جستجوی استاد راهنمای دانشگاهی

Olumuyiwa Matthewمشاهده پروفایل

مدرس ارشد

• Multitenancy systems
• Multi-tenant databases
• Database security
• +۵ مورد دیگر

Olumuyiwa Matthew serves as a Senior Teaching Fellow at the School of Computing, University of Portsmouth, where he teaches database units at undergraduate and master's levels, research methods, and academic/professional skill development. He holds a formal position as unit coordinator for Database Management (level 7), PARD (level 6), and Database Principle (level 5) units. His educational background includes a Bachelor of Technology (B.Tech) in Computer Science, MSc in Information Technology Management, and PhD in Computer Science from University of Wolverhampton (2016). Previously, he worked as a Visiting Lecturer at the School of Mathematics and Computer Science, University of Wolverhampton. Dr. Matthew's research focuses on critical areas of modern computing infrastructure, particularly multitenancy systems and multi-tenant databases where he established scientific guidelines for evaluation and adoption. His work extends to database security, cloud computing architectures, knowledge management systems, and system usability metrics. Recent publications demonstrate growing interest in machine learning applications for decision-making, bias detection, and healthcare systems, particularly examining cultural considerations in Nigerian mobile health applications. His research fingerprint shows strong connections to database management systems, cloud architectures, application development, and decision-making processes. He is currently accepting PhD students and has been involved in research projects including the development of an AI/ML-enhanced platform for maintenance and operations spares management for Entec International Ltd. (2022-2024). His teaching responsibilities span database principles, research methods, and professional skill development across multiple academic levels.

• Natural Language Processing (NLP)
• Computational Linguistics
• Translation Technology
• +۶ مورد دیگر

Professor Ruslan Mitkov is a leading academic at Lancaster University in the School of Computing and Communications , focusing on Natural Language Processing (NLP) , Computational Linguistics , and Translation Technology . His career spans roles as Director of the Research Institute of Information and Language Processing at the University of Wolverhampton and Distinguished Visiting Professor at the University of Alicante. PhD: Technical University of Dresden (supervised by Nikolaus Joachim Lehmann) Education: Humboldt University of Berlin Research interests include anaphora resolution , NLP for language disorders , translation memory systems , and text simplification . His work bridges technical innovation with societal impact, particularly in autism assistance through NLP tools. Recent publications focus on cross-lingual NER, low-resource language models, and explainability in NLP. Key trends include biomedical NLP, multilingual benchmarks, and incremental learning for translation systems. Scientific Recognition : Editor of the Oxford Handbook of Computational Linguistics Executive Editor for Natural Language Engineering (Cambridge University Press) Editor-in-Chief for John Benjamins' NLP book series Over 300 refereed publications Leadership roles include founding the Erasmus Mundus Master’s Programme in Technology for Translation and Interpreting (EM TTI) and directing the Responsible Digital Humanities Lab . He has chaired major conferences like RANLP and HiT-IT , and will deliver keynotes at 20+ international events from 2023-2024.

Mahnoor Shahidمشاهده پروفایل

پژوهشگر

• Neuro Symbolic AI
• Symbol Grounding
• Natural Language Processing
• +۶ مورد دیگر

Mahnoor Shahid is a Researcher at the University of Duisburg-Essen , affiliated with the Faculty of Computer Science and the Chair of Information Systems and Sustainable Supply Chain Management . Specializes in Neuro Symbolic AI , aiming to create explainable and adaptable AI systems PhD candidate with prior experience as a Research Assistant at DFKI Education : B.Sc. Computer Science (NED University, Pakistan, 2014-2017) M.Sc. Data Science and Artificial Intelligence (Saarland University, Germany, 2020-2023) Research Interests : Symbol Grounding and Common Sense Reasoning in AI Natural Language Processing and Language Models Domain Adaptation and Transfer Learning Machine Learning Privacy for responsible AI Recent Research Trends : Developing neuro-symbolic architectures for generalization and compositional reasoning Advancing NILM (Non-Intrusive Load Monitoring) with domain adaptation techniques Exploring prompt analysis in LLM platforms for inverse generativity Building python packages for BERT probing and joint learning Applying creative AI to text-to-image generation Investigating web security through ML-based threat detection Labs & Projects : Active in the Rhine-Ruhr Institute of Information Systems Maintains open-source python packages for NILM analysis Key contributor to tools like nilm_analyzer and nilm_analyzer_tutorials

