جست‌وجوی استاد و پژوهشگر

• Energy systems integration
• Grid modeling and simulation
• High performance computing
• +۵ مورد دیگر

Deepthi Vaidhynathan is a Researcher IV in the Computational Science Center at the National Renewable Energy Laboratory (NREL), where she works in the Complex System Simulation and Optimization Group. Her research focuses on energy system integration, grid modeling and simulation, and high performance computing for scientific applications in power systems and building energy management. Her educational background includes: Bachelor of Electronics and Communication Engineering from Anna University Master of Electrical and Computer Engineering from the University of Colorado Boulder Deepthi's research spans energy systems integration, grid modeling, high performance computing, computer architecture, and control theory. She applies quantum computing principles and microprocessor optimization to develop advanced simulation tools for modern power grids with high renewable energy penetration. Her work bridges theoretical algorithms with practical energy system challenges. Analysis of her 2024-2025 publications reveals concentrated expertise in hybrid simulation techniques for inverter-dominated power grids and AI-enhanced control frameworks. She specializes in partitioning algorithms for large-scale electromagnetic transient simulations and neural-network-based predictive control for building energy systems, addressing critical needs in grid stability and renewable integration. Deepthi is an active contributor to NREL's Complex System Simulation and Optimization Group, which develops high-fidelity tools for modeling next-generation energy infrastructure. The group's work supports U.S. Department of Energy initiatives for grid modernization and renewable energy deployment. No scientific awards are documented in available sources. Public records indicate no formal student advising roles or disclosed grant funding details.

Craig Douglasمشاهده پروفایل

استاد

• Dynamic Data-Driven Application Systems
• Big Data
• High Performance Computing
• +۶ مورد دیگر

Dr. Craig Douglas is a Professor of Mathematics and Statistics at the University of Wyoming's College of Engineering and Physical Sciences, with an Adjunct Professor appointment in Computer Science. He earned his Ph.D. from Yale University and is globally recognized for pioneering research in Dynamic Data-Driven Application Systems (DDDAS) for Big Data, high-performance computing, and multigrid algorithms for partial differential equations. He co-developed the first commercial DDDAS for oil and gas pipeline monitoring, deployed across 100+ countries. His work bridges theoretical mathematics and practical engineering challenges. Research Interests Dynamic Data-Driven Application Systems (DDDAS) High-Performance Computing (HPC) Multigrid Algorithms Big Data Analytics for Energy and Environment Computational Science Applications Recent Publications Advances in DDDAS frameworks Computational wildfire modeling CO 2 sequestration simulation Finite-difference and finite-element methods Cache optimization for multigrid algorithms Email: cdougla6@uwyo.edu

Khaled R. Ahmedمشاهده پروفایل

دانشیار

• Big Data
• Deep Learning
• Parallel Computing
• +۶ مورد دیگر

Khaled R. Ahmed is an Associate Professor and Graduate Program Director at the School of Computing, Southern Illinois University. He serves as Director of the Big Data & Software Engineering Lab (BASE Lab), focusing on software engineering support for big data systems and deep learning algorithms. His research bridges computer science with practical applications in agriculture, safety, and environmental monitoring. Dr. Ahmed's research interests center on Big Data Software Engineering, Deep Learning, Parallel and Distributed Computing, and Computer Vision. His laboratory collaborates extensively with the agriculture college on precision agriculture projects, securing federal funding from USDA-NIFA for research on livestock monitoring and crop analysis. His work demonstrates a clear trajectory from foundational distributed systems research to applied deep learning solutions for real-world problems. His recent publications reveal a strong focus on agricultural applications of computer vision, including weed detection systems, livestock methane emission monitoring, and soybean growth stage analysis. The BASE Lab has developed innovative approaches using Vision Transformers, DETR, and RetinaNet for precision agriculture applications, with several papers published in top venues like CVPR and IEEE Transactions. USDA-NIFA Research Award for Detecting Subacute Ruminal Acidosis USDA-NIFA Research Award for Artificial Intelligence for Greener Livestock Illinois Innovation Network Seed Grant Outstanding Achievement Award at World Congress in Computer Science (2020) Dr. Ahmed has successfully mentored numerous graduate students, with several receiving Outstanding Thesis Awards. His BASE Lab actively recruits Ph.D. and MSc students with backgrounds in Deep Learning, Big Data, and Software Engineering. He maintains international connections through Fulbright programs, particularly with researchers from India and Egypt interested in AI and Deep Learning applications. The BASE Lab maintains strong industry and research collaborations, particularly in precision agriculture where computer vision techniques are applied to solve practical farming challenges. Current projects include real-time video analysis systems for safety applications, drone-based monitoring for natural disaster recovery, and weapon detection systems for public safety.

