Morten Grud Rasmussen is an Associate Professor at the Department of Mathematical Sciences, Faculty of Engineering and Science, Aalborg University. His research spans renewable energy systems, mathematics education, and problem-based learning (PBL). He has contributed to projects like 'myPBL-VRMath: Micro-PBL in Mathematics using Virtual Reality' (2024-2025) and 'PBL og matematik: Hvordan kan PBL fungere på universitetets grundfag?' (2018-2019). His work emphasizes integrating VR for abstract mathematics teaching and optimizing renewable energy systems. Collaborations include projects on near-optimal solutions for highly renewable energy systems and pedagogical innovations in engineering education. Research interests include energy system flexibility, socio-economic analysis of renewables, and PBL methodology. He has published on topics ranging from solar power generation to interdisciplinary curriculum design. His recent work explores student experiences with VR-based learning and PBL contextualization in mathematics education. Notable contributions include modeling renewable energy alternatives and exploring storage synergies in pan-European grids. He actively participates in educational workshops and collaborates with institutions like the University of Agder, Norway.
Dr Craig Lobsey is an Adjunct Research Fellow at the University of Southern Queensland (USQ), affiliated with the Centre for Sustainable Agricultural Systems (CSAS) and the Centre for Agricultural Engineering (CAE). He holds a BEng(Hons) and PhD from the University of Sydney. His research focuses on proximal sensing, spectroscopy, precision agriculture, spatial modelling, software development, and electronic engineering. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and serves as Vice-chair of the International Union of Soil Sciences' Working Group on Proximal Soil Sensing (WG-PSS). Teaching responsibilities include courses such as ENG1004 Engineering Problem Solving Principles, MEC3303 Mechanical and Mechatronic System Design, and MEC2902 Mechanical Practice 2. His supervision spans interdisciplinary areas including agriculture, aerospace engineering, avionics, and electronics. Current research affiliations emphasize sustainable agricultural systems and engineering innovations.
Ting Lei is an Associate Professor in the Department of Geography at the University of Kansas, located in Malott Hall #1021. His primary research interests focus on Geographic Information Science (GIS), including algorithmic development, geospatial computational methods, network analysis, location theory, and web GIS. He also explores remote sensing applications and advancements in GIS technology such as data structures, databases, computational geometry, and open-source software. His teaching covers GIS principles, transportation geography, and geo-computational methods. Dr. Lei's publications emphasize spatial data conflation, transportation network vulnerability, and optimization models for facility location. Recent works include studies on optimal spatial data matching, hub center interdiction problems, and unified location-allocation approaches integrating GIS and distributed computing. His research addresses real-world challenges in urban planning, water resources management, and celestial imaging analysis. Key research trends in his articles include computational GIS advancements, transportation infrastructure resilience, and interdisciplinary applications of geospatial technologies. No specific scientific awards are listed, though his extensive publication record highlights scholarly contribution. Advising and grants details are not provided in the text, but his active research in multiple geospatial domains indicates engagement with academic and applied projects.
Nikesh Bajaj is a Lecturer in Data Science and Director of Education at Queen Mary University of London (QMUL), affiliated with the School of Physical and Chemical Sciences. He holds a PhD from QMUL and the University of Genova, focusing on auditory attention prediction from physiological signals. Previously, he worked as a Research Associate at Imperial College London, exploring cardiac electrophysiology via ECG imaging, and as a Research Fellow at the University of East London on deception detection in financial conversations (resulting in a U.S. patent). His research spans signal processing, machine learning, and biomedical applications, including projects like PhyAAt (auditory attention modeling), ATAR (EEG artifact removal), and computational models for cardiac arrhythmia. He also collaborates with industry on gamification, VR/AR, and forensic interaction analysis. Key Research Areas: ECGI inverse problems, EEG artifact correction, deception detection, and data-driven healthcare. Teaching: Leads courses on data science programming, exploratory data analysis, and machine learning principles at QMUL. Software: Developed Python libraries spkit , phyaat , and pylfsr for signal processing and ML. Publications: Over 20 peer-reviewed papers across biomedical signal processing, machine learning applications, and game design. Recent work emphasizes AI-driven cardiac diagnostics and mixed-reality user experience metrics.
