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Athanasios AngeloudisView profile

Associate Professor

• Environmental Fluid Mechanics
• Offshore Renewable Energy
• Coastal Processes
• +4 more

Dr Athanasios Angeloudis is a Reader (Associate Professor) and Director of Impact in the School of Engineering at the University of Edinburgh. He is affiliated with the Institute for Infrastructure and the Environment, leading research on hydro-environmental applications and offshore renewable energy systems. His academic roles include coordinating the Edinburgh Fluid Dynamics Group and serving as a member of NERC’s Peer Review College. Dr Angeloudis holds a PhD in Hydro-environmental Engineering (2014) and an MEng in Civil Engineering (2010) from Cardiff University. He is Chartered with the Technical Chamber of Greece (CEng) and a member of the Institution of Civil Engineers. His research focuses on environmental fluid mechanics, coastal processes, numerical methods, and data analytics. His teaching responsibilities include courses like Civil Engineering Hydraulics and Water Engineering Transport and Treatment . He has secured significant research funding, including the NERC Industrial Innovation Fellowship, and leads projects such as CoTide (tidal stream energy optimization) and ILIAD (maritime data services). Key research contributions span tidal energy systems, climate change impacts on river flows, and computational modeling frameworks like Thetis-SWAN . His work emphasizes collaboration with industry and international partners to advance sustainable energy solutions and environmental resilience.

Elliot J. CrowleyView profile

Senior Lecturer

• Machine Learning
• Automated Machine Learning
• Low-Resource Deep Learning
• +3 more

Dr Elliot J. Crowley is a Senior Lecturer in Electronics and Electrical Engineering at the University of Edinburgh, serving as Discipline Programme Manager. He co-leads the Bayesian and Neural Systems research group. His research focuses on simplifying machine learning, automated ML, low-resource deep learning, and engineering applications. He holds an MEng in Engineering Science and a DPhil (PhD) from the University of Oxford, with postdoctoral experience at Edinburgh's School of Informatics. He leads the EPSRC New Investigator Award and participates in the dAIEdge Horizon Network. Notable contributions include foundational work in neural architecture search (NAS), probabilistic methods for model efficiency, and applications in computer vision. His courses, such as the Data Analysis and Machine Learning module, emphasize practical Python-based learning for engineering students. Key awards include an EPSRC grant and recognition through distinguished papers at ASPLOS 2021. His team includes current PhD students (Linus Ericsson, Miguel Espinosa) and former advisees (Chenhongyi Yang at Meta, Jack Turner at Qualcomm). Research spans from NAS algorithms to ethical machine learning practices, with a focus on bridging theoretical advances and real-world engineering challenges.

Bruno A. OlshausenView profile

Professor

• Computational Neuroscience
• Visual Perception
• Sensory Coding
• +4 more

Professor Bruno A. Olshausen is affiliated with the Helen Wills Neuroscience Institute and the School of Optometry at the University of California, Berkeley. He also serves as the Director of the Redwood Center for Theoretical Neuroscience , focusing on computational models of sensory coding and visual perception. Ph.D. in Computation and Neural Systems (Caltech, 1994) M.S. and B.S. in Electrical Engineering (Stanford, 1987 and 1986) His research investigates how the brain processes sensory information by developing probabilistic models of natural images and neural circuits. Key contributions include sparse coding models that replicate receptive field properties of the primary visual cortex (V1), and work on extending these models to learn invariances and hierarchical structures. He has also collaborated with electrical engineers to design low-power analog memory systems inspired by brain computation, and developed software tools like SPARSENET and SPARSEPYR for neural signal processing. His work spans computational neuroscience, theoretical modeling, and interdisciplinary applications in vision science. As an educator, he has co-instructed courses such as Vision Science 206D (Neuroanatomy of the visual system) and Vision Science 212B (Visual neurophysiology), and independently taught Vision Science 265: Neural Computation at Berkeley and Psychology 290 at UC Davis. He co-edited the book Probabilistic Models of the Brain: Perception and Neural Function (MIT Press, 2002) and organized workshops at institutions like the Gordon Research Conference and Nature Neuroscience .

