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Sheng Shen is a Professor in the Mechanical Engineering Department at Carnegie Mellon University (CMU) , with courtesy appointments in the Departments of Electrical and Computer Engineering and Materials Science and Engineering . He earned his Ph.D. in Mechanical Engineering (Minor in Electrical Engineering) from Massachusetts Institute of Technology (MIT) , and his B.S. and M.S. from Huazhong University of Science and Technology in China. Prior to joining CMU in 2011, he conducted postdoctoral research at UC-Berkeley . Education: Ph.D., Mechanical Engineering, MIT (2010) B.S. & M.S., Power Engineering & Engineering Thermophysics, Huazhong University of Science and Technology (2000 & 2003) Research interests include nanophotonics , nanoscale energy transport and conversion , nanofabrication , and advanced manufacturing , with applications in thermal management , light sources and devices , thermal emission control , solar energy conversion , infrared sensing , and multifunctional materials . His work leverages interdisciplinary expertise in thermal and optical measurements , material synthesis , device fabrication , and theoretical modeling . Recent publications highlight advancements in infrared radiation control , thermal interface materials , metasurface engineering , and graphene-based nanosystems . His scientific awards include: NSF CAREER Award DARPA Director's Fellowship DARPA Young Faculty Award Elsevier/JQSRT Raymond Viskanta Award CMU Dean's Early Career Fellowship Philomathia Foundation Research Fellowship Hewlett-Packard Best Paper Award Best Paper Award, Julius Springer Forum Advising spans Ph.D. and postdoctoral researchers in nanoscale energy systems, with alumni contributing to solar energy conversion , infrared sensing , and flexible electronics . His lab receives funding from ARL, DARPA, DOE, DTRA, NASA, NSF, and ONR , and recently secured a DURIP award for instrumentation.

• Nanophotonics
• Nanoscale Energy Transport and Conversion
• Advanced Manufacturing
• +7 more

Robert JOLIET is an Associate Professor of Finance at IÉSEG School of Management in France. He holds a Ph.D. in Economics and Management from HEC-ULg Management School (Belgium) and has held various academic leadership roles, including Director of Academic Development and Academic Director of the PGE (MSc in Management). His research focuses on finance, portfolio management, SRI (Socially Responsible Investing), and corporate finance, with a particular emphasis on international expansion, capital structure, and sustainable investment strategies. Education: 2008: Ph.D. in Economics and Management, HEC-ULg Management School, Belgium 2004: Master of Advanced Studies (DEA), Management Sciences (Finance), University of Liège, Belgium 2002: Master of Science in Business Engineering (Finance), University of Liège, Belgium Research interests span SRI fund dynamics, corporate social responsibility valuation, international debt policies, and market liquidity. Notable contributions include analyzing the impact of SRI mutual funds on corporate governance and environmental practices, and exploring the stability of sovereign debt structures in the Eurozone. Awards include the 2005 FORTIS Award (Third Prize) and the 2003 E.G. de Barsy Doctoral Fellowship. He has published extensively in journals like Economics Letters and Journal of Banking & Finance . Academic leadership roles include directing programs and departments at IÉSEG, alongside coordinating the Finance Track and managing academic development initiatives. His teaching focuses on financial data analysis, active portfolio management, and investment strategies in capital markets. He is a member of the LEM (Laboratoire d'Économie Moderne) research group and contributes to interdisciplinary research in finance and economics.

• Finance
• Portfolio Management
• SRI (Socially Responsible Investing)
• +3 more

