Prof. Martin Messner is a University Professor at the University of Innsbruck, leading the Controlling Institute within the Department of Organization and Learning. His work focuses on management accounting, performance measurement, and the intersection of accounting practices with organizational behavior. He holds editorial leadership roles, including Editor-in-Chief of Accounting, Organizations and Society (2021–present) and previously served on the Management Accounting Research editorial board (2015–2019). Prior to his current position, he was an Assistant Professor at HEC Paris (2006–2011). Research Interests: His research explores performance measurement systems, ethics in management control, and the evolving roles of accounting in organizational narratives and governance. He investigates topics like algorithmic decision-making, narrative reporting, and entrepreneurial governance structures. Professional Activities: Member of the European Accounting Association’s Publications Committee Co-organizer of the MASOP Workshops (2008–2023) His publications span academic journals and edited volumes, addressing themes such as zero-based budgeting, data scientist identities, and the ethical implications of control systems. He actively bridges theoretical and practical accounting issues through interdisciplinary collaboration.
Mennatallah El-Assady serves as Assistant Professor at ETH Zurich's Department of Computer Science, where she leads the Interactive Visualization and Intelligence Augmentation Lab (IVIA). Her academic trajectory includes research fellowships at ETH's AI Center and doctoral work at University of Konstanz and OntarioTech University, establishing her expertise at the intersection of visualization and artificial intelligence. Her research focuses on advancing responsible data-driven decision-making through human-centered analytics, with particular emphasis on explainable machine learning systems. Dr. El-Assady combines data mining techniques with visual interfaces to create transparent AI workflows, specializing in text data analysis. Her work bridges computational linguistics, digital humanities, and information visualization to develop tools that make complex AI processes interpretable for end users. Recent publications demonstrate growing focus on generative AI's impact on visualization practices and sophisticated frameworks for interactive machine learning. Her research consistently addresses the challenge of maintaining human agency in increasingly automated systems, with applications spanning political debate analysis, musicology, and healthcare data interpretation. The trend shows increasing sophistication in evaluation methodologies for visual analytics systems. Best Paper Award: Honorable Mention for 'Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework' Dr. El-Assady actively shapes her field through workshop leadership including ArgVis (Argument Visualization), Vis4DH (Visualization for Digital Humanities), and VISxAI (Visualization for AI Explainability). Her teaching includes 'Interactive Machine Learning- Visualization and Explainability' at ETH Zurich, training next-generation researchers in human-centered AI development. She maintains strong industry connections with coverage in Forbes and ETH News regarding human-AI collaboration frameworks. The Interactive Visualization and Intelligence Augmentation Lab (IVIA) develops cutting-edge tools including explAIner for transparent machine learning, LingVis for linguistic analysis, VisArgue for debate structure visualization, and VALIDA for political deliberation analysis. These projects share a common thread of enhancing human understanding through carefully designed visual interfaces that expose AI decision processes.
Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.
Prof. Dr. Evi Hartmann holds the Chair of Business Administration, especially Supply Chain Management at Friedrich-Alexander University Erlangen-Nuremberg (FAU) within the Department of Business, Economics, and Social Sciences. She is actively involved in multiple research focus areas including sustainability, energy markets and energy system analysis, and insurance and risk. Her academic leadership extends across interdisciplinary collaborations with engineering, mathematics, and industry partners. Dr. Hartmann studied industrial engineering at the University of Karlsruhe (TH), received her doctorate in 2002 from the Institute of Technology and Management at the Technical University of Berlin, and completed her habilitation in business administration in 2008. Prior to her academic career, she worked as a consultant at AT Kearney from 1998 to 2005, followed by a junior professorship for 'Purchasing and Supply Management' at the Supply Chain Management Institute at the European Business School. Her research program focuses on supply chain management, purchasing, and strategic foresight, with particular emphasis on application-oriented approaches that bridge theory and practice. Current research trajectories include supply chain resilience in crisis situations (including pandemic response), digital transformation through Industry 4.0 technologies, sustainable and low-carbon supply chains, and the integration of strategic foresight methodologies in logistics decision-making. Her work frequently employs Delphi studies, bibliometric analyses, and multi-tier case studies to examine complex supply chain phenomena. Analysis of her recent publications reveals a strong trend toward interdisciplinary research that combines supply chain management with digital transformation, sustainability, and crisis response. Her work increasingly examines the intersection of technology adoption (particularly Industry 4.0), organizational culture, and supply chain resilience across multiple industries including automotive, food, and maritime logistics. Prof. Hartmann is recognized as the author of two academic bestsellers in her field, though specific awards are not detailed in available materials. Her research has been published in top-tier journals including IEEE Transactions on Engineering Management, International Journal of Production Research, and Journal of Cleaner Production. Her research program demonstrates extensive industry collaboration, with numerous projects involving real-world implementations and close partnerships with companies. She leads research initiatives examining the practical implications of digital transformation, sustainability challenges, and resilience strategies in supply chain operations. Current projects include studies on digital ecosystems, physical internet applications, and the future of freight forwarding technologies. Prof. Hartmann participates in several research networks including the Energy Campus Nuremberg (EnCN) and collaborates with the Department of Mathematics on gas networks and markets research. She is also involved with the Nuremberg Energy Region (Energieregion Nürnberg eV) and contributes to interdisciplinary research centers focused on sustainable development and digital transformation in supply chains.
