Prof. Dr. Jörg Hackermüller is a computational biologist with expertise in Omics data integration Toxicology Environmental risk assessment Non-coding RNA biology . He serves as Head of the Department of Computational Biology and Chemistry at the Helmholtz Centre for Environmental Research (UFZ) since 2024 and holds a Professorship at the Faculty of Mathematics and Computer Science at Leipzig University since 2021. His research focuses on Developing AI methods for chemical toxicity prediction Multi-omics integration for mechanistic toxicology Data standardization in environmental monitoring Non-coding RNAs as biomarkers in disease and toxicity and has produced 15+ recent publications spanning tools like multiGSEA and deepFPlearn+ . He collaborates with teams across UFZ Leipzig University Novartis Fraunhofer Institute and leads projects like InCeTo and SafePol , integrating exposome research with systems biology.
Yuqing Wang is a Postdoctoral Researcher in the Department of Computer Science at the University of Helsinki, Finland, actively contributing to software engineering research with expertise in anomaly detection for microservices and test automation maturity. Contactable via yuqing.wang@helsinki.fi and phone +358505934630/+358294151310, Wang participates in major EU and Academy of Finland projects including LUMI AI Factory (2025-2028) and MuFAno (2023-2026). Research focuses on two interconnected domains: anomaly detection in cloud-native systems using meta-learning for cross-system log analysis and trace categorization, and test automation maturity assessment frameworks. Recent work pioneers datasets like LO2 for microservice API anomalies and tools like LogLead for integrated log processing, while earlier studies establish quantitative links between test automation maturity and product quality in open-source ecosystems. Publications reveal an evolving trajectory from foundational test automation maturity models (2018-2020) toward advanced AI-driven anomaly detection (2024-2025), with 2022-2023 bridging both domains through empirical studies on agile practices and maturity impacts. Current work emphasizes cross-system generalization and multimodal fusion for microservice reliability. No scientific awards are documented in available sources. Wang contributes to two significant grants: the EU Horizon Europe LUMI AI Factory developing AI service infrastructure (2025-2028), and the Academy of Finland MuFAno project advancing multimodal anomaly detection for microservices (2023-2026). These projects drive collaboration with industry partners on real-world system reliability challenges.
Full Professor at the School of Informatics, Aristotle University of Thessaloniki (AUTh), Greece. Previously served as Associate Professor (2015-2020), Assistant Professor (2008-2015), and Lecturer (2002-2008) at the same institution. Also worked as an Informatics Teacher in Greek Secondary Education from 1989-2002. His research focuses on Learning Technologies with emphasis on Conversational Agents in Education, Learning Analytics, Computer-Supported Collaborative Learning, Computational Thinking, Teaching Machine Learning at School, and Massive Open Online Courses (MOOCs). His recent work heavily investigates the application of AI, particularly conversational agents and large language models, in educational contexts. His publications show a strong trend toward AI applications in education, particularly focusing on conversational agents, learning analytics, and automated grading systems. The research spans multiple educational contexts from K-12 to higher education, with particular attention to student self-regulation, collaborative learning, and ethical considerations in AI implementation. 3 Best paper awards at international conferences Interview by Educational Technology Magazine (2013) Cubes Coding project - Winners of Open Education Challenge 2014 Cubes Coding project - Winners of NUMA Competition 2014 Has supervised 5 completed PhD theses, 4 ongoing PhD theses, over 60 Master's theses, and over 120 undergraduate theses. Led the colMOOC project (2018-2020), a €999,000 EU-funded project on integrating conversational agents and Learning Analytics in MOOCs. Also coordinated the T4E project (2020-2022) on Teachers' Fast-paced Distance Training on Tele-education and a MOOC in Greek on Introduction to Programming with Python. Previously served as Director of the Software and Interactive Technologies Laboratory (SWITCH Lab) until 2020, Member of AUTH Educational Policy Committee until 2020, and Chair of the Scientific Supervisory Board of the 2nd Experimental Junior High School in Thessaloniki until 2020.
