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 .
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Konstantinos Anastassiadis is a Professor at the Center for Molecular and Cellular Bioengineering (CMCB) of Dresden University of Technology , leading the Stem Cell Engineering group at the Biotechnology Center (BIOTEC) . His research focuses on unraveling molecular pathways regulating stem cell self-renewal and lineage commitment, with a strong emphasis on genetic engineering tool development and epigenetic mechanisms during cellular reprogramming. The lab utilizes mouse and human embryonic stem cells, neural stem cells, mesenchymal stromal cells, and induced pluripotent stem cells (iPSCs) in their investigations. Core Research Areas: Molecular regulation of stem cell fate Epigenetic mechanisms (e.g., UTX/UTY histone demethylases) Genetic engineering tool development (Flp, Dre, Vika recombinases, CRISPR protocols) Conditional immortalization systems for rare cell expansion Publications highlight his contributions to understanding: Role of histone methyltransferases (MLL1, MLL2, Setd1b) in hematopoiesis and cancer Epigenetic regulation during mouse development and spermatogenesis Genetic tools for protein tagging, transposon-mediated BAC transgenesis Interactions between stem cells and niche microenvironments Transcriptional and mechanical markers during reprogramming Collaborations span immunology , developmental biology , and bioinformatics . The lab actively participates in teaching activities at CMCB and maintains a focus on translational applications of stem cell research.
Sebastian Schemm is a Heisenberg Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, a position regarded as equivalent to a non-permanent Associate Professor. He leads research within the Atmosphere-Ocean Dynamics group and previously held an ERC Starting Grant-funded Assistant Professorship (without tenure track) at ETH Zurich. Education and Career Path PhD (2013) and MSc (2010), ETH Zurich, Switzerland Postdoctoral researcher, University of Bergen, Norway (2014–2017) Postdoctoral researcher, Laboratoire de Météorologie Dynamique, ENS Paris (2017–2018) Assistant Professor (ERC Starting Grant), ETH Zurich (2020–2024) Heisenberg Fellow, DAMTP, University of Cambridge (2025–present) Research Focus Schemm’s work centres on atmospheric and climate dynamics, spanning turbulence to planetary scales. Core themes include the physics of extratropical cyclone life cycles, jet-stream and storm-track dynamics, Rossby waves and teleconnection patterns, high-resolution atmospheric modelling, and the integration of machine-learning techniques for parameter estimation, data assimilation, and kilometre-scale global simulations. He also contributes to large-scale initiatives such as ECMWF’s WeatherGenerator. Scientific Awards and Editorial Service DFG Heisenberg Fellowship (2025) ERC Starting Grant (2020–2024) European Meteorological Society Young Researcher Medal (2019) Co-Editor, Weather and Climate Dynamics (EGU) Co-Editor, Quarterly Journal of the Royal Meteorological Society PhD Supervision & Funding He currently supervises PhD students at both Cambridge and ETH Zurich, with funding streams including the Cambridge CREATES Doctoral Training Partnership and Swiss/EU grants. Ongoing students explore reinforcement-learning parameterisations, jet-stream–storm-track relationships, mid-latitude eddy energetics, machine-learning ensemble forecasting, and Bayesian parameter estimation in LES. Active Projects EU Horizon project WeatherGenerator (led by ECMWF) PASC HiRAD-Gen : High-Resolution Atmospheric Downscaling Using Generative Models
Péter Juhász is a Stipendiary Lecturer at Brasenose College and a Postdoctoral Research Associate in the Department of Physics at the University of Oxford. He holds an MA from the University of Cambridge and a DPhil from the University of Oxford. His research centers on quantum physics, specifically quantum computing and ultracold atomic systems. Juhász investigates Bose-Einstein condensates of dipolar gases, focusing on stability, loss mechanisms, and novel trapping geometries. He employs deep learning for atom cloud analysis and develops experimental protocols for large condensate production. His work combines theoretical modeling with advanced experimental techniques in ultracold physics. He previously served as Viscogliosi Fellow and Policy Adviser at the Permanent Observer Mission of the Holy See to the United Nations. He is President of the Cambridge–Oxford Alumni Club of Hungary and volunteers with the Order of Malta's soup kitchen initiatives.
