Eamonn Keogh is a Professor in the Computer Science and Engineering Department at the University of California, Riverside. His pioneering work centers on the Matrix Profile, a transformative approach to time series data mining enabling efficient solutions for motif discovery, anomaly detection, and similarity search. His algorithms (STAMP, STOMP, SCRIMP, DAMP, SCAMP) offer exact, parameter-free, and scalable solutions across domains like seismology, bioinformatics, and industrial IoT. Research areas include: Development of ultra-fast algorithms for time series joins and motif discovery at unprecedented scales (breaking the 100 million barrier) GPU acceleration for time series mining Domain-agnostic methods for semantic segmentation and anomaly detection Novel primitives like Time Series Chains, Snippets, and Consensus Motifs His work is highly cited and recognized by industry and academia, with applications ranging from NASA's Cassini mission to detecting BGP anomalies in computer networks.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.
R. Edwin García is a Professor at the School of Materials Engineering at Purdue University, where he has been faculty since 2005. He holds appointments in the Materials Engineering department within Purdue's College of Engineering, specifically in the School of Materials Engineering located in the Neil Armstrong Hall of Engineering at Purdue's West Lafayette campus. His educational background includes: B.S. in Physics from the National University of Mexico (1996) M.S. in Materials Science and Engineering from Massachusetts Institute of Technology (2000) Ph.D. in Materials Science and Engineering with a minor in Applied Mathematics from Massachusetts Institute of Technology (2003) Professor García's research focuses on the design of materials and devices through the development of a fundamental understanding of the solid state physics of individual phases, their short and long range interactions, and associated microstructural properties and time evolution. His current research emphasizes establishing relationships between material properties and resultant performance and degradation in electrochemical systems. He integrates computational approaches ranging from kinetic Monte Carlo, phase field and level set methods, to finite elements, finite volumes, and symbolic computing. His work particularly addresses microstructure design, crystallographic texture, and grain boundary science and engineering to control the topology of underlying phases and establish practical relations between processing, microstructure, and material properties. His recent publications demonstrate a strong focus on lithium-ion battery technology, ferroelectric materials, and computational modeling of material behaviors. The research trends show increasing integration of machine learning with traditional computational methods, exploration of novel sintering techniques like flash sintering, and deeper investigation into the fundamental mechanisms of material degradation in energy storage systems. His work spans multiple length scales from atomistic to continuum modeling, reflecting a comprehensive approach to materials design and analysis. Professor García teaches several courses including MSE 230 (Structure and Properties of Materials), MSE 350 (Thermodynamics of Materials), MSE 597G (Modeling and Simulation of Materials), MSE 597I (Introduction to Computational Materials), and MSE 597N (Physical Properties of Crystals). He mentors graduate students in areas related to computational materials science, battery technology, and microstructural evolution. His research group, the Laboratory of Computational Microstructures, focuses on developing home-grown analytical theories and algorithms to resolve relevant time and length scales in materials systems. The group's work has significant implications for portable power sources, including rechargeable batteries and fuel cells, as well as for ferroelectric ceramic applications.
Lucia Carichino is an Assistant Professor in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT). She holds a PhD in Mathematics from Purdue University and a BS/MS in Mathematical Engineering from Politecnico di Milano, Italy. Her research focuses on mathematical and computational models of multiscale biological systems, particularly fluid-structure interaction in biological contexts like ocular blood flow and microswimmers. She emphasizes integrating experimental data with mathematical models to advance medical understanding. Carichino teaches courses such as Differential Equations, Linear Algebra, and oversees undergraduate research projects. In 2023, she received the National Science Foundation LEAPS-MPS award for her work on computational modeling of eye-contact lens interactions. Her research has been published in high-impact journals and presented at conferences. She actively collaborates on projects addressing glaucoma, ocular hemodynamics, and biomedical applications. Education: PhD in Mathematics, Purdue University BS and MS in Mathematical Engineering, Politecnico di Milano, Italy Research interests include fluid dynamics, numerical methods, and mathematical biology. Her work bridges theoretical models with biomedical applications, such as optimizing gene therapy delivery and analyzing ocular physiology under varying environmental conditions (e.g., altitude). She explores topics like sperm motility, computational simulations of biological systems, and the interplay between fluid dynamics and biological structures. Her recent articles highlight advancements in ocular pharmacokinetics, contact lens interactions, and altitude effects on intraocular pressure. These studies underscore her expertise in multiscale modeling and fluid-structure interaction. Carichino also contributes to educational initiatives, fostering a collaborative classroom environment. Notable awards include the NSF LEAPS-MPS award (2023). She advises student research projects and collaborates with colleagues, such as Maki, on interdisciplinary studies. Her work is supported by grants and has led to presentations at ophthalmology and mathematics conferences. Carichino’s lab focuses on computational modeling of biological systems, particularly in ophthalmology and microscale fluid dynamics. Her team develops tools to simulate complex physiological processes, aiding in medical diagnostics and treatment strategies.
