جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Optimization
• Federated Learning
• +۸ مورد دیگر

Peter Richtarik is a Professor at the King Abdullah University of Science and Technology (KAUST), specializing in Machine Learning, Optimization, and Federated Learning. He actively teaches courses such as Stochastic Gradient Descent Methods and mentors PhD and MS students like Konstantin Burlachenko, Kai Yi, and Lukang Sun. His research spans distributed optimization, parameter-efficient fine-tuning, and theoretical frameworks for non-convex and non-smooth problems. Recent work includes 2025 contributions to Bernoulli-LoRA (theoretical PEFT) and Gluon (LMO-based optimizers). He co-developed Thanos (block-wise pruning) and BurTorch (CPU-optimized training framework). His publications focus on communication efficiency ( ATA , LoCoDL ), differential privacy ( DP-RBCD ), and stochastic proximal methods. Richtarik received the Charles Broyden Prize for his work on quasi-Newton methods. He frequently presents at workshops like MLSS in Senegal and FLOW seminars.

Anthony Blochمشاهده پروفایل

استاد

• Geometric Mechanics
• Nonlinear Dynamics
• Hamiltonian Mechanics
• +۱۰ مورد دیگر

Anthony Bloch is the Alexander Ziwet Collegiate Professor of Mathematics and Professor of Mathematics at the University of Michigan, serving as Chair of the Department of Mathematics. He is affiliated with the Center for the Study of Complex Systems (CSCS) in Weiser Hall. His research focuses on: Geometric Mechanics : Hamiltonian/Lagrangian mechanics, symplectic geometry, and integrable systems including Toda lattices and rigid body dynamics Nonlinear Dynamics : Nonholonomic systems with nonintegrable constraints, stability analysis, and continuous-discrete flow relationships Control Theory : Nonlinear and optimal control applications extending to quantum dynamics and astrophysical systems Recent publications demonstrate geometric methods applied to nonholonomic control, virtual constraints, and stabilization, with growing interdisciplinary work in network dynamics, quantum control, and cosmological models like cosmic reheating. Through the Center for the Study of Complex Systems, Professor Bloch collaborates across disciplines to investigate complex phenomena in natural and engineered systems, leveraging geometric frameworks to address fundamental questions in mechanics and dynamics.

Chun-Hua Guoمشاهده پروفایل

استاد

• Matrix analysis
• Scientific computing
• Numerical linear algebra
• +۳ مورد دیگر

Chun-Hua Guo is a Professor in the Department of Mathematics and Statistics at the University of Regina, Faculty of Science. His research focuses on matrix analysis, scientific computing, and applications in tensor computations and nonlinear matrix equations. He teaches advanced courses such as MATH 869 Numerical Analysis. Dr. Guo’s recent work emphasizes iterative methods for solving eigenvalue problems of nonnegative tensors, matrix equations arising in nano research, and convergence analysis of numerical algorithms. His publications address topics like Newton-Noda iteration, modified Newton methods for Z-eigenpairs, and algebraic Riccati equations associated with M-matrices. His research trends highlight advancements in computational techniques for matrix functions (e.g., matrix pth root), tensor Perron pairs, and stability analysis of iterative algorithms. These contributions bridge theoretical linear algebra with practical computational challenges in engineering and scientific domains. Dr. Guo’s advising and grants involve developing efficient numerical methods for complex systems. His work has implications for fields requiring high-precision matrix computations, such as control theory, stochastic modeling, and nano-material simulations.

