader of three research groups: Dependence Measures , Neuroimaging Genetics , and Data Science & IoT He has secured grants from UNSW Research Infrastructure Scheme and NSERC , with industry collaborations including BNP Paribas (credit risk) and Olea Medical (stroke treatment analytics). Current teaching includes Statistical Inference (ZZSC5905) and Data Science (DATA3001) at UNSW.

Torsten Hoeflerمشاهده پروفایل

استاد

• Deep learning Systems
• Parallel programming models
• High performance and datacenter networking
• +۸ مورد دیگر

Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.

• Wireless Communication
• Network Optimization
• Machine Learning
• +۳ مورد دیگر

Athanasios Gkelias is a Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. His research focuses on advanced wireless communication systems, network optimization, machine learning applications, and quantum computing methodologies. He contributes to interdisciplinary projects involving IoT coalitions, adversarial signal detection, and cognitive behavior analysis. Key research areas include: (1) Network resource management in Software-Defined Networks (SDN), (2) Localization systems for indoor environments, (3) 3D reconstruction using photometric stereo and origami-based modeling, and (4) distributed optimization frameworks for ad-hoc networks. His recent work explores quantum approaches to combinatorial optimization and self-supervised learning methods. Publications span 20+ years with notable contributions in network coding, vehicular ad-hoc networks (VANETs), and cross-layer design for wireless mesh networks. His work emphasizes practical implementations through frameworks like iVisher for caller ID spoofing detection and cooperative MAC protocols for multi-antenna systems. No scientific awards are listed in the provided information. His research has been applied in healthcare technology through emotion understanding systems for Alzheimer’s patients and in battlefield communication through IoBT coalitions. Active participation in Imperial College's Engineering faculty reflects his commitment to advancing telecommunications and network science.

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +۴ مورد دیگر

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

Mario Di Francescoمشاهده پروفایل

استاد

• Wireless Networking
• Mobile and Ubiquitous Computing
• Internet of Things
• +۴ مورد دیگر

Mario Di Francesco is a Professor at the Department of Computer Science, Aalto University. He holds the position of Professor in Computing Systems (ComputingSystems) Research Area and leads multiple research projects in networking and IoT. Previously, he was a research associate and adjunct faculty at the University of Texas at Arlington (2012–2014), and completed his PhD in Information Engineering at the University of Pisa under Prof. Giuseppe Anastasi. He also served as a research fellow at Scuola Superiore Sant'Anna in 2009. Research Interests : His work focuses on wireless networking, mobile and ubiquitous computing, and the Internet of Things (IoT). Key areas include edge computing, fog networks, virtual/augmented reality, and AI-driven resource management. His research contributes to UN Sustainable Development Goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), and industry/infrastructure development (SDG 9). Publications : His recent work spans topics like private virtual reality conversations, cloud-based network measurement frameworks (CLAIM), microservice connectivity analysis, and edge-DNN optimization. These publications reflect a trend toward integrating AI with distributed systems for real-time, low-latency applications in IoT and edge computing. Awards : Recipient of the Google Internet of Things Technology Research Award (2016) and MobileHCI Honorable Mention (2020). Grants & Projects : CRUISE (2023–2025): Cross-system architecture for autonomous wireless networks using lifelong machine learning. MeXICO (2020–2024): Mobile cross-reality through immersive computing. ASPECT (2016–2018): Spectrum utilization for mobile video. Labs & Teams : Leads research groups focused on edge computing, IoT systems, and immersive technologies. Collaborates with international partners on projects like fog radio networks and autonomous system design.

