جست‌وجوی استاد و پژوهشگر

Sebastian Seungمشاهده پروفایل

استاد

• Neuroscience
• Machine Learning
• Connectomics
• +۵ مورد دیگر

Sebastian Seung is a Professor at Princeton University , affiliated with both the Department of Computer Science and the Princeton Neuroscience Institute . His career spans Harvard University (Ph.D., 1990), Bell Laboratories, and Massachusetts Institute of Technology before joining Princeton in 2014. An External Member of the Max Planck Society and 2008 Ho-Am Prize recipient, Seung merges machine learning with neuroscience . Research Focus : Pioneering connectomics , Seung developed technologies for reconstructing neural circuits from high-resolution brain images, including FlyWire for collaborative brain mapping. His work explores brain function, development, and plasticity , drawing parallels between fly visual systems and convolutional networks . Awards & Affiliations : 2008 Ho-Am Prize in Engineering External Member, Max Planck Society Technical Contributions : Led breakthroughs in 3D connected component labeling and high-throughput EM imaging for mammalian brains, partnering with NIH’s BRAIN Initiative to scale connectomics to whole mouse brains. Seung’s team has shifted from EM analysis to interpreting connectomes , focusing on neural circuit function and biological mechanisms in flies and mice. His lab alumni network spans institutions, advancing AI and neuroscience globally.

Flavio Anselmettiمشاهده پروفایل

استاد

• Sedimentary records as indicators for environmental and climate change
• Paleoseismology and natural hazards
• Sediment budgets of source-to-sink systems
• +۳ مورد دیگر

Prof. Flavio Anselmetti is a Full Professor of Quaternary Geology and Paleoclimatology at the University of Bern , where he has been employed since 2012. He also serves as the Managing Director of the Institute of Geological Sciences . Prior to this, he held significant roles at ETH Zurich, including SNSF Assistant Professor, and at EAWAG Dübendorf as Head of the Sedimentology Group, while remaining a Titular Professor at ETH Zurich until 2012. His academic training includes a Diploma in Geology (1990) from the University of Basel , followed by a PhD (1994) from ETH Zurich and University of Miami (USA) . He spent three years as a Postdoctoral Fellow and Research Associate at the University of Miami between 1994 and 1997. Paleoseismology : Using lacustrine and marine sediments to reconstruct earthquake and tsunami records Climate Change Studies : Investigating Holocene and Pleistocene climate shifts through sediment cores Sediment Dynamics : Analyzing source-to-sink systems and glacial sediment properties His recent publications focus on sedimentary archives in Science Advances , Quaternary Research , and Environmental Modelling and Software , with an emphasis on deep learning applications in sediment analysis , multi-proxy paleoenvironmental reconstructions , and human impact assessments over millennia. He has contributed to major projects in the Alpine foreland and Caribbean regions . Prof. Anselmetti leads the Quaternary Geology and Paleoclimatology Research Group and collaborates with international teams on ICDP-DOVE drilling campaigns and multibeam bathymetry mapping in Swiss lakes. His work extends to geohazard assessment , carbonate-siliciclastic shelf dynamics , and subaqueous fault analysis in active tectonic zones.

• Human-Computer Interaction
• Extended Reality (XR)
• Mobile Health Sensing
• +۶ مورد دیگر

Karan Ahuja is the Lisa Wissner-Slivka & Benjamin Slivka Assistant Professor of Computer Science at Northwestern University, directing the Sensing, Perception, Interactive Computing & Experiences (SPICE) Lab. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University (2023) and a B.Tech. in Computer Science (2017). His research focuses on creating technologies that sense and understand human behavior, with applications in mobile health, extended reality, and natural user interfaces. Key projects include LemurDx for ADHD diagnosis, EITPose for wearable hand pose tracking, and MobilePoser for full-body pose estimation via consumer IMUs. Awards include Forbes 30 Under 30 (2024), MIT 35 Innovators Under 35 Asia Pacific, and ACM SIGCHI's Outstanding Dissertation Award. He has worked at Google, Apple, Microsoft Research, Meta Reality Labs, and IBM Research. His lab emphasizes real-world deployments, with technologies licensed and integrated into products used by millions. Prospective students are invited to join his lab at Northwestern via a dedicated application form. Research spans embedded systems, computer vision, and on-device ML, with a focus on impactful applications in healthcare and XR.

