جست‌وجوی استاد و پژوهشگر

Gary Shiuمشاهده پروفایل

استاد

• String Theory
• Particle Physics
• Cosmology
• +۳ مورد دیگر

Prof. Gary Shiu is a Professor of Physics at the University of Wisconsin-Madison, leading research at the intersection of string theory, particle physics, and cosmology. He is affiliated with the Department of Physics and has held academic appointments at institutions like the Hong Kong University of Science and Technology and the CERN. His research focuses on quantum gravity, the Swampland program, inflationary cosmology, and AI applications in physics. He has received notable awards including the Guggenheim Fellowship, Kavli Frontiers Fellowship, and Chancellor’s Distinguished Teaching Award. Education: PhD in Physics (Cornell University, 1998), BSc in Physics (Chinese University of Hong Kong, 1993). Academic roles include founding director of the Center for Fundamental Physics at HKUST and co-initiator of the Physics ∩ ML seminar series. He advises graduate and undergraduate students in theoretical physics and cosmology, with notable advisees contributing to projects in dark energy, string vacua, and machine learning. Research highlights include formulating the Weak Gravity Conjecture in AdS space, developing methods for cosmological parameter inference using topological data analysis, and exploring the String Genome Project. His work bridges theoretical physics with experimental observables, leveraging advanced computational techniques and interdisciplinary collaborations. Key awards include the Kellett Mid-Career Award, Vilas Associate Award, and multiple fellowships from prestigious societies. He actively participates in international conferences and editorial boards, contributing to initiatives like the Gordon Research Conference on String Theory and Cosmology. Labs/Teams: Theoretical and Computational Cosmology Group, AI ∩ Universe Initiative, and collaborations in string phenomenology and machine learning applications.

Wei Baoمشاهده پروفایل

دانشیار

• Networking
• Distributed Machine Learning
• AI for Networked Systems
• +۴ مورد دیگر

Dr. Wei Bao is an Associate Professor in the School of Computer Science at the University of Sydney, part of the Faculty of Engineering. He leads the I-Net (Intelligent Networking) Group and holds a B.Eng. from Beijing University of Posts and Telecommunications (2009), M.A.Sc. from the University of British Columbia (2011), and Ph.D. from the University of Toronto (2015). His research focuses on distributed machine learning, AI-driven network systems, and intelligent network optimization, with industrial collaborations at companies like Link Group. Education: Bachelor of Engineering, Beijing University of Posts and Telecommunications (2009) Master of Applied Science, University of British Columbia (2011) Ph.D., University of Toronto (2015) Research Interests: Dr. Bao's work addresses challenges in distributed machine learning, AI integration into network systems, and optimizing network performance. He emphasizes practical applications through industry partnerships, aiming to bridge academic research with real-world impact. His current projects include federated learning frameworks, IoT communication sharing architectures (e.g., sTube+), and edge computing optimizations. Publications Trends: His recent work focuses on federated learning algorithms (e.g., Federated Learning with Nesterov Accelerated Gradient ), edge computing optimizations ( SOAR: Smart Online Aggregated Reservation ), and partial label learning techniques. These reflect a blend of theoretical advancements and applied systems research. Awards: Multiple Distinguished TPC Member awards (INFOCOM 2020-2024) Best Paper Awards at ACM MSWiM (2019), IEEE NCA (2016), and others Advising & Grants: Dr. Bao supervises PhD candidates in distributed systems and machine learning. His grants include projects like Pioneering Federated Real-Time Video Analytics (ARC DP 2025) and industry collaborations via the University of Sydney's Industry Program. He also leads the Master of Computer Science program at the University of Sydney. Labs & Teams: He directs the I-Net Group, focusing on intelligent networking and distributed systems. Collaborations span institutions like The Hong Kong Polytechnic University (Dr. Dan Wang) and York University (Dr. Uyen Trang Nguyen).

Edoardo Serraمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۵ مورد دیگر

Edoardo Serra is an Associate Professor in the Department of Computer Science at Boise State University (BSU), a role he has held since July 2021. He previously served as an Assistant Professor at BSU from 2015 to 2021 and holds a joint appointment as a Senior Researcher at Pacific Northwest National Laboratory (PNNL) since June 2021. Since January 2023, he has co-directed the Computing Ph.D. Program at BSU and serves as General Chair of the 2024 ACM CIKM Conference. His academic journey includes a Ph.D. in Computer Science Engineering from the University of Calabria, Italy (2012), followed by postdoctoral positions at the University of Calabria and the University of Maryland. He also served as a Visiting Researcher at UCLA (2010–2011). His research focuses on AI/ML applications in cybersecurity, graph representation learning, generative AI, and robust AI systems. Notable projects include: NSF-funded cybersecurity curriculum integration Department of Defense-funded analysis of terrorist networks Idaho Department of Commerce precision agriculture initiatives Key research areas include graph neural networks, adversarial robustness, and ML-driven security solutions. His work has been recognized with awards such as Best Application Paper (2021) and Best Paper Award (2018). He actively contributes to professional service roles, including program chairs and editorial boards. Current projects emphasize AI ethics, generative models, and scalable graph algorithms. He advises on applied AI consulting for industry and government, focusing on model interpretability and cybersecurity implications.

