Berta María Guijarro Berdiñas is a Researcher in the Department of Computer Science and Artificial Intelligence at the University of A Coruña , Spain. She is affiliated with the Laboratory for Research and Development in Artificial Intelligence and teaches courses like Machine Learning , Development of Intelligent Systems , and Programming at both undergraduate and postgraduate levels. Research Focus: Her work lies at the intersection of Artificial Intelligence , Machine Learning , and Knowledge-Based Systems . Key contributions include frugal learning (limited data), anomaly explanation , and distributed learning for edge devices. She applies these to areas like health informatics , forest fire management , and human-robot interaction . Recent Publications span explainable AI , anomaly detection , multi-agent systems , and low-power machine learning . Her articles appear in top venues like Expert Systems with Applications and IEEE Transactions on Neural Networks and Learning Systems . Grants & Projects include EU-funded initiatives, Spanish Ministry of Science grants, and regional collaborations. She focuses on AI for healthcare , smart systems , and distributed learning .
Brennan Bean is an Assistant Professor in the Mathematics and Statistics Department at Utah State University's College of Arts & Sciences. His work focuses on geospatial modeling, statistical methods for extreme weather analysis, and machine learning applications in structural and environmental engineering. Recent publications highlight expertise in snow load prediction, Bayesian entropy, and interdisciplinary data science. Notable contributions include optimizing design methods for insulated concrete wall panels and addressing deployment challenges for ML models in engineering contexts. Research trends span geospatial data integration, climate change impact assessments, and educational interventions in STEM. Key subfields include ground snow load mapping, extreme value statistics, climate downscaling, and high-dimensional ecological modeling.
Professor Mark Handley is a Professor of Networked Systems in the Department of Computer Science at University College London. His research focuses on network architecture, protocols, and systems with a particular emphasis on low-latency networking, datacenter networks, and network security. Education: Doctor of Philosophy, University College London (1997) Bachelor of Science (Honours), University College London (1988) Professor Handley's research spans multiple areas of computer networking with a focus on practical, deployable solutions. His work addresses fundamental challenges in network architecture, including low-latency routing, congestion control, network security, and datacenter networking. He has made significant contributions to Multipath TCP, congestion control algorithms, and network security protocols. His research often bridges theoretical foundations with practical implementation, ensuring real-world applicability of his innovations. His recent publications demonstrate a continued focus on cutting-edge networking challenges, particularly in low-latency routing, datacenter networks, and network security. The trend shows increasing attention to space-based networking, in-switch processing, and novel approaches to congestion control. His work consistently addresses the fundamental tension between theoretical network design and practical deployment constraints. Scientific Awards: IEEE Internet Award (2012) Usenix NSDI Best Paper Award (2011) ACM SIGCOMM Test of Time Award (2011) Roger Needham Award (2007) Professor Handley has served on numerous program committees including ACM SIGCOMM and Usenix NSDI. His work has influenced both academic research and industry standards, with contributions to IETF RFCs including Multipath TCP and TCP encryption. He has mentored numerous students and researchers who have gone on to make significant contributions in the networking field. His research group at UCL focuses on next-generation network architectures, with particular expertise in low-latency routing, datacenter networks, and network security. The group maintains strong collaborations with industry partners to ensure practical relevance of their research.
Nick Virgilio is a Full Professor in the Department of Chemical Engineering at Polytechnique Montréal . His research focuses on soft matter interfaces, polymer blends, and advanced hydrogel systems for biomedical and catalytic applications. Director, Research Laboratory on Surfaces, Interfaces and Soft Matter Member, Research Center for High-Performance Polymer and Composite Systems (CREPEC) Research interests include interfacial phenomena in multiphase systems, self-assembly of soft materials, nanoparticle-hydrogel composites, Pickering emulsions, and polymer microstructure engineering. Scientific awards include the 2010 Canadian Macromolecular Science Thesis Prize and the 2004 Polytechnique Montréal Master's Thesis Award. Recent publications highlight his work in macroporous hydrogels for cancer cell capture, nanoparticle synthesis in soft matrices, and interfacial control of polymer blends. His studies frequently appear in high-impact journals like ACS Applied Materials & Interfaces , Green Chemistry , and Macromolecules . Students under his supervision have explored topics from biofilm mechanics to lunar environment polymer systems across 4 PhD and 6 Master’s theses completed or ongoing.