• Parallel and Distributed Computing
• Computer Networks
• Multicast Communication
• +۳ مورد دیگر

Dave Robinson, PhD, is an Adjunct Instructor Level 1 in Mathematics at Ferris State University within the College of Arts, Sciences and Education. He brings extensive expertise in parallel and distributed computing to his teaching role. Education: PhD in Computer Science, Michigan State University MS in Computer Science, Michigan State University BS in Computer Science, University of Michigan Robinson’s research spans the design and optimization of high-performance parallel systems. His work emphasizes efficient communication algorithms for wormhole-routed networks, multicast strategies in torus and hypercube topologies, and performance monitoring techniques for shared-memory multiprocessors. Across his publications, a clear trend emerges: advancing scalable and low-latency communication in massively parallel computers. His contributions have consistently targeted algorithmic improvements that reduce communication overhead and enhance overall system throughput in distributed-memory architectures. Scientific Awards: None explicitly listed in the provided text. Advising & Grants: No current or former students, funded projects, or grant details were cited in the source material. Labs & Teams: No laboratory or research group affiliations were mentioned.

Lester I. McCannمشاهده پروفایل

استاد

• Computer Science education
• database management systems

Lester I. McCann is a Professor of Practice in the Department of Computer Science at the University of Arizona, with his office located in GS 819. He earned his Ph.D. from North Dakota State University in 1994 and maintains active contributions to computer science research and education. Educational Background: Ph.D., North Dakota State University, 1994 Professor McCann's research centers on Computer Science education and database management systems. He developed innovative educational tools including "Graph Magic" for visualizing graph algorithms and "Guided slides" for tablet-based flexible lectures. His database work addresses relational algebra comprehension and concurrency control in high-performance computing environments, bridging theoretical concepts with practical pedagogy. His publication timeline from 1990-2008 reveals a strategic shift from foundational database research (multidatabase systems, concurrency control) toward educational technology development. This evolution highlights sustained commitment to enhancing computer science instruction through software tools, assignment design, and visualization techniques across algorithms, data structures, and database courses. Scientific Awards: No awards mentioned in the provided text. Available documentation contains no references to student advising relationships, research grants, or laboratory affiliations. His professional activities appear focused on classroom innovation and publication without indication of grant-funded projects or mentored graduate students in the source materials.

Jeffrey Considineمشاهده پروفایل

دانشیار مدعو

• Algorithms
• Randomized Data Structures
• Compressed Parallel Execution
• +۵ مورد دیگر

Jeffrey Considine is an Adjunct Associate Professor in the Faculty of Computing & Data Sciences (CDS) at Boston University, where he returned in 2024 after 19 years in industry. He holds a PhD in Computer Science from Boston University, specializing in distributed randomized algorithms and data structures. Education: PhD in Computer Science from Boston University (2005) His research spans theoretical computer science , algorithms , compressed parallel execution , and generative AI , with industry applications in bioinformatics and linguistics . Recent work includes compressed parallel execution for game solving and mathematical hypothesis testing, with plans to collaborate across disciplines. The trends in his publications reflect a focus on distributed systems , data aggregation , and overlay networks , evolving into neural implicit representations and generative AI . His teaching includes DS 542: Deep Learning for Data Science and foundational modules in the OMDS program. Scientific Awards IEEE ICDE 2014 Influential Paper Award for work on approximate aggregation techniques His industry experience includes 15 years at Cogo Labs as Chief Scientist and a brief tenure as CTO at Solved Technologies. He is available to advise master’s theses, with one project extending into ongoing research.

• Computer Architecture
• Distributed Systems
• Machine Learning
• +۵ مورد دیگر

Chitaranjan Das is a Professor in the Department of Computer Science and Engineering at Penn State University . His work aligns with the United Nations Sustainable Development Goals , particularly contributing to advanced computing infrastructure and global technological innovation. Das actively collaborates with researchers like Anand Sivasubramaniam , Mahmut Kandemir , and Vijay Narayanan on cutting-edge projects. Key Research Areas: Computer Architecture, Network-on-Chip (NoC) systems, Quality of Service (QoS), Spiking Neural Networks (SNNs), Cloud Computing, and Bioinformatics. Das leads projects funded by the National Science Foundation (NSF), including SHF: Medium: Exploring an Edge Platform Design Trajectory for Next Generation XR Applications and CNS Core: Small: Embracing cross stack heterogeneity in next-generation cloud platforms . His research outputs span over 300 publications, with recent works focusing on Edge Computing, Serverless Computing, and hardware acceleration for genomic data analysis. Recent Publications (2024-2025) highlight his interdisciplinary approach, integrating Machine Learning with FPGA-based acceleration for nanopore sequencing, optimizing VR streaming on edge devices, and enhancing serverless computing on heterogeneous hardware. His projects often bridge the gap between system design and algorithmic innovation , with a strong emphasis on real-world applications in cloud platforms and neural networks.

