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.
Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.
Dr. Crystal Senko is an Assistant Professor and Canada Research Chair in Trapped Ion Quantum Computing at the Institute for Quantum Computing (IQC) , University of Waterloo. Her research focuses on quantum simulations, quantum computing with trapped ions, and qudit-based systems. She holds a Ph.D. in Physics from the University of Maryland (2014) and a B.Sc. in Physics from Duke University (2009). Her research interests span Quantum Computing , Quantum Simulation , Trapped Ion Manipulation , and Photonics . Key projects include optimizing qudit-based quantum computing protocols and developing photonic crystal waveguides for atom-photon interactions. Recent work emphasizes trapped ion efficiency, laser noise mitigation, and programmable quantum simulators. Dr. Senko has authored influential papers on trapped ion systems, including studies on multi-level qudit control, nanophotonic cavity coupling, and non-thermalization in spin chains. Her work bridges theoretical quantum models and experimental advancements in scalable quantum hardware. Awards: Canada Research Chair (Trapped Ion Quantum Computing) Teaching: Courses include Quantum Physics 2 (PHYS 334), Quantum Mechanics 1 (PHYS 701), and Special Topics in Quantum Information Processing (PHYS 768/QIC 890). Labs/Teams: Affiliated with IQC and previously contributed to Harvard’s Center for Ultracold Atoms.
Claudio Canizares is a University Professor and Hydro One Endowed Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. He also serves as Executive Director of the Waterloo Institute for Sustainable Energy (WISE). With a career spanning over 30 years, his research focuses on power systems stability, smart grids, microgrids, and renewable energy integration. He has secured nearly $118 million in grants and supervised 180+ researchers/students. Education: PhD (1991) and MSc (1988) in Electrical Engineering from University of Wisconsin-Madison; Electrical Engineering Diploma (1984) from Escuela Politécnica Nacional, Ecuador. Research Interests : Nonlinear systems theory, FACTS/HVDC applications, energy storage systems, microgrid stability/control, renewable integration in remote communities, and smart grid analytics. His work emphasizes bridging academic research with industrial applications through collaborations with utilities and tech firms. Key Achievements : IEEE Transactions on Smart Grid Editor-In-Chief; multiple IEEE Fellowships (IEEE, Royal Society of Canada, Canadian Academy of Engineering); 2017 IEEE PES Outstanding Educator Award; 2016 IEEE Canada Electric Power Medal. His publications (370+) include landmark papers on microgrid stability definitions and control frameworks, cited over 29,000 times. Teaching: Recently taught ECE 140 (Linear Circuits), ECE 467 (Power Systems Analysis), and graduate courses ECE 6601PD/ECE 6613PD on power systems modeling and analysis.
Prof. Dr. Amelie Hagelauer holds a professorship in Micro- and Nanosystem Technology at the TUM School of Computation, Information and Technology, Technical University of Munich. Her work focuses on advanced electronics and systems integration across quantum computing hardware, resistive memory technologies, and high-frequency RF systems. She has contributed to innovations in superconducting qubit readout architectures, multi-level RRAM designs, and 3D-integrated CMOS-compatible quantum devices. Research interests span quantum hardware design, nanoelectronic devices, RF front-end systems, and emerging memory technologies. Her work emphasizes practical implementation challenges such as low-power operation, high-voltage handling in RF switches, and wafer-scale fabrication processes. Recent projects include D-band radar systems, energy-efficient 60 GHz transceivers, and antenna tuning solutions for 5G applications. Publications from 2023-2025 showcase advancements in resistive switching device characterization, mitigation of TLS losses in superconducting qubits, and reconfigurable AI accelerators using RRAM-based digital twins. Her work bridges theoretical device physics with practical integrated circuit design, addressing scalability and reliability in next-gen electronics. Awards and grants: None explicitly listed in provided texts. Active collaborations include EU-funded projects on quantum computing platforms and TUM's Electronic Photonic Integration initiatives. Leads research teams in microsystem technology with emphasis on cross-disciplinary approaches combining CMOS processes, MEMS, and quantum engineering.