Scientia Professor Robert Kohn is a distinguished academic at the University of New South Wales, holding a position in the School of Economics within the UNSW Business School. With a career spanning several decades, Professor Kohn has established himself as a leading expert in statistical methodology and econometric modeling. His research has significantly contributed to Bayesian statistics and computational methods for complex data analysis. Professor Kohn's research focuses on advanced statistical methodologies including Bayesian methodology, variable selection and model averaging, nonparametric regression models, time series modeling, multivariate Gaussian and non-Gaussian regression, and Markov chain Monte Carlo simulation algorithms. His work bridges theoretical statistics with practical applications across economics, finance, and cognitive science. His research demonstrates a consistent trajectory toward developing more efficient computational methods for complex statistical models, with recent work emphasizing variational Bayesian methods, particle filtering techniques, and applications to time series analysis. Analysis of his recent publications (2022-2025) reveals a strong focus on advancing computational statistical methods, particularly in Bayesian inference for complex models. His work shows increasing integration of machine learning techniques with traditional statistical methods, especially in handling high-dimensional data and complex time series structures. Professor Kohn has made significant contributions to variational inference methods, particle-based computational techniques, and applications to financial time series and cognitive modeling. Professor Kohn has maintained an exceptionally productive research career with continuous publication output since the 1970s, demonstrating remarkable longevity and adaptability in his research focus as statistical methodologies have evolved. His work shows strong international collaboration, particularly with researchers in Australia, the United States, and Europe, reflecting his standing in the global statistical community.
Maisha T. Winn serves as the Excellence in Learning Graduate School of Education Professor at Stanford University and is Faculty Director of the Stanford Accelerator for Learning's Equity in Learning Initiative. She leads the Futuring for Equity Lab as Principal Investigator and holds significant leadership positions including President-Elect of the American Educational Research Association and membership in the National Academy of Education. Dr. Winn's research examines how non-dominant youth and communities develop literate trajectories across historical and contemporary settings. As an ethnographer by training, she investigates how communities depicted as under-resourced create their own educational practices, processes, and institutions. Her scholarship bridges historical analysis with contemporary educational practice to build more just, collaborative, and equitable futures in education. Her work spans restorative justice in education, Black literacy studies, and transformative justice approaches, with particular attention to the school-to-prison pipeline, independent Black institutions, and futures-oriented educational frameworks. Dr. Winn analyzes how historical educational models can inform contemporary practices that center community knowledge and cultural identity. Andrew W. Mellon Fellow at CASBS (2022-23) American Educational Research Association Fellow Member of the National Academy of Education Dr. Winn advises doctoral students including Christina Hewko and Misbah Naseer, and leads research initiatives focused on educational equity. Her Futuring for Equity Lab develops frameworks for understanding how marginalized communities have historically created educational spaces that affirm their identities and knowledge systems, with direct implications for contemporary educational practice and policy. Her research team collaborates with community organizations and schools to translate scholarly insights into practical applications that promote educational justice, particularly in areas of school discipline reform and literacy education for marginalized youth.
