جست‌وجوی استاد و پژوهشگر

Matt Amyمشاهده پروفایل

استادیار

• Quantum Computing
• Programming Languages
• Compilers
• +۳ مورد دیگر

Dr. Matt Amy is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), holding the Canada Research Chair in Quantum Computing. His research focuses on quantum compilers, programming languages, and formal verification of quantum programs. He also explores quantum circuit optimization and models of quantum computation. Education: PhD in Computer Science (University of Waterloo, 2019), M.Math in Quantum Information (2013), and B.Math in Computer Science (2011), all from the University of Waterloo. Research Interests: Quantum compilers and languages, circuit optimization, formal verification, and quantum computation models. His work bridges theoretical foundations with practical implementations, emphasizing efficient quantum software development. Recent research trends include advancing quantum compilation techniques, exploring NP-hard optimization problems in quantum circuits, and developing formal methods for quantum program analysis. His work on symbolic synthesis and equational theories for quantum circuits demonstrates a focus on foundational algorithmic challenges. Scientific Awards: Canada Research Chair (2025–present) Advising and Grants: While no current advisees are listed, his research is supported by grants focused on quantum computing and formal methods. He collaborates with industry through SFU’s School of Computing Science. Labs and Teams: Involved with the Tangent Lab, a research group exploring quantum algorithms and software systems at SFU.

Ram Alagappanمشاهده پروفایل

استادیار

• Storage systems
• Distributed systems
• Operating systems
• +۴ مورد دیگر

Ram Alagappan is an Assistant Professor at the University of Illinois Urbana-Champaign's Siebel School of Computing and Data Science, Department of Computer Science. He co-leads the Distributed And Storage Systems Laboratory (DASSL) and focuses on improving reliability and performance in computer systems. PhD from University of Wisconsin-Madison (2019) Postdoctoral researcher at VMware Research (2020-2022) Research interests span storage systems, distributed systems, and operating systems, with emphasis on: Crash consistency and reliability Consensus protocols and fault tolerance Non-volatile memory optimization Datacenter storage abstractions Recent publications highlight advancements in shared log abstractions (LazyLog, SOSP 2024), replication for KV stores (IONIA, FAST 2024), and fault tolerance in disaggregated datacenters (SplitFT, EuroSys 2024). His work also includes foundational studies on crash vulnerabilities and consistency models. Scientific recognitions include: Best Paper Awards at SOSP 2024, FAST 2020, FAST 2018, FAST 2017 NSF CAREER Award (2023) NetApp Faculty Fellowship (2024) Teaching honors: Listed as Excellent/Outstanding Teacher at UIUC (CS598 Storage Systems, 2022-2023) Ranked 1st in student evaluations for CS739 Distributed Systems at UW-Madison (2020) Grants and service: NSF CAREER Award, IBM-IL Discovery Grant Program Committee roles at OSDI, SOSP, EuroSys, HotStorage Labs & Teams: Co-leader of DASSL research group at UIUC.

Per Lynggaardمشاهده پروفایل

استاد

• Integrated Circuit Design
• Wireless Sensor Networks (WSN)
• Machine Learning
• +۸ مورد دیگر

Per Lynggaard is a Professor of Electronics at the Technical University of Denmark (DTU) , leading the B.Eng. program in Electronics. Previously, he held an Associate Professor role at Aalborg University, combining academic excellence with a robust industrial career in technical-scientific research and development. Education: M.Sc. in Electrical Engineering and Information Technology (EE and IT) Ph.D. in Electronics from Aalborg University Research Interests: Focus on Integrated Circuit Design, Wireless Sensor Networks (WSN), Machine Learning, IoT, and Smart City Technologies . His work emphasizes energy-efficient systems, cybersecurity in IoT, AI-driven interference mitigation, and sustainable energy harvesting solutions. He has contributed to UN Sustainable Development Goals through projects addressing smart infrastructure and environmental monitoring. Projects & Collaborations: Leads and participates in EU-funded initiatives such as InnoTech (2023–2025) for green transition solutions and TransportTech (2023–2026) for Industry 4.0 logistics. Active in cybersecurity research via projects like Jamming Against Critical Wireless Communication , aiming to protect critical infrastructure. Awards: Recognized with multiple honors and rewards during his industrial career, though specific names are not listed. His work has been cited widely, with notable impact in IoT security and energy-efficient systems. Advising & Grants: Supervises Turnip T.N. in a PhD project on 6G security protocols. Engaged in securing funding for projects like F2D2: The Community for Dynamic Data (2021–2030), focusing on dynamic data systems and cybersecurity. Labs & Teams: Collaborates in interdisciplinary teams such as the InnoTech TaskForce and F2D2 Community , advancing IoT and AI integration. His research bridges academia and industry, with outputs spanning smart cities, healthcare IoT, and sustainable energy systems.

