Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .
Professor Ben Goldys is a distinguished academic at The University of Sydney's School of Mathematics and Statistics, where he conducts research at the intersection of pure mathematics and applied sciences. His work spans multiple disciplines including stochastic analysis, partial differential equations, and financial mathematics, with significant contributions to both theoretical frameworks and practical applications in science and finance. Goldys' research interests center on stochastic (ordinary and partial) differential equations and their applications. His specific focus areas include stochastic partial differential equations, stochastic geometric PDEs, stochastic boundary value problems, stochastic fluid dynamics, ergodic theory of infinite-dimensional diffusions, and applications in financial mathematics such as interest rate derivatives, credit risk, and stochastic volatility. His work bridges pure mathematical theory (Functional Analysis, PDEs, Ergodic Theory) with complex real-world problems across multiple domains. His research aligns with the University of Sydney Faculty of Science Research Strengths including Understanding the Universe, Fundamental Laws of Nature, Complex Systems, and Next Generation Materials. Professor Goldys has secured multiple significant research grants from the Australian Research Council, including recent projects such as 'Mathematics for future magnetic devices' (2024), 'Mathematics for breaking limits of speed and density in magnetic memories' (2019), and 'Novel Approaches for Problems with Uncertainties' (2015). His current research projects focus on geometric stochastic partial differential equations and applications in micromagnetism, mean field games in finance, stochastic boundary value problems, and stochastic Navier-Stokes equations on the rotating sphere. He maintains extensive international collaborations with institutions in Germany (University of Tuebingen), Italy (LUISS University), Poland (Institute of Mathematics Polish Academy of Sciences), and the United Kingdom (University of York), working on projects involving optimal control, stochastic systems with memory, and geometric stochastic PDEs. Goldys is an active member of the Applied Mathematics Research Group and The University of Sydney Nano Institute, contributing to interdisciplinary research initiatives that connect mathematical theory with cutting-edge technological applications.
Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford
Leslie Ann Goldberg is a Senior Research Fellow at St Edmund Hall and Professor of Computer Science at the University of Oxford. She currently serves as Head of the Department of Computer Science (on sabbatical 2025-26) and focuses on foundational problems in Algorithms and Complexity Theory , particularly randomised algorithms for network communication, machine learning, and statistical physics models. Her research includes solving Aldous' 1987 conjecture on backoff protocol instability (with John Lapinskas), developing rigorous mathematical analysis frameworks for algorithmic efficiency, and advancing approximate counting techniques via Markov Chain Monte Carlo methods (with Andreas Galanis and collaborators). Key projects involve graph homomorphisms , Moran process dynamics , and #BIS complexity class analysis. Recent publications (2023-2024) span topics like Sybil defense mechanisms, low-temperature sampling on random graphs, and parameterised subgraph counting modulo 2. Her work demonstrates cross-disciplinary impact in computational biology, statistical physics, and database theory. Scientific Awards include Best Paper Prizes at ICALP 2016, ICALP 2010, and IPEC 2017. She supervises PhD student Paulina Smolarova and collaborates extensively with researchers in Oxford and beyond.
Nori Franco serves as Professor in the Department of Physics at the University of Michigan and Chief Scientist at RIKEN's Theoretical Quantum Physics Laboratory in Japan. His dual appointments reflect his significant contributions to both American and Japanese academic communities, with continuous service at Michigan since 1990 and at RIKEN since 2002. His research spans quantum information, condensed matter physics, and quantum optics, with particular focus on light-matter interactions, superconducting qubits, optomechanics, and quantum open systems. Franco's work bridges theoretical foundations with experimental implementations, especially in circuit quantum electrodynamics and quantum computing applications. Analysis of his recent publications reveals a strong emphasis on non-Hermitian quantum systems, quantum control techniques, and applications of quantum information science to fundamental physics problems. His research group consistently produces highly cited work, with publications appearing in top journals across quantum physics and condensed matter disciplines. Scientific Awards: Charles Hard Townes Medal (2024) - sole recipient for fundamental contributions to quantum optics and quantum information processing Research Doctorate Honoris Causa from University of Messina (2024) Highly Cited Researcher for eight consecutive years (2017-2024) Member of Academia Europaea (2023) Willis E. Lamb Medal (2023) for quantum electronics research Throughout his career, Franco has secured significant research funding and mentored numerous students and postdoctoral researchers. His work has received international recognition through invitations to deliver prestigious lectures including the Stanislav Ulam Lecture and Sir Nevill Mott Lecture in 2024. His research group maintains strong collaborations across multiple continents, reflecting his global impact on quantum physics. At RIKEN, Franco leads the Quantum Information Physics Theory Research Team within the Quantum Computing Center, directing cutting-edge theoretical work that complements experimental efforts in quantum computing hardware development.
