Jonas Kusch is an Associate Professor at the Department of Data Science, Norwegian University of Life Sciences, specializing in numerical analysis and its applications in scientific computing and machine learning. His work focuses on dynamical low-rank approximation, particularly in developing low-rank neural networks with geometry-aware training algorithms that respect the differential geometry of matrix manifolds. Research spans computational quantum mechanics , radiation transport , and machine learning . Key contributions include energy-stable integrators for kinetic equations and multi-fidelity optimization algorithms for fission criticality. Publications emphasize low-rank methods for solving inverse problems, time-dependent systems, and uncertainty quantification in hyperbolic equations.
PD Dr. Lara Schlaffke is a senior researcher at the Ruhr University Bochum 's Medical Faculty, leading the Neuroimaging Working Group at Bergmannsheil University Hospital. Her career bridges neuroscience and clinical neurology through advanced MRI techniques. Habilitation in Experimental Neurology (2022) Head of Quantitative Muscle Imaging Group (2020–present) Postdoctoral research at University Medical Center Utrecht (2016–2018) Her research focuses on quantitative MRI biomarkers for rare neuromuscular diseases, leveraging translational models to link imaging with histology. Key areas include non-invasive disease monitoring, treatment evaluation (e.g., gene therapies), and diagnosis facilitation through Muscle microstructure analysis Disease progression dynamics DTI/vBm for brain-muscle interaction Recent publications (2013–2024) demonstrate expertise in Neuroimaging , with 2024 works covering optoacoustic imaging in Pompe disease and 2023 articles on post-COVID-19 muscle abnormalities. Earlier studies explore brain plasticity in drummers (2020), multicenter MRI standardization (2019), and tactile learning mechanisms (2014). Technical specializations include Diffusion Tensor Imaging (DTI) Voxel-Based Morphometry (VBM) Machine learning for MRI segmentation
Sami Brandt is a Professor in Data Science and Machine Learning at the IT University of Copenhagen. Specializing in Audio-Visual Computing, they lead a research group focused on cutting-edge applications in computer vision, neural networks, and extended reality (XR). Their work bridges mathematical imaging with practical technological solutions. Research Interests: Latent Space Analysis in Diffusion Models Non-rigid Structure-from-Motion Techniques Human Motion Prediction and Smoothing Algorithms Tensor-based Methods for Emotion Recognition Metaverse Applications for Parkinson's Disease Therapies Scientific Awards: Best Student Paper Award (2022), 11th International Conference on Pattern Recognition Applications and Methods Projects: MotiVerseP (2024-2025): Motivation for movement therapies in a metaverse for Parkinson's disease XTREME (2024-2026): Extended Reality Environment for Immersive Experience of Art and Music
Professor Artur d'Avila Garcez is a leading academic in Neural-Symbolic Computation at City St George's, University of London, where he serves as Professor of Computer Science and Director of the Research Centre for Machine Learning. He earned his PhD in Computing from Imperial College London (2000) and holds prestigious fellowships from the British Computer Society (FBCS) and Higher Education Academy (FHEA). With over 150 publications across journals like Artificial Intelligence and Neural Computation , and conferences including AAAI and NeurIPS, he pioneers hybrid AI systems merging machine learning with symbolic reasoning. Education: PhD (2000) and MEng (1993) in Computing Awards: Nuffield Foundation Grant (2002-2004), Daiwa Foundation Grant (2006) His research focuses on Neural-Symbolic Computing , integrating logic-based reasoning with deep learning for explainable AI. Key contributions include Logic Tensor Networks and Knowledge Extraction from CNNs, impacting fields like medical diagnostics and financial technology. He has mentored 22 PhD students and co-authored two foundational books in the field. He serves as Editor-in-Chief for Neurosymbolic AI and on editorial boards of Machine Learning and Journal of Logic and Computation . His work addresses trust in AI , accountability , and human-like computing through EU and industry-funded projects like Smart Big Data Platform and Safety Validation of Autonomous Vehicles.
