جست‌وجوی استاد و پژوهشگر

• Energy Demand
• Demand Side Response
• Time Use and Energy Consumption
• +۱۲ مورد دیگر

Jacopo Torriti is a Professor at the University of Reading, affiliated with the School of the Built Environment and the Department of Construction Management and Engineering. His research lies at the intersection of energy demand, social practices, and policy, with a focus on achieving sustainable and equitable energy transitions. His primary research interests include energy demand, demand side response, time-use behavior, electricity pricing, energy policy, smart meters, peak electricity demand, and energy flexibility. His work integrates empirical analysis of time-use and energy data with modeling and policy evaluation to understand how daily activities shape energy consumption and how interventions can shift demand in time and space. The 15 most recent publications highlight a consistent focus on the relationship between human behavior, time-use patterns, and electricity demand. Key themes include the social and spatial dimensions of energy use, the design and impacts of time-of-use tariffs, demand flexibility, and the role of occupant behavior in building energy performance. His research employs diverse methods, including statistical modeling, neural networks, and critical policy analysis, often drawing on large-scale survey data from the UK and other European countries. While no specific scientific awards are mentioned in the provided text, his extensive publication record in top-tier journals and edited volumes, along with contributions to policy reports, reflects significant scholarly impact. Torriti has supervised or collaborated with several researchers, including Yunusov, Ramirez-Mendiola, Lorincz, Curtis, and Smith, on projects related to demand flexibility, time-use analysis, and demand response. He has been involved in research grants and projects funded by organizations such as the DEMAND Centre and has contributed to policy-oriented work for institutions like Citizens Advice and ResPublica. His work often involves interdisciplinary collaboration across energy, sociology, and policy studies. He is a key contributor to research on energy demand through the Centre for Research into Energy Demand Solutions (CREDS) and the DEMAND Centre, where his work on the social and temporal aspects of energy use has been influential. His research helps inform policy and practice on how to manage peak demand, integrate renewable energy, and design equitable energy tariffs.

Michael Galdeمشاهده پروفایل

استادیار

• Cybersecurity
• Malware Analysis
• Industrial Control Systems (ICS) Security
• +۱۰ مورد دیگر

Michael Galde is an Assistant Professor at the University of Arizona's College of Engineering, Department of Electrical and Computer Engineering, where he specializes in cybersecurity education and research. He holds a Master's in Cybersecurity and a Bachelor's in Political Science from the University of Nebraska and brings over a decade of experience from defense intelligence, industrial cybersecurity, and academic instruction. MS in Cybersecurity, University of Nebraska BA in Political Science, University of Nebraska His research and teaching focus on malware analysis , Industrial Control Systems (ICS) security , reverse engineering , and hands-on cyber operations training . He develops advanced courses and practical tools to strengthen cybersecurity resilience in critical infrastructure. His work bridges technical depth with educational accessibility, empowering the next generation of cyber professionals. His recent technical projects reflect a strong trend in applied cybersecurity research , especially in OT/ICS monitoring , network visualization , and the integration of AI and NLP into security operations . Projects like GRID-LM, IAES-SOC, and PCAPMap demonstrate innovation in real-time threat detection, large-scale data analysis, and user-friendly tooling for cyber defense. Notable scientific credentials include: Global Industrial Cyber Security Professional (GICSP) GIAC Response and Industrial Defense (GRID) Michael Galde actively contributes to cybersecurity through teaching (80% responsibility), university service (20%), research (2024–present), and external consulting (15%). He mentors students through project-based learning and leads initiatives in developing robust educational and technical frameworks. His research team works on projects including SPINE, DaRIA, and IAES-SOC, focusing on scalable NLP ecosystems and intelligent network monitoring. He leads multiple research and development efforts, including the IAES-SOC for OT network monitoring, PCAPMap for traffic visualization, and SPINE for NLP infrastructure. These projects are supported by hands-on development and integration with tools like Wazuh, ELK stack, Scapy, and Bokeh.