Dr. Kristin A. Persson is a Professor and Daniel M. Tellep Distinguished Professor in Engineering at the University of California, Berkeley's Department of Materials Science and Engineering. She leads the Persson Group at Lawrence Berkeley National Laboratory (LBNL), focusing on atomistic computational methods for energy materials. As director of the Materials Project, she pioneers high-throughput computing and data-driven approaches to accelerate material discovery for clean energy applications, including batteries, electrolytes, and photocatalysts. Her research spans lithium-ion and multivalent batteries, with a focus on electrolyte design, interfacial chemistry, and sustainable materials. Persson has directed the Materials Project since its inception, a global initiative to computationally predict material properties and provide open-access data. She holds affiliations with LBNL’s Energy Sciences Area and collaborates with industry and academia on projects like the Electrolyte Genome and piezoelectric materials databases. Key achievements include election to the National Academy of Engineering (2025), Royal Swedish Academy of Sciences (2024), and Fellowships from the AAAS (2022) and APS (2021). Her group’s work has produced over 200 publications, with recent highlights on disordered cathodes, ML-driven material predictions, and circular polymers. Persson advises a dynamic team of ~50 graduate students, postdocs, and staff, fostering interdisciplinary innovation in energy storage and materials informatics. Awards include DOE’s Distinguished Scientist Fellowship (2024), Cyril Stanley Smith Award (2022), and Web of Science Highly Cited Researcher recognition (2020). Her lab’s infrastructure supports projects from computational workflows to experimental collaborations, with a focus on translating theory into real-world energy solutions.
Suresh Krishna is an Associate Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on the neurophysiological and computational basis of sensory processing, attention, and eye movements, with applications to brain-machine interfaces and human health. He works with human subjects, non-human primates, and open datasets using in-vivo electrophysiology, eye-tracking, and computational modeling. Research interests include visual attention mechanisms, saccadic eye movement control, neural coding of motion perception, and the interplay between attention and decision-making. His work bridges basic neuroscience with translational applications such as improving neural prosthetics and understanding perceptual disorders. Recent work highlights how neural remapping processes during saccades underlie spatial perception, and how attention modulates neural activity patterns in visual cortex. The lab's publications reveal critical insights into the temporal dynamics of attentional shifts and their neural substrates, particularly in areas MT and MST. Dr. Krishna's team also investigates auditory temporal processing in the inferior colliculus, exploring correlations between neuronal responses to sound modulation. Their findings contribute to understanding how sensory systems encode temporal information across modalities. Research is conducted in the M2B3 Lab (http://m2b3.lab.mcgill.ca), which integrates experimental and computational approaches to study brain mechanisms underlying perception and action. No specific awards are listed, but ongoing work involves major contributions to primate neurophysiology and translational neuroscience.
Colin J Akerman is Professor of Neuroscience and Group Leader in the Department of Pharmacology at the University of Oxford, concurrently serving as Corange Fellow and Medical Tutor at Corpus Christi College. His research investigates fundamental mechanisms of synaptic circuit formation and plasticity, with direct implications for epilepsy, dementia, and schizophrenia through multidisciplinary approaches integrating electrophysiology, optical imaging, and computational modeling. His primary research interests encompass Synaptic Plasticity, Neural Circuit Formation, and Excitatory-Inhibitory Balance, with specific focus on neuronal progenitor influences on connectivity, chloride dynamics in inhibitory transmission, and learning mechanisms in disease contexts. The lab employs custom-built equipment and molecular tools to probe synaptic function across in vivo , in vitro , and in silico platforms, emphasizing how activity-dependent processes shape neural networks during development and disease. Recent publications (2023-2025) reveal strong thematic convergence on intracellular chloride regulation in sleep-wake cycles, cortical circuit assembly from embryonic progenitors, and innovative optical tools for neural monitoring. This work bridges molecular neuroscience with systems-level understanding of synaptic plasticity, particularly regarding ionic mechanisms in epilepsy and sleep homeostasis. No scientific awards or fellowships are explicitly documented in the source materials. Professor Akerman currently mentors four PhD students (Vourvoukelis, Selfe, Wang, Gemayel) and multiple postdoctoral researchers, having previously trained scientists now leading independent groups in Toronto, Edinburgh, Cape Town, Oxford, and London. His research is funded by the European Research Council, Innovative Medicines Initiative, and Wellcome Trust, supporting investigations into synaptic mechanisms underlying neurological disorders. The Akerman Group, established in 2008, operates as an integrative neuroscience hub within Oxford's Pharmacology Department. The 10-member team combines expertise in patch-clamp electrophysiology, optogenetics, multiphoton imaging, and computational modeling, with current projects spanning neuronal progenitor biology, inhibitory synaptic plasticity, and learning rule implementation in neural networks. The lab emphasizes technical innovation, regularly developing custom instrumentation and molecular tools for neural observation and manipulation.