Long Cai is a Professor at the California Institute of Technology, affiliated with the Biology and Biological Engineering department. He pioneered the field of spatial genomics and co-developed transformative technologies such as seqFISH and MEMOIR. Research Interests: His work focuses on decoding biological systems through spatial genomics, integrating molecular imaging with computational analysis to uncover cellular organization in tissues. Key areas include developmental biology, neuroscience, kidney regeneration, and cancer biology. Publications: Recent studies highlight applications of spatial transcriptomics in kidney disease, brain nuclear architecture, and multi-omics tissue mapping. His research emphasizes creating high-resolution atlases of cellular dynamics. Scientific Awards: NIH Director’s Pioneer Award (2022) Labs & Collaborations: He leads the Cai Lab, which develops cutting-edge imaging tools in collaboration with the Elowitz Lab and other interdisciplinary teams.
Professor Hala Zreiqat AM is a leading biomedical engineer at The University of Sydney , serving as the Director of the ARC Training Centre for Innovative BioEngineering . A Fellow of all major Australian academies (AAS, ATSE, FAHMS, FRSN), she develops 3D printed bioceramics for bone regeneration while championing diversity through initiatives like the IDEAL Society and BIOTech Futures mentorship program. Her work bridges academia, clinical practice, and industry in musculoskeletal research . Research Focus: Her lab creates synthetic bone scaffolds that mimic natural bone architecture, strength, and porosity, enabling non-rejected bone regeneration via patient-matched implants. Key applications include orthopaedic, dental, and maxillofacial repair , with over $18M in competitive funding and multiple patents. Current projects explore AI-driven scaffold performance prediction and anti-senescence strategies for aging-related bone loss. Scientific Trends: Recent publications highlight 3D printed nanovoxelated ceramics , antisenescence biomaterials , and multifunctional theranostic platforms . Her team integrates machine learning for scaffold design, atom probe tomography for interface analysis, and two-photon imaging for cellular monitoring in 3D environments. 2021-2022 Fulbright Senior Scholar 2018 NSW Premier's Woman of the Year 2019 Eureka Prize for Innovative Use of Technology Fellow of Australian Academy of Science (2021) Over $18M in research funding Teaching & Leadership: She designed core courses like Tissue Engineering and Nanomaterials in Medicine , mentoring 158 students in 2020 alone. As Chair of CAAR (2020-2023), she strengthens Australia-Arab collaborations. Her lab trains early-career researchers , with alumni now in academia and industry.
Ibo van de Poel is a Full Professor at Delft University of Technology , within the Faculty of Technology, Policy and Management , specifically in the Section Philosophy . He holds the prestigious Antoni van Leeuwenhoek professorship , awarded to excellent young researchers, and is a leading figure in Design for Values , engineering ethics , and technology as social experimentation . Education: Van de Poel studied Philosophy of Science, Technology and Society at the University of Twente , with a propaedeutic exam in Mechanical Engineering . He earned his PhD in Science and Technology Studies (STS) in 1998 from the University of Twente , under the supervision of Prof. Dr. A. Rip. Research Interests: His research spans three major themes: Design for Values : Investigating how moral values can be integrated into engineering design, including empirical studies on how engineers handle value conflicts. Moral Responsibility in R&D Networks : Exploring the "problem of many hands" in large-scale research and development projects, developing formalizations and empirical methods. Ethics of Emerging Technologies : Examining ethical issues in new technologies like nanotechnology and synthetic biology , with a focus on technology as social experimentation . Publications and Impact: Van de Poel has authored over 25 international peer-reviewed articles and 15 book contributions . He is co-editor of major works including the Handbook of Philosophy of Technology and the Engineering Sciences (Elsevier, 2009), Philosophy and Engineering (Springer, 2010), and Ethics, Engineering and Technology (Wiley-Blackwell, 2011). His recent work includes leading the VICI project "New Technologies as Social Experiments" , funded by NWO. Scientific Awards: Antoni van Leeuwenhoek Professorship (TU Delft) VICI Grant for "New Technologies as Social Experiments: Conditions for Morally Responsible Experimentation" Advising and Grants: Van de Poel has led and contributed to multiple high-impact projects, including: NWO Project on "Moral Responsibility in R&D Networks" (2007–2012) EU Project on ethical issues in synthetic biology (2009–2011) VICI Project on "New Technologies as Social Experiments" (ongoing) He has also supervised PhD students and contributed to educational innovations, including the development of the AGORA web-based program and the first Dutch textbook on ethics and technology . Labs and Teams: Van de Poel is an active member of 4TU.Ethics & Technology , a federation of Dutch technical universities focused on ethics and technology. He contributes to the Simon Stevin Series in the Ethics of Technology and participates in various reading groups and PhD communities within the network.
