Reed Essick is an Assistant Professor at the Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto. His research focuses on experimental gravity, astrophysical signals, and nuclear physics, with particular emphasis on neutron stars, black holes, and gravitational waves. He develops advanced statistical methods like hierarchical Bayesian inference and nonparametric analysis for interpreting observational data from pulsars and gravitational wave detectors. Dr. Essick collaborates extensively with international observatories such as LIGO, Virgo, and KAGRA, contributing to cutting-edge projects like multimessenger astronomy and precision cosmology. His work bridges computational astrophysics with observational techniques, addressing fundamental questions about dense matter and strong-field gravity. Key contributions include studies on gravitational wave equation-of-state constraints, pulsar timing analysis, and the application of machine learning to detector data. His research leverages both ground-based interferometers and space-based observations to explore extreme astrophysical environments.
Sigrid Källblad Nordin is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Department of Mathematics (Division of Probability, Mathematical Physics, and Statistics). Her research focuses on Mathematical Finance, Probability Theory, and Stochastic Analysis, with an emphasis on measure-valued processes, martingale optimal transport, and model uncertainty. She holds a DPhil from the University of Oxford (2014). Her work bridges theoretical advancements in stochastic control, optimization, and financial applications. Recent research includes Bayesian optimal adaptive control, robust option pricing, and dynamically consistent investment strategies under uncertainty. She teaches courses such as Financial Mathematics and Financial Derivatives, and supervises PhD students Linn Engström and Chaorui Wang. Publications span journals like Annals of Applied Probability , Finance and Stochastics , and SIAM Journal on Control and Optimization , reflecting contributions to optimal transport, stochastic processes, and financial modeling. She is currently hiring a new PhD student and welcomes inquiries about master thesis supervision.
Claire Acevedo is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. Her lab, the Fracture and Fatigue of Skeletal Tissues Laboratory (F² Lab), focuses on understanding mechanisms of deformation, fracture, and biological responses in skeletal tissues and biomaterials across molecular to macro scales. She holds a Ph.D. from the Swiss Federal Institute of Technology Lausanne (EPFL) and completed postdoctoral research at UC San Francisco and UC Berkeley/Lawrence Berkeley National Laboratory. Dr. Acevedo’s research is funded by the National Science Foundation (NSF), National Institutes of Health (NIH), and the Advanced Light Source. Her work bridges biomechanics, materials science, and high-energy X-ray physics to address bone fragility in aging and diabetes. Key projects include investigating collagen cross-linking effects on bone mechanics and developing novel imaging techniques like deep learning-enhanced synchrotron micro-CT. Education: Ph.D., Swiss Federal Institute of Technology Lausanne (EPFL) Postdoctoral Research: UC San Francisco & UC Berkeley/Lawrence Berkeley National Lab Previous Faculty Position: University of Utah (Mechanical Engineering) Recent contributions include the NSF CAREER Award for studying fracture mechanisms in fragile bones and an NIH R21 grant to explore collagen-level diabetes impacts. Her lab collaborates with the University of Utah Tanner Dance Program to develop K-12 educational initiatives linking dance with biomechanics. Publications span topics like synchrotron imaging innovations, diabetes-induced bone fragility, and collagen nanomechanics. Students in her lab have contributed to advancements in fatigue testing, cross-link analysis, and imaging algorithms. Awards: NSF CAREER Award (2024) NIH R21 Grant (2023) Alice L. Jee Award (2022) Nikon Small World Image of Distinction (2024) The F² Lab hosts a dynamic team with ongoing projects on glycemic effects, synchrotron techniques, and biomaterial design. Future work emphasizes translating findings into clinical fracture prevention strategies and educational outreach.