Dr. Trina Myers is a researcher at James Cook University Queensland , with a focus on interdisciplinary applications of Information and Communication Technology (ICT) in environmental science, healthcare, and education. Her work spans eco-informatics , semantic technologies , and human-computer interaction , particularly in co-design methodologies for marginalized communities. Key Affiliations: James Cook University Queensland Research Themes: Sensor networks, coral reef modeling, telemedicine, educational technology Dr. Myers' recent publications highlight trends in dynamic optimization for logistics, health data quality analysis, and co-design frameworks for special education. Her work often integrates gamification and collaborative tools to address real-world challenges in sustainability and healthcare. She has contributed to advancing ontology development and environmental monitoring systems , with projects like the Tropical Data Hub and Semantic Reef platform. While no explicit awards or advisees are documented in the provided corpus, her collaborations span disaster management , marine conservation , and inclusive technology design .
Sabine Storandt is a Lecturer at the Department of Computer Science, University of Freiburg, with a focus on algorithm design and transportation systems. She has contributed significantly to research in route planning, electric vehicle navigation, and public transit optimization. Her work emphasizes practical applications of theoretical algorithms in real-world scenarios. Her research interests include algorithms for vehicle navigation, route optimization, and facility location problems. She has received notable awards, including the Best Paper Award at VLDB 2014 and the INFOS Award for her PhD thesis on 'Algorithms for Vehicle Navigation.' In teaching, she has led courses such as Information Retrieval (as a tutor), Randomized Algorithms (lecture + tutorial), and Information Extraction (seminar). She has also collaborated on projects like DORC (Distributed Online Route Computation) and Enabling E-Mobility, addressing challenges in transportation and energy efficiency. Her recent publications highlight advancements in electric vehicle infrastructure, public transit planning, and efficient route algorithms, reflecting her expertise in bridging theoretical computer science with practical transportation solutions.
Dr. Karsten Tabelow is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, Germany, affiliated with the group Stochastic Algorithms and Nonparametric Statistics . His work bridges mathematical statistics and biomedical imaging through advanced computational methods. PhD in physics (2001) and Diplom in physics (1997) from Freie Universität Berlin Key projects: Matheon grants F10 and A3 on biomedical imaging Current focus: adaptive smoothing , quantitative MRI , and nonparametric statistical modeling His research has produced foundational contributions in neuroimaging software , including R packages adimpro , dti , fmri , and qmri , as well as SPM toolboxes like ACID-Toolbox and aws4SPM . He extends these methods to inverse problems in magnetic resonance and electron microscopy. Collaborations span neuroscience (Charité), semiconductor physics (Leibniz Institute), and biomedical engineering (Max Planck Institute). Recent work includes physics-informed data assimilation for intracranial pressure estimation and mathematical research data infrastructure through the MaRDI initiative under Germany's NFDI. Publications emphasize open science principles using R/knitr workflows. Grants include Matheon projects and MaRDI development. Software contributions are distributed under GNU GPL within R and SPM ecosystems. Talks highlight applications in presurgical planning , in-vivo histology , and cross-disciplinary data management .
