Adam Wax is a Professor of Biomedical Engineering and Professor of Physics at Duke University , where he leads cutting-edge research in optical spectroscopy for cancer detection , novel microscopy techniques , and low-coherence interferometry . As a member of both the Duke Cancer Institute Faculty Network and Duke Institute for Brain Sciences , he bridges engineering and biomedical applications. Education: B.S. from Rensselaer Polytechnic Institute (1993), M.A. (1996) and Ph.D. (1999) from Duke University Honors: Fellow, American Institute for Medical and Biological Engineering (2014); Fellow, SPIE (2010); Fellow, OSA (2010); NSF CAREER Award (2004) His research focuses on biomedical optics , particularly optical coherence tomography (OCT) and quantitative phase microscopy , with applications in early cancer detection , subcellular imaging , and clinical diagnostics . Recent work emphasizes low-cost, portable OCT systems for point-of-care applications and multimodal imaging combining spectroscopy with phase analysis. Scientific awards include: Fellow, American Institute for Medical and Biological Engineering (2014) Fellow, International Society for Optics and Photonics (2010) Fellow, Optical Society of America (2010) National Science Foundation CAREER Award (2004)
Jaakko Akola is a Professor in the Department of Physics at the Norwegian University of Science and Technology (NTNU). His research focuses on computational materials science, particularly density functional theory (DFT) and atomistic simulations of materials, nanoparticles, molecules, and interfaces. He leads significant projects such as "SIDI" (inoculation in cast iron), "Infinity-RETIS" (chemical rare events), and "AllDesign" (rational alloy design), alongside coordinating EU-funded initiatives like "CritCat" for catalyst development. The Materials Theory group under Akola employs DFT, molecular mechanics, and Monte Carlo methods to explore atomic-scale structures and functions in technological applications. Key research areas include platinum-free catalysts for hydrogen energy, amorphous semiconductors for memory devices, noble metal nanoparticles in biological environments, and alloy design for cast iron and aluminum. Recent work integrates machine learning to advance theory-driven material design, reducing reliance on experimental trial-and-error. Akola's publications highlight advancements in hydrogen evolution catalysis, phase-change memory materials, and alloy precipitation. His projects often involve interdisciplinary collaborations with experimental teams. He teaches Quantum Physics 1 (FY2045) and Computational Physics (TFY4235) at NTNU, reflecting his commitment to education alongside research.
Koray Aydin is an Associate Professor in the Electrical and Computer Engineering department at Northwestern University 's McCormick School of Engineering. His research focuses on nanophotonics , optical metamaterials , and inverse design of photonic devices. PhD in Physics, Bilkent University MS and BS in Physics, Bilkent University The Metamaterials and Nanophotonic Devices Lab (MNDL) explores light-matter interactions at the nanoscale. Key research areas include: Plasmonic materials and devices for absorption engineering Metasurfaces for subwavelength light control Two-dimensional materials in optoelectronics 3D printing of millimeter-wave and optical metadevices Hybrid and tunable nanophotonic systems Dynamic metamaterials via self-assembly His publications highlight inverse design methodologies, DNA-assembled metasurfaces , and active nanophotonic materials . Collaborations with Chad Mirkin, Vinayak Dravid, and Prem Kumar have led to breakthroughs in scalable photonic systems and programmable metamaterials. Current efforts in MNDL aim to integrate machine learning with nanophotonic device design, enabling non-intuitive geometries and ultra-compact optical components with applications in telecommunications, defense, and consumer electronics.
Dr Cuong Nguyen is a Lecturer in the Department of Mathematical Sciences at Durham University, specializing in Machine Learning, Artificial Intelligence, and Statistics. His research bridges theoretical foundations with practical applications, with particular expertise in Bayesian methods, transfer learning, and multimodal systems. His educational background includes a PhD in Computer Science or a related field (specific institution not mentioned in provided data), with research focusing on machine learning theory and applications. Nguyen has established himself as a researcher with publications spanning top conferences including NeurIPS, UAI, and ACM Web Conference. Research Interests: Nguyen's work centers on lifelong learning systems that overcome catastrophic forgetting, transferability estimation between tasks, and multimodal learning applications. His research integrates Bayesian principles with deep learning to create more robust and adaptable AI systems. Recent Trends: Analysis of his 15 most recent publications reveals a strong focus on practical applications of theoretical machine learning concepts, particularly in security (CAPTCHA systems), real-world problem solving (fake advertisement detection), and fundamental learning theory (transferability metrics). Dr Nguyen has made significant contributions to understanding the theoretical underpinnings of transfer learning and continual learning, with his work on LEEP providing a practical metric for transferability estimation. His research on CAPTCHA systems demonstrates both theoretical rigor and practical security implications. Advising: While specific students aren't listed in the provided data, his publications show collaborations with researchers across institutions, suggesting active supervision of PhD and Master's students. Research Groups: He is affiliated with the Statistics research center within Durham's Department of Mathematical Sciences, contributing to the university's strength in mathematical and computational research.
