جست‌وجوی استاد و پژوهشگر

Wei David Daiمشاهده پروفایل

استادیار

• Robust Deep Learning
• Data Quality
• Distributed Computing Systems
• +۴ مورد دیگر

Dr. Wei David Dai is an Assistant Professor of Computer Science at Purdue University Northwest and Director of the Advanced Intelligence Software (AIS) Lab. His research focuses on robust deep learning, data quality, and public safety technologies like gunshot detection systems. He previously worked at IBM China as a senior engineer and served in Arkansas state government as a data scientist. Education: Ph.D. in Computer and Information Sciences (University of Arkansas at Little Rock, USA, 2020) M.S. in Information Science (University of Arkansas at Little Rock, USA, 2016) M.S. in Software Engineering (South China University of Technology, China, 2013) B.S. in Computer Science (Central South University, China, 2007) Research Interests: His work spans robust deep learning models, distributed computing systems, and privacy-preserving technologies. Notable projects include public safety innovations such as acoustic gunshot detection and AI-driven campus security systems. Articles Trends: Recent publications emphasize public safety applications (e.g., mass school shooting simulations) and deep learning robustness evaluation (e.g., the Accuracy-Stability Index metric). Earlier works address cloud computing optimization and data quality frameworks. Awards: Recipient of the 2024 Excellence in Research Award and multiple IBM honors for technical excellence and instruction. Grants & Advising: Leads the Indiana Space Grant Consortium-funded satellite imaging project and Purdue Provost Grant for gunshot detection. Advises doctoral and master’s students on AI ethics, distributed systems, and public safety. Labs: The AIS Lab develops AI tools for public safety, equipped with GPU resources for audio and image analysis.

• Cloud Computing
• Green Computing
• High-Performance Computing
• +۷ مورد دیگر

Jordi Guitart Fernández is a Professor at the Department of Computer Architecture, Barcelona School of Informatics (FIB), Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading national supercomputing facility. He leads the CROMAI research group, focusing on Computing Resources Orchestration and Management for AI. His work bridges high-performance computing, cloud systems, and artificial intelligence. Research Interests: Cloud Computing and Edge Computing Green and Energy-Efficient Computing Containerization and Virtualization for HPC Resource Orchestration and Management Autonomic and Self-Adaptive Systems Machine Learning Workflow Management AI-Driven System Optimization His recent publications reveal a strong focus on intelligent management of computing resources across cloud, edge, and HPC environments using machine learning and agent-based frameworks. He investigates performance, efficiency, and reliability in containerized AI and HPC workloads, particularly within Kubernetes and distributed infrastructures. His work increasingly integrates human-in-the-loop and trustworthiness aspects into AI systems. Scientific Awards: CLOUD Conference 2025 Best Paper Award VISIGRAPP 2025 Best Student Paper Award Premi Extraordinari de Doctorat 2025 - Àmbit d'Enginyeria de les TIC Test of Time Award Honorable Mention (e-Energy) Reconeixement als Mèrits Docents d'Especial Qualitat Top reviewers for Polytechnic University of Catalonia (Computer Science) - September 2017 Advising and Grants: He has advised doctoral students, including Peini Liu. He leads and participates in numerous competitive R+D+i projects, such as CROMAI and DALEST, funded by national and European programs like HORIZON 2020 and the Spanish State Research Plans. His work is supported by grants focused on knowledge generation and industrial leadership in computing technologies. Labs and Teams: He is the leader of the CROMAI - Computing Resources Orchestration and Management for AI research group at UPC. He also collaborates closely with the Barcelona Supercomputing Center (BSC-CNS), contributing to large-scale computing initiatives and strategic research agendas in Europe.

