Gloria Milena Fernandez Nieto is a Research Fellow in the Faculty of Information Technology at Monash University. She holds a master's in Systems and Computer Engineering from Universidad de Los Andes (Colombia) and a PhD in Learning Analytics from the University of Technology Sydney. Her research focuses on Teamwork Analytics, learning feedback mechanisms, and educational technology, particularly in designing tools to support teacher and student reflection. She contributed to the UN Sustainable Development Goals through her work in education technology. Her collaborations span institutions globally, including the Connected Intelligence Centre. Notable outputs include co-designing knowledge management tools for educators and developing learning analytics dashboards. She received the Best Paper Award (2020) for collaborative research. Her articles emphasize multimodal learning analytics, dashboard design, and data storytelling. Projects like the 'Data Storytelling Editor' and 'Evidence-based Multimodal Learning Analytics' highlight her focus on bridging educational theory and practical tool development.
Magdalini Eirinaki is a Professor and Academic Program Coordinator for the MS in Artificial Intelligence at San José State University's Charles W. Davidson College of Engineering. With a career spanning two decades, her work bridges recommender systems , machine learning , and smart city applications . PhD in Computer Science (2006), Athens University of Economics and Business MSc in Advanced Computing (2000), Imperial College London BSc in Computer Science (1998), University of Piraeus Her research focuses on machine learning and recommender systems with extensions to generative AI , privacy-sensitive algorithms , and social network analysis . Recent publications explore federated learning , multi-resolution diffusion models , and autonomous network defense using reinforcement learning. Current projects include NSF-funded CollaborAIte (2024) EU Horizon/Marie Sklodowska-Curie's MUSIT (2024) IBM SkillsBuild Cloud Credits for Sustainability (2024) She has received multiple teaching and mentorship awards including: Newnan Brothers Award (2019) Applied Materials Award (2017) 5-time SJSU Distinguished Faculty Mentor Award Dr. Eirinaki advises students in AI , ML , and smart city projects, with recent graduates presenting at IEEE CAI (2025) and CSU Conference (2025).
Professor Jacek Banasiak holds a prestigious DST/NRF SARChI Chair in Mathematical Models and Methods in Biosciences and Bioengineering at the University of Pretoria, Department of Mathematics and Applied Mathematics. He also maintains strong academic ties with Lodz University of Technology in Poland where he serves as a research professor in the Department of Mathematical Modeling. His career spans several decades with extensive contributions to mathematical modeling, particularly in population dynamics, fragmentation-coagulation processes, and epidemiological modeling. Professor Banasiak's research interests center on mathematical modeling of biological and physical processes, with particular expertise in singular perturbation theory, semigroup theory, and transport equations on networks. His work bridges theoretical mathematics with practical applications in epidemiology, ecology, and population biology. He has developed sophisticated mathematical frameworks for understanding fragmentation-coagulation phenomena, malaria transmission dynamics, and savanna ecosystem modeling. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on climate-based malaria models, multiscale epidemiological systems, and mathematical analysis of growth-fragmentation equations. His publications appear in top-tier journals across mathematical analysis, epidemiology, and mathematical biology fields, demonstrating the interdisciplinary nature of his work. DST/NRF SARChI Chair in Mathematical Models and Methods in Biosciences and Bioengineering Author of numerous influential publications spanning from 1984 to 2025 Editor of special issues and author of several books including 'Introduction to Mathematical Methods in Population Theory' (2025) Professor Banasiak has supervised over 15 PhD students, many of whom have gone on to successful academic careers. His mentoring spans topics including fragmentation-coagulation with transport effects, telegraph systems on networks, and mathematical modeling of malaria transmission. His research has attracted significant funding, particularly through his SARChI Chair position which supports advanced mathematical research in biosciences and bioengineering.
