جست‌وجوی استاد و پژوهشگر

Omar Hegazyمشاهده پروفایل

استاد

• Power Electronics Systems
• Electric Vehicles
• Battery Management Systems
• +۷ مورد دیگر

Omar Hegazy is a Professor in Electrical Engineering and Power Electronics at Vrije Universiteit Brussel (VUB), affiliated with the MOBI - Electromobility Research Centre. He leads research in power electronics systems, electric vehicle drivetrains, and energy management. His work focuses on reliability, WBG semiconductors, and sustainable transportation systems. Education details are not explicitly provided, but his extensive publication record and project leadership imply advanced academic qualifications. Research interests include power electronics, battery management systems, hybrid/fuel cell vehicles, and V2X technologies. His projects address challenges in electric vehicle infrastructure, grid integration, and renewable energy systems. Key trends in his articles include digital twin development for electric trucks, advanced thermal management of SiC devices, and optimization of DC charging systems. His work emphasizes practical applications like modular converters, fault-tolerant drives, and interoperable charging solutions. Awarded Best Master Thesis (2019), Best Paper (2024), and Optimal Design Recognition (2016) Supervised over 49 theses, including master's and doctoral studies in power electronics and EV systems Secured funding for projects like HiPower 5.0, HARPOONERS, and FLEXMCS Labs/Teams: Active in MOBI's Electromobility Research Centre, collaborating on advanced power electronics and e-mobility solutions. Involved in interdisciplinary teams for microgrid design and DC charging infrastructure.

• Structural Health Monitoring
• Machine Learning in Civil Engineering
• Earthquake Engineering
• +۹ مورد دیگر

Marco Martino Rosso is a Research Fellow at the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin, where he also serves as an external lecturer and teaching assistant in both DISEG and the Department of Mathematical Sciences (DISMA). He is affiliated with the Doctoral School (SCDOTT) and completed his PhD under the supervision of Professor Giuseppe Carlo Marano. His academic work bridges civil engineering with advanced computational methods, focusing on structural health monitoring, optimization, and machine learning applications. His research interests center on Structural Health Monitoring , Machine Learning in Civil Engineering , Earthquake Engineering , Structural Optimization , Operational Modal Analysis , and AI-driven diagnostics for infrastructure. He applies deep learning, neural networks, and hybrid modeling techniques to problems such as damage detection, post-earthquake assessment, tunnel and bridge monitoring, and dynamic analysis of timber and concrete structures. His recent publications, spanning from 2023 to 2025, demonstrate a strong trend toward integrating artificial intelligence with structural engineering, particularly in automating modal analysis, optimizing structural forms, and enhancing seismic resilience. These works appear in journals like Mechanical Systems and Signal Processing , Computers & Structures , and Bulletin of Earthquake Engineering , as well as in proceedings of international conferences such as IOMAC and EWSHM. Marco Rosso has not received any explicitly mentioned scientific awards in the provided text. However, his extensive publication record and active role in research projects indicate strong recognition in his field. He has contributed to teaching as a course collaborator in subjects including Dynamic Identification of Structures , Statistics , Construction Techniques , and Safety Assessment and Retrofitting of Structures . He has also been involved in the ARTISTE 2025 Summer School, indicating engagement in advanced training programs. While no formal lab or team name is specified, his frequent collaborations with researchers such as Angelo Aloisio, Giuseppe Carlo Marano, and Jonathan Melchiorre suggest he is part of a vibrant research group focused on intelligent structural systems and data-driven engineering at Politecnico di Torino.

Leila Moslemi Naeniمشاهده پروفایل

مدرس ارشد

• Project Management
• Fuzzy Methods
• Mathematical Modelling
• +۵ مورد دیگر

Dr. Leila Moslemi Naeni is a Senior Lecturer at the University of Technology Sydney (UTS), School of Built Environment, with a dual appointment in the Faculty of Design, Architecture and Building. She previously served as a Lecturer at UTS (2016-2018) and Sessional Lecturer at Curtin University (2015-2016). PhD in Computer Science (University of Newcastle, 2017) MSc in Industrial Engineering (Sharif University of Technology, 2007) BSc in Industrial Engineering (Iran University of Science and Technology, 2004) Her research focuses on project management under uncertainty, integrating fuzzy systems and mathematical modeling with applications in construction, ESG reporting, and disruptive technologies. She developed innovative methods for statistical control charts in project monitoring and leads research on leveraging blockchain and digital tools for sustainable infrastructure. Recent publications highlight her work on resource-constrained scheduling algorithms, ESG integration in megaprojects, and gamification in project management education. She serves as Review Editor for Frontiers in Environmental Science and on the Editorial Board of Smart and Sustainable Built Environment . 2016 PMI NSW Research Award 2022 Walt Lipke Award 2013 FEBE Postgraduate Research Prize As an active research mentor, she supervises PhD students in machine learning applications, disaster management technologies, and social infrastructure investments. Her teaching emphasizes simulation-based learning, collaborating with Oulo University (Finland) to quantify educational value.