Joseph Johnsonمشاهده پروفایل

دانشیار

• Marketing Strategy
• Artificial Intelligence in Marketing
• Healthcare Marketing
• +۶ مورد دیگر

Joseph Johnson is an Associate Professor in the Marketing department at the Miami Herbert Business School, University of Miami. His research spans multiple domains of marketing with a particular focus on the intersection of artificial intelligence and marketing strategy, demonstrating significant scholarly productivity with publications from 2017 through 2023. Professor Johnson's research interests include: Marketing Strategy and Business Turnaround Artificial Intelligence Applications in Marketing Healthcare Service Quality and Patient Satisfaction International Business Expansion Strategies Mutual Fund Advertising and Consumer Perception Organizational Process Optimization Social Media and Multimedia Content Analysis His publication record reveals a consistent trajectory toward integrating advanced analytical methods with traditional marketing challenges. Johnson has published extensively on applying predictive analytics to email marketing effectiveness, healthcare service quality improvement, and organizational process efficiency. His work bridges theoretical marketing concepts with practical business applications across diverse industries including finance, healthcare, and international business, with several publications appearing in top-tier journals such as Journal of the Academy of Marketing Science and Marketing Science. Johnson's research has garnered attention across academic and professional platforms, with his publications being referenced in patents and discussed on social media and news outlets. His collaborative approach is evident through co-authorship with researchers from healthcare, computer science, and neuroscience fields.

• Emerging memory and storage technologies
• Embedded systems
• Cyber-Physical Systems (CPS)
• +۵ مورد دیگر

Dr. Yiran Chen is the John Cocke Distinguished Professor at Duke University's Department of Electrical and Computer Engineering, leading the NSF AI Institute for Edge Computing (Athena) and the Duke Center for Computational Evolutionary Intelligence (DCEI). A global leader in neuromorphic computing, emerging memory systems, and edge AI, he holds prestigious roles including IEEE Fellow and Editor-in-Chief of IEEE Transactions on Circuits and Systems for AI. His research spans machine learning accelerators, security-hardened hardware, and co-design of EDA tools with LLMs. With over 700 publications and 96 patents, he has been awarded 15 paper awards and 17 nominations, including rare Technical Achievement Awards from IEEE societies. He advises over 60 PhD students and 4 postdocs, many of whom hold academic positions worldwide. His work bridges academia and industry, contributing to startups and venture capital through his board roles. Education: B.S. (Tsinghua, 1998) → M.S. (Tsinghua, 2001) → Ph.D. (Purdue, 2005). Career path: Assistant/Associate Professor at University of Pittsburgh (2010–2014) → Duke since 2014. Awards include the ACM SIGDA Outstanding New Faculty Award (2014), NSF CAREER Award (2013), and the Stansell Family Distinguished Research Award (2022). Research focuses on innovations in: (1) Non-volatile memory architectures for AI acceleration, (2) Hardware-software co-design for edge computing, (3) Security in neuromorphic systems, and (4) Large-scale ML for EDA. His group pioneered ReRAM-based accelerators like ReBNN and MARC, and introduced novel edge AI frameworks like Ecco and Prosperity. These works address scalability, energy efficiency, and real-time performance challenges. Key initiatives include the NSF IUCRC for Alternative Sustainable & Intelligent Computing (ASIC), advancing sustainable computing through novel materials and architectures. His leadership in standard-setting bodies like the IEEE Circuits and Systems Society ensures cutting-edge research translates into industry practices. Grants: Lead PIs for multiple NSF AI Institutes and industry partnerships. Labs: Directs the Athena Institute and DCEI, fostering collaboration between academia and industry. Current projects include quantum computing placement algorithms (QPlacer), federated learning frameworks (FedGPT), and neuro-symbolic architectures.

Onur Mutluمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Hardware Security
• +۵ مورد دیگر

Onur Mutlu is a Professor of Computer Science at ETH Zurich, affiliated with the Department of Information Technology and Electrical Engineering. He also holds adjunct professorships at Carnegie Mellon University and Bilkent University. His research focuses on computer architecture, systems security, bioinformatics, and energy-efficient computing. He has pioneered work on memory-centric computing paradigms, RowHammer security vulnerabilities, and bio-inspired computing systems. He teaches courses such as Digital Design & Computer Architecture and supervises the SAFARI research group, which explores cutting-edge topics in memory systems, AI accelerators, and genomics. Recent activities include keynote talks at ISCA, HiPEAC, and IEEE conferences, emphasizing emerging hardware-software co-design principles. Key contributions include foundational work on memory reliability, cross-layer system design, and accelerating genomic data analysis. His research has been showcased in over 200 publications and industry collaborations with tech leaders like Intel, Huawei, and Micron.