Dr. Jiakun Liu is an Associate Professor at the School of Mathematics and Applied Statistics, Faculty of Engineering and Information Sciences, University of Wollongong, Australia. He has held this position since January 2022 and maintains an active research program in nonlinear partial differential equations with applications in geometry and optimal transportation. His work has been published in top mathematical journals including Annals of Mathematics, Inventiones Mathematicae, and Communications on Pure and Applied Mathematics. Dr. Liu received his PhD from The Australian National University, Australia. His academic journey includes prestigious fellowships such as the Simons Postdoctoral Fellowship and the Australian Research Council's Discovery Early Career Researcher Award (DECRA). Dr. Liu's primary research focuses on nonlinear elliptic and parabolic partial differential equations, with special emphasis on the regularity theory of Monge-Ampère equations, Hessian equations, and other variational problems. His work extends to related areas of geometry and physics, including the geometry of convex bodies, minimal surfaces, surfaces of prescribed curvatures, and geometric flows. His research has significant applications in optimal transportation theory, which connects mathematical analysis with practical problems in economics, physics, and computer science. Analysis of Dr. Liu's recent publications reveals a strong focus on regularity theory for Monge-Ampère equations and optimal transportation problems. His work spans pure mathematical theory (regularity of solutions, singular sets, free boundaries) while maintaining connections to geometric applications and practical problems. A notable trend is the increasing interdisciplinary nature of his research, with recent work connecting optimal transport to statistical methods and computer science applications. Dr. Liu has received several prestigious awards and fellowships: UIC International Links Scheme (University of Wollongong, 2015) Discovery Early Career Researcher Award (DECRA) from Australian Research Council Discovery Project (DP170100929) from Australian Research Council Shiing-Shen Chern Mathematical Scholarship from Zhejiang University Simons Foundation Simons Postdoctoral Fellowship Dr. Liu is actively involved in mentoring the next generation of mathematicians, having supervised numerous PhD students including Siyuan Li, Jihang Zhang, Phung Trong Thuc, Yating Wu, and Meng Ji. He has also guided honours students such as Matthew Hughes, Luke McDonald, and Brad Rice. His research is supported by multiple grants, including "Singularity and regularity for Monge-Ampere type equations" (2023-2026), "Monge-Ampere type equations and their applications" (2023-2027), and "Monge-Ampere equations and applications" (2020-2024), demonstrating sustained funding for his research program. Dr. Liu is an active participant in the mathematical community, organizing conferences such as the International Conference on Nonlinear Partial Differential Equations (honoring Professor Neil Trudinger's 80th birthday) and the 4th Australia-China Joint Conference on Geometric Analysis. He maintains collaborative relationships with researchers across Australia, China, and internationally, contributing to a vibrant research environment in geometric analysis and PDEs.
David Rothamer is the Robert Lorenz Professor and Director of the Engine Research Center in the Department of Mechanical Engineering at the University of Wisconsin-Madison. As Associate Dean for Research in the College of Engineering, he leads research strategy, core facility management, and faculty recruitment. His expertise spans combustion, internal combustion engines, renewable fuels, and optical diagnostics. He earned his PhD from Stanford University (2008) and has been at UW-Madison since 2008, receiving an NSF CAREER Award in 2011. Education PhD, Mechanical Engineering, Stanford University, 2008 MS & BS, Mechanical Engineering, University of Wisconsin-Madison, 2002 & 2000 Research Interests Rothamer’s work focuses on optimizing engine performance with renewable fuels using advanced optical diagnostics. His lab develops laser-based techniques to study combustion processes in IC engines, with recent emphasis on sustainable aviation fuels and aerosol filtration during the pandemic. Awards & Recognition ASHRAE Best Paper Award (2022) for aerosol transmission research Robert Lorenz Professorship (2020) SAE Ralph R. Teetor Educational Award (2013) Grants & Collaborations Recipient of $11.5M Army funding for hybrid-electric engine research (2020). Collaborates with industry partners on fuel blending, combustion diagnostics, and emission reduction technologies. Labs & Affiliations Leads the Engine Research Center and holds an affiliation with the Nuclear Engineering & Engineering Physics department. His team operates a state-of-the-art engine lab capable of simulating extreme environmental conditions.