Jane Wangمشاهده پروفایل

استاد

• Biological Physics
• Theoretical Condensed-Matter Physics
• Computational Fluid Dynamics
• +۴ مورد دیگر

Affiliations and Roles Professor Wang holds dual appointments as Professor of Physics and Mechanical and Aerospace Engineering at Cornell University. She is affiliated with the Sibley School of Mechanical and Aerospace Engineering and the College of Arts and Sciences. Education B.S. in Physics, Fudan University, Shanghai, China (1989) Ph.D. in Physics, University of Chicago (1996) NSF-NATO Postdoctoral Fellow, Theoretical Physics, Oxford University (1997) Visiting Member, Courant Institute of Mathematical Sciences, NYU (1997-1999) Research Her research focuses on the physics of living organisms, particularly insect flight dynamics , biophysics , and computational modeling . Key projects include: Dragonfly righting reflex mechanisms Neuro-mechanical control in fruit flies Unsteady aerodynamics and fluid-structure interactions Awards and Honors Simons Fellowship in Theoretical Physics (2020) Radcliffe Fellowship (2007) Cornell Provost's Award for Distinguished Scholarship (2005) David and Lucile Packard Fellowship (2002) Labs and Collaborations Her work integrates experimental and computational approaches, often conducted in collaboration with institutions like the Janelia Research Campus (HHMI) and the Joint Texas Experimental Tokamak (J-TEXT).

Ahmed Sherifمشاهده پروفایل

دانشیار

• Cybersecurity
• Cryptography
• Autonomous Vehicles
• +۲ مورد دیگر

Ahmed Sherif is an Associate Professor specializing in cybersecurity and privacy-preserving systems, likely affiliated with the University of Southern Mississippi. He holds a Ph.D. from Tennessee Technological University (2017) and M.S. from Egypt Japan University of Science & Technology (2014). His research focuses on cryptographic solutions for autonomous vehicles, IoT networks, and smart grid security. Research interests span privacy-preserving systems across transportation and critical infrastructure, with emphasis on authentication protocols and hardware security. Teaches courses including Digital Logic, Network Security, and Advanced Security Techniques. Publications show strong focus on privacy engineering in emerging technologies (2021-2025), particularly in vehicular/drone networks and medical AI. No awards listed. Research includes federated learning for medical diagnosis and hardware-accelerated security frameworks.

• Marine Renewable Energy
• Wind Energy Systems
• Drivetrain Design and Reliability
• +۷ مورد دیگر

Amir R. Nejad is a Professor in the Department of Marine Technology at NTNU, leading the Marine Energy Systems and Autonomics (MESA) research group. He holds roles such as Chair of the EAWE WindEurope Scientific Track Committee and co-chair of the Drivetrain Technical Committee at the European Academy of Wind Energy (EAWE). His research focuses on stochastic design, reliability-based operation, dynamic modeling, and fault detection in marine and offshore renewable systems. Nejad is an editorial board member for journals like Wind Energy Science and Ocean Engineering , and a member of ISO committees on drivetrain health monitoring. Education: PhD in Marine Technology, NTNU (2015) MSc Subsea Engineering, University of Aberdeen (2012) BSc Mechanical Engineering, Tehran University (2009) His research interests emphasize offshore wind drivetrain reliability, condition monitoring, and digital twin applications. Recent work includes studies on blade bearing fatigue estimation, SCADA-based lifetime extension of drivetrains, and wake steering techniques in floating wind farms. Key awards include multiple 'Best Lecturer' honors at NTNU and international conference recognitions. His research has been supported by grants such as the Nowitech Fellowship and DNV Education Fund. Key Projects: Marine System Dynamics and Vibration Lab (MD Lab) oversees projects on wind turbine drivetrains, floating offshore systems, and digital twin integration. Lab/Teams: MD Lab focuses on advancing marine energy systems through experimental and computational studies.

• Machine Learning
• Computer Vision
• Medical Imaging
• +۷ مورد دیگر

Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.