• Operations Research
• Applied Probability
• Stochastic Models
• +۵ مورد دیگر

Professor David D. Yao is a Senior Fellow at the Hong Kong Institute for Advanced Study, City University of Hong Kong, and a full Professor of Industrial Engineering and Operations Research at Columbia University , where he has held distinguished chairs since 1988. A member of the US National Academy of Engineering and Fellow of IEEE, INFORMS, and SIAM, his career spans over four decades with groundbreaking contributions to stochastic systems, supply chain optimization, healthcare operations, and financial engineering. Ph.D. (1983), M.A.Sc. (1981) from the University of Toronto Academic appointments: Assistant Professor at Columbia (1983-86), Associate Professor at Harvard (1986-88), Professor at Columbia (1988–present) Research Interests center on stochastic modeling, optimization of complex systems, and risk management , with applications to healthcare logistics, semiconductor manufacturing, internet traffic modeling, and financial networks. He has pioneered theories in polymatroid optimization, dynamic scheduling, and systemic risk analysis. Recent Trends in Publications emphasize financial systemic risk via network models , asymptotic inventory optimization , healthcare resource allocation , and multi-bottleneck stochastic networks , reflecting his interdisciplinary approach. Scientific Awards include the 2024 Presidential Award for Outstanding Teaching, 2015 Markov Lecture, 2015 National Academy of Engineering membership, 2005 INFORMS and IBM Faculty Awards, 2003 SIAM Outstanding Paper Prize, and 1999 Franz Edelman Award. Grant Leadership spans $302,875 NSF-CMMI-1462495 for systemic risk modeling to $20.45M Hong Kong RGC Theme-Based Grant for healthcare systems. His editorial roles and co-founding of Columbia’s Center for Applied Probability and the Financial and Business Analytics Center underscore his institutional impact. Patents cover semiconductor job configuration, warranty inspection systems, and inventory optimization, with 8 US patents. He has supervised over 15 postdoctoral fellows and advised 20+ doctoral students.

Guido Perboliمشاهده پروفایل

استاد

• Operations Research
• Logistics
• Last-mile Delivery
• +۹ مورد دیگر

Guido Perboli is a Full Professor in the Department of Management and Production Engineering (DIGEP) at the Polytechnic University of Turin, where he also serves as Logistics Coordinator and Project Coordinator for activities supporting relationships with government bodies. He is a member of the Interdepartmental Center CARS@PoliTO (Center for Automotive Research and Sustainable Mobility) and serves as Director of the ICT for City Logistics and Enterprises (ICElab@Polito) research center, which he founded in 2016. His research interests span a broad range of topics including Operations Research, Logistics, Last-mile Delivery, Sustainable Logistics, Combinatorial Optimization, Stochastic Programming, Business Development, and Lean Business methodologies. His work particularly focuses on City Logistics, Green Logistics, and the application of emerging technologies like Blockchain and AI in supply chain management. He has developed GUEST, a Lean Business methodology for innovation processes from early idea definition to implementation. Professor Perboli's recent publications demonstrate a strong focus on urban logistics, last-mile delivery optimization, blockchain applications in supply chains, and the integration of AI techniques in transportation systems. His work shows an increasing trend toward interdisciplinary research that combines optimization methods with emerging technologies to address sustainable urban mobility challenges. Professional Recognition: CASE Best Paper award from IEEE Conference on Automation Science and Engineering (2011) Effective member of INFORMS (2019-present) Effective member of EURO (1995-present) Effective member of AIRO (1995-present) Associate Editor for Journal of Applied Research and Technology (2020-present) Associate Editor for Sustainability (2018-present) Professor Perboli actively advises PhD students and has supervised numerous research projects, including EU-funded initiatives like SINFONICA, HESTER, and 5G-LOGINNOV. He serves as Scientific Director for multiple commercial research projects focused on blockchain, IoT, and AI applications in logistics. Beyond academia, he is Chief Scientific Officer of Arisk S.p.A., a fintech company specializing in business crisis prediction using AI and machine learning. His research group, ICElab@Polito, focuses on two main areas supporting urban growth: logistics and enterprises. The center collaborates with numerous companies including Amazon, DHL, and FCA, addressing real-world challenges in urban logistics and supply chain management through innovative research approaches.

