Bo Zhu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. His research focuses on computational approaches for complex physical systems, including fluid dynamics, topology optimization, and robotics control. He holds a Ph.D. from Stanford University and completed postdoctoral research at MIT CSAIL. He has been recognized with the NSF Career Award (2022) and multiple best paper awards at SIGGRAPH conferences. Education: B.E.-M.S., Software Engineering, Shanghai Jiao Tong University Ph.D., Computer Science, Stanford University Postdoc, EECS, MIT Research Interests: Develops numerical algorithms and machine learning techniques to simulate fluidic systems, soft materials, and multi-scale phenomena. His work emphasizes vorticity preservation, real-time simulation, and physics-based AI integration. Key Contributions: Pioneered Particle Flow Map (PFM) methods for fluid simulation, developed open-source libraries like SimpleX and PFM Hub, and contributed to projects like Genesis physics engine. Over 50 peer-reviewed publications in top venues (SIGGRAPH, NeurIPS, IEEE TVCG). Awards: NSF Career Award (2022) Best Paper Honorable Mention (SIGGRAPH 2025) Best Paper Award (SIGGRAPH Asia 2024) Grants & Projects: Leads NSF-funded research on Physical AI Design, collaborating with Sandia National Labs on real-time CFD solvers. Active in open-source software development for computational physics and graphics.
Amit Lal is a Professor in the School of Electrical and Computer Engineering at Cornell University, with affiliations in Biomedical Engineering, Applied Engineering Physics, and Mechanical and Aerospace Engineering. He is a member of key research centers including Cornell CCMR, NBTC, and KAUST-CU. Education: B.S. in Electrical Engineering, California Institute of Technology, 1990 Ph.D. in Electrical Engineering, University of California, Berkeley, 1996 Prof. Lal's research focuses on the development of integrated microsystems using micro- and nanoscale fabrication. His work spans ultrasonic MEMS, low-power IoT sensors, atomic microsystems, and bio-robotics. He directs the SonicMEMS Laboratory, advancing technologies in GHz ultrasonics, inertial sensing, and chip-scale manipulation of particles. His interdisciplinary interests include biomedical imaging, solid-state devices, nanotechnology, and plasma science. His recent publications highlight innovation in energy harvesting, MEMS gyroscopes, and biologically integrated systems. The works reflect strong trends in autonomous sensing, miniaturized power sources, and hybrid bio-electromechanical systems, particularly for medical and navigation applications. Scientific Awards and Honors: NSF CAREER Award Whitaker Foundation Award Department of Defense Exceptional Service Award Best Program Manager Award, DARPA IEEE Ultrasonics and Frequency Control Symposium Best Paper Award IEEE NEMS Best Paper Award Robert M. Scharf 1977 Professor, Cornell Engineering HHMI Visiting Scientist, Janelia Farms Intel Fellowship (awarded to advisee) Prof. Lal has advised numerous students who have gone on to win awards and publish impactful research. He has secured significant research funding through DARPA and other agencies, managing and initiating multiple high-impact programs. His leadership extends to service on technical committees for IEEE conferences and journals, including Transducers and the IEEE Sensors Council. He has also contributed to academic recruiting within ECE. He leads the SonicMEMS Laboratory , a multidisciplinary research group focused on transforming sensing, communication, and computation at the microscale. The lab fosters collaboration across engineering and life sciences, pushing the boundaries of what integrated microsystems can achieve.
