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Martin SeifridView profile

Assistant Professor

• Organic Materials
• Self-Driving Labs
• Materials Informatics
• +7 more

Martin Seifrid is an Assistant Professor at NC State University's Department of Materials Science and Engineering (College of Engineering). His research focuses on designing organic materials with controlled structures for applications in energy storage, healthcare, and neuromorphic computing. He leads the Data-Driven Organic Materials Lab, which develops self-driving labs powered by machine learning to accelerate materials discovery. Key areas include organic mixed ionic-electronic conductors, materials informatics, and automation through tools like Chemspyd. His work integrates synthesis, characterization, and computational methods. Recent efforts emphasize automated experimentation frameworks and open-source robotics interfaces for chemical synthesis. Notable projects include optimizing organic photovoltaic materials and understanding thin film morphology in semiconductors. Publications highlight advancements in self-driving lab systems, machine learning for material design, and novel material characterization techniques. He collaborates widely, contributing to interdisciplinary initiatives in autonomous laboratory development. His lab is equipped with state-of-the-art equipment for automated chemistry and advanced material analysis. While currently no scientific awards are listed, his research has been featured in prominent journals like Science , Chemistry of Materials , and Journal of Materials Chemistry A . He actively mentors graduate students in materials science and engineering through collaborative projects at NC State.

• Dark Matter Detection
• Quantum Physics
• Condensed Matter Physics
• +3 more

De Candia Antonio is an Associate Professor at the Consiglio Nazionale delle Ricerche (CNR), affiliated with the Institute for Superconductors, Innovative Materials, and Devices. His research focuses on dark matter detection, quantum phase transitions, and neural network dynamics. He has contributed to the DarkSide experiment, a leading effort in direct dark matter detection using liquid argon time projection chambers (LAr TPC). His work spans particle physics, condensed matter, and statistical mechanics. Key research interests include the development of advanced detectors for dark matter searches, quantum systems under environmental influence, and critical phenomena in neural networks. He has published over 140 articles in top journals like Physical Review Letters , Physical Review B , and Journal of Instrumentation , with a focus on experimental astrophysics, theoretical quantum mechanics, and computational methods. His recent work explores directional dark matter signatures, quantum phase transitions in spin-boson models, and applications of deep learning in news virality prediction. He collaborates on the DarkSide-50 and DarkSide-20k projects, aiming to improve sensitivity to sub-GeV dark matter particles and core-collapse supernova neutrinos. Lab/Team: Part of the DarkSide collaboration, a multinational team advancing liquid argon detector technology for fundamental physics research.

• Quantum Physics
• Condensed Matter Physics
• Particle Physics
• +5 more

Giulio De Filippis is an Associate Professor at the CNR Institute for Superconductors, Innovative Materials and Devices in Naples, Italy. His research focuses on quantum systems, particularly quantum phase transitions, dark matter detection, and the interplay between dissipation and quantum coherence. He is a key contributor to the DarkSide experiment, an international project searching for dark matter using liquid argon technology. Additionally, he explores theoretical aspects of non-Hermitian systems, topological phases, and quantum thermodynamics. De Filippis has co-authored over 130 peer-reviewed articles, including high-impact studies on dissipative quantum systems and detector physics. His work bridges fundamental quantum theory with experimental applications, such as optimizing energy conversion in quantum engines and analyzing dark matter signals in dual-phase liquid argon detectors. He collaborates with institutions globally, contributing to both the DarkSide-50 detector and the Recoil Directionality (ReD) project. His research emphasizes the development of advanced detector technologies, such as the ReD TPC, and theoretical frameworks to understand quantum phenomena under non-equilibrium conditions. While no formal awards are listed, his extensive publication record and experimental leadership reflect his significant contributions to the field.

