Daniele Dell'Aglio is an Associate Professor at the Department of Computer Science , Aalborg University , affiliated with the Technical Faculty of IT and Design . His research focuses on privacy-preserving data synthesis, knowledge graphs, and semantic data integration. Academic Rank: Associate Professor Department: Computer Science School: Technical Faculty of IT and Design University: Aalborg University Research Interests : Development of privacy metrics for synthetic data generation Heterogeneous graph representation in knowledge graphs Interactive evaluation tools for data privacy Transformer-based biomedical data extraction Integration of gut-brain axis scholarly data Scientific Awards : Best paper at the Deep Learning for Knowledge Graphs workshop (2022) Collaborative Projects include HEREDITARY (Heterogeneous semantic data integration for gut-brain interplay) as Principal Investigator, and partnerships with NASA on Mars exploration data processing. His work spans privacy-preserving technologies, knowledge graph engineering, and biomedical text mining applications.
Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.
Audrey Giremus is a Professor at the Institut de Mécanique et d'Ingénierie de Bordeaux (IMS Bordeaux) , affiliated with the Signal and Image Processing research group (SPECTRAL team). Her work bridges signal processing, statistical modeling, and interdisciplinary applications in environmental science and biomedical engineering. Key affiliations: IMS Bordeaux, BRGM (Bureau de Recherches Géologiques et Minières), CNRS (Centre National de la Recherche Scientifique) Collaborations: STMicroelectronics, Stellantis, Thales, CEA, Slb Research Interests focus on: Advanced signal/image processing algorithms Bayesian inference and particle filtering Applications in GPS navigation, fuel cells, and material science Statistical methods for high-dimensional data Thermal source localization and acousto-thermal coupling Coastal erosion detection using Markov chains Recent publications demonstrate expertise in GPS interference compensation , fuel cell degradation modeling , and biomarker validation . Collaborative projects with institutions like BRGM and CEA highlight her cross-disciplinary impact. Grants/Projects include: PEPR Risks (IRiMa) - CNRS OneWater PEPR - CNRS Technical platforms mentioned: Accelerated Aging Nanocomponent Testing (ATLAS, Cacyssee) Thermal Analysis (Elorga, Tamis) Terahertz and Organic Electronics
Adam Robert Kobus is a Lecturer at the Department of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics, and Computer Science, Maria Curie-Skłodowska University (UMCS) in Lublin, Poland. His academic profile includes roles in research and education, with a focus on speech processing and biomedical engineering. University: Maria Curie-Skłodowska University School: Faculty of Mathematics, Physics, and Computer Science Department: Department of Neuroinformatics and Biomedical Engineering Academic Rank: Lecturer Dr. Kobus specializes in speech recognition algorithms for detecting non-fluencies and disorders, combining techniques like linear prediction coefficients , neural networks , and Continuous Wavelet Transform (CWT) . His work integrates machine learning and biomedical signal processing to develop automated tools for analyzing speech patterns. Research trends include speech disorders diagnostics , neural network applications , and wavelet-based signal analysis . His publications emphasize computational linguistics and biomedical informatics . Dr. Kobus is based at the Institute of Computer Science, room 511, Lublin. He can be contacted via email at adam.kobus@mail.umcs.pl .