• Distributed Algorithms
• Nonconvex Optimization
• Machine Learning
• +۲ مورد دیگر

Ying Sun is an Assistant Professor in the Department of Electrical Engineering at the University of Minnesota's College of Science and Engineering. Her research focuses on distributed algorithms, nonconvex optimization, and machine learning, with applications in wireless communications and robotic swarm systems. Develops decentralized learning frameworks for data heterogeneity Designs risk-aware motion planners for large-scale robotics Works on sparse regression and covariance matrix estimation Advances gradient tracking and bilevel optimization techniques Recent work includes federated learning synchronization, tensor recovery, and robust transfer learning algorithms. She has contributed to the theoretical understanding of optimization landscapes and practical implementations of distributed systems.

Sahra Sedighمشاهده پروفایل

دانشیار

• Dependable Networks and Systems
• Critical Infrastructure
• Cyber-Physical Systems
• +۷ مورد دیگر

Sahra Sedigh is an Associate Professor of Electrical and Computer Engineering at Missouri University of Science & Technology, with a courtesy appointment in the Department of Computer Science. She is a Research Investigator at the Intelligent Systems Center and Faculty Ombuds, while serving as a Senator for ECE in the Faculty Senate. Her research focuses on dependable networks/systems for critical infrastructure, including power grids, water distribution networks, and transportation systems. B.S.E.E. from Sharif University of Technology M.S.E.E. and Ph.D. in Electrical and Computer Engineering from Purdue University Her work involves modeling cyber-physical systems, software-based electromagnetic immunity analysis, and structural health monitoring. She has secured funding from the National Science Foundation, US Department of Transportation, European Commission, Ford, and Samsung. As a Fellow of the National Academy of Engineering's Frontiers of Engineering Education Program and holder of a Purdue Research Foundation Fellowship, she combines academic excellence with industry experience in high-availability network systems. Scientific awards include National Academy of Engineering Fellowship Purdue Research Foundation Fellowship Senior Member IEEE IEEE-HKN and ACM memberships She teaches CpE 5410 Introduction to Computer Communication Networks and contributes to labs like the Center for Intelligent Infrastructure. Her research spans cybersecurity, fault tolerance, and energy-efficient computing, with applications in transportation, education, and environmental monitoring.

Brandon Cookمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Quantum Transport
• Electronic Structure Methods
• +۴ مورد دیگر

Brandon Cook is the Programming Environment & Models Group Lead at the National Energy Research Scientific Computing Center (NERSC) within the HPC Technology Department. He specializes in system-level performance analysis, productivity tools development, and future programming model exploration for high-performance computing (HPC) environments. PhD in Physics (2012), Vanderbilt University, United States His research interests include high-performance computing (HPC), quantum transport methods, electronic structure applications in material science, GPU acceleration, resource disaggregation, and performance modeling. His work bridges hardware architecture analysis with software optimization to enhance scientific computation efficiency. Beyond his leadership role, Brandon investigates network congestion impacts, communication motifs in distributed systems, and fault-tolerant numerical methods. His publications emphasize intra-rack resource management, GPU-aware MPI, and directive-based approaches for quantum/nuclear calculations. Collaborations span institutions including Oak Ridge National Laboratory and Springer Nature publications.

Rodrigo Bragaمشاهده پروفایل

استادیار

• Cognitive Neuroscience
• Brain Network Organization
• Functional Brain Mapping
• +۵ مورد دیگر

