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Panagiotis PAPADAKISView profile

Associate Professor

• Robotics
• Computer Vision
• 3D Shape Analysis
• +4 more

Panagiotis PAPADAKIS is an Associate Professor (HDR) in Informatics & Robotics at IMT Atlantique Bretagne/Pays de la Loire, Department of Informatics. He leads the RAMBO team at CNRS Lab-STICC and is an associate member of the IRL CROSSING. His research focuses on cognitive robotics, computer vision, and artificial intelligence with applications in assistive robotics, urban search and rescue (USAR), and 3D environment analysis. He has over €1M in research funding and has co-authored numerous top-tier publications in computer vision and robotics journals/conferences. Educations: PhD in Information Technology, National University of Athens (2009) Informatics and Telecommunications Degree, National University of Athens (2005) Research Interests: Robot Control and Navigation 3D Shape Analysis & Matching Urban Search and Rescue (USAR) Robotics Computer Vision for Assistive Technologies Cognitive Robotics and Human-Robot Interaction Key Projects: Leading RAMBO team in CNRS Lab-STICC Coordinated multi-robot assistance systems for smart environments Development of digital twin-driven smart home solutions Teaching: Postgraduate courses in Computer Vision/Perception at University of Rome Programming courses at ENSTA ParisTech Undergraduate Computer Graphics at University of Athens

Gheorghi GuzunView profile

Associate Professor

• Data Management
• Machine Learning
• Data Compression
• +4 more

Dr. Gheorghi Guzun is an Associate Professor in the Department of Computer Engineering at San José State University (SJSU), where he has served since 2017. He holds a Ph.D. in Electrical and Computer Engineering from the University of Iowa (2016) and a BSc in Electrical Engineering from the Technical University of Moldova (2010). His research focuses on scalable data management systems, machine learning, and hardware-driven software design, with applications in energy-efficient data centers, infrastructure monitoring, and predictive analytics. He is a recipient of the NSF CAREER Award for his work on efficient AI systems. Education background: Ph.D. in Electrical and Computer Engineering, University of Iowa (2016) BSc in Electrical Engineering, Technical University of Moldova (2010) Research interests span data compression, large-scale indexing, and algorithm optimization for machine learning. His projects emphasize scalability and performance in distributed systems, with recent work addressing energy efficiency and sparsity-driven methods for AI. He also develops educational technologies, such as competitive learning platforms to enhance student engagement. Notable achievements include the NSF CAREER Award and contributions to infrastructure monitoring systems, flood event modeling, and predictive university admission yield estimation. His lab's work is documented at https://g-guzun.github.io/ . Grants and advising: His NSF-funded CAREER project explores scalable sparsity-driven methods for AI systems. He collaborates on projects involving distributed data analytics and maintains an active research lab at SJSU.

Jonas LatzView profile

Lecturer

• Bayesian Inference
• Uncertainty Quantification
• Stochastic Processes
• +5 more

Jonas Latz is a Lecturer in Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, uncertainty quantification, stochastic processes, and their applications in computational mathematics and inverse problems. He has contributed to areas such as physics-informed neural networks, stochastic gradient methods, and medical imaging modeling. Key research interests include developing robust algorithms for Bayesian inverse problems, analyzing stochastic dynamical systems, and advancing numerical methods for partial differential equations. His work bridges theoretical foundations with practical applications in fields like tumor growth modeling and medical image reconstruction. Recent Achievements : Recipient of the SIAM Activity Group Uncertainty Quantification Early Career Prize (2024) SIAM Student Paper Prize (2020) SIGEST Award (2023) Dr. Latz collaborates internationally on topics such as adversarial machine learning, deep learning methods for PDEs, and stochastic sampling techniques. His research emphasizes rigorous mathematical analysis alongside computational innovation.