Sangmi Lee Pallickaraمشاهده پروفایل

استاد

• Big Data
• Data Science
• Data Engineering
• +۱۳ مورد دیگر

Sangmi Lee Pallickara is a Professor of Computer Science and the Clare Booth Luce Professor at Colorado State University. She is affiliated with the Department of Computer Science in the College of Natural Sciences. Her research is supported by major agencies including the National Science Foundation, Department of Homeland Security, ARPA-E, and NIFA, with ongoing projects in AI Institutes, Cyberinfrastructure, and CyberPhysical Systems. Research Interests: Her work focuses on Big Data systems for scientific applications, including scalable storage, retrieval, metadata management, predictive analytics, and interactive visualization. She applies these to domains such as agriculture, atmospheric science, environmental monitoring, and epidemiology. Her research integrates data science, distributed systems, and deep learning to enable scalable knowledge extraction from high-velocity, voluminous datasets. Publication Trends: Her recent publications emphasize scalable solutions for geospatial and spatiotemporal data, including efficient storage (e.g., ATLAS), visualization (e.g., Glance, Iris), and deep learning (e.g., Argus, CloudNet). There is a strong focus on real-world applications such as wildfire prediction, satellite data imputation, and precision agriculture, leveraging generative models, embeddings, and ensemble methods. Scientific Awards: NSF CAREER Award IEEE TCSC Award for Excellence in Scalable Computing Best Paper Award at IEEE/ACM UCC 2019 Best Paper Award at IEEE CLUSTER 2019 Best Paper Award at IEEE/ACM UCC 2014 Finalist for Best Paper Award at IEEE BDCloud 2018 Advising and Grants: She advises numerous Ph.D. and Master’s students, many of whom have gone on to careers in industry and academia. Her research is funded by the NSF (AI Institutes, CPS), DHS, ARPA-E, NIFA, and the Environmental Defense Fund. She also leads the SWiFT outreach program for K–12 STEM education. Labs and Teams: She leads a vibrant research group focused on Big Data systems, with students working on distributed storage, deep learning for satellite imagery, spatiotemporal analytics, and interactive visualization. The team collaborates with domain scientists in agriculture, climate, and public health.

Luis Ortizمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computational Game Theory
• +۱۲ مورد دیگر

Luis Ortiz is an Associate Professor at the University of Michigan - Dearborn in the Department of Computer and Information Science , College of Engineering and Computer Science. He previously held faculty positions at Stony Brook University (2008-2015), University of Puerto Rico, Mayaguez (2007-2008), and postdoctoral roles at MIT (2004-2006) and University of Pennsylvania (2001-2004). His research spans Artificial Intelligence , Machine Learning , Game Theory , and Graphical Models , with applications to networks, security, finance, and health. Education: Ph.D., Computer Science, Brown University (2002) B.S., Computer Science, University of Minnesota, Twin Cities His recent publications focus on graphical games, causal inference, and probabilistic modeling, with contributions to Nash equilibrium computation, strategic network analysis, and multi-agent systems. He has received multiple NSF awards , including a CAREER grant. Scientific Awards: 2019 NSF Research Award 2011 NSF CAREER Award 2008 NSF Faculty Diversity Award Ortiz has advised projects in computational game theory , machine learning , and network security . His research grants include NSF funding and industry support from Huawei/FutureWei Technologies. He has led projects on interdependent defense games, wireless localization, and causal strategic inference in networked systems. Labs & Teams: Collaborates with the Stony Brook Game Theory Summer Festival , University of Pennsylvania's Computer and Information Science Department , and industry partners in cellular networking and security.

Olivier Verscheureمشاهده پروفایل

استاد مدعو

• Stream and Big Data Mining
• Geospatial Analysis
• Large-scale Data Management
• +۴ مورد دیگر

Olivier Verscheure serves as the Executive Director of the Swiss Data Science Center (SDSC), a national R&D center organizationally hosted by both École Polytechnique Fédérale de Lausanne (EPFL) and ETH Zurich. He also holds multiple Adjunct Professor appointments at EPFL, specifically within the School of Computer and Communication Sciences (SIN and SSC) and the School of Engineering (SEL). His educational background includes: Ph.D. in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), June 1999 Verscheure's research focuses on the intersection of data science and real-world applications. His work centers on stream and big data mining, geospatial analysis, and large-scale data management. These technical capabilities are applied across diverse domains including personalized health and medicine, Intelligent Transportation Systems, telecommunications, smart building technologies, Smart Grid infrastructure, healthcare analytics, and waste water management systems. His approach emphasizes creating practical data science solutions that address complex challenges in these sectors while considering the constraints of real-world deployment. An analysis of his recent publication record reveals a strong focus on real-time data processing and analytics, particularly for transportation and urban systems. His work frequently addresses challenges in handling massive time series data, developing efficient architectures for low-latency analytics, and creating practical applications for smart city infrastructure. There's a clear progression from theoretical data science contributions to production-ready systems that can process billions of data points daily, demonstrating his ability to bridge research and practical implementation. His notable achievements include: Two IBM Outstanding Technical Achievement Awards Best Paper Award for his research Student Best Paper Award Verscheure has substantial experience in research leadership and mentoring. During his tenure at IBM, he managed the Exploratory Stream Analytics research group and led a technical and management team of approximately 40 people at the IBM Research lab in Ireland. He has served on PhD committees at major universities and published nearly 100 research papers that have garnered over 2,400 citations. His work has resulted in more than 40 US and international patents, demonstrating both academic and practical impact. As Executive Director of the Swiss Data Science Center, Verscheure oversees a distributed multi-disciplinary team working across domains including personalized health, transportation, earth and environmental science, social science and digital humanities, and economics. The center aims to federate data providers, data and computer scientists, and subject-matter experts around a cutting-edge analytics platform while addressing security and privacy issues. Under his leadership, the SDSC develops embedded data science support, offers end-to-end data science services, and fosters a community to share tools and knowledge in data science.

