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Ilse C.F. IpsenView profile

Professor

• Numerical Linear Algebra
• Randomized Algorithms
• Probabilistic Numerical Analysis
• +8 more

Ilse C.F. Ipsen is a Distinguished Professor in the Department of Mathematics at North Carolina State University, with a joint appointment in the Department of Statistics. She is a Fellow of the American Mathematical Society and the Society for Industrial and Applied Mathematics (SIAM), and delivered the Olga Taussky-Todd Lecture at ICIAM 2023. Institution: North Carolina State University School: College of Sciences Department: Department of Mathematics Rank: Professor Email: ipsen@ncsu.edu Her research centers on numerical linear algebra, randomized algorithms, and probabilistic numerical analysis. She has made significant contributions to error analysis in floating-point computation, matrix perturbation theory, and the development of randomized methods for large-scale data problems. Her work bridges theoretical foundations with practical applications in data science, quantum physics, and computational statistics. She is the author of the textbook Numerical Matrix Analysis (SIAM, 2009) and serves as Editor-in-Chief of the SIAM Book Series on Data Science. The recent publications highlight a strong trend toward probabilistic numerical methods, uncertainty quantification, and randomized matrix algorithms. Themes include error analysis for inner products and summation, Bayesian solvers for linear systems, and scalable methods for trace and diagonal estimation. Applications span biobank data analysis, quantum physics, and PageRank computation. Scientific Awards: Fellow of the American Mathematical Society (AMS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Olga Taussky-Todd Lecture, ICIAM 2023 She has advised multiple graduate and undergraduate students, including J.T. Holodnak, T. Wentworth, and R. Rehman. Her collaborative research has been supported by interdisciplinary grants, particularly in quantum physics and data-intensive computing. She has served on editorial boards of leading journals such as SIAM Review , Acta Numerica , and Numerische Mathematik , and chaired SIAM activity groups, reflecting her leadership in the applied mathematics community. She is actively involved in the development of software and algorithms, including the MATLAB package kappa_SQ for randomized sampling, and has contributed to discussions on communicating applied mathematics through case studies.

Angelika PeerView profile

Professor

• Robotics
• Control Systems
• Human-Robot Interaction
• +15 more

Angelika Peer is a Full Professor in the Faculty of Engineering at the Free University of Bozen-Bolzano, Italy, a position she has held since November 2017. Previously, she served as a Full Professor at the Bristol Robotics Laboratory, University of the West of England, UK (2014–2017), and as a senior researcher and lecturer at the Technical University of Munich, Germany, where she was also a TUM-IAS Junior Fellow. Her academic base is firmly within engineering, particularly in intelligent and human-centered systems. Diploma in Electrical Engineering and Information Technology, Technical University of Munich, 2004 Doctor of Engineering, Technical University of Munich, 2008 Her research interests are centered on intelligent systems that bridge humans and machines. Key areas include robotics, control systems, human-robot interaction (HRI), emotion recognition through physiological signals, shared control, and intelligent manufacturing. She investigates how dynamic models and machine learning can improve safety, adaptability, and efficiency in human-machine collaboration. Her work spans both theoretical control design and practical applications in Industry 5.0, sustainable production, and assistive technologies. Her recent publications (2023–2025) reflect a strong trend toward data-driven and model-based intelligent systems. Topics include energy-based safety control in HRI, neural-network-based trajectory adaptation in powder compaction, emotion intensity estimation using NARX models and appraisal theory, and task prediction in digitalized shop floors using support vector regression. These works demonstrate expertise in integrating dynamic modeling, machine learning, and real-time control with applications in manufacturing and human-centered computing. Angelika Peer has advised PhD students including Ehtisham Ul Hasan and Isabel Francisca Sota Machado Barradas. She is involved in research initiatives such as the Human-centered Intelligent Systems (HCIS) macro area at her university, promoting interdisciplinary work at the intersection of computer science, engineering, and human factors. Her research is supported by collaborations and publications in high-impact journals and conferences, with a strong emphasis on open access and peer-reviewed dissemination.

