Michael T. Monaghan is a Professor at the Faculty of Biology, Chemistry and Pharmacy, Freie Universität Berlin, and leads the Research Group in Molecular Ecology and Genomics at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB). His work bridges evolutionary biology, molecular ecology, and environmental monitoring, focusing on freshwater systems and urban aquatic habitats. Education: B.S. (The Ohio State University, 1995), M.S. (Idaho State University, 1998), Dr. sc. nat. (ETH Zürich, 2002) Key Positions: Senior Scientist/Research Group Leader at IGB (2008–present), Visiting Professor at Duke-NUS Medical School (2016), Guest Professor at Ehime University (2015–2016) Dr. Monaghan's research explores freshwater biodiversity dynamics using molecular tools like metabarcoding and phylogenomics. His team investigates how environmental changes—climate warming, urbanization, pollution—affect microbial and insect communities, with a focus on eDNA applications and ecohydrology. His publications (2022–2025) span topics such as climate-driven plankton shifts, urban habitat adaptation by bees, toxin genetics in algae, and metagenomic monitoring of aquatic systems. Collaborative projects like BiNatUr and ODER~SO emphasize urban resilience and disaster ecology. Teaching includes data science in biology, evolutionary ecology, and environmental genomics at Freie Universität Berlin and the Institute of Computer Science. He mentors doctoral and master’s students in molecular ecology and trains interns in biodiversity genomics. Lab Members: Dr. Dagmar Frisch (Scientific Staff) Dr. Katrin Kiemel (Postdoc) Elisabeth Funke (Research Technician) Doctoral Candidates: Onur Erk Kavlak, Athena Karapli-Petritsopoulou, Daniel Wewer
Dr. Ken Ferens is an Assistant Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba. He serves as the Computer Engineering Champion in the Centre for Engineering Professional Practice and Engineering Education and directs the Applied Cognitive Intelligence (ACI) Research Group. Dr. Ferens is a senior member of the Institute of Electrical & Electronics Engineers (IEEE), Chair of the EduManCom Chapter of the IEEE, Vice-Chair of the Computer and Computational Intelligence Chapter of the IEEE, and Chair of the Industry, Teaching Assistants, and Student Forums for Engineering Curriculum Review and Improvement. Ph.D. (Computer Engineering), University of Manitoba, 1996 M.Sc. (Computer Engineering), University of Manitoba, 1991 B.Sc. (Electrical Engineering), University of Manitoba, 1989 Dr. Ferens has over 33 years of research experience in computational intelligence, focusing on cognitive machine learning, artificial intelligence, cognitive computational intelligence, chaos theory applications, agent-based models, and various optimization algorithms including simulated annealing, genetic algorithms, artificial neural networks, and particle swarm optimization. His research applies these techniques to develop software and hardware intrusion detection systems for cybersecurity applications. He teaches graduate-level courses on Computer Network Security and Applied Computational Intelligence, providing students with theoretical background and hands-on experience in state-of-the-art security methods. Analysis of Dr. Ferens' recent publications reveals a strong focus on applying cognitive and chaotic computational techniques to cybersecurity challenges, particularly malware detection and network intrusion detection. His work increasingly integrates complexity theory, fractal analysis, and hybrid optimization approaches to enhance security systems' effectiveness. There's a clear progression toward more sophisticated machine learning architectures applied to increasingly complex security scenarios, with growing emphasis on real-world IoT and network security applications. Best Paper Award at IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 2022) Best Paper Award at IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 2015) Best Journal Paper Award for 2013 (Journal of ICT Research and Applications) Best Poster Award at 12th International Conference on e-Health Networking, Application & Services (2010) Best Paper Award at IASTED International Conference on Computer, Electronics, Control, and Communication (1991) Dr. Ferens collaborates with national and international industry partners including the Department of Advanced Information Management, Content Technology Canadian Tire Corporation (CTC), and Magellan Aerospace. His research group has received funding supporting the Cyber-security Research Program, developing practical applications of computational intelligence for security systems. He has supervised numerous graduate students in the Electrical and Computer Engineering department, focusing on research at the intersection of machine learning and cybersecurity. Dr. Ferens leads the Applied Cognitive Intelligence (ACI) Research Group within the Department of Electrical and Computer Engineering, which focuses on applying cognitive, chaotic, and computationally intelligent algorithms to build intrusion detection systems. The group collaborates with industry partners to develop practical security solutions while providing students with hands-on research experience in cutting-edge security technologies. Their work spans both theoretical algorithm development and practical hardware implementation for real-world security applications.