Prof. Dr.-Ing. Holger Blume serves as Vice President for Research and Transfer at Leibniz University Hannover while maintaining his academic position as Professor in the Architectures and Systems Section within the Faculty of Electrical Engineering and Computer Science. He holds multiple leadership positions including Chairperson of the Research Commission and Central Ethics Committee, Executive Board member of eNIFE (Leibniz Research Initiative for Neurosciences), and membership in both the Laboratory of Nano and Quantum Engineering and L3S Research Centre. His research interests span computer architecture, hardware design, signal processing, AI accelerators, hearing aid technology, and biomedical engineering. His work bridges theoretical computer science with practical applications in automotive systems, medical devices, and quantum engineering. Professor Blume's research demonstrates strong interdisciplinary connections between electrical engineering, computer science, and biomedical applications, with particular emphasis on hardware-oriented solutions for real-world problems. Analysis of his recent publications (2023-2025) reveals a strong focus on hardware acceleration for AI and signal processing applications, particularly in automotive radar/LiDAR systems and hearing aid technology. His work shows consistent innovation in RISC-V processor design, specialized hardware for mathematical functions, and biomedical applications of engineering principles. The research demonstrates a clear trajectory toward energy-efficient, specialized computing architectures for specific application domains. As Vice President for Research and Transfer, Professor Blume oversees significant research initiatives at Leibniz University Hannover, which hosts multiple Clusters of Excellence including PhoenixD (Photonics, Optics, and Engineering), QuantumFrontiers, and Hearing4all. The university participates in numerous collaborative research centers and junior research groups funded by DFG, BMBF, and EU programs. Professor Blume is actively involved in multiple research facilities including the Laboratory of Nano and Quantum Engineering and the L3S Research Centre. His work connects with Leibniz University's research focuses on optical technologies, quantum optics and gravitational physics, and biomedical research and technology. His leadership positions indicate strong involvement in shaping the research strategy and ethical framework of the university's scientific endeavors.
Sinead O'Keeffe is a Research Fellow at the University of Limerick in the Faculty of Science and Engineering , specifically within the Department of Electronic and Computer Engineering . Her research bridges the technical domain of optical fiber sensor development with critical applications in radiation therapy and sports medicine. Primary Research Themes Medical radiation dosimetry using optical fiber sensors Brachytherapy dose monitoring systems Sports injury prevention in Gaelic football and running Mental health literacy in rural farming communities Key Technical Contributions Development of scintillation-based dosimeters Characterization of perfluorinated polymer fibers 3D printed sensor systems for clinical and rehabilitation applications Interdisciplinary Applications Prostate cancer radiotherapy dose measurement Mental health intervention programs for athletes Work-family conflict analysis in Irish farming Email: sinead.okeeffe@ul.ie
Jürgen Hesser is a Professor at the Mannheim Medical Faculty , Heidelberg University, specializing in Experimental Radiotherapy and Medical Imaging . His research focuses on solving inverse problems in imaging, particularly for CT reconstruction , brachytherapy planning , and low-dose imaging . Current affiliations: Clinic for Radiotherapy and Radiooncology, Mannheim University Hospital Collaborative ties: Interdisciplinary Center for Scientific Computing (IWR) and Center for Bioinformatics (ZITI) at Heidelberg University Research interests center on anisotropic total variation techniques for medical and industrial applications, including MR-guided interventions and real-time radiation therapy . His work has led to a 1000x speed improvement in brachytherapy planning algorithms. Recent publications highlight expertise in image reconstruction (CT/X-ray), noise optimization , machine learning for cancer classification, and big data management solutions. His methods are applied to both clinical and industrial imaging challenges. Additional contributions include scientific data infrastructure development and variance stabilization techniques for medical sensors. The research group maintains strong interdisciplinary links with Physics, Mathematics, and Computer Science faculties.
Eugene Y. Vasserman is an Associate Professor in the Department of Computer Science at Kansas State University's College of Engineering, where he also serves as Director of the Center for Cybersecurity and Trustworthy Systems. His office is located in 2171 Engineering Hall, and he holds office hours on Tuesdays from 2:30 pm to 4:00 pm and Wednesdays from 11:30 am to 1:00 pm during the Fall 2025 semester. Dr. Vasserman's research spans multiple critical areas of cybersecurity including network and distributed system security, privacy and anonymity, censorship resistance, operating system security, medical and IoT security, usable security, and applied cryptography. His work bridges theoretical security concepts with practical implementations, addressing real-world challenges in diverse domains from medical systems to blockchain technologies. His recent publications reveal a research trajectory that has evolved from foundational network security work to increasingly interdisciplinary research intersecting with artificial intelligence, medical systems, and cybersecurity education. The 15 most recent publications demonstrate his ongoing commitment to both theoretical advances and practical security solutions across multiple domains. Outstanding short paper award for 'Hypersparse Traffic Matrix Construction using GraphBLAS on a DPU' at IEEE HPEC 2023 Best graduate student poster award for 'Empowering pre-service teachers to utilize programming in the classroom' at ASEE Midwest Conference 2013 Dr. Vasserman has mentored numerous graduate students through the Systems and Network Security (SyNeSec) Lab, with alumni now working at organizations including Paycom, Corelight, Sandia National Labs, Microsoft, and Cerner. He teaches multiple cybersecurity courses each semester including CIS 525: Introduction to Network Programming, CIS 755: Systems Security, and CIS 351: Cyber Defense Basics, demonstrating his long-term commitment to cybersecurity education since at least 2010. As Director of the Center for Cybersecurity and Trustworthy Systems, he leads initiatives that address critical security challenges across multiple domains, fostering collaboration between researchers, students, and industry partners to develop trustworthy systems for the future.