Chinthaka Pathum Dinesh Herath Gedaraمشاهده پروفایل

عضو هیئت علمی

• Graph Signal Processing
• Data Analytics
• Machine Learning
• +۸ مورد دیگر

Chinthaka Pathum Dinesh Herath Gedara is an Assistant Teaching Professor in the Multidisciplinary Graduate Engineering Programs at Northeastern University. He holds a PhD in Signal and Image Processing from Simon Fraser University (2021). His research focuses on graph signal processing, machine learning, computer vision, and smart grids. He has published extensively in top journals like IEEE Transactions on Image Processing and Pattern Analysis and Machine Intelligence. His recent work addresses challenges in 3D point cloud processing, image super-resolution, and power system forecasting. Education: PhD, Signal and Image Processing, Simon Fraser University, 2021 Research interests include developing novel algorithms for graph-based data analytics, generative AI applications, and optimization techniques. His work bridges theoretical foundations and practical applications in computer vision and smart energy systems. While no grants or awards are explicitly listed, his publications reflect active engagement in high-impact interdisciplinary research. No student advisees or lab affiliations are documented in the provided materials.

Alfonso Sorrentinoمشاهده پروفایل

استاد

• Hamiltonian Dynamical Systems
• Nonlinear Analysis
• Differential Geometry
• +۳ مورد دیگر

Alfonso Sorrentino is a full professor of mathematical analysis at the Department of Mathematics, University of Rome Tor Vergata. He specializes in Hamiltonian dynamical systems, with expertise in nonlinear analysis, differential geometry, and mathematical physics. He earned his Ph.D. in mathematics from Princeton University (2008) and held academic positions at the Fondation des Sciences Mathématiques de Paris (2008–2009) and the University of Cambridge (2009–2012). His research focuses on invariant structures in Hamiltonian systems, billiard dynamics, and spectral rigidity. Key recognitions include the Fubini Prize (2018), Barcelona Dynamical Systems Prize (2019), Best Paper Award (Gold medal, 2020), and Frontiers of Science Award (2023). His work bridges pure mathematics and applications, emphasizing geometric and analytic approaches to dynamical systems. Recent studies explore topics like invariant Lagrangian graphs, spectral properties of billiards, and homogenization of Hamilton-Jacobi equations on networks. His contributions reflect a commitment to advancing theoretical frameworks in dynamical systems and their interdisciplinary implications.

Jamshid Mohammadiمشاهده پروفایل

استاد

• Structural Engineering
• Civil Engineering
• Risk Analysis
• +۵ مورد دیگر

Jamshid Mohammadi is the Interim Provost and Professor of Civil and Architectural Engineering at Illinois Institute of Technology (IIT), within the Armour College of Engineering. He holds a Ph.D. in Civil Engineering (Structural Engineering) from the University of Illinois at Urbana-Champaign. His research focuses on structural integrity, seismic damage analysis, bridge performance, and risk assessment in transportation systems. Research Projects: He leads studies on bridge fatigue, seismic vulnerability, structural health monitoring, and disaster resilience. Notable projects include investigating horizontally curved bridges, seismic damage to skewed bridges, and probabilistic models for fatigue failure in metals. His work often involves collaborations with institutions like NASA and the Illinois Department of Transportation. Publications & Books: Mohammadi has authored over 150 peer-reviewed articles and two influential books: Systems Engineering, with Economics, Probability and Statistics and NDT Methods Applied to Fatigue Reliability Assessment of Structures . His work bridges theoretical models with practical engineering solutions. Expertise: His expertise spans system reliability, highway bridge analysis, and probabilistic methodologies for infrastructure assessment. He advises on temporary structure design, post-disaster risk mitigation, and lifecycle cost optimization. Grants & Recognition: His projects are funded by federal and state agencies. While no specific awards are listed, his extensive publications and leadership roles highlight his impact in civil engineering education and practice.