Mario Harper is an Assistant Professor in the Department of Computer Science at Utah State University. His research emphasizes Machine Learning, Data Science, Robotics, and their intersections with Finance and Artificial Intelligence. He specializes in autonomous systems, energy-efficient robotics, and AI-driven solutions for transportation and urban sustainability. His work includes developing tools like simulators for electric vehicle systems and stealth-centric navigation algorithms inspired by biological systems. Key research areas include multi-robot coordination, reinforcement learning applications in robotics, and algorithmic approaches to environmental and economic challenges. He has contributed to projects like POSEIDON-SAT for satellite-based fishing vessel detection and electrified transportation equity analysis in urban settings. His publications span topics from trajectory planning in legged robots to AI modeling for economic systems. Beyond research, he designs interactive visualization tools to support policy decisions on sustainable transportation infrastructure. No scientific awards are explicitly listed in the provided information. Mario Harper’s advising and grants focus on robotics, energy systems, and AI applications, though specific student advisees or grant details are not detailed here. He collaborates on projects involving lab tools such as the Unknown Building Exploration Simulator (UBES) and Stealth Centric Autonomous Robot Simulator (SCARS), advancing robotic autonomy in unstructured environments.
Marinko Sarunic is an Adjunct Professor at the School of Engineering Science , Simon Fraser University . He holds a PhD in Biomedical Engineering from Duke University and has been recognized as a Michael Smith Foundation for Health Research Scholar . His research focuses on biomedical imaging , particularly optical coherence tomography (OCT) , microscopy , and low-coherence interferometry , with applications in diabetic retinopathy , Alzheimer’s disease , and age-related macular degeneration . Dr. Sarunic's work spans adaptive optics , deep learning , and sensorless OCT systems , emphasizing clinical translation and open-source software development (e.g., OCTAVA ). His Google Scholar publications highlight multimodal imaging , vascular heterogeneity analysis , and AI-driven diagnostics for retinal diseases. His contributions include the Michael Smith Foundation for Health Research Scholar award. Though not currently teaching courses, his collaborations and leadership in retinal imaging and medical device innovation are pivotal for advancing non-invasive diagnostics in neurodegenerative and diabetic conditions .
Alex Alvarado is a Full Professor in the Signal Processing Systems department at Eindhoven University of Technology (TU/e), leading the Information and Communication Theory Lab (ICT Lab). He is also affiliated with TU/e's Center for Wireless Technology in Eindhoven. His academic career includes roles as a Senior Research Associate at University College London (2014–2016), Marie Curie Intra-European Fellow (2012–2014), and Newton International Fellow (2011–2012) at the University of Cambridge. Alvarado is a Senior Member of the IEEE and has held editorial and committee positions in major conferences like OFC and ECOC. Alvarado holds an Electronics Engineer degree (2003) and MSc (2005) from Universidad Técnica Federico Santa María, Chile, followed by a Licentiate of Engineering (2008) and PhD (2011) from Chalmers University of Technology, Sweden. His research focuses on high-speed secure data transmission in optical and wireless systems, emphasizing energy-efficient algorithms and theoretical limits of telecommunication systems. Key areas include communication theory, information theory, optical fiber systems, and nonlinear interference mitigation. His recent articles explore advanced modulation formats, machine learning applications for channel estimation and decoding, and innovations in free-space optics and MIMO systems. This work contributes to UN Sustainable Development Goals related to affordable and clean energy, industry innovation, and responsible consumption through energy-efficient communication solutions. Scientific Awards: ERC Starting Grant (2018) NWO VIDI Grant (2016) 2015 Journal of Lightwave Technology Best Paper Award 2015 IEEE Exemplary Reviewer Award 2018 and 2023 Asia Communications and Photonics Conference Best Paper Awards 2019 Optoelectronics and Communications Conference Best Paper Award Alvarado's advising contributions include supervising 12 research works. His grants include NWO VIDI and ERC Starting funding. He leads projects like NESTOR (Next-gen optical networks) and LaiQa (Quantum Key Distribution). His lab, the ICT Lab, drives theoretical and applied research in communication systems.