Marcus Herrmann is a Professor of Aerospace and Mechanical Engineering at Arizona State University's School for Engineering of Matter, Transport and Energy. He is also affiliated with the Center for Negative Carbon Emissions. His research focuses on fluid mechanics, multiphase flows, atomization processes, and numerical methods for discontinuous interfaces. Herrmann holds a PhD in Mechanical Engineering from RWTH Aachen University (2001) and a Diplom (1995). His career includes a postdoctoral fellowship at Stanford University's Center for Turbulence Research (CTR) and a visiting scientist position at the University of Technology Eindhoven, Netherlands. He has secured major grants from NASA, NSF, and industry partners like Honeywell, focusing on atomization modeling, supersonic crossflows, and turbulence simulations. Research interests span computational fluid dynamics, multiphase flow simulation, and LES/DNS methodologies. His recent work emphasizes high-fidelity numerical techniques for particle-resolved simulations and phase interface dynamics. Teaching includes courses like MAE 561 (Computational Fluid Dynamics) and MAE 384 (Advanced Math Methods for Engineers). He actively advises students through research and dissertation roles. Notable projects include modeling wax deposition in pipelines and developing novel approaches for interface dynamics in turbulent flows. His work bridges fundamental fluid mechanics with industrial applications like combustion systems and porous media modeling.
Professor Patricia H. Reiff is a Professor in the Department of Physics and Astronomy at Rice University and Associate Director for Outreach Programs at the Rice Space Institute. Her research focuses on space plasma physics, magnetospheric dynamics, auroras, and space weather. She has led missions like the Magnetospheric Multiscale (MMS) and contributed to the Dynamics Explorer, Polar, and Cluster missions. Reiff has pioneered public education initiatives, including the Discovery Dome portable planetarium system and the 'Totality!' planetarium show, reaching global audiences. She has trained fourteen PhD students and directs the Master of Science Teaching (MST) program, which has produced 36 teacher alumni as of 2024. Education: B.S. Physics (Oklahoma State University, 1971), M.S. Space Science (Rice University, 1974), and Ph.D. Space Physics and Astronomy (Rice University, 1975). Her research uses data from missions like MMS and citizen science projects like Citizen CATE to study magnetic reconnection and space weather effects. Key research interests include solar wind-magnetosphere-ionosphere interactions, magnetospheric reconnection, and the societal impacts of space weather. She has authored over 160 refereed publications and holds an H-index of 40. Reiff’s awards include AGU Fellow (1997), the Athelstan Spilhaus Award (2009), and NASA Group Achievement Awards. She is a vocal advocate for STEM education, frequently appearing in media to discuss eclipses and space science, including her role as a solar eclipse tour guide and science commentator.
Sujan Pal is a Hydroclimate Scientist at Argonne National Laboratory , focusing on hydrometeorology, hydroclimatology, and land-atmosphere interactions through numerical modeling and field experiments. He serves as an associate mentor for multiple observational systems in the Atmospheric Radiation Measurement (ARM) user facility and monitors environmental data at Argonne Testbed for Multiscale Observational Science (ATMOS). Ph.D., University of Illinois at Urbana-Champaign (2017-2021) M.S., The University of Arizona (2015-2017) B.E., Jadavpur University (2010-2014) His research spans hydrometeorological modeling, urban climate impacts, and machine learning applications in environmental science. He leads high-resolution flood simulations and contributes to the DOE-funded CROCUS project studying urban climate change in Chicago. Recent publications highlight his work on convection-permitting climate modeling, extreme rainfall dynamics, and flood risk assessment across South America, the U.S. Northeast, and Argentina. His methodologies integrate field data with advanced computational tools. ARM Service Award 2025 Argonne Commercialization Excellence Award 2024 Argonne IMPACT Awards (2024, 2023, 2022) He actively collaborates with national user facilities and contributes to spatiotemporal modeling of water-related hazards as an Associate Editor for Frontiers in Water .
Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .
Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.
Leonardo Chamorro is a Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations in Earth Science and Environmental Change, Aerospace Engineering, and Civil and Environmental Engineering. His research focuses on fluid dynamics, renewable energy systems, and turbulence modeling. He holds a Ph.D. in Civil Engineering from the University of Minnesota (2010) and has held academic positions at UIUC since 2013, advancing to Full Professor in 2024. Chamorro's work spans experimental and theoretical investigations of wind and hydrokinetic energy, geophysical flows, and particle dynamics. His research group, the Renewable Energy & Turbulent Environment Group (RE-TE-G), explores topics like tidal flow multifractality, vortex dynamics, and bio-inspired robotics. Key achievements include Nature and Lab on a Chip cover articles, and contributions to turbulence modeling for tidal energy systems. He has received awards such as the Best Paper Award in Energies (2018) and recognition for pandemic-related research (2021). His editorial roles include associate editorships at journals like Journal of Renewable and Sustainable Energy and Frontiers in Energy Research . Chamorro has supervised numerous graduate students and postdocs, contributing to over 150 peer-reviewed publications since 2009.
Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.