Dr. Hamish Alexander serves as Senior Lecturer in Surgery at the University of Queensland School of Medicine and Interim Clinical Lead of the Cranial Stream. As a Staff Specialist Neurosurgeon, he maintains active clinical practice with expertise in cranial and spine surgery, specializing in neuro-oncology at Brisbane-based hospitals. His academic qualifications include: Neuroscience and medical degrees from Otago University, New Zealand Master of Philosophy (M.Phil) from University of Queensland focused on Immunotherapy for Gliomas Fellowship of the Royal Australasian College of Surgeons (2016) Neurosurgical oncology fellowship at Memorial Sloan-Kettering Cancer Centre, New York Dr. Alexander's research integrates neuro-oncology with surgical innovation, particularly through 3D-printed simulation models that enhance training safety and precision. His work on glioma immunotherapy targets molecular pathways like EPHA3/ephrin A5, while his rural neurosurgery initiatives address critical gaps in regional emergency care. The Cranial Stream team under his leadership develops protocols for complex tumor resections and spinal procedures. Analysis of his 15 most recent publications (2021-2025) reveals three dominant research streams: neuro-oncology (40% of output, including glioma cell line development and targeted therapies), surgical simulation via 3D printing (40%, covering material science and safety validation), and management of rare cranial/spine pathologies (20%, including metastatic disease and conus medullaris tumors). This multidisciplinary approach bridges laboratory research with clinical implementation. Professional affiliations include: Neurosurgical Society of Australasia Congress of Neurological Surgeons As Interim Clinical Lead of the Cranial Stream, Dr. Alexander oversees a multidisciplinary team developing standardized protocols for emergent craniotomies and complex tumor resections. His simulation workshops using 3D-printed models have been implemented across regional Queensland hospitals to address neurosurgical workforce shortages. Current projects focus on optimizing burr hole drilling safety through VOC emission studies and advancing recurrent glioblastoma models via the QCELL-R resource.
Professor Michael S. Floater is affiliated with the Department of Mathematics at the University of Oslo , specializing in Differential Equations and Computational Mathematics . His research spans approximation theory, numerical analysis, and geometric modeling, with a focus on polynomial and spline-based data approximation, recursive subdivision techniques, and optimal function spaces. Fields of Interest: Approximation Theory, Numerical Analysis, Geometric Modelling, Computational Mathematics, Differential Equations Recent publications address B-spline properties, supersmoothness in Alfeld splits, and advanced polynomial interpolation methods. His work often intersects with applications in computer-aided geometric design (CAGD) and numerical solutions to partial differential equations. He serves on the editorial boards of Computer Aided Geometric Design (CAGD) and BIT Numerical Mathematics , and has supervised technical reports and collaborative projects in computational methods.
Konstantin Fackeldey is a researcher at the Technical University of Berlin and heads the Efficient Large Scale Computing group at the Zuse Institute Berlin (ZIB) . His roles include Deputy Director of the Central Institute SETUB and leadership in the Activity Group Mathematics of Data Science at MATH+. He also serves as a high school mathematics teacher and contributes to the Q-Master program. His research spans drug discovery using high-performance computing and machine learning , with a focus on Protein-Ligand Docking (VirtualFlow Project) and Markov State Models . Collaborative projects include Quantum Skills in Teacher Education and co-organizing the Workshop on Tensor Methods for Quantum Simulation . He holds a habilitation in Applied Mathematics from TU Berlin (2015). Key contributions include developing algorithms for adaptive virtual screening and non-equilibrium molecular dynamics . His work bridges mathematical theory with practical drug design , involving interdisciplinary collaborations across institutions.
Marek Miśkiewicz serves as an Assistant Professor in the Department of Cybersecurity and Computational Linguistics at Maria Curie-Skłodowska University in Lublin (UMCS), Faculty of Mathematics, Physics and Computer Science. He also teaches at the Polish-Japanese Institute of Information Technology (PJATK) and Kozminski University, demonstrating extensive experience in cybersecurity education across multiple institutions. Dr. Miśkiewicz holds a PhD in theoretical physics and has evolved his research focus toward interdisciplinary applications at the intersection of computer science and biotechnology. His primary research interests include DNA-based security systems, cryptographic algorithms leveraging DNA properties, and cybersecurity awareness among youth. He has developed innovative approaches for using DNA as a key element in modern security systems, focusing on authentication, identification, and ownership protection of physical objects through forgery-resistant tagging methods. His publication record shows a clear transition from theoretical nuclear physics (2002-2008) to applied DNA-based security systems (2019-2023), with notable publications in Nature Communications and other high-impact journals. Recent work emphasizes practical educational approaches to cybersecurity through hands-on CTF (Capture The Flag) exercises and virtual laboratories for online learning environments. As an academic educator, Miśkiewicz has authored numerous educational materials on cybersecurity, cryptography, and programming, with a particular focus on developing practical, hands-on learning experiences for students. His research methodology combines theoretical computer science with experimental biotechnology approaches, creating a unique interdisciplinary research profile that bridges traditionally separate scientific domains.