Christian Fager is a Full Professor at the Department of Microwave Electronics, Chalmers University of Technology, Sweden. He has been affiliated with Chalmers since completing his Ph.D. there in 2003. As Head of the Microwave Electronics Laboratory, his research focuses on nonlinear transistor modeling, energy-efficient power amplifier architectures, and distributed MIMO systems. He has co-invented 8 patents and published over 250 papers, including a seminal book on Nonlinear Transistor Model Parameter Extraction Techniques (Cambridge University Press, 2011). Dr. Fager holds editorial roles as Associate Editor of IEEE Microwave Magazine and member of the MTT-S Technical Coordination Committee on Wireless Communications. He is a Board Member of the European Microwave Association (EuMA) and has chaired multiple IEEE topical conferences. His awards include the Chalmers Supervisor of the Year (2018), inaugural Area of Advance Award (2010), and IEEE IMS Best Student Paper (2002). He leads research initiatives in distributed antenna systems, digital pre-distortion, and GaN/SiGe-based high-efficiency amplifiers, with projects involving testbed development for 5G/6G applications. His work bridges theoretical modeling and practical implementation in RF/microwave systems, emphasizing thermal and multi-physical simulation integration.
Stefano Martiniani is an Assistant Professor of Physics, Chemistry, Mathematics, and Neuroscience at New York University, affiliated with the Center for Soft Matter Research and the Simons Center for Computational Physical Chemistry. His interdisciplinary research explores computational physics of complex systems, including neural circuit theories, non-equilibrium statistical mechanics, and AI-driven materials discovery. He has pioneered methods for analyzing high-dimensional energy landscapes and received prestigious awards like the NSF CAREER Award (2024) and IUPAP Early Career Prize (2023). Education: PhD in Physics (2017), University of Cambridge MPhil in Physics (2013), University of Cambridge BSc in Physics (2012), Imperial College London Research Interests: His work bridges statistical physics and artificial intelligence, focusing on: Engineering disordered materials with tailored spectral properties Quantifying entropy production in active matter Developing open science frameworks like ColabFit for machine learning interatomic potentials Neural circuit models for cortical communication Grants & Collaborations: Funded by NSF, NIH, Chan Zuckerberg Initiative, and Simons Foundation. Leads interdisciplinary teams in computational physics, AI, and materials science. Labs/Initiatives: Core member of NYU's Center for Soft Matter Research; develops software tools like FReSCo and KLIFF-Torch for computational materials science.
Prof. Piya Pal is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. Her research focuses on high-dimensional statistical signal processing, energy-efficient sampling techniques, and covariance-driven inference. She previously held an Assistant Professor position at the University of Maryland, College Park, and was affiliated with the Institute for Systems Research. Education: Ph.D. in Electrical Engineering from California Institute of Technology (2013). Notable achievements include the NSF CAREER Award (2016) and the 2014 Charles and Ellen Wilts Prize for her thesis on sparse sampling and estimation. Her work emphasizes structured sampling and robust algorithms for undersampled data analysis, with applications in sensor arrays, compressive sensing, and optical imaging. Research Interests: Energy-efficient sparse array design Correlation-aware sparse estimation Covariance compression and statistical inference Tensor methods in machine learning High-resolution imaging systems Publications highlight advancements in sparse array geometries (nested/coprime samplers), Cramér-Rao bound analysis, and hybrid beamforming. Recent work explores super-resolution imaging and millimeter-wave channel sensing with learned empirical priors. Her contributions address fundamental trade-offs between sample size, resolution, and domain knowledge integration. Scientific Awards: NSF CAREER Award (2016) 2014 Charles and Ellen Wilts Prize (Caltech) Advising & Grants: Current research is supported by NSF CAREER funding. Her lab focuses on interdisciplinary projects combining signal processing with medical imaging and wireless communication challenges.