• Machine Learning
• Remote Sensing
• Artificial Intelligence
• +9 more

Jordi Muñoz Mari is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. He is a key member of the Image and Signal Processing (ISP) research group within the Image Processing Laboratory (IPL), a recognized research institute (ERI) at the university. His research focuses on the intersection of artificial intelligence, remote sensing, and environmental science. Key areas include machine learning for Earth observation, biophysical parameter retrieval, crop yield forecasting, climate modeling, and causal inference in ecological systems. He also explores applications of AI in education, particularly generative models for assessment and digital learning tools. The most recent publications highlight a strong trend toward physics-aware machine learning, uncertainty quantification, and large-scale applications using platforms like Google Earth Engine. His work spans climate extremes (medicanes, hurricanes, droughts), agricultural monitoring, biodiversity, and educational technology, demonstrating interdisciplinary innovation. Jordi Muñoz Mari completed his PhD at the University of Valencia in 2004 on telemonitoring systems for cardiac patients. His ongoing research involves advanced signal processing, kernel methods, and AI-driven geoinformation systems.

Neal MageeView profile

Associate Professor

• Solution Architecture
• Cloud Computing
• Event-Driven Computing
• +7 more

Neal Magee is an Associate Professor at the School of Data Science, University of Virginia, where he also serves as Faculty Director of Systems Architecture. He plays a key role in developing data science curricula and teaching systems-focused courses across degree programs. Education: Ph.D. in Religion, Syracuse University Master's in Divinity, Princeton Theological Seminary B.A. in Philosophy and Religion, Flagler College His research focuses on solution architecture, cloud computing, event-driven systems, and cloud-native design patterns . He brings extensive industry experience to academia, having worked over a decade in cloud architecture, DevOps, and systems administration, including leading a zero-downtime migration of a Fortune 1000 client to AWS. His recent publications and projects emphasize the application of containers and APIs in biomedical and genomics research, reflecting a strong trend toward scalable, secure, and collaborative computational platforms in data-intensive domains. Scientific Awards and Recognitions: AWS Certified Solution Architect AWS Certified Developer Member of the AWS Customer Council GitHub Global Campus Teacher Cloud Native Foundation contributor Magee has been instrumental in advancing research computing at UVA, notably as principal architect of the NSF-funded ACCORD platform for collaborative research on sensitive data. He previously served as Director of Solutions and DevOps Services for UVA Research Computing and continues to support faculty and students through tooling, automation, and hands-on development training. He leads initiatives in cloud-native systems and is deeply involved in building scalable, fault-tolerant infrastructures for scientific computing. His work bridges technical architecture with educational outreach, ensuring robust systems empower both research and learning.

Anastasia AilamakiView profile

Professor

• Data Systems
• Database Systems
• Resource Optimization
• +4 more

Anastasia Ailamaki is a Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences and the Department of Computer Science. She is also a member of the Board of Directors of the Technical University of Crete (TUC), where she is a distinguished alumna, having earned her Master of Science in Electrical and Computer Engineering. Her leadership in data systems research has been recognized with the prestigious ERC Advanced Grant. Her research focuses on foundational approaches to optimizing system resources through declarative data systems. This work lies at the intersection of database systems, computer architecture, and performance modeling, aiming to improve efficiency and scalability in modern data-intensive applications. Her interests include query processing, data storage optimization, and the integration of hardware-aware techniques into database design. The awarded ERC project, "A Foundational Approach to Resource Optimization through Declarative Data Systems," represents a significant advancement in the field, funded with 2.5 million euros, highlighting the relevance and impact of her research agenda. ERC Advanced Grant Professor Ailamaki has advised numerous students throughout her career and has led major research initiatives, though specific names and grants beyond the ERC are not detailed in the current text. Her role on the External Advisory Committee and Board of Directors at TUC reflects her ongoing commitment to academic leadership and institutional development. She is actively involved in shaping the future of data systems research at EPFL and through her collaborations with institutions like the Technical University of Crete. Her lab or research group likely focuses on systems for data science and performance-aware computing, although specific team details are not provided.