Josep Maria Porta Pleite is an Associate Researcher at the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of the Spanish National Research Council (CSIC) and Universitat Politècnica de Catalunya (UPC). He leads the Kinematics and Robot Design (KRD) research group and has been actively contributing to robotics and computational kinematics since 2007. His work bridges theoretical algorithm development and practical applications in robotics, molecular biology, and environmental toxicology. Porta’s research spans motion planning , robot kinematics , SLAM , and planning under uncertainty . He has made significant contributions to solving complex kinematic problems in closed-chain systems and molecular conformational spaces. His work often involves developing efficient algorithms and open-source software tools such as the CuikSuite , Cuik-KDtree , and Pose SLAM , which are widely used in robotics research. His recent publications (2019–2025) reveal a strong trend toward interdisciplinary applications, particularly in zebrafish behavioral analysis and neurotoxicology , where computational methods are applied to assess environmental contaminants. He also continues to advance core robotics problems, including trajectory optimization, hand-eye calibration, and closed-form solutions in rotation geometry. His work appears in top-tier journals such as IEEE Transactions on Robotics , Mechanism and Machine Theory , and Science of the Total Environment . Porta has served as an associate editor for IEEE Transactions on Robotics (2015–2018) and has supervised numerous students and collaborators. He has led long-term software development efforts and secured research funding through national and European projects. Scientific Contributions: Lead developer of the CuikSuite for motion analysis of closed-chain systems. Coordinator of the KRD research group since 2011. Contributor to ambient intelligence and robot localization during his postdoc at the University of Amsterdam. He advises multiple students in robotics, computer vision, and biomedical applications, and his team develops tools for path planning, singularity analysis, grasp optimization, and molecular modeling. There is no indication of part-time status, retirement, or former affiliation.
Stephanie C. Hicks is an Associate Professor in the Department of Biomedical Engineering at the Whiting School of Engineering and in the Department of Biostatistics at Johns Hopkins University. She is also affiliated with multiple research centers, including the Malone Center for Engineering in Healthcare, the Center for Imaging Science, the Center for Computational Biology, the Johns Hopkins Data Science Lab, and serves as a preceptor in the Department of Genetic Medicine and the Department of Biochemistry and Molecular Biology. B.S. in Mathematics from Louisiana State University (LSU) M.A. and Ph.D. in Statistics from Rice University, advised by Marek Kimmel and Sharon Plon Postdoctoral training with Rafael Irizarry at the Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health Dr. Hicks is an applied statistician whose research lies at the intersection of genomics and biomedical data science. Her work focuses on developing computational methods using statistics and machine learning to address challenges in single-cell genomics, epigenomics, and spatial transcriptomics. She implements these methods as open-source software, guided by a 'problem-forward' philosophy that emphasizes real-world applicability. Her research has led to contributions in spatially aware quality control (e.g., SpotSweeper), self-supervised learning for spatial domain detection, and benchmarking of feature selection methods. Her recent publications reflect a strong trend in integrating machine learning—especially deep learning and transformers—into genomic data analysis. She has received numerous honors, including the COPSS Emerging Leader Award, the Teaching in the Health Sciences Young Investigator Award, the Myrto Lefkopoulou Distinguished Lectureship (2023), and election as a Fellow of the American Statistical Association. These awards recognize her leadership, contributions to statistical science, and commitment to education and reproducibility. Dr. Hicks is actively involved in mentoring and advising, with students such as Kinnary Shah, Jianing Yao, Michael Totty, and Boyue Guo contributing to her research. She also contributes to the academic community through editorial roles, including Associate Editor for Reproducibility at the Journal of the American Statistical Association and membership on the editorial board of Genome Biology . She co-hosts the popular podcast The Corresponding Author and is a co-founder of R-Ladies Baltimore, promoting diversity and inclusion in data science. Her work is supported by her affiliations with interdisciplinary research hubs at Johns Hopkins, where she collaborates across engineering, medicine, and data science. She is also deeply engaged in science communication, education, and advocacy, notably through her public commentary on the importance of federal research funding and participation in events like Stand Up for Science.