Guofu Niuمشاهده پروفایل

استاد

• Semiconductor Devices
• Compact Modeling
• RF Circuit Design
• +۳ مورد دیگر

Guofu Niu is a Professor in the Department of Electrical and Computer Engineering at Auburn University, holding the Ed and Peggy Reynolds Family endowed chair. His research specializes in semiconductor device physics, compact modeling of SiGe heterojunction bipolar transistors (HBTs), FinFETs, and cryogenic electronics for RF and power applications. Key research areas include RF linearity characterization, avalanche effects, thermal noise, and low-temperature device performance. He has developed advanced models (e.g., Mextram) for circuit simulation tools, enhancing the accuracy of semiconductor design workflows. Publications focus on nano-scale device reliability, tunneling currents, and optimization of RF amplifiers, with applications in 5G technology and extreme-environment electronics. Collaborative projects span industry and academia to advance semiconductor modeling frameworks.

• Vehicle Routing Problems
• Scheduling Algorithms
• Genetic Algorithms
• +۳ مورد دیگر

Rémy DUPAS is an Associate Professor with HDR (Habilitation à Diriger des Recherches) specializing in operations research, scheduling algorithms, and vehicle routing optimization. His work focuses on dynamic systems, genetic algorithms, and real-time logistics solutions. He has contributed extensively to the field through publications in journals like European Journal of Operational Research and International Journal of Innovative Computing and Applications . His research interests include vehicle routing problems (VRP), cyclic scheduling in manufacturing systems, and metaheuristics for dynamic optimization. He has developed innovative approaches using genetic algorithms and neural networks to address real-world logistics challenges, such as time-dependent travel times and flexible time windows. He has advised four PhD students, including Xin ZHAO (2004–2008), Haiyan HOUSROUM (2002–2005), and Guillaume CAVORY (1997–2000), focusing on topics like dynamic vehicle routing and evolutionary algorithms. His HDR thesis, Amélioration de performance des systèmes de production : apport des algorithmes évolutionnistes aux problèmes d’ordonnancement cycliques et flexibles , underscores his expertise in optimizing industrial systems. Key contributions include book chapters on metaheuristics for vehicle routing in dynamic contexts and conferences on topics like the traveling repairman problem and simulation platforms for dynamic vehicle tours.

• Wireless Communications
• Signal Processing
• MIMO Systems
• +۳ مورد دیگر

Konstantinos Nikitopoulos is a Professor at the University of Surrey , UK, specializing in Wireless Communications and Signal Processing . His research focuses on MIMO Systems , Open-RAN , and Non-Linear Processing for next-generation wireless networks. His recent work explores Analogue Processing for Tbps Wireless Systems and Neuromorphic Computing in MU-MIMO detection. He has developed frameworks like MIMO-SoftiPHY and SACCESS for software-based radio acceleration and power-efficient network design. Key Publications : Power-Efficient RIC, NL-COMM, NeuroMIMO Collaborators : Rahim Tafazolli, George Katsaros, Marcin Filo His research impacts 6G Network Development through innovations in Beamforming , Channel Estimation , and Software-Defined Radios .

László Kozmaمشاهده پروفایل

استادیار

• Data Structures
• Algorithms
• Algorithmic Adaptivity
• +۷ مورد دیگر

László Kozma is an Assistant Professor at the Theoretical Computer Science group of the Institute of Computer Science (Freie Universität Berlin). He obtained his PhD from Saarland University under Raimund Seidel, followed by postdoctoral positions at Tel Aviv University and TU Eindhoven. His research focuses on self-adjusting data structures , adaptive algorithms , and combinatorial optimization with applications to problems like the Traveling Salesman Problem, binary search trees, and geometric data structures. Academic Affiliation: Freie Universität Berlin (since 2018) Education: PhD in Computer Science (Saarland University, 2016); postdoc at Tel Aviv University and TU Eindhoven. His work explores the intersection of data structures, combinatorial algorithms, and geometric methods. He has made significant contributions to problems involving pattern-avoidance in inputs, saddlepoint detection , and self-adjusting heaps . Key areas include: Adaptive algorithms for pattern-avoiding inputs Optimal tree and heap structures Geometric and stochastic approaches to optimization Complexity analysis of classical algorithms Recent publications highlight efficient solutions for exponential cut problems (ESA 2025), balanced TSP partitioning (EuroCG 2025), and randomized saddlepoint algorithms (ESA 2024). His research often bridges theory and practice, exemplified by the smooth heap implementation and fun projects like Recursi and Cuckoo Hashing visualization.

