Marcelo Mattar is an Assistant Professor of Psychology and Neural Science at New York University, leading the Mattar Lab. His research focuses on the neural computations underlying memory, decision-making, and reinforcement learning. He holds a Ph.D. in Psychology from the University of Pennsylvania and has held academic positions at NYU, UC San Diego, and postdoctoral roles at Princeton University and the University of Cambridge. His work bridges computational neuroscience and artificial intelligence, aiming to model how the brain uses internal models for planning and decision-making. Education: Ph.D. in Psychology (Computational and Cognitive Neuroscience), University of Pennsylvania, 2016 M.A. in Statistics, University of Pennsylvania, 2016 B.A. in Electronics Engineering, Instituto Tecnologico de Aeronautica, Brazil, 2010 Research Interests: The lab develops mathematical models of learning and decision-making, leveraging reinforcement learning, Bayesian statistics, and neural networks. Experiments involve human behavioral studies and neuroimaging, with collaborations in animal electrophysiology and computational psychiatry. Key Contributions: His work explores how episodic memory and hippocampal replay support flexible decision-making. Recent studies highlight parallels between human cognition and AI systems, such as language models' metacognitive abilities and brain-inspired algorithms. Awards: Newton International Fellowship, Royal Society (2018–2019) Lab Team: The lab includes postdocs, PhD students, and undergraduates from diverse fields like cognitive science, neuroscience, and computer science. Current members are listed on the lab's website. Lab Location: Meyer Hall, 6 Washington Place, New York, NY 10003.
Jonathan Külz is a Researcher at the Technical University of Munich (TUM) , affiliated with the Department of Informatics 6 - Chair for Cyber Physical Systems under Prof. Matthias Althoff. His research focuses on Modular Robotics , Reinforcement Learning , Cyber-Physical Systems , and Control Systems . His work includes algorithmic synthesis of modular robot compositions, model-based manipulator co-design, and unifying benchmarks for robotics. He has supervised multiple Master’s theses Bachelor’s theses Practical courses on topics like Robot Workspace Representation and Dynamic Model Identification . Notable supervised projects include Autonomous Navigation of Reachbot and Task-Based Modular Robot Configuration Synthesis . His recent publications span Robotics , Benchmarking , and Computational Social Science . Key trends include Computationally efficient assessment of robot capabilities Deep reinforcement learning for robotics Analysis of political discourse polarization Jonathan emphasizes structured thesis supervision, requiring exposés, shared folders, and protocol-driven meetings. He advocates for LaTeX in scientific writing and tools like NotebookLM and Zettlr for research documentation.
Fethiye Irmak Dogan is a Postdoctoral Research Associate at the University of Cambridge's Department of Computer Science and Technology, working in the Affective Intelligence and Robotics Laboratory. She holds a Ph.D. in Computer Science from KTH Royal Institute of Technology (2023), an M.Sc. and B.Sc. in Computer Engineering from Middle East Technical University (METU). Her research focuses on human-robot interaction, continual learning, and socially appropriate robot behaviors leveraging explainability. She has conducted robotics research at KTH's Division of Robotics, Perception and Learning and collaborated internationally, including a visiting scholar stint at Georgia Institute of Technology. Education highlights include: B.Sc., Computer Engineering, METU (2015) M.Sc., Computer Engineering, METU (2018), with research at Kovan Robotics Lab Ph.D., Computer Science, KTH (2023), with visiting research at Georgia Tech Research interests emphasize deploying autonomous robots in human environments, resolving ambiguous user instructions through explainability, and enabling socially intelligent robot behaviors. Recent work explores continual learning for context adaptation, multimodal frameworks for human-robot collaboration, and vision-language models for wellbeing assessment in children. Key projects include BT-ACTION (modular instruction understanding), GRACE (LLM-driven socially appropriate actions), and STREAK (continual learning for household tasks). Her contributions span robotics, AI ethics, and human-centered design, with a focus on real-world applications in healthcare and education.