• Superconductivity
• Quantum Engineering
• Condensed Matter Physics
• +3 more

Pepe Giovanni Piero is a Full Professor at the CNR Institute for Superconductors, Innovative Materials and Devices. His research focuses on advanced superconducting materials, quantum technologies, and photon detection systems. Key areas include Josephson junction dynamics, quantum computing components, and vacuum energy experiments. He leads projects on superconducting single-photon detectors for quantum communication and lidar applications, as well as foundational studies in quantum gravity through the Archimedes experiment. Research interests span superconductivity, quantum engineering, and applied photonics. His work bridges theoretical models with experimental advancements in superconducting circuits and materials. Recent efforts emphasize tunable qubit systems, flux parametric amplifiers, and precision measurements of vacuum energy effects. Publications highlight contributions to superconducting device characterization, quantum error mitigation, and collaborative projects like the Archimedes experiment aiming to detect gravitational vacuum energy. He actively contributes to open-source simulation tools for superconducting circuits and explores applications in quantum key distribution and environmental lidar systems.

Faisal QureshiView profile

Professor

• Computer Vision
• Deep Learning
• Camera Networks
• +5 more

Dr. Faisal Qureshi is a Professor of Computer Science in the Faculty of Science at Ontario Tech University, where he leads the Visual Computing Lab. He holds a concurrent role as Guest Professor at Mid Sweden University. His research focuses on visual sensor networks, computer vision, and deep learning, with applications in smart camera networks, video surveillance, and hyperspectral imaging. He earned his PhD in Computer Science from the University of Toronto (2007), following degrees from Punjab University (BSc 1993) and Quaid-e-Azam University (MSc Electronics 1995). Key research areas include multicamera tracking, graphics for vision, and neural networks for computer vision. He has pioneered work on self-organizing visual sensor networks and developed computational models for autonomous visual perception. His teaching includes courses on computer graphics, algorithms, and advanced computer vision. He has authored over 100 peer-reviewed publications, including a Best Paper Award at CRV 2017 for optical flow estimation research. Dr. Qureshi’s lab develops innovative solutions for video summarization, optical flow estimation, and activity analysis. His work bridges theoretical computer science with real-world applications in robotics, aerospace, and environmental monitoring. Recent projects explore adversarial machine learning for satellite communications security and hyperspectral image compression using neural representations.

Brian A. WandellView profile

Professor

• Vision Science
• Neuroscience
• Educational Technology
• +4 more

Brian A. Wandell is the Isaac and Madeline Stein Family Professor at Stanford University, holding primary appointment in Psychology and courtesy appointments in Electrical Engineering, Ophthalmology, and the Graduate School of Education. He directs the Center for Cognitive and Neurobiological Imaging and served as Deputy Director of the Wu Tsai Neurosciences Institute (2013–2021). His research integrates vision science, neuroimaging, and computational modeling to study brain development, visual disorders, and reading mechanisms. Wandell’s lab develops MRI analysis tools and imaging software frameworks like Flywheel.io, impacting medical imaging and automotive vision systems. Education: B.S. in Math/Psych (University of Michigan, 1973); Ph.D. in Social Sciences (UC Irvine, 1977). Honors include the Proctor Medal (2021), George Miller Prize (2016), and membership in the National Academy of Sciences (2003). Research focuses on visual neuroscience, MRI methodologies, and educational technology. Key projects include modeling retinal encoding, developing open-source tools (ISETBio), and studying cortical circuits for reading. His work bridges basic science and commercial applications via co-founded companies like Flywheel.io. Advisees include doctoral students Austin Kuo, Joshua Ryu, and Itamar Terem, with postdoc mentorship of Xi Mou. Awards span neuroimaging validation, optics modeling, and contributions to vision science. His labs emphasize reproducible research and software innovation, contributing to global neuroimaging standards through platforms like Flywheel.io.

