Karen M. Feigh is a Professor and Associate Chair for Research at Georgia Tech's Daniel Guggenheim School of Aerospace Engineering, with a courtesy appointment in the School of Interactive Computing. She directs the Georgia Tech Cognitive Engineering Center and is affiliated with the Vertical Lift Research Center of Excellence (VLRCOE) and the Institute for Robotics and Intelligent Machines (IRIM). Her work spans computational cognitive modeling, socio-technical systems, and adaptive automation. Education: B.S. in Aerospace Engineering (Georgia Tech), MPhil in Aeronautics (Cranfield University, UK), Ph.D. in Industrial and Systems Engineering (Georgia Tech) Research Focus: Feigh's research explores human-machine interaction in aviation, space operations, and robotics. She designs cognitive work support systems using field work, human-subjects studies, and mathematical modeling. Recent projects emphasize human-autonomy teaming and the human experience of machine learning across domains. Leadership & Collaborations: She leads the Cognitive Engineering Center (CEC), founded in 2005, which brings together aerospace engineers, computer scientists, and education researchers. The CEC contributes to air/space procedures, military decision-making systems, and autonomous vehicle technologies. Awards & Distinctions: David S. Lewis Professorship (2025) AIAA Wilbur and Orville Wright Graduate Award (2006) Zonta International Amelia Earhart Fellowship (2005) NSF Graduate Research Fellow (2001-2006) Marshall Scholar (2001-2003) Grants & Advisory Roles: Feigh has led and collaborated on FAA, NIA, ONR, NSF, and NASA-sponsored projects. She serves as Associate Editor for the Journal of Cognitive Engineering and Decision Making and previously chaired the Human Factor and Ergonomics Society’s Cognitive Engineering and Decision Making Technical Group.
Konstantinos Alexakos is a Professor and Program Coordinator for General Science (GSCI) and Science Education (covering biology, chemistry, physics and earth science for grades K-12) at the School of Education, Brooklyn College, City University of New York (CUNY). He also holds a professorship in The Ph.D. Program in Urban Education at the Graduate Center, CUNY. His academic work focuses on science education, teacher development, and the integration of mindfulness practices in educational settings. His educational background includes: B.S. in Physics from The City College of New York - CUNY (1989) M.A. in Education in Physics and General Science from New York University (2000) M.Phil. in Science Education from Columbia University (2004) Ph.D. in Science Education from Teachers College, Columbia University (2005) Alexakos's research interests span multiple interconnected domains within science education. He has made significant contributions to understanding mindfulness and wellness in educational contexts , particularly how contemplative practices can enhance teaching and learning environments. His work explores emotional dimensions of science education , examining how teachers and students navigate emotional landscapes in classrooms. He has pioneered research on cogenerative dialogue and coteaching as mechanisms for improving educational practice. His scholarship also addresses diversity, equity, and inclusion in science education, with particular attention to race, gender, and cultural considerations. Alexakos investigates teacher identity formation and the challenges science educators face in urban settings, while also developing frameworks for authentic inquiry that empower teachers as researchers. His recent work increasingly focuses on holistic approaches to education that integrate physical, emotional, and intellectual dimensions of learning. Analysis of his recent publications (2017-2022) reveals a clear trajectory toward integrating contemplative practices with science education. His scholarship demonstrates a consistent focus on teacher development through authentic inquiry, with increasing emphasis on wellness, mindfulness, and emotional dimensions of teaching and learning. The publications show a progression from theoretical frameworks to practical applications of heuristics and contemplative practices in educational settings. His collaborative work with Kenneth Tobin forms a substantial portion of his output, indicating a productive research partnership focused on transforming educational practices. The publications span multiple formats including books, book chapters, and journal articles, demonstrating versatility in scholarly communication. His scientific awards and recognitions include: Brooklyn College Student Technology Fee Award for "Science Lab Probes" ($12,000) PSC-CUNY Research Award (PSCREG-38-335) for "Self and Science Teacher Attrition" ($6,000; 2007-08) PSC-CUNY Research Award (PSCOOC-37-30) for "The Science Teacher, Subjective Constructs of Science Teaching, and the 'Organic Link'" ($5,990; 2006-07) Alexakos has been actively involved in mentoring students through various teaching practicums and research activities. His courses include Natural Science for Early Childhood & Childhood Education, General Science for Child & Elementary School, Physical Science for Childhood Teachers, and multiple student teaching practicum courses. He has developed the master of arts in teaching (MAT) in science education program at Brooklyn College that infuses research and practice in science teaching in urban settings. His grant activities include research projects on teacher attrition, the science teacher as the organic link, and wellness practices in educational contexts. He has also secured funding for technology implementation in science education through the Student Technology Fee Award. While not explicitly mentioned as leading a specific laboratory, Alexakos is deeply involved with the Urban Science Education Research Seminar at the CUNY Graduate Center, where he has presented and organized numerous sessions. He has collaborated extensively with colleagues including Kenneth Tobin, Maria Powietrzynska, and Leah Pride on research related to emotions, mindfulness, and wellness in educational settings. His work with the Association of Greek American Professional Women (AGAPW) on "Inspiring Women in Science, Technology, Engineering, and Mathematics (STEM)" demonstrates his commitment to supporting underrepresented groups in STEM fields.