Alexander Müller-Hermesمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Linear Algebra
• Functional Analysis
• +۱ مورد دیگر

Alexander Müller-Hermes is an Associate Professor at the Department of Mathematics, University of Oslo. His research focuses on quantum information theory with emphasis on mathematical questions in quantum Shannon theory and entanglement. He also explores functional analysis and convex geometry inspired by quantum phenomena. Before joining UiO, he held a Marie Skłodowska-Curie fellowship at University Claude Bernard Lyon 1 and was a postdoc at the Centre for Mathematics in Quantum Theory (QMATH), University of Copenhagen. He earned his PhD in Mathematics from Technical University Munich in 2015. Teaching includes advanced courses like Quantum Information Theory (MAT4430) and Linear Algebra (MAT1120). His research interests span quantum communication, entanglement theory, operator algebras, and functional analysis, with over 20 peer-reviewed publications since 2014. His work on fault-tolerant quantum coding and entanglement monotones has advanced theoretical foundations in quantum information. He collaborates with the Operator Algebras research group at UiO and is part of the QOMBINE project on quantum computation and many-body theory.

• Computational Photography
• 3D Vision
• AI Security
• +۱۲ مورد دیگر

Dr. Wan Renjie is an Assistant Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He holds a BEng in Network Engineering from the University of Electronic Science and Technology of China and a PhD from Nanyang Technological University (NTU), Singapore. Prior to joining HKBU, he was a Wallenberg-NTU Presidential Postdoctoral Fellow (2020–2022) and a guest researcher at Peking University (2019–2020). His research focuses on computational photography, 3D vision, AI security, digital watermarking, and neural representations . He explores robustness and security in vision models, especially concerning NeRFs and 3D Gaussian Splatting, and develops methods for low-light enhancement, reflection removal, and domain adaptation. Dr. Wan has published in top-tier venues including TPAMI, IJCV, CVPR, ICCV, NeurIPS, AAAI, and ECCV . His recent work emphasizes copyright protection for neural 3D models , adversarial attacks in multimodal and event-based systems, and medical image reconstruction. He is actively mentoring PhD students and research assistants. VCIP 2020 Best Paper Award Outstanding Reviewer, ICCV 2019 He teaches courses such as Introduction to AI and ML (COMP3057) , AI Application Development (COMP3065) , and Python for Data Analysis and Machine Intelligence (COMP7035) . Dr. Wan leads a dynamic research group with ongoing projects on watermarking, 3D reconstruction, and AI security, and he is currently recruiting new PhD students and research assistants.

Christine Tardifمشاهده پروفایل

استادیار

• MRI-based Neuroimaging
• Quantitative MRI (qMRI)
• Biophysical Modeling
• +۸ مورد دیگر

Christine Tardif is an Assistant Professor in the Department of Biomedical Engineering and the Department of Neurology and Neurosurgery at McGill University. As head of the McConnell Brain Imaging Centre lab at the Montreal Neurological Institute, she develops advanced MRI techniques for in-vivo brain imaging, focusing on quantitative mapping of myelin and cortical microstructure. Her work spans methodological innovation (e.g., multi-modal biophysical modeling) and translational applications across preclinical (7 Tesla) and clinical (3 and 7 Tesla) systems. Undergraduate: B.Eng. in Computer Engineering, McGill University (2004) Master's: M.Sc. in Bioengineering, Imperial College London (2006) PhD: Biomedical Engineering, McGill University (2011) Her research explores myelin dynamics in health and disease, emphasizing its role in neural conduction, brain plasticity, and cognitive functions. The lab investigates dysmyelination in psychiatric disorders (e.g., bipolar disorder) and neurodegenerative conditions (e.g., multiple sclerosis) using relaxometry , magnetization transfer , and diffusion-weighted imaging . Recent methodological work includes 3D MERMAID sequences for motion-insensitive diffusion imaging and optimization of magnetization transfer saturation maps. Current projects integrate ultra-high field MRI with histological validation in preclinical models (e.g., marmoset brain sections), aiming to bridge microstructural metrics with macro-scale brain function. Applications span Alzheimer's disease risk assessment via white matter alterations, synaptic density mapping in psychosis, and cortical laminar differentiation studies.