Austen LamacraftView profile

Professor

• Condensed Matter Physics
• Quantum Mechanics
• Ultracold Atoms
• +5 more

Prof Austen Lamacraft is a Professor at the University of Cambridge's Cavendish Laboratory, affiliated with the Theory of Condensed Matter group in the Department of Physics. His research focuses on quantum phase transitions, magnetism in atomic gases, ultracold atoms, and nonequilibrium phenomena in quantum systems. He has contributed to understanding dynamics of impurities in one-dimensional quantum liquids, integrable systems, and the interplay between magnetism and superfluidity. His work bridges theoretical physics with cold atom experiments, exploring topics like quantum hydrodynamics, spin-orbit coupled Bose gases, and quantum noise correlations. He has also developed methods for analyzing many-body quantum systems using reinforcement learning and machine learning techniques. Research interests include: quantum phase transitions, ultracold atomic physics, integrable systems, and non-equilibrium dynamics. His studies often involve exact analytical solutions and numerical methods to model complex quantum phenomena. Key contributions include theoretical predictions of dynamical magnetism in atomic gases, analysis of impurity motion in quantum fluids, and exploration of operator spreading in noisy spin systems. His recent work addresses quantum measurement-induced phase transitions and stochastic processes in dual-unitary circuits. Lamacraft has published extensively on quantum dynamics, entropy production, and interdisciplinary applications of statistical mechanics. He collaborates with experimental groups to guide cold atom experiments and has developed tools for multimodal data integration in physics research.

Duane LohView profile

Associate Professor

• Nanotechnology
• Biophysics
• Materials Science
• +3 more

Duane Loh is an Associate Professor at the National University of Singapore (NUS), affiliated with the Centre for BioImaging Sciences. His research focuses on developing computational methods to analyze microscopy data, combining machine learning with physical priors. He earned his PhD from Cornell University in 2010 and has pioneered techniques in X-ray and electron microscopy, particularly in single-particle imaging and nanocrystal dynamics. Education: PhD in Physics, Cornell University (2010) Research interests include structural dynamics of biomolecules, nucleation processes in nanocrystals, and improving imaging techniques through algorithmic innovations. His group, the Computational Lens Group, has developed methods like 'Dragonfly' for X-ray laser data analysis and 'Pop-out Metrology' for high-throughput TEM imaging. Recent work emphasizes machine learning applications in microscopy, such as classifying structural motifs in nanoparticles and studying ice nucleation in supercooled water. His team's advancements in data-driven analysis have enabled insights into biological systems and materials at nanometer scales. Key achievements include the NUS Early Career Research Award and recognition as a Lee Kuan Yew Postdoctoral Fellow. He leads interdisciplinary projects involving collaborators from physics, chemistry, and computational science. Current projects explore correlative imaging techniques for butterfly wing structures and developing AI-driven microscopy tools for real-time analysis. His group's GitHub repository hosts open-source software like 'Dragonfly' for single-particle reconstruction.

Justin ChanView profile

Assistant Professor

• Digital Health Systems
• Mobile and Wearable Systems
• Wireless Sensing
• +6 more

Justin Chan is an Assistant Professor at Carnegie Mellon University, affiliated with the School of Computer Science (S3D Department) and the College of Engineering's Electrical and Computer Engineering Department. He leads the Semantic Signals Lab, focusing on creating mobile systems for physiological intelligence and augmented perception. His research spans AI-enabled healthcare systems, real-time mobile intelligence, and acoustic-tactile sensing for robotics and accessibility. Education: Ph.D. (expected 2023) in Computer Science & Engineering from the University of Washington, M.S. (2018) from the same institution, and a B.A. (2015) in Computer Science from Dartmouth College with high honors. Research Interests : His work combines wireless sensing, deep learning, and signal processing to develop technologies like smartphone-based ear infection detection (FDA-listed), newborn hearing screening (TUNE initiative in Kenya), and cardiac arrest detection via smart speakers. These innovations emphasize equitable healthcare access and real-world impact. Awards : SIGMOBILE Doctoral Dissertation Award Runner Up IEEE Pervasive Computing Emerging Rockstar American Academy of Audiology Research Award Grants & Projects : NIH-backed TUNE project, partnerships with NGOs like the Global Foundation for Children with Hearing Loss, and collaborations with startups like Wavely Diagnostics (FDA-listed ear infection tech) and Sound Life Sciences (acquired by Google). Leads development of privacy-preserving protocols for contact tracing (CovidSafe/WA Notify). Labs & Teams : Director of the Semantic Signals Lab, focusing on interdisciplinary projects merging hardware, software, and clinical applications.