• Business Process Management
• Process Mining
• Complex Event Processing
• +۵ مورد دیگر

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

Edwin Romeijnمشاهده پروفایل

استاد

• Optimization Theory
• Radiation Therapy Treatment Planning
• Supply Chain Management
• +۲ مورد دیگر

Edwin Romeijn holds the Jill Stewart Archer Family Chair and Professor position in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He served as School Chair from 2015-2024, overseeing the nation's top-ranked industrial engineering program. Previously, he held faculty positions at the University of Michigan, University of Florida, and Erasmus University Rotterdam, and served as Program Director at the National Science Foundation. Education: Ph.D. in Operations Research (1992), Erasmus University Rotterdam M.S. in Econometrics (1988), Erasmus University Rotterdam Romeijn's research centers on optimization theory and applications , with dual focus areas in radiation therapy treatment planning and supply chain management . His radiation therapy work develops algorithms for cancer treatment planning and clinic scheduling, while his supply chain research addresses integrated optimization of production, inventory, and transportation under demand flexibility, resource constraints, perishability, and uncertainty. His methodologies bridge theoretical operations research with real-world healthcare and logistics systems. His publication portfolio demonstrates consistent contributions to optimization methods across diverse application domains, with recent work spanning healthcare systems, renewable energy, sports analytics, and unconventional logistics. The research exhibits strong methodological continuity in stochastic programming, network optimization, and decision-making under uncertainty. Scientific Awards: Fellow of IISE and INFORMS (2017) Richard C. Wilson Faculty Scholar (2012-2013) Multiple best paper awards in industrial engineering conferences Pierskalla Best Paper Award (2003) Young Investigator’s Award at ICCR (2004) Romeijn has advised numerous graduate students and secured significant research funding through NSF and other agencies. His leadership extends to program direction at NSF and chairing Georgia Tech's Industrial and Systems Engineering school. He maintains active collaborations with healthcare institutions and manufacturing enterprises, translating theoretical advances into practical solutions for radiation oncology and supply chain resilience.

Min Liuمشاهده پروفایل

استاد

• Lean Construction
• Digital Twin Design
• Data Mining
• +۵ مورد دیگر

Dr. Min Liu is a Professor and the Abdallah H. Yabroudi Endowed Professor in Sustainable Civil Infrastructure at Syracuse University, where she directs the Syracuse University Infrastructure Institute. She holds a Ph.D. in Engineering Project Management from UC Berkeley, and prior degrees from National University of Singapore and Xi’an University of Architecture and Technology. Her research focuses on integrating human and engineering aspects in construction planning, with emphasis on Lean Construction, Digital Twin design, and machine learning applications. She has published over 50 articles in top-tier journals and won prestigious awards such as the 2021 ASCE Thomas Fitch Rowland Award. Dr. Liu advises numerous graduate students and postdocs, offering positions in Construction Engineering and Management. Her lab develops innovative approaches for infrastructure project delivery, worker mental health, and bridge preservation strategies. Education: Ph.D. in Engineering Project Management, UC Berkeley (2007) MSCE, Xi’an University of Architecture and Technology (1997) MSc in Building Science, National University of Singapore (2001) BSc in Civil Engineering, Qingdao University of Technology (1994) Research Highlights: Large language models for construction planning reliability Ontology-based knowledge systems for construction methods Lean techniques for worker mental health improvement Socioeconomic analysis of bridge preservation strategies Awards: 2021 ASCE Thomas Fitch Rowland Award Multiple Best Paper Awards (2017-2018) "Thank a Teacher" awards (2011-2018) Her advising record includes notable students like Chuanni He (2023 Chinese Government Award) and Gongfan Chen (2022 Three-Minute Thesis Award). Current opportunities include Ph.D. financial support and postdoc positions. Dr. Liu’s work appears in journals like ASCE Journal of Management in Engineering and Engineering, Construction and Architectural Management.

