جست‌وجوی استاد و پژوهشگر

Vladimir Itskovمشاهده پروفایل

دانشیار

• theoretical neuroscience
• applied algebraic topology
• neural networks
• +۳ مورد دیگر

Vladimir Itskov is an Associate Professor in the Department of Mathematics at The Pennsylvania State University, affiliated with the Eberly College of Science. His research focuses on theoretical neuroscience, applied algebraic topology, and neural networks. He holds a Ph.D. in Mathematics from the University of Minnesota (2002) and a B.S. from Moscow Institute of Electronics and Mathematics (1995). His career includes roles at the University of Nebraska-Lincoln (2009–2014), Columbia University’s Center for Theoretical Neuroscience (2006–2009), and Rutgers University (2004–2006). Research interests include understanding neural coding, network dynamics, and topological methods in neuroscience. Notable work involves applying algebraic topology to analyze neural correlations and developing models for neural network behavior. He has received grants from NIH, NSF, and DARPA, focusing on projects like olfactory coding and neural network dynamics. His lab, the Mathematical Neuroscience Laboratory, develops computational tools and collaborates on interdisciplinary projects. Software packages are hosted on GitHub (nebneuron repository). Publications span journals such as PNAS, SIAM, and Neural Computation, addressing topics from clique topology to competitive network dynamics. His theoretical contributions emphasize bridging data-driven neuroscience with mathematical rigor.

• System Infrastructure for Big Data Processing
• High-Performance Computing (HPC)
• Storage Systems
• +۵ مورد دیگر

Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.

Brian Vermeireمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Aerodynamics
• High-Performance Computing
• +۶ مورد دیگر

Brian Vermeire is an Associate Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. His research focuses on computational fluid dynamics, aerodynamics, high-performance computing, turbulence modeling, numerical methods, and optimization. He leads the Computational Aerodynamics Laboratory, emphasizing scale-resolving simulations and high-order numerical techniques. Key interests include large eddy simulation (LES), direct numerical simulation (DNS), and gradient-free optimization. His work often involves developing advanced algorithms for unstructured grids and high-performance computing platforms. Research Interests: High-order numerical methods Implicit/explicit time integration schemes Polynomial adaptation for adaptive meshing Aeroacoustic shape optimization Large eddy simulation (LES) and direct numerical simulation (DNS) Software development for CFD (e.g., PyFR) Recent work trends show strong focus on hybridized flux reconstruction methods, energy-conservative algorithms, and industrial adoption of high-fidelity simulations. Major contributions include scalable implementations for petascale computing and open-source tools like PyFR. His group collaborates on applications such as wind turbine aerodynamics and low-pressure turbine design. Labs/Teams: Computational Aerodynamics Laboratory (website: link )

Ioannis Emirisمشاهده پروفایل

استاد

• Computational Geometry
• Algebraic Algorithms
• Robotics
• +۴ مورد دیگر

Ioannis Z. Emiris is a Professor in the Department of Informatics & Telecoms at the National & Kapodistrian University of Athens and concurrently serves as President and General Director of the ATHENA Research Center in Greece. He holds a BSc in Computer Science from Princeton University (1989) and a PhD in Computer Science from UC Berkeley (1994). His research spans computational geometry, algebraic algorithms, robotics, structural bioinformatics, and optimization. He is a leading expert in sparse elimination theory, geometric modeling, and algorithmic algebra. Affiliations: ATHENA Research Center, National & Kapodistrian University of Athens, INRIA Sophia Antipolis (France via joint AROMATH team). Education: BSc (Princeton), PhD (UC Berkeley). Research Interests Emiris's work focuses on geometric algorithms, algebraic systems, and their applications. His contributions include advancements in sparse elimination theory, computational geometry for high-dimensional data, and robotics. He has developed algorithms for polynomial system solving, Voronoi diagrams, and geometric predicates for ellipses. Articles Overview His recent work bridges theoretical advances with practical applications, such as deep learning for protein structure prediction (HydraProt) and geometric algorithms for high-dimensional data analysis. He explores intersections between algebraic geometry and computational methods, with applications ranging from robotics to bioinformatics. Scientific Awards Best Paper Award at ISSAC 2003 and 2010 MSCA Network GRAPES (2019-2023) Advising & Grants Emiris has supervised numerous students and researchers, contributing to interdisciplinary projects. He has secured grants for initiatives like the GRAPES network and has led teams in algorithm design and geometric software development. His work on MARS (Maple/Matlab/C Resultant-Based Solver) exemplifies his focus on practical algorithm implementation. Labs & Teams He directs the Lab of Geometric & Algebraic Algorithms and collaborates with the AROMATH team at INRIA. His research group develops open-source tools for computational geometry and algebraic computations.