Aarti Singh is a Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF AI Institute for Societal Decision Making. She leads research at the intersection of machine learning, statistics, and decision making, with applications to scientific and societal domains. Her work focuses on designing principled interactive algorithms for learning and decision making under uncertainty. Education: Ph.D. in Electrical Engineering, University of Wisconsin-Madison (2008) M.S. in Electrical Engineering, University of Wisconsin-Madison (2003) B.E. in Electronics and Communication Engineering, University of Delhi (2001) Research Interests: Professor Singh's research centers on developing interactive machine learning algorithms that go beyond finding input-output associations to make higher-level decisions about the most informative data and actions. Her work spans autonomous decision making, including active sampling, stochastic optimization, bandits, and reinforcement learning that are statistically optimal, computationally tractable, and robust. She also investigates human factors in decision making, designing algorithms that model and leverage human feedback while accounting for bias, memory effects, and calibration. Her research has applications in material science, cosmology, and peer review systems. Research Trends: Professor Singh's recent publications demonstrate a strong focus on reinforcement learning, particularly in developing more efficient and robust algorithms for decision making under uncertainty. Her work bridges theoretical foundations with practical applications, spanning from fundamental algorithm development to real-world implementation in scientific domains. There's a clear trajectory toward integrating human factors into decision-making algorithms, with significant contributions to peer review systems and preference learning. Scientific Awards: NSF Career Award United States Air Force Young Investigator Award A. Nico Habermann Faculty Chair Award Harold A. Peterson Best Dissertation Award Multiple paper awards Advising and Grants: Professor Singh has advised numerous PhD and master's students, many of whom have gone on to faculty positions or research roles at leading institutions. Her research is supported by prestigious grants from ONR, Simons Foundation, AFRL, ARL, and NSF. She serves as General Chair (2025) and Program Chair (2020) for the International Conference on Machine Learning (ICML) and has held leadership roles in multiple professional organizations. Research Team: Professor Singh leads a vibrant research group within the Machine Learning Department at CMU, with current PhD students working on topics including reinforcement learning, human-AI collaboration, and decision making under uncertainty. She also directs the NSF AI Institute for Societal Decision Making, which brings together researchers from multiple disciplines to develop AI systems that support human decision making in societal contexts.

• Reinforcement Learning
• Bandit Algorithms
• Active Learning
• +7 more

Tomaso Aste is a Professor of Complexity Science at the Department of Computer Science, University College London (UCL). He founded the Financial Computing and Analytics group and co-founded the UCL Centre for Blockchain Technologies. His work bridges complex systems, data science, and finance, with applications in blockchain, fintech, and market modeling. Education: PhD in Physics (Politecnico di Milano, 1994); Laurea in Physics (University of Genoa, 1990) Prior Appointments: Reader at University of Kent's School of Physics; Associate Professor at Australian National University's Applied Mathematics His research focuses on data-driven modeling of complex systems , particularly financial systems, complex networks, and statistical physics. He has pioneered information filtering networks and topological machine learning methods for financial applications, including portfolio optimization, risk assessment, and cryptocurrency analysis. Recent publications highlight his expertise in financial time-series analysis, blockchain technology, and AI-driven modeling. Articles explore topics like limit order books, cryptocurrency market fragility, and topological neural networks. His work has influenced regulatory technology (RegTech) frameworks and digital economy strategies. Scientific Awards : Marie Curie Individual Fellowship University of Genoa graduate study specialization Fellowship Bacheflor Boncompagni-Ludovisi Foundation Fellowship Awarded fellowships from European Commission and academic foundations support his interdisciplinary research. He has held editorial roles at journals like Philosophical Magazine and Granular Matter , and contributed to professional societies including American Physical Society and Australian Research Council panels. Teaching & Academic Leadership : Co-created four UCL Master's programs: Financial Risk Management, Computational Finance, Financial Technologies, Emerging Digital Technologies Coordinates executive training on AI, blockchain, fintech, and regtech for regulators and private firms Teaches graduate-level courses in Data-Driven Modeling, Data Science, and Advanced AI

• Complex Systems
• Financial Mathematics
• Statistical Physics
• +6 more

Farzad Mashayek is a Professor and Department Head of Aerospace and Mechanical Engineering at the University of Arizona, College of Engineering. He is a member of the Graduate Faculty and leads the Computational Multiphase Transport Laboratory. His research integrates high-fidelity simulations, machine learning, and experimental validation across diverse domains in fluid dynamics and energy systems. Educational Background: PhD in Mechanical Engineering, State University of New York at Buffalo, Buffalo, NY MS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran BS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran His research interests include turbulent reacting flows, plasma dynamics, electrostatic atomization, solid-ion and lithium batteries, computational fluid dynamics, and machine learning applications in engineering. He employs high-order spectral element methods, phase-field modeling, and deep neural networks to study complex multiphysics phenomena such as drop impact, battery degradation, and turbulence modeling. The recent publications reflect a strong trend toward integrating machine learning with multiphysics simulations, particularly in battery safety (thermal runaway prediction), materials characterization (STEM image analysis), and fluid dynamics (modal analysis of turbulence). His work often involves collaboration with experimental groups to validate models, especially in dental aerosol suppression and electrohydrodynamics. Scientific Awards: Sustained Service Award, American Institute of Aeronautics and Astronautics (AIAA), Spring 2022 Best Presentation Award, The 20th International Conference on Computational Mathematics, Parallel and Distributed Computing, Summer I 2018 Dr. Mashayek has secured funding from NSF (GOALI program) for controlled coating via charged droplet deposition. He advises graduate students and postdoctoral researchers in computational mechanics and energy systems, fostering interdisciplinary research. He has contributed to engineering education, particularly during the pandemic, with active learning strategies in online instruction. He leads a dynamic research team focused on advancing simulation tools and applying them to real-world challenges in energy, manufacturing, and public health.

