Christian Erik Kampmann is an Associate Professor at the Department of Strategy and Innovation, Copenhagen Business School. He holds a Ph.D. in Management from MIT and an engineering background from DTU, bridging technical rigor with socio-economic research. Education: MIT (Ph.D. in Management), DTU (Engineering) Research Interests focus on system dynamics as applied to sustainable energy transitions, electric mobility, and green urban mobility. His methodological work enhances structural dominance analysis and eigenvalue techniques for complex system modeling. Recent publications address feedback loop gains, market misperceptions of feedback, and comprehensive analytical approaches for policy modeling, reflecting his interdisciplinary focus on sustainability challenges. Teaching includes courses on system dynamics, sustainable business strategy, and quantitative business research, with supervision of theses on electric mobility and product-service sustainability. External engagements involve board membership (Magasin du Nord, 2018-2020) and computer modeling consultancy (Zerolytics, Whitebox).
Prof. Dr. Ulrich Kleinekathöfer is a Full Professor of Theoretical Physics at Constructor University (formerly Jacobs University Bremen) in the School of Science. His research focuses on computational physics and biophysics, particularly on light-harvesting complexes, membrane transport, and quantum dynamics in biological systems. He leads the Computational Physics and Biophysics research group and coordinates the MSCA Doctoral Training Network "PhotoCaM". His educational background includes: PhD from Max-Planck-Institut für Strömungsforschung, Göttingen (1996) Diploma in Physics from Universität Göttingen (1993) Habilitation in Physics from Technische Universität Chemnitz (2002) Prof. Kleinekathöfer's research spans multiple areas of computational biophysics and theoretical physics. His primary interests include excitation energy transfer in light-harvesting complexes , molecular transport through membrane channels and nanopores , and quantum dynamics in open systems . His group develops and applies advanced computational methods including molecular dynamics simulations, quantum chemistry calculations, and machine learning approaches to study these phenomena. A significant portion of his work focuses on photosynthetic systems, particularly how energy is transferred and converted in natural light-harvesting complexes, with implications for renewable energy technologies. His recent publications demonstrate a strong trend toward integrating machine learning with traditional computational methods, particularly in the fields of quantum chemistry and molecular dynamics. There's a clear focus on multifidelity approaches that balance computational efficiency with accuracy. His work spans from fundamental quantum dynamics to applied research on antibiotic transport mechanisms, showing remarkable breadth while maintaining depth in computational methodology development. His notable recognition includes: Tan Chin Tuan Exchange Fellowship, NTU Singapore (2019) Prof. Kleinekathöfer has supervised numerous PhD students and postdoctoral researchers, with a current group comprising several PhD candidates and research associates. His research is supported by multiple funding sources including the Deutsche Forschungsgemeinschaft (DFG), European Union through MSCA Doctoral Network PhotoCaM, and previously through the Innovative Medicines Initiative "Translocation" and Marie Curie Training Program "Translocation". His collaborative network spans internationally, with partnerships at institutions in Germany, USA, Greece, and Switzerland. The Computational Physics and Biophysics Group operates within Constructor University's research infrastructure, utilizing high-performance computing resources for their simulations. The group maintains active collaborations with experimental groups to validate and inform their computational models, creating a strong interdisciplinary research environment focused on understanding fundamental biophysical processes at the molecular level.
Kostas Papakonstantinou is an Associate Professor in the Department of Civil Engineering at Penn State University, affiliated with the College of Engineering. His research bridges Artificial Intelligence (AI) with Civil Engineering, focusing on uncertainty quantification and decision-making under uncertainty. Research Areas: Uncertainty Quantification Stochastic Control Deep Reinforcement Learning Bayesian Analysis Nonlinear Filtering Computational Mechanics Infrastructure Management Rare Events Quantification His work emphasizes AI-driven solutions for structural life-cycle management, infrastructure systems, and autonomous operations. Funded by the NSF and USDOT, his projects include AI-enabled fiscally constrained life-cycle asset management and Deep reinforcement learning for multi-asset infrastructure management . Scientific Awards: NSF CAREER Award: Optimal engineering decision-making under uncertainties for enhanced structural life-cycle He teaches graduate courses on Uncertainty and Reliability in Civil Engineering (CE 566) and Computational Analysis of Randomness in Engineering (CE 597) .