Assoc. Prof. Dr. Michael Stefan Bittermann is an academic at Istanbul Technical University , Department of Architecture. His career spans multiple institutions including Technical University Delft and Maltepe University, with roles ranging from Doctor Lecturer to research leadership positions. Doctorate from Delft University of Technology (2005) Post-doctoral Fellowship (80k€) at Delft University of Technology (2009) Currently Assistant Professor at ITU Previously held academic positions in Germany and The Netherlands His research focuses on Information Technologies in Architecture and Design , with key contributions in: Architectural Design Computational Intelligence Visual Perception Evolutionary Algorithms Fuzzy Logic Applications Multi-Objective Optimization Scientific activities reflect strong collaboration with Dr. Özer Ciftcioglu and others, with projects like the Interactive Computational Design initiative (2018-2020). Research outputs (42 total) show consistent contributions since 2006 in: IEEE Transactions on Systems, Man, and Cybernetics Nexus Network Journal CAADRIA proceedings IEEE Congress on Evolutionary Computation International collaborations span institutions in: The Netherlands (Delft University) Germany (Bittermann Weiss GmbH) Turkey (Istanbul Technical University) Japan (CEC conferences) USA (SIGGRAPH, NAFIPS)
Prof. Dr. Ralph Bergmann is a full professor at the University of Trier since 2004 and leads the Experience-Based Learning Systems research group. Since 2020, he serves as topic-field leader for experience-based learning systems at the Trier Branch of the German Research Center for Artificial Intelligence (DFKI) . He has directed approximately 35 EU/DFG/BMBF-funded projects and authored over 200 papers (h-index 40) with four books and 13 edited proceedings. Academic Rank: Professor (Business Information Systems II) Key Collaborations: DFKI, KI-AIM, KIAFlex, DZW (Digital Twins), myRPA, SPELL Ralph Bergmann's research focuses on hybrid AI systems that combine data-driven methods (machine learning, case-based reasoning) with semantic technologies (ontologies, knowledge graphs). His work addresses knowledge-intensive processes like emergency call handling, healthcare discharge management, smart factory automation, and political argumentation analysis. He explores similarity assessment, workflow flexibility, and context-sensitive reasoning through frameworks like ProCAKE and CBRkit . Recent publications highlight IoT data integration (SensorStream, DataStream XES), large language models for knowledge engineering, and graph neural networks for similarity ranking. Application domains span Industry 4.0 , oncology decision support, water resource management, and clinical guideline conformance checking. His teaching includes courses on Data Mining , Semantic Technologies , and Research Internships in business informatics, with consultation hours held both in-person and online. Current projects like KI-AIM and SPELL emphasize AI anonymization in medicine and semantic platforms for control centers .
Professor Amir H Gandomi is a leading academic in data science and artificial intelligence at the University of Technology Sydney, where he serves as Professor of Data Science at the Data Science Institute within the Faculty of Engineering and Information Technology. An ARC DECRA Fellow with over 450 journal papers and 14 books, his research has garnered more than 70,000 citations with an H-index exceeding 110. Ranked 18th among over 17,000 researchers in Genetic Programming bibliography and 24th in Artificial Intelligence & Image Processing by Stanford University, Prof. Gandomi is recognized as one of the world's most influential scientific minds. His research interests span machine learning, evolutionary computation, global optimization, and big data analytics, with applications across healthcare, structural engineering, environmental science, and cybersecurity. He has developed innovative frameworks like Adaptive Strategy Management for large-scale optimization and Boundary Update methods for constrained optimization problems. His work bridges theoretical advancements with practical implementations in diverse fields including medical diagnostics, renewable energy site selection, and smart infrastructure. Prof. Gandomi's publication portfolio demonstrates consistent high-impact contributions across multiple disciplines, with recent work focusing on AI-driven healthcare solutions, optimization algorithms, and climate modeling. His research shows strong interdisciplinary connections between computer science, engineering, and medical applications, with particular emphasis on practical implementations of theoretical frameworks. The breadth of his work reflects both deep technical expertise and the ability to apply computational methods to solve real-world problems across various domains. 2024 IEEE TCSC Award for Excellence in Scalable Computing (MCR) 2023 Achenbach Medal from Stanford University 2022 Walter L. Huber Prize (highest-level mid-career civil engineering research award) 2025 Sigma Xi Young Investigator Award 6 consecutive years as Clarivate Analytics Highly Cited Researcher AmCham Alliance Award in AI As a dedicated educator and mentor, Prof. Gandomi has supervised numerous research students in evolutionary machine learning, structural health monitoring, and uncertainty-aware AI systems. His funded research projects include Amazon Research Awards for medical report generation, Climate Change AI grants for drought prediction, and Digital Finance CRC projects for cyber threat detection. He leads the Data Science Institute's efforts in developing practical AI solutions while maintaining strong industry partnerships and international collaborations across multiple continents.
