جست‌وجوی استاد و پژوهشگر

Alexander Fletcherمشاهده پروفایل

استاد

• Mathematical Biology
• Computational Science
• Tissue Homeostasis
• +۳ مورد دیگر

Professor Alexander Fletcher is a faculty member in the School of Mathematical and Physical Sciences at the University of Sheffield, holding the title of Professor of Mathematical Biology and School Director of Research and Innovation. He previously held roles including Senior Lecturer (2022–2024), Lecturer (2019–2022), and Vice-Chancellor's Fellow (2015–2019). His research focuses on mathematical modeling of biological systems, particularly how tissue-level behaviors emerge from cell-level processes in embryos, cancer, and developmental contexts. Education: DPhil in Mathematics (University of Oxford, 2005–2010), MSc in Mathematical Modelling & Scientific Computing (Oxford), and an undergraduate degree in Mathematics from the University of Cambridge. Research Interests: Development of discrete and continuous models to study tissue homeostasis, cancer formation, and embryonic proportioning. Techniques include individual-based simulations and multiscale modeling. Grants: Lead Principal Investigator on BBSRC-funded projects (e.g., 'Inferring epithelial tissue mechanics through data-efficient multi-fidelity modelling') and EPSRC grants. Collaborative grants include Wellcome Trust-funded work on hypothalamic development. Teaching: Courses include MAS116 Mathematical Investigation Skills and MAS2007 Mathematics and Statistics in Action. Research Group: Member of the Mathematical Biology research group at Sheffield. Labs/Teams: Active in computational biology and mathematical modeling initiatives, contributing to software development like Chaste for multiscale modeling.

Thomas Guillermeمشاهده پروفایل

پژوهشگر ارشد

• Macroevolution
• Macroecology
• Phylogenetics
• +۳ مورد دیگر

Dr Thomas Guillerme is an NERC Independent Research Fellow in the School of Biosciences at the University of Sheffield, UK. His research focuses on macroevolutionary and macroecological processes, integrating morphological and molecular data to understand trait evolution and biodiversity dynamics. He develops computational tools (e.g., dispRity , treats ) for analyzing phylogenetic and trait data. Education: PhD in Zoology (Trinity College Dublin, 2015), MSc in Palaeontology (Université de Montpellier, 2012), BSc in Biology (Université de Montpellier, 2010). Research Interests: Traits diversity and disparity through time, morphological character evolution in phylogenetics, functional ecology, and the application of R packages for biodiversity analysis. He advocates for open science and collaborates with groups at the Natural History Museum and Imperial College London. Software Contributions: dispRity (disparity analysis), treats (tree/traits simulations), SIDER (stable isotope predictions), and tools for morphometric analysis. Future Work: Expanding trait-based analyses in ecology and bridging macroevolutionary with macroecological questions. Actively mentoring researchers and promoting open-access science.

• Machine Learning
• Computer Vision
• Multimodal GenAI
• +۳ مورد دیگر

Xiatian Zhu is a Senior Lecturer at the University of Surrey's Faculty of Engineering and Physical Sciences, affiliated with the Surrey Institute for People-Centred AI and the Centre for Vision, Speech and Signal Processing (CVSSP). He holds a PhD from London and is a Fellow of the Higher Education Academy (FHEA). His research focuses on scalable machine learning, including self-supervised learning, multimodal GenAI, and computer vision applications for autonomous systems. Dr. Zhu's research interests span machine learning, computer vision, and multimodal generative AI, with emphasis on autonomous driving, 3D object detection, and domain adaptation. His work integrates vision-language models for open-set recognition and leverages novel neural architectures for real-time applications. His publications demonstrate a strong focus on autonomous driving perception, 3D generative modeling, and domain-agnostic AI. Key trends include multi-sensor fusion, self-supervised representation learning, and computationally efficient transformers. Recent works also explore audio-visual synthesis and adversarial robustness. Awards: 2016 Sullivan Doctoral Thesis Prize (Best UK Computer Vision Doctoral Thesis) He leads the X-Up Lab and collaborates extensively on grants related to AI safety, efficient 3D generation, and multimodal systems. His team focuses on real-world applications in intelligent transportation and human-centred AI.

