D. Spinellis is a Professor at Athens University of Economics and Business with continuous affiliation since 2000, specializing in Software Engineering within the broader field of Computer Science. His research spans critical domains: Software Engineering Computer Science Data Engineering Artificial Intelligence Cybersecurity High-Performance Computing Recent publications (2024-2025) demonstrate consistent focus on practical system-level challenges including Linux analysis on supercomputers, graph processing efficiency, AI-generated content ethics, security system modernization, and data workflow engineering. These works bridge theoretical research with real-world software applications. Professor Spinellis actively engages public discourse through media coverage of his 2025 study on AI-generated publication fraud, which received significant attention across news outlets, academic platforms, and social media including X, Facebook, and Bluesky.
Randy Buckner is a Professor of Psychology and Neuroscience at Harvard University, where he directs the Buckner Lab. His research investigates the organization of large-scale human brain networks and their relationship to cognition, behavior, and neuropsychiatric disorders. The lab employs multimodal approaches including neuroimaging, behavioral testing, and computational modeling to examine how genetic variation influences brain architecture and contributes to conditions like Alzheimer's disease and schizophrenia. Current projects focus on detecting early network dysfunction in individuals at genetic risk for neurological disorders and mapping individualized brain organization across the lifespan. Research emphasizes precision neuroimaging techniques to capture individual variability in brain structure and function, with implications for early intervention strategies. Buckner's work spans multiple neuroscience domains including: Large-scale brain network organization Genomic influences on neural circuitry Precision mapping of individual neuroanatomy Neurodegenerative disease biomarkers Advanced neuroimaging methodologies Contact: Northwest Building, Room 280.06, 52 Oxford St, Cambridge, MA 02138. Email: randy_buckner@harvard.edu
Nathaniel Bastian is an Adjunct Professor in the Department of Data Analytics and Business Analytics at Pennsylvania State University's Smeal College of Business. He specializes in artificial intelligence foundations and applications, with a focus on defense, cybersecurity, and data-driven decision systems. Research Focus: His work spans stochastic optimization, federated machine learning, uncertainty modeling, multimodal data fusion, and generative AI. Key application domains include military systems, national security, IoT infrastructure, healthcare analytics, and financial technology. Funded Projects: Principal investigator for six defense/AI initiatives: Generative Methods for Cyber (NSA) Evaluating Model Robustness to ML Data Contamination Attacks (Army C5ISR) Assessment of Intelligent Autonomous Cyber Decision-Support Systems (Army C5ISR) Enabling Safe Reinforcement Learning (ARL) Principles of Robust Learning and Inference (ARL) Optimization for Learning and Decision Making (ARL) Publication Trends: Recent articles (2020-2021) demonstrate strong emphasis on AI robustness in high-stakes domains: 85% focus on cybersecurity/defense applications, with recurring themes of uncertainty quantification, adversarial resilience, and military AI systems. Methodological contributions center on Bayesian networks, generative models, and ensemble techniques. Instructional Activities: Teaches graduate courses on analytics implementation (BAN 888) and data-driven decision frameworks (DAAN 881).
Jean-Noël Grad is a postdoctoral researcher at the Institute for Computational Physics , University of Stuttgart. His work focuses on computational physics and software engineering for soft matter simulations. Institute for Computational Physics, University of Stuttgart Research Interests: Grad specializes in computational physics and soft matter simulations, with a particular focus on molecular dynamics and simulation frameworks. He contributes to open-source software development and sustainability in research computing. Workshop Contributions: He regularly organizes and participates in workshops on soft matter simulation tools like ESPResSo, PyStencils, and LbmPy. His roles include co-organizer of events such as "Simulating soft matter across scales" and "Simulating energy materials with ESPResSo". Teaching Activities: Grad teaches the ESPResSo Block course annually, focusing on practical applications of soft matter simulation software. Software Contributions: Active in multiple software projects including ESPResSo (core contributor), pyMBE (co-developer), and tools like EESSI and waLBerla for cross-platform simulation environments.