Professor David Thomas holds the position of Professor in Computer Engineering at the University of Southampton's Electronics and Computer Science Department. His research focuses on the intersection of software and hardware, particularly leveraging FPGAs for novel digital architectures and event-driven computing. He has a notable academic trajectory, having previously served as a Lecturer and Senior Lecturer at Imperial College London before joining Southampton in 2021. Dr. Thomas is actively involved in supervising PhD students and contributes to interdisciplinary research projects funded by the EPSRC, such as the SONNETS initiative exploring scalable event-triggered systems. Education: BSc in Computer Science (Imperial College London), PhD in Digital Architectures (Imperial College London). Postdoctoral roles included Research Associate and Research Fellow at Imperial's Department of Computing. Research Interests: Event-driven computing, FPGA-based systems, high-level synthesis, and high-performance computing. His work emphasizes practical implementations of theoretical models, such as custom processors and application-specific accelerators. Current projects include optimizing random number generation for FPGAs and exploring meta-programming techniques for hardware design. Advising and Grants: Supervises multiple PhD students in areas like neuromorphic computing and algorithm optimization. Active in securing funding for distributed system architectures and FPGA-based solutions. Labs/Teams: Member of the Cyber Physical Systems research group. Collaborates with interdisciplinary teams on projects like POETS (Partially Ordered Event-Triggered Systems) for large-scale parallel computing.
Professor Dmitry Turaev is a Professor in Dynamical Systems at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His primary role includes teaching courses such as Dynamical Systems and Bifurcation Theory. He is affiliated with the Applied Mathematics and Mathematical Physics groups and the Mathematics research and teaching staff. His research focuses on dynamical systems, chaos theory, bifurcation theory, and their applications in physics and engineering. Education: Ph.D. in Mathematics (details inferred from academic position). Research interests span applied and pure mathematics, with a strong emphasis on dynamical systems, including Hamiltonian systems, homoclinic tangencies, and chaotic behavior in reversible systems. Turaev's work explores complex phenomena such as the emergence of Lorenz-like attractors, Fermi acceleration, and the breakdown of symmetry in dynamical systems. His recent publications highlight studies on pseudohyperbolic attractors, chaotic dynamics in symmetric networks, and nonuniformly expanding random systems. Turaev advises numerous PhD students, reflecting his active role in nurturing the next generation of researchers in dynamical systems. He maintains a lab/working group within the Dynamical Systems group at Imperial College, collaborating with colleagues like Jeroen Lamb and Martin Rasmussen. His research often intersects with interdisciplinary topics like quantum physics and nonlinear optics.
Garnet K. Chan is the Bren Professor of Chemistry and Director of the Rudolph A. Marcus Center for Theoretical Chemistry at the California Institute of Technology. He received his B.S. from the University of Cambridge in 1996 and his M.A. and Ph.D. from the University of Cambridge in 2000. Dr. Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, focusing on quantum many-particle phenomena and the numerical methods to simulate them. His group has developed numerous methodologies including density matrix renormalization and tensor network algorithms, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings, and new quantum Monte Carlo algorithms. His work addresses problems that appear naively exponentially hard but where understanding of physics, particularly entanglement structure, allows for calculations of polynomial cost. Analysis of his recent publications reveals a strong focus on quantum simulation techniques, particularly tensor network methods applied to strongly correlated systems. His research spans fundamental theoretical developments to practical applications in quantum computing, molecular simulation, and materials science, with increasing integration of machine learning techniques and GPU acceleration in computational chemistry frameworks. Dr. Chan leads an active research group at Caltech dedicated to simulating chemical and physical systems at the level of many-particle quantum mechanics. His group has welcomed numerous researchers including Kasra Hejazi, Zuxin Jin, Zhihao Cui, Ke Liao, Henrik Larsson, and Wenyuan Liu. He teaches courses in Physical Chemistry (Ch 21 abc) and Advanced Quantum Chemistry (Ch 225), contributing significantly to theoretical chemistry education at Caltech.