• Digital Forensics
• Image Processing
• Multimedia Analysis
• +۴ مورد دیگر

Giulia Boato is an Associate Professor at the University of Trento’s Department of Information Engineering and Computer Science (DISI). She teaches courses in Probability and Multimedia Data Security. Her expertise spans Cyber Security, Digital Forensics, and Multimedia Analysis, focusing on image and signal processing for data protection, forensics, and anti-forensics. She collaborates internationally with institutions like Tampere University of Technology and Dartmouth College, co-advising PhD students and contributing to European projects like LIVINGKNOWLEDGE and GLOCAL. Education: PhD in Information and Communication Technology (2005), M.Sc. in Mathematics (2002), Scientific Lyceum (1998) with bilingual Italian-German certification. Past roles include Assistant Professor at DISI (2006–2018) and visiting researcher at the University of Vigo (2006) and University of Innsbruck (2018). Research interests include multimedia data protection, image forensics (tampering detection, computer vs. natural data discrimination), and intelligent data management. She leads projects on social media forensics, event-based retrieval, and synthetic media detection. Awards include Best Paper at IEEE WIFS 2012 and Top 10% Paper at MMSP 2012. Professional contributions include roles as co-chair of workshops, Technical Program Committee member for ICIP and ICC, and reviewer for journals like IEEE Transactions on Information Forensics and Security. She has advised PhD theses and contributed to datasets like TrueFace and WILD for synthetic media analysis.

• Databases
• Spatial Data Management
• Geospatial Systems
• +۳ مورد دیگر

Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.

Andrew Ngمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Robotics
• +۴ مورد دیگر

Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.

• Laser Physics
• Plasma Physics
• Machine Learning in Physics
• +۷ مورد دیگر

Ewan Dolier is a Research Fellow in the Department of Physics at the University of Strathclyde, Faculty of Science. He is actively involved in cutting-edge research on laser-driven ion acceleration and plasma physics, working within the SCAPA (Scottish Centre for the Application of Plasma-based Accelerators) facility. His work bridges experimental physics and machine learning techniques to optimize and diagnose high-energy particle beams. His research interests include: Laser-Plasma Interactions Machine Learning for Physics Optimization Proton and Ion Beam Acceleration Synthetic Diagnostics using Neural Networks High Repetition Rate Laser Systems Relativistic Transparency Regime Physics The recent trend in his publications shows a strong focus on integrating artificial intelligence and deep learning models into the control and analysis of laser-driven particle acceleration experiments. His work spans experimental design, data-driven optimization, and advanced diagnostics using scintillating fiber spectrometers and synthetic models. His scientific contributions have been presented at major plasma physics conferences and published in high-impact journals such as Communications Physics and High Power Laser Science and Engineering . Notable projects include: External Experiment at the Gemini High-Power Laser Facility (Deep Learning for Ion Acceleration) Development of High Repetition-Rate Target Systems at SCAPA Doctoral Training Partnership research (2019–2024) He collaborates extensively with leading researchers such as Paul McKenna and Ross Gray, and contributes to multi-investigator datasets and simulations. Ewan Dolier completed his PhD in 2024 with a thesis on advancing laser-driven ion acceleration using machine learning and instability analysis.

Erik Linsteadمشاهده پروفایل

دانشیار

• Machine Learning
• GPU Programming
• Autism Spectrum Disorder
• +۵ مورد دیگر

Dr. Erik Linstead is an Associate Professor and Senior Associate Dean at Chapman University, affiliated with the Fowler School of Engineering, School of Pharmacy, and George L. Argyros College of Business and Economics. His expertise spans Machine Learning, GPU Programming, Autism Spectrum Disorder, Assistive Technologies, Predictive Analytics, and Virtual Reality. Education: Bachelor of Science, Chapman University Master of Science, Stanford University Ph.D., University of California, Irvine Dr. Linstead's research integrates machine learning with diverse domains, including autism treatment, environmental monitoring, and software engineering. His recent publications focus on coral reef health, land surface temperature trends, and embedded machine learning systems. His scholarly work includes collaborations in remote sensing, medical informatics, and neurodiversity support. Articles highlight his interdisciplinary approach, applying AI to ecological challenges (e.g., Red Sea coral reefs, Nile Basin droughts) and human-centered technologies (e.g., VR therapy for autism, medication adherence analysis).