Niclas Jansson is a researcher at the PDC Center for High Performance Computing at KTH Royal Institute of Technology. He holds an M.S. in Computer Science (2008) and a Ph.D. in Numerical Analysis (2013) from KTH. His career spans roles such as postdoctoral researcher at RIKEN Advanced Institute for Computational Science (2013-2016) and visiting scientist at RIKEN (2018-2021), where he contributed to the Japanese exascale program Flagship 2020. A core focus of his research involves extreme-scale computing and numerical method development. He is a key developer of RIKEN's multiphysics framework CUBE , the HPC branch of FEniCS , and the spectral element flow solver Neko . His work is currently supported by a Swedish Research Council Starting Grant aimed at enhancing high-order spectral element methods for exascale fluid simulations. Niclas has published extensively on topics such as GPU acceleration , adaptive finite element methods , in situ visualization , and extreme-scale turbulence modeling . He also teaches Computational Fluid Dynamics (SG2212) at KTH.
Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University, where he leads research on efficient data analysis methods. His work focuses on improving both system efficiency (e.g., high-performance data algorithms) and human efficiency (e.g., user productivity with data systems). Research Focus: Patel's group specializes in database systems, query optimization, hardware acceleration, and human-data interaction. Their interdisciplinary work spans: Transactional processing and real-time analytics Query optimization techniques Hardware-algorithm co-design Natural language interfaces for data systems Memory-efficient data processing Professional Activities: Co-founded four technology companies (Paradise, Locomatix, Quickstep, DataChat). Serves on program committees for premier conferences including SIGMOD and CIDR (as co-chair). Teaches database systems courses at CMU. Awards: Received Best Paper Award at DaMoN 2010 for work on cluster efficiency.
Dr. Lipeng Wan is a tenure-track Assistant Professor of Computer Science at Georgia State University (GSU), located at 25 Park Place, room 733. He holds a B.Eng. in Communication Engineering from Nanjing University of Science and Technology (2008), an M.Eng. in Information and Communication Engineering from Southeast University (2011), and a Ph.D. in Computer Science from the University of Tennessee, Knoxville (2016). Prior to joining GSU, he served as a Computer Scientist at Oak Ridge National Laboratory (ORNL), first as a postdoctoral researcher (2016–2018) and later as a full-time research staff member (2018–202?). His research focuses on big data management and analytics , high-performance and data-intensive computing , and resilience and performance optimization for distributed systems . Key interests include scientific data workflows, I/O innovations for exascale systems, and error-controlled data compression frameworks like MGARD and HPDR. Dr. Wan’s recent work emphasizes adaptive data transmission (e.g., JANUS), load balancing in cloud environments (SciLance), and optimizing file access patterns on HPC systems. His publications address challenges in exascale computing, including I/O performance, geographically distributed data management, and feature-preserving compression for climate simulations. He leads research at GSU in collaboration with national labs like ORNL, focusing on advancing scalable data management techniques for high-performance computing applications.
Tommaso Ciarli is a Senior Research Fellow at the Science Policy Research Unit (SPRU) within the University of Sussex Business School. His research focuses on technological change, economic development, and the impact of innovation on employment and inequality. He holds PhDs from the University of Birmingham and the University of Ferrara, and has held academic positions at institutions including the Max Planck Institute for Economics and the University of Bologna. Ciarli has led multiple funded projects addressing structural change, SDG alignment, and conflict economics. He is currently engaged in initiatives like STRINGS (Sustainable Development Goals research steering) and TRansit (modelling economic transition risks). His work bridges policy, science, and technology to address global challenges. Key research themes include: economic structural change, science trajectory analysis, conflict's economic impacts, environmental sustainability, and innovation's societal role. He has extensive experience with agent-based modeling and macroeconomic frameworks. Notable projects include assessing STI metrics in African nations, modeling transition risks, and analyzing conflict effects on private economic activity. Ciarli has advised governments and international organizations like UNIDO and the Economic Commission for Latin America. Recent work emphasizes the interplay between technological dynamics and labor markets, particularly automation's regional impacts. He collaborates globally on projects funded by ESRC, GCRF, DFID, and the European Union. His contributions span policy design, empirical analysis, and theoretical frameworks linking innovation to inclusive growth. Key grants include the GCRF-funded STRINGS project ($\approx$3M), the Rebuilding Macroeconomics initiative, and DFID-supported capacity-building programs in East Africa. Awards include prestigious fellowships and research leadership roles. His interdisciplinary approach integrates economics, complexity science, and policy analysis to address 21st-century challenges like sustainable transitions and equitable development.