• Turbulent Reacting Flow
• Plasma Flow
• Electrostatic Atomization
• +9 more

Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)

• Scientific Computing
• Uncertainty Quantification
• Turbulence Modeling
• +6 more

Wojciech Matusik is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He leads the Computational Design and Fabrication Group and is a member of the Computer Graphics Group. His research spans computer graphics, robotics, and AI-driven manufacturing, with a focus on computational design, tactile sensing, and material science. Matusik holds a PhD in Computer Science from MIT (2003), an MS from MIT (2001), and a BS from UC Berkeley (1997). His work includes groundbreaking projects like differentiable cloth simulation (DiffCloth), AI-enhanced molecular design, and tactile sensing gloves. He has received prestigious awards such as the MIT TR35 (2004), DARPA Young Faculty Award (2012), and Ruth and Joel Spira Teaching Award (2014). Matusik teaches courses on computer graphics, machine learning, and computational fabrication at MIT. Key research themes include: Robotics: Robotic assembly, tactile interaction, and soft robotics Graphics: 3D holography, procedural material generation Manufacturing: Additive fabrication, topology optimization His recent articles explore AI-driven molecular synthesis, holographic displays, and tactile-enabled VR systems. Matusik collaborates on open-source tools like the WiReSens tactile platform and Simit language for sparse systems.

• Computer Graphics
• Computational Design and Fabrication
• Computer Vision
• +7 more

PD Dr. Christian Zillinger is a researcher at the Karlsruhe Institute of Technology (KIT), specifically within the Department of Mathematics. He leads the Junior Research Group "Stability and Instability in Fluids and Materials" (AP6) as part of the CRC 1173. His office is located at Kollegiengebäude Mathematik (20.30), room 2.024 in Karlsruhe, Germany. Dr. Zillinger obtained his PhD under the supervision of Herbert Koch at the University of Bonn. Following his doctorate, he served as an assistant professor (NTT) at the University of Southern California and was a postdoctoral fellow at BCAM (Basque Center for Applied Mathematics). He recently completed his habilitation thesis titled "On Mixing and Resonances in Fluid Systems" at KIT in 2023. Dr. Zillinger's research focuses on partial differential equations motivated by physical problems, particularly in fluid dynamics and material sciences. His work encompasses several key areas: Mixing as a (de)stabilizing mechanism in fluids and inviscid damping Cascades of resonances and instabilities in fluids and plasmas Convex integration and microstructures in materials, including rigidity and flexibility phenomena Magnetic fluids and magnetohydrodynamics Partial dissipation in the Boussinesq equations His recent publications demonstrate a strong focus on stability and instability phenomena in fluid systems, with particular attention to mathematical analysis of PDEs governing fluid behavior. He has made significant contributions to understanding echo chains, resonance phenomena, and damping mechanisms in various fluid models. His work bridges theoretical mathematics with applications in physics and materials science, often employing advanced analytical techniques to address challenging problems in nonlinear PDEs. Dr. Zillinger actively teaches courses at KIT, including "Klassische Methoden für partielle Differentialgleichungen" (Classical Methods for Partial Differential Equations), "Introduction to convex integration," "Introduction to Kinetic Equations," and seminars on microstructure in materials and fluid dynamics. He leads the Junior Research Group "Stability and Instability in Fluids and Materials" which is part of the Collaborative Research Centre (CRC) 1173 at KIT, focusing on wave phenomena. This research group investigates mathematical aspects of stability and instability in physical systems, with applications to fluid dynamics and material science.