Dora ErdosView profile

Senior Lecturer

• Algorithm Design
• Data Mining
• Combinatorial Optimization
• +3 more

Dora Erdos is a Senior Lecturer and Director of Undergraduate Studies in the Department of Computer Science at Boston University. She specializes in algorithmic challenges, data mining, and combinatorial optimization with a focus on network-based problems. Her work bridges theoretical computer science and practical applications in education technology and network analysis. Education: PhD in Computer Science, Boston University (2015) MSc in Pure Mathematics, Eotvos University (Advisor: Andras Frank) Postdoctoral Research at Brown University's Raphael Lab (Advisor: Ben Raphael) Research Interests: Erdos investigates algorithms for network analysis, including centrality measures, graph reconstruction, and optimization problems in educational systems. She develops scalable methods for tensor factorization and content placement in navigational networks. Her work often integrates combinatorial approaches with real-world applications. Professional Roles: As Director of Undergraduate Studies, Erdos oversees academic advising and curriculum development. She emphasizes student accessibility, maintaining office hours and encouraging direct communication via email (edori@bu.edu). Recent Research Trends: Her publications (2011–2017) focus on network-centric problems such as centrality evaluation frameworks, boolean tensor decomposition, and team formation algorithms for educational scheduling. These contributions highlight her dual expertise in theoretical algorithm design and applied educational technology.

Christopher GilliamView profile

Assistant Professor

• Signal Processing
• Radar Systems
• Sampling Theory
• +5 more

Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.

Arno SiebesView profile

Professor

• Data Mining
• Pattern Mining
• Minimum Description Length Principle
• +2 more

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

• Visualization
• Computer Graphics
• Flow Visualization
• +3 more

Tino Weinkauf is a Professor of Visualization and Head of the Division of Computational Science and Technology at KTH Royal Institute of Technology in Stockholm. His work bridges computer science and applied mathematics, with a focus on visualization and topological data analysis. He leads research in visualizing complex data from fields like fluid dynamics, neurobiology, and human-computer interaction. Education: Ph.D. in Computer Science (not explicitly stated in provided texts, but inferred from career trajectory). Research interests include flow visualization, topological methods for data analysis, and interactive visualization techniques. He develops tools like the TopoInVis Toolkit (TTK) and contributes to infrastructure such as the Swedish Research Infrastructure for Visualization Support (InfraVis). His work emphasizes applications in turbulence modeling, biomedical imaging, and user-centered design. Teaching: Responsible for courses such as Advanced Topics in Visualization and Computer Graphics , Information Visualization , and Introduction to Visualization and Computer Graphics . Supervises degree projects in Computer Science and Engineering across specializations like Machine Learning and Interactive Media Technology. Publications focus on topological data analysis, flow segmentation, and algorithm optimization. Notable projects include binary segmentation of turbulent flows and interactive reward tuning systems for preference elicitation. Labs/Teams: Leads the Division of Computational Science and Technology at KTH, fostering interdisciplinary research in computational methods and visualization technologies.

• Privacy-Preserving Algorithms
• Differential Privacy
• Federated Learning
• +6 more

Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (QMUL), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Libera Università di Bolzano (2018) and a Diploma in Media Informatics from the University of Munich (2014). His research focuses on developing algorithms and data structures for managing large and sensitive datasets, with a particular emphasis on privacy-preserving techniques like differential privacy and federated learning. He has held postdoctoral roles at the University of Warwick and King's College London before joining QMUL in 2023. Research Interests: Privacy-preserving algorithms, differential privacy, federated learning, data management systems, randomized algorithms, and efficient query processing. His work bridges theoretical foundations with practical applications, aiming to enable secure data sharing while preserving individual privacy. Teaching: Leads undergraduate modules in Database Systems and Operating Systems at QMUL. His teaching emphasizes foundational concepts in computer science through rigorous coursework and practical projects. Grants and Funding: Currently supervises a PhD studentship titled 'Privacy-Preserving Algorithms: Unlocking Data Sharing for Medical Sciences & Machine Learning', funded by QMUL and open to UK home students. The role involves exploring privacy-preserving frameworks for collaborative data analysis. Professional Contributions: Serves as a reviewer for top-tier conferences (NeurIPS, ICML, SIGMOD, ICDE) and journals (IEEE TKDE, Data & Knowledge Engineering). Actively involved in conference organization, including NeurIPS Area Chair (2024) and ICDT Proceedings Chair (2024). Labs and Collaborations: Affiliated with the Centre for Fundamental Computer Science at QMUL, fostering interdisciplinary research in theoretical and applied computing. Engages with industry partners on privacy-enhancing technologies and data management solutions.