• Biophotonics
• Biomedical Optics
• Super-Resolved Microscopy
• +6 more

Professor Paul French is a leading academic in the Department of Physics at Imperial College London, affiliated with over 20 research groups including the Photonics Group and the Francis Crick Institute. He holds a BSc (Hons) in Physics (1983) and a PhD in Laser Optics (1987) from Imperial College. His research focuses on advancing biomedical optics, particularly in fluorescence imaging technologies for cell biology and clinical diagnostics. He has pioneered open-source microscopy platforms like openFrame and easySTORM to democratize access to advanced imaging tools. Roles: Professor of Physics, former Head of Photonics Group (2001-2013), Vice Dean (Research) for Faculty of Natural Sciences (2015-2023) Affiliations: Artificial Intelligence Network, CRUK Convergence Science Centre, Pancreatic Islet Biology and Diabetes Consortium Research interests span super-resolved microscopy (SRM), fluorescence lifetime imaging (FLIM), and quantitative phase imaging (QPI). His work emphasizes translational applications in cancer, infectious diseases, and drug discovery. Recent innovations include PolSpec hyperspectral imaging and VP-CLEM-Kit for correlative microscopy. Notable awards include the Royal Society University Research Fellowship (1989–1994). His group collaborates globally, with satellite labs at the Francis Crick Institute.

• Biomedical Engineering
• Materials Science
• Regenerative Medicine
• +4 more

Dame Molly Stevens is a Professor of Biomedical Materials and Regenerative Medicine at the University of Oxford, holding the John Black Professorship and serving as Deputy Director of the Kavli Institute. She retains a part-time strategic Professorship at Imperial College London and collaborates with the Crick Institute. Her research focuses on bioengineering solutions for regenerative medicine and biosensing, spanning drug delivery, biomaterials, and tissue engineering. Her group is highly interdisciplinary, integrating materials science, engineering, and molecular dynamics. Stevens has led major initiatives like the UK Regenerative Medicine Platform and holds leadership roles in global health and scientific organizations. Notable awards include the NovoNordisk Award (2023) and election to the Royal Society and Royal Academy of Engineering. Education: BSc (Hons) Pharmaceutical Sciences, University of Bath PhD in Biophysical Investigations, University of Nottingham Postdoctoral training at MIT with Prof. Robert Langer Research Interests: Her work bridges living and non-living systems, developing bioactive materials for regenerative medicine, biosensors, and therapeutics. Key areas include nanomedicine, enzyme-responsive nanoparticles, and extracellular vesicle engineering. She emphasizes interdisciplinary collaboration, involving data scientists and surgeons to address clinical challenges. Articles Trends: Recent publications highlight innovations in anti-counterfeiting materials, nanozyme-based diagnostics, and bioactive coatings for bone repair. Her work often integrates nanotechnology with biological systems, emphasizing practical applications in healthcare and security. Awards: NovoNordisk Award (2023) Royal Society Fellowship Foreign Member, U.S. National Academy of Engineering (2019) Grants & Advising: Led the UK Regenerative Medicine Platform and directed the EPSRC Early-Warning Sensing Systems initiative. Advises on numerous grants and collaborates with industry partners. Supervises a large group of students and postdocs in regenerative medicine and biosensing. Labs & Networks: Leads the Stevens Group at Oxford’s Kavli Institute, maintaining ties with Imperial’s interdisciplinary networks. Active in the Crick Institute’s research collaborations and the EPSRC-funded Interdisciplinary Research Centre.

