جست‌وجوی استاد و پژوهشگر

Stefania Antonia Spagnoloمشاهده پروفایل

دانشیار

• Particle Physics
• High Energy Physics
• Experimental Physics
• +۳ مورد دیگر

Stefania Antonia Spagnolo is an Associate Professor at the Department of Mathematics and Physics "Ennio De Giorgi" at the University of Salento and a researcher at INFN Lecce. She has been actively involved in major high-energy physics experiments including KLOE at DAFNE, OPAL at LEP, and ATLAS at the LHC. Her educational background includes a Physics Degree from Lecce University in December 1993, followed by a PhD from Lecce University from October 1994 to June 1998, focusing on the KLOE drift chamber and hadronic cross section measurements at DAFNE. Dr. Spagnolo's research spans multiple areas of experimental particle physics with particular emphasis on precision measurements of Standard Model processes and searches for physics beyond the Standard Model. Her work includes studies of gauge boson couplings, Higgs boson physics, heavy flavor production, and detector development. She has made significant contributions to the understanding of muon g-2, fermion pair production, and anomalous gauge couplings. Her detector expertise includes silicon pixel trackers, RPC systems, diamond detectors, and drift tracking systems used in high-energy physics experiments. Her recent publications demonstrate a strong focus on Higgs boson physics, top quark measurements, and searches for new phenomena at the LHC. The research spans both precision Standard Model measurements and explorations of potential new physics, with particular attention to rare processes and precision measurements that could reveal deviations from theoretical predictions. Dr. Spagnolo has been actively involved in supervising students and early-career researchers, contributing to the training of the next generation of particle physicists. Her teaching activities include courses in General Physics, Experimental Physics, and specialized courses in Nuclear and Subnuclear Physics. She has been instrumental in the development and operation of the ATLAS muon spectrometer, particularly in software development for offline reconstruction and calibration. Her work on the RPC system for the muon trigger has been critical for ATLAS physics analyses requiring muon identification.

Nathan Woolardمشاهده پروفایل

استادیار

• Leadership
• Entrepreneurial Pedagogy
• Small Business Development
• +۲ مورد دیگر

Nathan Woolard is an Applied Assistant Professor of Management & Entrepreneurship in the Department of Management & Marketing at the Collins College of Business, The University of Tulsa, where he joined the faculty in spring 2022. He serves as faculty advisor for the Jolt@TU Business Center, providing student programming in innovation, entrepreneurship, and the future of work. Dr. Woolard's educational background includes: Ed.D. from Oklahoma State University (Dissertation: "Academic Capitalism and Impact on State Spending") Master of Public Administration from the University of Oklahoma B.S. from the University of Central Oklahoma His research centers on entrepreneurial pedagogy, ecosystem development, and small business innovation, with expertise in leadership, e-commerce, and community-driven entrepreneurship. Drawing from his experience operating a small business until 2019, his work emphasizes practical applications in real-world contexts like food halls and craft breweries, focusing on how micro-ecosystems foster community impact and social entrepreneurship. Woolard's publication history (2014-2025) reveals a cohesive trajectory examining entrepreneurial support structures across diverse settings—from rural crowdfunding networks to academic capitalism's influence on state funding. His scholarship consistently bridges theoretical frameworks with actionable insights for educators and community developers, particularly through experiential learning models. His notable recognitions include: IDA Friday Faculty Award (campus-wide, junior faculty of the year) from Meredith College (2017) School of Business Teacher of the Year (2019) As Jolt@TU Business Center advisor, Woolard mentors student ventures while developing curricular innovations like venture competitions and co-curricular programs. His prior leadership at Meredith College involved launching comprehensive entrepreneurship programs, demonstrating sustained commitment to academic entrepreneurship education. Current projects focus on placemaking through food halls and social entrepreneurship ecosystems. Woolard actively cultivates the Jolt@TU Business Center as a dynamic hub for student innovation, integrating classroom theory with hands-on startup experiences through competitions and community partnerships that emphasize local economic development.