• Machine Learning
• Medical Imaging
• Computer Vision
• +۳ مورد دیگر

Bjørn Leth Møller is a Researcher at the Department of Computer Science , University of Copenhagen , affiliated with the Image Analysis, Computational Modelling and Geometry section. His work bridges machine learning, medical imaging, and geometric modeling to address healthcare challenges. Research Focus: Medical AI, Computer Vision, and Explainable AI Department: Image Analysis, Computational Modelling and Geometry Email: bjm@di.ku.dk Research Interests include AI-driven clinical decision support, neural network interpretability for medical imaging, and sensor-ML integration for biomedical diagnostics. His projects aim to enhance real-time disease monitoring and improve transparency in AI models. Recent Publications (2024–2025) focus on ulcerative colitis severity classification, neural explanation masks for medical image models, and sensor-based tissue analysis. These works reflect interdisciplinary applications of machine learning to gastroenterology, diagnostics, and biomedical engineering.

• Neuroinformatics
• Brain-Computer Interfaces
• Causal Modeling
• +۳ مورد دیگر

Moritz Grosse-Wentrup is a Professor at the Faculty of Computer Science, University of Vienna , where he leads the Neuroinformatics research group. His work spans neuroscience, machine learning, and brain-computer interfaces , focusing on causal modeling and neurotechnology. Location: Kolingasse 14-16, Room 02.49, 1090 Wien Email: moritz.grosse-wentrup@univie.ac.at Phone: +43-1-4277-79610 Research interests include: Developing causal frameworks to bridge neuronal activity and cognition Designing interpretable machine learning algorithms for BCI applications Neurorehabilitation using virtual reality and EMG-BCI systems Advancing causal inference and feature importance in AI models Open-source tools like PyTES for neurostimulation research Recent publications highlight trends in auditory BCI, causal recourse, and Riemannian geometry-based classification . His work integrates neuroscience, computer science, and clinical applications to improve communication for impaired patients and motor rehabilitation. Students mentored include Alex Markham (2021), Gunnar König (2023), and Jiachen Xu (2023). He teaches foundational courses in data analysis, signal processing, and neuroinformatics at the University of Vienna.

Ashwin Ashokمشاهده پروفایل

استادیار

• Cyber-physical systems
• Internet-of-Things (IoT)
• Mobile and Wearable Computing
• +۵ مورد دیگر

Dr. Ashwin Ashok is an Assistant Professor in the Department of Computer Science at Georgia State University, directing the Mobile Cyber Physical Systems Lab. He holds a Ph.D. from Rutgers University and a postdoctoral background at Carnegie Mellon University with expertise in vehicular networks, visible light communication, and mobile systems. His research focuses on cyber-physical systems, IoT, and wearable technologies, with contributions to computer vision, underwater communication, and environmental sensing. Education: B.Tech., National Institute of Technology-Warangal (2006) M.S., Texas A&M University (2008) Ph.D., Rutgers University (2014) His research interests span visible light communication, vehicular networks, and sensor systems, with a focus on real-world applications like autonomous systems and environmental monitoring. Recent work includes advancements in non-line-of-sight optical communication and digital rehabilitation systems. Publications reflect a strong emphasis on interdisciplinary innovation, combining communication systems with environmental and healthcare applications. Awards include the Best Teaching Assistant and Research Excellence Awards from Rutgers. Key Grants: CAREER: Towards Practical Mobile Visible Light Communication (2022) He leads initiatives like the OpenRadon Lab for soil radon modeling and collaborates on projects such as the Cosmic Climate Cuboid Nanosatellite for space radiation studies.

• Machine Learning
• Health Informatics
• Data Mining
• +۵ مورد دیگر

Dr. Rittika Shamsuddin is an Assistant Professor in the Department of Computer Science at Oklahoma State University. She holds a PhD in Computer Science from The University of Texas at Dallas and a BA in Computer Science and Biology from Mount Holyoke College. Her research focuses on improving interdisciplinary communication between computational fields and healthcare/biology through machine learning and algorithm development. Key areas include healthcare data analysis platforms, explainable AI, and addressing challenges like data scarcity and privacy in medical applications. Education: PhD, Computer Science & Data Science, University of Texas at Dallas (2013–2016) BA, Computer Science & Biology, Mount Holyoke College (2008–2012) Research Interests: Dr. Shamsuddin develops tools and frameworks to bridge computational and medical domains. Her work emphasizes healthcare-specific machine learning platforms, interpretable AI systems, and solutions for data scarcity. She also explores applications in rural healthcare, disease progression modeling, and ethical AI integration in medical decision-making. Recent projects include System-of-Systems ML frameworks for XAI and synthetic data generation for rare medical conditions. Grants & Funding: RET Site: SecurAIty Nexus (2024–2027) RET Site: Big Data & Machine Learning for Educators (2021–2024) Labs & Teams: She leads a research group focused on healthcare AI and collaborates with medical institutions. Her lab website hosts ongoing projects and tools like CEFEs for ECG interpretability and synthetic data generation frameworks.