Dr. Britnie Delinger Kane is an Associate Professor of Literacy Education at Zucker Family School of Education , The Citadel . She also serves as the Director of the Center for Literacy Excellence and Program Coordinator for Literacy Education . Prior to her work at The Citadel, she taught at the University of Colorado, Denver . Ph.D. in Literacy Education, Peabody College, Vanderbilt University M.Ed. in Secondary English Education, Peabody College, Vanderbilt University B.A. in English, University of South Carolina’s Honors College Research Interests center on equitable teacher learning , disciplinary literacy , writing instruction , and STEM education . Her work explores preservice teacher education, collaborative teacher talk, and instructional coaching. She has published in journals like American Educational Research Journal , Journal of the Learning Sciences , and Journal of Teacher Education . Recent Publications include studies on mathematics coaching, disciplinary literacies, adolescent literacy efficacy, and trauma-sensitive instruction. These works emphasize professional learning communities , content-specific coaching , and equity in instructional design .
Dr. Thomas Britz is a Senior Lecturer at the School of Mathematics and Statistics, UNSW Sydney . He is a member of the Combinatorics Research Group and serves as Chief Editor of Parabola and Managing Editor for the Australasian Journal of Combinatorics . PhD in Mathematics (Aarhus University, 2003) Research: Discrete Mathematics, Combinatorics, Graph Theory, Matroid Theory, Coding Theory, Design Theory, and applications to renewable energy, bioinformatics, criminology, and more His research has been supported by grants including an ARC Discovery Grant and international fellowships. He has supervised numerous PhD, Master’s, and Honours students across diverse projects. Recent publications focus on harmonic Tutte polynomials, group divisible designs, and closeness centrality in graphs, reflecting his expertise in foundational and applied combinatorics. Awards : Vice-Chancellor’s Award for Student Wellbeing (2021), Science Dean's Award for Excellence in Student Wellbeing (2021), Vice-Chancellor's Award for Contributions to Student Learning (2015) He contributes to outreach activities , editorial service, and university committees, including the Education Excellence Committee and Sustainability Committee at UNSW.
Jessica Young Schmidt is an Associate Teaching Professor in the Department of Computer Science at North Carolina State University, serving as ABET Coordinator since 2017 and Course Coordinator for CSC226: Discrete Mathematics. Her primary focus is undergraduate education, particularly CSC116: Introduction to Computing – Java and CSC226, where she implements flipped classroom models and comprehensive end-of-semester exercises. Her educational background includes: Ph.D. in Computer Science, North Carolina State University, 2012 M.S. in Computer Science, North Carolina State University, 2009 B.S. in Computer Science and Mathematics, Roanoke College, 2007 Dr. Schmidt's research centers on computer science education, emphasizing active learning, instructional design, and software engineering pedagogy. She has pioneered methods like collaborative software engineering exercises for CS1 materials and frameworks for assessing critical thinking, with her work on end-of-semester integration earning a SIGCSE 2020 award. Her publications reveal an evolution from privacy policy analysis (2009-2012) toward educational innovation in recent years. Her scholarly contributions demonstrate consistent focus on curriculum development and assessment, particularly in integrating software testing throughout computer science education and enhancing student comprehension through structured reflection. She has received the following recognition: Third Best Paper, SIGCSE 2020 Technical Symposium (Experience Reports and Tools Track) As ABET Coordinator, Dr. Schmidt drives curriculum assessment and continuous improvement, while her CSC226 coordination ensures pedagogical consistency across sections. Her teaching innovations directly support student success in foundational computer science concepts.
