جست‌وجوی استاد و پژوهشگر

Maitreyee Royمشاهده پروفایل

دانشیار

• Optical Physics
• Quantum Dots
• Contact Lens
• +۶ مورد دیگر

Associate Professor Maitreyee Roy is a researcher at the School of Optometry and Vision Science, University of New South Wales (UNSW Sydney). She specializes in Optical Physics, Quantum Dots, 3D Optical Imaging, and Artificial Neural Networks with applications in biomedical instrumentation, nanophotonics, and optometry/ophthalmology. Her research includes: Developing novel imaging technologies like Full-Field Optical Coherence Microscopy Investigating blue-blocking lens effects on visual/non-visual systems Creating AI-driven ocular disease detection systems Visualizing tear film dynamics using non-toxic quantum dots 3D reconstruction of OCT images for glaucoma diagnosis Recent publications focus on machine learning applications for glaucoma staging, quantum dot tear film imaging, and 3D medical visualization techniques. She collaborates with international labs at UC Berkeley, Tokyo Tech, and Mexico's INAOE. Awards: NMI World Metrology Day Outstanding Achievement (2013, 2011) ProSciTech Trans-Tasman Award (2006) Fellow of Optical Society of America JSPS Invitation Fellowship (2004) She supervises postgraduate and undergraduate students in projects related to optical coherence tomography, artificial intelligence applications, and quantum dot bioimaging. Professional memberships include Optical Society of America, SPIE, and Australian Standards Committee for eye protection.

James Tompkinمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Vision
• Human-Computer Interaction
• +۳ مورد دیگر

James Tompkin is an Associate Professor in the Department of Computer Science at Brown University, specializing in visual computing. His research focuses on computer graphics, computer vision, and human-computer interaction, with an emphasis on techniques for image/video creation, editing, analysis, and interaction. His lab develops methods for scene reconstruction (especially from multi-camera systems), dynamic scene modeling, and applications in 2D, multi-view, and VR/AR displays. Research interests include neural radiance fields (NeRFs), Gaussian splatting, time-of-flight sensing, and generative adversarial networks (GANs). He has collaborated extensively with industry partners (Adobe, Amazon, Meta) and received funding from NSF, DARPA, NASA, and UK/EPSRC. His work is disseminated via top-tier venues like CVPR, SIGGRAPH, and ECCV. Teaching includes visual computing topics, and his lab maintains active projects on GitHub and project webpages. Office hours are held weekly, with scheduling via Google Calendar integration.

Jingfeng Zhangمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Adversarial Machine Learning
• +۳ مورد دیگر

Jingfeng Zhang is a Lecturer at the University of Auckland's School of Computer Science and a Visiting Research Scientist at RIKEN Center for Advanced Intelligence Project. Holding a PhD from the National University of Singapore, he's supervised by Prof. Masashi Sugiyama at RIKEN and Prof. Mohan Kankanhalli at NUS. Education: PhD (2016-2020) at NUS, BEng (2012-2016) at Shandong University's Taishan College Current research focuses on Trustworthy Machine Learning , Adversarial Robustness , and Foundation Model Security His publication history shows a strong focus on adversarial learning techniques across multiple top conferences (ICML, NeurIPS, ICLR). He's developed methods for improving model robustness against various attacks including backdoor attacks, clean-label poisoning, and adversarial noise. The work spans from theoretical foundations to practical implementations in neural network training. He supervises several students including: Zihao Luo (Master's, UoA) Xin Chen (PhD, UoA with Prof. Gill Dobbie) Di Zhao (PhD, UoA with Prof. Yun Sun Koh and Prof. Gill Dobbie) Professional service includes organizing workshops at ACML and TrustML, serving as Area Chair at SOICT, and reviewing for top venues in machine learning and AI.

