Find Academic Supervisors

Julia Camps is a postdoctoral research associate at the University of Oxford, Department of Computer Science. Her work bridges Computational Biology and Health Informatics, focusing on cardiac digital twin development for precision medicine applications. She specializes in combining data-driven and mechanistic approaches for in silico clinical trials, particularly through Purkinje network modeling and ECG-based calibration. Education: Informatics Engineer (2014) and Master's in Artificial Intelligence (2015-2017) from Universitat Politècnica de Catalunya PhD in Computer Science (2017-2021) at Oxford, completed within the Computational Cardiovascular Science research group under Prof Blanca Rodriguez Current role: postdoc in Prof Rodriguez's group since 2021, focusing on post-myocardial infarction disease progression Software development: open-source cardiac digital twin tools available on GitHub Her research interests center on creating patient-specific cardiac digital twins using multimodal clinical data. This work enables virtual therapy evaluation and in silico clinical trials through: Integration of statistical inference and machine learning techniques Development of Purkinje network models from clinical ECG data Electrophysiological and repolarization sequence modeling Gait detection algorithms for Parkinson's disease applications Recent publications (2024-2025) demonstrate trends in: GPU-accelerated cardiac electrophysiology simulations (MonoAlg3D) Topology-informed ECG electrode localization Sex-specific electromechanical cardiac modeling Multi-modal characterisation of diabetic cardiac deterioration Pro-arrhythmic risk assessment for stem cell therapies

• Computational Biology
• Health Informatics
• Machine Learning
• +3 more

Professor Yizhou Sun is affiliated with the University of California Los Angeles (UCLA) and the Henry Samueli School of Engineering and Applied Science . Her academic work focuses on Machine Learning , Artificial Intelligence , and Graph Neural Networks within the Computer Science department. Her research spans High-Level Synthesis , Causal Inference , and Computational Biology , with recent publications addressing neural network compression, language model safety, and dynamical system modeling. The trends in her recent 2025 and 2024 publications emphasize Deep Learning , Graph Theory , and Language Model Optimization , reflecting interdisciplinary applications in Biomedical Data , Hardware Design , and Physical Simulation .

• Machine Learning
• Artificial Intelligence
• Graph Neural Networks
• +3 more

Prof. Dr. Andrea Sick is Professor of Cultural and Media History/Theory at the University of the Arts Bremen (HfK Bremen) where she leads the Digital Media study program. She founded the Binational Artistic PhD Program, collaborating with institutions including Leiden University, University of Groningen, and University of Gothenburg. Her research explores intersections between technology, art, and cultural production through both academic and curatorial work. Education includes studies in German Studies, Political Science, Cultural Studies, and Art History at Heidelberg, Bremen, and Hamburg universities, culminating in a 2001 PhD from Universität Hamburg on cartographic knowledge systems. She co-directs the art-science project thealit Frauen.Kultur.Labor , producing exhibitions, conferences, and publications. Research focuses span: Technological media's impact on artistic production Art-biology-information technology intersections Performance art history/theory Queer Studies frameworks Artistic research methodologies Generative AI in cultural analysis Her work critically examines how digital tools transform cultural documentation and archival practices. Recent projects include: Latent Museums (AI analysis of museum collections) The Dynamic Archive (ongoing digital archiving project) Re Capitulating.Queer (2024-2026 curatorial project) Administrative roles include: Dean of Art and Design (2012-2018) Co-Rector for Research & International Affairs (2018-2023) Lead of Binational Artistic PhD Program (since 2022) Awards include the Heinrich-Böll-Stiftung doctoral scholarship (1998-2001). She mentors 12+ PhD students in artistic research, including projects on sonic archaeology, queer archives, and decolonial practices.

• Media Theory
• Cultural History
• Artistic Research
• +5 more

Dr. Aaron Schurger is an Assistant Professor in the Psychology Department at Chapman University’s Crean College of Health and Behavioral Sciences. He is also a member of the Institute for Interdisciplinary Brain and Behavioral Sciences. Schurger holds a BA from Indiana University, and MA and PhD from Princeton University. His research focuses on the neuroscience of volition, consciousness, and decision-making, particularly exploring the readiness potential (RP) and its implications for free will debates. His work challenges classical interpretations of the RP using computational models, suggesting it reflects stochastic neural processes rather than preconscious decisions. Recent contributions include studies on the origins of the RP in spiking neural networks, critiques of causal structure theories of consciousness, and interdisciplinary analyses of free will. His findings emphasize that the RP may not indicate preconscious decision-making but instead arise from natural neural fluctuations during decision thresholds. Schurger collaborates across neuroscience, philosophy, and cognitive science, contributing to debates on consciousness, action initiation, and neural correlates of subjective experience. His research also addresses methodological rigor in studying unconscious processing and integrates computational models with empirical data, as seen in studies on movement timing and neural stability during perception. While no specific grants or labs are explicitly listed, his affiliations suggest involvement in interdisciplinary projects at Chapman.