Patrick Laub is a Senior Lecturer at the UNSW School of Risk and Actuarial Studies, where he teaches courses in artificial intelligence and machine learning with a focus on risk and insurance applications. His academic work bridges the gap between advanced computational methods and practical actuarial problems. Patrick holds a joint PhD in computational applied probability completed between the University of Queensland and Aarhus University. He also possesses degrees in software engineering and mathematics, providing him with a strong interdisciplinary foundation for his research. His research focuses on computationally challenging problems in actuarial data science, with particular emphasis on natural catastrophe modeling and artificial intelligence applications. Key areas of investigation include Hawkes processes for modeling contagion in insurance claims, Approximate Bayesian Computation for fitting complex insurance loss models, and Empirical Dynamic Modeling for analyzing complex temporal dependencies. His work addresses critical challenges in risk assessment, particularly for extreme events where traditional statistical methods may be inadequate. Analysis of Patrick's recent publications reveals a strong trend toward integrating advanced statistical methodologies with practical actuarial applications. His work spans from theoretical developments in point processes and Bayesian inference to practical implementations in computational environments. A notable theme is the application of machine learning techniques to traditional actuarial problems, particularly in the areas of catastrophe modeling and risk prediction. Patrick has developed and taught innovative courses since 2022, including 'Artificial Intelligence & Deep Learning and their Applications to Risk and Insurance' (ACTL3143 and ACTL5111) and 'Statistical Machine Learning for Risk and Actuarial Applications' (ACTL5110). These courses reflect his commitment to preparing students for the evolving landscape of data-driven risk management in the insurance industry. His research is supported by UNSW's strong infrastructure for computational research, though specific lab affiliations are not explicitly mentioned in the available information. Patrick maintains an active research program with numerous publications across statistics, actuarial science, and computational methods.
Christopher McComb is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University's College of Engineering. He leads research in sociotechnical systems, machine learning for engineering design, and human-AI collaboration. He is affiliated with the Block Center for Technology and Society, Manufacturing Futures Institute, NextManufacturing Center, and Wilton E. Scott Institute for Energy Innovation. Previously, he was an assistant professor at Penn State, where he directed the Center for Research in Design and Innovation and led the Technology and Human Research in Engineering Design Group. Ph.D., Mechanical Engineering, Carnegie Mellon University M.S., Mechanical Engineering, Carnegie Mellon University B.S., Civil Engineering and Mechanical Engineering, California State University-Fresno His research centers on human-AI teaming , sociotechnical systems , and computational design , with applications in additive manufacturing, STEM education, and energy systems. He explores how machine learning can enhance engineering design processes, particularly through human-centered AI, generative design, and agent-based modeling. His work emphasizes the integration of human cognition and behavior into AI systems to improve collaboration and innovation. The 15 most recent publications (2025) demonstrate a strong trend in AI-driven design automation , neural surrogate modeling , human-AI interaction , and data generation for engineering simulations . Topics span from using large language models for material selection and design concept generation to developing datasets and benchmarks for advanced manufacturing and CAD systems. There is a clear emphasis on real-world applications in aerospace, finance, and global manufacturing, particularly in Africa. National Science Foundation Graduate Research Fellow McComb has received research funding from NSF, DARPA, and private corporations, and has collaborated with Boeing through their Visiting Professorship Program. He advises students in mechanical engineering and design, and leads the Human+AI Design Initiative and the Design Research Collective. His research has been applied in partnerships with NASA and in addressing manufacturing challenges in Africa. He leads or contributes to interdisciplinary research teams focused on AI in design, additive manufacturing, and energy systems. His labs and initiatives include the Human+AI Design Initiative and the Design Research Collective, which foster collaboration between human-centered design and artificial intelligence.
Rajesh Nandy is an Associate Professor in the Department of Population and Community Health at the University of North Texas Health Science Center's College of Public Health. His research focuses on developing novel statistical methods for solving real-world problems in clinical trials, neuroimaging, and biomedical applications. He holds a PhD in Probability/Statistics from the University of Washington and has collaborated on over 20 sponsored projects spanning neurodegenerative diseases, audiology, and cancer research. Education BS and MS in Statistics, Indian Statistical Institute PhD in Probability/Statistics, University of Washington Research Interests Nandy specializes in multivariate statistical analysis, ROC methods, and machine learning applications. His work addresses challenges in functional MRI artifact correction, noise reduction in medical imaging, and epigenetic risk factors for Alzheimer's disease. He has pioneered methods for analyzing neuroimaging data and optimizing clinical trial designs. Grants & Collaborations His active projects include: NIA-funded Health & Aging Brain Study exploring cognitive decline mechanisms NEI-supported research on ocular pathogenesis and tissue engineering NCI projects investigating tumor-derived signaling pathways Epidemiological studies on noise-induced hearing loss and recreational audio device risks Labs & Teams Nandy collaborates with multidisciplinary teams across neuroscience, oncology, and audiology, contributing statistical expertise to translational biomedical research initiatives.
