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Nico FranzView profile

Professor

• Biodiversity Data Science
• Insect Systematics
• Evolution
• +3 more

Nico Franz is a Professor in the Department of Ecology and Evolutionary Biology at the University of Kansas, and serves as Krishtalka Director of the Biodiversity Institute and Natural History Museum. He holds a Ph.D. in Systematic Entomology from Cornell University (2005), an M.Sc. in Biology from the University of Costa Rica (1999), and a Prediploma in Biology from the University of Hamburg (1996). His research focuses on evolutionary biology, insect systematics (specializing in weevils), and the development of innovative biocollections infrastructure and biodiversity data science services. He leads NSF-funded projects such as iDigBio Phase 3 and the NEON Biorepository operations. Education: Ph.D. Systematic Entomology, Cornell University (2005) M.Sc. Biology, University of Costa Rica (1999) Prediploma Biology, University of Hamburg (1996) His research interests include biocollections infrastructure, biodiversity data science, insect systematics and evolution, and societal engagement through initiatives like the Symbiota portal network. He has extensive experience in teaching undergraduate research and courses in general entomology. Key grants include NSF DBI 2027654 (iDigBio Phase 3) and NSF DBI 1724433 (NEON Biorepository). His work emphasizes FAIR data principles, taxonomic curation, and global biodiversity data integration. He collaborates widely on projects addressing ecological monitoring, taxonomic conflict resolution, and digital specimen management. He directs the Biodiversity Institute, overseeing collections and research divisions including Entomology, Botany, and Informatics. His lab focuses on advancing biodiversity knowledge through data-driven approaches and infrastructure development.

Leocadio Blanco BercialView profile

Assistant Professor

• Zooplankton Ecology
• Molecular Phylogenetics
• Biodiversity
• +3 more

Dr. Leocadio Blanco Bercial is an Assistant Professor at Arizona State University (ASU), affiliated with the School of Ocean Futures and the School of Life Sciences. He is also a Senior Global Futures Scientist at the Bermuda Institute of Ocean Sciences (BIOS), now integrated into ASU. His research focuses on zooplankton ecology, integrating molecular and morphological tools to study diversity, population connectivity, and evolutionary processes in marine systems. Education: PhD in Biology (University of Oviedo, Spain, 2009), M.S. in Biology (2002), and B.S. in Biology (2000), all from the University of Oviedo. Research Interests: Plankton community diversity, population connectivity in marine organisms, phylogenetic relationships within Copepoda, and integrating diversity measurements into ocean biogeochemical models. His work emphasizes understanding how diversity is maintained in open ocean ecosystems and its implications for climate change. Key Projects: Includes studies on zooplankton mediation in particle formation, the VAMPIRE autonomous profiling vehicle, and the BIOSSCOPE project examining plankton ecology in the Sargasso Sea. Courses taught include Ocean Futures, Ecology, and special topics in marine sciences. Lab Affiliation: Zooplankton Ecology Lab, focusing on molecular and classical ecological approaches to study marine plankton diversity and dynamics. Collaborative efforts include global initiatives on copepod phylogeny and population genomics.

• Medical Informatics
• Healthcare Information Systems
• Database Systems
• +3 more

Ao.Univ.Prof. Dipl.-Ing. Dr.techn. Ernst Schuster is an Associate Professor in the Department of Databases and Artificial Intelligence at Technische Universität Wien. His research focuses on healthcare information systems, medical informatics, and telemedicine infrastructure. He has supervised numerous theses on topics ranging from cancer documentation systems (e.g., HNOOncoNet) to mobile health applications and medical data security. His work emphasizes interdisciplinary collaboration between informatics and healthcare domains. Key research interests include: Database systems for medical applications Telemedicine and remote patient monitoring Software engineering for healthcare IT Decision support systems in clinical settings Recent publications highlight innovations in: Medical data version control systems Usability engineering for healthcare interfaces Telecommunication security frameworks AI-driven diagnostic tools Supervised over 30 graduate students since 2002, contributing to advancements in medical informatics and health technology. Active in developing web-based medical documentation systems and e-learning platforms for medical education.