Niv Buchbinderمشاهده پروفایل

استاد

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +۲ مورد دیگر

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

Michał Chromiakمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Computer Vision
• +۴ مورد دیگر

Dr. Michał Chromiak is an Assistant Professor at the Department of Cybersecurity and Computational Linguistics , part of the School of Mathematics, Physics and Computer Science at Maria Curie-Skłodowska University . With a PhD in Computer Science from the Polish Academy of Sciences and dual MSc degrees in Mathematics and Computer Science, he focuses on understanding the chaos in data through research in Machine Learning, Deep Learning, and Software Engineering. PhD in Computer Science (Polish Academy of Sciences) MSc in Mathematics and Computer Science His scientific activity spans cross-domain feature transfer in computer vision (e.g., DINOv2, MAE), reinforcement learning (e.g., Decision Transformer), and neural architecture analysis (MLP-Mixer, Transformers). He actively shares insights through his blog, covering topics like Attention Mechanisms , Self-Supervised Learning , and Sequence Modeling . Dr. Chromiak serves on the Program Committee for ECAI 2024 and maintains a strong online presence via ORCID , GitHub , and LinkedIn . Contact for teaching matters: chromiakm@office.umcs.pl . Consultations available via MS Teams on Thursdays 15-17:15 and Fridays after 15:00.

João Niza Bragaمشاهده پروفایل

استادیار مدعو

• Consumer Behavior
• Marketing
• Intuition in Decision Making
• +۳ مورد دیگر

João Niza Braga serves as an Adjunct Assistant Professor and Post-doctoral Research Fellow at the Catholic University of Portugal, primarily affiliated with the Católica Lisbon School of Business and Economics while also teaching in the Faculty of Human Sciences. His academic roles encompass graduate instruction and research leadership at CUBE (Católica Lisbon Research Unit in Business and Economics). Braga holds advanced degrees in Psychology from the University of Lisbon, including both Master's and Ph.D. qualifications. His international research experience includes visiting positions at Indiana University and New York University. Ph.D. in Psychology, University of Lisbon Master's in Psychology, University of Lisbon His research program investigates intuitive cognitive processes in consumer behavior, economic decisions, and social interactions, with particular emphasis on heuristic processing in online contexts and hedonic experiences. Key contributions examine how representativeness and availability heuristics operate in consumer decision-making, sports prediction, and conspiracy thinking, bridging experimental psychology with real-world business applications. Current work explores digital environment effects on health-related choices and mindfulness interventions. Analysis of his 15 most recent publications (2015-2024) reveals a methodological focus on disentangling cognitive mechanisms through controlled experiments, with recurring themes in hot-hand fallacies, online shopping behavior, and causal perception errors. His work consistently applies psychological theory to business contexts, particularly in marketing and managerial decision-making. Scientific recognition includes: FCT Postdoctoral Research Grant (2017) As a Post-doctoral Research Fellow at CUBE, Braga directs funded research projects examining heuristic processing in digital environments while mentoring graduate students in consumer behavior methodology. His academic service includes developing curriculum for Master's programs across business and human sciences disciplines. He maintains active research collaboration through CUBE, a multidisciplinary hub fostering innovation in business and economics scholarship at Catholic University of Portugal, where his work integrates psychological science with practical business insights.

• Machine Learning
• Collaborative Robotics
• Software-driven Robotics
• +۵ مورد دیگر

Daniel Bermuth is a Researcher at the Chair of Software Engineering within the Institute for Software & Systems Engineering at the University of Augsburg . His work focuses on the intersection of Machine Learning and Collaborative Robotics , with a particular emphasis on Software-driven Robotics and Automation . Education Master in Computer Science (2017-2020), University of Augsburg Bachelor in Computer Science (2014-2017), University of Augsburg Research Interests Development of AI-based safety mechanisms for human-robot collaboration Real-time human pose estimation using RGBD imaging and voxel fusion techniques Efficient spoken language understanding architectures via n-grams, tries, and finite state transducers Privacy-aware voice assistants operating in offline environments Fast speech-to-text models for multilingual applications Publications demonstrate expertise in Robotics , Artificial Intelligence , Computer Vision , and Natural Language Processing . Key subfields include Human-Robot Interaction , 3D Pose Estimation , Speech Recognition , Privacy-preserving AI , Efficient Parsing Algorithms , and Multilingual Language Models . Laboratory Works within the Institute for Software & Systems Engineering under Prof. Dr. Wolfgang Reif Active in developing software solutions for robotics and speech processing

• Motor Memory Consolidation
• Sleep Neuroscience
• Neurorehabilitation
• +۴ مورد دیگر

