جست‌وجوی استاد و پژوهشگر

William Robert Norrisمشاهده پروفایل

استاد پژوهشی

• Robotics
• Control Systems
• Machine Learning
• +۳ مورد دیگر

William Robert Norris is a Research Professor and Clinical Associate Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign (UIUC). He is the Founding Director of the Center for Autonomous Construction and Manufacturing at Scale (CACMS) and an affiliate in multiple departments, including Mechanical Science and Engineering, Electrical and Computer Engineering, and the Coordinated Science Lab. His expertise spans robotics, control systems, and autonomous systems, with a focus on machine learning, decision analysis, and adaptive systems. Education: Norris holds a Doctor of Philosophy (2001), Master of Business Administration (2007), and two Master of Science degrees (1997, 1996), all from UIUC, with concentrations in Control Theory, Robotics, and Systems Engineering. Research Interests: His work emphasizes optimal machine design, decision analysis, machine learning (including deep learning and reinforcement learning), adaptive systems, and control theory. He has pioneered advancements in autonomous vehicle navigation, sensor fusion, and robotics applications in construction and agriculture. Professional Highlights: Norris led the development of the first large-scale autonomous vehicle from concept to deployment. His contributions include innovations in Kalman filtering, fuzzy logic control, and autonomous system architectures. He has authored over 100 publications, including peer-reviewed journal articles and conference proceedings. Awards: Dean's Award for Early Innovation (2024), Robert A. Jewett Endowment (2020), William A. Chittenden Award (1998). Labs/Teams: My Lab, Center for Autonomy, Coordinated Science Lab (CSL), Discovery Partners Institute (DPI). Grants/Consulting: Active collaborations with the US Army Corps of Engineers, Deere & Company, and startups like Robust Smart Control Solutions LLC.

Dany Vargheseمشاهده پروفایل

پژوهشگر ارشد

• Inductive Logic Programming
• Explainable AI
• Computer Vision
• +۲ مورد دیگر

Dr. Dany Varghese is a Research Fellow in the Department of Computer Science at the University of Surrey's School of Computer Science and Electronic Engineering. He specializes in Learning and Reasoning using Inductive Logic Programming (ILP), focusing on developing transparent and interpretable machine learning systems. As a Fellow of the Higher Education Academy (FHEA), he maintains strong connections with Jyothi Engineering College in India where he previously served as Assistant Professor (2015-2019). His research develops computationally efficient methods for human-like learning from minimal data, particularly for critical applications like plant disease detection and medical diagnostics. He created the PyGol system - an explainable machine learning framework implementing Meta Inverse Entailment principles that outperforms traditional deep learning approaches in few-shot learning scenarios. Dr. Varghese has developed several influential tools including PyILP (Python interface for ILP systems), InfIntE (for microbial interaction inference), and contributes to the Meta Inverse Entailment framework. His collaborative work spans healthcare diagnostics, agricultural technology, and microbial ecology. Scientific Awards: Fellow of the Higher Education Academy (FHEA) Teaching Experience: Lab coordinator for Data Mining & Machine Learning (University of Surrey) Former Assistant Professor at Jyothi Engineering College (India) Lecturer at Government Engineering College (India)

Lyndon Drakeمشاهده پروفایل

پژوهشگر ارشد

• Theological Ethics
• Artificial Intelligence Ethics
• Indigenous Theology (Māori)
• +۳ مورد دیگر