• Machine Learning
• Enterprise Architecture
• Conceptual Modeling
• +3 more

Philipp-Lorenz Glaser is a PreDoc Researcher at the Department of Business Informatics, Technische Universität Wien. His research focuses on conceptual modeling, enterprise architecture, machine learning applications, and knowledge graph construction. Role: PreDoc Researcher (E194-03) Contact: philipp-lorenz.glaser@tuwien.ac.at His work explores methods for encoding semantic information in conceptual models to improve machine learning applications, with a focus on FAIR datasets and hybrid modeling approaches. Key themes include: Enterprise architecture modeling practices Graph-based data representation Integration of textual and graphical modeling Development of modeling tools (e.g., Sprotty-based solutions)

• Reading Comprehension
• Linguistics
• Educational Psychology
• +3 more

Julie Van Dyke is an Assistant Clinical Professor at the Child Study Center, Yale University. Her research focuses on reading comprehension mechanisms, linguistics, and educational interventions, employing eye-tracking technology and computational methods. She explores how text formatting, acoustic features, and linguistic structures influence comprehension across populations including ELL students and adolescents. Notable contributions include databases like DerLex and CompLex, analyzing word reading behavior through eye-movement studies. Her work bridges cognitive science with practical educational applications, addressing both theoretical and applied challenges in literacy development. Her academic contributions extend to special issue curation and peer-reviewed publication processes, reflecting her role in advancing scholarly discourse in reading research. While no explicit awards or student advisement are noted in the text, her prolific output since 2019 demonstrates sustained engagement with language processing, phonological awareness, and syntactic-semantic interactions in comprehension.

Yusuf OzbekView profile

Teaching Professor

• Machine Learning
• Deep Learning
• Neural Networks
• +4 more

Yusuf Ozbek is a Teaching Professor in the Information Systems Program at Northeastern University's College of Engineering. He has been teaching since 2007, blending industry experience (Web Programmer, CAD Engineer) with academic roles. Dr. Ozbek holds a Ph.D. in Interdisciplinary Engineering (2012) and MS degrees in Engineering Management (1997) and Computer Systems Engineering (2004), all from Northeastern University, alongside a BS in Mechanical Engineering from Yildiz Technical University (1994). Research Interests : Machine learning, deep learning, neural networks, big data systems, web technologies, and information security. His work spans brain-computer interfaces, distributed applications, and manufacturing optimization strategies. Education : PhD (2012): Interdisciplinary Engineering, Northeastern MS (2004): Computer Systems Engineering, Northeastern MS (1997): Engineering Management, Northeastern BS (1994): Mechanical Engineering, Yildiz Technical University Publications focus on real-time brain interfaces using Hadoop, Q-learning for manufacturing maintenance, and big data applications. His 2017 Martin W. Essigman Outstanding Teaching Award reflects his dedication to pedagogy. Awards : 2017 Martin W. Essigman Outstanding Teaching Award Teaching : Courses include Web Programming, Database Design, Algorithms, and Big Data Systems. His work bridges industry practice and academic rigor.

Haug Dag Trygve TruslewView profile

Professor

• Corpus linguistics
• Syntactic theory
• Formal semantics
• +4 more

Dag Trygve Truslew Haug is a Professor of Linguistics and Greek/Latin at the University of Oslo, affiliated with the Faculty of Humanities and the Department of Classics. He holds concurrent roles as a Professor of Linguistics and has served as a Visiting Professor at the University of Oxford. His academic journey includes degrees from the University of Oslo: Cand. mag. (1996), Cand. phil. (1998), and Dr.art. (2001) in Ancient Greek linguistics. Haug’s research focuses on corpus linguistics, syntactic theory, formal semantics, and Indo-European languages, with significant contributions to computational linguistics and historical linguistics. He leads projects like the DAMOS database for Mycenaean studies and the PROIEL treebank for early Indo-European texts. His awards include membership in the Norwegian Academy of Science and Letters (2016) and the HM King of Norway's Gold Medal for Young Scholars (2002). Haug is actively involved in teaching Greek, Latin, and comparative Indo-European linguistics, and serves on curriculum committees for these disciplines. His research groups include Digital Humanities, Grammar and Meaning, and Language Change. Key areas of innovation involve integrating formal semantics with dependency grammar and developing computational tools for analyzing ancient languages.