Paul Wiegert is a Full Professor in the Department of Physics and Astronomy at the University of Western Ontario , where he has been since 1996 after positions at York University and Queen's University. He is a member of the Institute for Earth and Space Exploration (IESX) and the Centre for Planetary Science and Exploration (CPSX) . His research spans asteroid dynamics , exoplanet systems , and celestial mechanics , with notable work on Earth co-orbital asteroids like (3753) Cruithne and Earth's first Trojan asteroid 2010 TK7. Education : PhD in Astronomy (University of Toronto, 1996) Research Domains : Planetary Science, Astronomy, Big Data Analytics His recent publications focus on interstellar transport mechanisms , asteroid impact risks , and exomoon detection . Key findings include quantifying risks from asteroid 2024 YR4's potential lunar impact and demonstrating the feasibility of detecting alpha Centauri-origin material in our solar system. He actively supervises graduate students like Cole Gregg and participates in NSERC-funded summer research programs for undergraduates. For planetary defense, he has analyzed collision probabilities for Apophis and developed meteoroid hazard models for spacecraft. His work appears in Planetary Science Journal , Nature Astronomy , and Astrophysical Journal Letters , with media coverage in 60+ outlets and 126 X (Twitter) mentions .
Professor Ioannis Katakis is a Faculty Member at the University of Nicosia, where he is affiliated with the School of Sciences and Engineering and the Department of Computer Science. He has held various academic positions across multiple institutions including Aristotle University of Thessaloniki, University of Cyprus, Cyprus University of Technology, Open University of Cyprus, Hellenic Open University, Athens University of Economics and Business, and National and Kapodistrian University of Athens. His educational background includes a PhD in Machine Learning for Automated Text Classification (2005-2009), a Master's in Information Systems (2005-2007), and a Bachelor's in Computer Science (2000-2004), all from Aristotle University of Thessaloniki. Professor Katakis specializes in several cutting-edge areas of computer science and data analysis. His primary research interests include Mining Social, Web and Urban Data , Sentiment Analysis and Opinion Mining , Data Streams , and Multi-label Learning . His work bridges theoretical machine learning approaches with practical applications in social media analysis, healthcare informatics, privacy protection, and smart city technologies. He has published extensively in top venues including CIKM, ECML/PKDD, IEEE TKDE, and ECAI. His recent publications demonstrate a clear trend toward applying machine learning techniques to real-world problems with societal impact. He has focused on areas such as GDPR compliance in smart devices, sentiment analysis in crowd-sourced content, healthcare applications including drug reaction classification and brain disease monitoring, and privacy protection in wearable technologies. His work often involves multi-modal data analysis and addresses challenges in data streams and multi-label classification. Professor Katakis has made significant contributions to his field, with his research cited over 4,200 times. He serves as an Editor for the journal Information Systems and has edited four special issues in journals such as DAMI and InfSys. He regularly contributes to the academic community by serving on program committees for major conferences including ECML/PKDD, WSDM, DEBS, and IJCAI, and by reviewing for prestigious journals like TPAMI, DMKD, TKDE, TKDD, JMLR, TWEB, and ML. He has been actively involved in European research projects, notably serving as Quality Assurance Coordinator and Senior Researcher for projects such as VAVEL (www.vavel-project.eu) and INSIGHT (www.insight-ict.eu). His grant activities demonstrate a strong focus on collaborative, interdisciplinary research with practical applications in urban data management, social media analysis, and healthcare informatics. He has organized three workshops at major conferences (ICML, ECML/PKDD, EDBT/ICDT) and has extensive experience translating research into practical applications through his involvement in European projects.
Professor Robin Hendry is a faculty member at Durham University , serving as Co-Director of CHESS (Centre for Humanities Engaging with Science and Society) and actively engaged in research leave . His academic work bridges philosophy of science , history of chemistry , and metaphysical questions in physical sciences , with a focus on scientific realism , emergence , and chemical substances . Robin's research explores the ontological relationship between chemistry and physics , arguing for a realist view of chemical substances and strong emergence in chemical systems. He has authored influential works such as 'The Metaphysics of Chemistry' and co-edited The Routledge Handbook of Emergence (2019), contributing to debates on downward causation and scientific modeling . 2023 : Structure, essence and existence in chemistry 2021 : Elements and principles, Structure and emergence 2020 : Trusting Atoms 2019 : Emergence in Chemistry, Philosophy of Chemistry (Stanford Encyclopedia) Recent scientific awards include John Templeton Foundation funding for the Durham Emergence Project (2013-16) and AHRC grants for 'Structure in Chemistry' (2012). He has supervised PhD students in philosophy of science and served as Director of Research in the Department of Philosophy.