Dr. Christos Papavassiliou is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on instrumentation electronics, memristor modeling, signal integrity, and novel device technologies such as SiGe devices, RF MEMS, and ReRAM. He leads the Space Lab and collaborates with the National Centre for Scientific Research in Athens. He holds senior membership in IEEE and is a member of the IET. Education: Ph.D. in Applied Physics, Yale University (1983–1989) MPhil in Applied Physics, Yale University (1983–1988) MS in Applied Physics, Yale University (1983–1985) B.S. in Physics, MIT (1979–1983) Research Interests: Memristor-based neuromorphic computing and stochastic systems High-performance instrumentation hardware and data acquisition Multi-state memristive memory and selectorless arrays Integration of memristors with CMOS for hybrid circuits Applications in biomedical wearables and edge AI deployment Key Contributions: Developed novel memristor models for circuit simulation Pioneered work on memristor-based true random number generators Advanced understanding of resistive drift and energy-constrained storage Designed FPGA-based systems for analog circuit emulation Labs & Teams: Active in the Space Lab at Imperial College, focusing on interdisciplinary research in electronics and space applications.
Andreas Malikopoulos is a Professor at Cornell University's School of Civil & Environmental Engineering and Director of the Information and Decision Science Lab (IDS Lab). Previously, he held roles as the Terri Connor Kelly and John Kelly Career Development Professor at the University of Delaware (UD) and founding Director of UD's Sociotechnical Systems Center. He also served as the Alvin M. Weinberg Fellow at Oak Ridge National Laboratory (ORNL), Deputy Director of ORNL's Urban Dynamics Institute, and Senior Researcher at General Motors R&D. His research focuses on cyber-physical systems (CPS), stochastic control, and learning-driven approaches for optimizing energy efficiency and sustainable mobility in smart cities and transportation systems. Education: PhD (Mechanical Engineering, University of Michigan, 2008), M.S. (Mechanical Engineering, University of Michigan, 2004), Diploma (National Technical University of Athens, 2000). Research Interests: Analysis and control of CPS, stochastic scheduling, game theory, and mechanism design applied to emerging mobility systems (e.g., autonomous vehicles, electric vehicles). He emphasizes integrating learning and control for socially optimal solutions in transportation networks. Awards: IEEE ITS Young Researcher Award (2019), UD’s Outstanding Junior Faculty Award (2020), Alvin M. Weinberg Fellowship (2010), and recognition as a NAS Kavli Frontiers of Science Scholar (2012). He is an IEEE Senior Member, ASME Fellow, and serves on editorial boards of leading journals. Teaching: Focuses on optimal decision-making, control theory, and emerging mobility systems. Courses include stochastic optimal control and game theory at Cornell. Labs: Leads the IDS Lab, which develops scalable frameworks for CPS and smart city applications. Current projects include coordinated routing for mixed-traffic systems and AI-driven recommendations for autonomous vehicles.
Dr. Kaiqun Fu is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. and M.S. in Computer Science from Virginia Tech (2021 and 2016). His research focuses on spatial data mining, spatiotemporal event analysis, graph neural networks, and urban computing applications such as traffic impact prediction and social media-driven insights. He also explores physics-informed machine learning for power systems and interdisciplinary topics like 'deaths of despair' in rural areas. Education: Ph.D. in Computer Science, Virginia Tech, 2021 M.S. in Computer Science, Virginia Tech, 2016 Research Interests: His work emphasizes machine learning and deep learning applications in spatial-temporal domains, including: Graph neural networks for traffic incident prediction Social media analysis for urban challenges Physics-informed models for power grid stability Citation forecasting in scientific publications Grants & Projects: NSF CRII ($174,734): Spatiotemporal impacts of traffic events via graph neural networks (2024–2026) NSF EAGER ($300,000): Socio-economic impacts of emerging technologies (2024–2026) SDSU RSCA ($10,118): Graph transformer-based location learning (2023–2024) Professional Involvement: He chairs ACM SIGSPATIAL's SRC committee, serves on SDSU's Computer Science curriculum committees, and is an IEEE member. He co-edits Frontiers in Big Data and advises on interdisciplinary projects like climate-impacted grid security (NSF RII Track-2, $750,000). Labs/Teams: Collaborates with interdisciplinary groups focusing on smart cities, data-driven infrastructure resilience, and GeoAI applications.