Lama Séoudمشاهده پروفایل

استادیار

• Medical Imaging
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Lama Séoud is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. She holds a Ph.D. in biomedical engineering from Polytechnique Montréal and has postdoctoral experience in industry and research at the National Research Council of Canada. Her research focuses on computer vision and computational medical imaging, with applications in healthcare, robotics, and industrial settings. She collaborates closely with clinicians, industrial partners, and artists to develop solutions for human motion analysis, medical image processing, and 3D imaging techniques. Educations: Ph.D. in Biomedical Engineering, Polytechnique Montréal (2012) M.Sc.A. in Biomedical Engineering, Polytechnique Montréal Diploma in Biomedical Engineering, École Supérieure d’Ingénieurs de Beyrouth (Lebanon) Research Interests: 3D imaging and analysis, human motion analysis, medical image computing, computer vision, machine learning. Her work integrates deep learning with 3D data acquisition and analysis, addressing challenges in healthcare (e.g., scoliosis, breast asymmetry) and industrial human-robot interaction. Key Collaborations: Centre de recherche du CHU Sainte Justine, Regroupement de recherche en intelligence artificielle appliquée aux enfants gravement malades, Institut Transmedtech, and Institut de génie biomédical. Teaching: INF8725 (Digital Signal and Image Processing), INF8801A (Multimedia Applications), GBM6700E (3D Reconstruction from Medical Images). Grants & Support: Received funding from the Quebec Research Fund for AI and health innovation projects. Labs & Teams: Active in multidisciplinary teams focusing on biomedical imaging, robotics, and AI for clinical applications.

• Computational Mathematics
• Geophysical Fluid Dynamics
• Numerical Software
• +۳ مورد دیگر

Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.

Garnet K. Chanمشاهده پروفایل

استاد

• Theoretical Chemistry
• Condensed Matter Physics
• Quantum Information Theory
• +۴ مورد دیگر

Garnet K. Chan is the Bren Professor of Chemistry and Director of the Rudolph A. Marcus Center for Theoretical Chemistry at the California Institute of Technology. He received his B.S. from the University of Cambridge in 1996 and his M.A. and Ph.D. from the University of Cambridge in 2000. Dr. Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, focusing on quantum many-particle phenomena and the numerical methods to simulate them. His group has developed numerous methodologies including density matrix renormalization and tensor network algorithms, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings, and new quantum Monte Carlo algorithms. His work addresses problems that appear naively exponentially hard but where understanding of physics, particularly entanglement structure, allows for calculations of polynomial cost. Analysis of his recent publications reveals a strong focus on quantum simulation techniques, particularly tensor network methods applied to strongly correlated systems. His research spans fundamental theoretical developments to practical applications in quantum computing, molecular simulation, and materials science, with increasing integration of machine learning techniques and GPU acceleration in computational chemistry frameworks. Dr. Chan leads an active research group at Caltech dedicated to simulating chemical and physical systems at the level of many-particle quantum mechanics. His group has welcomed numerous researchers including Kasra Hejazi, Zuxin Jin, Zhihao Cui, Ke Liao, Henrik Larsson, and Wenyuan Liu. He teaches courses in Physical Chemistry (Ch 21 abc) and Advanced Quantum Chemistry (Ch 225), contributing significantly to theoretical chemistry education at Caltech.

