جست‌وجوی استاد و پژوهشگر

Huey-Wen Linمشاهده پروفایل

دانشیار

• Lattice Quantum Chromodynamics (QCD)
• Hadron Structure
• High-Performance Computing
• +۳ مورد دیگر

Huey-Wen Lin is an Associate Professor at Michigan State University with joint appointments in the Department of Computational Mathematics, Science and Engineering and the Department of Physics & Astronomy. She joined MSU in 2016 as a high-energy particle theorist specializing in lattice Quantum Chromodynamics (QCD). Her research utilizes high-performance computing to study proton/neutron structure and fundamental particle interactions. Education She earned her Ph.D. in Physics from Columbia University in 2006. Research Focus Her work bridges theoretical particle physics and computational mathematics, with emphasis on: Lattice QCD simulations of hadronic structure Parton distribution functions and gluon dynamics Machine learning applications in particle physics Quantum computing algorithms for QCD Precision studies of nucleon properties Recent publications demonstrate strong focus on gluon distributions in hadrons, lattice-informed machine learning frameworks, and theoretical tools for neutrino scattering experiments. Her work consistently integrates high-performance computing with fundamental physics. Awards and Leadership NSF CAREER Award (2017) for research on "Constraining Parton Distribution Functions for New-Physics Searches" Organizing Chair of the 36th International Symposium on Lattice Field Theory Contributions She has co-edited foundational texts ( Lattice QCD for Nuclear Physics ) and develops computational methodologies advancing precision in hadronic physics. Her leadership in large-scale collaborations (e.g., CTEQ-TEA) strengthens synergy between lattice calculations and experimental particle physics.

Rongrong Wangمشاهده پروفایل

دانشیار

• Applied and Computational Harmonic Analysis
• Machine Learning
• Neural network training and generalization
• +۶ مورد دیگر

Rongrong Wang serves as Associate Professor in both the Department of Computational Mathematics, Science and Engineering (CMSE) and Department of Mathematics at Michigan State University, based in the Engineering Building with contact email wangron6@msu.edu . Her academic journey includes: B.S. in Mathematics and B.A. in Economics from Peking University, Beijing Ph.D. in Applied Mathematics from University of Maryland College Park under John Benedetto and Wojciech Czaja Postdoctoral fellowship at University of British Columbia with Ozgur Yilmaz and Felix Herrmann Her research spans Applied and Computational Harmonic Analysis , Machine Learning , and Compressed Sensing with focus areas including neural network training dynamics, learning theory, tensor analysis, and inverse problems. She investigates theoretical foundations of deep learning while developing applications for medical imaging and signal processing. Recent publications (2024-2025) demonstrate strong interdisciplinary work at the intersection of deep learning theory and medical imaging, particularly exploring edge-of-stability phenomena in neural networks and diffusion-guided reconstruction techniques. Her work also advances tensor decomposition methods and in-context learning mechanisms in language models. Professor Wang actively recruits self-motivated graduate and undergraduate students with backgrounds in mathematics, computer science, or electrical engineering for research opportunities in her lab.

Nikolaj Normann Holmمشاهده پروفایل

پژوهشگر ارشد

• Data Science
• Multimorbidity Research
• Chronic Heart Disease
• +۴ مورد دیگر

Nikolaj Normann Holm is a Postdoc researcher at the Department of Applied Mathematics and Computer Science, Statistics and Data Analysis at the Technical University of Denmark (DTU). His research focuses on applying data science techniques to healthcare problems, particularly in the areas of multimorbidity, chronic heart disease, and mortality prediction. His research interests center on developing and applying advanced statistical and machine learning methods to analyze complex healthcare data. Key areas include multimorbidity clustering, disease progression modeling, and mortality prediction using register-based data. His work bridges the gap between data science and medical research, creating tools that help understand complex disease patterns in population-level healthcare data. His recent publications demonstrate a strong focus on using variational autoencoders for disease clustering, analyzing co-occurring diseases in chronic heart conditions, and validating machine learning models for mortality prediction. His research consistently applies rigorous data science methodologies to address pressing healthcare challenges, particularly in the Danish healthcare system context. Dr. Holm actively participates in academic activities including conference presentations, examinations, and teaching. He has organized workshops on chronic disease selection algorithms and served as supervisor for research projects focusing on disease clustering and mortality analysis.