Nicolò Cesa-BianchiView profile

Professor

• Machine Learning
• Online Learning
• Multi-Armed Bandits
• +4 more

Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, Department of Computer Science (Dipartimento di Informatica), and affiliated with the DEIB Department at Politecnico di Milano. His research focuses on foundational aspects of machine learning, particularly online learning, multi-armed bandits, reinforcement learning, and graph analytics. He is an ELLIS Fellow and a corresponding member of the Accademia Nazionale dei Lincei. Research interests include the design and analysis of algorithms for prediction, clustering, and online decision-making, with applications to digital markets, social networks, and bioinformatics. Notable contributions span cooperative online learning, multitask learning, and bandit algorithms. He co-authored the influential book Prediction, Learning, and Games (2006). Professional roles include Board member of ELLIS, co-director of the Milan ELLIS unit, and involvement in EU initiatives like ELSA (Secure & Safe AI) and ELIAS (AI for Sustainability). He teaches graduate courses on statistical methods, machine learning, and reinforcement learning, with a focus on theoretical foundations. Key awards: ELLIS Fellowship (2020), Corresponding Member of the Accademia Nazionale dei Lincei (Italian National Academy of Sciences). His work bridges theory and practice, addressing challenges in adaptive systems, market design, and algorithmic fairness. Current projects explore distributed learning, regret minimization in adversarial environments, and interpretable models.

William PhanView profile

Assistant Professor

• Market Design
• Game Theory
• School Choice Mechanisms
• +2 more

Dr. William Phan is an Assistant Professor of Economics in the Department of Economics at North Carolina State University's Poole College of Management. He earned his PhD in Economics from the University of Rochester. His research focuses on theoretical and applied market design, particularly: School choice mechanisms and assignment algorithms Matching theory with applications to education and resource allocation Game theory and incentive design Fair division and representation in allocation systems His recent work examines crowding effects in school choice and representation in reserve systems.

• Statistical Signal Processing
• Pattern Recognition
• Machine Learning
• +5 more

Ulisses Braga-Neto is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from The Johns Hopkins University (2002), with earlier degrees including an M.S. from the Federal University of Pernambuco (1992). His research focuses on statistical signal processing, pattern recognition, and machine learning, with applications in bioinformatics, materials informatics, and environmental modeling. He leads the TAMIDS Scientific Machine Learning Lab and has authored over 100 publications. Key research areas include physics-informed neural networks for environmental and engineering problems, error estimation in classification systems, and gene regulatory network inference. His recent work emphasizes machine learning applications in agriculture (e.g., cotton detection in corn fields using UAS), CO2 sequestration modeling, and wildfire prediction. He has contributed to foundational texts like Error Estimation for Pattern Recognition (Wiley-IEEE, 2015). Prof. Braga-Neto's interdisciplinary approach bridges computer science, engineering, and biology. His lab develops algorithms for data-poor environments, with applications in genomics, proteomics, and metagenomics. He advises graduate students through Texas A&M's ECE program, emphasizing rigorous statistical methods alongside machine learning innovation.

Stephen HicksView profile

Professor

• Composite Steel and Concrete Structures
• Structural Reliability
• Vibration Serviceability of Floors
• +3 more

Professor Stephen Hicks is a Professor in Civil Engineering and Leader of the Civil and Environmental Engineering Discipline Stream at the School of Engineering, University of Warwick. He has held senior roles in research institutes like the Heavy Engineering Research Association (HERA) and the Steel Construction Institute (SCI), contributing to national and international standards development. Education: PhD (University of Cambridge, 1998), BEng (University of London, 1993) Research focuses on composite steel-concrete structures, structural reliability, and vibration serviceability. He leads teaching modules including ES3E1 Design Project and ES2C2 Civil Engineering Design. Key projects include Eurocode 4 revisions and steel-concrete composite systems development. Grants include leadership on Steel-CLT composite design (WSP UK) and EU-funded Eurocode 4 standardization projects. He chairs CEN Subcommittee SC4 for Eurocode 4 and contributed to Australasian standards like AS/NZS 2327. Engaged in governance roles with organizations such as EPD Australasia and Steel Construction New Zealand.

