David Bindel is an Associate Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences, College of Engineering, and Cornell Ann S. Bowers College of Computing and Information Science. He earned his Ph.D. in Mathematics from the University of California, Berkeley in 2006. His research focuses on applied numerical linear algebra, eigenvalue problems, and their applications in plasma physics, network analysis, and nonlinear systems. He develops methods for analyzing complex systems, including magnetic confinement in stellarators, stability of MHD systems, and community detection in networks. His work bridges theoretical foundations with practical computational tools, such as formal verification of linear algebra algorithms and scalable Gaussian process models. Bindel’s research explores the interplay between structure and computation, leveraging eigenvalue analysis to address challenges in computer vision, opinion dynamics, and engineering design. He has contributed to advancements in numerical methods for large-scale systems, including iterative solvers, spectral approximation techniques, and stochastic optimization. His interdisciplinary approach spans applied mathematics, computer science, and physics, with applications in fusion energy, machine learning, and network science. Recent work highlights include high-order expansions for magnetic confinement, adaptive filtering for dynamical systems, and Bayesian optimization strategies. His publications emphasize rigorous analysis alongside computational scalability, addressing both theoretical and practical aspects of modern scientific computing. Despite no explicitly listed awards, his contributions reflect significant impact in his fields.
Danai Koutra is an Associate Professor in Computer Science and Engineering at the University of Michigan, Ann Arbor, and an Amazon Scholar. Her research focuses on large-scale graph mining, graph neural networks, and interpretable machine learning methods for understanding complex networks. Key roles include leading the GEMS Lab and contributing to projects like DeltaCon (graph similarity) and VoG (graph summarization). She holds a PhD from Carnegie Mellon University and has authored over 80 publications in top venues like KDD, SDM, and NeurIPS. Educations: PhD in Computer Science, Carnegie Mellon University (2015) MS in Computer Science, Carnegie Mellon University (2015) Diploma in Electrical & Computer Engineering, National Technical University of Athens (2010) Research Interests: Her work spans graph mining, anomaly detection, knowledge graph completion, and applications in neuroscience, healthcare, and social networks. Recent projects include MAGNET (multi-agent graph networks) and GT2VEC (multimodal graph-text encoders). Grants & Awards: Recipient of the 2025 PECASE award, NSF CAREER Award (2019), and the 2016 ACM SIGKDD Dissertation Award. Active in organizing conferences like KDD and ECML/PKDD. Labs & Teams: Directs the GEMS Lab, collaborating on projects like FIDDLE (clinical data preprocessing) and SpecGreedy (dense subgraph detection). Engages in interdisciplinary efforts, including M-DICE (urban mobility analysis with Detroit).
Martin Z. Bazant is the E. G. Roos (1944) Professor of Chemical Engineering and Professor of Mathematics at the Massachusetts Institute of Technology (MIT), holding the Digital Learning Officer role in the Department of Chemical Engineering. His research focuses on mathematical modeling of electrochemical systems, transport phenomena, and applied mathematics, with significant contributions to battery technology and electrochemical energy storage. He is affiliated with MIT’s Department of Mathematics and the MIT Energy Initiative (MITEI), leading initiatives like the Center for Battery Sustainability and D3BATT. Education: Ph.D. from Harvard University (1997), M.S. and B.S. from the University of Arizona (1993, 1992). His work bridges theory and application, addressing challenges in lithium-ion batteries, solid-state systems, and electrolyte dynamics. Notable achievements include pioneering studies on coupled ion-electron transfer mechanisms and phase separation in battery materials. He is an elected member of the National Academy of Engineering (2025) and a Fellow of the Electrochemical Society (2023). As an educator, he develops MOOCs on transport phenomena and contributes to digital learning initiatives. His research group explores advanced battery diagnostics, machine learning for materials science, and environmental applications of electrochemical processes. Key collaborations include startups like Lithios, Inc., and leadership roles in professional societies such as the International Electrokinetics Society.