Haipeng Shen is a Professor of Innovation and Information Management at HKU Business School, The University of Hong Kong, serving as Associate Dean (EMBA and IMBA) and holding the Patrick S C Poon Professorship in Analytics and Innovation. He chairs the Business Analytics and Innovation program and joined HKU in 2015 after previously holding a professorship at the University of North Carolina at Chapel Hill. His academic credentials include: PhD in Statistics, The Wharton School of Business, University of Pennsylvania, 2003 MA in Statistics, The Wharton School of Business, University of Pennsylvania, 2000 BS in Mathematics, School of Mathematical Sciences, Peking University, 1998 Professor Shen's research focuses on data-driven decision making under uncertainty, with expertise spanning big data analytics, business analytics, healthcare analytics, and service engineering. He develops advanced statistical and machine learning methodologies to solve complex operational problems in call centers, optimize stroke care protocols, and enhance financial risk modeling, emphasizing real-time applications in high-stakes environments. Analysis of his recent publications reveals a consistent interdisciplinary approach bridging operations research, statistics, and domain-specific knowledge. His work demonstrates strong methodological innovation in time-series forecasting for service systems, risk assessment frameworks for medical complications, and covariance structure analysis for financial markets, with direct translational impact on business operations and clinical outcomes. His scientific contributions have been recognized with prestigious awards including: Most Influential Publication Award from China Stroke Association (2018) Fellow of the American Statistical Association (2015) Best Advisor of the Year Award from Academy of Asian Business (2018) Elected Member of International Statistical Institute (2015) Cluster Chair for Big Data Analytics at INFORMS International (2015) As an academic leader, Professor Shen has secured significant research funding from organizations including The Xerox Foundation and National Institute on Drug Abuse. He serves as Associate Editor for Management Science, Journal of the American Statistical Association, and Technometrics, while mentoring graduate students in statistical methodology and applied analytics. His current initiatives position HKU Business School at the forefront of healthcare innovation through big data analytics, driving collaborations with medical institutions to transform stroke care and hospital operations in Asia.
Matt Nassar is an Associate Professor of Neuroscience and Assistant Professor of Cognitive and Psychological Sciences at Brown University. He leads the Learning, Memory and Decision Lab, which is part of the Department of Neuroscience and the Robert J. & Nancy D. Carney Institute for Brain Science. His research focuses on understanding how the brain flexibly processes information to achieve complex and adaptive behaviors through computational approaches that bridge cognitive psychology and neuroscience. Education: PhD, University of Pennsylvania (2012) BA, Colgate University (2004) Nassar's research examines how different cognitive systems—learning, memory, and perception—leverage common computational principles to optimize decision-making. His work particularly focuses on how the brain balances stability and flexibility in processing information, how uncertainty is represented and utilized in learning, and how neural computations underlie complex behaviors. Through computational modeling and empirical research, he investigates how modular information-processing systems impact decisions and complex behavior in dynamic environments. His research integrates methods from cognitive psychology, neuroscience, and computational modeling to address fundamental questions about human cognition. Analysis of Nassar's recent publications (2020-2024) reveals a strong focus on computational neuroscience applied to decision-making, learning, and psychiatric conditions. His work frequently employs Bayesian modeling approaches to understand belief updating, uncertainty processing, and structure learning. Key themes include the neural basis of flexibility in learning, computational mechanisms underlying psychiatric symptoms, and age-related changes in cognitive processing. His research bridges cognitive psychology, neuroscience, and computational modeling to provide insights into both healthy cognition and disorders such as depression and schizophrenia. Scientific Contributions: Developed computational models of belief updating and learning under uncertainty Investigated neural mechanisms of stability-flexibility tradeoffs in cognition Examined age-related differences in learning and memory processes Explored computational mechanisms underlying psychiatric conditions Studied the role of noise correlations in neural learning systems Investigated how prefrontal cortex representations shape decision processes Nassar actively mentors researchers in his lab, with recent announcements highlighting postdocs joining from prestigious institutions like Max Planck UCL and Freie Universität Berlin. His lab appears to receive significant research funding, supporting multiple postdoctoral positions and research projects. Collaborations span multiple departments at Brown University, particularly with researchers in Cognitive and Psychological Sciences, Neurology, and Psychiatry. The lab has produced numerous high-impact publications in top journals including Nature Human Behaviour, Brain, and eLife. The Learning, Memory and Decision Lab, led by Nassar, is an active research group that uses computational models to understand how the brain represents and stores information for effective decision making. Recent lab announcements (as of February 2025) indicate the lab is expanding with new postdoctoral researchers joining from Harvard, Max Planck UCL, and Freie Universität Berlin, suggesting strong research momentum and funding support. The lab appears to be well-integrated within Brown's neuroscience community, with collaborations spanning multiple departments and research centers.