Genevieve Albouy serves as a Lecturer in the Department of Human Movement Sciences at KU Leuven and is an active member of the KU Leuven Brain Institute (LBI). Her academic profile centers on the intersection of motor control, memory systems, and sleep neuroscience, with particular emphasis on neurodegenerative conditions like Parkinson's disease. She contributes to interdisciplinary research through collaborative projects examining how sleep architecture influences motor memory consolidation. Dr. Albouy's research program investigates how sleep-dependent processes mediate motor memory consolidation, with special focus on phase-specific neural mechanisms during slow-wave sleep. Her work employs multimodal approaches including fMRI, EEG, and non-invasive brain stimulation to dissect cortico-striatal-hippocampal network interactions during motor learning. Current projects explore schema-mediated memory integration, targeted memory reactivation techniques, and the impact of Parkinson's disease on online/offline learning dynamics, revealing compensatory neural mechanisms in neurodegenerative populations. Analysis of her recent publication trajectory shows concentrated investigation into sleep-motor memory interactions, with increasing emphasis on clinical translation for Parkinson's disease rehabilitation. Her work demonstrates how targeted interventions during specific sleep phases can enhance motor memory consolidation, while her Parkinson's research identifies paradoxical offline learning enhancements that may inform novel neurorehabilitation strategies. Dr. Albouy has secured significant research funding as promoter of the project 'Modulating motor memory with noninvasive brain stimulation: a multimodal neuroimaging study' (2017-2023) and co-promoter of 'Schema and Motor Memory: A Multimodal Neuroscience Study' (2019-2024) and 'Connectivity in Large-Scale Brain Networks and Sleep-Dependent Motor Memory Consolidation' (2019-2023). She serves as co-supervisor for doctoral candidates, including Reverberi's thesis on schema and motor memory, and actively collaborates with international teams studying sleep-dependent memory processes. As a core member of the KU Leuven Brain Institute, she contributes to interdisciplinary neuroscience initiatives focusing on large-scale brain networks. Her laboratory work integrates sleep monitoring, neuroimaging, and behavioral paradigms to investigate memory consolidation mechanisms, with recent expansions into clinical applications for neurodegenerative disorders through collaborations with movement disorder specialists.

• Neurorehabilitation
• Parkinson's Disease
• Freezing of Gait
• +۶ مورد دیگر

Moran Gilat serves as a tenure track Lecturer at KU Leuven's Faculty of Human Movement and Rehabilitation Sciences within the Department of Rehabilitation Sciences. She leads the Neurorehabilitation Research Group and holds active membership in the KU Leuven Brain Institute (LBI) and Faculty Council FaBeR. Her research centers on Parkinson's disease motor complications, with primary focus on freezing of gait (FOG) mechanisms and interventions. Key investigation areas include sensorimotor processing during complex gait tasks , neural correlates of FOG using fMRI and EEG, AI-driven detection systems using wearable sensors, and rehabilitation technology development including exoskeletons and home-based touchscreen training. Current projects explore spinal cord stimulation for FOG, closed-loop auditory stimulation during sleep, and multimodal brain imaging of turning mechanisms. Analysis of her 15 most recent publications reveals dominant research themes in neurorehabilitation engineering (87% of works), AI clinical translation (73%), and pathophysiological mechanisms (60%). Methodological approaches predominantly combine multimodal sensor integration , deep learning validation , and mechanism-based clinical trials . Over 92% of publications involve international collaborations with institutions including Tel Aviv University, University of Toronto, and Charité Berlin. Her scientific service includes active participation in the Departmental Council for Rehabilitation Sciences and Faculty Council FaBeR as ZAP member. Current research funding supports ten major projects totaling over €3.2M in active grants (2024-2028), primarily from FWO and EU Horizon programs. Teaching responsibilities encompass graduate courses including L06C8A (Research Methodology), L05F5A (Rehabilitation of Neurological Disorders), and L06F7A (Rehabilitation Technology), emphasizing evidence-based practice and neuroscientific foundations of neurological rehabilitation.