• Big data
• Data science
• Data analytics
• +۱۲ مورد دیگر

Paolo Garza is an Associate Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he also serves as Coordinator of the College of Computer, Film and Mechatronics Engineering. He is a member of the DBDM research group and the SmartData@PoliTO laboratory, and actively contributes to academic governance through roles in teaching coordination and PhD program committees. Education: Bachelor’s in Computer Engineering, Polytechnic University of Turin (2001) PhD in Computer and Systems Engineering, Polytechnic University of Turin (2005) His research centers on data science, big data analytics, data mining, and machine learning , with applications in emergency management, real-time communications, Earth observation, and cybersecurity. He has led and participated in numerous national and commercial research projects, including AI4CTI and NODES (PNRR), and has collaborated with industry partners like Cisco Systems. His work bridges theoretical algorithm development and practical deployment in critical systems. The recent publications reflect a strong trend toward multimodal AI, crisis informatics, and intelligent networking . Articles span computer vision for environmental monitoring (e.g., burned area detection, canopy estimation), ML for real-time communication quality, multimodal document understanding, and crisis response systems. His team leverages deep learning, transformers, and vision-language models across diverse domains. Scientific Service: Associate Editor, Knowledge and Information Systems (2024–) Associate Editor, Expert Systems with Applications (2022–) General Co-Chair, IEEE AICT Conferences (2022, 2023) He mentors several PhD students and leads funded research initiatives focused on AI for sustainable industry and cyber threat intelligence. His teaching includes graduate courses on big data processing, distributed architectures, and data science lab methods. He has also directed commercial training programs and research contracts in machine learning and cybersecurity. Research Labs & Teams: DBDM - Database and Data Mining Group (DAUIN) SmartData@PoliTO - Big Data and Data Science Laboratory LAB 5 - Research Laboratory (DAUIN)

• Relational Machine Learning
• Deep Learning
• Complex Network Science
• +۴ مورد دیگر

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, leading the Relational Machine Learning Group . Her research bridges machine learning and complex network science to develop robust, data-efficient models with applications in molecular biology. Previously, she held positions at Harvard T.H. Chan School of Public Health and ETH Zurich. PhD in Systems Design (2016) from ETH Risk Center Mathematics and Physics BSc/MSc from TU Darmstadt Her work focuses on sparse training methods and theoretical deep learning , addressing challenges like computational efficiency and adversarial robustness. Recent publications explore: Sparse training via implicit sparsification GNN optimization through rescaling and rewiring Integration of domain knowledge in biomedical modeling Theoretical guarantees for batch normalization and lottery tickets Scientific awards include: Zurich Dissertation Prize (2016) CSF Best Contribution Award (2016) She actively advises PhD students and collaborates with interdisciplinary teams in biostatistics and systems biology.

• 3D Reconstruction
• Scene Understanding
• Autonomous Driving
• +۷ مورد دیگر

Stefano Gasperini is a PostDoc Researcher (part-time) at the Technical University of Munich (TUM), affiliated with the Chair of Computer Science Applications in Medicine (CAMP) under Prof. Nassir Navab. He also serves as Co-Founder & CEO of Visualais, a startup focusing on 3D rendering from smartphone images. His primary roles include advancing reliable 3D scene understanding for autonomous driving and medical applications. He holds a PhD (submitted 2023) in Reliable 3D Scene Understanding from TUM, supervised by PD Dr. Federico Tombari, with prior industry experience at BMW and Airbus. Education : PhD in Informatics (2019-2025), TUM Master in Informatics (2017-2019), TUM Bachelor in Computer Engineering (2013-2016), University of Bologna Exchange at UC Santa Cruz (2015-2016) Research Interests : 3D reconstruction, scene understanding, autonomous driving, robustness in computer vision, domain generalization, and medical image analysis. His work bridges theoretical advancements with practical applications in automotive and medical fields, emphasizing reliability under challenging conditions. Publications : Gasperini’s research focuses on robust 3D perception, adversarial augmentation techniques, and domain generalization. Recent work includes pioneering methods like 3D adversarial augmentations for domain adaptation and novel view synthesis using NeRF extensions. Awards : Outstanding Reviewer awards at CVPR 2025, ECCV 2024, ICCV 2023 Deutschlandstipendium scholarships (2017–2019) Summa Cum Laude (2016) and Provost’s Commendations (2015) Advising & Collaboration : Supervises PhD candidates across TUM and industry partners (BMW, Huawei, Mercedes-Benz). Coordinates the reconstruction research group at CAMP and collaborates on projects with AIT, BMW, and academic institutions. Labs & Teams : Active in TUM’s DHM, IFL, and NARVIS labs, focusing on medical imaging, autonomous systems, and surgical data science. Leads the Visualais startup to commercialize 3D reconstruction technologies.

• Geometric Deep Learning
• Topological Deep Learning
• Topological Data Analysis
• +۷ مورد دیگر

Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.