Bassam Bamiehمشاهده پروفایل

استاد

• Control Systems
• Dynamical Systems
• Quantum Control
• +۷ مورد دیگر

Bassam Bamieh is a Professor of Mechanical Engineering at the University of California, Santa Barbara (UCSB), with affiliate roles in Electrical and Computer Engineering and the Center for Control, Dynamical Systems and Computation (CCDC). His research focuses on control systems, dynamical systems, and their applications in fluid mechanics, quantum control, and network science. He holds fellowships from IEEE and IFAC and has received accolades such as the NSF Early Career Award and IEEE Distinguished Lecturer designation. Education: B.Sc. in Electrical Engineering and Physics from Valparaiso University (1983), M.Sc. and Ph.D. in Electrical and Computer Engineering from Rice University (1986, 1992). Formerly an Assistant Professor at the University of Illinois at Urbana-Champaign (1991–98). Research interests span robust and optimal control, distributed systems, shear flow turbulence, and thermoacoustic energy conversion. He has authored over 200 publications and pioneered work in spatially invariant systems and network controllability. His teaching includes courses on linear systems, vibrations, and control systems design. Awards include the IEEE Axelby Award (twice), Hugo Schuck Best Paper Award, and recognition for student research mentorship (e.g., Outstanding Student Paper Awards at CDC and IFAC NecSys22). His group collaborates across disciplines, integrating mathematical analysis with engineering applications.

• Compiler Optimization for High Performance Computing
• Optimization of Sparse/Dense Matrix/Tensor Computations
• Scalable Machine Learning
• +۱ مورد دیگر

P. (Saday) Sadayappan is a Professor in the School of Computing at the University of Utah. He serves as a lead researcher in high-performance computing, with a focus on compiler optimization and algorithm-architecture co-design. His current projects include NIH SBIR Phase 2 funding for large-scale image analysis and NSF grants for tensor applications and cyber-infrastructure for AI. Research Interests : Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Research Trends in Publications : His work emphasizes optimizing computational workflows for emerging hardware architectures, with a focus on accelerating machine learning and scientific computing through compiler-level innovations. Recent trends include co-design for CNNs, sparse matrix optimizations, and distributed algorithms. Scientific Awards : ACM SIGPLAN Most Influential PLDI Paper Award (2018) Grants & Projects : NSF (2022–2027): Comprehensive Framework for Tensor Applications NSF AI Institute ICICLE (2021–2026): Cyber-infrastructure for environmental AI NIH SBIR (2023–2025): Next-gen machine learning for image analysis Labs & Teams : Collaborates with institutions like Ohio State University and RNET Technologies on projects involving parallel computing, sparse algorithms, and compiler design.

Keith D. Paulsenمشاهده پروفایل

استاد

• Biomedical Engineering
• Medical Imaging Methodologies
• Cancer Therapeutics
• +۴ مورد دیگر

Keith D. Paulsen is the MacLean Professor of Engineering at Dartmouth College’s Thayer School of Engineering and holds the title of Professor of Radiology & Surgery at the Geisel School of Medicine. He serves as Scientific Director of the Center for Surgical Innovation at Dartmouth-Hitchcock Medical Center and Co-Director of the Translational Engineering in Cancer Research Program at the Norris Cotton Cancer Center. His roles emphasize interdisciplinary collaboration between engineering, medicine, and oncology. Paulsen earned a BSc in Biomedical Engineering from Duke University (1981), followed by MS (1984) and PhD (1986) degrees in Engineering Sciences from Dartmouth College. His research focuses on biomedical imaging, cancer therapeutics, and image-guided surgery, with particular expertise in optical and electromagnetic methodologies. He has pioneered technologies such as fluorescence-guided surgery, quantitative scatter imaging, and non-linear image reconstruction techniques, aiming to enhance surgical precision and cancer diagnosis. His awards include fellowships from OSA, SPIE, AIMBE, IEEE, and the National Academy of Inventors. Paulsen’s work has led to startups like CairnSurgical (where he serves as CTO) and InSight Surgical Technologies, translating research into clinical tools. Key projects include intraoperative imaging systems for brain and spine surgery, microwave imaging for breast cancer, and optical molecular imaging for real-time surgical guidance. Paulsen teaches advanced computational methods (ENGS 205, 105) and courses on medical device innovation (ENGM 189.1/2). His lab, part of Dartmouth’s Optics in Medicine cluster, collaborates with radiology, surgery, and oncology departments to develop clinical technologies funded by NIH, NCI, and DoD grants.