Thu Nguyen is a Research Fellow in the Department of Holistic Systems at Simula Metropolitan, a leading Norwegian research institute specializing in data science and computing. Her work focuses on developing robust methodologies for handling missing data across critical domains including healthcare diagnostics and environmental monitoring systems. Her primary research interests include: Advanced missing data imputation techniques Machine learning explainability under incomplete data Healthcare informatics and medical data analysis Environmental data processing for air quality prediction Multimodal data integration challenges Analysis of her publication record reveals a strong trajectory in developing computationally efficient imputation algorithms with real-world applications. Key trends include the application of principal component analysis and generative models to healthcare datasets (sperm tracking, medical imaging), environmental monitoring (air quality prediction), and multimodal systems. Her work consistently bridges theoretical statistical foundations with practical implementation needs, emphasizing model transparency and performance validation. Dr. Nguyen actively collaborates within Simula Metropolitan's data science ecosystem, contributing to interdisciplinary projects that address fundamental challenges in data completeness and usability across scientific domains.
Ronghui Gu is the inaugural Tang Family Associate Professor of Computer Science at Columbia University's Fu Foundation School of Engineering and Applied Science. He leads a research group focused on building verified systems software and serves on program committees for major conferences including PLDI, POPL, OSDI, and SOSP. His educational background includes: Ph.D. in Computer Science from Yale University (2016), where he received the Distinguished Dissertation Award B.S. in Computer Science from Tsinghua University (2011), graduating with Highest Distinction (3 out of 140) Gu's research centers on certified software systems, spanning programming language design, OS kernel development, formal semantics, compiler development, proof engineering, and concurrency. His work bridges theoretical foundations with practical systems, particularly in the areas of formal verification for operating systems, distributed protocols, and quantum computing. He has pioneered approaches that combine formal methods with machine learning techniques to automate verification tasks that were previously intractable. Analysis of his publication record shows a clear trajectory from foundational work on verified operating systems (CertiKOS, mCertiKOS) to broader applications in distributed systems (DistAI, DuoAI), quantum computing (Gleipnir, Giallar, HyperQ), and blockchain security. His recent work increasingly focuses on automation techniques that make formal verification practical for real-world systems. His notable achievements include: OSDI Jay Lepreau Best Paper Award (2021) SOSP Best Paper Award (2019) Multiple Amazon Research Awards (2021-2025) NSF CAREER Award (2023) VMware Systems Research Award (2023) CACM Research Highlight Gu has secured substantial research funding including a $4.5 million DARPA grant for Verified Enclave Layers. He has advised numerous PhD students who have gone on to positions at top institutions and companies. As founder of CertiK, a Web3 cybersecurity unicorn valued at $2 billion, he has successfully translated academic research into real-world impact, securing over $300 billion in cryptocurrency assets. His lab maintains active collaborations with industry partners including VMware, AWS, Google, and quantum computing companies, focusing on making formal verification practical for critical systems.
Eva Pettersson is a Researcher in computational linguistics at Uppsala University's Department of Linguistics and Philology. She is affiliated with the Swedish National Language Bank and collaborates with researchers across multiple institutions, including the University of Gothenburg where she works with Lars Borin on corpus linguistics projects. Her academic work bridges computational methods with historical language analysis. Dr. Pettersson's research focuses on the intersection of digital humanities and computational linguistics, specializing in the processing and analysis of historical texts. Her work spans multiple domains including natural language processing for historical documents, historical cryptology, corpus development, and linguistic analysis of diachronic language change. She has made significant contributions to Swedish historical linguistics through her development of specialized resources and tools. Her publication record demonstrates a consistent trajectory of innovation in historical text processing, with recent work focusing on medieval scribal habits, named entity recognition in 19th century Swedish, rhetorical structure analysis of historical petitions, and historical cryptanalysis. These publications reveal a progression from foundational work on spelling normalization to increasingly sophisticated applications of NLP techniques to historical documents across multiple centuries. Dr. Pettersson has developed significant linguistic resources including the Swedish Diachronic Corpus and the HistCorp collection of historical corpora and tools. These resources have become essential for researchers working on historical Swedish language and provide standardized datasets for computational analysis of language change over time. Her collaborative work extends to international projects like the DECRYPT initiative for historical manuscript decryption and the development of specialized databases for historical ciphers. Through these projects, she has established herself as a key figure in the application of computational methods to historical linguistic materials and cryptological challenges.