Theodoros Evgeniouمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Business Analytics
• +۵ مورد دیگر

Role & Affiliation: Theodoros Evgeniou is a Professor of Decision Sciences and Technology Management at INSEAD since 2001, and Director of the INSEAD Executive Education programme 'Transforming your Business with AI'. He holds a dual academic role in Technology and Business, focusing on Machine Learning, AI ethics, and business process optimization. His work bridges academic research and industry application, with affiliations including OECD Network of Experts on AI and the BCG Henderson Institute. Education: Four degrees from MIT: BSc in Computer Science BSc in Mathematics Master of Computer Science PhD in Computer Science Research Interests: Evgeniou’s research spans 30+ years in AI innovation, including business process optimization, AI regulation, and novel machine learning methods. His work appears in top journals like Science , Nature Machine Intelligence , and Lancet Digital Health . Key areas include AI ethics, healthcare applications, and strategic decision-making with data analytics. Articles Trends: Recent publications focus on XAI narratives, UGC strategies in digital content, AI robustness in healthcare, and pandemic impacts on academic resilience. These reflect interdisciplinary approaches to AI’s societal and business implications. Grants & Advising: Served as OECD AI expert, BCG advisor, and co-founder of Tremau (B2B SaaS for ethical AI). Consults widely on ML applications and regulatory frameworks. Labs & Teams: Co-founded Tremau to promote safe digital ecosystems, and collaborates with INSEAD’s AI initiatives. Active in academic-industry partnerships through case studies and executive education programs.

Shah Muhammad Hamdiمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Deep Learning
• +۵ مورد دیگر

Shah Muhammad Hamdi is an Assistant Professor in the Department of Computer Science at Utah State University (USU), leading the Hi-dimensional Data Analytics and Mining (HiDAM) lab. He previously held a faculty position at New Mexico State University (NMSU). He earned his Ph.D. and M.S. in Computer Science from Georgia State University (2020) and a B.Sc. from Rajshahi University of Engineering and Technology (2014). His research focuses on machine learning and data mining for graphs, time series, and spatiotemporal data, with applications in solar physics, neuroscience, and social networks. He has received notable grants, including NSF SHINE and CRII awards, and has published extensively in top venues like AAAI, ICMLA, and IEEE Big Data. Education: Ph.D. & M.S. in Computer Science, Georgia State University (2020) B.Sc. in Computer Science and Engineering, Rajshahi University of Engineering and Technology (2014) Research Interests: Graph representation learning and feature selection Time series classification and counterfactual explanations Applications in solar flare prediction, neuroscience (fMRI analysis), and social media discourse Recent Achievements: His work includes pioneering methods for solar flare prediction using multivariate time series and functional networks, as well as developing frameworks for explainable AI and data augmentation. He leads initiatives in machine learning cyberinfrastructure for multivariate time series and functional networks, funded by NSF grants. Awards: NSF SHINE Award (2023): Solar flare prediction via graph-based ML NSF CRII Award (2022): Cyberinfrastructure for multivariate time series analysis Advising & Grants: Ph.D. students: Reza EskandariNasab, Onur Vural, Santosh Chapagain Recent publications span solar physics, explainable AI, and social media analytics Labs & Teams: HiDAM Lab at USU focuses on high-dimensional data analytics, integrating machine learning and domain-specific applications.