Sergei Gukov is the John D. MacArthur Professor of Theoretical Physics and Mathematics at the California Institute of Technology (Caltech), where he has been a faculty member since 2005. He serves in the Division of Physics, Mathematics and Astronomy, with primary affiliation in the Department of Mathematics. His research bridges the fields of mathematics and theoretical physics, focusing on deep connections between geometry, topology, and quantum field theory. Gukov received his B.S. from Moscow Institute of Physics and Technology in 1997, followed by an M.S. and Ph.D. from Princeton University in 2001. He joined Caltech as an Associate Professor in 2005, was promoted to Professor in 2007, and was named the John D. MacArthur Professor in 2021. His research spans several interconnected areas at the frontier of mathematics and physics. A central theme is the exploration of quantum topology and its connections to mathematical physics. He has made significant contributions to the geometric Langlands program, gauge theory, and the categorification of knot and 3-manifold invariants. His recent work increasingly incorporates machine learning approaches to mathematical problems, reflecting his interest in the intersection of traditional mathematical research and modern computational techniques. Gukov's work often reveals deep connections between seemingly disparate areas of mathematics and physics, such as the relationship between Rozansky-Witten geometry and Coulomb branches in supersymmetric gauge theories. Gukov's publications demonstrate a consistent focus on the mathematical structures underlying quantum field theories and their topological implications. His recent work shows an increasing emphasis on computational approaches to mathematical problems, particularly through his interest in mathematics and machine learning. The recurring themes across his research include the application of physical insights to solve mathematical problems and the discovery of new mathematical structures through physical reasoning. He serves on the editorial boards of several prestigious journals including the Journal of Knot Theory and Its Ramifications, Communications in Mathematical Physics, and Letters in Mathematical Physics. Gukov is also active in the academic community, having delivered plenary talks at major conferences such as the First International Congress of Basic Science and presenting at String Math 2023 on the potential impact of AI on mathematical research. Gukov teaches Ma 146 ab, Introduction to Knot Theory and Quantum Topology, a course that reflects his research interests. He also runs a seminar on Mathematics and Machine Learning, held Tuesdays from 2-3pm in East Bridge Conference room 114, demonstrating his commitment to fostering interdisciplinary research at the intersection of mathematics and computational methods.
Ralf Haefner is an Assistant Professor in the Departments of Brain & Cognitive Sciences and Physics & Astronomy at the University of Rochester, holding this joint appointment since 2014. His interdisciplinary research bridges neuroscience and physics to investigate computational principles of perception and decision-making. Education and professional background: PhD, Oxford University, 1999 Visiting Research Fellow, Department of Neurobiology, Harvard Medical School Swartz Fellow, Sloan-Swartz Center for Theoretical Neurobiology, Brandeis University Haefner's research program centers on computational neuroscience , with primary focus on how the brain forms perceptual beliefs and uses them for decisions through Bayesian modeling . He employs machine learning tools to construct mathematical models explaining neural responses and behavior, particularly in the visual domain. His work addresses neural representation of uncertainty, causal inference mechanisms, and probabilistic computation in cortical circuits. Analysis of recent publications (2023-2025) reveals three dominant trends: (1) causal inference frameworks applied to motion perception and segmentation, (2) Bayesian modeling of perceptual biases and confidence computations, and (3) integration of generative and discriminative neural computations. His work extends beyond traditional neuroscience into scientific methodology through 'Generative Adversarial Collaborations' for improving research discourse. Honors and Awards: Swartz Fellowship, Sloan-Swartz Center for Theoretical Neurobiology NSF CAREER Award (2022) for 'Approximate inference at the intersection of neuroscience and machine learning' Haefner secured significant research funding through his NSF CAREER award, which supports foundational work on probabilistic inference at the neuroscience-ML interface. While specific students aren't listed, his active publication record and lab infrastructure suggest ongoing mentorship of graduate students and postdocs. His research has clinical relevance as shown by studies on perceptual abnormalities in autism spectrum disorder, indicating translational potential for understanding neurological conditions.