Dr. Jakub Szewczyk is a postdoctoral researcher at the Institute of Psychology , Jagiellonian University, Kraków, Poland. He holds a PhD from Radboud University (Netherlands) and has previously worked at prestigious institutions including the University of Illinois at Urbana-Champaign (USA), the Donders Center for Brain, Cognition, and Behavior (Netherlands), and the Max Planck Institute (Netherlands). Current affiliation: Jagiellonian University Department: Experimental Psychology Academic rank: Research Fellow His research focuses on psycho/neurolinguistics , particularly predictive mechanisms in linguistic and visual processing. He employs multimodal methodologies including EEG, fMRI, deep neural networks (LLMs, convolutional networks) , and behavioral measures. Key themes include semantic networks, bilingualism, prediction error representations, and the neurocognitive role of the N400 ERP component. Recent publications address topics like stimulus dependencies in brain encoding, bilingual language control adaptations, and neural signatures of prediction during natural listening. His work has been funded by the Polonez BIS grant (2022/47/P/HS6/02294) from Poland’s National Science Centre.
Zak Mhammedi is a Postdoctoral Associate at the Massachusetts Institute of Technology working with Sasha Rakhlin on theoretical problems in machine learning. His research spans Reinforcement Learning, Control Theory, and Optimization with strong theoretical foundations. His educational background includes: PhD in Computer Science, 2021, Australian National University MPhil in Computer Science, 2016-2017, The University of Melbourne Mhammedi's research focuses on bridging theoretical guarantees with practical algorithms, particularly in online learning, projection-free optimization, and reinforcement learning. His work often addresses fundamental limitations in statistical learning while developing computationally efficient methods. He has made significant contributions to understanding risk-monotonicity, developing parameter-free online learning algorithms, and creating efficient exploration strategies for reinforcement learning. His publication record shows a consistent trajectory in theoretical machine learning, with numerous papers in top-tier venues including NeurIPS, ICML, and COLT. His work demonstrates expertise across multiple subfields including PAC-Bayesian theory, online convex optimization, and control theory applications to machine learning. His scientific achievements include: Oral Presentation at NeurIPS 2021 (awarded to approximately 1% of submissions) Spotlight Presentation at NeurIPS 2020 Spotlight Presentation at NeurIPS 2018 (awarded to approximately 3% of submissions) Mhammedi actively contributes to the academic community through program committee service for COLT 2021 and 2022, and as a reviewer for major conferences including COLT, NeurIPS, and ICML across multiple years. His technical expertise spans adaptive methods, computational efficiency, constrained optimization, and theoretical aspects of deep learning and reinforcement learning.
Piyush Rai is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He also holds an Adjunct Assistant Professor position in Electrical and Computer Engineering at Duke University. His academic journey includes postdoctoral research at Duke University and the University of Texas at Austin, following his PhD from the University of Utah. His research interests focus on Machine Learning and Bayesian Statistics, with specializations in Latent Variable Models, Probabilistic Modeling, Approximate Inference, and Nonparametric Bayesian Methods. His work bridges theoretical foundations with practical applications in artificial intelligence and data science. Dr. Rai's publication record shows a consistent focus on tensor factorization, Bayesian nonparametrics, and scalable algorithms for large datasets. His work spans conferences including NIPS, ICML, UAI, and AISTATS, demonstrating strong contributions to both theoretical and applied machine learning. Best Student Paper Award at ECML-PKDD (2015) National Science Foundation (USA) EAGER Award (2015) Dr. Deep Singh and Daljeet Kaur Faculty Fellowship at IIT Kanpur (2015) NIPS 2013 Reviewer Award Sheldon Ekland-Olson Postdoctoral Fellowship (2012) He teaches advanced courses in Machine Learning and Probabilistic Machine Learning at IIT Kanpur, mentoring the next generation of researchers in statistical machine learning techniques. His collaborative work with Lawrence Carin and other researchers demonstrates strong interdisciplinary connections between institutions.