• Process Systems Engineering
• Large-Scale Optimization
• High-Performance Computing
• +2 more

Carl D. Laird is the John E. Swearingen Professor and Department Head of Chemical Engineering at Carnegie Mellon University. He leads an internationally recognized research program in process systems engineering, known for high-performance computing techniques in large-scale nonlinear optimization, parallel scientific computing, and open-source software development. Education: Ph.D. in Chemical Engineering, Carnegie Mellon University (2006) B.S. in Chemical Engineering, University of Alberta (2000) Research Focus: His work solves problems in non-traditional domains including public health, homeland security, critical infrastructure, and energy systems through advanced optimization methodologies. Current research integrates machine learning with optimization for improved decision-making in complex systems. Publication Trends: Recent work focuses on mathematical optimization frameworks, decomposition methods for large-scale problems, integration of machine learning surrogates, and applications in energy systems and chemical manufacturing. Research demonstrates consistent innovation in computational methods for engineering challenges. Awards and Honors: Steven J. Fenves Award for Systems Research INFORMS Computing Society Prize CAST Division Outstanding Young Researcher Award NSF CAREER Award Montague Center Teaching Excellence Award Wilkinson Prize for Numerical Software (for IPOPT development) Leadership and Funding: As director of the Center for Advanced Process Decision-Making, he oversees collaborative research with industry partners. His research has been supported by NSF, DOE, and industrial consortia.

TAY Seng ChuanView profile

Associate Professor

• Remote Sensing
• Image Processing
• Parallel Simulation
• +3 more

Dr. Tay Seng Chuan is an Associate Professor in the Department of Physics at the National University of Singapore (NUS). He is also the Deputy Director of the Centre for Remote Imaging, Sensing and Processing (CRISP) since 2001. His roles include coordination of the SM2/SM3 Bridging Courses under the Office of the Provost and Warden at Ridge View Residences . PhD in Computer Science from NUS (1999) Master of Science in Computer and Information Sciences from NUS (1994) His research interests span Remote Sensing , Image Processing , Parallel Simulation , Performance Modeling , High Level Architecture (HLA), and Information Technology Applications . He has contributed to Parallel Discrete-Event Simulation , Performance Analysis , and IT in Education . His publications focus on Time Warp Algorithms , Distributed Simulation , Parallel Processing , and Java-based Educational Tools . He has received multiple Meritorious Teaching Awards from NUS. Meritorious Teaching Award (2000) Meritorious Teaching Award (1999) Science Teaching Award (1997) In administration , he has served as Deputy Director at CRISP, Head of Education Division at CITA (1998–2001), and Webmaster of the Physics Department (1996–2001). He has also supervised UROPS projects on topics like Memory Management for Parallel Simulation and Remote Sensing Data Processing .

Sonya StaceyView profile

Adjunct Associate Professor

• Clinical Pharmacy
• Pediatric Pharmacology
• Medication Safety
• +3 more

Dr Sonya Stacey serves as an Adjunct Associate Professor at the School of Pharmacy and Pharmaceutical Sciences , The University of Queensland . Her work focuses on pediatric pharmacy , emphasizing medication safety , pharmacokinetics , and clinical pharmacology in hospital settings. Specializes in pediatric medication safety and adverse drug event analysis Develops clinical training frameworks for pharmacists Investigates antibiotic compatibility and dosing in critical care Active in respiratory pharmacology, particularly for cystic fibrosis Her recent research includes vaccine administration challenges ( 2025 ), antibiotic IV compatibility studies ( 2025 ), and safety evaluations of antifungal therapies ( 2024 ). Earlier work established standards for pharmacist training ( 2015 ) and advanced practice models in Australia ( 2014 ). Collaborative projects span multidisciplinary pediatric medicine safety initiatives and hospital pharmacy service development. As a co-author on over 20 publications, her work impacts healthcare policy and clinical practice. She contributes to the Safe and Effective Medication Collaborative and OPTMED-D research projects, addressing medication information continuity post-discharge.