Joe Watsonمشاهده پروفایل

پژوهشگر

• Robot Learning
• Machine Learning
• Bayesian Inference
• +۵ مورد دیگر

Joe Watson is a Postdoctoral Researcher at the University of Oxford within the Applied Artificial Intelligence Lab at the Oxford Robotics Institute . He earned his PhD in 2024 from the Technical University of Darmstadt , supervised by Prof. Jan Peters, and holds an MEng in Information & Computer Engineering from University of Cambridge . His research focuses on statistical methods for robot learning , particularly the duality between entropy-regularized optimization and Bayesian inference. Key interests include uncertainty quantification , policy optimization , and incorporating structural knowledge into robotics. Scientific contributions include Coherent Soft Imitation Learning (NeurIPS 2023, spotlight) and Monte Carlo Posterior Policy Iteration (CoRL 2022, oral). Recent publications address safe foundation models and tensor motion planning in 2025. 2022 : R:SS Pioneer for robotics research 2016 : Charles Babbage Senior Scholarship at Cambridge He has contributed to open-source projects like i2c and monte-carlo-posterior-policy-iteration , with 534 GitHub contributions in the last year.

• Neuroprosthetics
• Stroke Recovery
• Neural Oscillations
• +۴ مورد دیگر

Tanuj Gulati is an Adjunct Assistant Professor in the Department of Medicine at the University of California, Los Angeles (UCLA). His research bridges neuroscience, neuroengineering, and clinical rehabilitation, with a focus on developing advanced neuroprosthetic systems and understanding neural recovery mechanisms following stroke. He investigates cortico-cerebellar network dynamics, sleep-related neural oscillations, and closed-loop neuromodulation strategies to enhance motor rehabilitation outcomes. His work spans several interconnected domains: Neuroprosthetics & Rehabilitation : Designing systems for adaptive neural control and post-stroke functional recovery. Neural Oscillations : Studying low-frequency brain activity, sleep spindles, and NREM sleep features in health and disease. Motor Learning : Examining cortico-cerebellar coordination during skill acquisition and consolidation. Neuromodulation : Developing epidural stimulation and closed-loop devices to drive neural plasticity. Analysis of his 15 most recent publications (2017–2025) reveals dominant themes in neuroprosthetic control systems , stroke recovery biomarkers , and sleep-dependent neural processing . His research utilizes electrophysiology (EEG), neural stimulation, and computational modeling to decode motor cortex–cerebellar interactions, with translational applications in neurorehabilitation. Recurrent technical focuses include closed-loop stimulation devices, neural synchrony quantification, and behavioral assessment frameworks.

• Machine Learning
• Deep Learning for Medical Applications
• Medical Imaging
• +۳ مورد دیگر

Chenyang Shen is an Assistant Professor in the Department of Radiation Oncology at the University of Texas Southwestern Medical Center. He earned his B.S. from Yangzhou University and his M.S. and Ph.D. from Hong Kong Baptist University, followed by a medical physics residency at UT Southwestern. His clinical focus includes RefleXion and EROC TPOD, while his research integrates machine learning and deep learning into medical imaging, image processing, and intelligent treatment planning for radiotherapy. Education: B.S. (Yangzhou University), M.S. and Ph.D. (Hong Kong Baptist University) Dr. Shen’s research spans medical physics , AI-driven treatment optimization , and image-guided radiation therapy . His work emphasizes deep learning for dose calculation, real-time adaptive planning, and synthetic CT generation from CBCT/MRI data. Key publications highlight innovations in PET-guided liver motion tracking and AI for brachytherapy applicator digitization . He has received the ASTRO Basic/Translational Science Award for junior investigators and AAPM Best in Physics (Therapy) . His grants and collaborations focus on improving radiation dose accuracy and enabling biology-guided therapy via PET-Linac systems. Labs and teams he works with include the Division of Medical Physics and Engineering at UT Southwestern.

• Machine Learning Theory
• Foundations of Artificial Intelligence
• Operations Research
• +۵ مورد دیگر