Gianni Franchi is an assistant professor at ENSTA Paris , affiliated with the Computer Science and Systems Engineering Unit (U2IS) . His work focuses on theoretical deep learning , with a strong emphasis on uncertainty quantification, robustness, and explainability in machine learning models. Current affiliation: ENSTA Paris (U2IS) Academic rank: Assistant Professor Key collaborators: David Filliat, Emanuel Aldea, Andrei Bursuc, Antoine Manzanera His research spans uncertainty quantification , explainable AI , and reliable machine learning . He investigates methods like Bayesian neural networks, ensemble approaches, and deterministic uncertainty models. His work also addresses domain adaptation , self-supervised learning , and autonomous systems , particularly in trajectory forecasting and semantic segmentation for autonomous driving. Recent publications analyze probabilistic modeling for robustness, symmetry-aware Bayesian methods , and multi-modal datasets like InfraParis. He develops frameworks like Torch-Uncertainty and benchmarks such as MUAD for uncertainty types in autonomous driving. Key themes: Uncertainty Quantification Deep Learning Theory Autonomous Systems Explainable AI Dataset Creation Bayesian Methods
Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections
Dr. Victoria C. P. Chen is a Professor in the Industrial, Manufacturing, and Systems Engineering (IMSE) department at The University of Texas at Arlington (UTA), where she has served since 2002. She previously held positions at the Georgia Institute of Technology from 1993-2001. Dr. Chen has held several leadership roles at UTA, including Interim Department Chair (2012-2014), Director of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) (2008-2012, and again from 2017-present), and Director of Doctoral Studies (2019-present). She was also the George & Elizabeth Pickett Professor from 2015-2017 and was inducted into the UT Arlington Academy of Distinguished Teachers in 2019. Dr. Chen is actively involved with INFORMS (Institute for Operations Research and the Management Science), where she currently serves as Secretary on the Executive Board. Dr. Chen earned her B.S. in Mathematical Sciences from The Johns Hopkins University, and her M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. Her academic journey includes visiting professorships at the University of Genoa, Italy, and Iowa State University. Dr. Chen's research utilizes statistical perspectives to create new methodologies for operations research problems appearing in engineering and science. Her expertise includes the design of experiments, statistical modeling, and data mining, particularly for computer experiments and stochastic optimization. Through her statistics-based approach, she has developed computationally-tractable decision-making methods for many high-dimensional complex systems. Her work spans multiple domains including sustainability, energy, water management, healthcare, and law enforcement. Specific application areas include inventory forecasting, airline optimization, water reservoir networks, wastewater treatment, air quality monitoring, green building design, nurse assignment systems, and pain management programs. Her recent publications demonstrate continued innovation in mixed integer programming for electric vehicle charging stations, vacuum ultraviolet spectroscopy prediction, and sustainable building education. Senior Member, Institute for Operations Research and the Management Sciences (INFORMS) (2024) Data Mining Prize (Lifetime Achievement Award), INFORMS Society on Data Mining (2023) College of Engineering Teaching Award, UT Arlington (2021) Third Place Award, C3.ai COVID-19 Grand Challenge (2020) Academy of Distinguished Teachers, University of Texas at Arlington (2019) George & Elizabeth Pickett Professorship (2015-2017) As an educator and mentor, Dr. Chen has advised over 25 doctoral students across diverse research topics in operations research and systems engineering. She has secured substantial research funding from multiple sources including the National Science Foundation (over $1.5 million in active projects), Environmental Protection Agency, National Institute of Justice, and industry partners like Luminant and Dallas-Fort Worth International Airport. Her current research projects focus on decision analytics for sustainable urban environments, optimization for Texas water management, and statistical methods for pain management programs. She has served as Principal Investigator or Co-PI on more than 20 externally funded research projects totaling over $3 million in funding. Dr. Chen co-founded the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at UTA with Dr. H. W. Corley. This research center brings together faculty and students from multiple disciplines to address complex problems through advanced statistical and optimization methods. She also leads interdisciplinary research teams working on projects related to sustainable infrastructure, energy systems, and healthcare optimization, frequently collaborating with researchers from civil engineering, environmental science, and medical fields.