Hannah Spitzer is a Research Group Leader at the Institute for Stroke and Dementia Research (ISD) at Ludwig Maximilian University of Munich and an associated Research Group Leader at Helmholtz Munich's Computational Health Center. She leads the Spitzer Lab, focusing on computational analysis of multimodal brain datasets to advance understanding of neurovascular and neurodegenerative diseases. Her educational background includes: PhD in Computer Science from Heinrich-Heine University Düsseldorf and Research Center Jülich (2015-2020) Master's in Computer Science from RWTH Aachen (2013-2015) Bachelor's in Computer Science from RWTH Aachen (2009-2013) Dr. Spitzer's research integrates computational biology and machine learning to decode brain complexity, with emphasis on spatial omics analysis , interpretable image representation learning , and cross-modal data integration . Her group develops tools like squidpy and campa for spatial omics while applying graph neural networks to epilepsy lesion detection through the international MELD project, prioritizing biological interpretability in AI models. Recent publications reveal strong trends in leveraging graph neural networks for subtle brain lesion detection and creating computational frameworks for spatial omics integration. Her work consistently bridges advanced machine learning with clinical neuroscience to uncover disease mechanisms in neurodegeneration and vascular disorders. Dr. Spitzer actively mentors students including current PhD candidate Beatrice Guastella and alumni Deniz Fettahoglu (MSc) and Katia Berr (PhD). Her lab operates through major collaborations including the MELD epilepsy consortium and Helmholtz Imaging Project, with funding supporting computational pipeline development for small-vessel disease prediction and multimodal brain atlasing. The Spitzer Lab comprises postdoc Wasim Aftab and PhD student Beatrice Guastella, working on computational pipelines that integrate histology, spatial omics, and neuroimaging data to decode brain disease mechanisms through interpretable AI approaches.
Xiusi Chen is a Postdoctoral Research Fellow in the Blender Lab at the University of Illinois Urbana-Champaign (UIUC), working under Prof. Heng Ji. His research focuses on improving reasoning, alignment, and decision-making capabilities of Large Language Models (LLMs). Previously, he completed his Ph.D. in Computer Science at UCLA under Prof. Wei Wang, and earned M.S. and B.S. degrees in Computer Science from Peking University under Prof. Jun Gao. His educational background includes: Ph.D. in Computer Science, University of California, Los Angeles (UCLA), advised by Prof. Wei Wang M.S. in Computer Science, Peking University, advised by Prof. Jun Gao B.S. in Computer Science, Peking University, advised by Prof. Jun Gao Dr. Chen's research program targets three interconnected areas: advancing Large Language Models (particularly in low-resource reasoning and alignment), developing NLP applications for AI in Science and recommendation systems, and modeling complex decision-making processes in sports domains. His work bridges theoretical foundations with practical implementations, resulting in numerous publications in top-tier conferences including ACL, ICML, ICLR, and KDD. Analysis of his recent publications reveals a strong focus on making LLMs more efficient, reliable, and capable of complex reasoning tasks across diverse domains. His significant academic contributions include: 2023 Best Poster Award (Honorable Mention) at SDM 2023 2023 SIAM Student Travel Award 2021 and 2022 SIGIR Student Travel Grants from ACM SIGIR Multiple academic scholarships from Peking University Co-creation of the widely adopted Amazon Reviews'23 dataset (500k+ HuggingFace downloads) Dr. Chen actively serves the research community as Workshop Organizer for KDD 2025, Program Committee member for major conferences (KDD, WSDM, ICML, NeurIPS, ICLR, AAAI), and journal reviewer. He maintains a strong commitment to mentoring, offering dedicated time for students (especially from underrepresented groups) to discuss research and career development. Starting Fall 2025, he will be seeking academic positions to continue his research on language agents and decision-making systems. Currently based in the Siebel Center for Computer Science, Dr. Chen collaborates with the Blender Lab team on advancing NLP and LLM capabilities. His work has practical impact through widely adopted resources like the Amazon Reviews'23 dataset and theoretical contributions through his publications on reasoning frameworks and alignment techniques.
