جست‌وجوی استاد و پژوهشگر

Marco Carvalhoمشاهده پروفایل

استاد

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Marco Carvalho is a Professor in the Department of Electrical Engineering and Computer Science at Florida Institute of Technology's College of Engineering and Science. He also serves as Executive Director of the L3Harris Institute for Assured Information (HIAI) and holds the L3Harris Chair in Assured Information. Contact: mcarvalho@fit.edu . Education: B.Eng. and M.Sc. (Mechanical Engineering) from Federal University of Brasilia; M.Sc. (Computer Science, University of West Florida); Ph.D. (Computer Science, Tulane University, specializing in ML/Data Mining). Research Focus : Cybersecurity (moving target defense, cyber command/control), AI (machine learning, agent-based systems), human-automation teamwork in cyber operations, smart systems security, and software assurance. His work includes projects sponsored by DoD, DHS, and DoE, such as security for smart buildings and federated cybersecurity. Recent publications emphasize adaptive defense mechanisms, human-machine collaboration, and educational initiatives in cybersecurity. Notable projects include VINE (MTD experimentation), MIRA (cyber command support), and ARMED (adaptive resource management). Labs/Teams: Leading research at HIAI focused on critical mission systems and cyber resilience. Active in organizing workshops like the International Workshop on Adaptive Cyber Defense.

Monica S. Lamمشاهده پروفایل

استاد

• Virtual Assistants
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

Monica S. Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in the Computer Science Department at Stanford University's School of Engineering, where she has been a faculty member since 1988. She serves as Faculty Director of the Open Virtual Assistant Lab (OVAL) and holds a courtesy appointment in the Electrical Engineering Department. Lam received her B.Sc. from the University of British Columbia in 1980 and her Ph.D. in Computer Science from Carnegie Mellon University in 1987. Her research spans multiple domains with a focus on creating trustworthy virtual assistants based on Large Language Models. She is particularly renowned for her work on the Almond virtual assistant, which emphasizes privacy preservation and open-source development. Her research interests include virtual assistants, natural language programming, machine learning, distributed mobile computing, and compiler technologies. Lam's recent publications reveal a strong focus on addressing key challenges in virtual assistant technology, particularly around hallucination reduction in LLMs, multilingual dialogue systems, semantic parsing, and multimodal interactions. Her work consistently emphasizes privacy preservation, open-source development, and user control over data. Member of the National Academy of Engineering ACM Fellow NSF Young Investigator Award (1992) ACM Most Influential Programming Language Design and Implementation Paper Award (2001) Wikimedia Foundation's Research of the Year Award (2024) for eliminating LLM hallucinations Popular Science's Best of What's New Award in Security (2019) for Almond virtual assistant Lam has advised numerous PhD students who have gone on to prominent positions at institutions like MIT, Harvard, Google, and Amazon. Her research has been supported by significant funding including NSF grants and industry partnerships. She leads the Open Virtual Assistant Lab (OVAL), which focuses on developing open, privacy-preserving virtual assistant technologies that give users control over their data while maintaining functionality.

• Robotics
• Autonomous Systems
• Control Systems
• +۵ مورد دیگر

Jonay Tomás Toledo Carrillo is a Full Professor at the University of La Laguna, affiliated with the Department of Computer Science and Systems Engineering within the School of Computer Science and Systems Engineering. He leads the GRULL (Robotics Group of the University of La Laguna), focusing on robotics, control systems, and autonomous systems. He earned his PhD in 2008 with a thesis on nonlinear control strategies for quadrotor helicopters, advised by Dr. Leopoldo Acosta Sánchez. His research interests span robotics, autonomous navigation, sensor fusion, and assistive technologies. Key projects include developing mobility aids for visually impaired individuals and enhancing localization systems through adaptive algorithms. Recent work emphasizes real-time sensor processing (e.g., LSTM networks for odometry), magnetic field control for accessibility, and Kalman filter variants for multi-sensor integration. Publications highlight contributions to robotics applications, including autonomous vehicles, wheelchair navigation, and BCI systems. His work bridges theoretical control methods with practical implementations in unstructured environments. Collaborations include interdisciplinary efforts in computer vision, ontology-based navigation, and educational simulators like MNEME for memory hierarchy teaching. Dr. Toledo Carrillo’s research aligns with the university’s engineering and automation priorities, with a focus on real-world impact through low-cost, high-precision solutions. His team’s innovations address challenges in both robotics engineering and accessibility technologies.