Rev. Dr. Lyndon Drake is a Research Fellow in Theological Ethics and Artificial Intelligence at the University of Oxford, holding a DPhil in Theology (2023) and a PhD in Artificial Intelligence (2005). His dual expertise bridges biblical studies, economic theology, and AI ethics, with a focus on Māori post-colonial perspectives. Previously, he served as an Archdeacon in the Māori Anglican Church, managing multi-million dollar grants and leading teams of 20+ staff. His career spans finance (Barclays Capital, 2000–2011), AI research, and theological academia. Education : Bachelor of Commerce (Auckland, 1999) Bachelor of Science (Computer Science with Honours, Auckland, 1999) PhD in Artificial Intelligence (University of York, 2005) BA in Theology (First-Class Honours, Oxford, 2012) MSt in Theology (Hebrew Bible/Old Testament, Oxford, 2017) DPhil in Theology (Hebrew Bible/Old Testament, Oxford, 2023) Research Interests combine biblical economics (e.g., Jeremiah 32), Māori theological frameworks, and ethical AI applications. His work critiques financial systems through a theological lens, advocating for equitable capital markets. Key Contributions include Capital Markets for the Common Good (2017) and co-founding the Kingi Ihaka Research Centre in Aotearoa/New Zealand. His publications span biblical exegesis (e.g., prosbul in the Lord’s Prayer) and foundational AI algorithm research (SAT solving, constraint satisfaction). Awards : Prize-winning Oxford BA in Theology (top graduate of year). Grants & Leadership : Secured millions in church-related grants, built teams from scratch, and maintained part-time finance roles post-academia. Labs/Teams : Co-founder of the Kingi Ihaka Research Centre, integrating theological and computational ethics research.

Michel R.V. Chaudronمشاهده پروفایل

استاد

• Software Engineering
• Software Architecture
• Artificial Intelligence
• +۷ مورد دیگر

Michel R.V. Chaudron is a Full Professor and Chair of the Software Engineering group at Eindhoven University of Technology (TU Eindhoven), part of the Department of Mathematics and Computer Science. He also serves as an Adjoint Professor at Bandung Institute of Technology (since 2021) and previously held roles at Chalmers University of Technology (until 2022). His work aligns with the UN Sustainable Development Goals, particularly focusing on innovation and industrial development. Education: Michel obtained his Ph.D. in formal methods and programming calculi for parallel computing. His academic journey includes positions at Leiden University and earlier roles at TU Eindhoven. Research Interests: Michel’s primary research revolves around software architecture, design, and modeling (with a focus on UML), software composition , knowledge sharing , and AI integration in software development . He emphasizes empirical studies in industrial software engineering contexts and explores collaborative software design in virtual reality (VR). His work bridges theoretical formal methods with practical, industry-driven applications. Projects & Advising: Michel leads projects such as Conformance Management of Architecture and Implementation of Complex Software (2021–2027) and Trust4All (2005–2007). He has supervised 53 students but none are named in the provided text. His grants and collaborations span tool development (e.g., Arvisan) and empirical studies in VR and NLP-based architecture recovery. Labs & Teams: Michel is affiliated with the EAISI Foundational and EAISI High Tech Systems initiatives at TU Eindhoven, contributing to foundational AI research and high-tech systems development. He co-organizes international workshops like the Analytics and Mining of Model Repositories (AMMoRe) and teaches courses including Advanced Software Engineering and Course Design and Use of Software Architecture .

Uzay Kaymakمشاهده پروفایل

استاد

• Intelligent Decision Support Systems
• Computational Intelligence
• Fuzzy Set Theory
• +۶ مورد دیگر

Uzay Kaymak is a Full Professor and Chair of Information Systems in Health Care at Eindhoven University of Technology (TU/e), with concurrent appointments at JADS Den Bosch, EAISI Foundational, and EAISI Health. His work bridges computational intelligence and healthcare, developing adaptive decision support systems through linguistic-numerical data fusion. His educational background includes: MSc in Electrical Engineering (1992, Delft University of Technology) Chartered Designer Degree in Information Technology (1995) PhD in Control Engineering (1998, Delft University of Technology) Research Interests: Professor Kaymak pioneers computational intelligence frameworks integrating fuzzy set theory with machine learning for intelligent decision support . His work spans data/process mining , reinforcement learning , and clinical decision modeling , with applications in financial systems, economic analysis, and healthcare. Recent focus includes serious games for diabetes management and AI-driven water infrastructure monitoring , emphasizing model interpretability and real-world implementation. Publication Trends: His 2023-2025 output reveals intensified focus on environmental AI applications (water systems, desalination) and healthcare informatics . Publications demonstrate consistent integration of fuzzy logic with deep learning for interpretable models, particularly in leak detection, river monitoring, and clinical alert systems. The work shows strong industry-academia collaboration with practical implementations in Dutch hospitals and water utilities. Scientific Awards: Best Industrial Paper Award (2020) Best Student Paper Award (2019) Advising and Grants: With 91 supervised students, Professor Kaymak leads major EU-funded projects including SmartDATA (2020-2025) for AI in connected systems, DiaGame (2019-2025) for diabetes self-management games, and GOAL (2018-2021) for gamified active lifestyles. His Eurostars project PADS pioneered reinforcement learning for programmatic advertising, securing significant industry partnerships with Shell and healthcare institutions. Labs and Teams: He co-leads the EAISI Health initiative and TU/e's Clinical Informatics program, collaborating with Jheronimus Academy of Data Science (JADS) and Zhejiang University. His interdisciplinary team combines AI researchers, clinical informaticians, and environmental engineers to develop deployable solutions for UN Sustainable Development Goals in health and clean water.