• Computational Statistics
• Machine Learning
• Bayesian Inference
• +۳ مورد دیگر

Saifuddin Syed is a Florence Nightingale Bicentenary Research Fellow at the University of Oxford's Department of Statistics, supervised by Arnaud Doucet and funded by the CoSInES project. His research focuses on computational statistics and machine learning, particularly scalable Bayesian inference, Monte Carlo methods, and non-reversible parallel tempering. He completed his PhD in Statistics at the University of British Columbia under Alexandre Bouchard-Côté. Currently, he contributes to the Next Generation Event Horizon Telescope (ngEHT) collaboration, improving algorithms for modeling and imaging supermassive black holes. Research interests include parallel tempering, sequential Monte Carlo, information geometry, and statistical physics. His work spans methodological development in MCMC schemes and applications in complex systems. He is affiliated with the Computational Statistics and Machine Learning research group and Statistical Theory and Methodology at Oxford. Key contributions include advancing non-reversible parallel tempering techniques to enhance computational efficiency in high-dimensional sampling, with applications to astrophysics and statistical mechanics. His recent work emphasizes scalable algorithms and rigorous theoretical analysis of MCMC performance.

Jacob Laurelمشاهده پروفایل

استادیار

• Static Analysis
• Programming Languages
• Probabilistic Programming
• +۳ مورد دیگر

Jacob Laurel is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. His research focuses on static analysis of programming languages, particularly in probabilistic and differentiable programming. He actively recruits PhD students starting Fall 2025 for work in these areas. Research interests include static analysis techniques for probabilistic/differentiable languages, compiler optimizations, and formal methods for ensuring correctness in AI systems. His work spans topics like abstract interpretation, automatic differentiation, and uncertainty quantification in distributed systems. Notable contributions include the Diamont framework for uncertainty monitoring in distributed programs, the Statheros compiler for low-precision probabilistic inference, and foundational work on higher-order automatic differentiation analysis. He has served on program committees for OOPSLA, SAS, ECCV, and WFVML. Teaching includes a special topics course on probabilistic/differentiable programming in Spring 2025.

Walter Fichterمشاهده پروفایل

استاد

• Flight Control Systems
• Aerospace Engineering
• Satellite Dynamics
• +۵ مورد دیگر

Prof. Dr.-Ing. Walter Fichter is Director of the Institute of Flight Mechanics and Flight Control at the University of Stuttgart, within the Faculty of Aerospace Engineering and Geodesy. With 17 years of industrial experience at Airbus Defence and Space and Mitsubishi Electric, he has contributed to six satellite control systems and holds 14 patents. His research focuses on advanced flight control systems, autonomous UAVs, helicopter dynamics, and spacecraft navigation. Notable projects include the LISA Pathfinder mission's drag-free system and the MosaicGPS receiver for geostationary orbits. His expertise spans satellite aerodynamics, nonlinear control theory, and AI-driven autonomous flight. Current research includes energy-efficient soaring algorithms, distributed propulsion systems (e.g., Icaré aircraft), and real-time evasion maneuvers for eVTOLs. Prof. Fichter actively pilots aircraft (EU/US certifications) and serves on the AIAA Journal of Guidance, Control, and Dynamics advisory board. Key achievements include pioneering tilt-to-length noise estimation techniques for gravitational wave observatories and developing stall-prevention systems for gliders. His work bridges academic research with industrial applications, addressing challenges in space exploration, urban air mobility, and sustainable aviation technologies.