Benedikt Günther is a research scientist at the Technical University of Munich (TUM) working within the Chair of Biomedical Physics led by Prof. Dr. Franz Pfeiffer. His research focuses on the Munich Compact Light Source (MuCLS), a laboratory-scale inverse Compton X-ray source that provides synchrotron-like radiation for biomedical applications. Günther plays a key role in developing, optimizing, and characterizing this innovative technology, contributing to both its fundamental physics and practical medical applications. His primary research interests center around X-ray physics and imaging techniques, particularly laser enhancement cavities for inverse Compton X-ray sources, X-ray microscopy, dynamic phase-contrast imaging, and X-ray spectroscopy. Günther's work bridges fundamental physics with practical medical applications, developing instrumentation that brings synchrotron-quality imaging to conventional laboratory settings. His research has significant implications for improving medical diagnostics while making advanced imaging techniques more accessible. Analysis of Günther's publication record reveals a consistent focus on advancing compact X-ray source technology and its applications. His work demonstrates expertise in both theoretical modeling and experimental implementation, with publications spanning instrument development, imaging techniques, and specific medical applications. The research shows progression from fundamental source characterization to increasingly sophisticated biomedical applications, particularly in breast imaging, dental diagnostics, and materials science. 2019 Best Poster Award at the combined meeting of the 68th Denver X-ray Conference (DXC) & 25th International Congress on X-ray Optics and Microanalysis (ICXOM) for 'Full-Field Structured Illumination Super-Resolution X-ray Transmission Microscopy' Günther regularly presents his work at major international conferences including the International Particle Accelerator Conference, High-Brightness Sources and Light-driven Interactions Congress, and specialized X-ray imaging meetings. His research is conducted within the Munich Compact Light Source facility, a collaborative project involving physicists, engineers, and medical researchers working to develop laboratory-scale synchrotron technology for widespread biomedical use.
Professor Michael Manhart is affiliated with the Technical University of Munich (TUM) as an Extraordinary Professor in the Department of Hydromechanics . His research focuses on fluid mechanics, turbulent flow dynamics, and computational fluid dynamics (CFD) simulations, particularly in porous media and environmental fluid mechanics. Education: Not explicitly stated in the provided text. His recent publications investigate turbulent flow over random sphere packs, scalar transport at porous-turbulent interfaces, acoustic resonances in HVAC systems, and nonlinear oscillatory flow modeling. He employs advanced numerical techniques like direct numerical simulations (DNS) and large-eddy simulations (LES) to study flow structures, energy budgets, and particle transport mechanisms. Professor Manhart collaborates with researchers such as Yoshiyuki Sakai, Simon Wenczowski, and Daniel Quosdorf. His work addresses applications in environmental engineering, hydraulic modeling, and industrial fluid dynamics, with a strong emphasis on validating computational models against experimental data (e.g., PIV measurements). He leads the Professorship for Hydromechanics at TUM, conducting high-fidelity simulations and experimental studies on topics like wall shear stress estimation, sediment erosion around cylinders, and turbulence decomposition in complex flows.
Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he directs the UW Robot Learning Laboratory. He also serves as a Principal Research Scientist in the Seattle Robotics Lab at NVIDIA Research and co-chairs the IEEE Robotics and Automation Society Technical Committee on Robot Learning. Dr. Boots received his Ph.D. from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University, where he was a member of the Sense, Learn, Act (SELECT) Lab co-directed by Carlos Guestrin and his advisor Geoff Gordon. Prior to joining the University of Washington faculty, he was an Assistant Professor in the School of Interactive Computing within the College of Computing at Georgia Tech, and before that, he completed a post-doc in the Robotics and State Estimation Lab directed by Dieter Fox at the University of Washington. Professor Boots' research focuses on the intersection of machine learning, artificial intelligence, and robotics, with particular emphasis on developing theory and systems that tightly integrate perception, learning, and control. His work spans computer vision, state estimation, localization and mapping, high-speed navigation, motion planning, and robotic manipulation. His group develops algorithms drawing from deep learning and neural networks, nonparametric statistics, graphical models, nonconvex optimization, quantum physics, online learning, reinforcement learning, and optimal control. The research demonstrates a strong theoretical foundation while maintaining practical relevance to real-world robotic systems. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with robotics, particularly in model predictive control, motion planning, and learning-based approaches to robot control. His work shows increasing focus on developing theoretically grounded methods that can handle the complex, nonlinear dynamics of real-world robotic systems while maintaining computational efficiency. The publications span top venues including ICRA, CoRL, IROS, and NeurIPS, demonstrating broad impact across multiple subfields of robotics and AI. Finalist for Best Systems Paper at Conference on Robot Learning (CoRL-2021) Multiple papers selected for oral presentations at top robotics conferences Work recognized for theoretical contributions and practical applications in robot learning As director of the UW Robot Learning Laboratory, Boots leads a vibrant research group focused on fundamental and applied research in robot learning. The lab maintains strong collaborations with NVIDIA Research and has produced numerous high-impact publications that bridge theory and practice. Professor Boots teaches courses in autonomous robotics, machine learning, and reinforcement learning, contributing to both undergraduate and graduate education at the University of Washington.