Pedro Abdalla Teixeiraمشاهده پروفایل

استادیار مهمان

• Probability Theory
• High-Dimensional Statistics
• Machine Learning
• +۲ مورد دیگر

Pedro Abdalla Teixeira is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine, working with Professor Roman Vershynin. His research focuses on high-dimensional phenomena across probability, statistics, machine learning, and non-convex optimization. His recent publications demonstrate expertise in sparse recovery methods, covariance estimation, synchronization problems, and community detection algorithms. Current investigations include minimum norm interpolation, robust statistical estimation under adversarial conditions, and theoretical guarantees for non-convex optimization landscapes. His work frequently employs techniques from geometric functional analysis and graphical models.

• Cognitive Neuroscience
• Perception
• Data Science
• +۵ مورد دیگر

Pascal Wallisch is a Clinical Professor of Data Science, Psychology, and Neural Science at New York University, affiliated with the College of Arts & Science. His research focuses on understanding how subjective reality is constructed through perception and cognitive processes. He holds a PhD in Cognitive Neuroscience from the University of Chicago and has held progressively senior roles at NYU since 2007, including Post-Doctoral Fellowships and Clinical Professorships across departments. Education: PhD in Psychology (Cognitive Neuroscience), University of Chicago, 2007 MA in Psychology, University of Chicago, 2004 B.A. (Vordiplom) in Psychology, Free University of Berlin, 2000 Research Interests: Wallisch explores perceptual ambiguity, subjective reality construction, and interdisciplinary applications of data science in neuroscience. His work bridges cognitive psychology, computational modeling, and behavioral analysis, with notable studies on phenomena like 'The Dress' illusion and motion perception. He emphasizes educational innovation, including computational neuroscience training through platforms like Neuromatch Academy. Key Research Trends: Recent publications highlight themes in AI-assisted learning, perceptual disagreement mechanisms, and the neural basis of social behaviors. His work often integrates data-driven approaches to address questions in perception, psychopharmacology, and social psychology. Awards & Honors: 2021/22 CAS Teaching Innovation Award 2020 Teach/Tech Award 2016 Golden Dozen College Teaching Award Multiple postdoctoral and graduate fellowships from NIH, University of Chicago, and German academic institutions Grants & Advising: While specific grants are not listed, his roles reflect sustained institutional support. He has advised numerous students in interdisciplinary neuroscience and data science programs, fostering collaborative research environments like the FoxLab (https://www.foxlabnyu.com/). Labs & Teams: Associated with the FoxLab at NYU, focusing on computational neuroscience, perception, and educational technology. Collaborates across NYU’s Center for Data Science, Psychology, and Neural Science departments.

• Harmonic Analysis
• Information Theory
• Signal Processing
• +۳ مورد دیگر

Sinan Gunturk is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on harmonic analysis, information theory, and signal processing, particularly on efficient digital representations of analog signals. He holds a Ph.D. in Applied and Computational Mathematics from Princeton University (2000) and a B.S. in Mathematics and Electrical Engineering from Bogazici University, Turkey (1996). His work bridges sampling theory and quantization, with notable contributions to sigma-delta modulation and compressive sensing. He has held academic roles at NYU since 2001, including Director of Graduate Studies in the Mathematics Department. Gunturk is a recipient of the Alfred P. Sloan Research Fellowship (2006) and the Monroe H. Martin Prize (2005). His publications emphasize quantization theory, sparse recovery, and signal reconstruction, often addressing practical applications in digital signal processing. He has organized major conferences like SampTA 2009 and contributed to editorial boards of journals such as Foundations and Trends in Signal Processing .