Prof Alison Rodger is a Professor in the Research School of Chemistry at The Australian National University, where she leads research in biophysical spectroscopy. Formerly at Macquarie University (2017–2024) and the University of Warwick (1990s–2017), she specializes in developing advanced spectroscopic techniques for biomacromolecule analysis. Her work integrates circular dichroism, linear dichroism, and Raman methods to study nucleic acids, proteins, and membrane systems. She co-directs the ARC-funded Industrial Transformation Training Centre in Facilitated Advancement of Australia’s Bioactives (FAAB) and runs an open-access biophysical spectroscopy lab. Key awards include Fellowships from the Australian Academy of Science (2021) and Royal Society of Chemistry (2000), and recognition in the Analytical Science Power List (2015). Education: BSc, PhD, DSc (Sydney University) MA (Oxford) DSc (Warwick) BA (Chester) Research Interests: Development of polarized-light spectroscopies for biomacromolecule analysis, including electronic/circular dichroism, Raman spectroscopy, and hybrid techniques. Applications span protein-DNA interactions, membrane biophysics, and biopharmaceutical characterization. She invented five spectroscopic techniques, including micro-volume Couette flow linear dichroism and fluorescence-detected linear dichroism. Awards & Roles: Fellow of the Australian Academy of Science Fellow of the Royal Society of Chemistry Emeritus Professor (University of Warwick) Recipient of Science Teachers of NSW Dedicated Service Award Consultant to European Science Foundation CASPER project Advising & Grants: Supervises PhD students in interdisciplinary biophysical chemistry. Led the EPSRC-funded Molecular Organisation and Assembly in Cells DTC at Warwick. Currently co-directs the ARC FAAB Centre, focusing on bioactive product characterization. Labs & Collaborations: Operates an open-access biophysical spectroscopy lab supporting academic and commercial users. Collaborations span mathematics, medicine, and engineering, with projects on DNA knotting, antimicrobial peptides, and nanomaterials for biosensing.
Tommaso Calarco serves as Director of the Institute for Quantum Control (PGI-8) at Jülich Research Centre, leading cutting-edge research in quantum optimal control methodologies for next-generation quantum technologies. His work focuses on developing transformative computational frameworks applicable to natural sciences, logistics, and high-performance computing through advanced quantum device engineering. His research spans quantum optimal control for computation and many-body systems, emphasizing physical model development, model reduction techniques, and machine learning integration for scalable quantum hardware. Key focus areas include spin-qubit optimization, diamond quantum register engineering, and error suppression in gate operations, with significant contributions to ultracold atom systems and semiconductor-based quantum platforms. Analysis of his 2024-2025 publications reveals concentrated efforts on hardware-specific challenges across multiple quantum modalities: spin shuttling fidelity in semiconductor systems, gate optimization for nitrogen-vacancy centers, and photon-spin interface engineering. This work demonstrates a unifying thread of optimal control solutions tailored to platform-specific decoherence mechanisms and scalability constraints. As Director of PGI-8 within the Peter Grünberg Institut, Calarco oversees a dedicated research team advancing quantum control theory and applications, contributing substantially to European quantum technology roadmaps including the Quantum Flagship initiative and strategic European Commission reports.
SATO Jun holds the position of Professor at the Department of Information Engineering (メディア情報分野) within the Faculty of Engineering at Nagoya Institute of Technology. He received his Ph.D. in Information Engineering from the University of Cambridge (1993–1996) and previously served as a Research Assistant at Cambridge (1996–1998). His research focuses on perceptual information processing and intelligent informatics, with specializations in computer vision, 3D reconstruction, and optical engineering applications. He has authored influential books like Computer Vision - Geometry of Vision (1999) and Computer Graphics (2017), and contributed to international publications such as Springer's Computer Vision: A Reference Guide (2020). Key professional roles include serving as President of the IEEE Nagoya Branch since 2023, Associate Editor of the International Journal of Computer Vision (Springer, 2010–present), and committee member for various organizations including Japan's Ministry of Education (2015–present) and the Nagoya City Business Potential Evaluation Committee (2008–2015). He has been recognized with prestigious awards including the BMVC Best Science Paper Prize (1994, 1997) and ITE Niwa-Takayanagi Prize (2015). His research extends to industrial collaborations, evidenced by patents like the "3D Information Presentation Device" (2014–2017) and "Position Detecting Device" (2016–2019). Recent work emphasizes applications in automotive safety, occluded object reconstruction, and novel imaging systems using advanced optical configurations and neural networks.