Jindal Shah is a Professor and holds the Anadarko Petroleum Chair in Chemical Engineering at Oklahoma State University, where he also serves as the Graduate Program Director. He is affiliated with the Department of Chemical Engineering within the College of Engineering at Oklahoma State University. Dr. Shah received his educational training from prestigious institutions worldwide. He earned his Ph.D. in Chemical Engineering from the University of Notre Dame in 2005, followed by an M.S. in Environmental Engineering from the University of Cincinnati in 1999, and completed his undergraduate education with a B.Tech. in Chemical Engineering from the Indian Institute of Technology (IIT) Bombay in 1996. Dr. Shah's research focuses on the application of molecular simulation methodologies to understand molecular-level interactions that give rise to macroscopic phenomena. His primary research interests include Monte Carlo and Molecular Dynamics Simulations, Phase Equilibria, Ionic liquids, and Dye-sensitized solar cells. A significant portion of his work centers on designing novel biodegradable ionic liquids with properties suitable for chemical processes, with applications in next-generation batteries and carbon capture. He also investigates molecular-level interactions responsible for device efficiency in dye-sensitized solar cells to rationally design novel dye molecules. Additionally, Dr. Shah employs data science and machine learning techniques to correlate properties of ionic liquids and generate new molecules with desired properties. An analysis of Dr. Shah's recent publications reveals a strong focus on ionic liquids and their applications in energy storage and carbon capture technologies. His work consistently bridges fundamental molecular-level understanding with practical applications, particularly in developing electrolytes for batteries and CO2 capture systems. A notable trend is the integration of machine learning techniques with traditional molecular simulation methods to accelerate materials discovery and optimization. His research demonstrates a progression from fundamental molecular simulations toward applied technologies with significant environmental impact, particularly in climate action (SDG 13) and affordable clean energy (SDG 7). Dr. Shah has secured substantial research funding from multiple prestigious sources including the National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, and industry partners. His funded projects include 'Collaborative Research: Cyber Training-Implementation, Medium, Establishing Sustainable Ecosystem for Computational Molecular Science Training & Education' (NSF), 'Ionic Liquids for Direct Air Capture of CO2 using Electric-Field-Mediated Moisture Gradient Process' (DOE), and 'CAREER: Computation-Enabled Rational Design of Cytochrome P450 for Ionic Liquid Biodegradation' (NSF). These grants support his research in computational molecular science, CO2 capture technologies, and the development of biodegradable ionic liquids. As an educator, Dr. Shah has been actively involved in teaching graduate courses including Principles of Chemical Engineering Thermodynamics, Doctoral Thesis supervision, and specialized courses such as Machine Learning for Chemical Processes and Introduction to Chemical Process Analytics. His teaching philosophy integrates cutting-edge research with educational practice, preparing students for the computational challenges of modern chemical engineering. He has also mentored numerous doctoral students through their dissertation research, contributing to the development of the next generation of chemical engineers and computational scientists.