Peng Songمشاهده پروفایل

استادیار

• Computer Graphics
• Geometric Modeling
• Computational Design
• +۵ مورد دیگر

Peng Song is an Assistant Professor of Computer Science at Singapore University of Technology and Design (SUTD), affiliated with the Pillar of Information Systems Technology and Design (ISTD). His research focuses on computer graphics, geometric modeling, computational design, and fabrication, emphasizing the development of algorithms for designing functional real-world objects. He holds a PhD from Nanyang Technological University (2013) and prior research roles at EPFL, University of Science and Technology of China, and Nanyang Technological University. Education: PhD in Computer Science, Nanyang Technological University (2013) Master’s Degree, Harbin Institute of Technology (2010) Bachelor’s Degree, Harbin Institute of Technology (2007) His research interests span computational design of assemblies, 3D printing, robotics, and urban modeling. Notable projects include generative urban design, wireframe mesh modeling, and mechanisms for custom-fit medical devices. He has received awards such as the SMI 2024 Best Paper Award and SIGGRAPH 2022 Honorable Mention. Grants & Professional Roles: Leading Singapore MOE Academic Research Fund Tier 2 grants (2023–2024) Associate Editor for Computers & Graphics and Graphical Models (2024–present) Program Committee Member for SIGGRAPH Asia, Pacific Graphics, and SPM He teaches courses such as 'Graphics and Visualization' and 'Extended Reality' at SUTD. His group’s work is showcased on YouTube and his lab’s website.

Jake Levinsonمشاهده پروفایل

استادیار

• Algebraic Geometry
• Algebraic Combinatorics
• Schubert Calculus
• +۱۰ مورد دیگر

Jake Levinson is an Assistant Professor in the Department of Mathematics at Simon Fraser University, Faculty of Science. His research bridges algebraic geometry and combinatorics, focusing on the interplay between geometric structures and discrete mathematics. Levinson's primary research interests include: Algebraic Geometry, particularly moduli spaces of stable curves (M_{0,n}), Hilbert schemes, and intersection theory. Algebraic Combinatorics, with emphasis on Schubert calculus, Young tableaux, crystal structures, and combinatorial aspects of representation theory. Representation Theory of GL_n, flag and Grassmannian varieties, and equivariant cohomology. His recent publications reveal a strong trend in studying the combinatorial geometry of moduli spaces, especially M_{0,n}, through degenerations, multidegrees, and products of cohomology classes. He also investigates crystal-like structures on shifted tableaux and topological proofs of conjectures in real algebraic geometry. Collaborations with Maria Gillespie, Kevin Purbhoo, and Sean T. Griffin are frequent, indicating active research networks in combinatorial algebraic geometry. Levinson teaches advanced courses such as Math 819 Topics in Algebraic Geometry: Schemes, using foundational texts by Hartshorne and Vakil. His educational background includes a Ph.D. from the University of Michigan advised by David Speyer, an NSERC Postdoctoral Fellowship at LaCIM, and an Acting Assistant Professorship at the University of Washington. He also spent a year as an AI Resident at Google Research, reflecting an interdisciplinary reach. His undergraduate studies were at Williams College, with formative experiences at Budapest Semesters in Mathematics and the SMALL REU program. He has not received any scientific awards mentioned in the text. He does not appear to have advised any students listed publicly. He is involved in computational mathematics, having run a Lean workshop, and maintains an interest in mathematical exposition and public engagement through blog posts and mini-courses on Schubert calculus.

JASON HANNAمشاهده پروفایل

استادیار

• Cancer Biology
• Molecular Biology
• Angiosarcoma Development
• +۴ مورد دیگر

Dr. JASON HANNA is an Assistant Professor in the Department of Biological Sciences at Purdue University, affiliated with the College of Science. His research focuses on cancer cell and molecular biology, particularly studying microRNAs in angiosarcoma development and metastasis. He leads the Hanna Lab, which investigates vascular sarcomas such as angiosarcoma and epithelioid hemangioendothelioma (EHE), aiming to uncover genetic drivers, tumor suppressors like DICER1, and develop precision therapies for these aggressive cancers. Education includes a Ph.D. from Yale University and postdoctoral research at St. Jude Children's Research Hospital. His work integrates genetic models, in vivo studies, and cell-line investigations to address tumor initiation, progression, and therapeutic design. Research interests span molecular mechanisms of sarcomagenesis, microRNA-mediated regulation, and translational approaches to combat rare vascular cancers. His lab’s goals include identifying metastasis mediators and advancing targeted treatments for these poorly understood malignancies. Publications span digital imaging forensics (e.g., JPEG compression detection, printer source identification) and biomedical engineering (e.g., force myography for locomotion analysis), reflecting interdisciplinary collaboration. Awards and grants are not explicitly listed, but his work demonstrates sustained innovation in both biological and computational domains.