Dr. Christopher Hampson is a Senior Lecturer in Computer Science (Education) at King’s College London's Department of Informatics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He specializes in theoretical computer science, modal logics, and algorithmic complexity. He holds an MSc in Mathematical Logic from the University of Manchester and a PhD from King’s College London on modal logic topics. His research focuses on Modal and Temporal Logics Computational Complexity Formal Verification Counter Machine Theory Computer Science Education Recent work includes studies on non-repudiation in healthcare AI systems (2024), termination analysis of faulty counter machines (2021), and dialogue systems for handling misunderstandings (2022). He collaborates with the Computing Education Research Centre (CERC) to advance pedagogical tools and curricula in computer science. Awards: none explicitly listed. Supervision: no named advisees found. Active in research groups focused on formal methods and education innovation.
Dr. Molly Hathaway Goldstein is a Teaching Assistant Professor and Director of the Product Design Lab at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering's Industrial & Enterprise Systems Engineering (ISE) department. She holds courtesy appointments in Mechanical Science and Engineering, Curriculum & Instruction (College of Education), and Industrial Design (School of Art & Design). Her research focuses on design cognition, generative design methodologies, and engineering education, emphasizing student-centered approaches to design thinking and spatial visualization. Education: Ph.D. in Engineering Education, Purdue University (2018) M.S. in Systems & Entrepreneurial Engineering, UIUC (2006) B.S. in General Engineering, UIUC (2004) Research Interests: Molly’s work bridges engineering education and design practices, including generative design frameworks, empathy in human-centered design, and STEM education equity. She co-authored the Engineering Design Graphics textbook (3rd ed., 2022) and leads NSF-funded projects on generative design education. Her lab explores tools like AI-driven design software and spatial visualization training to enhance student outcomes. Teaching & Awards: Recognized for innovative pedagogy, she won the 2023 Collins Award and 2024 Everitt Award. Courses include Engineering Graphics & Design (SE 101) and Advancements in Generative Design (SE 598) . She pioneered active learning spaces in the Campus Instructional Facility and developed the Renaissance Engineering summer camp for middle schoolers. Grants: NSF funding for generative design education research (2020–2024) Labs/Teams: Product Design Lab, Siebel Center for Design Affiliate
Roger Beecham is Associate Professor in Visual Data Science at the School of Geography, University of Leeds, and Director of Research & Innovation at the Leeds Institute for Data Analytics (LIDA). He co-leads LIDA's Visualization and Science of Data Science programmes and serves as Programme Leader for GISc Distance Learning. His academic home resides within the Faculty of Environment, where he bridges geographical analysis with cutting-edge data science methodologies. His research spans Data Visualization, Spatial Statistics, and Applied Data Science across transport, health, crime science, and political geography domains. Beecham develops visualization techniques for analyzing large social science datasets, with particular focus on uncertainty quantification and methodological rigor. His work addresses the 'Forking Paths' problem in data analysis and promotes transparent scientific practices through visual analytics. Current projects include INFUZE (zero-carbon mobility) and SaferActive, funded by EPSRC, ESRC, ERC, NIHR, and Wellcome Trust. Beecham's scholarly contributions manifest in top-tier journals like IEEE TVCG, Accident Analysis & Prevention, and Transport Research Part C. His upcoming 2025 CRC Press book Visualization for Social Data Science synthesizes his methodological innovations. Research outputs demonstrate consistent focus on visual inference frameworks, spatial pattern analysis, and open-source implementation. EPSRC-funded transport safety research Alan Turing Institute Methods Challenge leadership Wellcome Trust health geography projects ERC spatial data science collaborations He supervises doctoral researchers including Juan P. Fonseca-Zamora, Juliana Novaes, and Seán Ó Héir through the SENSE CDT program. Teaching responsibilities include GEOG5009 Visualization for Social Data Science and GISc Distance Learning MSc coordination. Beecham maintains active GitHub repositories demonstrating reproducible research practices and collaborates extensively through the Institute for Spatial Data Science.