• FinTech
• Cyber Security
• Data Science
• +۴ مورد دیگر

Dr. KC Chan is a Senior Lecturer at the University of Southern Queensland (USQ) in the School of Business, specializing in Information Systems. He holds academic affiliations as an Honorary Senior Research Fellow at La Trobe University and a Visiting Fellow at the University of New South Wales (UNSW). With over 30 years of professional experience, his career spans academia, industry, and research. He has published over 100 refereed papers focusing on FinTech, Cyber Security, AI/ML applications, and data science. Dr. Chan's educational background includes a BASc and MASc from the University of Toronto and a PhD in Manufacturing Engineering from UNSW. His research interests emphasize the intersection of technology and business, particularly in financial systems security, blockchain applications, and data-driven decision-making. He has supervised numerous doctoral and master's students, including projects on quantum computing risk management, intrusion detection in web systems, and cognitive approaches to cryptocurrency frameworks. Dr. Chan's work bridges academia and industry, with notable contributions to smart building AI integration, manufacturing big data ecosystems, and healthcare informatics. His recent publications (2023-2025) address cryptocurrency trading analysis, mental health monitoring systems, and sustainable corporate strategies. He actively contributes to teaching advanced cybersecurity topics like CIS8500 and CIS8711.

Lan Duمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing (NLP)
• Active Learning
• +۴ مورد دیگر

Dr. Lan Du is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of IT. His research focuses on cross-disciplinary applications of machine learning and AI, particularly in text analytics, uncertainty estimation, knowledge distillation, and multi-modal learning. He leads projects addressing real-world challenges in public health, marketing, and clinical decision-making. Key collaborations include work with Victoria Police, Monash Health, and the National Health and Medical Research Council (NHMRC). Education: PhD in Computer Science (ANU, 2012), Bachelor of Information Technology (ANU, 2007), and B.Communication & IT (Flinders University, 2006). Research Interests: Machine/deep learning for NLP, active learning strategies, uncertainty quantification, and AI-driven solutions for healthcare and business analytics. His work bridges theoretical advancements with practical implementation, emphasizing translational research. Recent Projects (2021–2026): Includes AI models for predicting fracture outcomes (NHMRC-funded PRAISE study), risk prediction tools for pregnancy complications, and medical surveillance systems. He also collaborates on business insights derived from unstructured customer data. Teaching Commitment: Served as Chief Examiner and Lecturer for courses like FIT5149 (Applied Data Analysis) and FIT5196 (Data Wrangling). Lab/Team Involvement: Leads initiatives in AI for healthcare analytics and cross-modal learning, with active participation in Monash's research networks.

Yiren Zhaoمشاهده پروفایل

استادیار

• Large-scale GenAI Systems
• Hardware-Algorithm Co-design
• AI Security
• +۴ مورد دیگر

Dr. Yiren Zhao is an Assistant Professor (Lecturer) in Computer Engineering at Imperial College London, part of the Faculty of Engineering. He leads the DeepWok Research Lab and holds affiliations with the UKRI Artificial Intelligence for Engineering Biology Consortium and the University of Cambridge as a Visiting Researcher. His work focuses on Large-scale GenAI Systems, emphasizing hardware-algorithm co-design, efficiency, and security. He has secured over £5M in funding since 2022 and reviews for top conferences like ICLR, ICML, and NeurIPS. Dr. Zhao earned a BEng in Electrical and Electronic Engineering from Imperial College London (2016), followed by an MPhil and PhD in Computer Science from the University of Cambridge (2017–2022). His research spans GenAI acceleration, unstructured data processing (e.g., graphs), and system-level AI safety. Recent projects include MASE (unified ML system exploration) and ImpNet (imperceptible backdoor attacks). Awards: Apple Scholar in AI/ML (2020), Microsoft Research Award (2023), and Junior Research Fellowship at St John’s College (2021). Labs/Teams: DeepWok Lab (20+ members), involved in Imperial-X and AI-4-EB initiatives. Grants: Industry/government-funded projects totaling over £5M since 2022. His work bridges computer systems and AI, with notable contributions to quantization, adversarial attacks, and hardware-aware optimization. Collaborations include the Center for Spatial Computational Learning and CaRAML group.