Line Clemmensen serves as Associate Professor at DTU Compute (Department of Applied Mathematics and Computer Science), Technical University of Denmark, where she has held faculty positions since 2010. Her interdisciplinary work bridges statistical learning, machine learning, and real-world applications in mental healthcare, biotechnology, and agricultural informatics. Her academic credentials include: Ph.D. in Image Analysis and Computer Graphics from DTU (2010), thesis: "High-dimensional sparse data analysis" M.Sc. in Applied Mathematics from DTU (2006) Exchange studies at Universitat Politecnica de Catalunya, Barcelona (2004) Mathematical graduate from Falkonergården upper secondary school (2000) Clemmensen's research centers on machine learning and statistical learning with emphasis on low-resource modeling, representation learning, and AI evaluation methodologies. She applies these techniques to mental health (developing biosensor-based OCD monitoring systems), biotechnology (spectral data analysis for pharmaceutical quality control), and environmental science (crop health assessment via remote sensing). Her work consistently addresses challenges in data scarcity and model interpretability. Analysis of her recent publications reveals dominant trends in computational psychiatry (e.g., detecting OCD episodes through physiological signals and oxytocin biomarkers) and agricultural AI (linking soil microbiome composition to crop health via machine learning). Methodologically, she pioneers interpretable deep learning frameworks for low-resource settings and robust time-series analysis techniques for physiological data. She has supervised 10 PhD students to completion (including Jacob Søgaard Larsen on NIR management and Gudmundur Einarsson on psychiatric motion quantification) and mentored 13 Master's/Bachelor's students. Current funding includes the LundbeckFonden LF-Experiment grant for the FAST project (Fast Assessment of psychiatric Symptoms to Transform Mental Health Care). Within DTU Compute's Section for Statistics and Data Analysis, Clemmensen leads collaborations with Novo Nordisk (biostatistics), the Danish Meat Industry, and clinical psychiatry teams, focusing on real-world AI deployment in mental healthcare and industrial applications.
Benedikt Ehinger is a computational neuroscientist at the University of Stuttgart, leading an Emmy Noether research group on "EEG in motion" funded by DFG. He specializes in integrating EEG with eye-tracking, developing statistical methods for neuroimaging and creating open-source tools. DDFG Emmy Noether grant recipient (2019) Director of Computational Cognitive Science lab Co-developer of open-source tools: Unfold toolbox, ClusterDepth algorithm His research focuses on three main areas: 1. Methodological foundations of EEG/MEG analysis, particularly cluster-based statistics and deconvolution methods; 2. Eye-tracking methodology and visualization techniques; 3. Statistical modeling of human perception using linear mixed models and Bayesian approaches. Key trends in his recent publications include: Advancing EEG methodology with linear deconvolution and cluster permutation tests Developing open-source neuroscience tools in Julia/MATLAB Investigating perceptual inference and reliability estimation Creating art-science interfaces through "thesis art" projects Scientific contributions: Emmy Noether research group leadership Over 10 thesis supervisions (Master's/Bachelor's) Co-development of multiple open-source toolboxes Methodological innovations in ERP analysis and statistical testing As an educator, he creates interactive tutorials on statistical concepts and provides thesis art for each supervised student. His lab maintains strong software engineering practices with GitHub-hosted code repositories.
Lee Yuri serves as Professor and Dean of the College of Human Ecology at Seoul National University, where she leads the Department of Clothing and Textiles and directs the Fashion Retail & Service Lab. Her academic career spans over two decades with significant contributions to fashion retailing, consumer behavior, and technology integration in the fashion industry. Bachelor's and Master's degrees from Seoul National University (1989-1995) Ph.D. in Clothing and Textiles from Virginia Tech (1996-2000) Her research explores the intersection of fashion, technology, and consumer psychology, with particular focus on generational differences in retail technology adoption, cross-cultural consumer behavior, and digital transformation in fashion retail. Recent work examines AI applications in design, contactless service encounters, and the psychological impact of social media on fashion consumption. Professor Lee's publication trends reveal a strategic shift toward interdisciplinary research bridging fashion, data science, and human-computer interaction. Her work increasingly addresses pandemic-era retail challenges, sustainability in secondhand markets, and generational divides in technology adoption, establishing her as a leading voice in empirical fashion consumer research. Korean Society of Clothing and Textiles Outstanding Academic Paper Award (2023) As Editor-in-Chief of the International Journal of Costume and Fashion and active board member in seven academic societies, Professor Lee mentors numerous graduate students while advancing research through editorial leadership. Her grant-funded projects focus on consumer behavior analytics and retail innovation, with recent collaborations spanning fashion technology development and cross-cultural market studies. She directs the Fashion Retail & Service Lab (Room 305, Building 222), which operates as a hub for empirical research on retail innovation, consumer psychology, and technology implementation. The lab maintains strong industry partnerships and has pioneered methodologies for analyzing digital consumer behavior in fashion contexts.