Dr. Sirojan Tharmakulasingam serves as a Lecturer and Research and Development Coordinator at the Signals, Information & Machine Intelligence lab within the Faculty of Engineering at the University of New South Wales (UNSW) Sydney. His work bridges theoretical machine learning with practical applications in edge computing and high-performance systems. His research spans multiple cutting-edge domains including machine learning, artificial intelligence, data science, edge computing, and high-performance computing. Dr. Tharmakulasingam specializes in developing next-generation inference models by integrating machine learning, signal processing, mathematical modeling, and computing across diverse data types including images, video, audio, and quantum molecular data. His work has significant implications for scientific computing, telecommunications, and healthcare applications. Analysis of his publication trends reveals a strong focus on practical AI implementations, with increasing emphasis on edge computing solutions, quantum applications, and energy-efficient models. His recent work demonstrates progression from foundational machine learning techniques toward specialized applications in scientific computing and real-time systems. Dr. Tharmakulasingam holds a Doctor of Philosophy from UNSW Sydney and a Bachelor of Science of Engineering from the University of Moratuwa in Sri Lanka. His academic journey reflects a strong foundation in both theoretical and applied engineering principles. As Research and Development Coordinator for the Signals, Information & Machine Intelligence lab, he oversees critical research infrastructure and collaborations. His work location in Room 447 of the EE&T Building (G17) places him at the heart of UNSW's engineering research ecosystem, with access to the Mark Wainwright Analytical Centre's extensive facilities.
John Copp is an Adjunct Assistant Professor in the Department of Computing and Software at McMaster University , specializing in wastewater treatment modeling and process control. His work bridges environmental engineering with computational methods, focusing on simulation benchmarks, digital twin applications, and real-time monitoring systems. Adjunct Assistant Professor, Computing and Software, McMaster University Research Interests include: Wastewater treatment process modeling Respirometry-based control strategies Digital twin development for water resource recovery Automation and fault detection systems Biological nutrient removal optimization His scholarly output spans over 25 years with notable trends in: Development of benchmark simulation models (BSM1, BSM2) Integration of anaerobic digestion with activated sludge processes Multi-biomass modeling for enhanced nutrient removal Machine learning applications in water quality monitoring
Angelos Georghiou is an Associate Professor of Operations Management at the Department of Business and Public Administration, University of Cyprus. He previously held academic positions as Assistant Professor at McGill University (2016–2019), Post-Doctoral Researcher at MIT (2012–2013), and Postdoctoral Researcher at ETH Zurich (2013–2016). His work bridges computational methods in stochastic and robust optimization with applications in healthcare, energy systems, and operations management. Ph.D. in Operations Research, Imperial College London (2012) M.Sci. in Mathematics, Imperial College London (2008) His research focuses on stochastic optimization , robust optimization , and decision rules , addressing challenges in healthcare analytics , energy-efficient control systems , and machine learning integration . Recent publications in Management Science and Operations Research highlight his contributions to decision-dependent information discovery and computational frameworks. Key trends in his 15 most recent articles include: Advancing robust optimization techniques for multistage problems Applications in healthcare (psychiatric risk prediction) and energy systems Integration of machine learning with stochastic programming Development of tractable algorithms for complex decision environments Scientific Awards : Esdras-Minville Best Student Paper Award (2023) He serves on editorial boards of Operations Research Letters (Area Editor) and Management Science , and collaborates with institutions like MIT, ETH Zurich, and McGill University. His work with undergraduate students includes a 2021 paper in SIAM Undergraduate Research Online .
Burkhard Schipper serves as Professor of Economics at the University of California, Davis, with affiliated status in the Graduate Group of Applied Mathematics. His academic trajectory spans over two decades, establishing him as a leading theorist in strategic decision-making frameworks with applications across economics, finance, and political science. His educational foundation includes: Diplom-Volkswirt, University of Bonn (2000) Dr. rer. pol., Economics, University of Bonn, European Doctoral Program (2003) Schiper's research program centers on game theory, microeconomic theory, and experimental economics, with pioneering contributions to modeling unawareness in strategic interactions. His theoretical work develops formal frameworks for agents operating with incomplete awareness of game structures, while his experimental research investigates biological determinants of economic behavior, particularly steroid hormones' influence on risk attitudes and competitive bidding. This dual approach bridges abstract theory with empirical validation, yielding insights applicable to financial markets, policy design, and organizational behavior. Recent publications (2022-2025) demonstrate consistent focus on unawareness extensions across diverse contexts—from macroeconomic policy to auction design—while maintaining empirical rigor through experimental methods. His work shows increasing integration of biological variables with strategic models, reflecting interdisciplinary innovation in economic theory. His scientific recognition includes: UC Davis Hellman Fellowship (2009-10) Young Economist Award, European Economic Association (2003) Schiper secures major research funding from the Army Research Office and National Science Foundation, supporting his unawareness modeling and experimental programs. As Editor-in-Chief of the B.E. Journal in Theoretical Economics and Associate Editor of Mathematical Social Sciences, he actively shapes scholarly discourse. His teaching encompasses undergraduate and doctoral courses in microeconomics and game theory, transmitting advanced theoretical frameworks to new generations of economists. Though not explicitly detailed in source materials, his research likely operates through UC Davis' experimental economics laboratories with interdisciplinary collaboration across the Graduate Group of Applied Mathematics.