Amanda CostonView profile

Assistant Professor

• Algorithmic Fairness
• Causal Inference
• Machine Learning
• +3 more

Amanda Coston is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. Her research focuses on addressing challenges in algorithmic decision support systems and data-driven policy-making, emphasizing equity, validity, and reliability. She earned her PhD in Machine Learning and Public Policy from Carnegie Mellon University, advised by Alexandra Chouldechova and Edward H. Kennedy, and completed a postdoc at Microsoft Research's Machine Learning and Statistics Team. Her work spans causal inference, machine learning, and nonparametric statistics, with applications in criminal justice, healthcare, and public policy. Education: PhD in Machine Learning and Public Policy, Carnegie Mellon University (2019-2022) MS in Machine Learning, Carnegie Mellon University (2019) Bachelor of Science in Computer Science, Princeton University (2013) Research Interests: Her research investigates how algorithms and data systems can perpetuate or mitigate disparities in high-stakes domains. Key areas include counterfactual audits of racial bias in policing, fairness in predictive models, and validity in algorithmic decision-making. She develops methodologies to ensure equitable outcomes in applications like healthcare resource allocation and criminal justice risk assessments. Awards & Honors: 2024 Schmidt Sciences AI 2050 Early Career Fellowship 2023 FAccT Best Paper Award (Counterfactual Prediction Under Outcome Measurement Error) 2023 SaTML Best Paper Award (A Validity Perspective on Evaluating the Justified Use of Algorithms) 2022 Meta Research PhD Fellowship Teaching & Mentorship: She teaches Causal Inference (STAT 156/256) at Berkeley and has mentored students through programs like AI4ALL. Her teaching emphasizes ethics, fairness, and societal impacts of AI. Service: Referee for journals including Nature Human Behaviour, JASA, and Transactions on Machine Learning Research Steering Committee Member for ML4D Workshop (NeurIPS) Program Committee Member for FAccT and AAAI Labs & Collaborations: Amanda collaborates with interdisciplinary teams on projects involving policy design, algorithmic fairness, and healthcare equity. She co-organized the ML4D workshops at NeurIPS 2018-2019 and leads the FEAT reading group at CMU.

Yuhao ZhuView profile

Associate Professor

• Visual Computing
• Imaging
• Human Perception
• +5 more

Yuhao Zhu is an Associate Professor at the University of Rochester, holding joint appointments in the Department of Computer Science and the Department of Brain and Cognitive Sciences. He is affiliated with the Center for Visual Science and the Goergen Institute for Data Science. His research focuses on visual computing, spanning imaging (optics, image sensors), human perception/cognition, and computer systems (architecture, programming models). He earned his Ph.D. from The University of Texas at Austin and has held visiting positions at Harvard University and Arm Research. Education: Ph.D. in Electrical and Computer Engineering, The University of Texas at Austin, 2017 M.S.E. in Electrical and Computer Engineering, The University of Texas at Austin, 2015 B.S. in Computer Science and Engineering, Beihang University, 2010 Research Interests: Dr. Zhu explores interdisciplinary areas such as computational imaging, human visual perception modeling, and energy-efficient computing systems. His work bridges optics, cognition, and computer architecture, with applications in AR/VR, autonomous systems, and neural rendering. Key themes include optimizing systems using biological principles and developing tools for efficient visual computing. Awards: NSF CAREER Award (2020) University Research Award (2023, 2019) ISCA Hall of Fame (2023) ACM SIGARCH Dissertation Award (2018, Honorable Mention) Teaching and Service: He teaches courses on computer imaging, graphics, and systems architecture. He serves on program committees for top conferences (ISCA, ASPLOS, MICRO) and chairs university-wide committees on AI ethics and international collaborations. His lab, Horizon Lab, actively mentors students and fosters interdisciplinary research. Labs/Teams: The Horizon Lab at URCS focuses on visual computing, with projects in computational art, AR/VR optimization, and robotic computing systems. Collaborations span academia and industry (e.g., Meta, Arm).