Y. Charlie Huمشاهده پروفایل

استاد

• Mobile Systems
• Distributed Systems
• Operating Systems
• +۵ مورد دیگر

Y. Charlie Hu is the Michael and Katherine Birck Professor of Electrical and Computer Engineering and Professor of Computer Science (by courtesy) at Purdue University, where he leads the PurNET Lab and contributes to the Systems and Networking Group. His research spans Mobile Systems, Distributed Systems, Operating Systems, and Computer Networks , with a focus on energy-efficient AI systems and edge computing. His groundbreaking work on smartphone energy management has been widely adopted by the mobile industry and recognized with multiple test-of-time awards , including from ACM SIGOPS and ACM SIGMOBILE . He has received prestigious honors like the NSF CAREER Award , Honda Initiation Grant , and industry accolades from Google Research and Qualcomm . Notable Funded Projects: NSF's NeTS: Black-box Optimization of White-box Networks (2023-2026) Intel -NSF's SPLICE initiative His research has produced 15+ PhD graduates now in academia (University of Arizona, Virginia Tech) and industry (Google, Apple, Qualcomm). The articles reflect a career-long focus on edge computing , 5G network optimization , and energy-aware systems , with recurring themes in mobile AR/VR , video analytics , and network protocol design . Scientific Awards Honda Initiation Grant NSF CAREER Award Purdue Early Career Research Award Google Research Award Qualcomm Faculty Award ACM SIGOPS EuroSys Best Student Paper Award ACM MobiCom Best Community Paper Award IEEE Fellow ACM Distinguished Scientist Purdue PRF Innovator Hall of Fame

Twan Bastenمشاهده پروفایل

استاد

• Embedded Systems
• Cyber-Physical Systems
• Model-Driven Engineering
• +۳ مورد دیگر

Twan Basten is a Full Professor in the Electronic Systems group at Eindhoven University of Technology (TU/e). He leads research on embedded and cyber-physical systems, focusing on model-driven design, computational models, and system dependability. He holds an MSc (1993) and PhD (1998) in Computing Science from TU/e, advancing from Assistant to Full Professor by 2009, and became the Electronic Systems group chair in 2013. His research spans international projects (FP5-7, H2020, ECSEL) and Dutch initiatives (STW, NWO, RVO), with over 200 publications and seven best paper awards. He has co-supervised 21 PhD students and actively participates in program committees and conferences. His work contributes to UN Sustainable Development Goals through innovations in smart systems. Education: MSc in Computing Science, TU/e (1993) PhD in Computing Science, TU/e (1998) Research Interests: Explores design methodologies for embedded systems, including scenario-based design, real-time scheduling, and performance analysis. Specializes in model-driven engineering and computational models to ensure system dependability. Active in projects like TRANSACT (real-time systems) and SAM-FMS (flexible manufacturing). Key Contributions: Co-author of 1 book and over 200 scientific publications Recipient of seven best paper awards Co-supervised 21 PhD degrees Senior member of IEEE and lifetime member of ACM Labs & Teams: Leads the Model-Based Design Lab and contributes to EAISI High Tech Systems initiatives. Collaborates on tools like TRACE4CPS for execution trace analysis and CReTS for vehicle platooning simulation.

Barbara Wohlmuthمشاهده پروفایل

استاد

• Numerical Simulation
• Partial Differential Equations
• Discretization Techniques
• +۴ مورد دیگر