Guglielmo Scovazzi is a Professor at Duke University with appointments across multiple departments including the Department of Civil and Environmental Engineering, the Thomas Lord Department of Mechanical Engineering and Materials Science, and as Professor of Mathematics. His interdisciplinary research bridges computational mechanics, scientific computing, and engineering applications. Dr. Scovazzi earned his B.S/M.S. in aerospace engineering (summa cum laude) from Politecnico di Torino (Italy), followed by an M.S. and Ph.D. in mechanical engineering from Stanford University. Prior to joining Duke, he was a Senior Member of the Technical Staff at Sandia National Laboratories' Computer Science Research Institute. His research focuses on developing advanced numerical methods for computational mechanics, particularly finite element methods for fluid and solid mechanics. Key areas include multiphase porous media flows, computational methods for materials under extreme conditions, turbulent flow computations, and instability phenomena. His work emphasizes creating accurate computational approaches that reduce design/analysis costs for complex engineering problems involving fluid-structure interactions and transient phenomena in complex geometries. Dr. Scovazzi's most significant recent contribution is the development of the Shifted Boundary Method, an innovative computational framework that enables efficient simulations on complex geometries without requiring boundary-fitted meshes. This method has found applications in geomechanics, energy systems, and resilient infrastructure design. Kavli Fellow, National Academy of Sciences & Kavli Foundation (2018) Presidential Early Career Award for Scientists and Engineers (PECASE), White House (2017) Early Career Award, U.S. Department of Energy, Advanced Scientific Computing Research Program (2014) Dr. Scovazzi teaches multiple courses in computational mechanics including Nonlinear Finite Element Analysis and Introduction to the Finite Element Method. His research has been supported by substantial federal funding, and he actively collaborates across disciplines to address challenging problems in energy, environment, and infrastructure resilience through advanced computational methods.
Jun Liu is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the School of Engineering and Applied Sciences, University at Buffalo. His research focuses on advanced energy materials, nano/micro-mechanics, and self-powered systems, with applications in triboelectric energy harvesting and scanning probe microscopy. Education: PhD, Materials Engineering, University of Alberta (2018) MS, Materials Science, Shanghai University (2015) BE, Materials Science and Engineering, Nanchang University (2012) Research Interests: Development of tribovoltaic and triboelectric systems for self-powered electronics Mechanical energy harvesting via dynamic heterojunctions and Schottky contacts 3D-printed hydrogel structures for energy absorption and flexible electronics Nanoscale characterization using atomic force microscopy Design of nanocomposite sensors and catalytic materials Publication Trends: His work emphasizes triboelectricity, nanoscale energy conversion, and sustainable materials. Recent articles explore bionic tactile sensing, tunable hydrogels, and quantum dynamics in sliding interfaces. Awards: SONY Faculty Innovation Award (2021) Nature Springer MINE Young Scientist Award (2020) International Contest of Applications in Nano/Micro Technology Prize (2013) Laboratory: Advanced Energy Materials and Nanomechanics Lab at University at Buffalo.
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.
Prashant Mehta is a Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Laboratory . His research focuses on controlled interacting particle systems and machine learning applications , particularly in human activity recognition using motion sensors. Education: Ph.D. in Mathematics, Cornell University (2004) M.S. in Electrical & Computer Engineering, University of Massachusetts Amherst (1996) B.E. in Electrical & Electronics Engineering, Birla Institute of Technology & Sciences (1993) Mehta's work has pioneered the feedback particle filter (FPF) algorithm for nonlinear estimation, applied in robotic systems and gesture recognition. His research spans control of combustion instabilities in jet engines, mean-field games , and dynamical systems in aerospace engineering. His publications emphasize nonlinear control theory and stochastic filtering , with recent trends in sensor data pattern recognition and cyber-physical systems . He has received multiple scientific awards , including the MURI award for the Cyberoctopus project and Excellence in Undergraduate Advising Awards . Scientific Honors: MURI Award (2019) for Cyberoctopus Excellence in Undergraduate Advising (2010, 2008) Outstanding Teaching Assistant Award (1994) Senior Member, IEEE Control Systems Society Member, ASME Energy Systems Subcommittee Member, SIAM Dynamical Systems Group Mehta has supervised students like Jin Kim (IEEE CDC Best Student Paper, 2019) and co-founded the startup Rithmio , acquired by Bosch Sensortec . His laboratory develops gesture-detection filters for applications in soft robotics and human-machine interfaces .
