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.
Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.
Professor Asif Gill is Head of Discipline for Software Engineering at the School of Computer Science, University of Technology Sydney (UTS), where he was promoted to Professor of Computer Science in January 2024. He also serves as Director of the DigiSAS Research and Innovation Lab and is actively involved in the Global Big Data Technologies Centre at UTS. As a founder of both the DigiSAS Lab and the Future Generation Enterprise Architecture Community of Practice (FGEA CoP), he has established integrated teaching-research-engagement frameworks that translate academic research into practical applications while enhancing graduate employment opportunities. Professor Gill's research interests span Adaptive Enterprise Architecture , Agile Software Development , and Design Science Research & Innovation , with a particular focus on architecting large-scale data-intensive enterprise software systems. His work addresses challenges across academia, industry, government, and society, with significant contributions to AI systems architecture, digital identity management, and enterprise knowledge graphs. His applied research has resulted in numerous collaborations with organizations including the Reserve Bank of Australia, Revenue NSW, Capsifi, Data Zoo, and the NSW Department of Planning, Industry and Environment. His publication record includes 3 books and over 190 articles in major academic journals such as IEEE Transactions on Professional Communication, Information and Management, and Information Systems. His recent work demonstrates a consistent focus on cutting-edge topics in enterprise architecture, AI systems, and digital identity, with multiple publications appearing in 2024-2025. His research trajectory shows a clear evolution from foundational work in agile software development toward more sophisticated integration of AI, enterprise architecture, and data governance. Fellow of the Australian Computer Society (ACS) Fellow of DSE (ESCP Center for Design Science in Entrepreneurship) Senior Member IEEE Associate Editor, IEEE Transactions on Technology & Society Associate Editor, Springer Nature Discover Data journals Member, Data Sharing Committee, IFIP Technical Committee 8.1 Member, Standards Australia Software and Systems Engineering Committee IT-015 Professor Gill has successfully secured numerous research grants from 2019-2026, totaling significant funding for projects related to digital identity, enterprise architecture, and AI systems. His approach emphasizes industry-academia collaboration, with many projects involving direct partnerships with government agencies and industry organizations. He has supervised multiple PhD and Master's students through industry-sponsored scholarships and maintains active collaborations with researchers across multiple institutions. Leading the DigiSAS Research and Innovation Lab, Professor Gill has created an environment that bridges theoretical research with practical implementation. The lab focuses on developing frameworks and tools for adaptive enterprise architecture, with particular emphasis on AI-enabled systems, data governance, and digital identity solutions. His work on the Data Satellite Architecture represents a significant contribution to combating data pollution in federated digital ecosystems.
Jonathan Leake is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo. His research lies at the intersection of combinatorics, optimization, and theoretical computer science, with a focus on log-concave and Lorentzian polynomials and their applications in discrete and continuous settings. Assistant Professor, University of Waterloo (2022–present) Dirichlet Postdoctoral Fellow, TU Berlin (2020–2022) Postdoctoral Fellow, Institut Mittag-Leffler, Stockholm (Spring 2020) Postdoctoral Fellow, KTH, Stockholm (Fall 2019) James H. Simons Fellow, Simons Institute, UC Berkeley (Spring 2019) His research explores the deep connections between algebraic structures and combinatorial phenomena, particularly through polynomial capacity and Lorentzian polynomials. He applies these tools to problems in optimization, sampling, and representation theory. His work often involves developing new algebraic and analytic techniques to tackle longstanding conjectures and algorithmic challenges. The recent publications highlight a consistent focus on Lorentzian polynomials, capacity bounds, and their applications in combinatorics, optimization, and theoretical computer science. Key themes include matroid theory, log-concavity, sampling algorithms, volume approximation, and connections to Lie theory and representation theory. The research spans both theoretical developments and algorithmic applications, often in collaboration with leading researchers in the field. Dirichlet Postdoctoral Fellowship, TU Berlin Postdoc Fellowship in Algebraic and Enumerative Combinatorics, Institut Mittag-Leffler James H. Simons Fellowship, Simons Institute, UC Berkeley Jonathan Leake has advised or collaborated with several researchers, though formal advisees are not listed in the provided text. His work has been supported by prestigious fellowships and collaborations with institutions such as the Simons Institute and TU Berlin. He has taught courses including CO 250: Introduction to Optimization, MATH 239: Introduction to Combinatorics, and CO 739: Lorentzian Polynomials at the University of Waterloo and TU Berlin. While specific lab or research group names are not mentioned, Leake's collaborative work with researchers like Petter Brändén, Nisheeth Vishnoi, and Leonid Gurvits suggests active participation in research teams focused on algebraic combinatorics, optimization, and theoretical computer science. His publicly shared code for sampling from HCIZ densities and verifying positivity in Lie-theoretic contexts indicates an active computational research component.
Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD from Brunel University London (2015) and has extensive research experience in artificial intelligence and software engineering. His research focuses on: Artificial Intelligence and Intelligent Data Analysis Search Based Software Engineering (SBSE) Clustering algorithms and heuristic search methods Software refactoring and quality assessment Data mining applications in healthcare and education Analysis of his 15 most recent publications (2018-2022) reveals strong interdisciplinary work bridging computer science with healthcare (diabetes patient modeling, medical imaging) and education (chatbot design, algorithm visualization). His technical focus centers on clustering optimization, refactoring impact analysis, and explainable AI, with frequent use of empirical validation methods. Key collaborations include researchers like Stephen Swift, Steve Counsell, and Giuseppe Destefanis. Dr Arzoky has secured significant research funding through EPSRC grants including: AQUATIC project (EP/M024083/1): Assessing Test Suite Quality in Industrial Code FIAR-NET (EP/N011627/1): Fault Analyses in Industry and Academic Research Network His professional network includes active collaborations across computer science, healthcare informatics, and educational technology domains, with recent work extending into transformer models for healthcare SQL conversion and graph partitioning for software modularization.
Christian Tominski serves as an apl. Professor (non-tenured) at the University of Rostock, holding the außerplanmäßige Professur for Human-Data Interaction within the Institute for Visual and Analytic Computing. His academic work spans teaching in Visual Computing and Computer Science programs, with active research contributions in data visualization and visual analytics. His research focuses on multi-variate data visualization, time-series and geo-visualization, graph visualization, and coordinated multiple views. He investigates interaction techniques including interactive lenses, visual comparison, navigation, and guidance mechanisms, alongside computational aspects such as efficient algorithms and asynchronous processing for visualization systems. Recent work emphasizes task-driven approaches and analytic support for interactive exploration. Analysis of his publication trends reveals strong emphasis on visual analytics for complex data structures, particularly in process mining and multivariate graphs. His work consistently explores guidance frameworks, progressive computation models, and novel interaction paradigms for large high-resolution displays, bridging theoretical foundations with practical applications in visual data analysis. Tominski holds professional roles as a member of the Faculty Council of IEF and the System Technical Group of Computer Science Institutes at the University of Rostock. He actively participates in the Informatik-Forum Rostock (INFO.RO), contributing to the regional computer science community through collaborative initiatives and knowledge sharing.
Xia Ning is a Professor jointly appointed in the Department of Computer Science and Engineering , the Division of Medicinal Chemistry and Pharmacognosy (College of Pharmacy), and the Department of Biomedical Informatics at The Ohio State University . She also holds affiliation with the Translational Data Analytics Institute at OSU. Education: Ph.D. in Computer Science & Engineering, University of Minnesota, Twin Cities (2012) M.S. in Computer Science, University of Minnesota, Twin Cities M.S. in Statistics, University of Minnesota, Twin Cities B.S. in Computer Science, Chu Kechen Honors College, Zhejiang University, China Research Focus : The Ning Lab pioneers data-driven Artificial Intelligence, Machine Learning, and Big-Data analytics with targeted applications in drug discovery, medical informatics, health informatics, and e-commerce . Recent thrusts include generative AI for molecule design, graph neural networks for retrosynthesis, large-language models specialized for chemistry (LlaSMol) and e-commerce (eCeLLM), and reinforcement-learning frameworks for precision-medicine drug selection. The lab’s methodologies are intentionally generalizable, enabling spill-over benefits to domains such as social networks and system monitoring. Publication Trends : Over the past four years Professor Ning has released a steady stream of high-impact articles spanning retrosynthesis planning, LLM instruction tuning for scientific domains, reinforcement learning for drug discovery, and COVID-19 health-analytics . These works repeatedly integrate cutting-edge AI techniques (deep RL, graph Transformers, large-scale instruction datasets) with rigorous experimental validation in chemistry and biomedicine. Scientific Awards & Honors : Sanofi iDEA-TECH Award (2024) 10-Year Highest-Impact Award, International Conference on Data Mining (ICDM, 2020) Grants & Collaborations : Funding includes the Sanofi iDEA-TECH Award and collaborative grants with Amazon Web Services for COVID-19 knowledge graphs. Her open-source datasets (ECInstruct, SMolInstruct, CTKG) and models (G2Retro, LlaSMol, eCeLLM) are publicly released on HuggingFace and GitHub, fostering broad academic and industrial adoption. Labs & Teams : Professor Ning heads the Ning Lab at OSU, a multidisciplinary team focusing on AI/ML methodology and translational applications in health and medicine. The lab actively releases code and interactive web portals to accompany each major publication.
Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.
Michael Sirivianos is an Associate Professor at the Cyprus University of Technology (CUT), where he serves as Dean of the School of Engineering and Technology. He holds a PhD in Computer Science from Duke University (2010) and leads research in cybersecurity, disinformation detection, and social media analysis. He coordinates multiple EU-funded projects including ReCRED (Horizon 2020) and ENCASE (Marie Curie RISE), securing over €3M in research funding. Research Focus His work spans: Cybersafety : Detection of cyberbullying, hate speech, and inappropriate content targeting children Trust Systems : Device-centric authentication and blockchain applications Disinformation Analysis : Graph-based detection of fake news and state-sponsored manipulation Scalable Systems : Distributed databases and network infrastructure Achievements & Recognition Best Paper Award (2019) for work on state-sponsored disinformation Distinguished Paper (2018) for fringe web community analysis Spotlight Session recognition (2020) for child protection research Featured in NYT, Washington Post, and Wired for YouTube content analysis Leadership Co-directs the Network Systems Research Lab, serves on the Board of CYENS Centre of Excellence, and coordinates the Fact-check Cyprus Centre against Disinformation.