Associate Professor Jiwon Kim is a leading researcher in Transport Engineering at the University of Queensland's School of Civil Engineering. She serves as Director of Higher Degree by Research and was a DECRA Fellow from 2019-2022. Holding degrees from Korea University and Northwestern University, she specializes in AI/ML applications for transportation systems. PhD, Northwestern University BS & MS, Korea University Her research focuses on Artificial Intelligence and Machine Learning applications in transportation, including: Deep learning for traffic management Reinforcement learning in mixed traffic environments Multi-agent systems for urban mobility optimization Spatiotemporal trajectory analysis Recent publications demonstrate expertise in: Eco-driving strategies Traffic incident prediction Queue length estimation Crash risk modeling Scientific recognition includes: ARC DECRA Fellowship (2019-2022) She supervises doctoral students in: Transportation data analytics Autonomous vehicle systems Intelligent traffic management Current projects explore real-time traffic monitoring, synthetic mobility data generation, and connected vehicle technologies.
Jonathan Kirshner Jonathan Kirshner is a Professor of Political Science at Boston College and Emeritus Stephen and Barbara Friedman Professor of International Political Economy at Cornell University. His expertise spans International Relations, Political Economy (focusing on macroeconomics and money), and Politics and Film. He has held leadership roles, including director of the Reppy Institute for Peace and Conflict Studies (2007–2015), and received prestigious awards like the Provost’s Award for Distinguished Scholarship and the Stephen and Margery Russell Distinguished Teaching Award. Education: B.A. (Johns Hopkins University), M.A. and Ph.D. (Princeton University). Research Interests: Classical Realism, financial crisis implications, mid-century cinema politics, and Keynesian economics. Key works include An Unwritten Future: Realism, Uncertainty, and World Politics (2022), American Power After the Financial Crisis (2014), and When the Movies Mattered: The New Hollywood Revisited (2019). Recent Publications: Focus on US foreign policy trajectory, economic theory applications, and film analyses of 1970s America. His articles in Foreign Affairs , Security Studies , and Cineaste blend geopolitical analysis with cultural critique. Awards: Celebrated for scholarship and teaching, including recognition from the International Studies Association for Appeasing Bankers .
George H. Chen is an Associate Professor at Carnegie Mellon University , with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department . His research focuses on trustworthy machine learning methods for temporal reasoning , particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis . He has extensive experience in nonparametric methods requiring minimal data assumptions. Educational Background PhD in Electrical Engineering and Computer Science, MIT (2015) SM in Electrical Engineering and Computer Science, MIT (2012) BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) His work spans survival analysis , deep learning , and time series modeling , with applications in neurological prognostication , medical adherence , and health equity . He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques. Notable projects include advising the AgriTech startup CoolCrop , which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.
Rahul Mangharam is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania's School of Engineering and Applied Science, with a secondary appointment in Computer and Information Science. He directs the Safe Autonomous Systems Lab (mLAB) and is a founding member of the PRECISE Center. Mangharam serves as Penn Director for the Safety21 DoT National University Transportation Center ($20MM), Director of the Autoware Center of Excellence, and leads the F1Tenth Autonomous Racing Community. Education: Ph.D. in Electrical & Computer Engineering, Carnegie Mellon University M.S. in Electrical & Computer Engineering, Carnegie Mellon University B.S. in Electrical & Computer Engineering, Carnegie Mellon University His research bridges formal methods, machine learning, and control systems with applications in medical devices, autonomous systems, and energy-efficient buildings. Key focus areas include safety verification for autonomous vehicles, real-time control systems, and patient-specific cardiac modeling for clinical applications. Recent work explores conformal prediction for safe perception, differentiable control barrier functions, and explainable autonomous systems. Mangharam's publication trends show strong emphasis on autonomous systems safety (control synthesis, uncertainty quantification) and biomedical applications (cardiac modeling, clinical decision support). His 2022-2023 publications demonstrate cross-disciplinary approaches combining control theory, machine learning, and formal methods for robust autonomous systems. Awards and Honors: Presidential Early Career Award (PECASE) 2016 IEEE Benjamin Franklin Key Award 2014 NSF CAREER Award 2013 Intel Early Faculty Career Award 2012 National Academy of Engineers US Frontiers of Engineering (2012, 2018) Stephen J. Angelo Term Chair (2008-2013) He leads multiple major grants including NSF CAREER, DoT Safety21 Center ($20MM), DoE Energy-Efficient Building Hub ($160MM), and DARPA HACMS. Current PhD students include Zirui Zang. Mangharam founded the F1Tenth autonomous racing platform used globally for education and hosts international competitions through the Autoware Center of Excellence.