Ian PetersenView profile

Professor

• Control Theory
• Quantum Control Systems
• Systems Theory
• +5 more

Professor Ian Petersen is a faculty member at the Australian National University (ANU) since 2017, holding the position of Professor in the Research School of Engineering within the ANU College of Systems & Society. He previously held roles at UNSW from 1985–2016, including Scientia Professor and Australian Laureate Fellow. His academic journey includes a PhD (Electrical Engineering) from the University of Rochester (1984), MSc (Electrical Engineering) from the same institution, and a BEng (Electrical) with First Class Honours from the University of Melbourne (1979). Professor Petersen’s research focuses on Systems and Control Theory, with specialties in Robust Control Theory, Quantum Control Systems, Negative Imaginary Systems Theory, and applications in Quantum Optics, Flexible Structures, and Aerospace Systems. His work bridges theoretical advancements and practical engineering solutions, such as control in quantum optics, aircraft control, and fault detection in chemical processes. He has received numerous accolades, including Fellowships from the Australian Academy of Science, IEEE, and the International Federation of Automatic Control. Notable awards include the ISI Highly Cited Researcher distinction and the 2014 Best Paper Award at the World Congress on Intelligent Control and Automation. Petersen is actively involved in editorial roles, including Associate Editor for IEEE Transactions on Control Systems Technology and Automatica . His research has been supported by grants such as the Air Force Office of Scientific Research (USA) and Australian Research Council (ARC) Discovery Grants. Recent projects include Systems Theory Approach to Modelling and Robust Design of Quantum Information Processing Devices (AFOSR, 2016–2018) and an ARC Laureate Fellowship (2011–2016) focusing on quantum systems theory. Petersen’s work emphasizes interdisciplinary collaboration, addressing challenges in quantum technology, control systems, and energy networks.

• Nanotechnology
• Biomedical Engineering
• Drug Delivery Systems
• +1 more

Professor Ken Tye Yong is Associate Dean (External Engagement) for the Faculty of Engineering and Professor in the School of Biomedical Engineering at the University of Sydney. His research spans nanotechnology, biomedical imaging, drug delivery systems, and biophotonics, with emphasis on nanoparticle-based therapies and diagnostic platforms. He leads initiatives in external engagement through the Warren Centre and oversees research on nanomaterial applications in healthcare. Current projects include neuromorphic imaging cytometry, SERS-based cellular monitoring, and biomimetic tissue scaffolds. Publications demonstrate consistent focus on interdisciplinary approaches combining nanomaterials engineering with clinical applications. Research outputs frequently involve microfluidic systems, targeted drug delivery, and advanced imaging techniques. Recent work emphasizes neuroprotective therapies, wearable biosensors, and mRNA vaccine delivery platforms.

• Cognitive Neuroscience
• Computational Neuroscience
• Behavioral Neuroscience
• +4 more

Thomas Akam is an Associate Professor of Experimental Psychology at the University of Oxford's Department of Experimental Psychology. His research investigates neural mechanisms of flexible behavior in complex environments through cognitive mapping and computational modeling approaches. His work centers on how brains construct internal models for planning, generalization, and hidden structure inference, with primary focus on frontal cortex, hippocampus, and dopamine systems. He employs advanced methodologies including silicon probe electrophysiology, fiber photometry, and optogenetics, integrating theoretical neuroscience with machine learning principles to decode behavioral and neural data. Recent publications (2024-2025) reveal strong trends in neural circuit mapping of behavioral structure, dopamine-independent reward processing, and methodological innovation in behavioral paradigms. Key themes connect computational modeling of hidden-state inference with experimental validation of neural circuit mechanisms across multiple brain regions. Dr. Akam has developed two widely adopted open-source neuroscience tools: pyControl for behavioral experiments and pyPhotometry for fiber photometry analysis. He leads the Cognitive Circuits research group at Oxford, emphasizing collaborative development of rich behavioral tasks that generate quantifiable neural datasets while recruiting natural cognitive mechanisms.

• Physical Chemistry
• Data Science
• AI in Chemical Discovery
• +5 more

Professor Jeremy Frey is a faculty member at the University of Southampton, serving as Professor of Physical Chemistry and Head of the Computational Systems Chemistry Group. His career spans experimental and computational chemistry, with a focus on integrating AI and machine learning into scientific discovery. DPhil from University of Oxford NATO/SERC fellowship at Lawrence Berkeley Laboratory Research interests include molecular organization studies using laser spectroscopy, chemical informatics, and e-Science infrastructure. He investigates how digital tools enhance laboratory intelligence through AI-driven workflows and topological data analysis. Recent publications highlight his work on blockchain in scientific workflows, data management, and AI ethics in food supply chains. His research bridges physical chemistry, computer science, and interdisciplinary studies. RSC Chemical Information and Computer Applications Group - 2021 Inspirational Committee Award Jeremy leads major projects like the EPSRC Network+ on AI and Automated Scientific Discovery, collaborating with institutions including Royal Society, EPSRC, and European Union. He advocates for digital engagement in IUPAC and co-leads the Green Book project.