• Partial differential equations
• Fluid dynamics
• Material sciences
• +5 more

Prof. Dr. Cemal Ulutaş is a faculty member at Hakkari University , affiliated with the Faculty of Education in the Department of Mathematics and Science Education . He also holds administrative and advisory roles across multiple institutions: Dean of the Faculty of Education Board Member at Faculty of Engineering and Faculty of Fine Arts Coordinator at Education and Training Coordination Office Chairman of the Academic Evaluation and Quality Improvement Board Advisory Board Member at Career Development Application and Research Center His research focuses on materials science with an emphasis on thin film technology , particularly metal sulfides and oxides . He investigates structural, electrical, optical, and physical properties of films like γ-MnS, CuS, SnS, and Mn 3 O 4 , using methods such as chemical bath deposition and post-deposition annealing . Current projects examine the impact of variables like molar concentration, bath temperature, and substrate choice on thin film behavior. Recent publications (2010–2023) highlight systematic studies on metal chalcogenide thin films , including concentration gradients, substrate interactions, and thermal stability. Experimental techniques span X-ray diffraction , UV-Vis spectroscopy , and electrical resistivity measurements . Notably, his work bridges fundamental material science with applied engineering challenges, such as optimizing evaporative cooling systems in a 2010 study. As a thesis advisor, he has guided 2 graduate students . His academic contributions are documented in Google Scholar (10 citations, H-Index 620), Scopus (24 publications, H-Index 480), and Web of Science (15 citations, H-Index 9).

• Materials Science
• Thin Film Technology
• Optical Properties of Materials
• +3 more

Scott Barnett is a Professor of Materials Science and Engineering at Northwestern University , specializing in ceramic thin films and electrochemical systems for energy applications. His research spans over 25 years in solid oxide fuel cells (SOFCs) and lithium-ion battery electrodes, with a focus on 3D printing, thin electrolyte deposition, and microstructural evolution under operational stress. Education: Ph.D. and B.S. in Metallurgy and Physics from the University of Illinois at Urbana-Champaign. Research Interests center on: Reversible solid oxide cells for energy storage 3D tomographic imaging of electrode microstructures Novel processing methods for ceramic materials Electrochemical stability and degradation mechanisms Recent Publications highlight advancements in: Pressurization effects on oxygen electrodes 3D printed SOFCs with enhanced performance Nanoparticle infiltration for fuel electrode stability Impedance modeling of lithium-ion batteries Scientific Awards include: Fulbright Scholar (2015) Cheng Tsang Man Endowed Professorship (2008) Office of Naval Research Young Investigator (1986) AVS Fellow (1998) Advising Legacy encompasses over 30 graduate students and postdocs, including Nicholas Geisendorfer (NASA Space Technology Fellow) and Matthew Lu (now at Tesla), with notable alumni placements at institutions like Bloom Energy and Microsoft . His lab employs advanced characterization techniques to predict long-term durability of electrochemical devices.

• Solid Oxide Fuel Cells
• Energy Storage and Conversion
• 3D Printing
• +4 more

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +7 more

Dawn Y. Sumner is a Professor in the Department of Earth and Planetary Sciences at the University of California, Davis. Her research focuses on geobiology, paleobiology, and planetary science, particularly reconstructing ancient environments on Earth and Mars. She is a key member of NASA’s Mars Science Laboratory team, contributing to the Curiosity rover’s exploration of Gale Crater on Mars. Sumner’s work integrates field studies, lab analyses, and interdisciplinary approaches to understand microbial life’s role in shaping Earth’s history and potential habitability on other planets. Education: Ph.D., Massachusetts Institute of Technology (1995). Research interests include microbialite formation, Antarctic lake ecosystems, and the evolution of oxygenic photosynthesis. She investigates modern microbial communities in ice-covered lakes (e.g., Lake Vanda) to understand ancient environments and their biosignatures. Her scientific awards include the California Academy of Sciences Academy Fellow (2020) and Geological Society of America Fellow (2014). Sumner emphasizes inclusive education and supports student success in STEM through feminist research practices. Labs/Teams: W.M. Keck Center for Active Visualization in the Earth Sciences (KeckCAVES), Antarctic Lake Research Group.