Joel S. Hayworth is an Associate Professor in the Department of Civil Engineering at Auburn University's College of Engineering. His research focuses on environmental and ecosystem restoration, particularly in estuarine, terrestrial, and freshwater systems. He leads the Estuarine Environments Research Program (EERP), which investigates the fate of endocrine-disrupting chemicals (EDCs), PFAS, and oil spill residues in coastal environments. Dr. Hayworth's educational background includes a PhD in Civil Engineering (Hydrology/Hydraulics) from Auburn University, an MS in Hydrology from the University of Nevada, Las Vegas via the Desert Research Institute, and a BS in Geophysics from the University of California, Santa Barbara. He previously worked at the Tennessee Valley Authority Engineering Laboratory and the U.S. Air Force Research Laboratory, and founded Hayworth Engineering Science in 1999 before returning to academia in 2010. His research interests span environmental engineering, hydrology, hydraulics, estuarine science, pollutant fate and transport, and chemical fingerprinting. He has developed advanced analytical methods for detecting EDCs and PFAS in water, sediment, and biota. His work integrates field studies, laboratory experiments, and environmental modeling to understand complex hydrologic, geologic, chemical, and biological processes in human-impacted ecosystems. The 15 most recent articles highlight a strong trend in environmental contaminant analysis, particularly focusing on PFAS, oil spill residues, and endocrine disruptors. His research combines analytical chemistry with environmental modeling and field monitoring, often in collaboration with interdisciplinary teams. Key themes include the development of UHPLC-MS/MS and GC-MS/MS methods, fate and transport modeling of pollutants, and ecological risk assessment in estuarine systems. Dr. Hayworth's scientific contributions are supported by funding from agencies such as the Gulf Coast Ecosystem Restoration Council (RESTORE Council). His work has led to significant publications in journals like Science of the Total Environment , Marine Pollution Bulletin , and Water . He actively mentors students and collaborates with researchers like T.P. Clement, G.F. John, and V. Mulabagal. His projects, such as the restoration assessment of Cotton Bayou and Terry Cove, demonstrate applied science for environmental problem-solving. He has developed state-of-the-art analytical laboratories and partnered with coastal communities for long-term monitoring. His laboratory, the Estuarine Environments Research Program (EERP), conducts multi-year studies on endocrine disruptors in estuaries, develops innovative sampling and analysis methods, and trains the next generation of environmental engineers and scientists. The team works across disciplines to address complex environmental challenges in the Gulf Coast region.
Daan Christiaens is a tenure track lecturer at KU Leuven's Faculty of Medicine and Faculty of Engineering Sciences. He is affiliated with the Department of Electrical Engineering (ESAT) and Department of Imaging & Pathology, serving as a member of the Medical Imaging Division and the KU Leuven Brain Institute (LBI). His academic responsibilities include membership in the Faculty Councils of Engineering Sciences and Medicine. His research focuses on: Inverse problems in medical imaging reconstruction Neuroimaging techniques for brain analysis Advanced quantitative MRI methodologies Diffusion-weighted imaging for microstructural assessment Dr. Christiaens' recent publications (2023-2025) demonstrate a consistent focus on diffusion MRI innovations, including novel reconstruction algorithms, neonatal brain development mapping, and clinical applications for neurodegenerative disorders. Key technical themes include motion correction, multi-shell modeling, and AI-enhanced image processing, while clinical applications span Alzheimer's disease, cerebral palsy, and autism research. He leads significant research projects including: MRI reconstruction with dynamic field monitoring (2024-2028) Compressed sensing for microstructure imaging (2022-2026) Neonatal diffusion MRI network connectivity analysis (2024-2028) As a core developer of the MRtrix3 software framework for medical image processing, he contributes to essential tools in neuroimaging research.
Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.
Aviad Levis is an Assistant Professor at the University of Toronto's Department of Computer Science, starting July 2024. He is affiliated with the Dunlap Astronomical Data Science and Technology Group (DADDAA) and collaborates with the Toronto Computational Imaging Group alongside Kyros Kutulakos and David Lindell. Previously, he was a postdoctoral researcher at Caltech's Computing + Mathematical Sciences department under Katherine Bouman, working with the Event Horizon Telescope (EHT) collaboration. PhD in Electrical Engineering from the Technion (supervised by Yoav Schechner) Research focuses on computational imaging tools at the intersection of AI and physics Develops algorithms for 3D tomography in both cloud physics and black hole imaging Recipient of ERC Synergy grant for CloudCT space mission His research spans two major domains: Computational Climate Imaging through cloud tomography to improve climate models, and Black Hole Imaging with the EHT collaboration. He pioneered methodologies for 3D cloud structure recovery using scattered sunlight and contributes to dynamic 3D reconstructions of black hole environments. Current interests include non-linear inverse problems, equation discovery from data, and ML-accelerated scientific simulations. Recent publications highlight advancements in atmospheric tomography and black hole emission modeling. His work on CloudCT involves coordinated nano-satellites for 3D cloud imaging, while EHT contributions include first images of Sagittarius A* (2022) and ongoing development of algorithms for 3D structure recovery. The ERC Synergy grant underscores his impact on climate imaging technology. Personal Website Work Email
Markus Heinonen is an Academy Research Fellow at Aalto University's Department of Computer Science within the School of Science. His academic position is tied to Harri Lähdesmäki's Professorship, focusing on probabilistic machine learning. He holds a Doctoral degree in Engineering and Technology from the University of Helsinki (2013). His research integrates probabilistic modeling , deep learning , and differential equations , with applications in computational biology, drug discovery, and biophysics. Key themes include Gaussian processes, Bayesian inference, generative models, and their use in understanding complex biological systems like immune cell behavior (e.g., T cell receptor analysis in aplastic anemia) and molecular design. He leads major projects such as the Deep Learning with Differential Equations initiative (2020–2025), exploring continuous-time models and physics-informed neural networks. His work bridges theory and application, evidenced by collaborations in diffusion models , optimal transport , and single-cell analysis . Publications span over 65 peer-reviewed outputs, with recent emphases on robust neural network training, multi-target molecular prediction, and interpretable drug design frameworks. His research contributes to UN Sustainable Development Goal 3 (Good Health) through advancements in disease modeling and therapeutic development. He has undertaken visiting research roles at the University of California, San Francisco (2017) and Telecom ParisTech (2013–2014). Media highlights include recognition for work on TCR-epitope prediction and AI-driven enzyme engineering.