Steven R. Caliari is an Associate Professor in the Department of Chemical Engineering with a secondary appointment in Biomedical Engineering at the University of Virginia’s School of Engineering and Applied Science. He serves as the ChE Graduate Program Director and is a SEAS Copenhaver Fellow (2023). His research focuses on designing biomaterials to study cell-microenvironment interactions, addressing challenges in disease and tissue engineering. He holds a B.S. (2007, University of Florida), M.S. (2010), and Ph.D. (2013) in Chemical Engineering from the University of Illinois, followed by an NIH postdoctoral fellowship at the University of Pennsylvania. His research interests include biomaterials, mechanobiology, musculoskeletal tissue engineering, and advanced manufacturing for biological applications. His lab has pioneered viscoelastic hydrogel platforms and conductive collagen scaffolds, supported by NIH, NSF, DoD, and industry grants. Notable awards include the NSF CAREER Award (2021) and NIH MIRA (2020). Grants: NIH (NIGMS), NSF CAREER, V Foundation, UVA-Coulter Partnership Courses: Tissue Engineering (BME/CHE 4417), Transport Processes I (CHE 3321) Labs: Caliari Lab focuses on biomaterial design and mechanobiological studies His work bridges fundamental science and translational applications, emphasizing dynamic material systems for regenerative medicine and disease modeling.
Professor James Barlow is Co-Director of Imperial College London's Centre for Sectoral Economic Performance and holds a Professorship in the Department of Economics and Public Policy at the Imperial College Business School. He also serves as Academic Director for the MBA programme and Visiting Professor at Halmstad University (Sweden) and Honorary Professor at UCL Bartlett Real Estate Institute. His research focuses on structural challenges in healthcare innovation, housing, and construction sectors, with a particular emphasis on embedding innovations into healthcare systems. Barlow's education includes a background in geography and economics from the London School of Economics. He has held previous roles at the University of Westminster and Policy Studies Institute. His advisory work spans governments, healthcare organizations, and industries including medical technology and pharmaceuticals. He contributes to major initiatives like AGE-WELL (Canada) and the Industry Commons Foundation (Sweden). Research interests include healthcare innovation ecosystems, institutional logics, and frugal innovation. His recent book *Managing Innovation in Healthcare* synthesizes his work. He collaborates across disciplines, addressing challenges in telehealth, AI integration, and regulatory frameworks post-Brexit. Key affiliations include the Centre for Health Economics and Policy Innovation, Policy Innovation Research Unit (PIRU), and the NIHR Health Tech Research Centre. His work bridges academic research with practical policy and industry solutions, emphasizing scalable and sustainable business models.
Prof. Dr.-Ing. Hans-Georg Herzog is a Professor of Energy Conversion Technology at the Technical University of Munich (TUM), School of Engineering and Design. He has headed the Energy Conversion Technology group at TUM since 2002 and is a Senior Member of IEEE and member of VDE and VDI professional organizations. His research focuses on energy-efficient electromechanical drives and related technologies critical for modern electric and hybrid vehicles. Prof. Herzog's research interests encompass energy-efficient electromechanical drives, with key expertise in design and optimization of hybrid-electric and battery-electric powertrains, automated design methods for electromechanical actuators, energy and power management systems, and analysis of loss mechanisms in soft magnetic materials. His work bridges fundamental electromagnetic theory with practical automotive applications, particularly in fault-tolerant systems and reliability engineering for electric propulsion. His recent publication trends show a strong focus on vehicular power systems, with particular emphasis on electronic fuses, fault diagnosis in multiphase machines, wireless power transfer, and reliability analysis of electric aircraft propulsion systems. The research spans from fundamental electromagnetic modeling to practical automotive applications, with increasing attention to autonomous driving power requirements and next-generation vehicle electrical architectures. Prize for Good Teaching of the Free State of Bavaria (2010) Prof. Herzog leads a substantial research team including doctoral candidates and postdoctoral researchers who contribute to his extensive publication record. His research group collaborates with automotive industry partners on various grants focused on electric vehicle technology, power system reliability, and advanced electromagnetic systems. The team regularly develops novel methodologies for machine design, fault tolerance analysis, and power system optimization. The research is conducted within TUM's Energy Technology Workshop with specialized facilities for electrical machine testing, power electronics development, and automotive power system simulation. The team maintains strong connections with industry partners in the automotive and aerospace sectors, facilitating technology transfer from academic research to practical applications.