• Blockchain technologies
• Quantum Computing
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Ernestas Filatovas is a Senior Researcher and Chief Researcher in the Project at Vilnius University's Institute of Data Science and Digital Technologies (formerly Institute of Mathematics and Informatics), where he has been affiliated since 2013. He leads the Blockchain and Quantum Technologies Group, focusing on cutting-edge research at the intersection of quantum computing, blockchain, and artificial intelligence. Previously, he served as an Associate Professor and Lecturer at Vilnius Gediminas Technical University's Faculty of Fundamental Sciences from 2013 to 2019. Dr. Filatovas earned his Doctor of Technology in Computer Science Engineering from Vilnius University Institute of Mathematics and Informatics in 2012. His dissertation, supervised by Prof. Dr. Olga Kurasova, focused on the interactive solution of multi-criteria optimization problems. His research spans multiple high-impact domains, with particular expertise in blockchain technologies, quantum computing, artificial intelligence, and machine learning. He has pioneered work in quantum blockchain implementations, reproducibility of AI research through blockchain verification, and quantum machine learning applications. His research bridges theoretical computer science with practical applications in financial markets, healthcare, and distributed systems. His extensive publication record—over 50 scientific papers, with more than 25 in Clarivate Analytics-indexed journals—demonstrates consistent productivity and international collaboration. Recent work shows a clear trajectory toward quantum-enhanced AI systems, blockchain-based research verification frameworks, and quantum algorithms for practical problems. Laureate of the 4th LMA Young Scientists' Conference (2014) INFOBALT scholarship 2nd place winner (2014) Lithuanian State Science and Studies Foundation funding recipient (2009, 2010) Recognized as one of Lithuania's most active doctoral students Master's degree with honors (2006) Dr. Filatovas leads multiple significant research projects, including the 2021-2024 project 'Solving the problems of reproducibility of scientific research in the field of artificial intelligence using blockchain technologies' as team leader, and the 2023-2027 project 'Development and validation of quantum machine learning methods using prepared datasets' as Chief Researcher. He has also contributed to international collaborations such as the Spanish-funded 'High Performance Solutions for Modern Scientific Computing Challenges' (2019-2021). His popular science contributions, including the VU news portal article 'Quantum Computing: Who and Why?', demonstrate his commitment to science communication. As a key member of Vilnius University's Blockchain and Quantum Technologies Group, Dr. Filatovas contributes to Lithuania's growing reputation in quantum computing research and blockchain innovation, working closely with international collaborators across Europe.

Naimul Khanمشاهده پروفایل

دانشیار

• Machine Learning
• Medical Imaging
• Multimedia
• +۵ مورد دیگر

Naimul Khan is an Associate Professor and Associate Chair in Graduate Studies at Toronto Metropolitan University's Department of Electrical, Computer, and Biomedical Engineering. His research focuses on practical applications of machine learning, medical imaging, computer vision, and augmented/virtual reality technologies to solve real-world healthcare and multimedia challenges. PhD (Toronto Metropolitan University, 2014) MSc (University of Windsor, 2010) BSc (Bangladesh University of Engineering and Technology, 2008) His research interests span medical imaging automation, virtual reality systems for healthcare applications, and machine learning techniques for biomedical signal processing. He has developed innovative algorithms for ultrasound analysis, emotion recognition systems using EEG data, and segmentation approaches for diabetic foot ulcers. Publications demonstrate consistent focus on practical implementations across computer vision, medical diagnostics, and extended reality (XR) technologies. Scientific contributions include a Best Paper Award at the IEEE International Symposium on Multimedia (2017) and prestigious postdoctoral and graduate scholarships. He actively collaborates with industry partners through the TMU Multimedia Research Laboratory, teaching graduate courses like ELE 725 (Basics of Multimedia Systems) and COE 318 (Software Systems). Best Paper Award, IEEE International Symposium on Multimedia (2017) OCE TalentEdge Postdoctoral Fellowship (2014-2016) Ontario Graduate Scholarship (2013-2014) Queen Elizabeth II Scholarship in Science & Technology (2012-2013) As co-director of the TMU Multimedia Research Laboratory, Khan bridges academic research with industry applications through practical implementations of machine learning and extended reality technologies in healthcare domains.