Prof. Dr. Philipp A. Rauschnabel serves as Professor of Digital Marketing and Media Innovation at the University of the Bundeswehr Munich's Faculty of Business Administration. Recognized as one of the world's leading scientists in augmented reality, virtual reality, and metaverse research, he bridges academic theory with practical business applications across multiple industries. His educational background includes: Habilitation, Dr. habil; PD, Otto-Friedrich University of Bamberg Dr. rer. pol. (psychological brand management, 2014, summa cum laude), Otto-Friedrich University of Bamberg M.Sc. in Marketing and Distribution Management, Georg-August University of Göttingen (best of class) BA in Business (Marketing and Business Information Systems), Merseburg University of Applied Sciences (Top 10% graduate) Rauschnabel's research centers on the practical implementation of spatial computing technologies in marketing contexts. He has developed influential frameworks including the XR framework that redefines the relationship between augmented reality, virtual reality, and mixed reality. His work examines how these technologies transform consumer-brand relationships, workplace operations, and marketing strategies across retail, manufacturing, and service industries. He particularly investigates the psychological mechanisms through which AR/VR experiences influence consumer behavior, brand love, and purchasing decisions. Analysis of his recent publications reveals a clear progression from basic AR applications toward more sophisticated spatial computing environments and persistent metaverse experiences. His research demonstrates increasing sophistication in understanding contextual factors, emotional responses, and long-term consumer engagement with these technologies. The work consistently bridges theoretical marketing concepts with practical implementation challenges faced by businesses. His significant scientific recognition includes: Top 1% Scientist (Clarivate Highly Cited Researcher, 2023) Top 2% Scientist (Stanford List 2023) Microsoft Academics listed as one of the top 5 AR researchers worldwide based on citation output (2021) Named one of the top 15 AR marketing researchers worldwide (Kumar, 2021) Prof. Rauschnabel serves as Associate Editor for the Journal of Business Research and chairs the bdvb research institute. He has provided expert consultation to the German Federal Government on XR/Web3/Metaverse (2022) and regularly advises major corporations across multiple sectors including food, home appliances, insurance, automotive, and telecommunications across Germany, USA, Switzerland, China, and Austria. His practical consulting work complements his academic research, creating a valuable feedback loop between theory and practice. He leads multiple research initiatives on augmented reality marketing and spatial computing, collaborating with industry partners to develop practical applications of these emerging technologies. His work emphasizes the importance of understanding both the technological capabilities and human factors that determine successful implementation of AR/VR in business contexts.
Christoph Hertrich is a tenure-track professor for Applied Discrete Mathematics at University of Technology Nuremberg, where he conducts research at the intersection of discrete mathematics, theoretical computer science, and machine learning. His work particularly focuses on applying polyhedral geometry and combinatorial optimization techniques to neural network theory, with significant contributions to understanding the computational complexity and expressivity of neural networks. Hertrich received his BSc and MSc degrees from TU Kaiserslautern (2013-2018) working with Sven O. Krumke, followed by his PhD at TU Berlin (2018-2022) under the supervision of Martin Skutella. His doctoral thesis, titled "Facets of Neural Network Complexity," laid foundational work for his current research direction. Prior to joining UTN, he held postdoctoral positions at Université libre de Bruxelles (2023-2024) with a Marie Skłodowska-Curie fellowship under Samuel Fiorini, and at LSE London (2022-2023) with László Végh. He also served as a substitute professor for discrete mathematics at Goethe-Universität Frankfurt during the winter semester of 2023/24. Hertrich's research interests center on the mathematical foundations of neural networks, with particular emphasis on polyhedral geometry approaches. His work explores computational complexity questions related to neural network training and architecture, expressivity bounds, and connections to combinatorial optimization problems. He has made significant contributions to understanding the relationship between neural network depth and function representation, the complexity of counting linear regions in ReLU networks, and the application of extended formulations to neural network theory. His approach combines rigorous theoretical analysis with practical implications for neural network design and optimization. His recent publication record reveals a strong trend toward establishing fundamental theoretical limits and connections between deep learning and discrete mathematics. A significant portion of his work examines computational complexity of various neural network problems, often proving hardness results or establishing bounds on expressivity. He has also developed novel connections between polyhedral combinatorics and neural network architecture, demonstrating how techniques from operations research can inform deep learning theory. Marie Skłodowska-Curie fellowship Since February 2025, Hertrich has been supervising PhD student Moritz Stargalla at UTN. His research has been supported by prestigious fellowships including a Marie Skłodowska-Curie fellowship during his postdoctoral period in Brussels. He is organizing a workshop on "Polyhedral Geometry for Neural Networks" in March 2026 in Nuremberg, highlighting his leadership in this emerging interdisciplinary field.