Federico Corradiمشاهده پروفایل

استادیار

• Neuromorphic Computing
• Spiking Neural Networks
• Energy-Efficient Computing
• +۷ مورد دیگر

Federico Corradi is an Assistant Professor in the Electrical Engineering Department at Eindhoven University of Technology (TU/e), where he leads the Neuromorphic Edge Computing Systems Lab. He also holds the position of EAISI Foundational Assistant Professor at the Eindhoven Artificial Intelligence Systems Institute. His research focuses on neuromorphic computing and engineering, spanning from efficient computational models to novel microelectronic architectures for deep learning and brain-inspired algorithms. Dr. Corradi's academic background includes a Ph.D. in Neuroinformatics from the University of Zurich and an international Ph.D. from the ETH Neuroscience Centre Zurich (2015). Prior to joining TU/e in 2022, he worked at IMEC in the Netherlands (2018-2022) where he started a group focused on neuromorphic IC design. Before that, he was with Inilabs, a spin-off from the Institute of Neuroinformatics, developing event-based cameras and neuromorphic processors (2015-2018). His research interests center around understanding natural neural computation principles to develop energy-efficient sensing and computing technologies. Key areas include: Neuromorphic computing and engineering Spiking neural networks Event-based vision systems Energy-efficient microelectronic architectures Applications in robotics, machine vision, and biomedical signal analysis Dr. Corradi's recent publications demonstrate a strong focus on practical implementations of neuromorphic systems for edge applications. His work shows consistent innovation in translating theoretical neural models into efficient hardware implementations, with particular emphasis on handling temporal data and developing brain-realistic computational models. The research spans multiple domains including radar signal processing, optical computing, and error correction systems. He serves as an active review editor for Frontiers in Neuromorphic Engineering, IEEE, and IOP Science journals, and participates in technical program committees for several machine learning and neuromorphic conferences including ICTOPEN, AICAS, AIAI, ICONS, NEWCAS, DSD, and EUROMICRO. His work has received significant media attention with coverage from multiple news outlets and social media mentions. Dr. Corradi leads the Neuromorphic Edge Computing Systems Lab, which bridges theoretical neuroscience with practical engineering challenges, creating novel solutions for real-world problems. The lab's research has practical applications in robotics, autonomous systems, and biomedical technologies, reflecting his commitment to developing energy-efficient computing solutions that can operate effectively at the edge.

Jessie Galasso-Carbonnelمشاهده پروفایل

استادیار

• Software Engineering
• Software Reverse Engineering
• Knowledge Extraction
• +۵ مورد دیگر

Jessie Galasso-Carbonnel is an Assistant Professor in the Department of Electrical and Computer Engineering at McGill University's Faculty of Engineering. Her research focuses on software reverse engineering, knowledge extraction, variability management, and software repository mining. She holds a Ph.D. in Computer Science from the University of Montpellier (2018) and completed postdoctoral research at the French National Institute for Sustainable Development (IRD) and the University of Montréal's Geodes group. She is currently co-chairing several international conferences and workshops, including the Poster Track at MODELS’24 and the Doctoral Symposium at SPLC’24. Her academic background includes: Ph.D. in Computer Science, University of Montpellier (2018) Postdoctoral Researcher at IRD (2019–2020) and University of Montréal (2020–2023) Recipient of IVADO Postdoctoral Research Funding (2020) Research interests span: Software product line engineering Formal concept analysis AI-driven code recommendation systems Domain-specific language design Reproducible software development practices Recent contributions include advancements in: Automated repair of semantic errors in model transformations Social diversity metrics for collaborative problem-solving Low-code development frameworks for AI pipelines She actively contributes to the academic community through roles such as PC member for SANER’25 and co-chair of multiple international workshops. Her work intersects software engineering, AI, and formal methods to advance scalable and accessible software development practices.