Alain Hecq is a Full Professor in the department of QE Econometrics at the School of Business and Economics, Maastricht University. His research focuses on econometric methodologies, particularly in time series analysis, noncausal models, and financial econometrics. He has contributed significantly to the understanding of volatility dynamics, cryptocurrency markets, and inflation targeting regimes. His work often addresses policy-relevant questions in macroeconomics and financial markets. Key research interests include mixed causal-noncausal autoregressive models, volatility modeling with MARMA-GARCH frameworks, and the application of these techniques to real-world phenomena such as oil price bubbles and cryptocurrency volatility. He has also explored the credibility of central banking policies during crises, such as the Brazilian inflation-targeting regime during the pandemic. His recent work emphasizes methodological advancements in high-dimensional time series analysis, including spectral estimation, hierarchical regularizers for mixed-frequency data, and reduced-rank matrix autoregressive models. These contributions reflect a blend of theoretical rigor and practical applicability in addressing complex economic and financial problems. While no formal awards are listed, his extensive publication record and focus on cutting-edge econometric techniques underscore his scholarly impact. Advising and grant activities are not detailed in the provided information, but his research demonstrates sustained engagement with both academic and policy-oriented audiences.
Zhen Xie is an Assistant Professor in the Department of Computer Science at Binghamton University (SUNY), serving as Director of the Parallel Computing and Intelligent System (PCIS) Lab. He holds a PhD from the Chinese Academy of Sciences and a BA from Wuhan University of Technology. His research focuses on high-performance computing (HPC), machine learning, and their intersections, particularly optimizing performance for HPC and AI/DL applications across heterogeneous architectures. Research Highlights: Dr. Xie’s work emphasizes system-level performance optimization for ML and HPC, including GPU acceleration, memory optimization, and AI accelerator selection. His team has won the ACM Gordon Bell Special Prize (2022) for their GenSLMs project predicting SARS-CoV-2 evolution. Recent grants include a 2024 gift from OpenAI for AI testbed initiatives. Awards: ACM Gordon Bell Special Prize (2022), Impact Argonne Awards (2023) Lab: PCIS Lab explores middleware for parallel computing, targeting scientific simulations and big data analytics. Collaborations include Argonne National Lab and Lawrence Berkeley National Lab. Teaching: Teaches Distributed Systems (CS 457/557) and oversees independent studies. Previously trained researchers at Argonne’s ATPESC program. Grants & Collaborations: Subcontract with Lawrence Berkeley Lab (HEVI-LOAD), Argonne testbed expeditions, and OpenAI-funded projects. Active in DOE labs like Summit and Aurora supercomputers.
Professor Serge Guillas is a faculty member at the University College London (UCL) Department of Statistical Science. His research focuses on functional data analysis, uncertainty quantification, environmental statistics, and emulation of complex computer models. He leads the NERC consortium on Uncertainty Quantification of Natural Hazards and has held roles such as Work Package Leader for quantifying uncertainties in natural hazard models. His work integrates statistical methods with geophysical and climate modeling, emphasizing tsunami risk analysis, climate dynamics, and ozone exposure studies. Education: PhD in Statistics from Paris 6 University (2001), followed by roles at the University of Chicago, Georgia Institute of Technology, and UCL. Current roles include teaching STAT1006 and STAT7001 courses. Research interests span functional regression, spatial data analysis, and probabilistic hazard modeling. Recent work explores machine learning-driven climate models, ozone exposure health impacts, and real-time data assimilation software (ParticleDA.jl). He collaborates globally, including with institutions in Georgia, Italy, and Indonesia, to advance tsunami modeling and disaster risk reduction. Key awards include ESRC-DFID-NERC funding, MAPS Faculty Postgraduate Research Prize (for student Ah Yeon Park), and leadership roles in SIAM’s Uncertainty Quantification group. Active in mentoring PhD students and securing interdisciplinary grants. Labs/Teams: Involved with the UCL Institute for Risk & Disaster Reduction and leads statistical emulation efforts in climate and geophysical modeling. Collaborates on fusion reactor design (ExCALIBUR project) and global temperature uncertainty quantification (GETQUOCS initiative).