Novi Quadrianto is a Professor of Machine Learning at the School of Engineering and Informatics, University of Sussex, where he joined as a Lecturer in February 2014. He is currently a Principal Investigator on three active EU grants: BayesianGDPR (ERC), TANGO (EU Horizon RIA), and Act.AI (ERC Proof of Concept). He also holds an Adjunct Professor position in Data Science at Monash University, Indonesia, and serves as Strategic Lab co-Leader of the BCAM Severo Ochoa Strategic Lab on Trustworthy Machine Learning in Bilbao, Spain. His educational background includes a PhD in Machine Learning from the Australian National University (2012) and a BEng in Electrical and Electronics Engineering from Nanyang Technological University, Singapore. During his PhD, he conducted research at multiple international institutions including HIIT-Finland, Yahoo! Research-US, University of Alberta-Canada, Fraunhofer IAIS-Germany, and IST Austria. From 2012-2014, he was a Newton International Fellow of the Royal Society at the University of Cambridge. Professor Quadrianto directs the Predictive Analytics Lab (PAL) since 2017, which focuses on "Responsible AI" research developing AI models that embed fairness, accountability, transparency, and trustworthiness. His research spans algorithmic fairness, federated learning, and computer vision, with applications in sustainable development, healthcare, and finance. His work has been funded by prestigious organizations including the European Research Council, EPSRC, and HM Treasury. His publications reveal a strong focus on addressing challenges in AI fairness, robustness, and privacy, particularly in dynamic environments and heterogeneous data settings. Recent work explores performative prediction, diversity-driven learning, and efficient vision transformer inference, demonstrating his leadership in cutting-edge machine learning research. European Research Council ERC Proof of Concept Grant (2023) Guarantor Researcher for BCAM Severo Ochoa Excellence Accreditation (2023) European Lab for Learning and Intelligent Systems (ELLIS) Scholar/Fellow (2020) European Research Council ERC Starting Grant (2019) Newton International Fellowship (2012) Microsoft Research Asia Fellowship (2009) Professor Quadrianto currently supervises six PhD students and five postdoctoral researchers. He has served as Action Editor for Transactions on Machine Learning Research since 2022 and as Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence since 2016. He has also been an Area Chair for major conferences including NeurIPS, ICML, and AAAI. His PAL laboratory hosts a team of 15 members focused on inter-disciplinary AI research with domain experts across various sectors. The PAL Lab operates three innovation strands: AI for Sustainable Development (supporting UN SDGs), AI for Healthcare (transforming health outcomes), and AI for Finance (personalized loan decision-making). The lab also leads initiatives in Diversity & Inclusion in AI and offers Pro-Bono Office Hours to organizations seeking guidance on machine learning aspects.
Slava Rychkov is a Permanent Professor of Theoretical Physics at the Institut des Hautes Études Scientifiques (IHES), a position he has held since 2017. He specializes in strongly coupled quantum and conformal field theories, with applications across high energy physics, statistical mechanics, and condensed matter physics. His current research focuses on the conformal bootstrap and renormalization group techniques, including both perturbative and nonperturbative methods like tensor network renormalization. Education: Ph.D. in Physics, Princeton University (2002) Master of Science, Moscow Institute of Physics and Technology (1996) Recent research highlights include a groundbreaking connection between Deligne categories and symmetries of probabilistic loop ensembles in statistical physics, and a novel method for analytic continuation of Euclidean CFTs to Lorentzian signature. His work on the 2+ϵ expansion challenges established assumptions about critical exponents in 3D systems. Publications span topics from tensor renormalization group methods to rigorous mathematical approaches in the conformal bootstrap program. Scientific Awards: Jacques Solvay International Chair in Physics (2025) Grand Prix Mergier-Bourdeix, French Academy of Sciences (2019) New Horizons in Physics Prize (2014) As Deputy Director of the Simons Collaboration on the Nonperturbative Bootstrap, Rychkov leads efforts to rigorously analyze conformal field theories. His former advisees include prominent researchers at institutions like EPFL, Princeton, and Università di Genova. Current projects focus on resolving fundamental questions about critical phenomena and phase transitions using advanced mathematical physics tools.
Anson Kahng is an Assistant Professor in the Department of Computer Science and the Goergen Institute of Data Science at the University of Rochester. He previously held postdoctoral positions at the University of Toronto and completed his PhD at Carnegie Mellon University under the supervision of Ariel Procaccia, focusing on computational social choice. PhD, Computer Science, Carnegie Mellon University Undergraduate degree, Computer Science, Harvard College His research explores the intersection of computer science and democracy, developing frameworks like virtual democracy and liquid democracy while analyzing fairness in participatory budgeting and voting systems. He combines theoretical analysis with empirical methods, emphasizing interdisciplinary collaboration. Recent work includes advancements in ranked choice voting optimization, fairness metrics for elections, and structural analysis in cryo-electron tomography. He has published in top venues such as IJCAI, AAAI, NeurIPS, and ACM Transactions on Economics and Computation. NeurIPS 2019 Spotlight Presentation (top 2.5% of submissions) Kahng advises PhD students Alina Chadwick and Joe Saber, and has mentored multiple undergraduate researchers. He teaches courses on algorithmic game theory and computational statistics at the University of Rochester.