Nikolaus Rajewsky is a leading Professor at the Max Delbrück Center for Molecular Medicine (MDC) and Charité – Universitätsmedizin Berlin , where he founded and directs the Berlin Institute for Medical Systems Biology (BIMSB) . His lab integrates experimental (biochemistry, molecular biology) and computational (bioinformatics, physics) approaches to study RNA regulation in gene expression , with applications to developmental biology, regeneration, neurodegenerative diseases, and cancer . Using model systems like C. elegans , planaria, and human brain organoids, his team pioneers cutting-edge methods such as MirDeep , DistMap , and FLAM-seq for RNA analysis. His research focuses on single-cell transcriptomics , spatial RNA sequencing , and circular RNA (circRNA) regulation , revealing novel roles for circRNAs like CDR1as in neuropsychiatric disorders. Recent work includes 3D tumor microenvironment mapping and computational modeling of RNA metabolism in diseases. Scientific Awards : Gottfried Wilhelm Leibniz Prize (2012) EMBO Membership (2010) Honorary PhD, Sapienza University of Rome (2014) Berlin Science Award (2009) His team's recent articles highlight breakthroughs in 3D spatial transcriptomics , circRNA degradation mechanisms , and mitochondrial disease modeling using human brain organoids. The lab actively collaborates with clinical partners across Charité and European institutions, driving the LifeTime initiative for cell-based interceptive medicine.
Amalia Miliou is a Professor in the Department of Informatics at Aristotle University of Thessaloniki, where she has served since 1993, progressing through the academic ranks from Lecturer to her current position as Professor since 2022. She holds a PhD in Electrical and Computer Engineering from the University of Florida (1991) with specialization in Optoelectronics, following an MSc in the same field (1988) and a Physics degree from Aristotle University (1985). Her research focuses on optical communications systems, with specific expertise in optoelectronic circuits simulation, optical switching, optical RAM development, converged fiber-wireless technology, 5G networks, and secure optical communications using chaos theory. Over her career, she has supervised numerous graduate students across these research areas, with thesis topics spanning optical memory systems, fiber-wireless integration, chaos-based secure communications, and advanced optical network architectures. Her recent publications (2021-2024) demonstrate a strong focus on next-generation optical networking solutions for 5G/6G applications, including fiber-wireless convergence, optical memory systems for high-speed networks, and innovative approaches to optical signal processing. Her work bridges fundamental photonics research with practical telecommunications applications, particularly in addressing the bandwidth and latency challenges of modern mobile networks. Professor Miliou has served as the Coordinator of the LLP-ERASMUS student exchange program at the Department of Informatics since 1997 and has held various administrative positions including membership in the University Senate and General Assembly. She has led and participated in numerous research projects, most recently focusing on technological improvements for 5G systems through optical-wireless network development (2019-2021), next-generation healthcare applications leveraging 6G networks (2023-2027), and photonic integrated circuits for random access memory (2012-2015).
Marlene Behrmann is the Thomas S. Baker University Professor of Psychology and Cognitive Neuroscience at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. She leads the Behrmann Lab, which moved to the University of Pittsburgh in 2023. Her research focuses on visual cognition, object recognition, and neural mechanisms of perception, with a particular emphasis on face and word recognition. Behrmann holds a B.A. and M.A. in Speech and Hearing Therapy and a Ph.D. in Psychology from the University of Toronto. She is a leader in her field, recognized by her induction into the National Academy of Sciences (2015) and the American Academy of Arts and Sciences (2019). Her work combines neuropsychological studies of patients with brain damage, neuroimaging, and computational modeling to explore visual processing. Recent research highlights include studies on dorsal-ventral pathway interactions, functional reorganization post-hemispherectomy, and autism-related sensory processing differences. Behrmann has advised numerous graduate students and postdocs, contributing to their academic and professional development. Key awards include her National Academy of Sciences membership and American Academy of Arts and Sciences fellowship. Her lab collaborates widely, publishing in top journals like Cerebral Cortex , PNAS , and Trends in Cognitive Sciences . She also engages in translational research to improve interventions for perceptual and cognitive disorders.