Junhua DingView profile

Professor

• Data Science
• Artificial Intelligence
• Software Engineering
• +1 more

Junhua Ding is the Reinburg Endowed Professor Chair of Data Science at the University of North Texas, where he is based in Discovery Park, E292G. He holds a prominent academic position in the Department of Data Science, contributing to research and academic leadership in data-intensive fields. His educational background includes advanced degrees from institutions in both the United States and China: Ph.D. from Florida International University M.S. from Nanjing University, China B.S. from China University of Geosciences, China Dr. Ding's research spans several cutting-edge domains in computing and data science. His primary areas of interest include Data Science , Artificial Intelligence , Software Engineering , and Biomedical Computation , reflecting a strong interdisciplinary focus on applying computational methods to complex scientific and engineering problems. No recent publications are listed in the available information, so no trend analysis can be performed at this time. There are no scientific awards mentioned in the current profile. Dr. Ding advises students and may be involved in grant-funded research, though specific details about advisees or grants are not provided in the current text. He is likely affiliated with research initiatives at UNT's Discovery Park, a hub for technological innovation and interdisciplinary research, though specific lab or team affiliations are not detailed.

Noah RhodesView profile

Research Fellow

• Power Grid Optimization
• Mathematical Optimization
• Energy Security
• +2 more

Noah Rhodes is a CNLS Postdoctoral Associate at Los Alamos National Laboratory's Center for Nonlinear Studies (CNLS), working within the Theoretical Division (T-Division). His research focuses on mathematical optimization applications for power grid challenges, particularly infrastructure flexibility and wildfire risk mitigation. Office contact: TA-3, Building 1690, Room 123; email: nrhodes@lanl.gov. Educational background includes: B.S. in Electrical Engineering, University of Wisconsin-Madison (2019) Ph.D. Minor in Energy Analysis and Policy, University of Wisconsin-Madison (2022) Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2024) Rhodes' research centers on enhancing power grid resilience through optimization techniques, with emphasis on reducing infrastructure needs and managing wildfire-related outages. His work bridges theoretical mathematics with practical energy security challenges, developing frameworks for public safety power shutoffs and grid restoration under extreme conditions. His 2021-2025 publications reveal a trajectory from foundational grid restoration algorithms toward integrated environmental considerations. Recent work combines carbon intensity analysis with grid operations while advancing open-source tools (PowerModelsRestoration.jl, ElectricityEmissions.jl) for academic and utility applications. Key trends include stochastic optimization for emergency response and topology-aware power flow formulations. No scientific awards are listed in the provided materials. No student advisement or grant information is documented in the source text. Rhodes operates within CNLS—a hub for nonlinear science research at LANL—which fosters interdisciplinary collaboration on complex systems. His work interfaces with T-Division's theoretical frameworks while addressing real-world energy infrastructure vulnerabilities through computational modeling and optimization.

Mohammad GhassemiView profile

Assistant Professor

• Machine Learning
• Data Mining
• Natural Language Processing
• +7 more

Mohammad Ghassemi is an Assistant Professor in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. His work focuses on developing AI systems that integrate human and machine intelligence to address complex challenges in healthcare, decision-making, and social behavior. He has affiliations with MIT's Institute for Medical Engineering and Science, Standard & Poor's Financial Services, and Ghamut Corporation, and serves on multiple startup advisory boards. Education: PhD from MIT (Electrical Engineering and Computer Science), M.Phil. from University of Cambridge (Information Engineering), B.Sc. from New Mexico State University (Electrical Engineering & Applied Mathematics). Research: Combines machine learning, natural language processing, and crowdsourcing to predict health outcomes, enhance medical decision support, and design wearable AI for social interaction. Awards: Multiple US patents, media features in BBC, NPR, Wall Street Journal, and publications in Nature Scientific Data, Science Translational Medicine, and AAAI. Key projects include the Connect platform for health data sharing, large-scale medical databases, and an AI-driven wearable for social coaching. His lab emphasizes end-to-end systems for real-world impact in healthcare and human behavior analysis.

Xavier DevroeyView profile

Assistant Professor

• Software Engineering
• Test Automation
• Search-Based Testing
• +5 more

Xavier Devroey is an Assistant Professor of Software Engineering at the Faculty of Computer Science , University of Namur , where he co-leads the SNAIL Team . His research focuses on test automation for search-based and model-based software testing , test suite augmentation , DevOps , and variability-intensive systems . PhD in Computer Science (University of Namur, 2017) Master in Computer Science (University of Namur, 2010) Recent research highlights include: Automated Android safety/security audits (A3S3, 2025) REST API benchmarking infrastructure (2025) Energy consumption analysis through test execution (2025) Fuzzing approaches for Odoo integration testing (FuzzE, 2025) Scientific recognitions: 1st place Java Test Case Generation Tool Competition (2025) VAMOS 2024 Ten-Year Most Influential Paper SSBSE 2020 Best Paper Award ICST 2024 Distinguished Reviewer AST 2023 PC Reviewer Star He actively supervises PhD and master's students while organizing international conferences like ISSTA (2025) and Belgium-Netherlands Software Evolution Workshops (2024).