Daniel Vieweger serves as a Research Scientist at the Chair of Materials Engineering of Additive Manufacturing within the TUM School of Engineering and Design at the Technical University of Munich (TUM). Based in Garching bei München, Germany, he operates from room B1.2.02 at Freisinger Landstraße 52, with contact details including email (daniel.vieweger@tum.de) and phone (+49 89 289 55347). His role centers on advancing additive manufacturing research under Prof. Dr. Peter Mayr's leadership. His research focuses on materials engineering for additive manufacturing processes, with specialized expertise in advanced design methodologies for metal 3D printing systems. This work directly supports TUM's Project Week: Fundamentals of Advanced Design Methods for Additive Manufacturing initiative, addressing critical challenges in geometric complexity, material efficiency, and structural integrity. His contributions bridge academic research with industrial applications through TUM.Additive and TUM.Idea collaborations. Within TUM's innovation ecosystem, Vieweger participates in cross-disciplinary teams developing next-generation additive manufacturing protocols. The chair maintains strategic partnerships with aerospace and automotive industries to optimize material deposition techniques and post-processing methods. His current work emphasizes computational modeling of thermal dynamics during laser-based metal additive processes, contributing to TUM's leadership in sustainable advanced manufacturing.
Jukka Tuhkuri is a Professor at Aalto University's Department of Energy and Mechanical Engineering, specializing in ice mechanics and arctic marine technology . He serves as Editor-in-Chief of Cold Regions Science and Technology and became an Honorary Professor at University College London (Department of Earth Sciences) in 2023. His work spans numerical simulations using the Discrete Element Method (DEM) and experimental research in the Aalto Ice and Wave Tank, with fieldwork in both Arctic and Antarctic regions. Research Focus : Understanding ice fracture mechanics, sea ice ridge formation, and ice-structure interaction processes. He investigates how global warming alters ice conditions and affects loads on ships/marine structures, addressing risks from increased Arctic shipping activity. Scientific Awards 2023 POAC Founders Lifetime Achievement Award Teacher of the Year 2003 Espoo Ambassador 2012 1996 Best Dissertation Stipend from Helsinki University of Technology Collaborative Impact : His research directly informs offshore wind engineering and Arctic risk management frameworks through publications like Challenges with sea ice action on structures for Offshore wind (2023) and A comprehensive approach to scenario-based risk management for Arctic waters (2022).
Ebru Turanoglu Bekar is a Senior Lecturer at the Department of Industrial and Materials Science, Chalmers University of Technology, specializing in Smart Maintenance and Production Systems. She contributes to the Production Service Systems & Maintenance research group. Research Interests: Total Productive Maintenance (TPM), Artificial Intelligence applications in manufacturing, Multi-Criteria Decision Making, Performance Measurement systems Recent Focus: Development of data-driven algorithms for predictive maintenance, integration of digital twins in industrial contexts Key Projects: Factory SensAI (2025–2028) - Data integration for AI in manufacturing Trustworthy Predictive Maintenance TPdM (2022–2025)
Professor Guoyin Li is a faculty member at the School of Mathematics & Statistics , University of New South Wales (UNSW Sydney). He holds a Ph.D. from The Chinese University of Hong Kong (2007) and has been at UNSW since 2011, currently serving as Professor and Research Director. Research Interests His work spans optimization , variational analysis , and multilinear algebra , with applications in robust optimization , structural engineering , and machine learning . He specializes in nonconvex nonsmooth optimization , tensor eigenvalue problems , and exact semi-definite programming relaxations . Recent Publications His articles focus on robust optimization for structural design, nonlinear approximation techniques, and conic programming for uncertain data. Key journals include Foundations of Computational Mathematics , Mathematical Programming , and Computer Methods in Applied Mechanics and Engineering . Awards and Grants Fellow of the Australian Mathematical Society (2023) 2022 AustMS Medal 2024 Marguerite Frank Award ARC Discovery Grants (2021-2023, 2025-2027) ARC Research Hub Project (2017-2021) Professional Roles He serves on editorial boards of SIAM Journal on Optimization , Optimization Letters , and Journal of Optimization Theory and Applications , and has delivered plenary lectures at international conferences in Austria, Spain, and Canada.