Vassilios Tzerpos is an Associate Professor at the Lassonde School of Engineering, York University, where he has been since 2001. He holds a Ph.D. in Computer Science from the University of Toronto (2001). His research focuses on audio processing for musical applications, deep learning, digital signal processing, machine listening, and software engineering education. He directs the APTLY lab exploring music-technology intersections and leads the LaSSoftE lab developing socially-oriented software solutions. Education: Ph.D. in Computer Science, University of Toronto, 2001 Research Highlights: Dr. Tzerpos' work spans music information retrieval (e.g., automatic music classification), synthetic speech detection using neural networks, and software engineering pedagogy. His recent projects include Music-STAR for audio re-instrumentation and OER-based learning path creation systems. He has pioneered methods in design pattern detection and software clustering evaluation. Grants & Labs: Leads two research groups: APTLY (music-tech) and LaSSoftE (social impact software). Active in developing adaptive cybersecurity solutions against DoS attacks and refining software architecture recovery techniques. Key Themes in Publications: Recent work emphasizes machine learning applications in music technology and cybersecurity, with foundational contributions to software clustering methodologies and design pattern detection algorithms. His work bridges theoretical computer science with practical applications in education and creative industries.
FANG Yuan is a tenured Associate Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He holds the prestigious Lee Kong Chian Fellowship and leads research in artificial intelligence and data science. His institutional affiliation includes: School of Computing and Information Systems, Singapore Management University Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2014) Bachelor of Computing (First Class Honors), National University of Singapore (2009) - Top student in Computer Science Research Focus: Dr. FANG specializes in data mining, machine learning, and AI with emphasis on graph learning, information networks, recommendation systems, and knowledge graph applications. His work bridges theoretical foundations with practical applications in social analytics, biomedical informatics, and digital transformation, often employing advanced neural network architectures. Publication Trends: Recent works (2024-2025) demonstrate strong focus on graph machine learning innovations, including graph foundation models, prompt-based learning for dynamic graphs, and LLM-graph integrations. Key themes include few-shot/zero-shot learning, non-homophilic graph processing, and applications in recommendation systems, bioinformatics, and NLP. Methodological advancements frequently involve contrastive learning, transformer architectures, and explainable AI techniques. Awards & Honors: Lee Kong Chian Fellow World's Top 2% Scientist (2024) by Stanford/Elsevier #1 Most Influential Paper at WWW'23 (GraphPrompt) - Paper Digest (2024-09) Top 5 Most Influential Papers at WWW'23 (GraphPrompt) - Paper Digest (2024-05) Top Computer Science Graduate, NUS (2009) Student Advising: Currently advises doctoral candidates including DONG Viet Hoang, LIU Ran, and NIU Yudong. Recently supervised Dr. Zhongzhou Liu's successful PhD defense (2024) on trustworthy recommendation systems. Professional Engagement: Regularly organizes tutorials at premier venues (WWW, KDD) and delivers invited talks internationally on graph learning advancements. Leads multiple research projects in collaboration with industry partners.
Hasan Ayaz, PhD, is an Associate Professor at Drexel University’s School of Biomedical Engineering, Science and Health Systems, and the Department of Psychology in the College of Arts and Sciences. He is a core member of the CONQUER Collaborative and has affiliations with the University of Pennsylvania and Children’s Hospital of Philadelphia. His research focuses on neuroengineering, neuroergonomics, and clinical applications of optical brain imaging, particularly using fNIRS and EEG. He has over 200 publications and has secured funding from federal agencies and industry partners. Dr. Ayaz serves on editorial boards for journals like PLOS One and Frontiers in Human Neuroscience and has organized international neuroergonomics conferences. Education: BSc (Electrical and Electronics Engineering, Boğaziçi University, Turkey), MSc and PhD (Drexel University). Research Interests: Neuroergonomics, functional neuroimaging, biomedical signal processing, neuroengineering, fNIRS, EEG, brain-computer interfaces, and mobile neuroimaging. His work aims to develop next-generation brain imaging technologies for applications ranging from aerospace to healthcare. Key Awards: Received a Wellcome LEAP Grant for Addiction Research in 2024. Grants & Advising: Extensive federal and corporate funding; no explicit student list provided. His research involves interdisciplinary collaborations and clinical partnerships. Labs/Teams: Leads the CONQUER Collaborative and contributes to the Cognitive Neuroengineering group at Drexel.
Matthew Holden is an Associate Professor in the School of Computer Science at Carleton University. He holds a PhD (2018) and MSc (2014) from Queen's University and a BScH (2012) from Western University. His research focuses on Surgical Data Science, applying machine learning to surgical time-series data from operating rooms and simulations to improve patient outcomes and surgical training. Key areas include real-time decision support, performance assessment, and surgical efficiency through domain-knowledge integration. Research interests emphasize machine learning for surgical workflows, skill assessment via sensor data (e.g., motion tracking, EEG), and computer-assisted interventions. Notable work includes automated proficiency evaluation in cataract surgery, ultrasound-guided procedures, and neurosurgical training. His contributions span medical robotics, surgical education, and clinical decision support systems. Publications highlight advancements in surgical workflow anticipation, tool detection, and skill metrics across domains like ophthalmology, emergency medicine, and neurology. Holden advocates for interdisciplinary approaches combining computational methods with clinical expertise to enhance healthcare delivery.