Dr. Igor V. Pivkin is a Full Professor at the Institute of Computing within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His academic journey includes degrees from Novosibirsk State University (B.Sc./M.Sc. Mathematics), Brown University (M.Sc. Computer Science and Ph.D. Applied Mathematics), and postdoctoral research at MIT's Department of Materials Science and Engineering. His research focuses on multiscale/multiphysics modeling , numerical methods , and large-scale simulations of biological and physical systems. Key areas include biophysics, cellular/molecular biomechanics, stochastic modeling, and coarse-grained molecular simulations. He leverages high-performance computing (HPC) and particle-based methods to address complex biological phenomena. His work spans diverse applications, from understanding cellular mechanosensitivity and biofilm engineering to modeling cancer cell behavior and red blood cell dynamics in the spleen. His contributions bridge computational science, biotechnology, and biomedical research. He has published extensively in top-tier journals, with recent work advancing automated biofilm analysis, deep learning for microbial classification, and systems biology approaches to metal bioleaching. His lab collaborates on interdisciplinary projects, emphasizing computational innovation for real-world biological challenges.
Miroslav Pajic serves as a Professor in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering. He also holds joint appointments as Associate Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science and Associate Professor of Computer Science. As Director of Master's Studies, he oversees the graduate program in Electrical and Computer Engineering and teaches numerous courses spanning embedded systems, cyber-physical systems design, and robotics. Education: Ph.D. in Electrical and Computer Engineering from University of Pennsylvania (2012) Miroslav Pajic's research focuses on the design and analysis of cyber-physical systems (CPS) with varying levels of autonomy and human interaction. His work spans the intersection of embedded systems, artificial intelligence, machine learning, control theory, formal methods, and robotics. He specializes in developing high-assurance autonomous systems with applications in robotics, automotive systems, and medical devices, with particular emphasis on CPS security and resilient autonomy. His research addresses fundamental challenges in creating systems that can operate reliably in uncertain environments while maintaining security against potential cyber attacks. Analysis of Pajic's recent publications reveals a strong interdisciplinary research program bridging theoretical foundations with practical applications. His work spans secure sensor fusion for distributed autonomy, medical applications of CPS (particularly deep brain stimulation for neurological disorders), and innovative sensing technologies for autonomous vehicles. A significant portion of his research addresses security challenges in cyber-physical systems, including stealthy GPS attacks on UAVs and methods for attack-resilient state estimation. His publications increasingly integrate machine learning techniques with traditional control theory to create more adaptive and robust autonomous systems. Pajic actively mentors graduate students and leads research groups focused on cyber-physical systems security and high-assurance autonomy. His research is supported by multiple grants, including the NSF AI Institute for Edge Computing (Athena), which he co-leads. He has received funding from various sources to support his work on secure and resilient cyber-physical systems, medical device security, and autonomous vehicle technologies. Pajic collaborates extensively with medical researchers on applications of cyber-physical systems in healthcare, particularly in deep brain stimulation for neurological disorders. His work bridges the gap between theoretical control systems and practical implementations in safety-critical domains, with a growing emphasis on translating research into real-world applications that improve system security and reliability.
Professor Stefan Goedecker is a distinguished faculty member in the Department of Physics at the University of Basel, Faculty of Science. He holds the position of Professor of Computational Physics and leads an active research group focused on developing advanced computational methods for materials science and quantum physics. Dr. Goedecker received his physics education at the Technical University Munich and the College of William and Mary, followed by a Ph.D. from EPFL Lausanne. His postdoctoral training included positions at Cornell University and the Max-Planck Institute in Stuttgart. In 2003, he was appointed Professor of Computational Physics at the University of Basel, where he has established himself as a leading researcher in computational methods development. His research interests center on computational physics with emphasis on electronic structure calculations, atomistic simulations, and the development of novel algorithms for materials science applications. His work has strong interdisciplinary connections spanning physics, mathematics, material sciences, chemistry, and computer science. Current research directions include machine learning applications in catalysis, fourth-generation neural network potentials for molecular chemistry, and methods for quantifying material synthesizability. Analysis of his recent publications reveals a strong focus on advancing computational methods for electronic structure calculations, with particular emphasis on machine learning potentials, molecular dynamics optimization, and accurate modeling of material properties. His work bridges theoretical physics with practical applications in materials science and nanotechnology, with increasing integration of artificial intelligence techniques into traditional computational physics frameworks. Machine learning for Catalysis (Ongoing) Fourth-Generation Neural Network Potentials for Molecular Chemistry (Completed) Towards Quantifying the Synthesizability of Materials (Completed) Professor Goedecker's research group operates within the Department of Physics at the University of Basel, which is part of the NCCR SPIN initiative focused on silicon-based quantum computing development. The department hosts over 20 research groups with more than 180 teaching staff members, creating a vibrant research environment for computational physics and quantum technologies.