• Scientific Workflow Management
• High-Performance Computing (HPC)
• Artificial Intelligence/Machine Learning (AI/ML)
• +۳ مورد دیگر

Ozgur Kilic is a Researcher at the Computational Science Initiative of Brookhaven National Laboratory, specializing in high-performance computing (HPC) and AI/ML-driven scientific workflows . His work focuses on optimizing computational efficiency, resource management, and data resilience in large-scale systems. Education: Ph.D. in Computer Science, State University of New York at Binghamton Research Interests: Ozgur investigates scientific workflow management, asynchronous task execution in heterogeneous environments, memory trace analysis, and performance modeling. His work addresses challenges in virtualization overheads and scalability for accelerated systems. Recent Publications Trends: His recent work emphasizes interoperability in HPC workflows, asynchronous execution frameworks, memory diagnostics, and redefining computational efficiency metrics like Amdahl's Law in accelerated systems. Laboratory Affiliation: Ozgur is part of the Computational Science Initiative at Brookhaven National Laboratory, contributing to data-driven discovery and HPC innovation.

Soon Myoung Chungمشاهده پروفایل

استاد

• Cloud Computing Security
• Hypervisor Security
• Parallel Computing
• +۸ مورد دیگر

Soon Myoung Chung is a computer science researcher with significant contributions in the areas of cloud computing security, parallel data clustering, and GPU-accelerated algorithms. The publications indicate long-standing research activity spanning from 2002 to at least 2022, suggesting sustained academic engagement. While no formal institutional affiliation is provided in the scraped content, the depth and consistency of work imply a faculty or research-oriented academic role. The research interests center around cloud security , especially hypervisor vulnerabilities and isolation breaches, parallel and distributed clustering algorithms for large-scale data, and 3D shape analysis using orthogonal moments. These fields reflect a strong focus on algorithmic efficiency, security in virtualized environments, and pattern recognition. The most recent articles show a trend toward leveraging GPU acceleration for real-time data processing in crisis management and enhancing anomaly detection in time series data. Earlier works emphasize foundational methods in association rule mining, text clustering, and combinatorial fusion for feature selection. Collectively, the publications demonstrate expertise in both theoretical algorithm design and practical implementation in high-performance computing contexts. Although no scientific awards are mentioned in the provided texts, the body of work has accumulated over 1,800 citations, indicating influence in the field. There is no information available about students advised, grants received, or leadership roles. No labs or collaborative teams are referenced in the scraped material.