Andrea Ferrero is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Mechanical and Aerospace Engineering (DIMEAS) . He leads research in aerospace propulsion, computational fluid dynamics (CFD), machine learning, and reduced-order modeling, with a focus on turbulent flows, turbomachinery, and combustion. Scientific Director for projects like NextGenSProDesT (2023–2025) and PARSEC (2024–2027). Active in commercial research collaborations with companies like Avio Aero and GE. His work bridges theoretical and applied research, emphasizing AI/ML integration in aerospace systems. Research Interests Andrea Ferrero's research spans: Aerospace Propulsion : Advanced rocket nozzles, hybrid engines, and hydrogen turbofans. Computational Fluid Dynamics : Turbulent and compressible flow simulations, reduced-order models (ROM). Machine Learning : Data-driven turbulence modeling, stress correction with physical constraints. Combustion & Propellants : UV-curable solid propellants, photocurable materials for additive manufacturing. Recent Publications His 2025 articles address hydrogen turbofan fuel systems, rocket nozzle retro-flows, and UV-curable propellants, reflecting trends in sustainable propulsion and AI-enhanced CFD. Earlier works include turbomachinery simulations, resonant igniters, and microwave material uncertainty analysis. Teaching & Students He teaches Innovative Approaches to Turbulent Flow Simulation and Reduced Basis Methods in doctoral programs. His PhD advisees explore topics like: Turbulent flow modeling (Giacomo Gedda, Lorenzo Folcarelli) Hybrid rocket engine optimization (Leonardo Stumpo) Cryogenic fuel systems (Alessandra Zumbo) Topological heat exchanger design (Alessandro Chiodi)
Chunhao Wang is an Assistant Professor in the Department of Computer Science and Engineering at an unspecified university. His research focuses on quantum algorithms, optimal control of quantum systems, and their applications in computational chemistry and machine learning. He has active NSF-funded projects on quantum control and continuous-time open quantum systems. Active NSF Grants: FET Small (2023-2026), CAREER (2023-2028) His work spans quantum computing, algorithm design, and solving high-dimensional problems in optimization and statistical mechanics. Recent publications highlight quantum speedups for classical algorithms and efficient simulation of non-Markovian systems. Collaborations include researchers like Li, X. and Wu, X.
Dr. Muting Hao serves as a Research Fellow at the Oxford Thermofluids Institute (OTI) and an Associate Member of Faculty in the Department of Engineering Science at the University of Oxford. Since 2024, she has held a Career Development Research Fellowship at St John's College, focusing on advancing computational fluid dynamics (CFD) to address global energy and pollution challenges in aviation technology through fundamental research and industrial collaboration. Dr. Hao completed her DPhil in 2022 at the University of Oxford specializing in numerical methods and conjugate heat transfer for gas turbine film cooling under Rolls-Royce sponsorship. Her academic background includes BSc and MSc qualifications, though specific institutions are not detailed in available records. Her research spans numerical methods in fluid dynamics, turbomachinery, turbine cooling, conjugate heat transfer, turbofan design, large eddy simulation (LES), turbulence, and machine learning integration. She investigates unsteady flows across gas turbines, compressors, steam turbines, and nuclear reactor coolant pumps, with current emphasis on aerospace CFD applications and AI-enhanced fluid dynamics modeling for sustainable aviation solutions. Analysis of her publication record (2018-2023) reveals consistent focus on film cooling optimization, turbulence modeling, and advanced CFD techniques for turbomachinery. Recent work increasingly incorporates machine learning approaches and high bypass ratio turbofan design, reflecting strategic alignment with industry needs for efficient propulsion systems. Dr. Hao's contributions have earned significant recognition: UKRI Computing Insight UK 2023 Jacky Pallas Award The Osborne Reynolds 2024 Best Poster Award She currently leads UKRI-funded research on high bypass ratio turbofan design and maintains active collaboration with Rolls-Royce on solver development for next-generation aircraft engines. Dr. Hao mentors potential DPhil students in fundamental fluid dynamics, aerodynamics, high-order numerical schemes, and machine learning applications in CFD, emphasizing practical solutions for aviation sustainability challenges. As a core member of OTI's CFD Methodology group, Dr. Hao operates at the intersection of academic research and industrial application, leveraging Oxford's infrastructure and Rolls-Royce partnerships to advance turbomachinery efficiency through cutting-edge computational approaches and emerging AI methodologies.