Muhammad AwaisView profile

Senior Lecturer

• Artificial Intelligence
• Trustworthy AI
• Self-Supervised Learning
• +5 more

Dr Muhammad Awais is a Senior Lecturer in Trustworthy and Responsible AI at the University of Surrey. He leads research on foundation models and self-supervised learning, with affiliations to the Surrey Institute for People-Centred Artificial Intelligence (PAI) and the Centre for Vision, Speech and Signal Processing (CVSSP). His expertise spans AI ethics, audio processing, medical AI, and biometric systems. Education: PhD in AI, MSc in AI, BSc Computer Engineering, BSc Mathematics and Physics. His research focuses on advancing AI through self-supervised learning techniques, with applications in healthcare (e.g., Parkinson’s Disease rehabilitation), transportation, and security systems. He has published extensively on topics like masked autoencoders, audio event classification, and age-invariant face recognition. Key contributions include DailyMAE (fast autoencoder pretraining), ASiT (audio-spectrogram transformers), and AiCareGaitRehabilitation (AI-driven gait rehabilitation). His work emphasizes ethical AI deployment and cross-modal learning.

Samanta RosatiView profile

Assistant Professor

• Biomedical Engineering
• Medical Imaging
• Machine Learning in Healthcare
• +5 more

Samanta Rosati is an Assistant Professor at the Department of Electronics and Telecommunications at the Polytechnic University of Turin. She holds a Master’s Degree and Ph.D. in Biomedical Engineering from the same institution. Her research focuses on biomedical data interpretation, signal processing, and medical device software development. Key areas include radiomics, AI for CAD systems, and clinical process modeling. Education: Ph.D. in Biomedical Engineering (2014), Politecnico di Torino Master’s Degree in Biomedical Engineering (2010), Politecnico di Torino Research Interests: Medical imaging analysis (e.g., CT/MRI segmentation) Machine learning applications in healthcare Wearable sensor systems for telemedicine Clinical workload modeling Current Projects: Development of a wearable multi-sensor array for heart failure monitoring AI-driven systems for personalized chemotherapy prediction Telemedicine frameworks for heart failure management Grants & Advising: Actively supervises projects on medical device software certification and hernia repair outcomes analysis. Collaborates on multi-center studies for cancer treatment response prediction. Labs/Teams: Leads projects in medical AI and wearable technology within the Department’s biomedical engineering group, focusing on bridging clinical needs with engineering solutions.

David MorrisonView profile

Lecturer

• Medical imaging
• Computer vision
• Deep learning
• +5 more

Mr. David Morrison is a Lecturer at the School of Computer Science, University of St Andrews. His research focuses on medical imaging, computer vision, deep learning, and generative models with applications in pathology and healthcare technology. He has contributed to projects such as the Automated Remote Pulse Oximetry System (ARPOS) and developed tools like wsipipe for digital pathology analysis. Research Interests: Medical imaging techniques, AI-driven diagnostics, metric search algorithms, and tangible computing applications. His work bridges theoretical computer science with practical healthcare solutions, emphasizing reproducibility and privacy in medical data. Selected Contributions: Published on cervical biopsy automation, endometrial cancer detection, and anonymizing pathology data using GANs. Collaborates with clinicians and engineers to advance digital diagnostics. Active in open-source tool development and public engagement through events like Doors Open @ Computer Science.

Melika PayvandView profile

Assistant Professor

• Neuroinformatics
• Artificial Intelligence
• Neuromorphic Computing
• +5 more

Melika Payvand is an Assistant Professor at the Institute of Neuroinformatics, University of Zurich and ETH Zurich, leading the Emerging Intelligent Substrates lab. She holds MSc and PhD degrees in Electrical and Computer Engineering from the University of California, Santa Barbara (2012 and 2016). Her research focuses on understanding natural intelligence and translating its principles into efficient artificial intelligence systems, particularly exploring hierarchical structures and memristive devices to reduce AI operational costs. She received the 2023 Swiss National Science Foundation Starting Grant and is actively involved in neuromorphic initiatives, including co-coordinating the NEUROTECH project and co-chairing the International Conference on Neuromorphic Systems (ICONS). Her work bridges neuroscience and engineering, emphasizing neuromorphic computing’s potential for scalable and energy-efficient AI. Key contributions include developing the Neurobench benchmarking framework and advancing 3D integration for neuromorphic systems. She also chairs the Neural Systems and Applications committee of the IEEE Circuits and Systems Society, reflecting her leadership in the field. Research interests span neuromorphic architectures, memristive technologies, and spiking neural networks. She has pioneered studies on temporal hierarchy, in-memory computing, and hardware-aware training methods. Her lab’s interdisciplinary approach integrates circuit design, machine learning, and neurobiology to create next-generation intelligent systems.