Irina Oleinikova is a Professor at the Norwegian University of Science and Technology (NTNU) in the Department of Electric Energy, Faculty of Information Technology and Electrical Engineering. She leads the Power System Operation and Analysis research group and serves as the NTNU Smart Grid Team Leader. She is a steering committee member of the European Energy Research Alliance (EERA) Joint Programme on Smart Grids and an expert in the International Smart Grid Action Network (ISGAN) WG6. Research Interests : Power System Operation, Digital Power System Protection and Control, Grid Resilience, Energy Flexibility, Cybersecurity in Power Systems, and Hydrogen Technology Integration. Her work focuses on advancing smart grids, grid flexibility, and cybersecurity through projects like FME CINELDI, HONOR, ASAP, and ZeroKyst. Key Projects : CINELDI : Developing intelligent electricity distribution grids. HONOR : Cross-sectoral energy flexibility markets. ASAP : Next-generation system protection schemes. ZeroKyst : Hydrogen and charging infrastructure along Norway’s coast. COSPAT : Stability of AC/DC transmission grids via co-simulation. Advising & Grants : Supervises PhD students in digital protection and cybersecurity. Active in projects funded by RCN, STATNETT, and EU Horizon 2020. Leads the Power System Operation and Analysis group and collaborates with SINTEF and industry partners. Labs/Teams : NTNU Smart Grid Team and the Power System Operation research group.
William Anderson is a Professor in the School of Aeronautics and Astronautics at Purdue University since 2001. He holds a Ph.D. in Mechanical Engineering (Pennsylvania State University, 1996), M.S. in Chemical Engineering (University of Arizona, 1984), and B.S. in Chemistry (Arizona State University, 1979). His research focuses on chemical propulsion systems, combustion dynamics, and rocket engine design methodologies. Key research areas include measurement and modeling of combustion instabilities, rocket combustor stability, and liquid propulsion systems. His work spans experimental and computational studies of thermoacoustic behavior, injector design, and hypergolic reaction mechanisms. He has led projects on resonance igniters, hydrogen peroxide/kerosene combustors, and multi-fidelity modeling frameworks. Anderson has been recognized with the C.T. Sun Research Award (2005) and multiple Best Paper Awards from AIAA conferences. He served as Global Engineering Program Director (2011–2014) and is an Associate Fellow of AIAA. His expertise is showcased in invited lectures at institutions worldwide, including Technical University of Munich and Harbin Institute of Technology. He has authored/co-authored books on rocket propulsion and combustion instability, including Rocket Propulsion (Cambridge University Press, 2018) and edited volumes such as Liquid Rocket Engine Combustion Instability (AIAA, 1995). His lab collaborates internationally on advanced propulsion technologies, emphasizing design, build, and test methodologies.
Nathan Youngblood is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Pittsburgh , with a secondary appointment in the Department of Physics and Astronomy . His research focuses on reconfigurable photonic materials and devices for energy-efficient artificial intelligence applications. Educational Background: PhD in Electrical Engineering from the University of Minnesota Postdoctoral research at the University of Oxford (2017–2019) His work explores photonic in-memory computing, neuromorphic systems, and phase-change materials to minimize computing latency and energy consumption. Recent publications highlight advancements in magneto-optical non-reciprocity, coherent crossbar arrays, and plasmonic-enhanced phase-change devices. Scientific Awards: NSF CAREER Award (2024) AFOSR Young Investigator Award (2024) William Kepler Whiteford Faculty Fellowship (2024) Dr. Youngblood's lab develops photonic accelerators like LightML and LightBulb for machine learning, emphasizing scalable integration and novel material applications in silicon photonics.