Sameer Borwankarمشاهده پروفایل

استادیار

• Digital Platforms
• Human-AI Interaction
• Online Marketplaces
• +۵ مورد دیگر

Sameer Borwankar is an Assistant Professor in the Department of Information Systems at the Desautels Faculty of Management, McGill University. His research focuses on digital platforms, human-AI interaction, and platform economics, employing empirical models and machine learning tools. He holds a Ph.D. in Management from Purdue University's Daniels School of Business, along with dual master's degrees in Economics and MBA from the same institution. Prior to academia, he worked at Cummins Inc.'s digital implementation team and as a software engineer in financial services. His research investigates content platform moderation, multi-sided platform ecosystems, and unstructured data analysis. Notable areas include misinformation dynamics on social media, crowdsourcing for fact-checking, and AI's societal impacts. He teaches courses on information systems project management, technology project management, and AI/deep learning fundamentals. Recent work explores geopolitical AI developments, including China's DeepSeek and Qwen models, and the implications of platform policies on misinformation. His interdisciplinary approach bridges technical analysis with policy implications, addressing real-world challenges like data privacy and platform governance. Dr. Borwankar maintains an active blog discussing AI ethics, platform governance, and technology policy. He advises students in McGill's MBA, MM in Analytics, and PhD in Management programs, emphasizing practical applications of theoretical concepts.

Kris Hauserمشاهده پروفایل

استاد

• Robot Motion Planning
• Robot Control
• Autonomous Systems
• +۶ مورد دیگر

Kris Hauser is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), with affiliate appointments in the Departments of Electrical and Computer Engineering and Mechanical Science and Engineering. He holds a PhD in Computer Science from Stanford University and B.A. degrees in Computer Science and Mathematics from UC Berkeley. Prior to UIUC, he served as faculty at Indiana University (2009–2014) and Duke University (2014–2019), where he founded the Intelligent Motion Lab. He has also consulted for Waymo since 2019 and is the Director of the Coordinated Sciences Lab Robotics Group at UIUC. His research focuses on robot planning and control, semi-autonomous systems, and applications in intelligent vehicles, medical robotics, and legged locomotion. Key methodologies include optimization, probabilistic methods, AI, and physics simulation. His work bridges theory with real-world applications, such as robotic surgery, warehouse automation, and autonomous driving. Prof. Hauser has received notable awards including the NSF CAREER Award, Siebel Scholar Fellowship, and multiple Amazon Research Awards. He actively engages in teaching advanced robotics courses at UIUC and has contributed to open-source robotics frameworks like the Klamp't package. His lab emphasizes interdisciplinary collaboration, with projects ranging from UV disinfection robots to telepresence avatars.

• Climate Dynamics
• Arctic Oceanography
• Numerical Modeling
• +۴ مورد دیگر

Dr. Ivan Kuznetsov is a Researcher in Climate Dynamics at the Alfred Wegener Institute (AWI) . His work focuses on Arctic Oceanography, numerical modeling, and integrating AI-driven tools for climate science. He actively contributes to major projects like the MOSAiC Expedition and develops models such as FESOM-C for coastal and global climate studies. Research Interests: - Climate dynamics and Arctic system interactions - Development of coupled physical-biogeochemical models - Application of Language Learning Models (LLMs) to enhance climate data analysis and dissemination - Coastal processes and sea-ice dynamics - Submesoscale and mesoscale ocean variability Notable Projects: - MOSAiC:OCEAN : Modeling the central Arctic Ocean's winter dynamics. - ClimSight : AI-powered climate information system. - FESOM-C : Coastal ocean model for high-resolution studies. Key Contributions: - Over 30 peer-reviewed articles on Arctic Oceanography, model development, and climate change impacts. - Lead in analyzing MOSAiC expedition data to understand Arctic mixed layer dynamics and sea-ice interactions.