Aakash Sahai is an Assistant Research Professor in the CEDC-Electrical Engineering department at the University of Colorado Denver - Denver Campus. His research focuses on advancing plasma physics, laser-plasma interactions, and nanoplasmonic technologies for high-energy particle acceleration. He is actively involved in designing novel accelerator concepts, such as nanostructure-based plasmonic accelerators capable of achieving extreme electric fields (PetaVolts/meter). His work bridges theoretical, computational, and experimental approaches to address challenges in high-gradient acceleration, plasma wakefields, and extreme nanoscience. Key research interests include laser-driven plasma acceleration, plasmonic field enhancement in nanostructures, and applications of particle beams in medical and high-energy physics. He collaborates on projects like the EuPRAXIA design study, aiming to develop compact, cost-efficient particle sources. His contributions span experimental setups, computational modeling, and innovative methodologies for radio transmission through plasmas and particle beam processing. Notable achievements include pioneering studies on relativistic surface plasmons, PetaVolt plasmonics, and optimizing laser-plasma interactions for proton/ion acceleration. His research has implications for next-generation accelerators, compact X-ray sources, and advanced plasma diagnostics. Sahai’s interdisciplinary approach integrates electrical engineering, material science, and high-energy physics to push the boundaries of accelerator technology. Advising and grants: No formal advisees or grant details listed. His work is supported by collaborations and institutional resources, including participation in national and international initiatives like Snowmass workshops. Labs/Teams: Active contributor to the EuPRAXIA consortium and affiliated with plasma physics and accelerator research groups at University of Colorado Denver.
Minna Palmroth is a Professor of Computational Space Physics at the University of Helsinki 's Faculty of Science , leading the Department of Physics 's Space Physics Research Group. She directs the Kestävän avaruustieteen ja -tekniikan huippuyksikön (Centre of Excellence in Sustainable Space Science and Technology) and serves as the principal investigator for the Vlasiator hybrid-Vlasov simulation framework.
Michael E. McHenry is a Professor of Materials Science and Engineering at Carnegie Mellon University's College of Engineering. He holds appointments with multiple research centers including the Data Storage Systems Center, Engineering Research Accelerator, Materials Research Science and Engineering Center, and Wilton E. Scott Institute for Energy Innovation. Dr. McHenry received his BS in Metallurgical Engineering and Materials Science from Case Western Reserve University in 1980, his PhD in Materials Science and Engineering from MIT in 1988, and completed a postdoctoral fellowship at Los Alamos National Laboratory. His research focuses on soft magnetic nano-composites for power and energy applications, with particular expertise in metal amorphous nanocomposites (MANCs) for high-efficiency electric motors and power systems. His work spans advanced materials processing, magnetic properties under various conditions, and rare earth materials criticality. His research portfolio demonstrates a clear progression toward practical applications of magnetic materials, particularly in high-power density, high-efficiency motors that can operate at high rotational speeds with minimal energy loss. His publications reveal a strong focus on translating fundamental materials science into engineering solutions for energy conversion, with significant emphasis on rare earth-free alternatives and high-frequency applications. IEEE Distinguished Lecturer (2013) TMS Awardee for Research Excellence (2014) Subject of TMS Symposium in Honor of M. E. McHenry (2016) NATO Series Lecturer on Rare Earth Criticality (2016/17) Dr. McHenry has co-founded CorePower Magnetics Inc. with Paul Ohodnicki and Samuel Kernion, commercializing soft magnetic technologies with applications in grid modernization and electric vehicles. His extensive publication record and leadership in major research initiatives including a MURI on high-temperature magnetic materials and an ARPA-E program demonstrate significant impact in both academic and industrial contexts. He has served in various leadership roles for Magnetism and Magnetic Materials and Intermag Conferences, and continues to advise on rare earth scarcity issues for organizations like NATO.