• Social Sciences
• Humanities
• Data Management
• +3 more

Elina Late is a Senior Research Fellow at the Department of Communication Sciences, University of Tampere. Her work bridges social sciences, humanities, and computer science through research data management, image data interaction, and academic collaboration frameworks. Key Research Areas: Data interaction in humanities, open science, learned societies, image data modeling Recent Publications: Focus on collaborative workflows, FAIR data principles, and cross-border academic networks Activities: Conference presentations at CHIIR '25, STI2024, and invited lectures on digital image research Datasets: Principal contributor to national and European learned society datasets (2018-2023)

Luis A. LeivaView profile

Assistant Professor

• Human-Computer Interaction
• Machine Learning
• Computational Interaction
• +12 more

Luis A. Leiva is an Assistant Professor in the Department of Computer Science at the University of Luxembourg’s Faculty of Science, Technology and Medicine. He leads the Computational Interaction research group and serves as director of the Interdisciplinary Lab for Intelligent and Adaptive Systems (ILIAS). His academic background includes a PhD in Computer Science (with honors), an MAS in Pattern Recognition and Artificial Intelligence, an MSc in Electrical Engineering, and dual BSc degrees in Industrial Design and Industrial Engineering, all from the Universitat Politècnica de València (UPV). His research lies at the intersection of Human-Computer Interaction (HCI) and Machine Learning , with a focus on computational interaction . He investigates methods to enable, explain, and support user interaction through computational models and data-driven techniques. His key areas include implicit interaction —leveraging behavioral signals such as mouse movements and gaze—and synthetic data generation , particularly using kinematic models to simulate human-like gestures. His work has contributed to usability evaluation, user engagement prediction, adaptive interfaces, and stroke gesture modeling. His recent publications span top-tier journals and conferences such as ACM Transactions on Computer-Human Interaction (TOCHI) , IEEE Transactions , CHI , IUI , and SIGIR . The research trends reflect a strong emphasis on multimodal interaction, affective computing, brain-computer interfaces, and deep learning applications in user interface modeling and personalization. Best paper award at IUI'25 Marie Skłodowska-Curie Actions (MSCA) Seal of Excellence (2019) Extraordinary PhD thesis award (2014) Outstanding Reviewer Recognition at CHI, ICMI, MobileHCI, and UIST Quality Teaching Awards finalist at Aalto University (2020) Luis A. Leiva has advised numerous MSc and BSc students at the University of Luxembourg and Aalto University and has secured significant research funding as PI or co-PI for projects such as SYMBIOTIK (EIC), SCRIPTOR (FNR), BANANA (CHIST-ERA), and SAMUSE (Audacity). He is an Associate Editor for the International Journal of Human-Computer Studies and Machine Learning with Applications , and actively serves on the program committees of major HCI and AI conferences. He is also a co-founder and former CTO of Sciling, an SME specializing in machine learning solutions, and has held postdoctoral positions at Aalto University and the PRHLT Research Center (UPV). He leads the Computational Interaction research group and the ILIAS lab, both dedicated to advancing intelligent, adaptive, and user-centered computing systems through interdisciplinary research combining machine learning, cognitive science, and interactive technologies.

David CarreraView profile

Associate Professor

• Computer Engineering
• Supercomputing
• Data-Intensive Computing
• +5 more

David Carrera is an Associate Professor in the Department of Computer Architecture at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), and serves as the head of the Data-Centric Computing group at the Barcelona Supercomputing Center (BSC-CNS). His research focuses on holistic integration of emerging supercomputing technologies, infrastructure management and optimization for data-intensive workloads, and collaboration with global industry leaders in Computer Science. Areas of expertise include task placement strategies, performance modeling for heterogeneous workloads, and data location strategies for data analytics. He founded the start-up NearbyComputing SL (2018), specializing in orchestration of 5G and IoT services. His work emphasizes technology transfer and industrial collaboration. Scientific Awards & Grants: Agustín de Betancourt y Molina Medal (2018) ERC Starting Grant (HiEST project, 2014) ICREA Acadèmia Award (2015) ERC Proof of Concept (Hi-OMICS, 2017) MIT-Spain "la Caixa" Seed Fund (2018)