Prof. Dr.-Ing. Katharina Schmitz serves as Institute Director and Vice Dean at the Institute for Fluid Power Drives and Systems, RWTH Aachen University. Her leadership within the Production Technology Cluster and extensive contributions to fluid power engineering establish her as a leading authority in mechanical engineering research and education. Her research spans fluid power systems, hydraulic component design, tribology, and physics-informed machine learning applications. She pioneers sustainable propulsion solutions through bio-hybrid fuels research while addressing fundamental challenges in polymer material behavior under hydraulic stresses. Current work focuses on carbon-neutral heavy-duty transportation, physics-based neural networks for lubrication modeling, and advanced control systems for electro-hydraulic actuators. Analysis of her 15 most recent publications reveals a dominant trend toward integrating physics-based modeling with deep learning to solve complex engineering problems. Her team consistently develops novel frameworks for cavitation prediction, flow rate determination, and material compatibility assessment - significantly advancing fluid power system reliability, efficiency, and digitalization. Scientific recognition includes: GfT Förderpreis 2023 for experimental and simulative investigation of partially hydrostatic relieved contacts in variable speed axial piston machines As head of the Institute for Fluid Power Drives and Systems, she leads cutting-edge research in sustainable fluid power technologies. The institute maintains strong industry partnerships while driving innovation in hydraulic component design, digital twins for condition monitoring, and next-generation propulsion systems through its position within RWTH Aachen's Production Technology Cluster.
Anastasia Volovich is a Professor of Physics at Brown University, specializing in theoretical physics with a focus on quantum field theory, string theory, general relativity, and their mathematical foundations. Her research explores hidden mathematical structures in scattering amplitudes to advance understanding of gauge and gravity theories. B.A. and M.A. in Physics, Moscow State University (1999) Ph.D. in Theoretical Physics, Harvard University (2002) Her work on scattering amplitudes involves cluster algebras, polylogarithms, and celestial holography. Recent publications address symbol alphabets, Landau singularities, and Yangian invariants in high-energy physics. Notable honors include: APS Fellowship (2019) IBM Einstein Fellowship (2017) Blavatnik National Finalist (2016-2018) Simons Investigator Award (2015) DOE Early Career Award (2011) NSF CAREER Award (2007) White House PECASE (2008) She has held editorial roles at Letters in Mathematical Physics and Physics Letters B , and her research is funded by the Department of Energy and Simons Foundation.
Ilie Sarpe is a postdoctoral researcher in the Division of Theoretical Computer Science at KTH Royal Institute of Technology, mentored by Prof. Aristides Gionis. He is an active member of both VandinLab and AIDA Lab, focusing on developing rigorous algorithms for temporal network analysis. His educational background includes: PhD in Computer Engineering from the University of Padova (2019-2023), with thesis on 'Efficient and Rigorous Techniques for the Analysis of Large Temporal Networks' Master's Degree in Computer Engineering (summa cum laude) from the University of Padova (2017-2019) Bachelor's Degree in Computer Engineering from the University of Padova (2014-2017) Sarpe's research centers on the design of scalable algorithms for data-mining problems, particularly in graph-mining and clustering. He specializes in probabilistic algorithms with rigorous theoretical guarantees, with a strong focus on temporal networks. His work integrates tools from sampling theory, probability, concentration inequalities, and statistical learning theory to develop efficient solutions for complex network analysis problems. His publications demonstrate a consistent focus on temporal network analysis, with recent work accepted at top venues including KDD 2024, WWW 2022, CIKM 2021, and SIAM SDM 2021. The research trajectory shows increasing sophistication in handling temporal motifs, dense subnetwork discovery, and centrality measures in evolving networks. His scientific recognition includes: SoBigData TNA Fellowship (2022) 3 years Ph.D. Fellowship (2019) Award for Scientific Degrees (2017) Two 'Mille e una lode' awards (2016, 2017) Sarpe actively supervises master's thesis projects in data mining at KTH and has secured research funding through the SoBigData TNA Fellowship. His research group affiliation with VandinLab and AIDA Lab provides collaborative opportunities across multiple institutions. He maintains active laboratory work focused on developing practical implementations of his theoretical algorithms, as evidenced by his GitHub repositories containing C++ implementations of his published methods.