• Neuroscience
• Cognitive Neuroscience
• Consciousness Studies
• +3 more

Dr. Craig R. Forest is a Professor at the Georgia Institute of Technology's Woodruff School of Mechanical Engineering, specializing in bioMEMS, neuroengineering, and high-throughput instrumentation. He leads the Precision Biosystems Laboratory, focusing on developing robotic tools for neuroscience and genomics. His research bridges mechanical engineering with biological systems, creating innovations like the PatcherBot for automated electrophysiology. Forest earned his Ph.D. (2007) and M.S. (2003) from MIT and B.S. (2001) from Georgia Tech. He has been recognized with awards including the 2013 Georgia Tech Class of 1940 W. Roane Beard Outstanding Teacher Award and Engineer of the Year (2013). His work emphasizes interdisciplinary collaboration, particularly through initiatives like CREATE-X and the Invention Studio, fostering student entrepreneurship and maker culture. Key contributions include ultra-high-throughput genomics tools, microfluidic systems, and acoustic reporter genes for medical imaging. Forest’s lab explores emerging fields like intracellular robotics in neuroscience and molecular communication networks, with applications in drug discovery and personalized medicine. Scientific awards highlight his impact in education and engineering innovation. His grants and collaborations span academic and industrial partnerships, advancing both theoretical and applied research in bioengineering and nanotechnology.

• Neuroengineering
• BioMEMS Technologies
• High-Throughput Genomics
• +4 more

Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.

• Neuroscience
• Neural Computing
• Quantum Algorithms
• +7 more

Dr. Pascale Biron is a Professor in the Department of Geography, Planning and Environment at Concordia University, Montreal. Her research focuses on river dynamics, stream restoration, flood modeling, and climate change impacts. She has been at Concordia since 1998 and holds professional affiliations with key organizations like the Canadian Geomorphology Research Group and the American Geophysical Union. Education: PhD from Université de Montréal and Leeds University (fluvial geomorphology) Research Interests Her work integrates fluvial geomorphology, computational fluid dynamics, and societal aspects of river management. Key themes include: River restoration for fish habitat Floodplain modeling using LiDAR Climate adaptation strategies Socio-hydrological dimensions of restoration Hydrodynamic processes at river confluences Recent Research Trends Recent publications emphasize global salmonid habitat analysis, machine learning for fluvial hazard detection, and the socio-environmental impacts of urban flooding. Her work bridges technical hydrology with policy-relevant solutions for sustainable water management. Grants & Advising Supervises over 15 graduate students (PhD/MSc) in topics ranging from flood modeling to bioengineering. Active in collaborative projects like the 'Freedom Space for Rivers' initiative and large-scale floodplain mapping. Labs & Teams Member of GRIL (Limnology Research Group) and RIISQ (Quebec Flood Network), contributing to interdisciplinary water security research.

• Hydro-geomorphology
• River dynamics
• Stream restoration
• +5 more

Professor Brendan Choat is a leading plant physiologist and Professor at the Hawkesbury Institute for the Environment, Western Sydney University. With a distinguished career in plant hydraulics and water relations research, he has established himself as a global expert in understanding how plants respond to drought stress. His work spans both natural ecosystems and agricultural systems, with particular emphasis on Australian native forests and crop species. Choat's research focuses on the intricate relationship between plant water transport systems and environmental stressors, particularly drought. His work examines how the xylem tissue functions as a hydraulic system that must balance water delivery to leaves while avoiding cavitation (embolism) that can lead to plant mortality. His groundbreaking research has demonstrated that many woody plant species operate close to their physiological safety margins with respect to drought, making them vulnerable to future climate changes. His laboratory employs cutting-edge non-invasive imaging techniques, including X-ray Micro Computed Tomography (microCT) and Magnetic Resonance Imaging (MRI), to directly visualize xylem function in living plants. This approach has allowed his team to address fundamental questions about how cavitation forms and spreads through plant vascular systems during drought stress. Analysis of Professor Choat's extensive publication record reveals a consistent focus on plant drought responses, with particular emphasis on Eucalyptus species and mangrove ecosystems. His research has increasingly incorporated large-scale monitoring approaches, remote sensing data, and trait databases to understand vegetation responses to climate extremes across broader spatial scales. Clarivate Highly Cited Researcher (2018-2024) ARC Future Fellowship (2013) Humboldt Fellowship for Experienced Researchers (2010) Thomson Reuters Citation and Innovation Award (2015) Professor Choat leads multiple significant research projects examining tree dieback in Australian forests, particularly focusing on Eucalyptus species. His work with citizen scientists through the 'Dead Tree Detective' project has provided valuable data on drought impacts across diverse forest biomes. He maintains active collaborations with researchers across Australia and internationally, contributing to large-scale initiatives like the AusTraits plant trait database. His research has direct implications for forest management, conservation strategies, and predicting ecosystem responses to climate change.