• Geobiology
• Paleobiology
• Geochemistry
• +3 more

Athanasios TRIANTAFYLLOU is an Associate Professor of Finance at IÉSEG School of Management in France. He holds a Ph.D. in Economics from the University of Athens (2017) and has held academic positions including Visiting Professor at Cornell University (2019), Lecturer at the University of Essex (2017–2022), and General Equilibrium Modeler at the National Technical University of Athens (2015). His research focuses on commodity markets, volatility forecasting, risk management, and monetary economics. Key contributions include analyzing commodity price uncertainty's impact on economic activity and financial markets. He has published extensively in journals like Journal of Futures Markets , Economics Letters , and International Journal of Finance and Economics . Education includes a BSc in Mathematics (2008), MSc in Accounting and Finance (2010), and M.Phil. in Economics (2013), all from Greek institutions. Professional experience includes risk management consulting at Ernst & Young (2016–2017). His teaching covers financial derivatives, multinational finance, and commodity markets. Research highlights include exploring the role of commodity price uncertainty in global economic dynamics and the predictive power of market information in corporate defaults. His work often bridges theoretical econometrics with practical policy implications, addressing issues like inflation dynamics driven by oil market risks and the tourism-led growth hypothesis in the Eurozone. TRIANTAFYLLOU’s publications emphasize volatility modeling, commodity market linkages, and the interplay between economic uncertainty and financial stability.

• Commodity Markets
• Risk Management
• Volatility Forecasting
• +4 more

Susan Pfeiffer is a Research Professor of Anthropology at the University of Toronto, affiliated with the Department of Anthropology within the Faculty of Arts and Science. Her research focuses on bioarchaeology, skeletal biology of past populations, and repatriation of human remains, particularly among Southern African Khoe-San and North American Great Lakes communities. She holds a Ph.D. (1976), M.A. (1972), and B.A. (1968) in Anthropology and Religion from the University of Toronto and the University of Iowa. Education: Ph.D. in Anthropology, University of Toronto (1976) M.A. in Anthropology, University of Toronto (1972) B.A. in Religion, University of Iowa (1968) Her research interests include paleopathology, adult age estimation, evolutionary theory, and collaborations with Indigenous communities. Notable works include her 2022 book Osteobiographies , which explores repatriation frameworks globally, and studies on Huron-Wendat ancestral repatriation and Southern African skeletal analysis. Dr. Pfeiffer has received prestigious awards such as the 2019 AAAS Fellowship and the 2016 J. Norman Emerson Medal. Her publications bridge bioarchaeological methodological advancements with ethical repatriation practices, emphasizing cross-cultural collaboration in anthropology. Key Awards: AAAS Anthropology Section Fellowship (2019) Ontario Archaeological Association Medal (2016) University of Toronto Teaching Excellence Award (2001) Her work integrates skeletal analysis with community partnerships, addressing both scientific and cultural dimensions of human remains research. Collaborations with First Nations and African groups highlight her commitment to ethical, culturally sensitive archaeology.

• Bioarchaeology
• Paleopathology
• Adult Age Estimation
• +6 more

Charles M. Jones is a Professor of Finance at Columbia Business School, Columbia University, with an extensive publication record spanning several decades. His research focuses on market microstructure, high-frequency trading, short selling, retail investor behavior, and stock market liquidity. His work has appeared in top finance journals including the Journal of Finance, with his most recent publication "Nonstandard Errors" appearing in the June 2024 issue. Professor Jones's research interests center on understanding how financial markets function at a granular level. His work on market microstructure examines the mechanics of price formation, liquidity provision, and the impact of trading technologies on market quality. His research on short selling has been particularly influential, investigating when short sellers trade, what information they possess, and how regulatory interventions like short sale bans affect market functioning. His more recent work has explored the rise of retail trading through platforms like Reddit and its implications for price discovery, particularly during events like the GameStop phenomenon and the COVID-19 pandemic. Analysis of his publication trends reveals a consistent focus on market efficiency and price discovery mechanisms, with increasing attention to retail investor behavior in recent years. His work spans both theoretical modeling and empirical analysis of market data, often utilizing high-frequency datasets to examine intraday trading patterns. The interdisciplinary nature of his research bridges finance, economics, and information science, contributing to both academic understanding and practical market regulation. Professor Jones has collaborated extensively with researchers across the globe, as evidenced by his numerous co-authored papers with scholars from institutions worldwide. His work has significant implications for market regulators seeking to understand the impact of technological changes and regulatory interventions on market quality and efficiency.

• Market Microstructure
• High-Frequency Trading
• Short Selling
• +7 more