• Digital Hardware Design
• Reliability Engineering
• Reconfigurable Devices
• +۳ مورد دیگر

Eleonora Vacca is a PhD student and Research Fellow in the Department of Automatic Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She holds a B.S. in Electronic Engineering from the University of Palermo (2018) and an M.S. in Electronic Engineering-Embedded Systems from Politecnico di Torino (2021). Her research focuses on digital hardware design, reliability engineering, reconfigurable devices, and AI applications in aerospace and safety-critical systems. She is a member of the Aerospace and Safety Computing Lab and the CAD - Electronic CAD & Reliability Group (DAUIN). Her work addresses challenges such as radiation effects mitigation in space missions, fault-tolerant AI accelerators, and real-time anomaly detection in satellite telemetry. She has contributed to projects like the RAMSES CubeSat-1 Development (2025-2026), funded by commercial contracts. In 2024, she won the Best Student Paper Award at the NEWCAS Conference for her research on radiation effects in space missions. Vacca collaborates on teaching, including assisting in the course 'Electronic Calculators' for Computer Engineering students. Her recent publications explore AI resilience in RISC-V ecosystems, radiation environment analysis for space missions, and gesture recognition systems for smart cities. She actively contributes to conferences such as the ACM International Conference on Computing Frontiers and the IEEE International Smart Cities Conference.

Russell Tessierمشاهده پروفایل

استاد

• Reconfigurable Computing
• FPGA Architecture
• CAD Algorithms for FPGAs
• +۴ مورد دیگر

Russell Tessier is a Professor and Department Head of Electrical and Computer Engineering at the University of Massachusetts Amherst, affiliated with the Manning College of Information and Computer Sciences. His research focuses on reconfigurable computing, FPGA architectures, and hardware security, with notable contributions in CAD algorithms for FPGAs, embedded systems, and multi-tenant FPGA vulnerability analysis. Education: B.S.C.S.E., Rensselaer Polytechnic Institute (1989) M.S. and Ph.D., Massachusetts Institute of Technology (1992 and 1999) Research Interests: Dr. Tessier's work spans FPGA security (e.g., side-channel attacks, power distribution vulnerabilities), reconfigurable cloud computing, and hardware acceleration for applications like SAR imaging and machine learning. His lab, the Reconfigurable Computing Group, develops open-source FPGA cores (e.g., FlexGrip GPGPU, DE4 NetFPGA) and explores cutting-edge security countermeasures. Awards and Honors: Chancellor's Leadership Fellow (2015-2016) NSF Information Technology Research Grant Lilly Teaching Fellow (2002-2003) Multiple College of Engineering Excellence Awards Grants and Projects: Active funding includes NSF SaTC grants on reconfigurable cloud security and NASA support for snowpack measurement systems. His research also addresses FPGA-based solutions for cybersecurity, such as intrusion detection and power-side channel mitigation. Labs and Teams: Leads the UMass Reconfigurable Computing Group, which collaborates on open-source FPGA tools, security frameworks, and embedded system designs. The group maintains platforms like the DE4 NetFPGA and FlexGrip architecture.

Brendan Dolan-Gavittمشاهده پروفایل

دانشیار

• Cybersecurity
• Program Analysis
• Virtualization Security
• +۵ مورد دیگر

Brendan Dolan-Gavitt is an Associate Professor in the Computer Science and Engineering Department at NYU Tandon School of Engineering and part of the NYU Center for Cybersecurity (CCS). He holds a Ph.D. in Computer Science from Georgia Tech (2014) and a BA in Math and Computer Science from Wesleyan University (2006). His research spans cybersecurity, program analysis, virtualization security, memory forensics, and embedded/cyber-physical systems, focusing on automating the understanding of large software systems to develop novel defenses. Research interests include developing techniques for static and dynamic analyses of real-world software to reveal hidden design assumptions. His work has been presented at top security conferences like USENIX Security, ACM CCS, and IEEE Security & Privacy. He led the development of the open-source PANDA platform for dynamic analysis. His publications primarily focus on AI-driven security solutions, vulnerability discovery, and automated testing tools. Recent work explores LLMs in offensive security, fuzzing enhancements, and secure code generation, emphasizing practical applications in cybersecurity. Scientific Awards: NSF CAREER Award for improving software vulnerability testing and education He leads the OSIRIS Lab, a student-run cybersecurity group, and collaborates on interdisciplinary projects addressing emerging security challenges through grants and industry partnerships.