Lisa Wruck is a Professor of Biostatistics & Bioinformatics at Duke University School of Medicine and a Member of the Duke Clinical Research Institute. Her work focuses on the design and analysis of pragmatic clinical trials, real-world evidence generation, and neurocognitive data, with applications in cardiovascular disease, stroke, and health disparities. Her educational background: Ph.D., Harvard University, T.H. Chan School of Public Health (2004) Dr. Wruck's research program centers on biostatistics and bioinformatics, with a strong emphasis on pragmatic clinical research and real-world evidence. She investigates the impact of social determinants of health on cognitive outcomes and cardiovascular disease, and is actively involved in data science workforce development initiatives to train the next generation of researchers. Her primary research interests include: Pragmatic Clinical Trials Real World Evidence Neurocognitive Data Analysis Data Science Workforce Development Cardiovascular Disease Epidemiology Stroke and Dementia Outcomes Her recent publications (2024-2025) demonstrate a consistent focus on cardiovascular outcomes, stroke, and dementia, often leveraging large pragmatic trials such as ADAPTABLE. Key themes include aspirin dosing strategies, racial and gender disparities in treatment outcomes, and the application of advanced statistical methods to real-world data for regulatory decision-making. Scientific awards: None mentioned. Dr. Wruck has secured substantial grant funding from diverse sources including the National Institutes of Health (NIH), industry partners (Bristol-Myers/Sanofi, Baxter), and academic institutions. Notable projects include: The Gut Brain Parkinson's Disease Consortium (2024-2029) RADx-UP CDCC (2020-2025) ADAPTABLE trial analyses (multiple grants) Her role as a professor involves mentoring graduate students and postdoctoral fellows in biostatistics and clinical research, though specific advisees were not listed in the provided text. She is actively involved in the Duke Clinical Research Institute and leads coordinating centers for multi-site studies, fostering collaborative research teams across institutions.
John Mazziotta, M.D., Ph.D., is a Professor at the David Geffen School of Medicine, UCLA , where he serves as Vice Chancellor of UCLA Health Sciences and CEO of UCLA Health System . He is Director of the Ahmanson-Lovelace Brain Mapping Center , a hub for neuroimaging research and innovation. His research focuses on brain mapping , neuroimaging , and cerebral metabolism , particularly in neurological and psychiatric disorders. He has pioneered applications of PET , fMRI , and diffusion tensor imaging to study conditions like Alzheimer's disease , epilepsy , schizophrenia , and developmental cognitive processes . Over his career, Mazziotta has co-authored over 150 peer-reviewed publications, with recent work emphasizing adolescent social cognition , white matter architecture , and neurodevelopmental trajectories . His studies often integrate machine learning and multimodal imaging to decode brain-behavior relationships. He collaborates extensively with teams at UCLA and International Consortium for Brain Mapping (ICBM) , contributing to standardized atlas-based image registration and neuroinformatics frameworks. His leadership roles in academic medicine complement his scientific contributions, driving institutional advancements in health sciences and clinical trials .
Ifat Levy is the Elizabeth Mears and House Jameson Professor of Comparative Medicine and Vice Chair for Diversity, Inclusion and Equity at Yale School of Medicine. She holds primary appointment in Comparative Medicine with secondary appointments in Psychology and Neuroscience. As Co-director of the Science Fellows Program, she leads initiatives supporting early-career researchers. Dr. Levy earned her PhD in Neuroscience from Hebrew University of Jerusalem (2004), following undergraduate studies in Physics (1994) and law (LLB, 1997) at Tel Aviv University. Her academic trajectory reflects an interdisciplinary approach bridging quantitative sciences with behavioral research. Her research focuses on neural mechanisms of human decision-making , particularly examining individual differences in risk and ambiguity processing. Using fMRI, eye-tracking, and physiological measurements, her lab investigates how decision traits contribute to pathological behaviors including PTSD, obesity, and eating disorders. Key research themes include value learning under uncertainty, neural representation of rewards/punishments, and lifespan changes in decision processes. Analysis of her recent publications reveals strong emphasis on PTSD neurobiology and computational decision neuroscience . Her work frequently employs advanced modeling techniques to understand how trauma exposure alters neural processing of risk and ambiguity, with particular attention to cerebellar contributions to traumatic memory and cannabinoid system involvement in emotional numbing. Dr. Levy maintains active collaborations with Yale's VA National Center for PTSD, Wu Tsai Institute, and Diabetes Research Center. Her Decision Neuroscience Lab serves as a hub for interdisciplinary research integrating behavioral economics, computational modeling, and neuroimaging approaches. As Vice Chair for Diversity, Inclusion and Equity, she implements initiatives to enhance representation across academic medicine. Her leadership in the Science Fellows Program demonstrates commitment to mentoring next-generation researchers through structured career development frameworks.
Professor Sanjiang Li is affiliated with the University of Technology, Sydney (UTS) as a Professor in the Faculty of Engineering and Information Technology , specifically within the Centre for Quantum Software and Information . With a PhD in Mathematics from Sichuan University and a BSc from Shaanxi Normal University, his research spans quantum computation , knowledge representation and reasoning , and formal verification of quantum systems . His recent work focuses on quantum circuit transformation , including novel methods like adaptive divide-and-conquer and Monte Carlo Tree Search frameworks. He has advanced symbolic verification techniques using tensor decision diagrams and explored classical-quantum hybrid algorithms for resource-efficient training. Dr. Li has received prestigious awards such as the ARC Future Fellowship and Alexander von Humboldt Research Fellowship . He supervises PhD students including Calum Holker and Guangxi Li , and contributes to quantum software development through funded projects like the Sydney Quantum Academy and ARC Discovery Projects .