Michael Qizhe Shieh is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the Tree and Rock AI Lab (TRAIL). He holds a PhD and Master's from Carnegie Mellon University (Machine Learning and Language Technologies) and a Bachelor's from Shanghai Jiao Tong University's ACM Class. His research focuses on Large Language Models, Deep Learning, and Natural Language Processing, with notable contributions to semi-supervised learning techniques like Noisy Student and UDA, and the RACE benchmark for reading comprehension. Education: PhD in Machine Learning, Carnegie Mellon University (2020) Master's in Language Technologies, Carnegie Mellon University (2018) Bachelor's in Computer Science, Shanghai Jiao Tong University (2016) His research explores robustness, safety, and scalability of AI systems. He has served as Area Chair for top conferences like NeurIPS, ICML, and ICLR. Current research directions include adversarial robustness, LLM self-evaluation, and alignment mechanisms. His lab, TRAIL, emphasizes foundational AI research. Selected contributions include: Developing UDA and Noisy Student techniques for semi-supervised learning Creating the RACE benchmark for exam-based reading comprehension Advancing methods for LLM safety and adversarial defense Prospective students are encouraged to apply to NUS's PhD program for collaborative research opportunities.
Kenneth BENOIT is the Dean and Full-time Professor of Computational Social Science at the School of Social Sciences, Singapore Management University (SMU). Previously, he served as Director of the Data Science Institute at the London School of Economics (LSE) from 2020 to 2024. He holds a PhD in Government from Harvard University, specializing in statistical methodology. His research focuses on computational methods for analyzing textual data, particularly political texts and social media. Key areas include text-as-data techniques, natural language processing, and the application of large language models in social sciences. He has pioneered methods combining machine learning with crowd-sourced coding to improve the accuracy of political text analysis. Ken’s work emphasizes the analysis of big data, electoral systems, and comparative party competition, with notable contributions to the European Parliament and policy positioning studies. His expertise extends to software development, including R packages like quanteda and spacyr , which are widely used in text analysis. His articles and publications span methodological innovations, policy analysis, and interdisciplinary applications. Notable projects include scaling political party positions and examining the role of AI in public policy. He is actively involved in academic leadership, having served on editorial boards and organized collaborative research initiatives like the CIVICA research hackathon. Beyond SMU, he maintains professional profiles on LinkedIn and GitHub , reflecting his commitment to open-source tools and scholarly collaboration.
Luisa Wellert is a Research Associate at the Innovative Educational Technologies Department within the Tübingen Center for Digital Education (TüCeDE) at the University of Tübingen since September 2023. She is also a PhD student at TüCeDE since October 2022. Education: Master of Arts in Intermedia and General Educational Science (2020-2022) from University of Cologne Bachelor of Arts in Media Studies and Educational Science (2016-2019) from University of Tübingen Research Focus: Her work centers on media pedagogy , media didactics , and adaptive learning systems , with particular emphasis on AI integration in educational contexts. Recent projects examine automated qualitative coding of AI tutoring dialogues using large language models and effectiveness of self-developed adaptive systems in schools. Publications: Recent work analyzes assessment methodologies in adaptive learning systems, compares performance-based and cognitive load-based evaluation approaches, and explores implementation challenges of DIY adaptive technologies. She actively contributes to open-source AI tutoring initiatives through the OSATI project. Presentations: Luisa presents her research at major conferences including EARLI, LEAD Research Meeting, and GEBF Conference, focusing on practical implementations of AI-based educational tools and their cognitive impacts. Professional Activity: Prior to her current role, she served as Project Manager for Educational Research at TüCeDE (2022-2023) and worked as a Research Assistant at mmb Institut GmbH (2021-2022) and mecodia GmbH (2018-2020). She also has experience in media education and empirical media research.