Matthias Ihme is a Professor in the Department of Mechanical Engineering and Photon Science Directorate at Stanford University. His research focuses on large-eddy simulation (LES) of turbulent reacting flows, aeroacoustics, combustion-generated noise, numerical methods, and high-order schemes. He holds a Ph.D. from Stanford University (2008), an M.Sc. in Computational Engineering from the University of Erlangen (Germany, 2002), and a Dipl.-Ing. in Mechanical Engineering from Munich University of Applied Sciences (Germany, 2000). His work bridges computational fluid dynamics, combustion science, and photon science, with notable contributions to supercritical fluid dynamics, machine learning integration in fluid simulations, and high-fidelity atmospheric transport modeling. Recent research emphasizes ultrafast cluster dynamics, shock-induced interface behavior, and stochastic ignition mechanisms in advanced fuel systems. Publications highlight interdisciplinary advancements, including physics-informed ML frameworks for reacting flows and experimental studies using X-ray photon correlation spectroscopy. His projects often involve high-performance computing and collaboration with national labs like SLAC.
Weichao Wang is a Professor and Chair of the Department of Software and Information Systems at the University of North Carolina at Charlotte (UNC Charlotte). He holds a Ph.D. in Computer Science from Purdue University (2005), with earlier degrees from Tsinghua University. His research focuses on securing pervasive systems, wireless networks, cloud computing, and critical infrastructures, integrating multi-disciplinary approaches like information theory and visualization. He leads efforts in cybersecurity education for K-12 and higher education. Key roles include organizing IEEE conferences (e.g., IPCCC) and editorial roles in journals like ITU Intelligent and Converged Networks. His awards include the 2024 AECT Crystal Award and multiple Distinguished TPC recognitions. Students under his advisement have contributed to areas like mobile cloud computing and network security. Professional contributions include service on over 50 conference committees and journal reviewing. Current research emphasizes cybersecurity in education, IoT defense, and AI-driven security systems. His lab explores immersive visualization for cybersecurity training and augmented reality applications.
Nan Marie Jokerst is the J. A. Jones Distinguished Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering and Executive Director of the Duke Shared Materials Instrumentation Facility. She previously served as Chair of the Duke Academic Council (2014-2015) and Associate Dean for six years. B.S. in Physics, Creighton University (1982) M.S.E.E., University of Southern California (1984) Ph.D. in Electrical Engineering, University of Southern California (1989) Her research spans chip-scale photonic sensing systems , III-V thin-film lasers on silicon , metamaterials , and heterogeneous integration . She develops optical systems for medical diagnostics, environmental monitoring, and security applications through the Jokerst Laboratory, which combines optical system design, optoelectronic device development, and semiconductor fabrication expertise. Her recent publications show strong focus on terahertz strain mapping using metamaterials, multi-pixel tissue characterization for cancer margin detection, and microfluidic sensing platforms with embedded photodetectors. Key trends include advancing non-destructive structural monitoring and miniaturized biomedical diagnostics through novel photonic integration techniques. IEEE Fellow (2003) Optica Fellow (2001) NSF Presidential Young Investigator Award IEEE Third Millennium Medal IEEE/HP Harriet B. Rigas Medal USC Viterbi School Alumni Award She has advised numerous graduate students in photonic device research and secured major grants including the NSF National Nanotechnology Coordinated Infrastructure ($6M, 2015-2021) and NNCI: North Carolina Research Triangle Nanotechnology Network ($20M, 2020-2026). Her leadership extends to co-founding Triangle Women in STEM and serving on the National Academies Board on Global Science and Technology. The Duke Shared Materials Instrumentation Facility under her direction provides critical cleanroom and characterization resources for interdisciplinary research.
Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.