Ion Androutsopoulos is a Professor of Artificial Intelligence in the Department of Informatics at Athens University of Economics and Business (AUEB), where he also serves as Head of Department. He is founder and co-director of AUEB's Natural Language Processing Group and an Adjunct Researcher at the Digital Curation Unit and "Archimedes" Research Unit of the Research Centre "Athena". His research spans multiple dimensions of Artificial Intelligence with a focus on Natural Language Processing. Key interests include: Machine learning in NLP, particularly deep learning and large language models Question answering and retrieval augmented generation for document collections Dialog systems for new languages and knowledge domains Sentiment analysis and emotion recognition from text and speech Detecting toxic posts and disinformation online Image-to-text generation for medical diagnostics NLP applications in biomedical, legal, and financial domains His recent publications demonstrate strong activity across medical AI (particularly ImageCLEFmed Caption competitions where his group consistently ranks 1st-2nd), legal NLP (LexGLUE benchmark), financial NLP (EDGAR-CRAWLER), and multilingual challenges. His work shows increasing emphasis on large language models, explainability, and practical applications. Notable awards include: Top 2% scientist worldwide (Stanford University database, 2023) Multiple AUEB Excellent Teaching Awards (2017-18, 2021-22, 2023-24) Three consecutive BioASQ awards (2018-2020) Multiple 1st/2nd place rankings in ImageCLEFmed Caption competitions (2021-2025) He actively organizes major events including the Athens Natural Language Processing Summer School (AthNLP) and SemEval tasks. His group maintains strong industry and research collaborations, particularly in medical AI applications where they've developed systems that generate diagnostic captions from medical images with state-of-the-art performance.
Mustafa Bilgic is a Professor and Chair of the Computer Science Department at Illinois Institute of Technology, where he also directs the Master of Artificial Intelligence program and the Machine Learning Laboratory. His research focuses on machine learning, active learning, explainable AI, and probabilistic graphical models, with applications in healthcare, social media analysis, and biomedical engineering. He has received funding from NSF, NIH, and Samsung, among others. Education: PhD in Computer Science, University of Maryland at College Park (2010) M.S. in Computer Science, University of Maryland at College Park (2006) B.S. in Computer Science, University of Texas at Austin (2004, with High Honors and Special Honors) Research Highlights: Dr. Bilgic's work emphasizes AI ethics, algorithm transparency, and interactive machine learning systems. Notable projects include analyzing political news engagement dynamics and developing frameworks for eliminating explanation noise in AI models. His lab explores tools like OrganoID for tracking organoid growth and IDGI for improving model interpretability. Awards: NSF CAREER Award (2014) ACM SIGKDD Best Student Paper Award (2008) Illinois Tech College of Computing Teaching Excellence Award (2021) Teaching and Leadership: Bilgic teaches advanced courses in AI, machine learning, and data mining. He leads initiatives to bridge AI theory and practical applications, emphasizing interdisciplinary collaboration. His administrative roles include overseeing the AI master’s program and fostering innovation in computing education.
Dr. Steven Manson is a Professor in the Department of Geography, Environment, and Society at the University of Minnesota's College of Liberal Arts, where he also served as Associate Dean for Research and Graduate Programs. He directs the Human-Environment Geographic Information Science (HEGIS) laboratory and leads major data science initiatives like the National Historical Geographic Information System (NHGIS) and IPUMS Terra. PhD in Geography, Clark University (2002) BA Honours in Geography, University of Victoria (1995) His research focuses on geographic information science and human-environment systems , using agent-based modeling and big data to analyze land use change, urban dynamics, and sustainability challenges. Recent work explores spatiotemporal data harmonization and geospatial cyberinfrastructure . The articles reveal trends in GIScience methodology , urbanization analysis , and data-intensive sustainability research . Key contributions include self-organizing map applications for health data and hybrid statistical-GIS techniques for environmental policy. Scientific accolades include: Ecological Society of America Sustainability Science Award NASA Earth System Science Fellow McKnight Land Grant Professorship As Principal Investigator for NHGIS and IPUMS Terra, he secured over $40M in NSF, NIH, and DOJ grants for spatiotemporal data infrastructure. Outreach initiatives include developing open geospatial textbooks adopted globally and collaborating with Twin Cities K-12 programs.