Associate Professor Paola Leardini holds dual roles as Director (Research) and Associate Professor at the University of Queensland's School of Architecture, Design and Planning, within the Faculty of Engineering, Architecture and Information Technology. Her work bridges design and performance in built environments, focusing on net-zero targets, urban resilience, and circular economy strategies in construction. She is a key researcher in the Centre for Future Building Structures and advises on Queensland’s social/affordable housing through the Design Excellence Panel. Leardini earned a PhD in building energy efficiency and environmental quality from Politecnico di Milano, supervised by Prof. P. Ole Fanger. Her educational journey includes studies in Milan, Berlin, Leicester, and Copenhagen. Research highlights include eco-retrofitting of historical districts (e.g., Milan’s Quartiere Mazzini), Passive House adaptations for subtropical climates, and timber construction innovations with the ARC Advance Timber Hub. She co-founded Passive House Institute New Zealand (PHINZ) and collaborates globally on projects like water-sensitive cities and flood-resilient urban design. Her recent publications emphasize timber’s role in sustainable construction, circular economy frameworks, and climate-responsive building systems. Advising includes leading an ARC Linkage project on adaptable housing while fostering industry partnerships through projects like Norman Creek Catchment flood resilience studies. She actively contributes to labs/teams advancing timber use and urban resilience, aligning with UQ's mission for future-ready built environments.
Iris D. Tommelein serves as the Roy W. Carlson Distinguished Professor in the Civil and Environmental Engineering Department at the University of California, Berkeley's College of Engineering, where she directs the Project Production Systems Laboratory (P2SL). A globally recognized pioneer in Lean Construction, she has revolutionized architecture-engineering-construction (AEC) practices through research, industry workshops, and leadership since co-founding the Lean Construction Institute in 1997. Her educational foundation spans multiple disciplines: Ph.D. in Civil Engineering (Construction Engineering and Management), Stanford University, 1989 M.S. in Computer Science (Artificial Intelligence), Stanford University, 1989 M.S. in Civil Engineering (Construction Engineering and Management), Stanford University, 1985 B.S. (5-year degree) in Civil Engineer-Architect, Vrije Universiteit Brussel, Belgium, 1984 Professor Tommelein's research centers on transforming construction processes through Lean principles and digital innovation . Her work pioneers takt planning for workflow reliability, industrialized construction for labor and sustainability challenges, and mistakeproofing to eliminate errors. She integrates digital twins , AI , and optimization to develop practical decision-support systems for supply chains, logistics, and production management. Recent focus includes modular offsite construction and Industry 4.0 applications. Analysis of her 2023-2025 publications reveals intensifying research on takt planning maturity models and industrialized construction feasibility , with growing emphasis on mass timber automation and visual management systems. Her work consistently bridges lean theory with practical implementation across megaprojects, subcontracting networks, and heavy civil engineering. Her exceptional contributions have earned: Lean Pioneer Award (Lean Construction Institute, 2015) National Academy of Construction induction (2019) PPI Technical Achievement Award (2022) Robert B. Harris Award (University of Michigan, 2024) ASCE Construction Management Award (2024) - first woman recipient in 51 years Through the P2SL, she leads industry-collaborative research on production system design, mistakeproofing frameworks, and digital transformation. Her grant-funded projects develop assessment tools for industrialized construction adoption and takt planning methods adaptable to diverse project types. She actively mentors graduate students and drives knowledge transfer via workshops and the annual Construction Innovation Day. The Project Production Systems Laboratory (P2SL) operates as a global hub for construction innovation, partnering with owners, contractors, and suppliers to implement lean production systems. Current initiatives include developing serious games for mistakeproofing training, optimizing work density methods for heavy civil projects, and creating digital twins for real-time construction management.