Nuno Loureiro is an Associate Professor at the Department of Nuclear Science and Engineering at MIT, with a secondary appointment in the Physics Department. He obtained his PhD in Physics from Imperial College London in 2005 and held postdoctoral positions at Princeton University and the UK’s Culham Centre for Fusion Energy before joining MIT in 2016. His research focuses on plasma physics , particularly theory and simulations of astrophysical and laboratory plasmas , including magnetic reconnection, turbulence, and kinetic effects. His work bridges classical plasma dynamics with emerging quantum computing applications. The 15 most recent publications highlight advancements in quantum algorithms for plasma simulations , magnetic reconnection mechanisms , and turbulence dynamics across relativistic and non-relativistic plasmas. Topics include plasmoid-mediated inverse energy transfer, data-driven fluid closures, and ion-acoustic instability impacts. 2015 Thomas H. Stix Award (American Physical Society) NSF CAREER Award
Prof. Prita Pant is a faculty member in the Department of Metallurgical Engineering and Materials Science at the Indian Institute of Technology Bombay (IIT Bombay). She holds a Ph.D. and M.S. in Materials Science from Cornell University and a B.E. in Metallurgical Engineering from Roorkee University. Her research focuses on the mechanical behavior of advanced materials, particularly thin films and shape memory alloys. B.E., Metallurgical Engineering, Roorkee University (1997) M.S., Materials Science, Cornell University (2001) Ph.D., Materials Science, Cornell University (2004) Her research interests include mechanical behavior of thin films , dislocation dynamics , nanoindentation studies , simulation of deformation processes , and Ni-Ti based shape memory materials . She employs both experimental and computational techniques to investigate microstructural evolution and deformation mechanisms in metals. The available publications indicate a strong focus on thin film mechanics , dislocation behavior , and microstructure-property relationships , particularly under mechanical loading. Her work bridges computational modeling and experimental characterization, with applications in structural and functional materials. There are no scientific awards explicitly mentioned in the provided text. While no specific students are listed, Prof. Pant leads the Computational Mechanics and Experimental Group (CMEG@IITB) , indicating active supervision of graduate students and involvement in research grants. Her research group website suggests ongoing funded projects and collaborative work. She is affiliated with the Computational Mechanics and Experimental Group (CMEG@IITB) , which conducts research on multiscale mechanics of materials, combining simulation and experimentation to understand deformation phenomena.
Beatriz Noheda is a Full Professor of Functional Nanomaterials at the University of Groningen's Faculty of Science and Engineering, where she chairs the Solid State Materials for Electronics group at the Zernike Institute for Advanced Materials. She also serves as the founding Director of the Groningen Cognitive Systems and Materials center (CogniGron). Her academic journey began with a PhD in Physics from the Autonomous University of Madrid in 1996, followed by research positions at Brookhaven National Laboratory and various European institutions before joining Groningen through the prestigious Rosalind Franklin Fellowship program in 2004. Her research interests span the physics of functional materials with particular emphasis on ferroelectric, piezoelectric, and multiferroic thin films . She investigates the relationship between structure and functionality, focusing on nano-domain control through strain engineering and the unique properties of domain walls. Her work bridges fundamental physics with two promising application areas: piezoelectric energy harvesting for low-power electronics and the development of novel materials for neuromorphic computing . This dual focus reflects her vision of enabling the next technological revolution through materials science. Noheda's publication record shows a clear evolution from fundamental structural studies of ferroelectric materials toward cutting-edge research in hafnia-based ferroelectrics and neuromorphic computing materials. Her most recent work focuses on oxygen migration in hafnium-zirconium oxide systems, metal-insulator transitions in nickelates, and the development of novel ferroelectric phases suitable for next-generation electronic devices. These publications demonstrate her leadership in advancing the field from basic understanding toward practical applications in memory devices and cognitive computing systems. Fellow of the American Physical Society (2011) - awarded for fundamental structural studies of new phases in perovskite-type ferroelectric materials and domain nanostructures IEEE Robert E. Newnham Ferroelectrics Award (2020) - for outstanding contributions to understanding giant piezoelectricity in lead zirconate titanate Member of the Netherlands Academy of Technology and Innovation (AcTI) (2022) Elected Senior member IEEE (2021) Rosalind Franklin Fellowship (2004) - enabling her successful academic career in Groningen Noheda has secured substantial research funding throughout her career, including a Rosalind Franklin Fellowship (2004-2009), VIDI-NWO Fellowship (2004-2008), TOP-NWO project on Functional Nanowalls (2007-2012), multiple Zernike Institute Dieptestrategie grants, and a significant TOP-PUNT grant (2016-2021). She has supervised numerous students and early-career researchers, contributing to the development of the next generation of materials scientists. Her leadership extends to editorial roles on prestigious journals including Science, Physical Review Applied, and npj Quantum Materials. As Director of CogniGron, Noheda leads an interdisciplinary center focused on developing materials and systems for cognitive computing. Her team combines expertise in functional oxides, nanoelectronics, and neuromorphic engineering to create novel computing paradigms inspired by the human brain. The center represents a strategic initiative at the University of Groningen to position itself at the forefront of cognitive systems research.