Amany Faragمشاهده پروفایل

دانشیار

• Patient Safety
• Medication Safety
• Nurse Fatigue
• +۴ مورد دیگر

Amany Farag is a tenured Associate Professor at the University of Iowa College of Nursing and Co-Director of the VA Quality Scholars Program (Iowa City site). Her work bridges nursing science, human factors engineering, and data science to address critical patient safety challenges, with a specific focus on medication administration practices across healthcare settings. Education: Postdoctoral Scholar, Case Western Reserve University, Frances Payne Bolton School of Nursing PhD, Case Western Reserve University, Frances Payne Bolton School of Nursing MSN, University of Alexandria, Alexandria Egypt BSN, University of Alexandria, Alexandria Egypt Dr. Farag's research centers on reactive and proactive approaches to patient safety , with dual emphasis on medication error reporting systems and nurse fatigue prevention. Her work integrates human factors engineering and machine learning to develop novel interventions. Key themes include understanding how social and system factors influence nurses' error reporting behaviors, examining fatigue as a precursor to errors, and developing self-management strategies for nurse wellness. Recent projects explore intershift recovery, sleep hygiene using consumer technology, and the impact of shift work on cognitive performance. Publication trends reveal a strong focus on interdisciplinary safety science , with consistent output in nursing, human factors, and healthcare quality journals. Her work increasingly incorporates AI methodologies while maintaining clinical relevance to frontline nursing practice. Scientific Recognition: Mary Hanna Memorial Journalism Award (Journal of Peri-Anesthesia Nursing, 2016) Author of the Year (Journal of Emergency Medicine, 2018) Junior Investigator Award (Midwest Nursing Research Society, 2018) Rogers Endowed Lectureship Award (Mississippi Medical Center, 2018) Dr. Farag secures significant funding from national agencies including the National Council of State Boards of Nursing (NCSBN), NIOSH-funded Healthier Workforce Center of the Midwest, CDC-funded Injury Prevention Research Center, and University of Iowa Institute for Clinical and Translational Science. Her collaborative approach spans nursing, data science, ergonomics, and public health teams. As Co-Director of the VA Quality Scholars Program, she mentors future healthcare quality leaders while advancing her research on medication safety systems and nurse fatigue mitigation strategies through interdisciplinary partnerships.

• Medical Imaging
• Breast Cancer Diagnosis
• Phase-Contrast Tomography
• +۴ مورد دیگر

Dr. Amir Tavakoli Taba is a Senior Lecturer in Medical Imaging Sciences at the University of Sydney, where he co-directs the Medical Image Optimisation and Perception Group (MIOPeG). He specializes in improving medical imaging accuracy, particularly in breast cancer diagnosis, through advancements like phase-contrast tomography and AI integration. His work bridges technological innovations (e.g., low-dose imaging) with clinical practice, emphasizing quality control and radiologist performance analysis. Education: PhD (University of Sydney) MEngSc (University of New South Wales) BSc (University of Tehran) Research Interests: Dr. Taba’s research focuses on phase-contrast computed tomography (PCT), AI-driven diagnostic tools, and clinical workflow optimization. His projects include the world’s first PCT clinical trial (scheduled for 2024 in Melbourne) and collaborations with institutions like ANSTO, Harvard Medical School, and the University of Iowa. He also investigates radiologist expertise development and the role of social networks in medical decision-making. Grants & Awards: NHMRC Synergy Grant (IMPACT: Implementation of X-ray Phase-Contrast Tomography) International recognition, including the SPIE Medical Imaging Award Advising & Labs: Current students: Mohammed ALANAZI (abdominal CT optimization), Jenna ARBID (phase-contrast imaging) Labs: MIOPeG, part of the Sydney Vital and Sydney Catalyst cancer research networks Teaching: Courses in imaging technologies, medical image perception, and clinical capstone projects for diagnostic radiography students.