Rodrigo Braga is an Assistant Professor of Neurology at Northwestern University's Feinberg School of Medicine in Chicago, where he leads the Human Cognitive Neuroscience Lab (Braga Lab). His academic journey includes an NIH Pathway to Independence Award (K99) as an Instructor at Stanford University working with Josef Parvizi and Russ Poldrack, and a Sir Henry Wellcome Postdoctoral Fellowship at Harvard University with Randy Buckner. He completed his PhD under the supervision of Dr. Robert Leech and Prof. Richard Wise. Dr. Braga's research focuses on unraveling the detailed organization of human brain networks, particularly association cortices that are expanded in humans and serve advanced cognitive functions like language, episodic memory, and social cognition. His groundbreaking work has demonstrated that canonical brain networks such as the default mode network actually comprise multiple parallel, closely interdigitated networks when resolved within individuals at high resolution. He employs individual-level whole-brain network mapping using fMRI combined with intracranial recordings and stimulation to understand how distributed networks interact during cognition. His publication record reveals a strong focus on brain network organization across multiple cognitive domains. Analysis of his 15 most recent articles shows consistent investigation of individual differences in brain architecture, with particular emphasis on network dissociation in episodic versus social cognition, multisensory integration, and the role of transmodal cortex in network integration. His work bridges basic neuroscience with clinical applications in neurologic and psychiatric disorders. NIH Pathway to Independence Award (K99) Sir Henry Wellcome Postdoctoral Fellowship Dr. Braga's research program is supported by significant grant funding, including the NIH K99 award that facilitated his transition to independence. His lab develops innovative methods to combine individual-level network mapping with intracranial recordings, suggesting substantial research infrastructure. While specific students aren't listed in the source material, his position as an independent principal investigator indicates he likely mentors graduate students and postdoctoral fellows in cognitive neuroscience. The Human Cognitive Neuroscience Lab (Braga Lab) specializes in high-resolution mapping of individual brain networks. Dr. Braga's approach of preserving individual brain circuitry rather than relying on group averages has revealed new organizational principles of the brain, including the discovery that what were thought to be single networks actually contain multiple parallel architectures when examined at sufficient resolution within individuals. His lab's work has implications for understanding both normal cognitive function and neurological disorders.

Torrey Wagnerمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Analytics
• +۷ مورد دیگر

Dr. Torrey Wagner serves as an Assistant Professor of Data Analytics at the Air Force Institute of Technology (AFIT), where he supports the Data Analytics graduate certificate program. He develops and teaches core courses including DASC 522 Machine Learning, SATI 532 Data Visualization & Communication, and DASC 511 Object-Oriented Programming using Python. His instructional focus centers on applying data analytics techniques to solve military problems through the CRISP-DM process framework. Dr. Wagner earned his PhD in Electrical Engineering with an Electro-optics focus from AFIT in 2010, an MS in Aerospace Systems Engineering from Loughborough University (UK) in 2004, and a BS in Electrical Engineering from the University of Minnesota in 2000. His educational background bridges electrical engineering, aerospace systems, and advanced data analytics. His research concentrates on applying predictive and generative artificial intelligence techniques to enhance Department of Defense organizational efficiency and performance. Dr. Wagner's scholarly work spans machine learning applications for defense systems, energy optimization for military operations, quantum computing advancements, and natural language processing solutions for defense contexts. He has developed specialized methodologies for military problem-solving through data analytics, with emphasis on practical implementation in operational environments. Dr. Wagner's publication record reveals a strategic evolution toward cutting-edge AI applications. Recent work demonstrates significant focus on large language models, retrieval-augmented generation systems, and quantum computing implementations. His research portfolio includes machine learning applications for aircraft engine cost prediction, satellite imagery analysis, seismic event classification, and energy system optimization for austere military environments. This work consistently bridges theoretical AI advances with tangible defense applications. As an educator and mentor, Dr. Wagner has guided dozens of students through their first publications and presentations. His students frequently co-author peer-reviewed journal articles and conference papers, primarily at the master's level with several doctoral candidates. He has contributed to AI education within military contexts through his chapter "Augmenting a Curriculum Review with Generative AI" in the AFIT Generative AI Teaching Guidebook and other educational resources. Dr. Wagner's research supports multiple AFIT initiatives focused on data analytics for defense applications. His work involves extensive collaboration with Department of Defense organizations to implement AI technologies in practical military settings, particularly in energy systems optimization, aircraft operations analytics, and decision support tools for commanders. Current projects include secure offline large language model implementations, quantum communication network resilience, and advanced data visualization techniques for military decision-making.