Thomas M. Antonsen Jr. is a Distinguished University Professor at the University of Maryland, holding joint appointments in the Department of Electrical and Computer Engineering and the Department of Physics. He is affiliated with the Institute for Research in Electronics & Applied Physics (IREAP), Maryland Energy Innovation Institute, and the Institute of Physical Science and Technology. His research focuses on plasma physics, nonlinear dynamics, and high-power coherent radiation sources. Antonsen earned his B.S., M.S., and Ph.D. in electrical engineering from Cornell University (1973–1977) and has held visiting positions at institutions such as the University of California, Santa Barbara, and the École Polytechnique in France. **Education:** B.S., Electrical Engineering, Cornell University, 1973 M.S., Electrical Engineering, Cornell University, 1976 Ph.D., Electrical Engineering, Cornell University, 1977 **Research Interests:** Antonsen’s work spans magnetically confined plasmas, laser-plasma interactions, and advanced vacuum electronics. He has pioneered adjoint methods for optimizing beam-wave interaction systems and contributed to the development of high-power microwave amplifiers. His recent projects include wave chaos in complex systems and machine learning applications in nonlinear dynamics. **Awards & Honors:** James Clerk Maxwell Award (American Physical Society, 2023) IEEE Marie Sklodowska-Curie Award (2022) University of Maryland Distinguished University Professor (2017) IEEE Fellow (2012) **Teaching & Mentorship:** Antonsen teaches courses such as Physics 132 (Biophysics), Electrodynamics, and Plasma Physics. He mentors graduate students in plasma physics and vacuum electronics through his research groups at IREAP and the Bright Beams Collective. **Labs & Collaborations:** His research is supported by grants from the Department of Energy, NASA, and the Office of Naval Research. Key collaborations include the National Institute of Standards and Technology (NIST) and the European XFEL facility.
Ioannis Gkioulekas is an Assistant Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science, with a courtesy appointment in Electrical and Computer Engineering. He leads the Computational Imaging Lab at CMU, focusing on joint hardware-software approaches to develop advanced imaging systems. His research spans computational imaging, computer vision, and graphics, addressing challenges like non-line-of-sight imaging, 3D sensing, and adaptive optics. Research interests include: Computational imaging systems design Non-line-of-sight and single-photon imaging LiDAR, SONAR, and interferometry applications Physics-based and differentiable rendering Probabilistic modeling and Monte Carlo methods Recent publications (2019-2021) demonstrate strong focus on waveguides, light transport simulation, 3D sonar reconstruction, and computational tomography. Common themes include inverse problems, wave-based imaging, and differentiable simulation techniques bridging graphics and sensing. Current advising includes Master's student Neham Jain and affiliates Bakari Hassan, John Liu, Bailey Miller, Sreekar Ranganathan, and Arjun Teh. Past students include PhD graduates Arpit Agarwal and Shumian Xin, and Master's student Shirsendu Halder.
Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.
Ahmed Hassoon is an Assistant Research Professor at the Johns Hopkins Bloomberg School of Public Health, with a primary appointment in the Department of Epidemiology and joint appointments in the Department of Neurology at the School of Medicine and the School of Engineering. He is affiliated with several key research centers, including the Welch Center for Prevention, Epidemiology and Clinical Research, the Center for Diagnostic Excellence, and the Center for Humanitarian Health. MPH, Johns Hopkins Bloomberg School of Public Health, 2014 MD, Baghdad College of Medicine, 2004 Dr. Hassoon’s research is centered on leveraging data science and artificial intelligence to improve healthcare quality and safety, particularly in diagnostic accuracy. His work spans applications in lung cancer, stroke, and emergency medicine, with a strong emphasis on reducing diagnostic errors using frameworks like symptom-disease pair analysis (SPADE). He is deeply engaged in developing AI models that reason across multiple clinical modalities to enhance patient outcomes. His recent publications highlight a strong trend in diagnostic safety, AI-driven interventions, and epidemiological analysis of misdiagnosis harms. Key themes include the validation of computable phenotypes, measurement of diagnostic error burden, and the impact of mental health on acute care diagnostics. Hubert Humphrey Fellowship Award, U.S. Department of State (2010) US President Appreciation Certificate, The White House (2011) Atlas Corps Award (2012) Sommer Scholarship Award (2013) State of Maryland Cigarette Restitution Fund Faculty and Translational Research Awards (2016–2017) Institute for Healthcare Improvement (IHI) Fellowship (2024) Dr. Hassoon is a recipient of the AHRQ K08 Career Development Award focused on improving cancer diagnostic safety at Johns Hopkins Hospital. He co-instructs graduate-level courses in data science and AI with Professor Brian Caffo, contributing to the training of the next generation of public health data scientists. He has not formally advised students listed in the provided texts. His collaborative research spans multiple institutions and has been widely disseminated through policy, news, and social media, indicating significant real-world impact. His work is deeply integrated with the Welch Center and the Center for Diagnostic Excellence, where he contributes to translational research initiatives aimed at improving clinical decision-making and patient safety through innovative data-driven approaches.