Mai OtsukiView profile

Associate Professor

• Human-Computer Interaction
• Augmented Reality
• Virtual Reality
• +5 more

Mai Otsuki is an Associate Professor in the Department of Information and Computer Science at Keio University's Faculty of Science and Technology, specializing in Human-Computer Interaction with a focus on Augmented and Virtual Reality systems. Her research spans nearly two decades, with an extensive publication record in top-tier venues including CHI, UIST, VRST, and IEEE Transactions on Visualization and Computer Graphics. Dr. Otsuki's primary research interests center on developing novel interaction techniques for augmented and virtual environments, particularly focusing on remote collaboration, spatial interfaces, and haptic feedback systems. Her work addresses fundamental challenges in how humans perceive and interact with virtual elements, with practical applications in remote instruction, industrial training, sports education, and service industry skill development. She has pioneered approaches for gaze representation, semantic labeling in AR, and physical interaction with virtual objects. Her recent publications (2023-2025) demonstrate continued innovation in applying AR/VR technologies to practical domains including industrial machinery operation, shopping experiences, and assembly tasks, with growing emphasis on physiological metrics and cognitive load assessment. Her work consistently bridges theoretical HCI principles with real-world applications, showing particular strength in experimental design and user-centered evaluation methodologies. Dr. Otsuki has established a robust collaborative network with researchers across Japan and internationally, frequently working with Hideaki Kuzuoka, Asako Kimura, Fumihisa Shibata, and Hideyuki Tamura. Her research group appears to focus on developing practical AR/VR systems that address specific human performance challenges in collaborative and instructional contexts.

Hongsong FengView profile

Visiting Assistant Professor

• Machine Learning
• Topological Data Analysis
• Computational Biology
• +3 more

Hongsong Feng is a Visiting Assistant Professor in the Department of Mathematics at Michigan State University (MSU), located in C309 Wells Hall. His research focuses on interdisciplinary applications of mathematics and machine learning to problems in computational biology, drug discovery, and topological data analysis. He specializes in combining advanced mathematical frameworks like persistent homology, algebraic topology, and differential geometry with machine learning techniques to analyze complex biological systems and molecular interactions. Key research interests include protein flexibility analysis, drug-target interaction networks, and the development of AI-driven methods for predicting protein-ligand binding affinities. His work often bridges theoretical mathematics with practical biomedical challenges, such as opioid use disorder treatment and cocaine addiction drug development. Recent projects involve leveraging transformers, knot theory, and generative network models to advance drug discovery and systems pharmacology. Notable contributions include methodologies like Persistent Sheaf Laplacian analysis, CAML (Commutative Algebra Machine Learning), and TIDAL (Topology-Inferred Drug Addiction Learning). These approaches integrate multiscale geometric learning and topological signal processing to model molecular dynamics and network complexities. His research also extends to numerical methods for solving elliptic interface problems and parabolic partial differential equations, reflecting a strong foundation in applied mathematics. Despite his extensive publication record, no specific academic awards or grants are explicitly listed in the provided materials. No formal advisees are mentioned, suggesting he may be early in his academic career or focused primarily on research collaborations.