Athanasios Liavas is a Professor at the Technical University of Crete in the School of Electrical and Computer Engineering , specializing in Signal Processing for Telecommunications and Information Theory . He has held administrative roles as Department Chair (2009-2011), Vice Chair (2011-2013), and Dean of the ECE School (2017-2021). Education: Diploma (1989) and PhD (1993) in Computer Engineering and Informatics from the University of Patras. Professional Background: Postdoctoral Marie Curie Fellow at INT, Evry (1996-1998); Lecturer at University of Ioannina (1999-2001); Assistant/Associate Professor at University of the Aegean (2001-2004) and Technical University of Crete (2004-present). His research focuses on Signal Processing for Telecommunications , Information Theory , and Tensor Decomposition . Recent work involves nonnegative tensor factorization , parallel algorithms , and fMRI data analysis , with applications in wireless communications and medical imaging . Articles show trends in optimization algorithms , LDPC code design , and MIMO system robustness . Scientific Awards include: Marie Curie Fellowship (1996-1998) Associate Editor, IEEE Transactions on Signal Processing (2005-2009) Elected Member, IEEE Signal Processing for Communications and Networking Technical Committee (2006-2011) He has taught courses like Telecommunications Systems II , Wireless Communications , and Information Theory , and supervised students such as Despoina Tsipouridou (PhD) and Alex Balatsoukas-Stimming (Graduate). He leads projects like Partensor (Parallel Tensor Toolbox) and COOPCOM (Cooperative Communications), and contributes to labs including the Telecommunications Laboratory .
Pouya Bashivan is an Assistant Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on developing computational models to explain and regulate neural responses during visual tasks requiring memory, combining machine learning, neuroscience, and cognitive science. Education : Ph.D. in Computer Engineering (2016), Postdocs in Machine Learning (2020) and Computational Neuroscience (2016-2020) His lab investigates: Topographical neural networks for visual cortex simulation Massively-multitask models for prefrontal cortex Saccade-driven visual exploration models Predictive hippocampus models for episodic memory Recent publications explore adversarial robustness, memory-augmented networks, and brain-state decoding. Current projects emphasize causal models, brain-AI alignment, and translating computational neuroscience into therapeutic applications. The lab is located in the McIntyre Medical Sciences Building, Room 1117, Montreal, Quebec.
Anne-Marie Oswald is an Associate Professor in the Department of Neurobiology within the Biological Sciences Division at the University of Chicago. Her research profile indicates active engagement in neuroscience research with a particular focus on cortical circuits, neural coding, and sensory processing systems. She maintains an active research program with publications spanning from 2011 to the present. Dr. Oswald's research interests span multiple areas of neuroscience, with particular emphasis on cortical circuit function, neural coding mechanisms, and sensory processing. Her work investigates how inhibitory interneurons shape cortical dynamics, how neural assemblies form during learning, and how sensory information is processed across different brain regions. Notably, she has also contributed to discussions on diversity in science through her publication "Curating more diverse scientific conferences" in Nature Reviews Neuroscience (2020). Her research employs a combination of electrophysiological, computational, and behavioral approaches to understand neural circuit function. Analysis of her publication record reveals a strong focus on cortical circuit mechanisms, particularly in the olfactory system. Her work demonstrates expertise in understanding how different interneuron subtypes (particularly parvalbumin and somatostatin-positive cells) regulate cortical dynamics, assembly formation, and sensory processing. Over time, her research has evolved from examining basic circuit mechanisms to investigating how these circuits support complex cognitive functions like odor discrimination and associative learning. The consistent presence of computational and systems neuroscience approaches throughout her publication history indicates a rigorous quantitative approach to understanding neural function. Dr. Oswald appears to be actively mentoring students and postdoctoral researchers, as evidenced by her consistent publication record with multiple collaborators. While specific grant information isn't provided in the available data, her sustained publication output suggests successful funding of her research program. Her work bridges cellular and systems neuroscience, contributing to our understanding of how microcircuit properties shape sensory processing and behavior.