Xiaoyang Wang is a Senior Lecturer in the School of Computer Science and Engineering (CSE) at the University of New South Wales (UNSW). He holds a Bachelor's and Master's degree in Computer Science from Northeastern University, China, and earned his PhD from CSE UNSW. Dr. Wang's research focuses on database systems with a special emphasis on query processing and data mining on large-scale graph, spatial, and streaming data. His expertise extends to data-driven machine learning, smart contract analysis on blockchain, and FinTech with financial network analysis. His work spans Graph Processing, Graph Neural Networks, Spatial Data Processing, AI for Databases (AI4DB), Database for AI (DB4AI), and FinTech applications. His publication record shows significant contributions to the field with 7 book chapters, 56 journal articles, 61 conference papers, 7 edited conference proceedings, and 4 conference abstracts. Recent publications (2022-2025) demonstrate his strong research trajectory in advanced graph processing techniques, neural network applications, and innovative database approaches. Key themes include hierarchical contrastive learning, robust attack frameworks, temporal graph processing, influence maximization, knowledge graph-enhanced reasoning, and rumor mitigation. Dr. Wang actively recruits PhD students interested in pursuing research in related fields and encourages current undergraduate and master's students at UNSW to contact him about research opportunities. He maintains an active research agenda with practical implications for industries dealing with large-scale network data, financial technology applications, and data-intensive systems. He can be reached at xiaoyang.wang1@unsw.edu.au and is located in Engineering building K17-501D at UNSW.
Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre
Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.
Antonios Deligiannakis is a Professor at the School of Electronic and Computer Engineering of the Technical University of Crete, specializing in database systems and distributed data processing. His academic career includes a postdoctoral position at the National and Kapodistrian University of Athens (2006-2007) and a visiting researcher role at AT&T Labs-Research (2003). His educational background includes: PhD in Computer Science, University of Maryland, USA (2005) Master's Degree in Computer Science, University of Maryland, USA (2001) Diploma in Electrical and Computer Engineering, National Technical University of Athens (1999) Professor Deligiannakis's research spans Databases , Stream Processing , and Sensor Networks , with pioneering work in Approximate Query Evaluation for massive datasets and Complex Event Processing in distributed environments. His contributions enable efficient analytics in resource-constrained settings through techniques like synopses-based engines and windowed outlier detection. His 15 most recent publications (2020-2025) reveal a dominant focus on distributed streaming analytics, with recurring themes of cross-platform integration, federated learning, and extreme-scale interactive systems. Key innovations include the INFORE framework for interactive analytics, DAG* for IoT workflow optimization, and communication-efficient federated learning techniques—demonstrating consistent translation of theoretical advances into production-ready platforms. Scientific Awards: No specific awards were listed in the provided material. Information about advisees and research grants was not provided in available documentation, though his leadership in the Distributed Information Systems and Applications laboratory suggests active mentorship and project direction. He directs research in the Distributed Information Systems and Applications laboratory, developing systems for real-time analytics across domains including maritime surveillance, financial technology, and IoT platforms, with emphasis on scalability and fault tolerance in geo-distributed environments.
Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.
Yuè Li is a Professor in the Department of Computer Science at McGill University, where he leads the Li Lab focused on machine learning applications in genomics and healthcare. His research develops computational methods for analyzing electronic health records (EHR), single-cell multi-omics data, and population genetics. Dr. Li teaches core courses including Applied Machine Learning (COMP 551), Machine Learning in Genomics and Healthcare (COMP 565), and Computer Programming for Life Sciences (COMP 204). His research interests span: AI methods for computational biology and translational healthcare Multi-modal EHR integration and clinical topic modeling Time-series health forecasting and trajectory analysis Single-cell transcriptomics and epigenomics Polygenic risk modeling and causal variant inference Regulatory genomics and functional annotation integration Publications demonstrate strong focus on transformer architectures for healthcare forecasting, Bayesian methods for genomic inference, and neural topic models for clinical phenotyping. Recent work emphasizes foundation models for single-cell data and federated learning for EHR analysis. Scientific Awards: KDD HealthDay2022 Best Paper Award for seed-guided topic modeling Dr. Li mentors graduate students and postdoctoral researchers working on machine learning applications in biomedical domains. Current lab members include Master's students Bo-Hong Wang, Claris Gu, Neda Esfehani, and Ruilin Wang, along with postdoctoral researcher Dr. Jun Bai. The Li Lab operates within McGill's School of Computer Science, developing computational frameworks to integrate heterogeneous biomedical data for improved disease understanding and clinical decision support.