• Signal Processing
• Bayesian Statistics
• Deep Learning
• +۴ مورد دیگر

Lars Hammarstrand is an Associate Professor at Chalmers University of Technology specializing in the Signal Processing research group. His work integrates model-based Bayesian statistics with deep machine learning for applications in visual localization, sensor fusion, and autonomous systems, with emphasis on robustness in real-world environments. His research focuses on bridging Bayesian inference and deep learning to solve challenges in autonomous vehicle perception. Key areas include visual localization under appearance changes, radar-camera sensor fusion, and out-of-distribution detection for safety-critical systems. Recent work explores neural radiance fields for radar, semi-supervised learning for mapping, and probabilistic hierarchical classification to address real-world uncertainties in autonomous driving. Analysis of his 2020-2025 publications reveals a trajectory toward unifying geometric and semantic understanding in autonomous systems. His work demonstrates increasing integration of neural radiance fields with traditional filtering techniques, while advancing open-set recognition capabilities. Notable contributions include road geometry estimation frameworks, extended object tracking with PHD filters, and methods to mitigate data leakage in localization benchmarks. No scientific awards were mentioned in the provided materials. Hammarstrand has contributed to academic supervision methodology through his publication on improving master's thesis supervision efficiency, though specific student names are not listed. The provided information contains no details about research grants or funding sources. He operates within Chalmers University's Signal Processing research group, which develops advanced algorithms for automotive perception systems, focusing on sensor fusion techniques that combine radar, camera, and motion data for robust environmental understanding in autonomous vehicles.

• Urban Climate Research
• Thermal Comfort
• Urban Air Quality
• +۶ مورد دیگر

Prof. Dr. Uwe Schlink is a leading Professor at the Institute of Meteorology, University of Leipzig, and Senior Researcher at the Department of Urban & Environmental Sociology, Helmholtz Centre for Environmental Research - UFZ. His work focuses on urban climate research , thermal comfort , urban air quality , and statistical modelling with Bayesian inference. He leads the working group on urban climate and personal exposure, bridging environmental science with societal resilience. Affiliation: University of Leipzig (since 2009) and UFZ (since 2013) Research Themes: Urban heat islands, personal exposure to environmental stressors, statistical climate models, and health impacts of air pollution His research spans environmental health , urban climatology , and resilient city planning , with significant contributions to understanding thermodynamic interactions between urban structures and climate. He has pioneered methods for high-resolution land surface temperature analysis and green infrastructure performance in mitigating heat stress. Recent publications (2023-2025) highlight his work on PM2.5-bound PAH exposure , anthropogenic heat impacts in Beijing, and Asian plateau climate dynamics . Collaborative projects address urban heat stress , green roofs , and health-focused urban planning .

Neill Campbellمشاهده پروفایل

استاد

• Visual Computing
• Machine Learning
• Computer Vision
• +۱۳ مورد دیگر

Neill Campbell is a Professor of Visual Computing and Machine Learning in the Department of Computer Science at the University of Bath . He is the Director of the Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) and co-director of the Centre for Mathematics and Algorithms for Data (MAD) . He holds an external position as Honorary Associate Professor at University College London and is a Royal Society Industry Fellow. His research spans visual computing, machine learning, and their applications in graphics, vision, and healthcare. His educational background includes a Master of Engineering and a Doctor of Philosophy in Engineering from the University of Cambridge, with his PhD focusing on Automatic 3D Model Acquisition from Uncalibrated Images. Neill Campbell’s research interests lie at the intersection of shape modeling , machine learning , and computer vision . He develops probabilistic and deep learning models to understand and generate visual content, with applications in digital humans, virtual production, biomechanics, and medical imaging. His work emphasizes uncertainty quantification, generative modeling, and alignment learning using Gaussian processes and Bayesian nonparametrics. He leads interdisciplinary projects that bridge computer science and mathematical sciences. The recent publications reflect a strong trend in probabilistic modeling , geometric deep learning , and medical applications . Topics include Gaussian process-based shape modeling, anomaly detection in safety-critical systems, and sparse approximations for geometric representations. His work increasingly integrates theoretical machine learning with real-world applications in health, engineering, and creative industries. Royal Society Industry Fellow Neill Campbell actively supervises PhD students across multiple Centres for Doctoral Training (ART-AI, SAMBa, CDE) and leads significant research grants such as MyWorld (UKRI Strength in Places Fund) and REMODEL (EPSRC). His group collaborates with industry leaders like Rolls-Royce, NVIDIA, Adobe, and DNEG, and he supports student internships and industrial placements. He is also involved in spin-out activities, including contributions to Forceteck Ltd. He leads the Visual Computing Group and is deeply embedded in research centers including CAMERA , MAD , and MyWorld . His team includes postdoctoral researchers and PhD students working on 3D reconstruction, biomechanics, inverse problems, and generative models, fostering a collaborative and interdisciplinary research environment.