Carlos Platero Dueñas is a Full Professor at the Department of Electrical, Electronic and Automatic Engineering and Applied Physics at the Universidad Politécnica de Madrid (UPM), where he has served for 31 years. He leads the research group Tecnologías para Ciencias de la Salud since 2015 and contributes to interdisciplinary research at the intersection of biomedical engineering, neuroscience, and artificial intelligence. Department: Electrical, Electronic and Automatic Engineering and Applied Physics Research Group: Tecnologías para Ciencias de la Salud (Health Science Technologies) Teaching: 34 years of academic experience, including 128 final projects supervised His research focuses on applying computational methods to neurodegenerative diseases , particularly Alzheimer's and Parkinson's, through neuroimaging analysis, predictive modeling, and hippocampal segmentation. Recent work includes AT(N) profiles for dementia prediction and machine learning techniques for clinical data modeling. The 15 most recent publications reveal a strong emphasis on Alzheimer's disease progression , hippocampal segmentation , and predictive analytics using neuroimaging and clinical markers. Key methodologies involve graph cuts algorithms, longitudinal modeling, and label fusion techniques applied to MRI and CT scans. Teaching contributions include: 128 final projects supervised (undergraduate and master's) 2 doctoral theses directed Active participation in university governance through the School Council and Researcher Staff Committee
Dr. Arie Levit is a Senior Lecturer (tenure track) in the Department of Theoretical Mathematics at Tel Aviv University's School of Mathematics, a position he has held since 2021. Previously, he served as a Gibbs Assistant Professor at Yale University from 2017. His academic career centers on pure mathematics with emphasis on structural properties of discrete groups and dynamical systems. His educational background includes: B.A in Mathematics from the Hebrew University of Jerusalem (2004) M.A in Mathematics from the Hebrew University of Jerusalem (2012) Ph.D. in Mathematics from the Weizmann Institute of Science (2017) under Prof. Tsachik Gelander Levit's research spans discrete groups, geometric and analytic group theory, and ergodic theory, with significant contributions to lattice theory, invariant random subgroups, character rigidity, and group stability. His work integrates algebraic, geometric, and probabilistic frameworks to solve fundamental problems in classification and rigidity of group actions, particularly in non-Archimedean and hyperbolic settings. Analysis of his 14 publications (2014-2024) reveals evolving focus from foundational lattice theory toward contemporary stability phenomena and character theory, with 60% of recent work (2022-2024) addressing permutation stability, Hilbert-Schmidt representations, and ergodic properties of group actions. Key methodological threads include the application of ergodic theory to group-theoretic classification and the development of analytical tools for stability problems. His scholarly recognition includes: Klein Prize (2017) ISF-BSF research grant (2020) As principal investigator of the ISF-BSF grant, Levit leads research on group stability and ergodic theory. His extensive collaborations with Gelander, Lubotzky, and Lazarovich demonstrate active mentorship within the global mathematics community. His work is conducted within Tel Aviv University's Theoretical Mathematics department, which maintains strong international partnerships in geometric group theory and dynamics.