• Federated Learning
• Wireless Communication Systems
• Machine Learning
• +3 more

Dr. Mairton Barros is an Assistant Professor in the Division of Computer Systems at Uppsala University, Sweden. Previously, he held a Marie Skłodowska-Curie Postdoctoral Fellowship at Princeton University and KTH Royal Institute of Technology. His research focuses on federated learning, wireless communications, and optimization algorithms. He received his PhD from KTH in 2019, with earlier degrees from the Federal University of Ceará, Brazil. Education: PhD in Electrical Engineering, KTH Royal Institute of Technology (2019) MSc in Telecommunications Engineering, Federal University of Ceará (2014) BSc in Telecommunications Engineering, Federal University of Ceará (2012) Affiliations: Postdoctoral Researcher, KTH (2019-2022) Visiting Researcher, Princeton University (2022) Research Interests: His work bridges machine learning and wireless communications, emphasizing federated learning, full-duplex systems, and mmWave technologies. Key contributions include optimizing communication efficiency in distributed learning frameworks and advancing smart antenna techniques for full-duplex networks. Professional Activities: He has organized conferences (e.g., IEEE SECON 2022, IEEE GLOBECOM workshops) and delivered tutorials on wireless machine learning at major IEEE events. Recent grants include funding from Ericsson Research (2022). Lab/Team: Leads research initiatives in distributed intelligence and edge computing within Uppsala’s computer systems division.

Matthias BeckerView profile

Associate Professor

• Real-Time Systems
• Embedded Systems
• Edge Computing
• +6 more

Matthias Becker is an Associate Professor at the Division of Electronics and Embedded Systems within the EECS School at KTH Royal Institute of Technology. He holds a Docent degree from KTH (2024) and has extensive experience in research, including postdoctoral roles at KTH and visiting research at CISTER in Portugal. His primary affiliations include the Digital Futures research center, focusing on edge computing and real-time systems. Education: B.Eng. in Mechatronics/Automation Systems, University of Applied Sciences Esslingen (2011) M.Sc. in Computer Science (Embedded Computing), University of Applied Sciences Munich (2013) Licentiate and PhD in Computer Science and Engineering, Mälardalen University (2015, 2017) Research Interests: Focuses on real-time systems, embedded computing, edge orchestration, automotive systems, and network-on-chip analysis. His work emphasizes safety-critical systems, scheduling algorithms, and design space exploration for multi-core platforms. Articles Trends: Recent publications emphasize edge computing safety, automotive latency optimization, and preemptive scheduling models. Key themes include resource management, timing predictability, and real-time communication protocols. Awards: Best Student Paper Award at RTAS 2021 Grants & Advising: Co-PI of the GPARSE project on edge safety. Supervised over 15 master’s theses on real-time scheduling and embedded systems. Labs/Teams: Active in Digital Futures and collaborates with industry partners on automotive and edge system projects.

Simon ColtonView profile

Professor

• Computational Creativity
• Artificial Intelligence
• Game Design
• +4 more

Simon Colton is a Professor of Computational Creativity, Games, and Artificial Intelligence at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on AI-driven creativity in domains like music, game design, and visual arts, emphasizing generative techniques and philosophical aspects of creative systems. He leads projects in automated composition, procedural content generation, and interdisciplinary AI applications. Education: Extensive background in AI and computational creativity, with a focus on generative systems. Research Interests: Computational Creativity, Generative AI, Music Generation, Game Design Automation, and Neuro-Symbolic Systems. His work explores how AI can autonomously create artistic content, including music, games, and visual art, with notable contributions to frameworks like ANGELINA for game design and The Painting Fool for automated art. Recent projects include AI-driven music composition and tools for creative support (e.g., Gamika, Danesh). Key grants include the £3M EPSRC-funded IGGI2 Centre for Doctoral Training, focusing on Intelligent Games and Game Intelligence. His lab, the Centre for Multimodal AI, advances research in cross-modal AI creativity.