Madeline CruiceView profile

Professor

• Aphasia Rehabilitation
• Quality of Life in Communication Disorders
• Stroke Recovery
• +3 more

Professor Madeline Cruice, based at City, University of London since 2002, is a leading expert in aphasia rehabilitation research. She holds a PhD in Speech Pathology from the University of Queensland (2001) and has pioneered innovations in clinical education, including the Classroom-to-Clinic program and Communication Skills Intensive in the NHS. Research Focus: Quality of life in aphasia, WHO ICF frameworks, communication disability assessment, and technology-enhanced interventions Grants: £609,000 (EPSRC), £410,000 (NIHR), £1,096,000 (NIHR HSDR), and £477,809 (Barts Charity) for projects like DARA, TANGO, COM-STAR, and MARS Her recent publications span virtual reality therapy (EVA Park), personalized language treatments , and communication partner training . She serves as Co-Chair of the international Collaboration of Aphasia Trialists (CATs-4) and has supervised 11 PhD/MRes students. Awards: Vice Chancellor’s Award for Local Community Contribution (2016), Research Supervision Award (2016), Student Voice Award (2011), and Walter and Eliza Hall Travelling Scholarship

• Modeling and Simulation
• Energy System Analysis
• Vehicle Grid Integration
• +3 more

Prof. Dr.-Ing. Marco Pruckner leads the Chair of Communication Networks at the University of Würzburg since 2022, following his assistant professorship in Energy Informatics at Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022). His research focuses on energy system analysis, vehicle grid integration, and future mobility systems using modeling and simulation methodologies. University of Würzburg (2022–present) Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022) UC Berkeley (Visiting Scholar, 2020) Key research areas: Modeling and simulation of integrated energy systems Digital twin development for local energy systems Optimization of electric vehicle charging strategies Water-energy nexus analysis and sustainability metrics Recent publications (2022–2025) demonstrate expertise in: Smart charging algorithms for vehicle grid integration Heat pump electrification and seasonal performance modeling Battery state-of-health estimation techniques Multi-objective optimization for data centers and energy systems His team at University of Würzburg includes Daniel Bayer, Paul Benz, Jonas Schiller, and Leo Strobel. Methodologies combine system dynamics, discrete-event simulation, and machine learning for energy and mobility system analysis.

Ethan GoldView profile

Lecturer

• Cyber Systems
• Computer Science
• Ocean Data Management
• +3 more

Ethan Gold serves as a Lecturer in the Department of Electrical Engineering and Computing at the United States Coast Guard Academy, joining the Cyber Systems program in 2019. His professional background integrates 9 years of oceanographic fieldwork (including 50+ research cruises aboard USCGC Healy) with 8 years of corporate IT security experience. His educational qualifications include: M.S. in Computer Science, Columbia University (2001) B.A. in Computer Science and Math Correlate, Vassar College (1997) Mr. Gold's research spans Cyber Systems , Ocean Data Management , and Robotics , with emphasis on data visualization for marine science. His interdisciplinary work bridges field oceanography and cybersecurity, leveraging extensive hands-on experience from research vessels to corporate environments. Early robotics publications evolved toward specialized ocean data systems, reflecting practical problem-solving in extreme operational settings. His publication history shows a clear trajectory from robotics research (2000-2001) to marine data management innovations (2014-2019), demonstrating consistent focus on data-intensive systems for challenging environments. Key themes include real-time data processing, visualization tools, and infrastructure design for scientific fieldwork. Mr. Gold has not listed any scientific awards or fellowships. He serves as Capstone Advisor for senior projects while teaching core courses including Introduction to Computing, Operating Systems, and Software Engineering. His industry and field experience provides practical context for student projects in cybersecurity and data systems. As part of the Cyber Systems faculty, Mr. Gold contributes oceanographic data management expertise to the academic program, connecting classroom instruction with real-world maritime operational challenges through his unique blend of seagoing and IT security experience.