John F Hughes is a Professor of Computer Science at Brown University's School of Engineering. His work bridges computer graphics and mathematics, with a focus on intuitive interfaces for 3D modeling and visualization. He has made significant contributions to sketch-based interfaces, art-based graphics, and shape modeling. Education: PhD in Mathematics, University of California, Berkeley (1982) MA in Mathematics, University of California, Berkeley (1982) BA in Mathematics, Princeton University (1977) Professor Hughes's research centers on computer graphics with strong mathematical foundations. He specializes in the modeling of shape and form at multiple scales, human-computer interaction, and art-based graphics. His work explores how artists' techniques can be utilized to enhance human-computer communication about shape. He has recently expressed interest in machine learning applications to graphics problems. His approach emphasizes informal modes of input and output, particularly sketching as a means to describe shape and expressive renderings for information communication. His publication record shows a consistent focus on sketch-based interfaces for 3D modeling, art-based rendering techniques, and mathematical approaches to graphics problems. Over time, his work has evolved from foundational mathematical approaches to more applied interactive systems, while maintaining a strong connection to mathematical principles. Recent publications indicate expanding interests into machine learning applications for graphics and computational approaches to sparse data. Scientific Awards: User Interface Software and Technology (UIST) Best Paper Award Professor Hughes has received substantial research funding from major technology companies and government agencies. His funded research includes a gift from Pixar supporting graduate fellowships in computer graphics (since 2000), research grants from Microsoft, NSF, and collaborations with IBM and Sun Microsystems. His work has been instrumental in advancing sketch-based interfaces and art-based rendering techniques, with applications ranging from character animation to document navigation interfaces. He maintains active collaborations with researchers across the computer graphics community, particularly in the areas of sketch-based interfaces and modeling. His work with Takeo Igarashi, Tomer Moscovich, and other collaborators has been highly influential in the computer graphics community, shaping how we interact with 3D content through intuitive sketching interfaces.
Cosimo Arnesano serves as an Assistant Professor of Clinical Data Sciences and Operations at the University of Southern California's Marshall School of Business. He holds dual Ph.D. degrees in Energy and Environmental Engineering and Biomedical Engineering, complemented by an MBA specializing in business analytics and operations/supply chain optimization. His industry background includes strategic roles at ThermoFisher Scientific (Strategy Manager, 3 years) and Zeiss Microscopy (Account Manager, 1.5 years). Education Ph.D. in Energy and Environmental Engineering Ph.D. in Biomedical Engineering MBA with concentrations in Business Analytics and Operations/Supply Chain Optimization Research Interests Dr. Arnesano's expertise spans analytics, artificial intelligence, corporate strategy, healthcare analytics, machine learning, operations management, statistics, and stochastic modeling. His work bridges biomedical imaging techniques with business applications, focusing on data-driven solutions for healthcare operations and business process optimization. He develops analytical models for classification, clustering, and association problems while leveraging third-party big data to enhance business insights and decision-making. Publication Trends His 15 most recent publications (2012-2022) reveal an interdisciplinary trajectory from biomedical imaging methodology to clinical applications. Early works (2012-2016) established