• Earth System Data Assimilation
• Ensemble Filter Algorithms
• High-Performance Computing
• +۳ مورد دیگر

Lars Nerger is the Head of the Data Assimilation Team within the Computing and Data Centre at the Alfred Wegener Institute (AWI). He specializes in Earth system data assimilation, focusing on ensemble filter algorithms and high-performance computing. His work integrates advanced computational methods with climate and ocean modeling to improve predictive capabilities. Research interests include ensemble Kalman filtering, non-linear systems, and scientific computing frameworks. He leads the development of the Parallel Data Assimilation Framework (PDAF), a widely used open-source tool for data assimilation in geoscience applications. His contributions enhance model accuracy in oceanography, climate dynamics, and cryosphere studies. Recent articles highlight advancements in data assimilation for ocean biogeochemistry, coupled climate models, and sea ice prediction systems. Collaborations span atmospheric chemistry, paleoclimatology, and environmental monitoring, reflecting his interdisciplinary impact. His team’s work supports operational forecasting and scientific understanding of Earth’s complex systems.

Stephen Thomasمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Big Data
• +۴ مورد دیگر

Stephen W. Thomas is an Assistant Professor & Distinguished Teaching Fellow of Management Analytics at the Smith School of Business, Queen’s University. He holds a PhD in Computer Science from Queen’s University and has expertise in Natural Language Processing (NLP), machine learning, and data analytics. His research focuses on practical and fair AI applications, including bias detection and ethical considerations in NLP models. He has held roles as Executive Director of Smith's Analytics & AI Ecosystem and directs the Master of Management Analytics and Master of Management in Artificial Intelligence programs. His work bridges academia and industry, with consulting experience in big data and AI. Education: PhD (Computer Science, Queen’s University 2012), M.S. (Computer Science, University of Arizona 2009), B.S. (Computer Science, New Mexico State University 2006). Research Interests: NLP applications in messy real-world data, fair AI, AutoML for NLP, temporal databases, and mining software repositories. He has published in IEEE Transactions journals and led initiatives like τBench for temporal benchmarking. Awards: Scotiabank Scholar, multiple Professor of the Year awards, Queen’s Graduate Award Scholarship, and Raytheon Advanced Scholarship Program recipient. He has advised over 15 students in analytics and AI projects. Teaching: Courses include Machine Learning & AI, Text Analytics, Big Data, and Mathematical Analysis across Smith’s programs. He is a frequent speaker on AI ethics and management applications.

Dorothea Blosteinمشاهده پروفایل

استاد

• Computer Science
• Biomechanics
• Graphics Recognition
• +۶ مورد دیگر

Roles and Affiliations: Dorothea Blostein is a Professor in the School of Computing at Queen's University, part of the Faculty of Arts and Science. She has held this position since 1988 after earning her Ph.D. (1987) and B.Sc. (1978) from the University of Illinois and an M.Sc. (1980) from Carnegie Mellon University. Research: Her work focuses on the interface between paper and electronic documents, with specialties in graphics recognition, document analysis, and biomedical computing. Key areas include tensegrity structures for biomechanical modeling, adaptive systems, and artificial fascial networks. She also explores music notation recognition and software engineering document classification. Collaborations include projects with NASA on tensegrity robotics and studies on concussion prevention through helmet fitting systems. Teaching: She teaches courses such as CISC 859 (Pattern Recognition) and CISC 324 (Operating Systems). Her pedagogical approach emphasizes hands-on projects and theoretical foundations in AI and algorithms. Labs and Teams: Her research leverages tools like the NASA Tensegrity Robotics Toolkit and ArtiSynth. She collaborates with institutions like the Gordon Lab for embryonic modeling and biomechanical engineers on cytoskeleton simulations.