Prof. Barbara Wohlmuth is a full professor in Numerical Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. She leads the International Graduate School of Science and Engineering at TUM and has held professorships at Stuttgart, Darmstadt, and Berlin universities. Her research focuses on numerical simulation of partial differential equations, multiscale solvers, and coupled multi-field problems with applications in engineering. Education: Studied mathematics at TUM and Université Joseph Fourier in Grenoble, received her doctorate from TUM in 1995, and completed habilitation in Augsburg. Visiting professorships in USA, France, and Hong Kong. Research interests include discretization techniques, predictive modeling, and interdisciplinary collaboration with engineering disciplines. Notable achievements: 2012 Gottfried Wilhelm Leibniz Prize (Germany’s highest academic honor in sciences), 2005 Sacchi-Landriani Prize. Publications emphasize advanced numerical methods in fluid dynamics, geophysics, and biomedical engineering. Active in editorial roles for international journals and scientific committees across Europe and USA. Elected member of Bavarian and European Academies of Sciences. Key contributions include: Development of robust numerical algorithms for exascale simulations Pioneering work in coupled multi-physics modeling Innovative methods for computational contact mechanics Leadership in graduate education initiatives

Michel Sannerمشاهده پروفایل

استاد

• Molecular Interactions
• Molecular Graphics
• Component-Based Software
• +۳ مورد دیگر

MICHEL SANNER is a Professor of Molecular Biology at the Department of Integrative Structural and Computational Biology at Scripps Research. He holds a PhD in Computer Science from the University of Haute Alsace, France (1992). His research focuses on computational methods for molecular interactions, molecular graphics, and component-based software development. Notable contributions include the AutoDock suite (for molecular docking), PMV (a molecular visualization environment), and Vision (a visual programming tool). His research group develops tools like AutoDock CrankPep for peptide docking and F2Dock for protein-protein interactions. These tools are widely used in drug discovery and structural biology. His work emphasizes software engineering principles to create adaptable computational pipelines for analyzing macromolecular structures and simulating interactions. Publications span topics like peptide-docking methodologies, ligand-binding site prediction, and GPU-accelerated docking algorithms. His articles highlight advancements in computational methods for understanding protein-ligand interactions, with applications in anticoagulant research and HIV/FIV protease inhibition. Collaborations include work with Arthur J. Olson and David S. Goodsell on docking methodologies.

• Edge Computing
• Distributed Systems
• Container Networking
• +۹ مورد دیگر

Tom Goethals is an FWO Junior Postdoctoral Fellow affiliated with the Department of Information Technology at Ghent University , where he conducts research in edge computing, container networking, and decentralized systems. Current role: IMEC Postdoctoral Researcher Research focus: Secure and intelligent edge service management for decentralized IoT applications His work explores edge intelligence , orchestration frameworks , and AI-driven network optimization , with trends in lightweight virtualization (e.g., Feather), Kubernetes adaptation for edge environments, and intent-based decentralized orchestration. Publications emphasize scalability, security, and energy efficiency in fog-native workflows. Scientific Awards : FWO Junior Postdoctoral Fellowship He collaborates with researchers like Bruno Volckaert and Filip De Turck on projects funded by the Research Foundation - Flanders (FWO) , including grants for edge container networking and decentralized learning frameworks. His projects align with Ghent University’s focus on smart city infrastructure and edge-to-cloud systems.

Hande Bensonمشاهده پروفایل

استاد

• Optimization
• Nonlinear Optimization
• Interior-Point Methods
• +۱۲ مورد دیگر

Hande Benson is a Professor in the Department of Decision Sciences and MIS at LeBow College of Business, Drexel University. She serves as the academic director of the Business and Engineering program and teaches in undergraduate and graduate programs in Business Analytics and Operations and Supply Chain Management. Research Interests: Dr. Benson specializes in optimization, particularly addressing modeling and computational challenges in large-scale nonlinear and mixed-integer optimization. Her work includes interior-point methods, regularization techniques, and the development of optimization software such as LOQO and MILANO. Recent Research Trends: Her recent publications span decision aggregation, multi-vehicle motion planning under communication constraints, and advanced interior-point algorithms. These works reflect a strong focus on algorithmic innovation, real-world applications in robotics and supply chains, and theoretical advancements in nonconvex optimization. Scientific Awards: Outstanding STAR Mentor, Drexel University (2017-2018) Distinguished Fellow, Center for Research Excellence, LeBow College of Business (2009-2012) Excellence in Research Award, LeBow College of Business (2005) Advising and Grants: While direct student advising is not explicitly listed, Dr. Benson has led significant research projects, including Multivehicle Path Coordination under Communication Constraints (Drexel Interdisciplinary Research Grant, $15,000) and Efficient Interior-Point Methods for Mixed-Integer Nonlinear and Conic Programming (NSF, $59,960). She has also contributed to executive education and consulting in financial, industrial, and governmental sectors. Editorial and Professional Service: Dr. Benson is actively involved in the academic community as Associate Editor for several leading journals, including Computational Optimization and Applications , Journal of Optimization Theory and Applications , Mathematical Programming Computation , and Optimization and Engineering .