Amy Ahnمشاهده پروفایل

پژوهشگر

• Autism Spectrum Disorder Diagnostics
• Developmental Psychology
• Behavioral Analysis Technology
• +۳ مورد دیگر

Amy Ahn serves as Associate Research Director in Harvard University's Department of Psychology, leveraging technology and multidisciplinary approaches to advance objective behavioral measurement for early risk identification and diagnostic acceleration. Her educational background includes: B.S. in Human Development from Cornell University Ph.D. in Developmental Psychology from the University of Miami Her research integrates computer vision, machine learning, and clinical psychology to develop objective metrics for autism spectrum disorder (ASD) diagnostics, focusing on social communication behaviors, facial expressions, and vocalizations in children. She pioneers automated tools like infant facial action recognition (AFAR) and AAC usage pattern analysis, while examining parent-infant interactions through paradigms like Face-to-Face/Still-Face. Her work bridges technological innovation with clinical application to improve early intervention pathways. Analysis of her 15 most recent publications reveals consistent advancement in objective ASD assessment tools, with increasing emphasis on machine learning applications (e.g., stacked autoencoders) and pandemic-related service disruptions. Her research trajectory shows progression from foundational infant behavior studies toward scalable diagnostic technologies and real-world implementation challenges. No scientific awards were mentioned in the provided text. The source material contains no information about student advising or grant funding. Dr. Ahn directs research operations at Harvard's Nock Lab, which specializes in technology-enhanced psychological assessment and intervention development for developmental disorders, with particular focus on objective behavioral metrics and early detection systems.

Madhushi Welikalaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Software Engineering
• +۶ مورد دیگر

Madhushi Welikala is a Researcher at the School of Computing , Ulster University, Belfast campus. Her work focuses on applying artificial intelligence to software engineering challenges, particularly in bug classification/localization and temporal analysis of code issues. M.Sc. in Computer Science with Artificial Intelligence from University of York B.Sc. (Hons) in Computer Science from University of Sri Jayewardenepura Research Interests span multiple domains: Core focus on Machine Learning and Artificial Intelligence applications Special interest in Edge Computing and Data Analytics Hybrid AI systems development combining Generative Adversarial Networks and Variational Autoencoders Scientific Awards highlight her innovative work: Gold Award at Asia-Pacific ICT Alliance (APICTA) 2019 Silver Award at National ICT Awards 2019 Sri Lanka Her industry experience at Zepz and Axiata Digital Labs informs her practical approach to software quality and large-scale system development. Current research aligns with UN Sustainable Development Goals, particularly in educational technology applications.

• Machine Learning
• Bioinformatics
• Software Engineering
• +۳ مورد دیگر

William Andreopoulos serves as an Assistant Professor in the Department of Computer Science at San José State University's College of Engineering. With a strong interdisciplinary background spanning computer science, bioinformatics, and molecular biology, he bridges computational methods with biological applications. His academic journey has taken him through prestigious institutions including Lawrence Berkeley National Laboratory, Columbia University, and TU Dresden. Ph.D. in Computer Science and Engineering, York University, Toronto, Canada (2006) M.Sc. in Computer Science, University of Toronto, Canada (2001) B.Sc. in Computing and Software, McMaster University, Canada (1999) Dr. Andreopoulos specializes in applying machine learning and computational approaches to biological problems, with particular emphasis on genomics, metagenomics, and bioinformatics. His research spans fungal genomics, microbial community analysis, plasmid identification, and the development of tools for omics data integration. He has extensive experience working with environmental data as well as cancer datasets from PCAWG and TCGA projects. His publication record reveals a strong interdisciplinary focus, with recent work spanning from fungal genomics and microbial identification to natural language processing applications. The research demonstrates consistent integration of machine learning techniques across diverse biological contexts, with notable contributions in developing computational pipelines for high-throughput sequencing data analysis. As an educator, Dr. Andreopoulos has mentored numerous graduate students through CS297/CS298 projects and CS280/CS180 courses, with students working on bioinformatics-related computational projects. His professional experience includes 8 years as a data scientist at the Joint Genome Institute, Lawrence Berkeley National Laboratory, where he developed software pipelines for automated processing of high-throughput sequencing data. His laboratory focuses on computational biology projects that require expertise in Java, Python, Linux command line, machine learning libraries, and data visualization tools. Current research directions include strain separation in microbial communities, plasmid identification, 16S sequence reconstruction, and deep learning applications to molecular biology problems.