• Computer Vision
• Robotics
• Signal Processing
• +۷ مورد دیگر

Prof. Guillermo Gallego is a Professor of Electrical and Computer Engineering at Technische Universität Berlin since 2019. He holds a Ph.D. from Georgia Institute of Technology (2011), with postdoctoral research at Technical University of Madrid (2011–2014) and University of Zurich/ETH Zurich (2014–2019). His research focuses on computer vision, robotics, and ocean engineering, particularly in camera autocalibration, structure-from-motion, and variational methods for ocean wave reconstruction. He has contributed to stereo vision systems for oceanographic remote sensing, developing algorithms for 3D/4D wave reconstruction and calibration refinement. His work bridges geometric optimization, numerical methods, and PDEs, with applications in robotics and environmental monitoring. Education: Ph.D. in Electrical & Computer Engineering (Georgia Tech, 2011), Master's in Mathematics (Georgia Tech, 2009), Telecommunications Engineering (Technical University of Madrid, 2004) Key research interests include: Camera autocalibration via Plücker coordinates and line geometry Space-time reconstruction of ocean waves using variational stereo methods Efficient 3D reconstruction pipelines for uncalibrated systems His publications analyze statistical properties of ocean waves, stereo imaging constraints, and optimization techniques for dynamic scenes. Research has been applied to offshore platforms in the Black Sea and Adriatic Sea. He collaborates with oceanographic institutions to improve wave measurement technologies and extreme wave prediction.

• Ultracold Quantum Gases
• Quantum Technologies
• Quantum Simulation
• +۴ مورد دیگر

Lindsay LeBlanc is an Associate Professor in the Department of Physics at the University of Alberta. She holds the Canada Research Chair (Tier 2) in Ultracold Quantum Gases and is a Fellow of the Canadian Institute for Advanced Research (CIFAR) Quantum Materials Program. Her research focuses on quantum simulation and hybrid quantum systems, leveraging ultracold atoms to explore phenomena in condensed matter physics and quantum technologies. She earned her Ph.D. in Physics from the University of Toronto (2011) and B.Sc. in Engineering Physics from the University of Alberta (2003). Her academic career includes roles as an NSERC Postdoctoral Fellow (2010–2013) at the Joint Quantum Institute and National Institute of Standards and Technology. LeBlanc's research laboratory investigates quantum many-body systems, including the creation of artificial gauge fields and the study of spin-orbit coupling in ultracold gases. Current projects aim to interface ultracold atoms with solid-state devices for quantum technologies, emphasizing coherence and hybrid systems. Her teaching includes Phys 146 (Fluids and Waves), Phys 294/295 (Experimental Physics), and a cross-listed course on Quantum Atomic and Optical Physics. She actively mentors graduate students and postdoctoral researchers, with current lab membership including four PhD and three MSc students. Key achievements include developing quantum memories based on superradiance, microwave-to-optical signal transduction, and reinforcement learning for optimizing ultracold gas production. She has been recognized with the AITF Strategic Chair (Tier 3) and has published extensively in journals like *Physical Review Letters*, *Nature Photonics*, and *Communications Physics*.

Chinmay Hegdeمشاهده پروفایل

دانشیار

• Machine Learning
• Algorithms
• Big Data
• +۶ مورد دیگر

Chinmay Hegde is an Associate Professor at the Tandon School of Engineering, NYU, with a joint appointment in Computer Science and Electrical Engineering. His research focuses on machine learning, signal processing, and their applications in cybersecurity, imaging, and materials science. He leads the DICE Lab, exploring data-driven solutions for complex engineering problems. Previously, he held positions at Iowa State University and MIT. Education: Ph.D. in Electrical and Computer Engineering, Rice University (2012) M.S. in Electrical and Computer Engineering, Rice University (2010) B.Tech. in Electrical Engineering, IIT Madras (2006) Research Interests: Machine Learning, Algorithms, Big Data, Signal Processing, Generative Models, Scientific Computing. Awards: NSF CAREER Award (2018-2023) NeurIPS Top Reviewer Award (2019) MRS Open Data Challenge Award (2019) Ralph Budd Award for Best Thesis (2013) Labs & Teams: Director of the DICE Lab, collaborating with industry partners on projects like real-time deepfake detection (GOTCHA), generative models for microstructure design, and AI-driven materials science.