Maryam MajediView profile

Assistant Professor

• Data Privacy
• Embedded Ethics
• Privacy Policies
• +3 more

Maryam Majedi serves as an Assistant Teaching Professor within the Department of Computer Science at the University of California, Santa Barbara (UCSB), where she spearheads initiatives integrating ethical frameworks into computing education and practice. Her academic appointment emphasizes pedagogical innovation in addressing critical challenges at the intersection of technology, privacy, and societal impact. Her educational credentials include: PhD in Computer Science from the University of Calgary MS in Computer Science from the University of New Brunswick Majedi's research centers on embedded ethics and data privacy, with pioneering work in developing educational modules that operationalize ethical considerations within computer science curricula. She investigates real-world applications including pandemic contact tracing systems, exposure notification technologies, and social networking platforms, creating unified frameworks for privacy policy modeling and visualization. Her scholarship bridges technical implementation with philosophical dimensions of digital ethics, emphasizing actionable pedagogical strategies for cultivating responsible technologists. Analysis of her 2020-2024 publications reveals a cohesive trajectory addressing ethical trade-offs in public health technologies, empirical validation of ethics-integrated curricula, and standardized approaches to privacy policy interpretation. This body of work demonstrates consistent focus on translating theoretical ethics frameworks into practical computing contexts, with increasing emphasis on pandemic-related technologies and policy compliance mechanisms.

Antigoni TsiamiView profile

Research Fellow

• Multimodal Signal Processing
• Robotics
• Speech Enhancement
• +7 more

Antigoni Tsiami is a Postdoctoral Research Associate at the National Technical University of Athens (NTUA), affiliated with the School of Electrical and Computer Engineering (ECE) and the CVSP Group. She has been a PhD student since 2013, focusing on audio and speech processing applications. Her work spans robotics, signal processing, and assistive technologies. Education: Diploma in Electrical and Computer Engineering from NTUA (2012) PhD candidate in the CVSP Group, ECE School, NTUA under Prof. Petros Maragos Research interests: Multichannel speech enhancement and acoustic source localization Audiovisual scene perception for human-robot interaction Far-field processing techniques MEMS microphone applications Multimodal perception systems for assistive robotics Home automation control via spoken commands Her research projects include BabyRobot and COGNIMUSE , exploring multimodal signal processing in cognitive robotics and human interaction scenarios. Recent publications demonstrate focus on integrating sensory data for practical robotics applications, emphasizing robustness in real-world environments. She collaborates with interdisciplinary teams including the CVSP Group and EU-funded initiatives. Advising/Grants: While no advisees are listed, her current postdoctoral role suggests ongoing research collaborations. She participates in grant-funded projects such as BabyRobot and COGNIMUSE. Labs/Teams: Active member of the CVSP Group and contributed to the IROS 2015 workshop on Cognitive Mobility Assistance Robots. Engaged with multidisciplinary teams in EU robotics initiatives.

Wael MohammedView profile

Researcher

• Robotics
• Industry 4.0
• Digital Twins
• +5 more

Wael Mohammed is a Postdoctoral Researcher and Doctoral Researcher at Tampere University's Automation Technology and Mechanical Engineering department. His research focuses on advanced robotics, Industry 4.0, and digital twin architecture in manufacturing systems. He holds an MSc in Automation Engineering (2017) and a BSc in Mechatronics Engineering (2010) from the University of Jordan. His work emphasizes human-robot collaboration, cognitive validation of AI systems, and machine vision for food manipulation. Key contributions include methodologies for digital twin implementation in factories and ontology-driven semantic frameworks for manufacturing processes. Mohammed has contributed to projects like the FASTory digital twin initiative and REMODEL's cable manipulation research. Research interests span robotics applications in food production, energy-efficient automation, and semantic integration of industrial IoT systems. He has reviewed for IEEE Transactions on Industrial Informatics and co-created datasets on robotic grasping and assembly line optimization. Ongoing work explores large language models for HRC reliability and multimodal human-robot interfaces.