Fabrizio Silvestriمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Web Search
• +۷ مورد دیگر

Fabrizio Silvestri is a Full Professor at Sapienza University of Rome's Department of Computer, Automatic and Management Engineering (DIAG), where he coordinates the Ph.D. program in Data Science. He leads the RSTLess research group focusing on Robust, Safe, and Transparent Deep Learning. Research interests: Artificial Intelligence, Machine Learning, Web Search, Natural Language Processing, Information Retrieval, Graph Neural Networks Research Trends from recent publications reveal: Advancements in sequential recommendation systems using topological and sheaf-based neural networks Focus on sustainable AI through eco-aware graph neural networks Counterfactual explanations for graph models and machine unlearning Security applications in dense retrieval and data poisoning defense Time series analysis for 5G network monitoring Integration of attention mechanisms and positional encoding in Transformers Scientific Achievements : ECIR 2018 Test of Time Award 3 Best Paper Awards (ECIR 2007, IEEE WI 2004, WSDM 2011 Runner-Up) Yahoo! Patent Milestone Award Recipient of Yahoo! Labs Excellence Program (LEAP) and Faculty Research Engagement Program (FREP) Finalist for ERCIM Cor Baayen Award (2005) Academic Leadership : Holds 9 industrial patents from Yahoo! and Facebook AI. Directed Facebook AI research groups combating malicious content. Ph.D. in Computer Science from University of Pisa with thesis on High-Performance Issues in Web Search Engines . Supervises thesis projects through the RSTLess group website .

Benedikt Wirthمشاهده پروفایل

استاد

• Optimisation and calculus of variations
• image processing
• scientific computing
• +۹ مورد دیگر

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

• Machine Learning
• Artificial Intelligence
• Federated Learning
• +۳ مورد دیگر

Dr. Jing Jiang is an Associate Professor in the School of Computer Science and a core member of the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). As an ARC DECRA Fellow, she has secured over AU$2 million in research funding through multiple ARC grants, CSIRO/Data61 projects, and industry collaborations. Her work bridges theoretical advances in machine learning with practical applications across various domains. Dr. Jiang's research focuses on machine learning, particularly federated learning, reinforcement learning, and foundation models. She explores how to make these technologies work effectively in heterogeneous environments, addressing challenges like data privacy, non-IID data distributions, and efficient communication. Her work spans both theoretical foundations and practical implementations for real-world applications. Her publications demonstrate a strong trend toward personalized federated learning approaches, with significant contributions to recommender systems, time series analysis, and weather forecasting. She has developed novel techniques like variational autoencoder approaches for federated collaborative filtering and adaptive prompt learning for foundation models on devices. Dr. Jiang has received several notable recognitions: ARC DECRA Fellow Awardee of the Australian International Postgraduate Research Scholarship (IPRS) Dr. Jiang has successfully led multiple major research projects, including two ARC Discovery Projects, one ARC Linkage Project, and a CSIRO/Data61 CRP project where she served as lead Chief Investigator. She has supervised numerous PhD and Master's students and actively collaborates with industry partners on applied research. As a core member of the Australian Artificial Intelligence Institute (AAII) at UTS, Dr. Jiang contributes to a vibrant research ecosystem focused on cutting-edge AI research. She collaborates closely with Professor Guodong Long and other researchers on various machine learning projects, and serves in leadership roles including program co-chair for major conferences like ADMA2023.

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +۸ مورد دیگر

Guodong Long is an Associate Professor at the University of Technology Sydney (UTS) in the Faculty of Engineering and Information Technology. He joined UTS in 2010 and earned his PhD there in 2014. His research focuses on federated learning, trustworthy AI, and pre-trained foundation models with applications in healthcare, IoT, and social media. PhD in Artificial Intelligence, University of Technology Sydney (2014) Leading the Foundation Model and Federated Learning research group (https://www.fmfl.group/) His work addresses challenges in frequency transformation for time series, privacy-preserving healthcare analytics, and spatio-temporal traffic forecasting. He has published extensively at top AI conferences like AAAI, ICLR, and NeurIPS, with significant citation impact (4,682 citations in 2022). Collaborations with industry partners have secured over $4M in external funding. Recent publications emphasize federated foundation models (ICLR'25), privacy-preserving recommendation systems (WWW'25), and adaptive time series analysis. His research integrates domain knowledge and graph learning for multivariate time series imputation and traffic prediction. Dr. Long actively contributes to academic leadership as General Co-Chair for WebConf 2025, Program Co-Chair for AI conferences, and reviewer for top venues. He supervises PhD and master's students and welcomes research visitors for extended collaborations.