Marina Gavrilovaمشاهده پروفایل

استاد

• Machine Learning
• Biometric Security
• Image Processing
• +۴ مورد دیگر

Marina Gavrilova is a Professor and Associate Head (Research and Strategic Planning) in the Department of Computer Science at the University of Calgary. Her research focuses on biometric security, machine learning, pattern recognition, and interdisciplinary computational sciences. She is a co-founder of the Biometric Technologies Laboratory and the SPARCS laboratory, and serves as Founding Editor-in-Chief of Springer's Transactions on Computational Sciences. Her work spans ethical AI frameworks, multimodal biometric systems, and healthcare applications. Dr. Gavrilova holds Senior ACM and IEEE membership statuses and has received prestigious awards including the Canada Foundation for Innovation Grant and the University of Calgary’s U Make a Difference Award. Her editorial roles include positions with IEEE Transactions on Computational Social Sciences, IEEE Access, and multiple biometrics journals. Her research explores emotion-aware de-identification systems, trustworthy AI, and bias mitigation in healthcare machine learning. Key contributions include advancements in generative adversarial networks, fusion strategies for multimodal data, and computational methods for medical imaging. She advocates for ethical AI practices and interdisciplinary collaboration to address societal challenges in security and privacy. Lab affiliations include the Biometric Technologies Lab and SPARCS Lab, which focus on computational security and interdisciplinary research. Ongoing work emphasizes social behavioral biometrics, autonomous systems, and AI-driven healthcare solutions.

• Theoretical Physics
• Particle Cosmology
• Geometric Phases
• +۳ مورد دیگر

Michael Morgan is a Professor in the School of Physics and Astronomy at Monash University. His research focuses on theoretical physics, including topological defects, particle cosmology, geometric phases, and X-ray imaging techniques. He has contributed to advancements in electron microscopy and phase contrast imaging, particularly in nanoscale field mapping and vector tomography. His teaching interests include electrodynamics, scattering theory, and quantum mechanics. Professor Morgan has led or participated in multiple research projects funded by organizations like the Australian Research Council (ARC). Notable projects include the 'Nanoscale field mapping in functional materials' and 'Electron Tomography of Electromagnetic Fields'. He has also been recognized with the Vice-Chancellor's Diversity and Inclusion Award in 2020. His work spans interdisciplinary collaborations, addressing challenges in materials science, medical imaging, and computational physics. Key contributions include developing novel microscopy techniques and applying mathematical models to reconstruct physical phenomena. His research has been published in high-impact journals, with a focus on enhancing precision and resolving power in imaging technologies.

Bjørn Tore Hjertakerمشاهده پروفایل

استاد

• Gamma-ray Tomography
• Multiphase Flow Measurement
• Hydrodynamic Modeling
• +۳ مورد دیگر

Bjørn Tore Hjertaker is a Professor at the Department of Physics and Technology, University of Bergen, specializing in Electronics and Measurement Technology. His research focuses on advanced measurement techniques for industrial processes, particularly gamma-ray tomography and multiphase flow analysis. He leads a research group developing novel instrumentation for energy systems, fluid dynamics, and environmental monitoring applications. Research interests span hydrodynamic modeling of offshore wind turbines, multiphase flow characterization in industrial pipelines, and advanced tomographic imaging techniques. His experimental approaches combine gamma-ray densitometry, electrical capacitance tomography, and computational fluid dynamics to solve complex engineering challenges. Publications demonstrate consistent focus on improving measurement accuracy for industrial processes through innovations in gamma-ray tomography, with applications in energy systems, carbon capture, and hydrocarbon flow monitoring. Recent work shows increasing emphasis on renewable energy applications including hydrodynamic optimization of floating wind turbines and closed aquaculture systems. Supervision activities include multiple Master's students working on projects related to hydrodynamic modeling of wind turbines, experimental hydrofoil design, multi-rotor optimization, and fish farm hydrodynamics. His group maintains active collaborations with University of Western Norway and international institutions.