Zahra Abbasiمشاهده پروفایل

استادیار

• Applied Electromagnetics
• RF/Microwave Sensing
• Chipless Sensors
• +۵ مورد دیگر

Dr. Zahra Abbasi is an Assistant Professor at the Department of Electrical and Software Engineering, University of Calgary (since 2021), with expertise in designing passive and active RF/microwave sensors for non-invasive real-time detection and monitoring. She holds a PhD in Nanosystems and Microsystems from the University of Alberta (2020) and a postdoctoral fellowship at the Waterloo Microfluidics Lab (University of Waterloo). Her research focuses on sensor systems for environmental, biomedical, and industrial applications, including microfluidic sensing, chipless tags, and wearable devices. Dr. Abbasi’s educational background includes degrees from Iran University of Science & Technology (BSc 2014, MSc 2016) and advanced training in Canada. She chairs IEEE MTT-S/AP committees in Alberta and has received notable awards such as the ASTech Finalist (2024) and Alberta Immigrant Impact Award (2023). Her work emphasizes collaboration across disciplines to address challenges in precision agriculture, healthcare, and environmental monitoring. Her sensor research spans diverse fields: microplastic detection in water, hydrocarbon contamination monitoring, real-time medical diagnostics (e.g., fibrinogen levels), and nutrient assessment in agriculture. Recent advancements include disposable sensors for medical implants and non-invasive hydration tracking for aging populations. Awards Finalist: ASTech Early Career Academic Change Maker (2024) Early Research Excellence Award (Schulich School of Engineering, 2023/2024) Alberta Immigrant Impact Award (2023) Advising & Labs Dr. Abbasi leads the Calgary Sensor Laboratory, fostering innovation in microwave and microfluidic sensor technologies. Her team collaborates on industrial and biomedical projects, emphasizing practical applications of novel sensor designs. While no named advisees are listed, her research group actively engages in multidisciplinary projects.

Majid Ghaderiمشاهده پروفایل

استاد

• Network Algorithms
• Secure Communication
• Machine Learning in Network Control
• +۳ مورد دیگر

Majid Ghaderi is a Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. His expertise spans network algorithms, secure communication, and machine learning applications in network control. He holds a Ph.D. in Computer Science from the University of Waterloo (2006), and M.Sc. and B.Sc. degrees in Software Engineering from Sharif University of Technology (2001 and 1999). Education: Ph.D. Computer Science, University of Waterloo, 2006 M.Sc. Software Engineering, Sharif University of Technology, 2001 B.Sc. Software Engineering, Sharif University of Technology, 1999 Research Interests: Dr. Ghaderi focuses on optimizing network algorithms, securing communication in distributed systems, and leveraging machine learning for network control. His work addresses challenges such as secure wireless protocols, SDN-based network management, and efficient resource allocation in data centers. He explores proactive traffic scheduling and anomaly detection in critical infrastructures like industrial control systems and vehicular networks. Publications Trends: His recent work emphasizes covert communication in heterogeneous networks, adaptive federated learning in edge environments, and low-overhead diagnostic systems for cloud networks. He also investigates cybersecurity defenses against hardware vulnerabilities and dynamic threat landscapes. Awards: Best in-session Presentation Award, IEEE INFOCOM 2018 Municipal Excellence Award, Government of Alberta 2018 Faculty of Science Excellence in Teaching Award 2012 Advising & Grants: While no specific advisees are listed, his research has been supported by grants focusing on network security, edge computing, and IoT applications. He teaches CPSC 441 (Computer Networks) and maintains an active lab focused on network systems and cybersecurity. Labs & Teams: His research group collaborates on projects involving software-defined networks, vehicular communication, and industrial IoT security. The team develops open-source tools for network monitoring and anomaly detection.