Thomas Yizhao Hou is the Charles Lee Powell Professor of Applied and Computational Mathematics at the California Institute of Technology, where he has served as a faculty member since 1998 and as Executive Officer of Applied and Computational Mathematics from 2000-2006. His research spans fundamental mathematical problems with significant implications for fluid dynamics and computational science. Hou received his B.S. in Mathematics from South China University of Technology in 1982, followed by an M.S. in 1985 and Ph.D. in 1987 from UCLA under the supervision of Prof. Bjorn Engquist. His academic journey includes positions at the Courant Institute and the Institute for Advanced Study before joining Caltech. Hou's research focuses on multiscale analysis and computation, interfacial problems, stochastic PDEs and uncertainty quantification, and the Millennium Problem concerning global regularity of 3D incompressible Euler and Navier-Stokes equations. His work on adaptive data analysis has led to significant methodological innovations. His research is characterized by the integration of rigorous mathematical analysis with computational approaches to tackle problems that have resisted traditional methods. His recent publications reveal a consistent focus on singularity formation in fluid equations, particularly the Euler and Navier-Stokes equations, with increasing sophistication in analyzing potential blowup scenarios. His work spans theoretical analysis, numerical verification, and the development of innovative mathematical frameworks for multiscale problems. Member of the National Academy of Sciences (2024) William Benter Prize in Applied Mathematics (2024) SIAM Ralph E. Kleinman Prize (2023) SIAM Outstanding Paper Prize (2018) Fellow of the American Mathematical Society (2012) Fellow of the American Academy of Arts and Sciences (2011) Hou has served in significant editorial roles including Founding Editor-in-Chief of the SIAM Journal on Multiscale Modeling and Simulation and Co-Editor-in-Chief of Research in Mathematical Sciences. His professional service includes membership on the SIAM Council and leadership roles at the Institute of Mathematics and its Applications. His research has been supported by numerous grants focusing on multiscale modeling, fluid dynamics, and computational mathematics.
Professor Manolis Gavaises is a leading academic in the field of mechanical engineering and computational fluid dynamics at City St George's, University of London, where he holds the position of Professor in the School of Engineering and Mathematical Sciences. He earned his PhD from Imperial College London and has been a faculty member since 2001, progressing to full Professor in 2009. His research is centered on advanced modeling of multi-phase flows, cavitation, and fuel injection systems, with extensive collaborations across Europe and industry partners such as Delphi, Caterpillar, and BP. Education: DIC, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 PhD, Mechanical Engineering, Computational Fluid Dynamics, Imperial College London, 1997 Diploma (5 years), Mechanical Engineering, National Technical University of Athens, 1992 His research interests span computational fluid dynamics, cavitation, fuel injection, atomization, high-pressure and supercritical flows, and alternative fuels . He has developed advanced numerical models and experimental techniques, including X-ray phase contrast imaging and high-pressure test rigs. His work integrates fundamental DNS and LES simulations with industrial applications in automotive, marine, aerospace, and medical devices such as heart valves. The recent publications reflect a strong trend toward real-fluid thermodynamic modeling (e.g., PC-SAFT), multi-component fuel behavior, cavitation erosion, and advanced diagnostics . His research increasingly incorporates machine learning and high-fidelity imaging to understand complex flow phenomena across energy, transportation, and biomedical domains. Scientific Awards and Recognitions: Richard Way Prize (1998) Arch T. Collwell Merit Award (1998) Best Oral Paper, SAE World Congress (2006) PE Publication Award, IMechE (2007) Best Presentation Award, Engine Combustion Processes (2009) Fellow, IMechE (2013) Fellow, IMA (2015) As a dedicated mentor, Professor Gavaises has supervised 13 PhDs to completion and currently guides 23 doctoral students. He has secured over €16 million in EU and UK funding, including multiple Horizon 2020 Marie Skłodowska-Curie ITN projects (CAFÉ, HAOS, IPPAD), which support 46 early-career researchers globally. He has created academic opportunities for post-docs and junior faculty, significantly advancing the research profile of his institution. He leads the International Institute of Cavitation Research (IICR), co-founded in 2011 with partners from Loughborough University, TU Delft, and Imperial College, supported by The Lloyd’s Register Foundation. His lab maintains strong experimental capabilities, including a 2000bar pressure flow rig with micro-transparent nozzles and collaborations with Argonne National Laboratory for X-ray imaging.