Chongfeng Weiمشاهده پروفایل

دانشیار

• Decision-making and Planning of Intelligent Vehicles
• Collective Autonomy
• Robotic System Design
• +۵ مورد دیگر

Dr. Chongfeng Wei is an Associate Professor (University Senior Lecturer) at the James Watt School of Engineering, University of Glasgow. Prior to joining the University of Glasgow, he was a lecturer at Queen's University Belfast. His research focuses on intelligent vehicles, autonomous systems, and human-robot interaction, with applications in transportation and robotics. Dr. Wei's research spans several key areas in autonomous systems and vehicle dynamics: Decision-making and Planning of Intelligent Vehicles Collective Autonomy: Perception and Planning Robotic System Design and Dynamical Control Dynamics and Control of Mechanical Systems Human Behaviour Study and Prediction His recent publications demonstrate a strong focus on human-vehicle interaction, with particular emphasis on pedestrian-vehicle interactions, decision-making frameworks, and control strategies for autonomous systems. His work often combines AI technologies, bio-designs, and first principles of dynamics and control to create smarter or human-acceptable autonomous systems. Dr. Wei serves as an Associate Editor for several prestigious journals including IEEE Transactions on Intelligent Transportation Systems (TITS), IEEE Transactions on Vehicular Technology (TVT), IEEE Transactions on Intelligent Vehicles (TIV), IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS), and Frontiers on AI and Robotics. He actively supervises PhD students and postdoctoral researchers, with current projects focusing on vision-based 3D flow prediction, pedestrian interaction modeling, and multimodal interaction for behavior prediction in autonomous driving.

• Autonomous Driving
• Intelligent Transportation Systems
• Human Factors
• +۳ مورد دیگر

Dr. Wenjing Zhao is a Research Associate in the Autonomous Systems & Connectivity research group at the University of Glasgow, specializing in autonomous vehicle systems and intelligent transportation solutions. Her work bridges engineering, human factors, and artificial intelligence to enhance vehicle safety and decision-making capabilities. Her research focuses on autonomous driving decision algorithms , human-vehicle interaction dynamics , and causal inference frameworks . She investigates critical safety scenarios like driver responses to collision warnings, develops attention-based exploration strategies for autonomous navigation, and applies Bayesian game theory to mixed-traffic environments. Her recent work extends to trustworthy generative modeling using causal graphs for image modification applications in autonomous systems. Analysis of her 2025 publications reveals a cohesive research trajectory centered on AI-driven transportation safety. Key trends include integrating human behavioral modeling with autonomous decision systems, developing robust game-theoretic approaches for human-automated vehicle interactions, and advancing causal machine learning for trustworthy perception. Her work spans high-impact transportation safety journals (Accident Analysis and Prevention), vehicular technology (IEEE Transactions), and computer vision conferences (ICDCS). Dr. Zhao contributes to the University of Glasgow's Autonomous Systems & Connectivity initiative, collaborating with researchers like Dezong Zhao and David Flynn on next-generation vehicle automation technologies. Her current projects emphasize real-world implementation of collision warning systems and decision-making frameworks for complex traffic environments.

Simone Severiniمشاهده پروفایل

استاد

• Quantum Computing
• Machine Learning
• Quantum Information
• +۳ مورد دیگر

Simone Severini is a Professor of Physics of Information at the University College London , affiliated with the Department of Computer Science . He is a Royal Society University Research Fellow and contributes to multidisciplinary groups including Intelligent Systems , UCL CS Quantum , UCL Quantum Science and Technology Institute , and CoMPLEX . Research Interests: His work bridges Quantum computing Machine learning Graph theory Quantum information theory Computational biology with a focus on quantum algorithms, classical simulation of quantum systems, and mathematical frameworks for physical correlations. Scientific Contributions: Recent publications span quantum state learning, non-Markovian dynamics, adversarial quantum learning, and graph isomorphism. His projects include Quantum Computing, Information, and Algebras of Operators and the Distributed Information initiative . Awards: Royal Society University Research Fellowship Best Paper Award at FCT2017