Sophia Natasha Wilson is a Research Fellow in the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in machine learning applications across interdisciplinary domains. She is affiliated with the SCIENCE AI Centre and holds a cross-departmental position at the Niels Bohr Institute . Her research bridges theoretical machine learning with practical implementations in healthcare, quantum computing, and environmental sustainability. University of Copenhagen Department of Computer Science (DIKU) Niels Bohr Institute SCIENCE AI Centre Her research focuses include: Quantum-enhanced machine learning algorithms Explainable AI for healthcare applications Environmental sustainability in computing Emotion-aware language models Quantum computing hardware optimization Public health risk modeling Her recent publications demonstrate cross-disciplinary work in quantum machine learning (hybrid optical processors, qubit stabilization), health informatics (hypothyroidism analysis, nursing values evaluation), and ethical AI (sustainable AI, fairness in recommender systems). Technical work also appears in non-Euclidean generative models and real-time adaptive systems . Current projects include quantum dot array simulation (QDarts platform) and federated learning for personalized medicine . She contributes to the TreeSense center for remote sensing of global tree resources and works on climate-aware AI frameworks.
Christoph Dellago is a full Professor of Computational Physics at the Faculty of Physics of the University of Vienna, where he has been a faculty member since 2003. He currently serves as Director of the Erwin Schrödinger Institute for Mathematics and Physics, Head of the Computational and Soft Matter Physics Group, and Project lead of EuroCC Austria - National Competence Centre for Supercomputing. Previously, he served as Dean of the Faculty of Physics (2009-2012) and Coordinator of the Doctoral College Advanced Functional Materials (DCAFM). Full Professor, Faculty of Physics, University of Vienna (2003-present) Director, Erwin Schrödinger Institute for Mathematics and Physics (2017-present) Head, Computational Physics and Soft Matter Group (2024-present) Coordinator, Doctoral College Advanced Functional Materials (DCAFM) Austrian Representative, Council of CECAM Dellago received his PhD in Physics from the University of Vienna in 1996, followed by postdoctoral research at UC Berkeley as a Schrödinger Fellow of the Austrian Science Foundation. His research focuses on developing computational methods to study rare events in condensed matter systems, particularly transition path sampling methodology for simulating nucleation, chemical reactions, and biomolecular reorganizations. He has pioneered the application of machine learning to molecular structure recognition and potential energy surfaces. Recent work examines self-assembly of nanocrystals, biopolymer folding, aqueous interfaces, phase separation in alloys, thermo-polarization, cavitation, and freezing phenomena. Analysis of Dellago's recent publications (2023-2025) reveals a strong emphasis on machine learning applications in computational physics, particularly neural network potentials for simulating water interfaces, crystal defects, and phase transitions. His work bridges traditional statistical mechanics with modern computational techniques, creating powerful tools for studying complex dynamical processes that occur on timescales far beyond conventional molecular dynamics simulations. The publications demonstrate increasing integration of machine learning with rare event sampling methods, reflecting the cutting-edge direction of computational statistical mechanics. Förderpreis der Stiftung Futura zur Förderung junger Südtiroler im Ausland (1997) The Raymond and Beverly Sackler Prize in the Physical Sciences (2005) UNIVIE Teaching Award of the University of Vienna (2014) Dellago leads an active research group with multiple PhD students and postdocs, focusing on computational statistical mechanics. His group develops trajectory-based sampling methods and machine learning approaches for molecular simulation. He has secured significant funding through EuroCC Austria and various research platforms including the Research Platform Accelerating Photoreaction Discovery and the Research Platform Erwin Schrödinger International Institute for Mathematics and Physics. His research has been supported by numerous grants enabling advanced computational infrastructure for high-performance simulations. The Dellago Group operates within the Computational and Soft Matter Physics division at the University of Vienna, with strong connections to the Research Network Data Science. The group collaborates extensively with international research institutions and maintains close ties with the Erwin Schrödinger Institute, which Dellago directs. Their research environment combines theoretical physics, computational chemistry, and machine learning expertise to tackle fundamental questions in condensed matter physics and soft matter systems.