Vijay Chidambaramمشاهده پروفایل

دانشیار

• Computer Systems
• Storage Systems
• Distributed Systems
• +۵ مورد دیگر

Vijay Chidambaram is an Associate Professor in the Department of Computer Science at The University of Texas at Austin, where he leads the UT Systems and Storage Lab. His research focuses on building next-generation storage systems with higher performance and stronger reliability, developing both storage systems and testing frameworks to ensure rigorous development. He is also part of the UT Data Systems group and the LASR research group at UT Austin. Ph.D. in Computer Science, University of Wisconsin–Madison M.S. in Computer Science, University of Wisconsin–Madison B.E. in Computer Science, College of Engineering, Guindy Chidambaram's research interests center on computer systems, particularly storage systems, distributed systems, and databases. His work addresses critical challenges in crash consistency, persistent memory file systems, and storage reliability. His group has made significant contributions to understanding and solving crash-consistency bugs, developing innovative testing frameworks, and building open-source storage systems that impact both academia and industry. They focus on innovation at both the data-structure level and systems level, with recent work exploring CXL-based database architectures and verification tools for storage systems. His research group has produced numerous influential publications in top-tier conferences including OSDI, FAST, ATC, and EuroSys, with several best paper awards. Their work spans from theoretical foundations of crash consistency to practical implementations of high-performance storage systems, with a consistent emphasis on open-sourcing their software to maximize impact. Distinguished Artifact Award at OSDI 2025 Best Paper Award at EuroSys 2023 Best Paper Award at ATC 2018 Best Paper Award at FAST 2018 NSF CAREER Award 2018 Microsoft Research Graduate Fellowship 2014 ACM SIGOPS Dennis M. Ritchie Dissertation Award 2016 Chidambaram actively mentors PhD students, with current advisees including Minh Phan, Talia Chen, Nitin Mathai, and Shurang Wu. His former students have gone on to positions at Microsoft Research, HP Labs, Apple, and academia. He has secured significant research funding, including NSF grants and industry collaborations. Beyond research, he serves on program committees for major systems conferences and co-founded the open-access Journal of Systems Research. He also teaches courses including Virtualization, Advanced Operating Systems, and Distributed Systems, and authored the CS Assistant Professor Handbook, a guide for new computer science faculty. His UT Systems and Storage Lab maintains close industry collaborations while pursuing fundamental research questions in storage systems. The lab is known for its rigorous approach to systems research, combining theoretical insights with practical implementations, and has developed multiple open-source systems that have influenced both academic research and industry practice.

Thomas Daggettمشاهده پروفایل

استاد مدعو

• Computer Architecture
• Distributed Systems
• Web Technologies
• +۴ مورد دیگر

Thomas Daggett is an Adjunct Professor of Computer Science at Connecticut College, holding this position since 2016. His educational background includes: B.S. in Engineering from the University of Connecticut M.S. from Rensselaer Polytechnic Institute Ph.D. from the University of Connecticut Professor Daggett specializes in computer architecture, distributed processing, web and collaboration technologies, and software engineering. His expertise extends to client/server computing, parallel algorithm development, and enterprise-scale software solutions. With over 18 years of industry experience, his work bridges theoretical computer science with large-scale pharmaceutical and defense applications. Professor Daggett's professional career spans leadership roles at major corporations and government institutions. He currently serves as director of Pfizer Inc's Advancing Content & Transactions (ACT) Group. Previously, he directed Pfizer's Content and Collaborations Team, acting as service delivery lead and platform architect for the Document Management Facility (DMF) Platform while serving as SharePoint and Enterprise Search architect. His industry experience includes positions at General Dynamics' Advanced Technologies Group (Electric Boat Division), Sapient in Cambridge, Massachusetts, Computer Sciences Corporation, and the Naval Undersea Warfare Center in Newport, Rhode Island.

Shangping Renمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Real-Time Scheduling
• Cloud Computing
• +۴ مورد دیگر

Shangping Ren is a Professor and Department Chair at the Department of Computer Science, San Diego State University (SDSU), within the College of Sciences. She earned her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1997. Research focus areas: Cyber-Physical Systems, Real-Time Scheduling, Cloud Computing, Medical Cyber-Physical Systems, Software Architecture, and Resource Optimization Key projects: EMBG System for Emergency Care, Medical Best Practice Guideline Modeling, Real-Time Task Scheduling, and Many-Core Virtualization Her recent publications emphasize medical cyber-physical systems (e.g., safety verification, implicit assumption detection) and real-time scheduling (e.g., rejuvenation techniques, resource degradation). She also explores cloud computing for DAG applications and mobile low-power sensing via Bluetooth/RFID. Grants: Funded by NSF, Air Force Research Laboratory, and Fermi National Accelerator Laboratory. Current projects include data science workforce training, medical guidance systems, and water infrastructure analytics. Collaborations: Works with Lui Sha, Chunhui Guo, Zheng Li, and institutions like University of Illinois, Yale University, and National Science Foundation.