innovations in fluorescence lifetime imaging and digital signal processing, while recent research (2017-2022) applies these techniques to cancer metabolism, stem cell tracking, and retinal organoid characterization. A consistent thread involves developing non-invasive optical biomarkers for real-time cellular analysis, directly informing his business analytics focus on operational efficiency and healthcare optimization. Teaching and Operations Undergraduate: Applied Business Statistics (BUAD-310), Basics of Project and Operations Management (BUAD-315) Graduate: Spreadsheet Modeling (DSO-427), Business Decision Modeling (DSO-536), Blended Data Business Analytics (DSO-528)
Kara Davis is an Associate Professor of Pediatrics at Stanford University School of Medicine, specializing in Hematology/Oncology. Her research focuses on pediatric blood cancers, particularly B-cell acute lymphoblastic leukemia (B-ALL), using single-cell and high-dimensional analysis to study tumor heterogeneity and treatment resistance. Education: B.A. from Pennsylvania State University, D.O. from Philadelphia College of Osteopathic Medicine, Pediatrics residency at A.I. DuPont Children’s Hospital, and Heme/Onc fellowship at Stanford. Her research integrates single-cell technologies like mass cytometry (CyTOF) and computational tools to: Model healthy B cell development vs. leukemic divergence Predict relapse risks via signaling signatures (e.g., mTOR activity) Investigate antigen escape mechanisms in CAR-T therapy Develop metabolic vulnerability-targeting strategies Design interpretable machine learning algorithms for clinical outcomes Key trends in her 15 most recent publications include: Advancing single-cell data ecosystems (tidyomics, TidyTOF, CyTOFIn) Optimizing CD22-CAR T cell manufacturing and safety Characterizing microbial DNA post-CAR-T therapy Modeling chromosomal translocations in KMT2A-AFF1 fusion leukemias Addressing glucocorticoid resistance via kinase inhibitors Scientific Awards : Anne T. and Robert M. Bass Endowed Faculty Scholar (2018) Zelencik Scientist (2021) She leads clinical trials on immunotherapies for relapsed/refractory pediatric cancers and mentors students in the Davis Lab, which emphasizes collaborative science and translational research. Lab members include postdocs, doctoral students, and research analysts working on projects spanning neuroblastoma, metabolic reprogramming, and computational biology.
Vikas Tomar is a Professor in the School of Aeronautics and Astronautics at Purdue University, where he has served since 2006. He holds a Ph.D. from Georgia Institute of Technology (2005), an MBA from UC Berkeley (2023), and Indian degrees from IIT Madras and NIT Kurukshetra. His research focuses on materials in extreme environments, including non-equilibrium phenomena (e.g., shock dynamics) and autonomy energy intelligence (e.g., battery management systems). He leads the Interfacial Multiphysics Lab, which has produced over 400 publications, 11 patents, and 16 PhD graduates. Key achievements include ASME Fellow status (2016), the AFoSR Young Investigator Award (2009), and multiple teaching/mentorship awards. His lab’s work spans experimental methods like Mechanical Raman Spectroscopy (patented) and computational models for battery safety and energetic materials. Education: MBA, UC Berkeley, 2023 Ph.D., Georgia Tech, 2005 M.Tech., IIT Madras, 2001 B.Tech., NIT Kurukshetra, 1998 Awards: University Faculty Scholar (2016–2021) W A Gustafson Best Teacher Award (2019–20) VAJRA Faculty Award (2019) Over 30+ patents, grants, and editorial roles in journals. Research interests include: Shock physics and nanosecond Raman spectroscopy Battery safety and AI-driven energy systems Interface mechanics in materials and biomaterials Lab contributions include pioneering work on battery thermal runaway prediction and smart management systems, as well as advanced imaging techniques for material characterization under extreme conditions.