Laxman Dhulipalaمشاهده پروفایل

استادیار

• Parallel Algorithms
• Graph Processing
• Parallel Clustering
• +۷ مورد دیگر

Laxman Dhulipala is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park, and a research scientist at Google Research in the Graph Mining team. He holds a Ph.D. from Carnegie Mellon University, advised by Guy Blelloch, and was a postdoctoral researcher at MIT with Julian Shun. Ph.D., Carnegie Mellon University Postdoctoral Research, MIT His research focuses on efficient parallel algorithms, particularly for graph processing and clustering. He explores theoretical and practical models of parallel computation aligned with modern hardware. His work spans parallel graph algorithms, computational geometry, and scalable systems for massive datasets. The recent publications demonstrate a strong trend in scalable and dynamic graph algorithms, with a focus on hierarchical clustering, connectivity, and benchmarking. Key themes include batch-dynamic updates, memory-efficient data structures, and practical parallel implementations for massive-scale problems. Many works appear in top venues such as SPAA, VLDB, NeurIPS, and SIGMOD. Best Paper Award at VLDB'25 Best Paper Award at SPAA'22 Best Paper Runner Up at VLDB'22 Distinguished Paper Award at PLDI'19 Best Paper Award at SPAA'18 Memorable Paper Award Finalist at NVMW'20 Honorable Mention, CMU SCS Dissertation Award Nominated for ACM Dissertation Award Dhulipala has advised and collaborated with numerous students and researchers, including Quinten De Man, Shangdi Yu, Jessica Shi, and others, contributing to influential projects such as Aspen, ParGeo, GBBS, and ParlayLib. He has received recognition for both theoretical and practical contributions to parallel computing. He teaches courses such as CMSC858N (Scalable Parallel Algorithms and Data Structures) and CMSC451 (Design and Analysis of Computer Algorithms). He is actively involved in building tools and frameworks for parallel algorithm development and evaluation, including benchmark suites and graph processing systems. His dual affiliation with academia and Google Research enables impactful, scalable research bridging theory and practice.

Yanda Taoمشاهده پروفایل

پژوهشگر

• Machine Learning Systems
• Distributed Deep Learning
• Automatic Parallelism
• +۲ مورد دیگر

Yanda Tao is a Researcher at the Department of Computing, Imperial College London, funded by Samsung since March 2024. He graduated with distinction from the MSc Advanced Computing program at Imperial College London (2022-2023) and holds an Ingénieur degree in AI from CentraleSupélec, Université Paris-Saclay (2019-2023). During his master's, he developed HetML , a system for heterogeneity-aware automatic parallel training of deep learning models under the supervision of Professor Peter Pietzuch at LSDS (Large-Scale Data and Systems Group). His research focuses on machine learning systems design for heterogeneous GPU clusters, optimizing cloud infrastructure for deep learning workflows, and improving distributed training efficiency. Technical expertise includes system architecture, parallel computing, and ML pipeline deployment gained through internships at Alstom (Data Scientist) and SAP France (Software Engineer). His work bridges theoretical system design with practical implementation challenges in large-scale ML environments. The HetML project (2023, in preparation) demonstrates his specialization in automatic parallelism techniques and heterogeneity-aware optimization for distributed deep learning. This aligns with his broader interest in scalable machine learning infrastructure and cost-efficient system design for democratizing large-scale AI applications.

• Computer Systems Performance Engineering
• Networking and Internet
• Cloud Computing
• +۷ مورد دیگر

Nicholas Bambos is the R. Weiland Professor in the School of Engineering at Stanford University, holding a joint appointment in the Department of Electrical Engineering and the Department of Management Science & Engineering. He served as the Fortinet Founders Department Chair of the Management Science & Engineering Department from 2016 to 2020. His academic career spans over three decades, with previous positions as an assistant professor (1989-1995) and tenured associate professor (1995-1996) at UCLA before joining Stanford in 1996. Prof. Bambos's primary research interests focus on the architecture and high-performance engineering of computer systems and networks, along with data analytics emphasizing medical and health-care applications. His work spans multiple domains including networking and the Internet, cloud computing, multimedia streaming, computer security, and digital health. Methodologically, his contributions extend to network control, online task scheduling, routing and distributed processing, and machine learning and artificial intelligence. His research has resulted in over 300 peer-reviewed publications that demonstrate a strong interdisciplinary approach, bridging theoretical computer science with practical healthcare applications. The trajectory of Prof. Bambos's recent publications reveals a strategic expansion from traditional networking and systems research into healthcare analytics, particularly opioid use prediction and digital health monitoring. His work increasingly integrates machine learning techniques with domain-specific medical knowledge, showing a clear evolution toward solving complex societal challenges through technological innovation. Many publications demonstrate collaborative work across engineering, medical, and data science disciplines, reflecting the growing importance of interdisciplinary research in addressing modern healthcare challenges. His significant scientific achievements have been recognized through numerous prestigious awards: R. Weiland Professorship in Engineering (2016-present) Eugene L. Grant Teaching Award (2014) IBM Faculty Award (2002) Cisco Systems Faculty Scholar (1999-2003) National Young Investigator Award from NSF (1992-1997) Prof. Bambos has graduated over 40 doctoral students who have gone on to leadership positions in academia, Silicon Valley industries, technology startups, finance, and venture capital. His research has been supported by significant funding, including a $30 million Stanford Networking Research Center which he directed from 1999 to 2005. Beyond traditional academic roles, he has served on various editorial boards, scientific committees, and as a consultant and co-founder of technology startups, demonstrating his commitment to translating academic research into real-world impact. He leads the Computer Systems Performance Engineering Lab (Perf-Lab) at Stanford, which comprises doctoral students and industry visitors engaged in various research projects. His lab serves as an interdisciplinary hub connecting theoretical computer science with practical applications in healthcare, energy, and networking domains. The lab's collaborative environment fosters innovation across traditional academic boundaries, reflecting Prof. Bambos's broader research philosophy of addressing complex problems through integrated, multi-disciplinary approaches.