Dr Rita Borgo is a Professor in Data Visualization and Head of the Human Centred Computing Group at King's College London's Department of Informatics. She holds a leadership role within the Faculty of Natural, Mathematical & Engineering Sciences and is affiliated with the Centre for Urban Science and Progress (CUSP) London. Her research focuses on interdisciplinary visualization challenges, including human-computer interaction, AI trust calibration, and epidemiological modeling. Education details are not explicitly stated in the provided text. Her work spans over 58 publications, emphasizing visualization techniques for large datasets, trust in AI systems, and urban science applications. Key projects include RAMPVIS (visual analytics for pandemic response) and Trusted Autonomous Systems Hub (AI ethics and human-machine partnerships). Research interests include data visualization, human factors, generative AI, and policy simulation. Recent articles explore trust calibration in AI, time-series visualization, and ethical clinical decision support systems. She has led grants totaling £multi-million, including EPSRC-funded initiatives. Collaborations with organizations like ContactEngine Limited highlight her industry engagement. Labs/Teams: Leads the Human Centred Computing Group and contributes to CUSP's urban data initiatives. Supervised student work includes a notable BSc thesis by Munkhtulga Battogtokh. Current projects address visualization in nuclear policy, social media mental health correlations, and trustworthy autonomous systems.
Vikram Deshpande is a Professor in the Department of Engineering at the University of Cambridge, UK, where he has been employed since 2010. He also maintains significant international connections, having served as a Visiting Professor at the Technical University of Eindhoven (2009-2017) and previously holding positions at the University of California, Santa Barbara and Brown University. His research spans multiple disciplines within solid mechanics and materials science, focusing on fundamental mechanisms that govern material behavior across different scales. His research interests encompass Mechanobiology , where he explores cellular organization mechanisms; Solid mechanics with applications to impact and failure; Data-driven mechanics approaches; Microarchitectured solids including mechanical metamaterials; Fluid-structure interaction in impact scenarios; Chemo-mechanics of battery materials; and Dislocation mechanics for understanding material deformation. His work uniquely bridges fundamental physics with practical engineering applications, particularly in developing materials with tailored mechanical properties. The analysis of his recent publications reveals a strong focus on mechanical metamaterials, cellular mechanics, and electro-chemo-mechanical phenomena in energy storage systems. His research demonstrates a consistent pattern of addressing fundamental scientific questions while maintaining strong connections to practical engineering applications, particularly in materials design, protective systems, and energy technologies. His publications frequently combine experimental approaches with sophisticated modeling techniques across multiple scales. 2024 Zdeněk P. Bažant Medal for Failure and Damage Prevention 2023 Fellow, Royal Academy of Engineering and International Member US National Academy of Engineering 2022 Warner T. Koiter Medal and William Prager Medal 2022 European Research Council (ERC) Advanced Grant 2021 Gili Agostinelli Prize and IIT Bombay Distinguished Alumnus Award 2020 Fellow, Royal Society of London and Rodney Hill Prize Professor Deshpande has served on numerous editorial boards including the Journal of the Mechanics and Physics of Solids (current Associate Editor), Modelling and Simulation in Materials Science and Engineering, and Proceedings of the Royal Society A. He chairs the Royal Society Sectional Committee 4 and serves on the Advisory Board of the European Mechanics Society EUROMECH. His leadership extends to directing the International Conference on Fracture and chairing the EUROMECH Mechanics of Materials Conference committee. His research group at Cambridge, accessible through cambridgesolidmechanics.co.uk, focuses on developing fundamental understanding of material behavior to enable the design of next-generation engineering materials.
Jie Xu is a Scientist at Argonne National Laboratory and a CASE Affiliated Scientist at the University of Chicago, Pritzker School of Molecular Engineering . Her research focuses on engineering durable, scalable, and sustainable polymer semiconductors for skin-like electronics and autonomous material discovery. Education : PhD in Chemistry (Nanjing University), Postdoctoral Fellow (Stanford University) Her research bridges polymer physics , self-driving laboratories , and AI-guided material synthesis to address challenges in stretchable electronics, recyclable polymers, and energy-efficient manufacturing. She pioneered polymer circuits that remain conductive under extreme deformation and developed the first roll-to-roll mass-production method for stretchable semiconductors. Her 15 most recent articles highlight advancements in AI-driven polymer discovery , biodegradable electronics , and multi-modal energy dissipation . Key themes include autonomous experimentation , hydrogen-bonded polymer systems , and machine learning for conjugated polymers , with applications in wearable medical sensors , soft robotics , and human-computer interfaces . Scientific accolades include the Materials Research Society Postdoctoral Award , MIT Technology Review’s Innovators Under 35 , and recognition as a Scialog Fellow . She serves on editorial boards for APL Machine Learning and Flexible Electronics , and her team at Argonne includes postdocs and students working on self-driving labs and degradable polymers .