Naresh R. Shanbhag is the Jack Kilby Professor in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. He serves as Director of the Systems on Nanoscale Information fabriCs (SONIC) Center and held the D.J. Gandhi Distinguished Visiting Professorship at IIT Mumbai from 2015-2020. Previously, he was a visiting faculty member at National Taiwan University (2007) and Stanford University (2014). Dr. Shanbhag received his doctorate from the University of Minnesota (1993) in Electrical Engineering. From 1993 to 1995, he worked at AT&T Bell Laboratories as the lead chip architect for AT&T's 51.84 Mb/s transceiver chips over twisted-pair wiring for Asynchronous Transfer Mode (ATM)-LAN and very high-speed digital subscriber line (VDSL) chip-sets. His research focuses on the design of energy-efficient machine learning, communications, and signal processing systems on resource-constrained embedded platforms. He explores fundamental trade-offs between energy efficiency, latency and accuracy of decision-making systems implemented in nanoscale technologies, with applications to computer vision, biomedicine, automatic target recognition, and imaging. His work spans four primary focus areas: Resource-efficient Machine Learning for the Edge, In-memory Computing (IMC), Energy-efficient High Data Rate Communications, and Shannon-inspired Statistical Error Compensation (SEC). Analysis of his recent publications reveals a strong emphasis on in-memory computing architectures (SRAM, MRAM, RRAM) for machine learning acceleration. His work consistently addresses energy-accuracy trade-offs, with increasing attention to security aspects of hardware implementations and applications to MIMO signal processing and edge AI systems. His research demonstrates a progression from theoretical foundations to practical silicon implementations. 2024 Semiconductor Research Corporation Innovation Award 2018 Semiconductor Industry Association/Semiconductor Research Corporation University Researcher Award 2018 IEEE International Symposium on Circuits and Systems Best Paper Award 2006 IEEE Fellow 1996 National Science Foundation CAREER Award Professor Shanbhag has mentored over 50 graduate students who now work at leading technology companies including Qualcomm, Amazon, Nvidia, Intel, and Apple. His research has been generously supported by the National Science Foundation, DARPA, AFRL, Semiconductor Research Corporation, Texas Instruments, Sandia National Laboratories, and industry partners including IBM, GlobalFoundries, and Intel Corporation. He led the Alternative Computational Models research theme (2006-2012) and was the founding Director of the SONIC Center (2013-2017), a 5-year multi-university center funded by DARPA and SRC. Currently, he leads research themes in the SRC and DARPA funded JUMP 2.0 Program's Center for Co-Design of Cognitive Systems and the Center for Ubiquitous Connectivity, and in the NSF IUCRC Center for Advanced Semiconductor Chips with Accelerated Performance (ASAP). As Director of the Systems on Nanoscale Information fabriCs (SONIC) Center, Professor Shanbhag leads a multidisciplinary team exploring novel computing paradigms for the nanoscale era. His group has benchmarked an extensive collection of in-memory computing and digital accelerator IC designs, maintaining a publicly available IMC benchmarking repository of metrics extracted from published IC prototypes. His research philosophy integrates concepts from information theory, statistical signal processing, detection and estimation, VLSI architectures, and digital and analog integrated circuits to develop energy-efficient systems from algorithms to silicon implementations.