• European Union Law
• State Aid Law
• Privacy Law
• +۳ مورد دیگر

Malgorzata Agnieszka Cyndecka is a Professor at the Faculty of Law, University of Bergen, specializing in EU law with a focus on state aid, privacy, and children's rights. Her work bridges legal theory and practice through analysis of EU case law, policy frameworks, and socio-legal challenges. Research interests include state aid compatibility assessments, judicial transparency, children's digital privacy rights, and maritime transport regulations. She has contributed to interdisciplinary projects like Artificial Intelligence in Education: Layers of Trust (EduTrust AI) and Machine Learning for the Anonymisation of Unstructured Personal Data (CLEANUP) . Notable achievements include winning the European State Aid Quarterly PhD Award (2016) . Her publications span peer-reviewed journals ( Journal of European Competition Law & Practice ), conference proceedings ( TJENI Annual Conference ), and encyclopedic entries ( Oxford University Press ). Cyndecka's work emphasizes real-world legal applications, such as balancing publicity of judgments with privacy rights, and addressing ethical dilemmas in data usage involving minors. She collaborates internationally, evidenced by co-authored works with scholars from Belgium and contributions to transnational legal research initiatives.

Tao Wangمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Health Data Science
• Multimodal Learning
• +۳ مورد دیگر

Dr. Tao Wang is a Research Fellow at King’s College London (KCL), affiliated with the Institute of Psychiatry, Psychology & Neuroscience (IoPPN) and the Department of Biostatistics & Health Informatics. He holds honorary research positions at leading UK hospitals and serves on Meta/Facebook’s Global Safety Policy Advisory Board on Mental Health and King’s AI Institute. His research focuses on advancing Natural Language Processing (NLP), Network Science, Multimodal Data Modeling, and AI applications in healthcare, public health, sociology, and law. He completed his PhD at the University of Southampton (2015–2018) and has contributed to over 45 publications, securing prestigious awards like the Alan Turing Fellowship and the HSJ Partnership Award for Best Mental Health Partnership. Education: PhD in Health Informatics, University of Southampton (2015–2018), supported by the UK ESRC Research Exchange at the University of Western Australia Research Interests: Natural Language Processing (NLP) for healthcare data Knowledge Graphs and Causal Inference in health analytics Clinical Decision Support Systems Multimodal Learning and AI ethics Key Projects: VIEWER: a visual analytics framework for mental healthcare Clinical Decision Support Systems using CogStack Pain analysis via knowledge graph embeddings Awards: Chair’s Prize at NIHR Maudsley BRC conference (2019) Early Career Research Award (2022) HSJ Best Mental Health Partnership Award (2022) HETT Best Data Innovation Award (2022) Labs & Teams: Precision Health Informatics Data Lab (IoPPN), King’s AI Institute.

Pratap Tokekarمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Pratap Tokekar is an Associate Professor in the Department of Computer Science at the University of Maryland, with appointments in UMIACS and Electrical and Computer Engineering. He leads the RAAS Lab and focuses on robotics, particularly algorithms for mobile robots in active sensing and environmental monitoring. His research spans AI, machine learning, and multi-robot systems. Education: Ph.D., Computer Science, University of Minnesota (2014); B.Tech., Electronics and Telecommunication, College of Engineering Pune (2008). Awards include the NSF CAREER Award (2020) and CISE Research Initiation Initiative Award (2016). Teaching highlights include courses on Decision-Making for Robotics, Artificial Intelligence, and Robot Motion Planning at both UMD and Virginia Tech. He advises six Ph.D. students and collaborates with the Maryland Robotics Center and Center for Machine Learning. Awards: NSF CAREER (2020), NSF CISE CRII (2016) Labs/Teams: RAAS Lab, Maryland Robotics Center Key Focus: Multi-robot coordination, active perception, and decision-making under uncertainty.