Rongjie Laiمشاهده پروفایل

استاد

• Computational Mathematics
• Machine Learning
• Applied Geometry
• +۴ مورد دیگر

Rongjie Lai is a Professor of Mathematics at Purdue University's Department of Mathematics, part of the College of Science. His research focuses on computational mathematics, machine learning, and applied geometry, with contributions to manifold learning, operator learning, and partial differential equations. He holds a position in the Department of Mathematics and is affiliated with the College of Science's broader computational and mathematical initiatives. His research interests include developing mathematical frameworks for geometric and topological data analysis, optimization algorithms for high-dimensional data, and applications in scientific computing. Notable work involves advancing neural network architectures for complex systems and developing efficient methods for solving PDEs on manifolds. Recent publications emphasize interdisciplinary applications, such as mean-field games, adversarial training in neural networks, and bio-inspired computing. His work bridges theoretical foundations and practical applications, impacting fields like computational biology and materials science. Despite his extensive output, no specific awards or grants are explicitly listed in the provided text.

Christopher K. Mayمشاهده پروفایل

عضو هیئت علمی

• Computer Vision
• Generative Adversarial Networks
• Image Synthesis
• +۳ مورد دیگر

Christopher K. May is an Assistant Teaching Professor in the Department of Computer Science at Purdue University, joining in Fall 2024. He holds a B.S., M.S., and Ph.D. in Computer Science from Purdue University, all completed between 2013 and 2024. His research focuses on computer vision, generative adversarial networks (GANs), and 3D rendering techniques, particularly in omnidirectional image synthesis and satellite imagery analysis. His educational background includes a decade of study at Purdue University, culminating in his doctorate in 2024. His work spans applications such as video frame interpolation, facade synthesis from satellite data, and interactive 3D rendering optimization. His research often intersects machine learning and computer graphics, addressing challenges in synthetic image generation and geospatial data processing. Dr. May's publications reflect a strong emphasis on generative models, with recent work exploring explicit camera control in omnidirectional synthesis (EpipolarGAN, 2024) and cube-based GAN architectures (CubeGAN, 2023). Earlier contributions include video folding techniques for framerate enhancement (2021) and satellite-based 3D building regularization (2020). No scientific awards or grants are explicitly listed in the provided information. His current position emphasizes teaching responsibilities alongside research.

Alexandra Golbyمشاهده پروفایل

استاد

• Neurosurgery
• Medical Imaging
• Image-guided Surgery
• +۴ مورد دیگر

Alexandra Golby, MD is a Professor of Neurosurgery and Radiology at Harvard Medical School, and Haley Distinguished Chair in the Neurosciences at Brigham and Women's Hospital. She directs the Golby Lab, a surgical brain mapping laboratory focused on advanced imaging technologies for neurosurgical applications. Her clinical expertise centers on brain tumor and epilepsy surgery, with a focus on lesions near critical brain structures. Dr. Golby holds multiple leadership roles including Director of Image-guided Neurosurgery and Co-Director of AMIGO at Brigham and Women's Hospital. Her research integrates disciplines such as computer science, applied mathematics, and biomedical engineering to improve surgical planning and intraoperative decision-making. Notable innovations include technologies for real-time tumor resection monitoring and low-cost neuronavigation systems (e.g., NousNav) for low-resource settings. Dr. Golby completed her BA at Yale University and MD at Stanford University School of Medicine, followed by neurosurgery residency at Brigham and Women's Hospital. Dr. Golby's translational work emphasizes global health equity, including Fulbright-supported initiatives to develop locally adapted medical technologies in Rwanda and Morocco. Her research spans image-guided neurosurgery, brain-computer interface applications, and neuro-oncology advancements.