James R. Greenمشاهده پروفایل

استاد

• Machine Learning
• Biomedical Informatics
• Computational Biology
• +۹ مورد دیگر

James R. Green is a Professor in the Department of Systems and Computer Engineering at Carleton University , where he has been a faculty member since 2005. He holds a PhD from Queen's University and is a licensed Professional Engineer (P.Eng.) and Senior Member of IEEE. His work integrates machine learning, biomedical informatics, and high-performance computing. His educational background includes: B.A.Sc. in Systems Design Engineering, University of Waterloo (1998) M.Sc.(Eng.), Queen's University (2000) PhD, Queen's University (2005) Dr. Green's research focuses on machine learning challenges in biomedical informatics , particularly class imbalance and rare event prediction. Key areas include protein structure, function, and interaction prediction; microRNA detection in unique species; non-contact neonatal monitoring; and accelerating scientific computing via parallel architectures like the Cell BE processor. His lab has developed several widely used bioinformatics tools such as PIPE, ProtDCal, and PCI-SUMO. His recent publications reflect a strong trend in computational biology and machine learning , with applications in proteomics, genomics, and medical diagnostics. He has published over 100 peer-reviewed papers and secured funding from NSERC, CIHR, CFI, ORF, OCE, MITACS, and IBM. Scientific and teaching recognitions include: Three teaching awards NSERC Best Project Award (twice: 2006-2007 and 2007-2008) Multiple student projects resulting in conference papers (e.g., CMBEC) He has supervised numerous undergraduate capstone projects in areas such as assistive technologies, robotic systems, and bioinformatics. His teaching portfolio includes courses in Pattern Classification, Machine Learning, Computer Architecture, and Biomedical Engineering. He leads an active research group that bridges computer engineering and life sciences, fostering interdisciplinary collaboration. Lab and research team initiatives include: Development of open-access web servers for protein analysis Collaborations with biologists and clinicians Integration of hardware and software for medical applications

• Software Correctness
• Cybersecurity
• Usability Engineering
• +۵ مورد دیگر

James Davis is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His research focuses on engineering robust computing systems through socio-technical approaches, emphasizing software correctness, security, and usability. He applies empirical methodologies to evaluate the practical impact of technical solutions. Research interests include software supply chain security, deep learning reproducibility, regular expression optimization, IoT cybersecurity, and the socio-technical challenges in system design. His work bridges theoretical foundations with real-world applications, addressing issues like regex denial-of-service (ReDoS), model reuse in AI, and developer practices for safety-critical systems. Recent publications span topics such as actor reputation metrics in software supply chains, AI safety for downstream developers, and edge-computing optimizations for vision transformers. His interdisciplinary approach integrates empirical studies, formal verification, and human-centered design principles. No scientific awards are explicitly mentioned in the provided materials. His advising record is currently unspecified, though his research group likely engages in collaborative projects with industry and academia. He contributes to initiatives like the Sigstore ecosystem and open-source security tooling, reflecting his commitment to practical impact.

Greg Stittمشاهده پروفایل

استاد

• Computer Engineering
• Embedded Systems
• Reconfigurable Computing
• +۵ مورد دیگر

Greg Stitt is a Professor in the Department of Electrical and Computer Engineering at the University of Florida, affiliated with the College of Engineering. His research focuses on reconfigurable computing, FPGA acceleration, embedded systems, and compiler design. He has received notable awards including the NSF CAREER Award (2012-2017) and the Undergraduate Teacher of the Year Award (2014). His work emphasizes elastic computing frameworks, intermediate fabrics for FPGA virtualization, and warp processors for dynamic hardware/software partitioning. Education: PhD, Computer Science, University of California-Riverside, 2007 BS, Computer Science, University of California-Riverside, 2000 Research Interests: Reconfigurable computing, FPGAs, GPUs, and their applications in high-performance computing Compiler optimization and synthesis techniques for embedded systems Elastic computing frameworks for heterogeneous systems Approximate computing and energy-efficient architectures Grants & Awards: National Science Foundation (NSF) grants for elastic computing (CNS-0914474) and intermediate fabrics (CNS-1149285) Recognition for contributions to FPGA-based scientific computing tools Teaching: Current courses include Reconfigurable Computing 2 and Digital Design Past course offerings span embedded systems, compiler design, and hardware architecture Labs & Teams: Active research in FPGA acceleration, novel architectures, and security for reconfigurable systems Contributions to the Novo-G scalable reconfigurable supercomputing project