Anuj Karpatneمشاهده پروفایل

دانشیار

• Knowledge-guided Machine Learning (KGML)
• Spatio-temporal Data Mining
• Environmental Science Data Analysis
• +۵ مورد دیگر

Anuj Karpatne is an Associate Professor in the Department of Computer Science at Virginia Tech, leading the Knowledge-guided Machine Learning (KGML) Lab. He joined VT in 2018 and earned tenure in 2023. His research focuses on integrating scientific knowledge into machine learning, pioneering the KGML paradigm to create explainable and generalizable AI solutions for interdisciplinary scientific challenges. He holds a PhD from the University of Minnesota (2017) and an M.Tech. from IIT Delhi (2011). Research Interests: Knowledge-guided ML frameworks (KG-Loss, KG-Neural Architectures, Hybrid-Science-ML) Applications in physics (fluid dynamics, quantum mechanics), environmental sciences (aquatic systems, remote sensing), and biology (organismal biology, virology) Interdisciplinary collaborations across 15+ institutions for NSF-funded projects Recent Contributions: Developed Fish-Vista dataset for biological trait analysis Co-organized KDD 2024 workshop on Generative Models for Sustainable Development Secured $18M NSF PIPP II grant as co-PI for pandemic prediction systems Grants & Leadership: Lead PI on NSF CAREER Grant ($586K) for unifying ML with scientific knowledge Co-PI on NSF Imageomics Institute ($15M) integrating biology with AI Site PI for macrosystems biology grant ($1.1M) modeling lake water quality Labs & Teams: KGML Lab at VT collaborates with 30+ scientists across disciplines Leadership roles in NSF PIPP Institute and Imageomics Center

Koorosh Aslansefatمشاهده پروفایل

استادیار

• Artificial Intelligence Safety and Explainability
• Probabilistic Modelling (Markov Modelling)
• Dependability Evaluation
• +۷ مورد دیگر

Koorosh Aslansefat is an Assistant Professor of Computer Science at the University of Hull, part of the Dependable Intelligent Systems Group (DEIS). He holds an M.Sc. in Control Engineering from Shahid Beheshti University (2014) and a PhD in Computer Science from the University of Hull (2021), focusing on the DREAM project for offshore wind farm maintenance optimization. His research spans AI safety, dependability evaluation, and safety-critical systems. **Education**: M.Sc. Control Engineering, Shahid Beheshti University (2014) PhD Computer Science, University of Hull (2021) **Research Interests**: Artificial Intelligence Safety & Explainability Markov Modelling & Probabilistic Systems Runtime Dependability Evaluation Offshore Wind Farm Maintenance Multi-Robot Systems Safety His work emphasizes real-time dependability analysis and safety assurance for complex systems, with notable contributions to SafeML and drone-assisted monitoring frameworks. **Awards**: IET Leslie H. Paddle Award (2020), Alan Turing Institute Postdoctoral Enrichment Award (2022), and grants from EU H2020 and EDF Energy. **Projects/Grants**: Leads initiatives like AKT TRUST-LLM (Safe LLMs for legal memory) and collaborates on offshore wind farm reliability (SESAME EU H2020). Active in grant-funded research with Innovate UK and the Engineering & Physical Sciences Research Council. **Teaching**: Courses include Real-Time Dependable Systems (MSc) and Safety-Critical Systems (BSc). Preparing a module on Responsible AI & Ethics.

Yiannis Papadopoulosمشاهده پروفایل

استاد

• Safety of AI
• Dependable Artificial Intelligence
• Safety of Machine Learning
• +۶ مورد دیگر

Professor Yiannis Papadopoulos is a leading academic in safety-critical systems and Dependable AI at the University of Hull's School of Computer Science, Faculty of Science and Engineering. His roles include teaching modules on Software Engineering, Digital Disruption, and Safety-Critical Systems. He holds a DPhil from the University of York and has over 25 years of research experience. Education: PhD/DPhil in Computer Science, University of York (2000) MSc in Advanced Manufacturing Technology, Cranfield University (1993) BSc/MSc in Electrical and Computer Engineering, Aristotle University of Thessaloniki (1991) Research Interests: Dependable AI, Safety of Machine Learning, and Autonomous Systems Model-Based Safety Engineering (HiP-HOPS method) Applications in Automotive, Aerospace, and Offshore Energy Generative Art and Philosophy-inspired Research Environments Publications: Recent work focuses on LLM safety, fairness in AI, and offshore wind maintenance. Key contributions include the Andromeda framework for safety assurance and the SafeML method for statistical safety monitoring. His research bridges bio-inspired AI with formal safety analysis, impacting industries globally. Grants & Projects: SESAME (EU H2020): Safety of Multi-Robot Systems (£482k) DEIS H2020: Dependability Engineering (£461k) EPSRC Edge AI Hub: Safety of Edge-AI (£214k) Academic Garden: His DEIS research group fosters a collaborative environment emphasizing ethical research and student well-being. Notable initiatives include Stoic philosophy seminars and interdisciplinary projects like the Timaeus digital art studio.