Mohammad Modarres is the Nicole J. Kim Eminent Professor at the University of Maryland within the A.J. Clark School of Engineering. He serves as Director of the Center for Risk and Reliability (CRR) and is a Professor of Nuclear Engineering in the Department of Mechanical Engineering. Dr. Modarres co-founded the world's first degree-granting graduate curriculum in reliability engineering at the University of Maryland and has established himself as an international expert in reliability and risk analysis. Dr. Modarres received his educational credentials from prestigious institutions: B.S. in Mechanical Engineering from Tehran Polytechnic M.S. in Mechanical Engineering from MIT M.S. and Ph.D. in Nuclear Engineering from MIT Dr. Modarres' research spans multiple critical areas in engineering risk and reliability. His primary interests include probabilistic risk assessment, uncertainty analysis, probabilistic physics of failure, and probabilistic fracture mechanics. His work encompasses both experimental investigations and sophisticated probabilistic model development. He has made significant contributions to materials degradation science, prognosis and health management systems, and nuclear safety analysis. His research bridges theoretical developments with practical applications in complex engineering systems, particularly in nuclear power and aerospace sectors. Analysis of Dr. Modarres' recent publications reveals a strong trend toward integrating advanced data science techniques with traditional reliability engineering. His work increasingly incorporates machine learning, deep learning, and entropy-based approaches to solve complex problems in prognostics and health management. There's a clear focus on multi-unit systems, particularly in nuclear power applications, and a growing emphasis on data-driven methodologies for remaining useful life estimation and failure prediction. His research maintains a strong foundation in probabilistic methods while embracing cutting-edge computational approaches. Dr. Modarres has received numerous prestigious honors and awards throughout his distinguished career: Nicole Y. Kim Eminent Professorship in Engineering Minta Martin Professorship in A.J. Clark School of Engineering University of Maryland Distinguished Scholar-Teacher (2019) Tommy Thompson Award for outstanding lifetime contributions to nuclear safety (American Nuclear Society) Fellow, American Nuclear Society Fellow, Institute of Electrical and Electronics Engineers (IEEE) Life Fellow of IEEE 1996 Maryland Inventor of the Year Award (in Information Sciences) FDA Commissioner Special Citation for Contributions to Risk Assessment Methods (2004) 2008 International Research Leadership Award (Society for Reliability Engineering, Quality and Operations Management) Honorary Doctorate from Universidad Da Vinci de Guatemala As the founding director of the Center for Risk and Reliability, Dr. Modarres has built a world-renowned program that has awarded over 500 Ph.D. and master's degrees. His research has been supported by significant grants from government agencies and industry partners, particularly in nuclear safety, aerospace reliability, and critical infrastructure protection. He has mentored numerous students who have gone on to become leaders in reliability engineering across various industries. His center collaborates extensively with the International Atomic Energy Agency (IAEA) and other international organizations on risk assessment methodologies. The Center for Risk and Reliability (CRR), which Dr. Modarres directs, serves as a hub for multidisciplinary research in risk and reliability engineering. The center has recently renovated its facilities to enhance collaboration among researchers from different engineering disciplines. CRR maintains strong partnerships with industry leaders including Amazon Lab126 (as evidenced by their collaboration on device durability research) and has been instrumental in advancing probabilistic risk assessment tools used in nuclear power plant safety analysis. The center hosts regular seminars, workshops, and international conferences, positioning itself at the forefront of risk and reliability research globally.