Scott T. BatesView profile

Associate Professor

• Bioinformatics
• Microbial Ecology
• Microbiology
• +3 more

Scott T. Bates is the Nils K. Nelson Associate Professor of Biological Sciences at Purdue University Northwest's Westville campus. He is affiliated with the Prairie Research Institute at the University of Illinois. His research focuses on bioinformatics, microbial ecology, and mycology, emphasizing molecular methods to study microbial assemblages and their environmental interactions. He also contributes to fungal taxonomy and bioinformatics software development. Education: Ph.D. in Biology, Arizona State University Research Interests: Dr. Bates investigates microbial ecosystems using modern molecular tools, explores spatial variations in microbial communities, and develops taxonomic frameworks for fungi. His work interfaces with natural history collections and software tools like the R package rMyCoPortal for data management. Publications Trends: Recent work emphasizes trait-based approaches in fungal ecology, computational tools for mycological data, and systematic documentation of fungal biodiversity across North America. Awards: No awards explicitly listed in the provided text. Advising & Grants: No specific grants or student advising details are mentioned here. His teaching spans bioinformatics, microbiology, and mycology at both undergraduate and graduate levels. Labs/Teams: Engaged in collaborative projects through the Prairie Research Institute and Mycology Collections Consortia networks.

Scott HaagView profile

Academic

• Health Informatics
• Machine Learning
• Computer Vision
• +7 more

Scott Haag is an Assistant Research Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics (CCI), with a co-appointment at The Children's Hospital of Philadelphia (CHOP). His work integrates computational science with pediatric health research, focusing on translational applications of machine learning and data systems. Education: PhD in Computer Science, Drexel University, 2019 MS in Ecology, Evolution, Systematics, and Population Biology, Rutgers University Graduate Certificate in Engineering Geophysics, Rutgers University BS in Natural Resources Management and Policy, Rutgers University Dr. Haag's research lies at the intersection of computer science and child health, employing techniques in machine learning, object segmentation, remote sensing, and combinatorial geometric modeling. His expertise extends to algorithm design, complexity analysis, relational database development, and the design and deployment of Application Programming Interfaces (APIs), all applied to health-informing systems. This multidisciplinary background enables innovative approaches to biomedical data challenges. No recent articles were listed in the source text, so no trend analysis can be provided. There are no scientific awards mentioned in the provided information. Dr. Haag is involved in a research-intensive academic environment at CCI, contributing to interdisciplinary projects linking computer science with healthcare. While no formal advisees or grants are listed, his affiliation with both Drexel and CHOP suggests active participation in funded research initiatives and collaborative teams focused on health informatics and computational medicine. He is associated with the research labs and infrastructure of Drexel’s College of Computing & Informatics, which are equipped with high-performance computing resources, specialized instrumentation, and robust network access, supporting advanced research in computer science and its applications.

Aurelie Anna Florence JacobView profile

Researcher

• Materials Science
• Metallurgy
• Thermodynamic Modeling
• +3 more

Dr. Aurelie Anna Florence Jacob is a researcher affiliated with TU Wien, specifically within the Computational Materials Development Research Group (E308-03-2) . Her primary role is as a Project Assistant with a Doctorate in Natural Sciences (Dr. rer. nat.). Her research focuses on advanced materials science, particularly in metallurgical processes, phase transformations, and computational modeling of steel microstructures. She specializes in thermodynamic and kinetic simulations of intermetallic phases (e.g., σ phase, G-phase) in high-alloyed steels, aiming to predict microstructural evolution and optimize material properties. Key research topics include: Thermodynamic modeling of phase equilibria in steel systems (Fe-Cr-Mn-Mo-Ni). Experimental and computational analysis of σ phase precipitation in hyper duplex stainless steels. Recrystallization kinetics in microalloyed steels and T₀-temperature evaluation. Development of databases for bainitic microstructures and Laves phase formation. Her collaborative work involves in-situ XRD experiments and CALPHAD-based simulations to understand phase evolution under thermal and mechanical processing. Jacob’s contributions bridge experimental materials characterization with predictive computational tools, advancing materials design for high-performance applications. Her recent publications highlight advancements in understanding phase stability, precipitation kinetics, and material recycling processes. She actively participates in international conferences, presenting findings on topics like sigma phase mechanisms and Gibbs energy reassessment for T₀-temperature predictions.