Dror Baronمشاهده پروفایل

دانشیار

• Quantum Information Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Dror Baron is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and has held positions including Postdoctoral Researcher at Rice University, Visiting Scientist at the Technion, and Quantitative Research Analyst at Menta Capital. His research focuses on the intersection of fast algorithms, signal processing, and information theory, with particular interests in quantum information science, machine learning, and communication systems. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (2003) M.Sc. in Electrical Engineering, Israel Institute of Technology (1999) B.Sc. in Electrical Engineering, Israel Institute of Technology (1997) Research Interests: Quantum error mitigation and algorithms Machine learning applications in signal processing Group testing and compressed sensing Generative adversarial networks (GANs) for system modeling Federated learning and privacy-preserving techniques Channel estimation and mmWave communication Awards & Honors: IEEE Senior Member (2010) M. E. Van Valkenburg Graduate Research Award (2002) Program for Outstanding Students, Technion (1994–1997) President's Roll every semester, Technion (1994–1997) Recent Contributions: His work includes developing algorithms to reduce testing quantities for pandemic management, advancing GAN-based receiver modeling, and exploring quantum advantage in finance. Collaborations span academia and industry, with notable contributions to federated learning security and compressed sensing theory.

Suman Chakravortyمشاهده پروفایل

استاد

• Estimation and Stochastic Controls
• Robotic Planning and Control
• Space Situational Awareness
• +۳ مورد دیگر

Suman Chakravorty is a Professor of Aerospace Engineering at Texas A&M University, part of the College of Engineering's Department of Aerospace Engineering. He leads the Estimation, Decision and Planning (EDP) Laboratory, focusing on nonlinear control, robotics, and space situational awareness. He holds a Ph.D. from the University of Michigan (2004) and a B.Tech from the Indian Institute of Technology Madras (1997). His research emphasizes stochastic controls, robotic planning, and data-driven modeling. Notable awards include the National Innovation Award (2017) and a Summer Faculty Fellowship from the Air Force (2010–2012). His work spans space object tracking, autonomous systems, and optimal control of complex systems like material microstructures. Key contributions include the SLAP framework for belief-space planning and advancements in particle Gaussian mixture filters. He advises students such as Utkarsh, Ran Wang, and Dilshad Raihan, and collaborates on projects like T-PFC control and randomized FISST techniques for multi-target tracking. His lab’s research integrates theory and practice, addressing challenges in non-Gaussian uncertainty, distributed estimation, and adaptive sampling. He promotes interdisciplinary approaches to robotics and aerospace systems, with applications in both academia and industry.

Gaurav Pandeyمشاهده پروفایل

دانشیار

• Autonomous Vehicles
• Robotics
• Computer Vision
• +۳ مورد دیگر

Gaurav Pandey is an Associate Professor in the Department of Engineering Technology & Industrial Distribution at Texas A&M University. He holds affiliated faculty positions in Electrical & Computer Engineering and Mechanical Engineering. His research focuses on autonomous systems, reinforcement learning, sensor fusion, and mapping/localization. He earned a PhD in Electrical Engineering from the University of Michigan (2013) after completing degrees at IIT Roorkee (B-Tech, 2006). Research interests include perception for autonomous vehicles, multi-agent motion prediction, and LiDAR-camera integration. He leads the Computer Vision & Robotics Lab, advancing navigation solutions for autonomous robots through deep learning and sensor fusion. Notable recognitions include IEEE Senior Member status (2020) and travel grants from IEEE IROS and AAAI. His work bridges theoretical machine learning with practical robotics applications. Education: PhD (2013), MS (2010) – University of Michigan B-Tech (2006) – IIT Roorkee Recent research trends emphasize infrastructure-enabled navigation systems and LiDAR intensity simulation. His lab collaborates with industry leaders like Ford, focusing on seasonal dataset development and sensor calibration. Active in both academic publishing and real-world robotics implementation, his work addresses challenges in autonomous system decision-making under uncertainty. Labs: Computer Vision & Robotics Lab at Texas A&M Key Collaborations: Ford Motor Company