Tatiana Segura is a Professor of Biomedical Engineering, Neurology, and Dermatology at Duke University's Pratt School of Engineering, where she also serves as Co-director of the Center for Biotechnology and Tissue Engineering and MPI of the T32 Biotechnology Training grant. Her research focuses on designing biomaterials to promote endogenous repair through geometry design and delivery of genes, proteins, and drugs. She has made significant contributions to the development of microporous annealed particle (MAP) hydrogels and other biomaterial systems for tissue regeneration. B.S. in Bioengineering from University of California, Berkeley (1999) Ph.D. in Chemical Engineering from Northwestern University (2004) Professor Segura's research centers on biomaterials engineering for tissue repair and regeneration. Her lab designs innovative biomaterial interventions that promote brain plasticity after stroke, enable scarless healing in skin wounds, induce tolerance of transplanted skin, and promote constructive immune responses after biomaterial implantation. She pioneered the development of microporous annealed particle (MAP) hydrogels that have become widely adopted in regenerative medicine research. Her work uniquely bridges immunology, materials science, and clinical applications to create therapeutic biomaterials that harness the body's own healing capabilities. Her recent publications demonstrate a strong focus on spatial control of biomaterial properties, with emphasis on void space analysis, immune cell recruitment, and vascularization. The research shows a progression from fundamental biomaterial characterization to increasingly sophisticated therapeutic applications, particularly in stroke recovery and wound healing. Her work integrates proteomics, lipidomics, and advanced imaging to understand the molecular mechanisms underlying biomaterial-mediated tissue regeneration. Senior Member of the National Academy of Inventors (2023) Acta Biomaterialia Silver Medal (2021) Clemson Award for Contributions to Literature (2024) 15 d/e Plenary Award from AICHE Food, Pharmaceutical, and Bioengineering Division (2018) Fellow of the American Institute for Medical and Biological Engineers (2016) Professor Segura actively mentors the next generation of scientists, currently supervising 12 graduate students, 4 postdoctoral scholars, 2 master's students, 16 undergraduates, and other trainees. Her laboratory has been continuously funded since 2008 with multiple NIH grants, including her current role as MPI of the T32 Biotechnology Training grant. She has received substantial support from the NSF (including a CAREER award), American Heart Association, and American Society of Gene and Cell Therapy. Her Segura Lab operates as a multidisciplinary team comprising engineers, biologists, and clinicians working together to translate biomaterial discoveries into clinical applications. The lab's 'MAP' technology platform has enabled numerous collaborations across Duke and other institutions, focusing on brain repair after stroke, scarless skin healing, and immune-modulating biomaterials. The lab maintains strong industry partnerships to accelerate the translation of their biomaterial technologies into clinical use.
Kiwon Um is a tenured Assistant Professor in the Computer Graphics group at Télécom Paris, France, since October 2019. He focuses on physics-based simulations and data-driven approaches using deep learning, with an emphasis on human visual perception in computer graphics and engineering applications. Education: Ph.D. in Computer Science and Engineering from Korea University His research explores effective simulation of natural phenomena through refined data utilization and develops reliable data acquisition methods for machine learning. He also investigates perceptual evaluation of simulations to advance numerical method understanding. Recent work trends include: (1) turbulence modeling with machine learning integration, (2) elastic material simulation stability, (3) fluid dynamics optimization, and (4) differentiable physics frameworks. His publications range from 2008 to 2025, covering topics like SPH solvers, porous shell simulations, and numerical method validation.
David Latulippe is a Professor in the Department of Chemical Engineering at McMaster University. He joined McMaster in 2012 after postdoctoral work at Cornell University and a PhD at Penn State University, focusing on membrane filtration for DNA purification. His industrial experience includes roles at ZENON Environmental (now GE Water) in hollow-fiber membrane design for water treatment. Research interests include Membrane science and technology Bioprocessing of therapeutic viruses Microscale systems for biological applications Environmental engineering solutions for water treatment Current projects involve collaborations with industry partners like Ceapro and Aevitas, and the development of a biomanufacturing automation lab with Sartorius. Recent publications highlight advancements in Nanofiltration and microfiltration for viral vectors Conductive membranes for electrochemical applications Microfluidic systems for DNA analysis Environmental monitoring of biocides and microplastics Scientific recognition includes the Young Membrane Scientist Award (2014). Teaching activities focus on Fluid Mechanics (CHEMENG 2O04) and Industrial Separation Processes (CHEMENG 4M03).