Remus Teodorescu is a Professor at AAU Energy , Aalborg University , specializing in Power Electronics System Integration and Materials . His work bridges Lithium-Ion Batteries , Modular Multilevel Converters , and Smart Battery Systems . Education : Not explicitly mentioned in the text. Research Interests focus on Battery Management Systems , AI-Driven Energy Optimization , and Power Electronics for renewable energy integration. Key projects include Digital Twin for Lithium-Ion Batteries and BMS-DC for Data Centers . Recent Publications (2025) emphasize Finite Set MPC , Gradient Descent Optimization , and AI in Battery Parameter Estimation . His 2024 work explores Physics-Informed Neural Networks and Fault-Tolerant Converters . Scientific Awards : Villum Foundation Grant (313 million kroner, 2021) Named world's best in electrical engineering (2023) Advising includes supervising PhD projects on AI-Accelerated Battery Twins and Data-Driven SOH Estimation . Collaborations span Energy Cluster Denmark and Villum Fonden .
Melissa A. Schilling is the Herzog Family Professor of Management at New York University Stern School of Business, where she is also Deputy Chair of the Management & Organizations Department and Director of the Innovation Initiative at the Fubon Center for Technology, Business and Innovation. She joined NYU Stern in 2001 and is a leading scholar in innovation and strategic management. Ph.D., Strategic Management, University of Washington, 1997 B.S., Business Administration, University of Colorado at Boulder, 1990 Melissa Schilling's research centers on innovation in high-technology industries, including smartphones, biotechnology, pharmaceuticals, and renewable energy. She explores platform ecosystems, network externalities, technological standards, and the cognitive and social traits of breakthrough innovators. Her work integrates strategic management with organizational behavior and technological evolution. She is particularly known for her studies on how firms can accelerate innovation adoption and create value in complex ecosystems. Her recent publications reveal a strong trend in digital transformation, platform competition, and the cognitive foundations of visionary leadership. She analyzes how firms manage innovation in platform-based markets and how breakthrough ideas emerge from outlier thinking. Her articles frequently appear in top journals such as Strategic Management Journal , Organization Science , and Management Science . Scientific Awards and Recognitions: National Science Foundation CAREER Award 2022 Sumantra Ghoshal Award for Rigour and Relevance in Management 2018 Leadership in Technology Management, PICMET Best Paper in Management Science and Organization Science, 2012 Broderick Prize for Excellence in Research, Boston University Melissa Schilling has advised numerous doctoral students and contributed to major research initiatives, including a National Academy of Sciences committee on electric vehicle deployment. She has secured grants from the NSF and Kauffman Foundation. She is a senior editor at Strategy Science and serves on the editorial boards of several leading management journals. She also leads the Innovation Initiative at the Fubon Center, fostering industry-academia collaboration in tech innovation. She is actively involved in research labs and innovation centers at NYU Stern, particularly those focused on technology ecosystems and digital transformation. Her leadership in the Fubon Center drives interdisciplinary research on how businesses can leverage technological change for competitive advantage.
Ashish Thatte serves as Associate Professor of Operations Management at Gonzaga University, teaching undergraduate courses including Operations Management (OPER 340), Lean Thinking (OPER 347), and Supply Chain Management (OPER 489), alongside graduate courses such as Global Operations (MBUS 640), Lean Thinking (MBUS 648), and Supply Chain Management (MBUS 699). His industry background encompasses manufacturing management and supply chain/logistics roles in multinational corporations. His academic credentials feature a Ph.D. and M.S. from the University of Toledo, complemented by an M.B.A. and B.E. from the University of Pune (India), plus APICS CPIM certification: CPIM, APICS Ph.D., University of Toledo M.S., University of Toledo M.B.A., University of Pune (India) B.E., University of Pune (India) Dr. Thatte's research centers on supply chain responsiveness, competitive advantage, and information systems in operations, with significant extensions into consumer behavior (particularly Gen Z purchasing in beauty care) and healthcare operations (emergency department efficiency and medical procedure cost analysis). His work bridges theoretical frameworks with practical applications across manufacturing, retail, and healthcare sectors. Analysis of his 15 most recent publications reveals three dominant research trajectories: (1) Supply chain responsiveness mechanisms and their competitive impact through supplier networks and modular manufacturing; (2) Cross-cultural consumer behavior examining regional variations in brand perception; (3) Healthcare operations optimization in emergency departments and procedural cost-effectiveness. His interdisciplinary approach connects operations theory with real-world business challenges across automotive, sports analytics, and global consumer markets. No scientific awards were documented in the available information. While the provided materials confirm his teaching responsibilities and publication record, specific details regarding student advisement, research grants, or laboratory affiliations remain undisclosed in the source materials.