Sophie Caron is an Associate Professor in the Department of Biological Sciences at the University of Utah, where she leads a research laboratory investigating fundamental mechanisms of brain function using Drosophila melanogaster as a model system. Her work focuses on how the brain generates internal representations of the external world, stores memories, and translates these into behavior through multisensory integration. Education: B.S. from Université de Montréal Ph.D. from New York University Dr. Caron's research centers on the Drosophila mushroom body—a critical brain center for learning and memory—with emphasis on multisensory integration mechanisms. Her lab investigates how the brain combines information from multiple sensory modalities (olfaction, vision, etc.) to form unified percepts, challenging traditional views of sensory processing. Key discoveries include the demonstration that mushroom body connectivity follows near-random patterns that maximize memory capacity, and the identification of cross-modal sensory pathways beyond olfaction. Current work explores evolutionary adaptations in neural circuits across Drosophila species and developmental mechanisms establishing sensory wiring. Analysis of her 15 most recent publications (2019-2024) reveals three dominant research trajectories: (1) high-resolution mapping of Kenyon cell inputs using advanced techniques like dye electroporation, (2) computational modeling of how connectivity patterns shape sensory representation and learning, and (3) evolutionary studies of circuit architecture across Drosophila species. Her work consistently bridges experimental neuroanatomy with theoretical frameworks, emphasizing the interplay between random and structured wiring principles in neural circuit design. Scientific Awards: NSF CAREER Award (2021) for research on brain size evolution and neuronal circuit adaptation Dr. Caron mentors graduate students in the University of Utah's Molecular Biology Program and directs an active laboratory utilizing genetic, imaging, and behavioral approaches. Her research program is supported by competitive grants including the NSF CAREER award, with collaborations spanning neuroscience and evolutionary biology. Current projects investigate multisensory integration mechanisms, evolutionary drivers of neural circuit specialization, and developmental basis of sensory wiring. The Caron Lab maintains specialized facilities for Drosophila neurogenetics, advanced microscopy, and behavioral analysis. Her team collaborates with the University of Utah's Brain Institute and Center for Cell and Genome Science, contributing to interdisciplinary initiatives in neural circuit mapping and evolutionary neuroscience. Ongoing work explores how sensory representations evolve in response to ecological pressures and how circuit architecture enables flexible behavior in complex environments.
Dr. Quirin Thomas Simon Vogel is a Senior Lecturer at the Department of Statistics, University of Klagenfurt. He previously held postdoctoral positions at the Technical University of Munich, New York University Shanghai, and served as an Interim Professor at Ludwig-Maximilians University of Munich. His research bridges probability theory with statistical mechanics and algorithmic applications. Current role: Senior Lecturer (2025) Previous roles: Postdoc (TUM, NYU Shanghai), Interim Professor (LMU Munich) His research focuses on: Random walks and their geometric/stochastic properties Randomized algorithms with applications in statistical models Quantum-inspired probabilistic systems (e.g. interacting bosonic loop soups) Large deviation theory for complex systems Percolation and phase transitions in particle models The articles reflect trends in probability theory, mathematical physics, and algorithmic applications. Key topics include high-dimensional percolation, Bose gas models, neural network theory, and stochastic geometry. The work combines rigorous mathematical analysis with interdisciplinary applications in physics and computer science. Scientific awards and functions cannot be determined from the provided data, as they describe other researchers. The department's research activities include projects on statistical learning, quantum models, and algorithmic probability, though Vogel's direct involvement in these specific funded projects isn't explicitly stated.
Pradeep Kumar is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), specializing in quantum cryptography, quantum optics, and fiber-optic communications. His research focuses on secure quantum communication systems and the application of quantum phenomena in information processing. Dr. Kumar received his PhD from IIT Madras in 2009 under the supervision of Anil Prabhakar. He completed his B.E. at M.V.J. College of Engineering, Visweswaraiah Technological University in 2002. His research interests span quantum cryptography and computation, quantum and nonlinear optics, and fiber-optics. Dr. Kumar's work primarily explores quantum key distribution systems, examining various approaches including frequency coding, decoy states, and spin wave-optical interactions. His research has significant implications for secure communications and quantum information processing. Dr. Kumar's publications demonstrate a consistent focus on quantum communication technologies, with particular emphasis on improving the reliability and security of quantum key distribution systems. His research trajectory shows progression from fundamental quantum state manipulation to practical implementations of quantum cryptography. He maintains an active research laboratory within the Advanced Centre for Electronic Systems (ACES) at IIT Kanpur, where he supervises graduate students working on quantum communication technologies and optical systems.