Youssef Marzoukمشاهده پروفایل

استاد

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +۴ مورد دیگر

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

Amneet Bhallaمشاهده پروفایل

دانشیار

• Fluid-Structure Interaction
• Multiphase Flows
• Aquatic Locomotion
• +۷ مورد دیگر

Dr. Amneet Bhalla serves as an Associate Professor in the Department of Mechanical Engineering within the College of Engineering at San Diego State University (SDSU). His primary contact email is asbhalla@sdsu.edu, with office located in Engineering Building Room 323-G, and phone number (619) 594-2043. Education: Ph.D., Mechanical Engineering, Northwestern University (2013) M.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2009) B.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2004-2008) Postdoctoral Training: University of North Carolina at Chapel Hill (Mathematics Department) and Lawrence Berkeley National Laboratory (Computational Research Division) Research Interests: Dr. Bhalla develops advanced numerical methods and high-performance computing techniques for computational fluid dynamics (CFD) and fluid-structure interaction (FSI) problems. His work spans aquatic locomotion, renewable energy device modeling, multiphase flows, vehicular aerodynamics, and bioengineering applications. He creates mathematical models to interrogate underlying flow physics for engineering design optimization, with emphasis on open-source software development through the IBAMR library. Publication Trends: Recent publications (2023-2025) focus on robust numerical frameworks for multiphase flows with phase change, acoustic streaming, and fluid-structure interaction. Key themes include mass conservation in level set methods, adaptive mesh refinement, and solvers for non-isothermal gas-liquid-solid systems. Applications range from aquatic locomotion and renewable energy devices to microfluidics and biomedical flows, demonstrating commitment to both theoretical advances and practical engineering solutions. Scientific Awards: No awards mentioned in the provided text Advising and Grants: Dr. Bhalla secured an NSF CAREER award (2023) for "Consistent Continuum Formulation and Robust Numerical Modeling of Non-Isothermal Phase Changing Multiphase Flows". As PI of the CFD Lab, he mentors graduate students in computational mechanics, leveraging prior industrial experience at ExxonMobil Upstream Research Company. His research integrates industrial practicality with academic rigor through collaborations with national laboratories. Laboratory and Team: The Computational Fluid Dynamics and Flow Physics Laboratory (CFD Lab) develops the open-source IBAMR software—a distributed-memory parallel implementation of the immersed boundary method with adaptive mesh refinement. The lab emphasizes transparency, community engagement, and reproducibility, establishing cross-institutional collaborations while advancing computational methods for complex flow phenomena in engineering and biological systems.

Hussein Gharakhaniمشاهده پروفایل

استادیار

• Agricultural Robotics
• Automation in Agriculture
• Artificial Intelligence
• +۳ مورد دیگر

Hussein Gharakhani is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University. He specializes in agricultural robotics and automation, focusing on robotic cotton harvesting systems, sensor integration, and precision agriculture applications. His research addresses challenges in end-effector design, object detection, and field testing of robotic prototypes. Dr. Gharakhani holds a Ph.D. in Biosystems Engineering from Mississippi State University, an M.S. in Mechanical Engineering of Agricultural Machinery from the University of Tehran, and a B.S. in Agricultural Machinery Engineering from the University of Tabriz. His academic background includes roles as a graduate research and teaching assistant, as well as industry experience as a research and application engineer. His research interests span robotic manipulators, artificial intelligence, 2D/3D perception, and off-road robotics. Key projects include developing vision-guided robotic harvesters, evaluating end-effectors, and exploring UAV applications in cotton farming. His work emphasizes practical solutions to enhance agricultural efficiency and sustainability through automation. No scientific awards or grants are explicitly listed in the provided text. Dr. Gharakhani’s advising and mentorship activities are not detailed here, though his academic role suggests involvement in graduate student guidance. His research is centered on advancing robotic systems for precision agriculture, with a particular focus on cotton production challenges and robotic harvesting innovations.