• Geometric Deep Learning
• Graph Neural Networks
• 3D Shape Analysis
• +۴ مورد دیگر

Michael Bronstein is the DeepMind Professor of Artificial Intelligence at the University of Oxford and Founding Scientific Director at the Aithyra Institute. He holds affiliations with Imperial College London (previous) and institutions like Stanford, MIT, and Harvard. His research focuses on geometric deep learning, graph neural networks, protein design, and non-human species communication. Bronstein received his PhD from the Technion in 2007 and has been awarded multiple fellowships and grants, including ERC, Google, and Amazon awards. Education: PhD in Computer Science, Technion, 2007 Research Interests: His work spans geometric deep learning, graph neural networks, 3D shape analysis, and applications in protein design. Notable projects include protein interaction design using surface fingerprints and advancing graph neural network architectures. He also explores AI in non-human communication, combining machine learning with biological systems. Publications: Recent work emphasizes knowledge graph foundation models, graph homomorphism analysis, and generative models for discrete data. His research bridges theoretical foundations (e.g., graph expressivity) with practical applications in biomedicine and AI. Awards: EPSRC Turing AI Fellowship Royal Society Wolfson Research Merit Award Academia Europaea Membership IEEE/IAPR/ELLIS Fellowships Advising & Grants: Supervises students in AI and graph learning. Active in securing ERC, Google, and industry grants. His entrepreneurial ventures include founding companies like Fabula AI (acquired by Twitter). Labs/Teams: Leads Graph Learning Research at DeepMind and collaborates with interdisciplinary teams in AI, biology, and quantum systems.

• Computer vision
• Machine learning

Dr. Zhongyan Zhang serves as an Associate Research Fellow at the School of Computing and Information Technology within the Faculty of Engineering and Information Sciences at the University of Wollongong, Australia, a position held since 2022. His academic foundation includes: PhD from University of Wollongong Master's degree from Beihang University, Beijing Bachelor's degree from Beihang University, Beijing Specializing in Computer Vision and Machine Learning , Dr. Zhang pioneers annotation-free methodologies for instance image retrieval. His research eliminates dependency on manual labels by exploiting dataset-internal supervisory signals, with innovations in unsupervised object discovery and spatial-context-aware feature representation that significantly enhance retrieval accuracy and user experience through region-based matching. Publication analysis (2015-2023) reveals an evolutionary trajectory from cascade classifiers for pedestrian detection to self-boosting unsupervised frameworks. His recent work demonstrates consistent focus on efficient, dataset-driven solutions that improve retrieval performance while maintaining algorithmic simplicity and low memory costs. No scientific awards are documented in source materials. The provided information contains no details regarding student advising, research grants, or laboratory affiliations.

Jesse Thalerمشاهده پروفایل

استاد

• Particle Physics
• Collider Phenomena
• Quantum Computing
• +۳ مورد دیگر

Jesse Thaler is a Professor in the MIT Physics Department and the Center for Theoretical Physics. He joined MIT in 2010 after a Miller Institute fellowship at UC Berkeley (2006-2009). His research focuses on collider phenomenology, quantum computing applications in particle physics, and machine learning methods for high-energy data analysis. He holds a Ph.D. from Harvard University (2006) and a Sc.B. from Brown University (2002). Key honors include the DOE Early Career Award (2011), Presidential Early Career Award (2012), Sloan Fellowship (2013), and MIT's Edgerton Award (2016). His work bridges theoretical physics with cutting-edge computational techniques, emphasizing jet substructure analysis, energy correlator studies, and symmetry discovery via AI. Thaler leads the MIT Center for Theoretical Physics' efforts in collider physics and co-founded the Institute for Artificial Intelligence and Fundamental Interactions (IAIFI). His research group collaborates on LHC Olympics challenges and explores quantum algorithms for jet clustering. Recent work includes developing Lorentz-equivariant neural networks and anomaly detection frameworks for CMS open data.