Christophe Croux is a full professor at the Faculty of Economics and Business (FEB) at KU Leuven and serves as the coordinator of the Operations Research and Statistics Research Group (ORSTAT). His research focuses on robust statistical methods, econometric modeling, and high-dimensional data analysis with applications in finance, marketing, and chemometrics. He has contributed extensively to the development of robust estimation techniques, sparse regression models, and volatility forecasting frameworks, often leveraging computational tools like R packages such as 'robustbase.' Key areas of research include robust correlation analysis, time series modeling (e.g., state space models, volatility spillovers), and machine learning applications for classification and prediction. His work emphasizes handling outliers and large datasets, with practical implications for financial risk management, credit scoring, and commodity market dynamics. Croux has published over 150 articles since 2015, spanning topics from robust canonical correlation to sparse vector autoregressive models. Notable contributions include the development of the robustbase R package, which provides essential tools for robust statistical analysis. His interdisciplinary approach bridges theoretical statistics with practical applications in economics and business analytics. He holds leadership roles in academic networks and committees, furthering the impact of his research in both academia and industry.
Dana Dachman-Soled is an Affiliate Associate Professor in the Department of Computer Science at the University of Maryland, with a joint appointment in the Department of Electrical and Computer Engineering. Her research focuses on cryptography, algorithmic fairness, and theoretical computer science, with applications to post-quantum security, privacy-preserving algorithms, and secure multiparty computation. She has advised at least one PhD student, Yvonne Zhou. Her work combines foundational cryptographic theory with practical implementations, addressing challenges such as tamper-resistant data encoding, fair machine learning, and secure communication protocols. In 2023, she received a $1M NSF award for research on post-quantum cryptography. She has contributed to advancements in non-malleable codes, leakage-resilient cryptography, and the security of lattice-based schemes like LWE and NTRU. Her research also explores ethical AI, including fairness in data classification and privacy-preserving synthetic data. She has published extensively in top venues like ITC, CRYPTO, and IEEE conferences, with a focus on cryptographic primitives, side-channel vulnerabilities, and algorithmic bias mitigation. Dr. Dachman-Soled collaborates widely, with grants supporting her work on non-malleable codes and the theoretical foundations of secure computation. Her lab engages in interdisciplinary projects at the intersection of computer science, mathematics, and cybersecurity.
Anton Feenstra is an Associate Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Bioinformatics department, as well as AIMMS and Integrative Bioinformatics. He holds a PhD (dr.) and an engineering degree (ir.). His research focuses on structural bioinformatics, protein structure prediction, computational biology, and bioinformatics algorithms. Key interests include protein-protein interactions, molecular dynamics, and knowledge graph applications in health and microbiota studies. Feenstra leads projects such as ELIXIR-NL (Digital Research Infrastructure) and has contributed to initiatives like BIOEXE and ENFIN. He teaches courses including Algorithms in Sequence Analysis and Fundamentals of Bioinformatics. His work spans over 86 publications, with recent contributions on protein interface prediction (PIPENN-EMB), microbiota-gut-brain axis analysis, and structural bioinformatics tools. Notable achievements include developing the PRALINE alignment toolkit and advancing machine learning methods for protein function prediction. Collaborations include work on Mycobacterium tuberculosis and SARS-CoV-2 protein analysis. His research aligns with UN Sustainable Development Goals, particularly in health and innovation.
Dr. Vincent Hargaden is an Associate Professor and Head of School at the School of Mechanical and Materials Engineering, University College Dublin. He holds a PhD in Decision Science from Rensselaer Polytechnic Institute and has over two decades of academic and industry experience. His research focuses on three core areas: Workforce Planning in professional services, Design and Optimization of resilient supply chains, and innovations in Engineering Education. He leads the UCD Master of Engineering Management program and has received multiple teaching awards including the 2017 University Teaching Excellence Award. Education: BE (Mechanical Engineering), University College Dublin MEngSc (Computer Integrated Manufacturing), NUI Galway MBA, UCD Michael Smurfit Graduate Business School PhD (Decision Science), Rensselaer Polytechnic Institute Professional Diploma in University Teaching & Learning, UCD Research Interests: Supply Chain Resilience & Risk Assessment Blockchain Applications in Construction & Engineering Digital Twin Technologies for Manufacturing Industry 4.0 Integration in Agri-Food Sectors Active Learning Pedagogies in Engineering Education Notable Achievements: Co-Principal Investigator in Enterprise Ireland Dairy Processing Technology Centre H2020 VALUMICS Project (Food Value Chain Dynamics) IBM Global PhD Fellowship (2010) UCD Learning Factory Initiative Participation