Yu-Wang Chenمشاهده پروفایل

استاد

• Decision Sciences
• Data Analytics
• Artificial Intelligence
• +۷ مورد دیگر

Yu-Wang Chen is a Professor of Decision Sciences and Business Analytics at Alliance Manchester Business School (AMBS), University of Manchester. He holds an adjunct role as a former Turing Fellow (2021–2023) at The Alan Turing Institute. His research focuses on Decision Sciences, Data Analytics, AI, and their applications in healthcare, maritime logistics, risk analysis, and social networks. He leads over £2 million in research funding from Innovate UK, EPSRC, and ERDF. Education: PhD in System Engineering (Shanghai Jiao Tong University), Postdoctoral Research Fellowship (Hong Kong Baptist University). Research Interests: Data-Driven Decision Making, Multiple Criteria Decision Analysis, Evidential Reasoning. Teaching: Programme Director for MSc Business Analytics (2015–2018 & 2020–2023). Editorial Roles: Associate Editor of Decision Analytics Journal, Editorial Board Member of multiple journals. Recent work explores social network dynamics in organizational decision-making and AI-driven solutions for maritime transportation optimization. His 80+ journal articles and 20+ conference papers address challenges in belief rule bases, risk assessment, and computational modeling. Grants: Projects include AI tools for mental health services, digital trust frameworks, and port sustainability. Awards: EPSRC Peer Review College membership, International Society of Multiple Criteria Decision Making.

Junjie Huمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Multilingual NLP
• +۵ مورد دیگر

Junjie Hu is an Assistant Professor at the University of Wisconsin-Madison, holding appointments in the Department of Biostatistics and Medical Informatics (School of Medicine and Public Health) and the Department of Computer Science (College of Letters and Science). He leads the Hulab research group focusing on natural language processing (NLP) and machine learning, with a particular emphasis on multilingual NLP, multimodal learning, and large language models (LLMs). His work aims to build robust AI systems for healthcare and communication applications. Affiliations: Biostatistics & Medical Informatics, Computer Science, Data Science Institute Education: PhD in Computer Science from Carnegie Mellon University (advisors: Jaime Carbonell & Graham Neubig) His research spans multilingual NLP, transfer learning, LLM alignment, and cultural-aware AI. Key interests include efficient multimodal models for high-stakes domains and cognitive language agents for complex reasoning. Recent projects explore LLM interpretability and societal impact through benchmarks like COMMA and cultural alignment studies. Dr. Hu teaches Advanced Natural Language Processing (CS 769) and has supervised over 20 graduate and undergraduate students. He has received awards including the CMU Dissertation Writing Grant and NAACL Best Demo Nomination. Current research focuses on safe and inclusive AI development through alignment techniques and ethical evaluation frameworks. Labs/Teams: Hulab (Human-Centered Language Agents Lab), collaborating with interdisciplinary teams on NLP and medical informatics applications.

• Fuzzy Sets
• Possibility Theory
• Imprecise Probability
• +۳ مورد دیگر

Didier Dubois is a CNRS Research Director (DR2) at the Institut de Recherche en Informatique de Toulouse (IRIT) within Université Paul Sabatier, Toulouse. He leads the ADRIA team (Argumentation, Decision, Reasoning, Uncertainty, Learning) in the 'Reasoning and Decision' thematic group. His work focuses on fuzzy sets, possibility theory, imprecise probability, decision theory, and uncertainty management in AI and operational research. Education: Ingénieur Civil de l'Aéronautique from ENSAE (1975) Docteur Ingénieur from ENSAE (1977) Docteur d'Etat from Université de Grenoble (1983) Habilitation à diriger des recherches from Université Paul Sabatier (1986) Research Interests: Explores mathematical models for uncertainty and vagueness, including numerical/qualitative possibility theory, risk analysis under partial ignorance, generalized possibilistic logic, qualitative decision theory, and applications in scheduling, information retrieval, and soft constraints. Key Contributions: Co-developed fuzzy set theory applications, edited influential volumes on uncertainty and decision-making, and pioneered formal frameworks for combining probability and possibility. Current research emphasizes bipolar knowledge representation and epistemic reasoning. Editing Roles: Co-Editor-in-Chief of Fuzzy Sets and Systems since 1999, and series editor for the Handbooks of Fuzzy Sets . Awards: Docteur Honoris Causa, Polytechnic School of Mons (1997) IFSA Fellow (1999) ISI Most Cited French Scientist (2001) IEEE Neural Network Society Pioneer Award (2002) Advising: Supervised numerous PhD students since 2005, including work on possibility theory, risk analysis, and decision fusion. Collaborates with institutions like IRSN, ONERA, and Airbus.