Matthieu Cord is a Professor at Sorbonne University and Scientific Director of valeo.ai, leading research in computer vision, deep learning, and computational cooking. He heads the MLIA team at ISIR Lab, focusing on multimodal models, transformers, and efficient architectures. Research areas include computer vision, large language models with vision, and AI-driven food analytics. Key projects: VISA-DEEP AI chair, Foundation VaViM models, and SmolVLA collaboration with Hugging Face. His recent work examines scalable multimodal models , trajectory prediction , and diffusion-based segmentation , with studies on in-context learning and biased shortcut learning in visual question answering. Articles highlight DeiT variants , fishr for OoD generalization , and STEEX for counterfactual explanations . Scientific awards include IUF Honorary Membership (2009), BMVC 2017 Best Paper, and ICIP 2018 Best Paper. As an advisor, he supervised PhD theses on topics like GAN editing , semantic segmentation , and multimodal retrieval . Current roles involve mentoring the 'Research Band' at MLIA and leading EU-funded initiatives like SCAPE. His work bridges theoretical AI exploration with practical applications in autonomous driving and food technology.
Dr. Nerijus Maliukevičius serves as an Assistant Professor at Vilnius University's Institute of International Relations and Political Science, where he conducts research and teaching within the Department of Political Behavior and Institutional Studies. His expertise centers on information warfare, strategic communication, and Russian geopolitical strategies, with extensive publications analyzing disinformation campaigns targeting Baltic and European states. Maliukevičius' research interests focus on Information Warfare , Russian Studies , and Propaganda Countermeasures , examining how technological advances reshape conflict dynamics. His work investigates Kremlin influence operations across media ecosystems, emphasizing social resilience frameworks and psychological defense mechanisms against hybrid threats. Key contributions include foundational studies on Russian information geopolitics and contemporary analyses of operations like Ghostwriter that exploit digital vulnerabilities in democratic societies. His publication trajectory reveals a strategic evolution from theoretical frameworks (2002-2008) toward actionable counter-strategies, with recent work prioritizing social resilience metrics and Nordic-Baltic defense coordination. This shift reflects escalating geopolitical tensions following Crimea's annexation and Ukraine's invasion, where his analyses of Russian media tactics have gained critical policy relevance across NATO and EU institutions. Maliukevičius has secured major research funding including the IARPA-funded SAGE project (2016-2019) for geopolitical forecasting and Lithuania's national disinformation study (2021). He regularly advises governments through high-impact presentations at NATO StratCom CoE, NED forums, and Lublin Triangle initiatives, translating academic insights into operational countermeasures against active measures. As co-leader of Vilnius University's Politics of Technology Research Group and Belarus Research Group, he directs collaborative investigations into AI-driven disinformation and authoritarian influence networks. His teams develop early-warning systems for hybrid threats while training next-generation analysts through specialized courses like Information Warfare and Strategic Political Communication.
Juan Camilo Gómez is an Associate Professor with tenure at the University of Washington Bothell's School of Business, specializing in game theory, bargaining, and microeconomic theory. He earned his Ph.D. in Economics from the University of Minnesota and his B.Sc. in Mathematics from Universidad de los Andes in Colombia. Education: Ph.D. Economics (1998–2003), University of Minnesota, Minneapolis, MN B.Sc. Mathematics (1990–1996), Universidad de los Andes, Bogotá, Colombia Research Interests: His research focuses on foundational and applied aspects of game theory, including bargaining models, coalition formation, cooperative solution concepts, and general equilibrium. He explores how efficiency can be achieved in strategic interactions, especially when agents may behave manipulatively or when traditional balance conditions do not hold. His work contributes to understanding how coalitions form and how outcomes can be predicted or designed to ensure equitable and efficient allocations. He also investigates the implications of reference points in bargaining and the role of aspirations in shaping cooperative behavior. Publications Overview: Across his publications, a clear trajectory emerges from foundational theoretical work—such as axiomatizing core extensions in cooperative games—to applied models that predict coalition formation and bargaining outcomes. His research consistently bridges rigorous mathematical frameworks with practical economic questions, including market interpretations of cooperative solutions and strategic pricing models like 'Pay What You Want'. Awards and Honors: 2010 MBA Professor of the Year, University of Washington Bothell Teaching and Advising: Gómez has taught a wide range of courses from undergraduate calculus and mathematical economics to graduate-level game theory and quantitative methods for business. He has held teaching roles at institutions including Macalester College, University of Copenhagen, Universidad de los Andes, and University of Minnesota. He has also co-directed undergraduate theses, such as that of Santiago Saavedra, and served on numerous academic committees including MBA Admissions and faculty hiring. Labs and Teams: While no specific lab is mentioned, his collaborative working papers with researchers like Camelia Bejan and P.V. Balakrishnan suggest active participation in research networks focused on game theory and economic modeling.