Prof. Dr.-Ing. Harald Klein holds the Professorship of Plant and Process Engineering at the TUM School of Engineering and Design (Technical University of Munich). His research focuses on process engineering analysis and synthesis, particularly thermal/chemical unit operations applied to power plant technology. Develops thermodynamic substance models & simulation tools Expertise in industrial process design and optimization Collaborates on hydrogen liquefaction and biofuel production Research trends: 2016-2017 publications show emphasis on sustainable energy systems Combines traditional process engineering with modern optimization Key domains: hydrogen technology, chemical reactors, and thermal systems
Dr. Curt von Keyserlingk is a Reader (equivalent to Associate Professor) in theoretical physics at King's College London, based in the Theory & Simulation of Condensed Matter Group within the Department of Physics, Faculty of Natural, Mathematical & Engineering Sciences. His research focuses on understanding complex quantum systems through both analytical and numerical approaches. His educational background includes an MMath from the University of Cambridge, followed by DPhil studies at the University of Oxford under Professor Steve Simon. Prior to his current position at King's, he held a postdoctoral research fellowship at the Princeton Center for Theoretical Science and was a lecturer at the University of Birmingham. Dr. von Keyserlingk's research centers on interacting quantum systems, studying exotic phenomena such as superconductivity, topological order, localization, and time crystallinity. His work bridges the gap between fundamental quantum mechanics and practical applications in quantum computing. He develops both analytical frameworks and numerical tools to understand how quantum systems evolve and behave under various conditions, with particular emphasis on non-equilibrium dynamics, quantum information processing, and topological phases of matter. His recent publications reveal a strong focus on quantum many-body systems, with particular attention to topological phases in three dimensions, operator dynamics in quantum systems, and the interplay between dissipation and quantum information. His work spans from fundamental theoretical questions about quantum thermalization to practical applications in quantum error correction and quantum computing architectures. Dr. von Keyserlingk is the recipient of a prestigious UKRI Future Leaders Fellowship, which supports his research on robust many-body quantum phenomena. His current projects include 'Robust Many-body Quantum Phenomena Through Driving And Dissipation' (2025-2028) and 'Robust many-body Quantum phenomena through Driving and Dissipation' (2022-2025). He actively supervises PhD students and runs the physics intercollegiate programme between King's College London and Royal Holloway, University of London. His research group focuses on developing new theoretical frameworks to understand quantum systems that could potentially be harnessed for quantum computing applications.
Sudhir Kumar is a Professor and Principal Investigator at Temple University, leading a research laboratory focused on molecular evolution, phylomedicine, and functional genomics. His lab develops mathematical methods, computational algorithms, and software packages for analyzing genomic variation across populations, pathogens, tumors, and species. Key contributions include the widely used MEGA software (www.megasoftware.net) for molecular evolutionary analysis and the TimeTree knowledge-base (www.timetree.org) that synthesizes evolutionary knowledge on species divergence times. Dr. Kumar's research interests center on integrating mathematical and computational techniques into evolutionary biology and biomedicine. His lab pursues a holistic paradigm where evolutionary and genomic patterns are discovered through comparative analysis of big datasets, then used to reveal underlying biological processes and develop predictive models. His work spans phylomedicine of genetic diseases, molecular phylogenomics, and the timetree of life, with recent innovations including Bayesian methods, machine learning algorithms, and statistical approaches for inferring molecular phylogenies, divergence times, and pathogenic mutations. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence and machine learning to evolutionary genetics, with multiple 2025 papers focused on sparse learning techniques, transformer-based models, and AI-assisted analytical protocols. His work increasingly bridges evolutionary biology with cancer genomics and precision medicine applications. His scientific achievements have been recognized with the prestigious 2025 George W. Beadle Award from the Genetics Society of America, which honors his "efforts to democratize evolutionary genetics." Dr. Kumar has mentored numerous doctoral candidates, postdoctoral researchers, and graduate students, many of whom have gone on to faculty positions at institutions including Oakland University and universities in Brazil. His lab includes current doctoral candidates working in bioinformatics and statistical molecular evolution, supported by technical staff including programmers, genome tech specialists, and informatics specialists. The Kumar Laboratory operates as an interdisciplinary research hub with multiple projects including MEGA (Molecular Evolutionary Genetics Analysis), TimeTree, myPEG (web-based evolutionary tools), and FlyExpress (a knowledge base for Drosophila melanogaster embryo images). The lab emphasizes green computing efforts aimed at democratizing scientific practice and making big data analytics more accessible.