• Interpretability
• Machine Learning
• Data Mining
• +۴ مورد دیگر

Martino Ciaperoni is a Postdoctoral Researcher and Adjunct Professor in the Department of Computer Science at Aalto University. He is affiliated with the research group of Adj. Prof. Gionis Aris. His primary focus is on advancing interpretable machine learning, algorithm optimization, and data mining techniques. Ciaperoni holds a doctoral degree (Tekn. toht.) in Computer Science from Aalto University (2024) and a Doctoral degree in Engineering and Technology from Università degli Studi di Roma 'La Sapienza' (2019). His research emphasizes interpretability in AI, multi-label classification, and efficient algorithms for speech recognition and Bayesian networks. Recent work includes exploring the Rashomon set of rule-based models, developing low-memory Viterbi decoding algorithms (SIEVE), and solving the Hadamard decomposition problem. His research spans topics like core decomposition in temporal networks and low-rank matrix approximation. Collaborations include international projects in computational linguistics, knowledge discovery, and algorithm design. He has contributed to open-source software, such as the 'Efficient Exploration of the Rashomon Set' codebase (Zenodo). His work balances theoretical rigor with practical applications, aiming to bridge gaps between complex algorithms and real-world problem-solving.

Qiang Shenمشاهده پروفایل

استاد

• Artificial Intelligence
• Fuzzy Systems
• Machine Learning
• +۵ مورد دیگر

Qiang Shen is a Professor and Pro Vice-Chancellor for the Faculty of Business and Physical Sciences at Aberystwyth University. He holds several prestigious fellowships, including the Royal Academy of Engineering and the Learned Society of Wales. His research focuses on artificial intelligence, fuzzy systems, machine learning, and their applications in remote sensing, medical imaging, and robotics. He leads projects funded by organizations like the British Council and Welsh Government, addressing challenges in data-driven decision-making and automated systems. Shen's academic career includes roles as Chair of the UK Research Excellence Framework (REF) 2029 Subpanel for Computer Science and Informatics. He has authored over 348 publications, with notable contributions in fuzzy rule interpolation, deep learning, and sensor data analysis. His work has been recognized with the IEEE Fuzzy Systems Pioneer Award (2024) and media coverage for innovations in robotics and environmental monitoring. Key research interests include fuzzy systems, pattern recognition, and interdisciplinary applications in health and environmental sciences. He supervises doctoral students in areas like federated learning, deep learning models, and remote sensing image processing. Collaborations span institutions globally, emphasizing knowledge interpolation and sustainable technology solutions.

Shengchao Qinمشاهده پروفایل

استاد

• Formal Methods
• Programming Languages
• Software Engineering
• +۳ مورد دیگر

Dr. Shengchao Qin is a Professor of Computer Science at Teesside University's School of Computing & Digital Technologies, specializing in formal methods and software verification. He holds a BSc and PhD from Peking University, with postdoctoral experience at the Singapore-MIT Alliance. As Associate Dean for Research & Innovation (2016-2019), he led strategic research initiatives. His research focuses on automated verification of software safety/security, program analysis, and cyber-physical systems. Major funded projects include EPSRC grants for resource analysis of embedded software (£403k) and inference mechanisms for separation logic domains (£213k). He actively contributes to academic service as Program Committee Chair for ICFEM/TASE conferences, steering committee member for formal methods symposiums, and editorial roles for IEEE Access and Science of Computer Programming. Collaborations span top institutions including National University of Singapore and Peking University. Research Supervision: Current PhD advisees: Chris Curry, Yang Liu, Colin Joy, Ndidi Ogbo Former supervisees: Zhang Zhang (PhD '20), Florin Craciun (Postdoc), Ryuta Arisaka (PhD '13)