Victor Albertمشاهده پروفایل

استادیار مدعو

• Quantum Information Science
• Theoretical Physics
• Quantum Error Correction
• +۳ مورد دیگر

Victor Albert is an Adjunct Assistant Professor of physics at the University of Maryland with an adjunct appointment in the University of Maryland Institute for Advanced Computer Studies. He simultaneously serves as a physicist at the National Institute of Standards and Technology (NIST) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His academic background includes: BS in physics from University of Florida Ph.D. in physics from Yale University (2017) Postdoctoral position at Caltech Dr. Albert's research focuses on theoretical quantum information science, with specific expertise in quantum error correction and open quantum systems. His work spans applications to quantum computing and atomic, molecular and optical (AMO) physics. He has developed significant contributions in bosonic error-correcting codes, quantum metrology, and quantum information processing for various quantum systems including planar rotors. His research demonstrates a strong theoretical foundation with practical applications for advancing quantum technologies. His publication record shows consistent contributions to quantum information science, with recent work focusing on concatenating bosonic codes with qubit codes, quantum information processing for planar rotors, noise effects in quantum metrology, and shadow tomography for quantum state characterization. Dr. Albert maintains active research collaborations through his positions at NIST and the University of Maryland, contributing to the advancement of quantum information science and quantum computing.

Craig Jonesمشاهده پروفایل

استادیار

• Medical Image Processing
• Artificial Intelligence
• Neural Networks
• +۲ مورد دیگر

Craig Jones is an Assistant Professor of Computer Science at Johns Hopkins University's Whiting School of Engineering. He is affiliated with the Malone Center for Engineering in Healthcare and contributes to the Precision Medicine Analytics Platform's Imaging and Data Science Subcommittees. BSc in Computer Science and Mathematics from Simon Fraser University MSc in Medical Biophysics from the University of Western Ontario PhD in Physics from the University of British Columbia His research focuses on applying artificial intelligence and neural networks to medical image processing, particularly for MRI, CT, optical coherence tomography (OCT), and ultrasound datasets. Key areas include 2D/3D image processing, anomaly detection, segmentation, and uncertainty quantification, with clinical applications in neurosurgery, ophthalmology, and oncology. Projects span robotic imaging, neuroendoscopic guidance, and cancer boundary detection. Recent publications highlight advancements in vision-language models for 3D medical imaging, automated segmentation of venous malformations, and AI-guided neurosurgical tools. Articles emphasize multimodal data fusion, self-supervised learning, and federated learning for rare cancer analytics. He received a $310,000 Department of Defense grant in 2022 to develop AI-guided treatments for venous malformations. His work bridges clinical imaging domains and computer vision as a member of the Radiology AI Lab (RAIL), a collaborative effort across Johns Hopkins Hospital, the Whiting School of Engineering, and the Applied Physics Laboratory.

• Computational Social Choice
• Algorithmic Game Theory
• Fairness in Machine Learning
• +۳ مورد دیگر

Anson Kahng is an Assistant Professor in the Department of Computer Science and the Goergen Institute of Data Science at the University of Rochester. He previously held postdoctoral positions at the University of Toronto and completed his PhD at Carnegie Mellon University under the supervision of Ariel Procaccia, focusing on computational social choice. PhD, Computer Science, Carnegie Mellon University Undergraduate degree, Computer Science, Harvard College His research explores the intersection of computer science and democracy, developing frameworks like virtual democracy and liquid democracy while analyzing fairness in participatory budgeting and voting systems. He combines theoretical analysis with empirical methods, emphasizing interdisciplinary collaboration. Recent work includes advancements in ranked choice voting optimization, fairness metrics for elections, and structural analysis in cryo-electron tomography. He has published in top venues such as IJCAI, AAAI, NeurIPS, and ACM Transactions on Economics and Computation. NeurIPS 2019 Spotlight Presentation (top 2.5% of submissions) Kahng advises PhD students Alina Chadwick and Joe Saber, and has mentored multiple undergraduate researchers. He teaches courses on algorithmic game theory and computational statistics at the University of Rochester.