R. Scott Kemp is an Associate Professor in the Department of Nuclear Science and Engineering at the Massachusetts Institute of Technology (MIT) and serves as the director of the MIT Laboratory for Nuclear Security and Policy. He teaches energy policy and advises students in the MIT Energy Studies Program. Additionally, he contributes to institutional governance through roles on MIT's International Policy Lab advisory board and the President's Committee for Distinguished Fellowships. His educational background includes a B.S. in Physics from the University of California, Santa Barbara, and a Ph.D. in Public and International Affairs from Princeton University. Kemp's research integrates physics and policy to address societal resilience, with emphasis on: Critical infrastructure vulnerabilities (electricity, gas, water systems) Strategic implications of hypersonic/advanced weapons Nuclear security frameworks Energy security and nonproliferation His work explores intersections of technology and global stability. Publications focus on nuclear security innovation, energy access, and proliferation risks, reflecting consistent themes in arms control verification, uranium enrichment, and sustainable nuclear energy deployment across diverse geopolitical contexts. Scientific awards include: Fellow of the American Physical Society Sloan Research Fellowship in Physics Spira Award for Excellence in Teaching He advises graduate students in energy policy and directs the Laboratory for Nuclear Security and Policy, leading research on treaty compliance, nuclear forensics, and emerging technology assessment.
Seth Frey is an Associate Professor in the Department of Communication at the University of California, Davis, with affiliate status at Indiana University's Ostrom Workshop and as Research Director at Metagov. His research focuses on computational social science approaches to understanding self-governance in complex social systems, particularly through the lens of online communities as model institutions. Education: Ph.D. in Cognitive Science and Informatics (complex systems), Indiana University, 2013 B.A. in Cognitive Science, UC Berkeley, 2004 Research Interests: Frey specializes in computational approaches to institutional analysis and the cognitive science of strategic behavior . His work examines how communities design governance systems to overcome collective action problems, with emphasis on: Emergent institutional structures in digital commons Policy-as-data through NLP and institutional grammar frameworks Cognitive mechanisms underlying cooperative behavior Design principles for participatory change in online platforms His methodology integrates large-scale data analysis, web-based experiments, and computational modeling across diverse contexts including Minecraft, Reddit, and professional sports ecosystems. Publication Trends: Recent publications (2023-2025) demonstrate a cohesive trajectory toward computational institutional analysis, with increasing focus on NLP-driven policy analysis (e.g., NLP4Gov), decentralized governance architectures (DAOs, multi-level platform governance), and the cognitive foundations of collective action. His work consistently bridges theoretical institutional analysis with practical applications in digital community design, showing particular growth in translating Ostrom's design principles into computational frameworks. Awards: Honorable Mention Award for Best Paper at ACM CSCW 2019 Advising and Grants: Frey mentors students interested in data science applications at the intersection of communication, cognition, and complex systems, emphasizing resourcefulness and intellectual curiosity. His research has secured substantial funding from: National Science Foundation (NSF) NASA Ford Foundation Google Open Source Foundation He actively encourages aspiring graduate students with strong self-directed research skills to explore computational approaches to social phenomena. Labs and Teams: He leads the Computational Communication Lab at UC Davis and co-directs the Institutional Grammar Research Initiative. Through Metagov, he develops the 'Governance API' framework for modular community governance. His past affiliations include Disney Research (Walt Disney Imagineering) where he applied complexity science to theme park systems, and the New England Complex Systems Institute (NECSI). Current collaborations span Ethereum governance, Minecraft server ecosystems, and Colorado's cannabis monitoring infrastructure.