Jie Liمشاهده پروفایل

مدرس ارشد

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

Dr. Jie Li is a Senior Lecturer at Teesside University's School of Computing, Engineering and Digital Technologies, specializing in Cybersecurity & Networks. He holds a PhD in Fuzzy Interpolation Systems from Northumbria University (2018), with prior master's and bachelor's degrees from the same institution. His research focuses on Machine Learning, Deep Learning, Computer Vision, and Generative AI, with applications in healthcare, IoT, and cybersecurity. Key projects include the D-PRIV data anonymization system (Innovate UK-funded, £90K), SuDs flood monitoring collaboration (£400K), and MC-IoT IoT security initiative. He has industry experience as AI Lead at THERAPYAUDIT and CTO of NewApex Ltd., developing healthcare AI systems and the StreetAway mobile app. Dr. Li actively engages in academic service, serving on program committees for IEEE conferences and the NCSC Cyberfirst Schools Scheme. His current research explores generative AI for synthetic data and real-time system optimization, aiming to bridge academic research with industrial applications.

Hua Fangمشاهده پروفایل

استاد مدعو

• Computational Statistics
• Data Science
• Biostatistics
• +۹ مورد دیگر

Hua Fang is an Adjunct Professor in the Department of Population and Quantitative Health Sciences at UMass Chan Medical School and the T.H. Chan School of Medicine. She serves as Principal Investigator of the Computational Statistics and Data Science (CSDS) lab, which focuses on developing computational methods for health and behavior studies through the integration of statistics and computer science. Her educational background includes: BA in Business English from Sichuan International Studies University, China MA in Financial Economics from Ohio University, United States PhD in Statistics from Ohio University, United States Dr. Fang's research spans multiple domains at the intersection of computational statistics, data science, and healthcare. Her work primarily focuses on developing advanced methods for analyzing longitudinal data, particularly in the context of behavioral interventions and health monitoring. She has pioneered the Multiple-imputation based Fuzzy Clustering (MIFuzzy) approach for trajectory pattern recognition in incomplete longitudinal data, which has been applied across various health domains including substance use, dietary patterns, and mental health. Her research integrates statistical theory with computational approaches to address challenges in missing data, pattern recognition, and real-time monitoring through wearable biosensors. She leads multiple NIH and NSF-funded projects exploring computational methods for health applications, with a particular emphasis on digital health interventions and precision medicine. Her recent publications demonstrate a strong trend toward digital twin technology, federated learning approaches for healthcare data, and advanced neural network architectures for medical applications. There's a clear progression from traditional statistical methods to more sophisticated AI-driven approaches, with increasing focus on privacy-preserving techniques like federated learning for multi-site clinical data analysis. Her work bridges computational statistics with practical healthcare applications, particularly in substance use detection, dietary pattern analysis, and real-time health monitoring. Dr. Fang's scientific recognition includes: Patent "System and methods for trajectory pattern recognition" (US20160358040A1), issued June 1, 2021 Best Paper Award for "Deep Learning-based adaptive beam forming for 5G mmWave Wireless body area network" at GLOBECOM2020 Abstract Citation Award from the Society of Behavioral Medicine As an advisor, Dr. Fang has mentored numerous graduate students and researchers who have gone on to positions at prestigious institutions including Harvard, Stanford, MIT, and faculty positions at universities. Her research is supported by multiple NIH grants including R01, R56, and P30 awards, as well as NSF funding for projects related to wireless body area networks and connected vehicle technology. Current major projects include iPAT (NIH/NIDDK R01), VIP (NIH/NIDDK R56), and several NSF-funded initiatives in wireless communication and machine learning. The Computational Statistics and Data Science (CSDS) lab, led by Dr. Fang, collaborates with researchers across multiple disciplines including psychiatry, behavior medicine, emergency medicine, immunology, infectious diseases, and healthcare systems. The lab works closely with the IoT and Data Engineering Lab and the UConn Center for mHealth and Social Media, creating an interdisciplinary research environment that bridges computational methods with real-world health applications. Current research focuses on developing computational tools for adaptive interventions, pragmatic clinical trials, causal inference, and risk prediction using longitudinal data from diverse health domains.