Nikhil Chopraمشاهده پروفایل

استاد

• Nonlinear control
• Robotics
• Control Systems
• +۵ مورد دیگر

Dr. Nikhil Chopra is a Professor in the Department of Mechanical Engineering at the University of Maryland, College Park, with affiliate appointments in Electrical and Computer Engineering. He earned his Bachelor of Technology from IIT Kharagpur (2001) and his M.S. and Ph.D. from University of Illinois at Urbana-Champaign (2003, 2006). As Director of Undergraduate Studies, he leads academic programs while advancing research in systems, control, and robotics. His work focuses on robotic system control, soft robotics, teleoperation, and machine learning integration. Research highlights include co-authoring the book *Passivity-Based Control and Estimation in Networked Robotics* (2015), co-chairing the IEEE Technical Committee on Telerobotics, and serving as Associate Editor for *Automatica* and related journals. His lab, the Semi-Autonomous Systems Lab, explores control-theoretic frameworks for robotics and optimization, collaborating with institutions like Sintef and IEEE RAS Technical Committees. Key projects involve underwater robotics navigation, cyber-physical system privacy, and distributed optimization algorithms. His team has exhibited strong presence at ICRA and IROS conferences, including awards for work on 3D water quality mapping and control frameworks. Current initiatives include robotic parasitic arrays for communication enhancement and secure bilateral teleoperation systems. Lab: Semi-Autonomous Systems Lab (SAS Lab) Affiliations: Institute for Systems Research, Maryland Robotics Center Recent Funding: NSF grants, industry partnerships

• Sustainable Development
• Low-Carbon Technologies
• Energy and Building Technology
• +۱۳ مورد دیگر

Dr. Behrang Vand Alimohammadisagvand is a Lecturer at the School of Computing, Engineering and the Built Environment, Edinburgh Napier University. His research focuses on sustainable energy systems, building performance, and smart energy management, with strong international collaboration across Iran, China, Finland, and the UK. B.Sc. and M.Sc. in Mechanical and Energy Engineering Ph.D. in Energy and Building Technology His research interests include sustainable development, low-carbon technologies, energy policy, thermal comfort in buildings, and energy management systems at multiple scales. He actively investigates model predictive control (MPC), demand response, energy sharing in communities, and low-temperature heating networks. His work bridges theoretical modeling and practical implementation in real-world buildings. Dr. Vand's recent publications highlight trends in integrated energy management , smart grid optimization , and decarbonization of building heating systems . His articles emphasize mathematical modeling, simulation, and control strategies to improve energy efficiency and reduce carbon emissions in the built environment. He supervises doctoral students working on smart IoT systems and low-temperature heat networks. His research is supported by funders such as the Academy of Finland and Energy Technology Partnership. Active supervisor for PhD project on smart IoT systems with real-time deep learning (since 2022) Director of Studies for PhD on heating Scottish public buildings with low-temperature networks (2018–2024) He is affiliated with the Institute for Sustainable Construction and contributes to the research theme Culture and Communities . His work aligns with global efforts toward net-zero emissions and sustainable urban development.