David M. Labyak is an Assistant Professor at Michigan Technological University's College of Engineering, affiliated with both the Manufacturing and Mechanical Engineering Technology and Mechanical and Aerospace Engineering departments. He teaches courses in computer-aided engineering, finite element methods, dynamic systems control, machine design, robotics dynamics, and Industry 4.0 concepts. PhD in Mechanical Engineering-Engineering Mechanics (2003) and MS in Mechanical Engineering (2000) from Michigan Tech Over 24 years of industrial experience in automotive, aerospace, mining, and consulting sectors His research interests span solid mechanics, finite element analysis, vibration analysis, machinability of metals, biomechanics, and helmet design optimization. Collaborative work includes dynamic testing, acoustic modeling, and workforce development initiatives. Recent publications highlight interdisciplinary work in vibration testing, metalcasting, and educational frameworks. Key areas include defect detection in additive manufacturing, dynamic fixture design, and experiential learning for mechatronics.
Scott McCrickard is an Associate Professor at the Department of Computer Science within the College of Engineering at Virginia Tech . He serves as the Co-director of Virginia Tech's Human-Centered Design Program , integrating interdisciplinary approaches into his research and teaching. Ph.D. , Computer Science, Georgia Tech (2000) M.S. , Computer Science, Georgia Tech (1995) B.S. , Mathematical Sciences (Computer Science emphasis), University of North Carolina at Chapel Hill (1992) McCrickard’s research focuses on Human-Computer Interaction (HCI) , particularly in outdoor environments. His work explores how technology can enhance experiences in nature, including the use of smartwatches for group fitness interventions, AR mobile games for social bonding, and diary studies as pedagogical tools. He also investigates mobile application development for low-literacy populations and social media integration in design processes. Recent publications highlight trends in HCI education , outdoor technology adoption, and social gaming dynamics . Key themes include leveraging diary methods for learning, designing context-aware notification systems , and analyzing indigenous knowledge preservation through self-organized online communities. As part of his leadership in the Human-Centered Design Program, he bridges computer science with design theory , emphasizing usability and social impact in technological solutions.
Dr. Elian Fink is an Associate Professor in the School of Psychology at the University of Sussex, focusing on developmental psychology and child social development. His research explores socio-cognitive antecedents of peer relationships, bullying behavior across neurotypical and neurodivergent groups, and parental influences on children’s social competencies. He holds a PhD in Developmental Psychology from the University of Sydney and has held academic roles including Senior Research Associate at the University of Cambridge and Visiting Scholar at the Centre for Family Research. Dr. Fink’s work is funded by grants such as the ESRC’s 'School Readiness: Connecting Viewpoints on Child and Family Well-being' and the LEGO Foundation’s study on children’s relationships through play. His research emphasizes longitudinal studies, cross-cultural comparisons, and collaborations with institutions like the Centre for Family Research and PEDAL at the University of Cambridge. Teaching interests include the Psychology of Childhood, Developmental Psychology, and Children’s Peer Relationships. He advises undergraduate, postgraduate, and PhD students, with drop-in hours for academic support. Key research themes include: Socio-cognitive foundations of peer relationships Bullying behaviors and neurodiversity Parenting and language environments in early childhood School readiness and well-being across cultures Recent studies investigate links between spatial language and numeracy, mental health trends during the pandemic, and cross-cultural validation of child well-being measures.
Svanhild Breive is an Associate Professor at the Department of Mathematical Sciences, University of Agder. Her research focuses on mathematics education in early childhood settings, particularly exploring how play and inquiry-based approaches enhance mathematical learning in kindergartens and early primary years. She has contributed significantly to understanding teacher-child interactions, classroom orchestration, and the role of physical environments in fostering mathematical thinking. Her work examines key themes such as the balance between structured and free play, embodied cognition in mathematics, and the application of semiotic theories to analyze learning processes. She has published extensively in journals like For the Learning of Mathematics and European Early Childhood Education Research Journal , and co-authored influential books including Lekende læring og lærende lek i begynneropplæringen (2022) and the Lekbasert læring series. Her research often intersects with broader educational psychology topics, such as curriculum design for preschool systems and the impact of pedagogical frameworks on child development. Collaborations with projects like the Agder Project highlight her commitment to evidence-based early education practices.