Juan Carlos Trujillo Mondejar is a Full Professor in the Department of Languages and Computer Systems at the University of Alicante's Higher Polytechnic School, where he leads the Lucentia Research Group. He has maintained active teaching responsibilities through 2025, instructing courses such as Business Intelligence and Process Management, Project Management of Information Technologies, and Database Technology. His academic credentials include: Doctor of Computer Engineering, Higher Polytechnic School of Alicante, University of Alicante (2001) Doctorate in Recognition, Interpretation and Machine Translation (2001) Computer Engineer, Polytechnic School of Alicante, University of Alicante (1995) Technical Engineer in Management Information Technology (1995) Professor Trujillo's research spans Business Intelligence, Big Data, Data Warehousing, OLAP, data mining, and strategic planning. His work integrates computer science with practical business applications, particularly in healthcare contexts where he applies EEG analysis and deep learning for ADHD diagnosis. He has directed 18 doctoral theses and supervised 35 final degree/master's projects in the last five years, emphasizing internationalization of students. His publication record demonstrates remarkable impact with over 200 conference papers at high-impact venues (ER, UML, DAWAK, CAiSE) and more than 60 JCR-indexed journal articles in top publications like DKE, DSS, IS, and InfSci. His work has earned him recognition as one of the most cited authors in Business Intelligence, with multiple papers ranking among the most downloaded in journals like Data & Knowledge Engineering. Notable achievements include: Co-editing 11 special issues for JCR journals Serving as PC-Chair for major international conferences (ER'18, ER'13, DOLAP'05) Senior Editor of Decision Support Systems (Q1 journal) until 2017 Articles with 152, 128, and 98 citations Multiple papers ranking among top downloaded articles in their respective journals Professor Trujillo has secured substantial research funding as Principal Investigator for national, regional, and Horizon 2020 projects, totaling 33 public research projects in the last five years. His technology transfer activities include 7 Intellectual Property Registrations and co-founding Lucentia LAB, S.L. in 2015, a spin-off company focused on Business Intelligence and Software Engineering technology transfer. He maintains active international collaborations with leading researchers including S. Rizzi, P. Vassiliadis, M. Golfarelli, Il-Yeol Song, and J. Mylopoulos. His current projects include a Smart City initiative with Elda City Council (2025) and an LLM-based data integration project for digital transformation at the University of Alicante (2024-2025).
Fredrik D. Johansson is an Associate Professor of Computer Science & Engineering at Chalmers University of Technology in Sweden, where he serves as Principal Investigator for the Healthy AI Lab. His academic career bridges theoretical machine learning with practical healthcare applications, focusing on causal inference methodologies for real-world decision making. His research interests center on causal inference in machine learning and decision-making with healthcare applications. Johansson's work spans multiple subfields including treatment effect estimation, missing data handling, reinforcement learning for healthcare, and Alzheimer's disease simulation. His research group develops both theoretical frameworks and practical tools for causal machine learning. Analysis of his recent publications reveals a strong emphasis on causal inference methodology with increasing healthcare applications, particularly in Alzheimer's disease research and treatment effect heterogeneity. His work consistently appears in top-tier machine learning venues including NeurIPS, ICML, AISTATS, and JMLR, demonstrating both theoretical rigor and practical relevance to healthcare decision making. As an academic advisor, Johansson currently supervises seven PhD students: Herman Bergström, Alessandro Margueritte, Ahmet Balcioglu, Adam Breitholtz, Newton Mwai Kinyanjui, Anton Matsson, and Lena Stempfle, indicating an active and growing research group. He leads the Healthy AI Lab at Chalmers University of Technology, which focuses on developing machine learning methods for healthcare applications with particular emphasis on causal inference. The lab has produced numerous software tools including Counterfactual Regression, Matching Kernels, and DLOREAN, along with datasets like IHDP-100, Jobs-Binary, and ADCB that have become standard benchmarks in the causal inference community.