Takako Fujioka is an Associate Professor of Music at Stanford University, affiliated with the Center for Computer Research in Music and Acoustics (CCRMA). Her research focuses on the neural mechanisms underlying auditory perception, auditory-motor coupling, and music-supported therapy for neurorehabilitation. She holds a Ph.D. in Physiology from the Graduate University for Advanced Studies, Japan, and M.Sc./B.Eng. degrees in Electrical Engineering from Waseda University. Her work combines neurophysiological techniques such as MEG and EEG to study brain plasticity in development, aging, and stroke recovery. Notable contributions include investigating how music influences motor and cognitive recovery in stroke patients, as well as exploring the neural basis of musical perception through rhythmic synchronization and pitch discrimination studies. Supported by awards from the Canadian Institutes of Health Research during her postdoctoral work at the Rotman Research Institute, her research bridges clinical neuroscience and music cognition. Dr. Fujioka’s expertise spans auditory neuroscience, neurorehabilitation, and technology-assisted music therapy. She has pioneered studies on tactile mapping for cochlear implant users and networked music performance systems, emphasizing cross-modal perception and human-technology interaction. Her findings contribute to both theoretical understanding of auditory processing and practical applications in medical and educational settings. Awards: Canadian Institutes of Health Research Awards (postdoctoral phase) Labs/Teams: CCRMA, Stanford Music Perception Laboratory, Rotman Research Institute collaborations Key Themes: Neuroplasticity, Music-Mediated Rehabilitation, Auditory-Motor Integration, Multisensory Processing Her recent work examines aging-related changes in binaural hearing and the role of beta/gamma oscillations in rhythmic processing. She advocates for translational research that connects neural mechanisms with real-world therapeutic interventions.
David S. Eisenberg is a Professor of Chemistry and Biochemistry and Biological Chemistry at the University of California, Los Angeles, where he also serves as Director of the UCLA-DOE Institute for Genomics and Proteomics and as an HHMI Investigator. His research focuses on protein interactions, particularly the structural basis for conversion of normal proteins to the amyloid state and conversion of prions to the infectious state. Dr. Eisenberg earned his undergraduate degree in biochemical sciences from Harvard College and his D.Phil. degree in theoretical chemistry from Oxford University on a Rhodes Scholarship. His postdoctoral research was on ice and water with Walter Kauzmann at Princeton and in protein crystallography with Richard Dickerson. He joined the UCLA faculty after his postdoctoral studies. Dr. Eisenberg and his research group focus on protein interactions in amyloid and prion diseases. These diseases involve protein aggregation where normal functional proteins convert to abnormal aggregated forms. Systemic amyloid diseases like dialysis-related amyloidosis result from fiber accumulation until organ failure, while neurodegenerative diseases like Alzheimer's, Parkinson's, ALS, and prion conditions appear to be caused by smaller oligomers. In 2005, his team determined the atomic-level structure for the amyloid fiber spine, revealing a 'steric zipper' of two parallel beta sheets packed across a dry interface. Since then, they've determined approximately 90 amyloid spines from 15 disease-related proteins. In 2010, they identified the structure of a toxic amyloid-related oligomer consisting of six anti-parallel beta strands forming a cylindrical barrel. His recent publications demonstrate continued innovation in amyloid research, with focus areas including structural prediction of amyloid formation, mechanisms of tau fibril disassembly in Alzheimer's disease, cryo-EM analysis of amyloid polymorphism, and structure-based design of inhibitors for amyloid toxicity. His work integrates computational, structural, and biochemical approaches to understand protein aggregation across multiple disease contexts. Dr. Eisenberg has received numerous prestigious awards and honors: National Academy of Sciences Member American Philosophical Society Member Institute of Medicine Member Howard Hughes Medical Institute Investigator Biophysical Society Emily M. Gray Award Harvard Westheimer Medal UCLA Seaborg Medal Technion - Israel Institute of Technology Harvey Prize in Human Health As Director of the UCLA-DOE Institute for Genomics and Proteomics and an HHMI Investigator, Dr. Eisenberg leads significant research initiatives in protein structure and aggregation. His laboratory combines X-ray crystallography, bioinformatics, and biochemical techniques to investigate protein interactions, with particular emphasis on amyloid-forming proteins and their role in disease. The Eisenberg Lab, located in Boyer Hall at UCLA, maintains an active research program investigating the structural basis of protein aggregation. The lab continues to build on its landmark discoveries of amyloid structures while exploring new frontiers in understanding protein misfolding diseases and developing potential therapeutic interventions.
Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.
Roberto Manduchi is a Professor of Computer Science and Engineering at the University of California, Santa Cruz, within the Baskin School of Engineering. His primary affiliation is with the Computer Science and Engineering department where he leads research in assistive technology for visual impairments. He holds a Dottorato di ricerca in Electrical Engineering from the University of Padova, Italy, and previously worked at Apple and NASA JPL before joining UCSC in 2001. His research focuses on mobile computer vision, inertial sensors, and location-aware systems to enhance spatial awareness and information access for blind and low-vision individuals. Key research areas include indoor navigation systems, screen magnification for low-vision readers, obstacle detection using augmented reality, and text accessibility assessment through specialized OCR pipelines. His work bridges computer vision, human-computer interaction, and accessibility design. Analysis of his recent publications (2022-2025) reveals strong emphasis on inertial-based indoor navigation (e.g., PALMS localization system, backtracking algorithms), screen magnification usability studies, and novel approaches to scene text access for blind users. His research consistently targets practical applications for visual impairment, with significant contributions to pedestrian dead reckoning, magnetic signature localization, and gaze-contingent interfaces. Manduchi serves on the scientific advisory board of Aira and is a board member of the Vista Center for the Blind and Visually Impaired. He leads the UCSC Computer Vision Lab where his team develops accessible computing solutions. His work includes both theoretical contributions to computer vision and tangible assistive applications, with recent projects focusing on smartphone-based inertial odometry, multi-scale tactile maps, and real-time obstacle cueing systems.
Luca Peretti is an Associate Professor in Electric Machines and Drives at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Department of Electrical Engineering, Division of Electric Power and Energy Systems. He works as a researcher in the EMD (Electric Machines and Drives) group and serves as Partner Director for KTH's strategic partnership with ABB. Education: M.Sc. in Electronic Engineering (2005) from University of Udine, Ph.D. from University of Padova (2008) Professional Experience: Postdoc at University of Padova (2009-2010), Principal Scientist at ABB Corporate Research (2010-2018), Associate Professor at KTH (2018-present) His research focuses on: Automatic parameter estimation in electric machines Multiphase drive systems Sensorless control algorithms Loss segregation in drive systems Condition monitoring of industrial and transportation applications Recent publications demonstrate expertise in variable phase-pole machines, harmonic plane decomposition, predictive control algorithms, and advanced modeling of permanent magnet motors. Key application areas include transportation electrification, wind energy systems, and industrial drive technologies. Scientific roles include: Associate Editor, IET Electric Power Applications Journal (2019-present) Theme Co-Leader, Swedish Electromobility Center (2020-present) Member, IEEE (2021-present) and IET (2006-present) He leads the strategic partnership with ABB and contributes to doctoral program committees at University of Padova.
Judith Driscoll is Professor of Materials Science at the University of Cambridge in the Department of Materials Science & Metallurgy. She holds the prestigious Royal Academy of Engineering Chair in Emerging Technologies and serves as a Visiting Staff Member at Los Alamos National Laboratory. As the founding Editor-in-Chief of APL Materials, she has significantly contributed to the materials science community. Dr. Driscoll's research focuses on Energy Efficient Oxide Materials for Information and Communications Technologies and energy devices. Her work spans the development of non-volatile memory, resistive switching devices, and ferroelectric materials for neuromorphic computing applications. She investigates oxide thin films for applications ranging from data storage to energy generation and conversion, with particular emphasis on creating more energy-efficient device technologies to handle the exponential growth of data-centric applications. Her recent publications demonstrate strong trends in developing novel oxide-based memory devices with improved energy efficiency, particularly for AI applications. The work shows significant progress in hafnium-zirconium oxide ferroelectrics, resistive switching mechanisms, and vertically aligned nanocomposite structures for enhanced device performance. These innovations address critical challenges in reducing the unsustainable energy demands of modern computing, particularly for artificial intelligence systems. Fellow of the Royal Academy of Engineering Fellow of the Materials Research Society Fellow of the American Physical Society Fellow of IOM3, IOP, and Women Engineers Society Fellow of the American Academy of Arts and Sciences Recipient of ERC Advanced Grant Editor-in-Chief of APL Materials Dr. Driscoll leads a vibrant research group that has secured significant funding including her Royal Academy of Engineering Research Chair, an ERC Advanced Grant, and an ECCS-EPSRC grant in collaboration with researchers from the USA. She has founded the Cambridge Centre for Neuromorphic Computing (Neucam) in 2023. Her group operates world-leading growth equipment including pulsed laser deposition with RHEED control, high temperature oxide sputtering, and spatial ALD systems. She collaborates extensively across the University of Cambridge and with international partners to solve complex materials challenges, with her group's role often being to identify optimal materials for functional goals, predict fabrication methods, and then create and characterize these materials.