Dr. Kidambi Sreenivas is an Associate Professor in Mechanical Engineering at the University of Tennessee at Chattanooga (UTC), affiliated with the College of Engineering and Computer Science. He holds a PhD in Mechanical Engineering and specializes in computational fluid dynamics (CFD), with a focus on unstructured multi-physics flow solvers and applications in aerospace, environmental systems, and biomedical engineering. His research bridges academia and industry, collaborating with NASA, the U.S. Navy, Department of Energy, and private companies. Dr. Sreenivas' research interests include rotating machinery simulations, pre-conditioners for non-ideal fluids, and real-world applications such as submarine hydrodynamics, wind farm optimization, aerodynamic efficiency of vehicles, and contaminant dispersal modeling. He has pioneered methods for simulating complex geometries and physics, including high-fidelity simulations of hypersonic vehicles, weapons bay cavities, and shock-wave interactions. Recent work emphasizes advanced CFD methodologies for high-speed flows, thermal effects on turbulence, and aerothermal characteristics of hypersonic test articles. His collaborations have led to practical solutions for drag reduction on Class 8 trucks and improved accuracy in wind turbine modeling. Dr. Sreenivas also contributes to educational initiatives, such as developing PIV systems for undergraduate fluid mechanics labs. His advising and grants reflect partnerships with federal agencies and private sectors, focusing on projects like microplastic sampling devices for stormwater management. These projects highlight his interdisciplinary approach to solving real-world engineering challenges through cutting-edge computational methods.
Dani S. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania with primary appointment in the Department of Bioengineering (School of Engineering and Applied Science) and secondary appointments in Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry. They serve as an external professor at the Santa Fe Institute and lead a research group focused on complex systems and network science. B.S. in Physics, Penn State University (2004) Ph.D. in Physics, University of Cambridge as Churchill Scholar and NIH Health Sciences Scholar (2009) Postdoctoral position at UC Santa Barbara and Junior Research Fellow at Sage Center for the Study of the Mind Their research integrates complex systems science, statistical mechanics, and applied mathematics to study network dynamics in physical and biological systems. Key areas include brain connectivity mechanisms, cognitive processes, neurological disease modeling, granular matter physics, and collective human curiosity. Bassett employs advanced methodologies including algebraic topology, network control theory, and multilayer network analysis to investigate how network architecture influences system function across diverse domains. Recent publications reveal a strong trend toward interdisciplinary network science applications, particularly in modeling human curiosity through Wikipedia navigation patterns and analyzing brain network reconfiguration during cognitive development. Their work bridges physics, neuroscience, and behavioral science with emphasis on topological network properties and dynamical processes. American Psychological Association's Rising Star (2012) MacArthur Fellow Genius Grant (2014) Lagrange Prize in Complex Systems Science (2017) Erdos-Renyi Prize in Network Science (2018) American Physical Society Fellow (2021) Web of Science Highly Cited Researcher (3 consecutive years) Bassett's research is supported by major agencies including NSF, NIH, DoD, ONR, and private foundations (MacArthur, Sloan, Paul Allen). Their lab actively recruits students from physics, engineering, neuroscience, and computer science backgrounds, emphasizing diversity in academic perspectives. Current projects include the 'Curious Minds' initiative exploring collective knowledge building and network-based models of neurological disorders. Bassett co-authored the MIT Press book 'Curious Minds: The Power of Connection' with philosopher Perry Zurn.