Vahid Seydiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Data Science
• Deep Learning
• +۶ مورد دیگر

Vahid Seydi is a Research Fellow in Data Science and Machine Learning at the School of Ocean Sciences, Bangor University, UK. He previously served as an Assistant Professor (2014–2020) and award-winning Lecturer (2010–2014) at Azad University South Tehran Branch, Iran. He holds a PhD in AI (2014) from Azad University, with prior M.Sc. (2007) and B.Sc. (2005) degrees in Computer Science and AI. Education : PhD in Artificial Intelligence (2014), Azad University M.Sc. in Artificial Intelligence (2007), Science and Research University B.Sc. in Software Engineering (2005), Azad University Research Interests : Deep Learning, Domain Adaptation, Generative Models, Explainable AI, Reinforcement Learning, Optimization, and applications in marine renewable energy and environmental sustainability. Key Contributions : His work focuses on ML-driven solutions for offshore energy systems, including predictive digital twins, passive acoustic monitoring, and domain adaptation for ecological impact analysis. He has contributed to projects like the SEEC Centre and EcoWind-ACCELERATE initiative. Awards : Global Talent Endorsement (UK Royal Society, 2023) Zillow Kaggle Competition Silver Medal (2017, top 2%) Excellence in Teaching Award (Azad University, 2014–2019) Grants & Projects : Smart Efficient Energy Centre (SEEC), 2019–2023 EcoWind-ACCELERATE, 2022–2026 Morlais Demonstration Zone, 2022–2024 Labs/Teams : Active in Bangor’s Ocean Sciences research groups, focusing on ML integration with marine energy and ecological data systems.

• Diabetes Management
• Endocrinology
• Telemedicine
• +۳ مورد دیگر

Morten Hasselstrøm Jensen is a Professor at Aalborg University Hospital's Department of Health Science and Technology, affiliated with the Faculty of Medicine. He holds additional roles as Data Lead Project Director at Novo Nordisk A/S and previously served as Senior Researcher at Steno Diabetes Center North Denmark. His research focuses on diabetes management, telemedicine, and glycemic control, particularly in insulin-treated populations. He leads projects like ADAPT-T2D (cloud-based personalized diabetes treatment) and Sten-O Starter (clinical effects of diabetes management tools). Education: Formal academic qualifications not explicitly listed in the provided text. Jensen's research interests include optimizing insulin therapy, predicting hypoglycemia, and leveraging machine learning for clinical decision support. His work emphasizes translating data into actionable insights for diabetes care, such as continuous glucose monitoring (CGM) analysis and telemonitoring systems. Key contributions include studies on cardiovascular risks in diabetes patients and adherence to insulin regimens. His articles explore topics like CGM data analysis, glycemic variability, and AI-driven treatment titration. Projects highlight interdisciplinary collaboration across clinical, engineering, and computational domains. Grants & Awards: No specific awards mentioned, but his projects (e.g., ADAPT-T2D) are funded through institutional and industry partnerships. Advising: Supervised 8 PhD students (names not listed). Jensen collaborates with Steno Diabetes Center and Novo Nordisk, contributing to clinical trials and digital health solutions aimed at improving diabetes outcomes through technology integration.

James Zouمشاهده پروفایل

دانشیار

• Machine Learning
• Biomedical Data Science
• Healthcare Technology
• +۴ مورد دیگر

James Zou is an Associate Professor of Biomedical Data Science at Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. His research focuses on advancing machine learning methodologies for healthcare and biomedicine, emphasizing reliability, human compatibility, and statistical rigor. He leads the Stanford AI Lab and is a Chan-Zuckerberg Investigator. Educated at Harvard (PhD 2014), he has held positions at Microsoft Research and U.C. Berkeley. His work bridges foundational ML theory with clinical applications, such as AI-driven cardiac function assessment (EchoNet-Dynamic), spatial transcriptomics (TISSUE), and oncology trial optimization (Trial Pathfinder). Research interests include generative AI for biomedical discovery, AI ethics, and scalable healthcare algorithms. His team collaborates with industry and academia to translate innovations into clinical tools. Notable achievements include FDA-cleared EchoNet AI and the BABEL framework for multiomic data integration. Awards include the Sloan Fellowship, NSF CAREER Award, and multiple grants from Google/Adobe/Amazon. He advises over 20 students and has published 69+ papers in top journals like Nature, Science, and PNAS. Current courses include Foundation Models for Healthcare and Deep Learning in Genomics.