Nicholas Tan Jeromeمشاهده پروفایل

پژوهشگر

• Scientific data management
• Real-time monitoring
• Low-latency computing
• +۵ مورد دیگر

Nicholas Tan Jerome is a Researcher at the Karlsruhe Institute of Technology (KIT), specifically working at the Institute for Process Data Processing and Electronics (IPE). His research focuses on scientific data management, real-time monitoring, low-latency computing, and scientific visualization for large-scale physics experiments and medical imaging applications. Dr. Jerome earned his PhD in Electrical Engineering from KIT in 2019, following an MSc (2010) and Dipl.-Ing. (2009) from University of Applied Science Mannheim. His educational background in electrical and automation engineering provides the foundation for his current research in scientific data systems. His research program addresses critical challenges in managing and visualizing data from large-scale scientific experiments. Dr. Jerome has developed innovative approaches for real-time data processing, low-latency visualization, and scientific data management systems. His work bridges computer science, electrical engineering, and domain-specific applications in physics and medical imaging, with particular focus on applying machine learning to time-series forecasting and causal inference in complex experimental setups. Analysis of his recent publications reveals a strong trajectory in neutrino physics data systems (KATRIN experiment) and advanced visualization frameworks (BORA). His work demonstrates consistent innovation in making scientific data more accessible and interpretable for researchers working with complex experimental setups. Dr. Jerome has received recognition through publications in high-impact journals including Science, Nature Communications, and Physical Review Letters. His collaborative approach is evident in his extensive co-authorship across physics, computer science, and biomedical domains. He has advised or collaborated with numerous researchers across disciplines, contributing to projects that integrate data acquisition, processing, and visualization for scientific discovery. His current work focuses on developing practical tools that help scientists make better, faster decisions from noisy and high-dimensional experimental data. Dr. Jerome leads development of the BORA framework for personalized data display in large-scale experiments and contributes to the KATRIN experiment's scientific data management infrastructure. His technical expertise spans real-time systems, scientific visualization, and machine learning applications for experimental physics.

Cesar A Uribeمشاهده پروفایل

استادیار

• Distributed Learning
• Optimization
• Decentralized Control
• +۳ مورد دیگر

Cesar A Uribe is a Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University. Previously, he was a Postdoctoral Associate at MIT's Laboratory for Information and Decision Systems (LIDS) until 2020 and held a visiting professor position at the Moscow Institute of Physics and Technology until 2022. His educational background includes M.Sc. degrees in Systems and Control from Delft University of Technology (2013) and Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), followed by a PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2018). His research focuses on distributed learning and optimization , decentralized control , algorithm analysis , and computational optimal transport . His work addresses fundamental challenges in collaborative learning across heterogeneous distributed systems, aiming to develop theoretical foundations for scalable, cooperative, and data-driven autonomous systems in infrastructure, environment, and health applications. He has been recognized with several prestigious awards: Ralph E. Powe Junior Faculty Enhancement Award 100k Strong in the Americas Innovation Award Google Research Scholar Award As a recipient of the Google Research Scholar Award and the SEC scholar (May-July 2024), he has secured research funding to advance decentralization paradigms. His current work with host Mikael Johansson (mikaelj@kth.se) creates synergies between the Digital Futures program and his long-term goal of establishing algorithmic foundations for robust socio-technical systems. His prior affiliation with MIT's Laboratory for Information and Decision Systems (LIDS) provided foundational experience in large-scale optimization frameworks.