Yang Songمشاهده پروفایل

استادیار

• Generative Models
• Diffusion Models
• Score-Based Modeling
• +۵ مورد دیگر

Yang Song is an incoming Assistant Professor in Electrical Engineering and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). Prior to joining Caltech, he leads the Strategic Explorations team at OpenAI. He received his Ph.D. in Computer Science from Stanford University under the supervision of Stefano Ermon and completed his Bachelor's degree in Mathematics and Physics from Tsinghua University. His educational background includes: Ph.D. in Computer Science, Stanford University Bachelor's in Mathematics and Physics, Tsinghua University Dr. Song's research focuses on building powerful AI models capable of understanding, generating, and reasoning with high-dimensional data across diverse modalities. He is particularly known for inventing foundational concepts and techniques in score-based diffusion models, which have revolutionized the field of generative AI. His work bridges theoretical advances with practical applications, particularly in image generation, medical imaging, and solving inverse problems. His research has demonstrated how score-based models can achieve state-of-the-art results in image generation while maintaining flexibility for various applications including medical image reconstruction. Analysis of his publication record reveals a strong trajectory in generative modeling, with a particular emphasis on score-based approaches and diffusion models. His work consistently addresses fundamental challenges in generative modeling including sample quality, training stability, computational efficiency, and application to real-world problems. His most recent work on consistency models represents a significant advancement toward making generative models practical for real-time applications. His notable achievements include: ICLR 2021 Outstanding Paper Award for Score-Based Generative Modeling through Stochastic Differential Equations NeurIPS 2021 Spotlight Presentation for Maximum Likelihood Training of Score-Based Diffusion Models Multiple ICLR Oral presentations for his work on consistency models Developing foundational techniques that power many modern AI image generation systems His GitHub repository for score-based generative modeling has gained significant traction in the research community, with over 1,700 stars, reflecting the impact of his work. His research bridges theoretical machine learning with practical applications, particularly in medical imaging where his techniques have shown promise for improving image reconstruction in CT and MRI.

Noa Maromمشاهده پروفایل

دانشیار

• Computational Materials Science and Engineering
• Quantum Materials
• Semiconductor Materials
• +۵ مورد دیگر

Noa Marom is an Associate Professor in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in Chemistry and Physics. She is a member of the Pittsburgh Quantum Institute (PQI) and an affiliate of the Wilton E. Scott Institute for Energy Innovation. Her research focuses on computational materials science, energy security, and quantum materials. Marom earned a B.A. in Physics and B.S. in Materials Engineering (cum laude) from the Technion-Israel Institute of Technology (2003) and a Ph.D. in Chemistry from the Weizmann Institute of Science (2010). She held postdoctoral positions at the University of Texas at Austin’s Institute for Computational Engineering and Sciences (ICES) before joining Tulane University as an Assistant Professor (2013–2016) and CMU in 2016. Her research interests include computational design of semiconductor materials, topological quantum computing, and crystal structure prediction. Key projects involve machine learning for materials discovery and quantum computing applications, such as optimizing semiconductor interfaces for stable qubits. Marom has received numerous awards, including the NSF CAREER Award (2016), DOE INCITE Awards (2017–2019), and the IUPAP Young Scientist Prize (2018). She serves as Associate Editor of npj Computational Materials. Her work spans collaborations with institutions like the Paul Scherrer Institute (Switzerland) and the Pittsburgh Supercomputing Center. Research highlights include computational studies of InAs/InSb semiconductors for quantum bits and machine learning-driven discovery of organic semiconductors.