Glenn H. Fredricksonمشاهده پروفایل

استاد

• Theoretical Polymer Science
• Complex Fluids
• Block Copolymers
• +۷ مورد دیگر

Glenn H. Fredrickson is the Mitsubishi Chemical Professor of Functional Materials in the Department of Chemical Engineering at the University of California, Santa Barbara, with additional appointments in the Materials Department. He directs the Mitsubishi Chemical Center for Advanced Materials (MC-CAM) and the Complex Fluids Design Consortium (CFDC), and previously chaired the Chemical Engineering department (1998-2001). He is an elected member of both the National Academy of Engineering and the National Academy of Sciences. Education Ph.D. Chemical Engineering, Stanford University (1984) M.S. Chemical Engineering, Stanford University (1981) B.S. Chemical Engineering, University of Florida (1980) Research Interests Fredrickson leads an internationally recognized program in theoretical and computational polymer science , focusing on self-assembly of block copolymers , complex fluids , and field-theoretic simulation methods . His group pioneered field-theoretic simulations (FTS) —numerical techniques to solve statistical field theories of polymers—enabling predictive design of advanced materials including high-performance plastics, ion-conducting electrolytes, and nanostructured membranes. Current themes include quantum-fluid analogs, machine-learning-accelerated discovery, and sustainable polymer formulations. Recent Publication Trends Between 2022 and 2025, Fredrickson’s group published extensively on block copolymer morphology control , polymer electrolytes for energy storage , phase-separation kinetics , and quantum many-body analogs in soft matter . Notable advances include machine-learning-enhanced self-consistent field theory, molecularly informed models for surfactant and polyelectrolyte systems, and the application of polymer field theory to spin-orbit-coupled Bose-Einstein condensates. Scientific Awards & Honors Election to National Academy of Sciences (2021) Materials Theory Award, Materials Research Society (2017) William H. Walker Award, AIChE (2016) Polymer Physics Prize, American Physical Society (2007) Election to National Academy of Engineering (2003) Alfred P. Sloan Fellow (1992) Camille and Henry Dreyfus Teacher-Scholar Award (1991) Presidential Young Investigator Award, NSF (1991) Research Group & Funding The Fredrickson Research Group comprises ~15 graduate students and several post-doctoral researchers. The group is supported by multi-agency grants including NSF, DOE, and industry partnerships through the Mitsubishi Chemical Center for Advanced Materials and the Complex Fluids Design Consortium. Laboratory & Collaborative Networks State-of-the-art computational facilities are housed in the Materials Research Laboratory (MRL) at UCSB. Collaborative projects extend to leading experimental groups worldwide, integrating theory with synthesis, characterization, and device testing to accelerate materials innovation.

Elena Tuzhilinaمشاهده پروفایل

استادیار

• Machine Learning
• Applied Statistics
• Computational Biology
• +۵ مورد دیگر

Elena Tuzhilina is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, specializing in machine learning, applied statistics, and computational biology. Her research focuses on statistical tools for chromatin 3D spatial structure reconstruction and analyzing emotional disorders' impact on brain function. Ph.D. in Statistics from Stanford University Specialist's degree from Moscow State University Two-year Data Science program at Yandex School Her research spans high-dimensional data analysis , dimension reduction , and statistical modeling in biological contexts. She has developed novel algorithms for chromatin conformation reconstruction and pandemic trajectory modeling. Recent publications focus on canonical correlation analysis , low-rank matrix approximation , and 3D genome architecture , with applications in computational biology and neuroscience. Dorothy Shoichet Women Faculty in Science Award JSM Student Travel Award Outstanding Teaching Assistance at Stanford Stanford Teaching Assistant Award Elena supervises PhD students and postdoctoral fellows across disciplines including statistical sciences, biochemistry, and applied mathematics. She has secured multiple grants including a NSERC Discovery Grant and University of Toronto Accelerator Grant .

Chen-Yu Weiمشاهده پروفایل

استادیار

• Interactive Machine Learning
• Reinforcement Learning
• Robust Learning Algorithms
• +۵ مورد دیگر

Chen-Yu Wei is an Assistant Professor in the Department of Computer Science at the University of Virginia. He holds a Ph.D. from the University of Southern California (2022), and M.S. and B.S. degrees from National Taiwan University (2015, 2012). His research focuses on interactive machine learning, emphasizing robust and adaptive algorithms for non-stationary/adversarial environments, sample-efficient reinforcement learning, and decentralized multi-agent systems. Education: Ph.D., Computer Science, University of Southern California, 2022 M.S., Electrical Engineering, National Taiwan University, 2015 B.S., Electrical Engineering, National Taiwan University, 2012 Research interests include reinforcement learning, game theory, and algorithmic economics. He has received prestigious awards such as the COLT and ALT Best Paper Awards (2021-2022) and the Simons-Berkeley Research Fellowship (2022). His work bridges theory and practice, addressing challenges in adversarial environments and multi-agent coordination. Current research group members include Haolin Liu (PhD), Braham Snyder (PhD), Kingsley Kim (Undergraduate), and Rishik Balerao (Undergraduate). Teaching includes courses on Reinforcement Learning, Artificial Intelligence, and Algorithmic Economics. He co-organizes the RL Meetup and Theory Seminar at UVA.