Giuseppe FRANZE'مشاهده پروفایل

استاد

• Constrained Predictive Control
• Networked Control Systems
• Cyber-Physical Systems
• +۷ مورد دیگر

Giuseppe FRANZE' is a Full Professor at the Department of Mechanical, Energy and Management Engineering (DIMEG) of the University of Calabria since 2022. He has over 200 publications in archival journals, book chapters, and conference proceedings, with a focus on constrained predictive control, networked control systems, and resilient control for cyber-physical systems. His research includes theoretical and applied projects funded by MIUR/MUR, European Union, and international institutions. IEEE Senior Member (2019) Associate Editor for IEEE/CAA Journal of Automatica Sinica Guest Editor for Special Issue on Resilient Control in Large-Scale Networked Cyber-Physical Systems Organized sessions at CoDIT, ETFA, CASE conferences Collaborations with Carnegie Mellon, Concordia University, Georgia Tech, and others His research spans constrained predictive control, fault-tolerant strategies, obstacle avoidance for autonomous vehicles, and machine learning integration in control systems. Recent articles emphasize resilient control under network attacks, encrypted MPC, and reinforcement learning for multi-agent systems. He received Best Paper and Best Reviewer awards at international conferences. Best Paper - CoDIT’19 Best Reviewer - IEEE ICAS 2021 FRANZE' has taught undergraduate and graduate courses in Automatic Control, Digital Control, and Robotics at the University of Calabria for over 25 years. He has chaired institutional committees, including the Degree Course Council for Automation Engineering and the Research Committee at DIMEG. His scientific partnerships include institutions like Concordia University, Northeastern University, and Université Libre de Bruxelles.

Mohammad Zubairمشاهده پروفایل

استاد

• High performance computing
• digital libraries
• autonomic computing
• +۳ مورد دیگر

Mohammad Zubair is a Professor in the Department of Computer Science at Old Dominion University's College of Sciences, specializing in high performance computing, digital libraries, autonomic computing, and peer-to-peer networks. His work bridges scientific computing and information systems with significant federal and industry collaborations. Education: Ph.D. in Computer Science, Indian Institute of Technology Delhi (1987) B.S. in Electrical & Electronics Engineering, University of Delhi (1981) Research Interests: Dr. Zubair's expertise centers on high performance computing for scientific applications and large scale data analytics. He develops scalable algorithms for scientific simulations (e.g., particle physics, structural health monitoring) and advances digital library systems through autonomic computing, collaborative classification, and metadata extraction. His work integrates parallel computing with domain-specific challenges in finance, physics, and digital preservation. Publication Trends: His recent publications (2017-2008) show strong focus on GPU-accelerated scientific computing (particle colliders, structural monitoring) and computational finance (binomial option pricing), while maintaining digital library research in collaborative classification and metadata extraction. The work increasingly addresses energy efficiency in cloud systems and leverages parallel architectures from multicore CPUs to GPUs. Scientific Awards: IBM Faculty Award (2006) IBM Faculty Award (2005) Grants and Funding: Dr. Zubair has secured over $800,000 in research funding since 2004, including projects like 'Design AMD Impl Scalable Opt Kernels for Lrg Sc Simu USN Fund3d On Emer' ($24,890, 2017-2018), 'Graduate Research Award Program On Public Sector Aviation Issues' ($35,519, 2016-2018), and foundational digital library work with the Library of Congress and NASA. He frequently collaborates with K. J. Maly and R. Mukkamala on autonomic systems and metadata extraction. Labs and Teams: His research integrates with ODU's high performance computing initiatives and digital library projects, particularly through collaborations with federal agencies (NASA, Library of Congress) and industry partners like IBM. Current work focuses on GPU-accelerated scientific simulations and energy-efficient cloud management systems.

• Machine Learning
• Computer Vision
• Data Compression
• +۳ مورد دیگر

Don Adjeroh is a Professor and Associate Chair in the Lane Department of Computer Science and Electrical Engineering at West Virginia University (WVU). He serves as Graduate Coordinator for CS programs and has led multiple NSF-sponsored projects including Bridges in Digital Health and Multi-Scale Integrative Approach to Digital Health . His research focuses span Machine Learning , Bioinformatics , Computational Biology , and Data Compression . NSF CAREER Award recipient Collaborated with institutions like University of Central Florida and University of Canterbury Led workshops on Plant Image Analysis and Long Non-Coding RNA research His academic contributions include co-authoring the book The Burrows Wheeler Transform: Data Compression, Suffix Arrays, and Pattern Matching (Springer, 2008) and developing software tools like BSMP and PSA for data compression and protein family modeling. He teaches graduate-level courses in String Algorithms and Information Theory , with former projects involving undergraduate and graduate students in DNA microarray image processing and 3D video compression research. Key Scientific Awards : NSF CAREER Award NASA-WV Space Consortium funding WV-EPSCOR grants NSF-CITeR support DOD-ONR/DHS/DOJ/NIJ/NHPRC sponsorships