Peter B. Noëlمشاهده پروفایل

استادیار

• Computed Tomography
• Interventional Imaging
• Tomographic Reconstruction
• +۷ مورد دیگر

Peter B. Noël is an Assistant Professor of Radiology at the Hospital of the University of Pennsylvania and holds academic affiliations with the Technical University of Munich (TUM). He is a member of the Institute for Diagnostic and Interventional Radiology and chairs the 'image reconstruction & x-ray' research group under Prof. Navab's Medical Informatics Applications department. His work integrates advanced imaging technologies with clinical applications. Education: PhD in Computer Science (2009, SUNY Buffalo), MSc in Computer Science (Toshiba Stroke Research Center), BEng in Biomedical Engineering (University of Applied Sciences Giessen, Germany). Research focuses on CT imaging advancements including diagnostic/interventional x-ray imaging, tomographic reconstruction algorithms, spectral imaging with photon-counting detectors, and GPU-based high-performance computing. He leads efforts to reduce radiation exposure in CT scans and translate cutting-edge imaging techniques into clinical practice. Awards: Behnker-Berger Foundation Research Award for contributions to CT radiation exposure reduction. Current projects include the 'image reconstruction & x-ray' group, DHM (Deutsches Herzzentrum München) collaborations, and leadership in labs like NARVIS (NArvIs) and RobUSt (Robotics and Ultrasound). His work is supported by national and industrial grants.

Mohsen Heidariمشاهده پروفایل

استادیار

• Quantum Computing
• Quantum Algorithms
• Theoretical Machine Learning
• +۳ مورد دیگر

Mohsen Heidari is an Assistant Professor in the Department of Computer Science at Indiana University, Bloomington. He is affiliated with the IU Quantum Science and Engineering Center (QSEc) and the NSF Center for Science of Information (CSoI). He previously held positions as a Visiting Assistant Professor at Purdue University and as a Postdoctoral Research Associate at CSoI. Ph.D. in Electrical Engineering (2019) and M.Sc. in Applied Mathematics (2017) from the University of Michigan His research focuses span quantum computing, theoretical machine learning, and information theory. Key themes include: Quantum algorithm design and sample complexity Fourier-based learning frameworks Quantum-classical duality in learning problems Information-theoretic approaches to biological systems Article trends show a strong emphasis on quantum-classical learning intersections (6/15 papers), Fourier analysis applications (5/15), and information-theoretic foundations (12/15). Notable venues include NeurIPS, IEEE Transactions, and ISIT. He directs research involving: Quantum Neural Network development Quantum measurement simulation Quantum data compression techniques Quantum algorithm implementation constraints

Jiaxuan Liمشاهده پروفایل

استادیار

• Seismology
• Distributed Acoustic Sensing
• Earthquake Rupture Dynamics
• +۷ مورد دیگر

Jiaxuan Li is an Assistant Professor of Geophysics in the Department of Earth and Atmospheric Sciences at the University of Houston's College of Natural Sciences and Mathematics. His research focuses on developing fiber-optic sensing technologies for seismic monitoring across diverse geological environments including volcanic, crustal, and glacial settings. Dr. Li's educational background includes a Ph.D. in Geophysics from the University of Houston (2015-2020) and a B.S. in Geophysics from Peking University (2011-2015). He previously held a postdoctoral position at Caltech Seismolab under Prof. Zhongwen Zhan. His research program centers on distributed acoustic sensing (DAS) applications, with major contributions in volcanic eruption forecasting through minute-scale magma migration imaging, earthquake rupture dynamics via high-frequency fault asperity analysis, and subsurface characterization for carbon sequestration and geothermal energy. Recent work demonstrates DAS capabilities as dense geodetic arrays for real-time volcanic monitoring systems deployed in Iceland through collaborations with the Icelandic Met Office and Reykjavik University. Analysis of Dr. Li's publication record reveals a strong emphasis on operationalizing fiber-optic networks for geophysical monitoring, with significant advancements in eruption early warning systems, earthquake source characterization, and subsurface imaging techniques. His work bridges fundamental seismological research with practical hazard mitigation applications. Dr. Li actively mentors graduate students and recently welcomed postdoc Dr. Tianfan Yan to his research team. His lab operates real-time DAS streaming systems for volcanic eruption monitoring in Iceland, developed through international collaborations involving the University of Houston, Caltech, Ljósleiðarann, and Reykjavik University. Current research directions include expanding DAS applications for carbon sequestration verification and deep geothermal reservoir characterization.

• Machine Learning
• Data Mining
• Bioinformatics
• +۱۱ مورد دیگر

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.