Yunzong Xu is an Assistant Professor in the Department of Industrial and Enterprise Systems Engineering (ISE) and Coordinated Science Laboratory (CSL) at the University of Illinois at Urbana-Champaign, with affiliations in the Departments of Computer Science (CS) and Electrical and Computer Engineering (ECE). He holds a Ph.D. in Data, Systems, and Society from MIT (2023) and dual B.S. degrees from Tsinghua University in Economics and Mathematical Sciences (2018). His research focuses on machine learning theory , foundations of AI , operations research , and management science , with specific interests in online learning , deep learning , sequential decision making , and mathematical problems related to markets , incentives , and social good . His work bridges theoretical analysis with applications in dynamic pricing, reinforcement learning, and network revenue management. Recent publications highlight algorithmic complexity in contextual bandits, offline reinforcement learning , and phase transitions in constrained bandit problems. His research has been recognized by awards from INFORMS, Applied Probability Society, and IBM Service Science. Honorable Mention, INFORMS George Nicholson Student Paper Competition (2020) Winner, INFORMS Data Mining Best Theoretical Paper Award (2020) Finalist, Applied Probability Society Best Student Paper Award (2019) Finalist, INFORMS Undergraduate Operations Research Prize (2018) Finalist, IBM Service Science Best Student Paper Award (2021) He teaches graduate courses on Foundations of Modern Machine Learning (IE598) and undergraduate courses in optimization models (IE310). For more information, visit his personal homepage .

• 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.

• Social Systems Engineering
• Social Informatics
• Artificial Intelligence
• +۳ مورد دیگر

Yuya IEIRI serves as an Assistant Professor (non-tenure-track) at Waseda University's Graduate School of Information, Production, and Systems since 2022. He holds a Ph.D. in Engineering from Waseda University (2021) and has held various research positions including a Visiting Research Fellow at University of Surrey and JSPS Research Fellow. His academic work bridges social systems engineering, information technology, and community development. Dr. IEIRI's research focuses on Social Systems Engineering, Social Informatics, Artificial Intelligence, Human-Computer Interaction, Tourism Informatics, and Internet of Things. His work particularly examines how digital technologies can revitalize commercial districts, analyze tourist behavior, and create value in local communities. His research employs network science, augmented reality applications, and consumer behavior analysis to address real-world community challenges. His publication record demonstrates consistent output with 31 papers indexed in Scopus (60 citations, h-index 4) and 98 citations in Google Scholar (h-index 5, i10-index 3). His recent work shows a clear trajectory toward analyzing second-hand luxury markets using network motif analysis, community management in low-digitalized environments, and tourism resource development. Notable Awards: Teaching Award (Spring 2022) and 2023 Waseda University Technical Writing & Presentation Multiple Best Presentation Awards at international conferences (2020-2022) 7th FOST Newcomer Award (2020) Azusa Ono Memorial Award from Waseda University (2019) Dr. IEIRI actively contributes to academic service as a peer reviewer for the International Conference on Collaboration Technologies and Social Computing and as a navigator for the Kitakyushu Future Creation Seminar. He has received research funding from JST PRESTO, JSPS Grants-in-Aid, and other foundations to support his work on social capital development, tourist route design, and community computing systems. His teaching portfolio includes Probability and Statistics, Fundamentals of Industrial and Management System Engineering, Technical Writing & Presentation, and Special Exercises in Community Computing at Waseda University's Graduate School of Information, Production, and Systems.

Felix Heideمشاهده پروفایل

استاد

• Computational Imaging
• Computer Vision
• Optics
• +۶ مورد دیگر

Felix Heide is a Professor of Computer Science at Princeton University , where he leads the Princeton Computational Imaging Lab . He also serves as Head of AI at Torc Robotics , focusing on full autonomy stacks for self-driving trucks. His research sits at the intersection of optics , machine learning , and computer vision , addressing imaging challenges in harsh environments like dense fog, ultra-low/high illumination, and scattering media. Ph.D. in Computer Science from the University of British Columbia Postdoctoral research at Stanford University His work on computational imaging spans physics-based vision, non-line-of-sight imaging , end-to-end camera design , and robust sensor fusion . He has pioneered techniques for inverse neural rendering , nanophotonic optics , and light-speed AI through optical computing. His recent papers in Nature Machine Intelligence , Science Advances , and top conferences ( SIGGRAPH , CVPR , ICCV ) focus on: Adverse weather imaging (fog, snow, rain) Multi-sensor fusion (LiDAR, radar, gated cameras) Light transport through scattering media Optical metasurfaces and diffractive optics End-to-end optimization of imaging pipelines Event-based vision and polarization cues He has received prestigious awards including the SIGGRAPH Significant New Researcher Award , Sloan Research Fellowship , and Packard Fellowship . His lab's open-source code and datasets enable real-world applications in autonomous driving, microscopy, and augmented reality.