Dr. Daniel J. Preston is an Assistant Professor of Mechanical Engineering at Rice University, leading the Preston Innovation Laboratory (PI Lab). He holds a B.S. from the University of Alabama (2012), M.S. and Ph.D. from MIT (2014, 2017), and postdoctoral training at Harvard University (2017-2019). His research focuses on energy efficiency, soft materials, fluid mechanics, and robotics, with applications in wearable technologies and sustainable systems. Key achievements include the NSF CAREER Award (2022) and pioneering work on necrobotics using biotic materials. The PI Lab collaborates widely, with projects involving biomimetic surfaces, smart textiles, and fluidic control systems. Education: B.S. Mechanical Engineering, University of Alabama (2012) M.S./Ph.D. Mechanical Engineering, MIT (2014/2017) Research Interests: Energy : Thermal management, heat transfer optimization, and waste heat recovery. Materials : Soft actuators, surface coatings, and functional biomaterials. Fluids : Interfacial phenomena, droplet dynamics, and fluid-structure interactions. Notable Publications: Recent work includes advancements in wearable haptic devices ( Science Advances ), necrobotics ( Advanced Science ), and fluidic logic textiles ( PNAS ). Over 50 peer-reviewed articles span multidisciplinary topics from magnetic levitation to virus decontamination. Honors: In addition to the CAREER Award, Dr. Preston has received the NSF Graduate Research Fellowship, Tau Beta Pi Fellowship, and Wunsch Foundation Award. His lab fosters innovation through grants and industry partnerships. Advising & Grants: Supervises ~15 graduate and undergraduate students, including NSF GRFP recipients. Active in mentoring initiatives like the Randall Research Scholars Program and AATCC grants. Labs & Teams: The PI Lab collaborates with MIT’s DRL, Harvard’s Whitesides Group, and institutions globally. Core projects include smart wearables, energy harvesting fabrics, and autonomous soft robots.
David Allcock is an Assistant Professor in the Department of Physics at the University of Oregon, part of the College of Arts and Sciences. His research focuses on ion trapping, quantum computing, and hybrid quantum systems, with an emphasis on manipulating atomic and molecular systems using electric and magnetic fields for quantum information applications. He leads the Ion Trapping Lab at UO, where he develops scalable quantum technologies and open-source control systems like ARTIQ and Sinara. His work bridges experimental physics with engineering, addressing challenges in qubit control, error mitigation, and large-scale quantum computer design. Education: MPhys from the University of Oxford (2007), D.Phil. in Physics from Oxford (2012). Prior to UO, he was a Lindemann Fellow at the National Institute of Standards and Technology (NIST) in Boulder, CO. His research includes innovations in trapped-ion qubit control, including laser-free entangling gates, scalable architectures, and applications in quantum sensing and dark matter detection. Key research themes include metastable qubit systems, photon scattering error mitigation, and the integration of superconducting detectors for state readout. He collaborates on open-source hardware-software stacks for quantum experiments and mentors students in quantum engineering through programs like the Quantum Technology Master’s Internship. Current projects explore hybrid quantum-classical interfaces and ultra-stable ion trap fabrication. His lab’s contributions span theoretical and experimental domains, with recent advances in geometric phase gates, microwave-driven control, and error-resilient qubit operations. The group also engages in interdisciplinary work linking quantum computing with precision measurement, such as SPUD (SPectroscopy for Ultralight Dark matter) and bosonic sensing tools.