• Plant hydraulics
• Xylem function
• Drought stress responses
• +6 more

Ulrich Vogt is a Professor in Applied Physics at Kungliga Tekniska Högskolan (KTH) and leads the X-ray Optics and Nanoimaging group within the Bio-Opto-Nano unit. He serves as Vice-head of the Applied Physics department for undergraduate education. His research focuses on developing advanced X-ray microscopy techniques, particularly at synchrotron facilities like MAX IV’s NanoMAX beamline. He specializes in X-ray optics, nanoimaging, and diffractive optical elements for applications in materials science, biology, and medicine. Key contributions include the design of the NanoMAX beamline, optimization of X-ray zone plates via metal-assisted chemical etching, and advancements in multi-beam ptychography. Vogt has pioneered compact X-ray microscopy systems using laser-plasma sources and liquid-jet targets. His work integrates nanofabrication, computational imaging, and synchrotron instrumentation to achieve sub-100 nm resolution in hard and soft X-ray regimes. Teaching responsibilities include courses on experimental physics, photonics, and X-ray applications. His lab collaborates internationally on projects like the European XFEL, emphasizing high-brightness sources and radiation-resistant optics. Recent innovations include adaptive multi-beam ptychography and stereo X-ray imaging for 3D nanoscale visualization. Research highlights span over 100 peer-reviewed articles, with a focus on coherence characterization, beamline instrumentation, and nanostructured materials. Vogt’s grants include a Röntgen-Ångström Cluster award supporting multi-beam ptychography and cryo-microscopy advancements.

• X-ray Optics
• Nanoimaging
• Synchrotron Radiation
• +3 more

Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.

• Machine Learning
• Artificial Intelligence
• Nonparametric Statistics
• +8 more

Lars-Olof Johansson is a Senior Lecturer at Halmstad University's School of Information Technology, specializing in Informatics. His research focuses on digital service innovation from a learning perspective, emphasizing collaboration between diverse stakeholders and knowledge exchange in innovation processes. He is actively involved in the LeaDS research program (Learning in a Digitalized Society) and teaches in the bachelor's program 'Digital Business Development' and the master's program 'Digital Learning'. His work bridges educational methodologies and technological innovation, particularly in fostering environments where interdisciplinary learning drives successful digital service creation. Notably recognized as an 'Excellent Teacher in Informatics,' he integrates practical experience with academic rigor, contributing to both scholarly discourse and pedagogical advancements. Key projects include SESMA (2019-2021), exploring sustainable mobility solutions, and ongoing collaborations in boundary practices for ICT innovation. His publications span topics like knowledgeability in digital service innovation, ethics in autonomous systems, and collaborative learning frameworks. His awards highlight his pedagogical impact, while his research addresses systemic challenges in innovation through interdisciplinary approaches.

• Digital Service Innovation
• Workplace Learning
• Information Systems
• +3 more

Oded Regev is a Silver Professor at the Courant Institute of Mathematical Sciences, New York University. He has previously held positions at Tel Aviv University and École Normale Supérieure, Paris (CNRS). His research spans theoretical computer science, cryptography, quantum computation, and machine learning applications in biological discovery. Education: PhD in Computer Science, Tel Aviv University (2001) Regev is renowned for his work in lattice-based cryptography, including the introduction of the Learning With Errors (LWE) problem and Gaussian measures. He also leads research using interpretable machine learning to decode RNA splicing logic and nuclear speckle dynamics, with implications in disease and therapeutics. His recent articles focus on quantum factoring, RNA localization, and geometric lattice bounds. Scientific Awards: European Research Council (ERC) Starting Grant (2008) 2018 Gödel Prize 2019 Simons Investigator Award Best Paper Awards: STOC 2003, Eurocrypt 2006 Regev mentors students and postdocs in both theoretical computer science and computational biology. His lab has secured funding from NSF, NIH, and Additional Ventures. He co-founded the TCS+ online seminar series and serves as Associate Editor-in-Chief for Theory of Computing .