Umang MathurView profile

Assistant Professor

• Formal Methods
• Concurrency
• Programming Languages
• +4 more

Umang Mathur is an Assistant Professor at the National University of Singapore's School of Computing, where he leads the FOCS Lab and is affiliated with PLSE@NUS. His research focuses on Formal Methods , Concurrency , and Decidability in Programming Languages and Software Engineering . PhD in Computer Science from the University of Illinois at Urbana-Champaign (advisor: Prof. Mahesh Viswanathan) Former Research Scientist at Facebook Inc. and Research Fellow at the Simons Institute Recipient of Google PhD Fellowship, 2024 CPP Distinguished Paper Award, 2023 ACM SIGPLAN Award, and ASPLOS 2022 Best Paper Award His recent work explores algorithmic techniques for detecting concurrency bugs , decidable program verification , and synthesis , with a focus on weak memory models, predictive monitoring, and automata-theoretic approaches. Articles span topics like causal concurrency, tree clock data structures, and probabilistic counting algorithms, reflecting interdisciplinary intersections of logic and systems research. Scientific Awards Google PhD Fellowship 2024 CPP Distinguished Paper 2023 ACM SIGPLAN Distinguished Paper 2022 ASPLOS Best Paper 2018 ESEC/FSE Distinguished Paper He advises PhD students in Formal Methods and supervises teams in the FOCS Lab. Teaching includes advanced modules on Automata Theory, Logic, and Verification at NUS.

• Antenna Design
• Evolutionary Algorithms
• Wireless Communications
• +7 more

Sotirios K. Goudos is a Professor at the Department of Physics, Aristotle University of Thessaloniki (AUTH), Greece, and Director of the ELEDIA@AUTH lab within the ELEDIA Research Center Network. His research focuses on antenna design, evolutionary algorithms, wireless communications, machine learning, and IoT applications. He holds a B.Sc. in Physics (1991), M.Sc. in Electronics (1994), Ph.D. in Physics (2001), and additional qualifications in Information Systems and Electrical Engineering. Prof. Goudos is a Senior Member of IEEE and serves as Editor-in-Chief of the Telecom open access journal (MDPI) and Associate Editor for IEEE Transactions on Antennas and Propagation, IEEE Access, and IEEE Open Journal of the Communication Society. He has organized multiple special issues in journals like EURASIP Journal on Wireless Communications and Networking and has authored/edited books on antennas and AI in networks. His awards include multiple IEEE Access Outstanding Associate Editor recognitions (2019–2023) and inclusion in Stanford University's top 2% scientists list (2020–2024). He teaches courses on telecommunications, Java programming, and microwave systems, and has supervised over two dozen master's students since 2009. His work spans antenna optimization, AI-driven communications, and IoT security, with contributions to 5G/6G, RIS systems, and smart agriculture. Prof. Goudos actively contributes to IEEE Greece Section leadership roles, including Secretary (2022) and Vice-Chair (2023–2024). His labs and teams focus on ELEDIA's research in electromagnetics, optimization, and AI applications.

Ahmad IbrahimView profile

Associate Professor

• Internet of Things
• Fog Computing
• Cloud Computing
• +7 more

Dr. Ahmad Ibrahim is an Associate Professor in Computer Science at the University of Birmingham Dubai, where he serves as the Undergraduate Programme Director and Year 2 Coordinator for BSc students. He is also the School Outreach Coordinator for the Computer Science program at the Dubai campus. Educational Background: Postgraduate Certificate in Higher Education (PGCHE), University of Birmingham, 2021 Fellow of the Higher Education Academy (FHEA), University of Birmingham, 2021 PhD in Computer Science, University of Pisa, Italy, 2016 MSc in Computer Science, Korea Advanced Institute of Science and Technology (KAIST), South Korea, 2009 BSc in Computer System Engineering, G.I.K Institute of Engineering Sciences and Technology, Pakistan, 2005 Dr. Ibrahim's research focuses on the Internet of Things, Quality of Service (QoS) prediction, Web services, and Cloud and Fog computing. His teaching interests include Object-oriented programming, Full Stack application development, Security and Networks, Operating Systems and Systems Programming, and Advanced Networking. His recent publications demonstrate a strong trend in fog and edge computing, with particular emphasis on QoS optimization, cost-effective deployment, and resource management in distributed environments. His work bridges theoretical modeling with practical application deployment strategies across cloud-edge continua. Scientific Awards and Recognition: Fellow of the Higher Education Academy (FHEA), 2021 Postgraduate Certificate in Higher Education (PGCHE), University of Birmingham, 2021 Dr. Ibrahim has supervised and contributed to numerous research projects in distributed systems and cloud computing, particularly through collaborations on European initiatives like SeaClouds. He has secured recognition for his teaching excellence and is actively involved in curriculum development and academic outreach.