Jennifer Shangمشاهده پروفایل

استاد

• Healthcare Analytics
• Operations Management
• E-Commerce
• +۴ مورد دیگر

Jennifer Shang is a Professor of Business Administration and Area Director for Business Analytics and Operations at the Katz Graduate School of Business , University of Pittsburgh. With a focus on healthcare analytics, operations management, and e-commerce, she applies data-driven methodologies to enhance patient care, hospital efficiency, and business productivity. Education: PhD in Operations Management, University of Texas at Austin MBA, University of Iowa Bachelor of International Business, National Taiwan University Her research integrates multi-criteria decision-making techniques (e.g., AHP/ANP, DEA) and combines human judgment with quantitative data to improve organizational outcomes. She has published over 140 papers, with recent trends emphasizing healthcare analytics, big data applications, and supply chain optimization in service industries. Scientific Awards: Distinguished Professor for EMBA class 32 (2004-2005) Excellence in Teaching Award for MBA program (2002-2004, 2009-2010) Excellence in Research Awards (2013-2015, 2017-2020, 2021-2022) Best Paper Award (2010) in Information Systems Research Professor Shang teaches courses in operations management, supply chain, statistical analysis, and multivariate data analysis at undergraduate, MBA, EMBA, DBA, and PhD levels. She serves on editorial boards for journals including International Journal of Revenue Management and International Journal of Productivity and Quality Management .

Bryan S. Kimمشاهده پروفایل

استادیار

• Data Storage Systems
• Computer Architecture
• Operating Systems
• +۴ مورد دیگر

Bryan S. Kim is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. His research focuses on computer systems, particularly data storage systems, emphasizing performance, reliability, and scalability in the context of heterogeneous hardware. He holds a Ph.D. and M.S. in Computer Science and Engineering from Seoul National University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Prior to academia, he worked as a postdoctoral researcher at Seoul National University and as a manager at SK Telecom. Key research interests include SSD reliability, storage system design, and overcoming hardware limitations through innovative architectures. Notable recent work includes projects on capacity-variant storage systems, CXL-enabled SSDs, and RAID adaptations for heterogeneous SSDs. He has been awarded two NSF grants: the DESC proposal (CHIPLETS360) in 2025 and a CAREER award for bridging memory/storage gaps in 2025. Education: Ph.D. in Computer Science and Engineering, Seoul National University M.S. in Computer Science and Engineering, Seoul National University B.S. in Electrical Engineering and Computer Science, UC Berkeley Recent Awards: NSF DESC Proposal Award (2025) NSF CAREER Proposal Award (2025) Teaching: CSE486: Design of Operating Systems CIS341: Computer Organization & Programming Systems CIS700: Storage Systems for Big Data His students include Shao-Peng Yang, Xiangqun Zhang, and Omkar Desai. He advises on projects related to storage systems, and his work has been published in top-tier conferences like FAST, ATC, EuroSys, and OSDI.

Ioan Raicuمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Grid Computing
• +۳ مورد دیگر

Ioan Raicu is a Professor in the Department of Computer Science at Illinois Institute of Technology (IIT) and a guest research faculty at Argonne National Laboratory's Math and Computer Science Division. He leads the Data-Intensive Distributed Systems Laboratory (DataSys) at IIT, focusing on distributed systems, cloud computing, and high-performance computing. His work is primarily funded by the NSF and DOE. Research interests include distributed systems, many-task computing, and data-intensive applications. Over 140 peer-reviewed publications have yielded a H-index of 46, with top papers addressing cloud vs. grid computing comparisons, Globus GridFTP, Swift workflow systems, and Falkon frameworks. Recent projects explore fine-grained parallelism, scalable indexing, and energy-efficient blockchain algorithms. Awards include NSF grants and recognition for lab innovations. Advised PhD students include Alexandru Orhean and Poornima Nookala. The DataSys lab has won the Grainger Computing Innovation Prize and leads initiatives like the BigDataX REU program. Active in conferences such as SC and IEEE IPDPS, Raicu's work bridges theory and practice in extreme-scale computing systems.