• Theoretical Computer Science
• Lattice-based Cryptography
• Quantum Computation
• +3 more

Elena Grigorescu is a Professor at the University of Waterloo, Department of Computer Science. She holds a Ph.D. from the Massachusetts Institute of Technology (2010), an M.S. from MIT (2006), and a B.A. from Bard College (2004). Her research focuses on sublinear-time algorithms, error-correcting codes, computational complexity, and learning theory. She explores foundational aspects of algorithms with constraints on time/space, privacy-preserving computation, and applications in graph theory and optimization. Her work includes advancements in spanner algorithms for network design, differential privacy in sublinear-time settings, and learning-augmented approaches for online optimization. Recent publications address trace reconstruction, privacy-utility trade-offs, and combinatorial optimization techniques. Grigorescu is actively involved in conferences like APPROX/RANDOM and IEEE Foundations of Computer Science, contributing to algorithmic theory and practical implementations. Her research emphasizes theoretical rigor while addressing real-world challenges in data analysis and distributed systems. No awards or formal advisees are explicitly listed in the provided information.

• Sublinear-time algorithms
• Error-correcting codes
• Computational complexity
• +3 more

Laurie Wilcox is a Full Professor in the Department of Biology at York University, affiliated with the Faculty of Science. Her research focuses on stereopsis, binocular vision, and depth perception, particularly exploring how the visual system processes binocular disparity signals. She leads a laboratory investigating cortical systems for fine and coarse disparities, with studies on amblyopia and applied collaborations with companies like Christie Digital and IMAX. Her work bridges basic neuroscience and applied research, addressing depth perception in 2D/3D displays and VR environments. Key interests include stereoscopic volume representation, perceptual grouping, and the impact of monovision on depth judgments. Recent studies examine lightness constancy in virtual reality, depth magnitude errors in 3D displays, and neural activation patterns in object-selective visual cortex. Wilcox has published extensively on binocular vision mechanisms, including coarse stereopsis in strabismus patients and the role of motion parallax in depth perception. Her applied projects evaluate visual fidelity in stereoscopic content, compression algorithms, and ergonomic considerations for XR devices. She also investigates how environmental context (e.g., familiar size, natural scenes) modulates depth perception accuracy across real and virtual environments. Her research emphasizes translational applications, aiming to optimize display technologies through insights from human visual processing. Ongoing work explores perceptual integration of binocular and monocular cues, attention modulation by depth, and the neurophysiological underpinnings of stereoscopic vision.

• Vision Science
• Neuroscience
• Binocular Vision
• +4 more

Anne J. Shiu is a Professor in the Department of Mathematics at Texas A&M University. She holds a Ph.D. in Mathematics (2010) from the University of California Berkeley with advisors Bernd Sturmfels and Lior Pachter. Her career includes postdoctoral positions at Duke University (2010-2011) and the University of Chicago (2011-2014), followed by a faculty role at Texas A&M since 2014. Research Focus: Algebraic, geometric, and combinatorial approaches to mathematical biology, specializing in biochemical dynamical systems, neural coding, parameter identifiability, algebraic statistics, and genomics. Academic Contributions: Over 15 recent publications spanning identifiability in compartmental models, multistationarity in reaction networks, convexity analysis of neural codes, and algebraic robustness in biochemical systems. Scientific Recognition: Association of Former Students Distinguished Achievement College-Level Award in Teaching (2019) Invited speaker for Ethel Ashworth-Tsutsui Memorial Lecture (2018-2019) Current Research: Investigates structural identifiability in biological models, focusing on compartmental systems, reaction networks, and neural codes through algebraic methods and computational tools. Her work bridges abstract algebra with practical biological applications, including parameter estimation and robustness analysis. Grant Support: Recipient of NSF CAREER award (2018-2023), prior NSF grants (2010-2017), and Simons Foundation Collaboration Grant (#521874, 2017-2018). Academic Leadership: Organized multiple international workshops/conferences including SIAM conferences and Banff workshop. Currently an Associate Editor for SIAM Journal on Applied Mathematics and serves on the AIM Scientific Research Board.

• Mathematical Biology
• Algebraic Geometry
• Dynamical Systems
• +3 more