Susana Araújo serves as Associate Researcher at CICPSI Research Unit within the University of Lisbon's Faculty of Psychology. She coordinates the Cognition in Context Group (CO2) since 2023 and holds Senior Investigator status in the Memory & Language team, specializing in neurocognitive mechanisms of reading acquisition and disorders using EEG-ERPs and eye-tracking methodologies. Education: PhD in Experimental and Cognitive Psychology (University of Algarve, Portugal; Donders Institute, Netherlands) Postdoctoral Fellowships (2012-2017) Her research critically examines literacy's impact on cognitive systems through investigations of visual word recognition , reading disorders , and rapid automatized naming . Current work explores how handwriting training influences graph recognition and identifies neurophysiological markers of dyslexia using multimodal assessment tools. Publication trends reveal sustained focus on cross-population comparisons (literate/illiterate adults) and meta-analytic approaches to naming-speed deficits. Her work bridges cognitive neuroscience with educational interventions, particularly in developmental dyslexia contexts. Scientific Recognition: FCT Postdoctoral Fellowship (2012-2017) FCT-Investigator Starting Grant (2017-2021) with highest Career Development score FCT CEEC-Ind Individual Contract She directs multiple FCT-funded projects including LEMON (2020-2024; €158,684) as Principal Investigator and VOrtEx (2018-2021; €183,590) as Co-PI. Her grant portfolio spans developmental dyslexia characterization, orthographic processing, and statistical learning deficits across eight funded projects since 2007. As CO2 Group Coordinator, she integrates multiple research teams under CICPSI while maintaining collaboration with the Max Planck Institute for Psycholinguistics where she served as Visiting Researcher in 2015.
Anastasios Zafeiropoulos serves as Assistant Professor at Harokopio University of Athens, specializing in Spatial Data Management and Analysis within the Postgraduate Studies Program for “Applied Geography and Spatial Management” (Direction C: Geoinformatics). His academic role encompasses teaching “Spatial Databases” and advancing research at the intersection of geospatial technologies and distributed computing systems. His research program focuses on Spatial Databases, Internet of Things (IoT), Cloud/Edge Computing, and 6G Network Orchestration, with significant extensions into Knowledge Graph applications for Sustainable Development Goals (SDGs) and socio-emotional learning in education. Key innovations include the EduCardia methodology for student competency assessment and frameworks for climate vulnerability analysis using knowledge graphs. Analysis of his 2024-2025 publications reveals three dominant thrusts: (1) AI-driven orchestration of 6G services across the computing continuum using reinforcement learning; (2) Knowledge Graph applications for SDG interlinkage analysis and materials science; (3) EU-funded IoT/Edge Computing project ecosystems. His work consistently bridges theoretical networking concepts with practical sustainability and educational applications. Dr. Zafeiropoulos actively contributes to EU-funded initiatives in IoT and Edge Computing standardization, particularly through AIOTI WG Standardisation. His project portfolio includes NEPHELE multi-cloud ecosystem development and O-RAN slice admission control research, demonstrating strong industry-academia collaboration in next-generation networking. He leads the development of innovative tools including Palindrome.js for distributed system visualization and the EmoSocio open-access emotional intelligence model, reflecting his commitment to translating research into practical educational and environmental solutions.
Faiz Hamid serves as an Associate Professor in the Department of Management Sciences at Indian Institute of Technology Kanpur. With a Ph.D. in Decision Sciences & Information Systems from IIM Lucknow (2012) and a B.Tech. in Computer Science and Engineering from Institute of Engineering & Management (2007), he brings strong technical and analytical expertise to his academic position. His research interests span Operations Research, Combinatorial Optimization, Network Optimization, and Data Science, with particular focus on transportation systems, pandemic response modeling, and revenue management applications. Dr. Hamid's scholarly work demonstrates consistent publication in high-impact journals including European Journal of Operational Research, Omega, Transportation Research, and IEEE Transactions on Signal Processing. His recent publications reveal a strong trend toward applying optimization techniques to real-world problems, particularly addressing pandemic-related challenges in transportation systems and developing sophisticated mathematical models for railway operations. His 2024 edited volume 'Optimization Essentials: Theory, Tools, and Applications' demonstrates his leadership in the field. Ph.D. Thesis recognized as runner-up for 2014 Best Dissertation Award by The INFORMS Technical Section in Telecommunications Best Paper Award at COSMAR 2010 Doctoral Conference, Indian Institute of Science, Bangalore Professor Dipak C Jain Best Paper Award at IMR Doctoral Conference 2010, IIM Bangalore Silver Medal, National Mathematics Olympiad 2002 Dr. Hamid has advised numerous students and collaborated extensively with researchers globally, particularly in transportation optimization problems. His professional journey includes industry experience as Associate Functional Architect at JDA Software and post-doctoral research at Telecom SudParis, France, before joining IIT Kanpur's faculty.
Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.
Teresa Cristina de Freitas Gonçalves is an Associate Professor at the Department of Informatics, School of Sciences and Technology, University of Évora, where she has been employed since 1999. She serves as an integrated researcher at the ALGORITMI research centre and is the Director of the VISTA Lab (Video, Image, Speech and text Analysis Lab), the unit of the ALGORITMI research centre at University of Évora. Her leadership roles include Director of the Master programme in Informatics Engineering and deputy Director of both the Master programme in Artificial Intelligence and Data Science and the Doctoral program in Computer Science. She earned her PhD in Computer Science from University of Évora and a MSc degree in Informatics Engineering from New University of Lisbon. Her academic journey at University of Évora has included significant leadership positions including Head of the Computer Science Department (2011-2015), Director of the Bachelor programme in Informatics Engineering (2016-2021), and Deputy Director roles for various undergraduate and graduate programs. Dr. Gonçalves' research focuses on intelligent systems, particularly Machine Learning approaches, with substantial contributions in evolutionary algorithms, information extraction and retrieval, and supervised learning across multiple data modalities including tabular data, text (in both Portuguese and English), and images (medical and satellite). Her work bridges theoretical advances with practical applications in healthcare, remote sensing, and natural language processing. She has successfully supervised 6 doctoral theses, 19 master theses, and 3 postdocs, and currently mentors 5 doctoral and 6 master students from diverse international backgrounds including Bangladesh, Cabo Verde, Nepal, Philippines, India, Sri Lanka, China, Mongolia, and Portugal. Her publication record includes over 100 scientific articles indexed by Scopus with 640 citations and an h-index of 12, demonstrating significant international impact with 56% of her work involving international collaboration. Her recent research shows a strong trend toward applying advanced machine learning techniques to healthcare applications, information retrieval systems, and remote sensing analysis, with particular emphasis on transformer networks, learning-to-rank methodologies, and multimodal data analysis. Dr. Gonçalves has made substantial contributions to the academic community through her service as a reviewer for over 50 articles in prestigious international journals and conferences, and as chair for major international conferences including IDEAL 2023, PROPOR 2020, SKIMA 2017 and 2018, and CLEF 2016. She serves on the board of APRP (Associação Portuguesa de reconhecimento de Padrões) and as a jury member for APRP prizes for best MSc and PhD theses. Her current research portfolio includes coordination of the Horizon Europe MSCA Staff Exchange HarmonicAI project and local coordination of WP6 in the NewSpace Portugal mobilising agenda. She is also actively involved in numerous other international research initiatives including Interreg VI-B Sudoe SenforFire, PRR CANTE, La Caixa INCOME, Erasmus+ KA220-HED REDINEST, Interreg POCTEP TID4AGRO, and ATTRACT DIH projects. Previously, she led the FCT AI in the Public Administration SNS24.Scout.IA project and coordinated the FEDER R&D NIIAA project. As Director of the VISTA Lab, Dr. Gonçalves leads a dynamic research team focused on video, image, speech, and text analysis. The lab serves as the Évora hub of the ALGORITMI research centre and has established strong international collaborations. Under her leadership, the VISTA Lab has developed innovative approaches in medical image analysis, natural language processing for Portuguese, and satellite image classification, with applications spanning healthcare, environmental monitoring, and public administration.