Chee-Wooi Ten is a tenured Professor in the Department of Electrical and Computer Engineering at Michigan Technological University, where he has served since 2010 and achieved tenure in 2016. He concurrently holds an Affiliated Professor appointment in Applied Computing and directs both the PSERC Site and ICC CPS Center. His institutional roles emphasize cyber-physical security integration within power infrastructure. His educational background includes: PhD in Electrical Engineering from University College Dublin (2009) MSc in Electrical Engineering from Iowa State University (2001) BSc in Electrical Engineering from Iowa State University (1999) Ten's research pioneers cyber-informed security engineering strategies for bulk power systems, focusing on quantifying rare events through system risk models and data science. His work bridges power grid interactions with robotics and transportation systems to advance decarbonization and electrification. Key methodologies include validating cyber-physical security frameworks against steady-state and dynamic grid approaches, with emphasis on attack/defense combinatorics and smart home technologies. This transdisciplinary approach supports the fourth industrial revolution's resilience requirements. His publication trends reveal strong focus on risk-aggregated substation testbeds using generative adversarial networks, cyber insurance models for power systems, and cascading failure analysis from switching attacks. Recent works increasingly integrate machine learning with physics-based modeling to address cybersecurity threats in inverter-based resource integration and distribution emergency operations. Ten has secured over $6.5M in active funding including: $2M DOE grant (MTU portion $105,000) for CyDERMS Center on DERs/Microgrids cybersecurity $704,409 CyManII award for secure digitalization in smart manufacturing $1.05M DOE ARPA-E grant for decarbonized freight transportation modeling NSF CyberCorps Scholarship for Service program ($3.38M) His grants consistently address risk management through data-driven and physics-based modeling, with industry partnerships through PSERC and utility collaborations. As ICC CPS Center Director, he leads research on cyber-physical security testbeds and coordinates the PSERC Summer Transformation School. His team develops validation frameworks for NERC CIP compliance while addressing practical pain points in OT cybersecurity for grid operators.
Natasha Zhang Foutz is a Research Associate Professor of Commerce at the McIntire School of Commerce, University of Virginia . Her work bridges Artificial Intelligence , Marketing Analytics , and Consumer Behavior , with a focus on Digital Content and Location-Based Services . She teaches Marketing Analytics , Entertainment Marketing , and Marketing Models across undergraduate to PhD programs. Ph.D. in Marketing, Cornell University M.S. in Statistics & Marketing, Cornell University B.S. in Economics, Fudan University Her research investigates: AI-Powered Entertainment Marketing : Using machine learning to analyze consumer behavior in digital content. Privacy and Ethical Data Use : Studying consumer trade-offs between privacy and public good, especially during crises like the COVID-19 pandemic . Location Analytics : Leveraging mobile data for urban economics, real estate, and emergency response modeling (e.g., MobiRescue for disaster logistics). Prosocial Consumer Behavior : Exploring how social capital and diversity drive innovation and policy compliance. Recent publications highlight trends in Reinforcement Learning for crisis management, Privacy-Preserving AI , and Freemium Pricing Models in digital markets. Her awards include the 2025 UVA Outstanding Researcher Award and the 2018 Mallen Award for motion picture studies. Natasha serves as an Area Editor for multiple journals, emphasizing Data Science in marketing. She has advised numerous PhD students and collaborated on projects analyzing Big Data in consumer mobility and platform economics.