Dr. Xiaofeng Qian is an Associate Professor in the Department of Materials Science & Engineering at Texas A&M University, with joint appointments in Physics and Astronomy, and Electrical & Computer Engineering. His research focuses on materials theory , quantum materials design , and high-throughput computational discovery , particularly for 2D materials and energy applications . Educational Background: Ph.D., Nuclear Science and Engineering, Massachusetts Institute of Technology (2008) B.S., Engineering Physics, Tsinghua University (2001) Research spans first-principles electronic structure methods , nonlinear optical responses , and multiscale modeling of electronic, thermal, and ionic transport. Key areas include quantum spin Hall effect , ferroelectric switching , and machine learning for materials prediction . Notable Awards: Dean of Engineering Excellence Award (2024) Engineering Genesis Multidisciplinary Award (2024) AZZ Faculty Fellow (2021) NSF CAREER Award (2018) Manson Benedict Fellowship (2006) Actively recruiting PhD, MS, and UG researchers with backgrounds in physics, materials science, or computational methods. Collaborates extensively on hybrid AI-materials projects and topological device concepts .
Professor Paul Midgley is a leading academic in Materials Science at the University of Cambridge's Department of Materials Science and Metallurgy, serving as Professor since 2007 and Head of Department from 2018–2020. He is a Fellow of Peterhouse College and holds multiple prestigious awards, including the Royal Society Fellowship and the Ernst Ruska Prize. Education: PhD in Physics (University of Bristol, 1991), MSc (Distinction) in Semiconductor Materials (1988), BSc (Hons) Physics (1987). Administration: Director of the Wolfson Electron Microscopy Suite, and active on various University committees including Research, Teaching, and REF. His research focuses on advanced electron microscopy techniques such as convergent beam diffraction, electron tomography, and nanostructure analysis, with applications in nanoscale materials science and 3D reconstruction using compressed sensing. He has pioneered methods like precession electron diffraction and multi-dimensional electron microscopy, contributing to fields like plasmonic nanoparticles and catalytic materials. Key Research Themes: Electron crystallography, nanomaterial characterization, energy materials, and defect analysis in perovskites. Midgley has delivered over 20 invited/plenary lectures globally, including at EUROMAT, the Welch Symposium, and the John Cowley Memorial Lecture. His grant income exceeds £16M as Principal Investigator. Labs/Teams: Leads the Wolfson Electron Microscopy Suite and collaborates internationally on microscopy advancements and materials innovation.
Dr. Yanqing Hu is an Associate Professor at the Department of Statistics and Data Science, School of Science, Southern University of Science and Technology (SUSTech). With a Ph.D. in Systems Theory from Beijing Normal University (2011) and postdoctoral experience at the Levich Institute, City University of New York (2011-2013), his work focuses on big data analysis of complex systems, particularly in social media dynamics, network resilience, and graph neural network applications. Ph.D.: Beijing Normal University (Systems Theory, 2011) Postdoctoral: Levich Institute, CUNY (2011-2013) Research spans complex network analysis, information spreading mechanisms, and predictability of network structures. His work combines theoretical frameworks with real-world applications in social networks, infrastructure systems, and brain connectivity. Recent publications explore information percolation in social media, resilience quantification in interdependent networks, and intrinsic structure predictability. These studies appear in high-impact journals like Nature Human Behaviour (IF: 24.3), Nature Communications (IF: 17.7), and PNAS (IF: 10). World AI Conference Youth Outstanding Paper Nomination Beijing Outstanding Doctoral Dissertation Award Guangdong Special Support for Young Talents Guangdong Outstanding Youth Fund Collaborations include leading researchers from Boston University, King's College London, and Shenzhen-Hong Kong Institute of Microelectronics. His work informs network defense strategies and efficient navigation mechanisms in complex systems.