Ignacio Cascos Fernandezمشاهده پروفایل

دانشیار

• Multivariate Analysis
• Probability Theory
• Data Depth
• +۷ مورد دیگر

Ignacio Cascos Fernandez is an Associate Professor at the Department of Statistics, School of Engineering, Carlos III University of Madrid. His research focuses on Multivariate Analysis (depth functions, multivariate quantiles) and Probability Theory (stochastic orders, random sets, financial risk measures). Key Research Areas Multivariate analysis with emphasis on data depth and quantiles Probability theory involving stochastic orders and random sets Applications to financial risk modeling and statistical process control Notable Awards IASC ERS Young Researchers Award (2006) Elected member of the International Statistical Institute (2008) PhD Supervision Advised José Manuel Cueto (2021) on multifactor models for equity returns Advised Maicol Ochoa (2022) on expectiles and multivariate analysis Recent Projects Developed data depth methodologies for biological age prediction Created statistical tools for complex data in finance and real estate analysis

Julia Salzmanمشاهده پروفایل

دانشیار

• RNA Biology
• Computational Biology
• Genomics
• +۴ مورد دیگر

Julia Salzman is an Associate Professor at Stanford University, affiliated with the School of Medicine in the Departments of Biomedical Data Science and Biochemistry , with courtesy appointments in Statistics and Biology . She serves as a member of interdisciplinary centers including Bio-X , Stanford Cancer Institute , and Sarafan ChEM-H . Salzman's research focuses on statistical computational biology , particularly RNA splicing , cancer genomics , and microbial systems . Her lab develops algorithms like SPLASH (Statistically Primary aLignment Agnostic Sequence Homing) and SICILIAN for precise splice junction detection, enabling reference-free analysis of genomic data across human , non-model organisms , and planetary health applications (e.g., oceanic systems , plants ). Her 15 most recent publications span 2024–2016, covering topics from cell-free RNA diagnostics to circular RNA in disease . Key methodologies include DEEPEST for gene fusion detection and SQUICH for logarithmic molecular sampling. These works highlight her contributions to cancer biology and viral pandemic response . Scientific Awards include Arc Ignite Investigator (2023) NSF CAREER AWARD (2016) Alfred P. Sloan Research Fellow (2014) Baxter Faculty Scholar Award (2014) Salzman's funding history includes NIH Pathway to Independence and NSF grants . She mentors PhD and Master’s students through courses like BIOMEDIN 290 and BIODS 228 , while leading the Salzman Lab in advancing computational genomics and biological modeling .

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Jiayan Qiu is a Lecturer (Assistant Professor) at the University of Leicester's College of Computing and Mathematical Science. Previously, he was a postdoctoral research fellow collaborating with Prof. Zhou Wang at the University of Waterloo's Department of Electrical & Computer Engineering. He holds a Ph.D. from the University of Sydney (USYD), advised by Prof. Dacheng Tao, and completed his MPhil and Honorable B.S. at the Australian National University (ANU). His research focuses on computer vision, machine learning, and artificial intelligence, with notable contributions to visual relationship modeling, image outpainting, depth estimation, and generative models. His work has been published in top-tier venues like IEEE TPAMI, CVPR, ECCV, and ACM KDD. Professional service activities include serving as a reviewer for prestigious journals (e.g., IEEE T-PAMI, T-IP) and conferences (CVPR, ICCV, NeurIPS), as well as a member of program committees for leading AI conferences. He also contributes to academic leadership as a Guest Editor for Frontiers in Signal Processing and MDPI-Electronics .