Abdullah Muzahidمشاهده پروفایل

دانشیار

• Computer Architecture
• Systems
• Artificial Intelligence
• +۵ مورد دیگر

Abdullah Muzahid is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University (since August 2024), previously serving as an Assistant Professor there since August 2018. Prior, he held an Assistant Professor role at the University of Texas at San Antonio (2012-2018). He earned his Ph.D. from the University of Illinois at Urbana-Champaign (2012), focusing on architectural support for debugging concurrency bugs under Prof. Josep Torrellas. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2012) M.S., Computer Science, University of Illinois at Urbana-Champaign (2009) B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology (2005) Research Interests: His work spans Computer Architecture , Systems , and Artificial Intelligence , with focus on multiprocessor architecture, parallel programming, debugging, and applying machine learning to system optimization. Recent projects include cache indexing via entropy estimation, DNN training acceleration, and hardware-software co-design for security. Awards: NSF CAREER Award (2017) Excellence in Research Award (UTSA, 2015 & 2017) W. J. Poppelbaum Award (UIUC, 2012) Intel Ph.D. Fellowship (2011) Grants & Advising: He leads NSF-funded projects on robust deep learning and stream processing systems. Advised 5 PhD graduates and currently mentors 4 PhD students. Served on program committees for ISCA, HPCA, MICRO, and as NSF panelist. Labs/Teams: Active in Texas A&M’s Computer Architecture group, collaborating on machine programming, hardware security, and AI-driven systems optimization.

• Algorithms
• Spectral Graph Theory
• Computer Science
• +۵ مورد دیگر

Dan Spielman is the Sterling Professor of Computer Science and holds joint appointments as Professor of Statistics and Data Science and Mathematics at Yale University. He is affiliated with the Department of Mathematics within the Faculty of Arts and Sciences. His research focuses on spectral graph theory, algorithms, linear systems, and their applications in computer science, mathematics, and statistics. He has been recognized as an ACM Fellow for his contributions to theoretical computer science and mathematics. Dr. Spielman's work bridges theoretical and applied domains, with notable advancements in graph sparsification, Laplacian solvers, and the resolution of the Kadison-Singer problem. His research also encompasses algorithmic design, optimization, and probabilistic methods. Key grants include NSF funding for projects like 'Generalized Algebraic Graph Theory: Algorithms and Analysis' (2016). His scientific awards include the ACM Fellowship (2011), acknowledging his impactful contributions to algorithms and complexity theory. Spielman’s interdisciplinary approach integrates spectral graph theory with practical applications, addressing fundamental problems in computation and mathematics.

Andreas Markoulakisمشاهده پروفایل

مدرس

• Behavioral Economics
• Experimental Economics
• Microeconometrics
• +۳ مورد دیگر

Andreas Markoulakis is a Lecturer in the Department of Economics at the University of Warwick. He teaches modules including EC134: Topics in Applied Economics, EC229: Economics of Strategy, and EC138: Introduction to Environmental Economics. His research spans Behavioral Economics, Experimental Economics, and Microeconometrics, with a focus on energy policy, environmental economics, and pedagogical innovations. He has conducted studies on the One-Minute Paper (OMP) teaching intervention, analyzing its impact on student engagement and comprehension across seminar sessions. This intervention, implemented in EC138, employs paper-based feedback to assess student understanding and has shown discrepancies in responses when framing questions differently (e.g., addressing hesitant students explicitly). Education: PhD in Economics from the University of Kent Key research areas: Energy security policy, creative production incentives, and the application of experimental methods in economics Teaching responsibilities include advising international students and assessing graduate teaching assistants His work on the OMP intervention highlights lower comprehension rates when questions explicitly acknowledge student hesitation, suggesting potential barriers for non-native English speakers or shy students. He also explores long-term learning retention and the correlation between OMP feedback and academic performance. Future research directions include expanding the OMP analysis to broader student demographics and integrating assessment with formal exam performance data. Contributions to educational practices include publishing findings on small-group teaching effectiveness and fostering transparent communication between instructors and students. Ongoing projects involve studying semiconductor economics' impact on labor markets and AI's role in creative industries.