Philip Daviesمشاهده پروفایل

استاد

• Intelligence Studies
• Security Studies
• Comparative Intelligence
• +۵ مورد دیگر

Professor Philip Davies serves as Professor of Intelligence Studies and Director of the Brunel Centre for Intelligence and Security Studies (BCISS) at Brunel University London's Department of Politics and History within the College of Business, Arts and Social Sciences. A political sociologist specializing in intelligence institution-building, he co-founded BCISS and developed the MA in Intelligence and Security Studies program. His academic journey includes a PhD in Sociology from the University of Reading (1997), MA in Social and Political Thought from York University Canada (1987), and BA (Hons) in Sociology from Trent University Canada (1984). PhD Sociology, University of Reading (1997) MA Social and Political Thought, York University Canada (1987) BA (Hons) Sociology, Trent University Canada (1984) Postgraduate Certificate in Teaching and Learning in Higher Education, Brunel (2006) Davies' research focuses on the organization and governance of intelligence agencies, national intelligence cultures, intelligence professionalization, and doctrine development. His work bridges practical intelligence operations with academic theory, examining how intelligence institutions interact with government machinery, international allies, and defense structures. A pioneer in comparative intelligence studies beyond the Anglosphere, he developed the theory of 'intelligence culture' and has conducted extensive research on UK, US, Indian, and Malaysian intelligence systems. His current work centers on defense and military intelligence concepts, doctrine evolution, and cyber-security policy. Analysis of his 15 most recent publications reveals a dominant trend in comparative intelligence studies with strong emphasis on British and US intelligence systems, expanding to include Malaysia and India. His work consistently examines intelligence politicization, accountability mechanisms, and the practical implementation of intelligence doctrine. The publications demonstrate increasing focus on military intelligence integration (evident in ISTAR/ISR debates) and historical case studies of intelligence failures/successes. Davies has made significant impact through practical applications of his research: Provided foundational input for UK Joint Intelligence Doctrine (JDP 2-00) and JDP 04 Understanding Advised on 2016 Investigatory Powers Act as Home Office consultative panel member Led EU INTCEN's Basic Intelligence Assessment course (2013-2019) Contributed to UK military Joint Intelligence Doctrine development (2010-2012) Delivered impact case studies for REF 2014 and REF 2021 through BCISS As an educator, Davies convenes postgraduate modules on Intelligence Concepts and Counterintelligence, while directing BCISS as a University Interdepartmental Research Centre. His media presence includes regular commentary on intelligence matters for outlets like LBC, particularly regarding the ISC Russia Report and Edward Snowden disclosures. He serves on editorial boards for Intelligence and National Security and International Journal of Intelligence and CounterIntelligence, and chairs the UK Political Studies Association/BISA Security and Intelligence Studies Group.

Paul Cisekمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Computational Neuroscience
• Motor System
• +۵ مورد دیگر

Paul Cisek is a Full Professor in the Department of Neuroscience at the Faculty of Medicine, University of Montreal. His research laboratory focuses on understanding how the brain controls behavior through the integration of decision-making and motor planning processes in the cerebral cortex. His work challenges traditional models that treat perception, decision-making, and action as separate serial processes. Dr. Cisek received his Baccalauréat in Computer Science from Rochester Institute of Technology (1991), followed by a Doctorat in Neurosciences and Physiologie from Boston University (1997). He completed postdoctoral training at both Université de Montréal and Queen's University (1996-2001). His research program centers on developing a theoretical framework describing how the brain interacts with the external world, with specific focus on how decision-making and motor planning processes are integrated in the primate cerebral cortex. Through his 'affordance competition hypothesis,' Cisek proposes that the brain operates through parallel processes where potential actions compete for selection rather than following a serial perception-decision-action model. His lab conducts behavioral, neurophysiological, and computational research, often using primate models to study neural mechanisms of real-time decision-making. The analysis of Dr. Cisek's recent publications reveals a strong focus on evolutionary perspectives in neuroscience, neural mechanisms of decision-making under time pressure, and the integration of biomechanical constraints in action selection. His work bridges cognitive neuroscience, motor control, and computational modeling to develop a more comprehensive understanding of how the brain selects and executes actions in dynamic environments. Dr. Cisek has been continuously funded by major Canadian research agencies including CIHR, NSERC, and FRQS since at least 2002 for his interdisciplinary studies of decision-making and motor planning. His current projects include 'Neural mechanisms of decision-making during natural interactive behavior' (2025-2031) and 'EthoLab: A platform for neurophysiological studies of natural behavior' (2025-2028). As an educator, Dr. Cisek has supervised numerous graduate students to completion of their degrees and currently teaches courses including NSC-3003 (Perception, action et neurocomputation), NSC-3084 (Neurosciences computationnelles), and NSC-6084 (Neurosciences computationnelles). His lab currently includes doctoral students, master's students, and postdoctoral fellows working on various aspects of decision neuroscience. Dr. Cisek is affiliated with several research groups including SNC (Groupe de recherche sur la signalisation neurale et la circuiterie), GRSV (Groupe de recherche en sciences de la vision), and CIRCA (Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage). His laboratory develops sophisticated experimental platforms for studying neural mechanisms of decision-making, including virtual reality environments for neurophysiological studies of natural behavior.

Marc Reichenbachمشاهده پروفایل

استاد

• Integrated Circuits
• Embedded Systems
• VLSI Design
• +۷ مورد دیگر

Prof. Dr.-Ing. Marc Reichenbach serves as the Chair of Integrated Systems at the Institute for Applied Microelectronics and Data Technology at the University of Rostock. His office is located at Albert-Einstein-Straße 26, 18059 Rostock, Room 102 (1st floor), with contact information including telephone (0381) 498 7270 and email marc.reichenbach@uni-rostock.de. Professor Reichenbach's research focuses on the intersection of hardware design and artificial intelligence, with particular expertise in memory technologies and computing architectures. His work spans several key areas: Development of specialized computer architectures for deep learning applications Advanced VLSI design and CPU architecture Emerging memory technologies, particularly RRAM (Resistive Random-Access Memory) FPGA-based acceleration systems Hardware implementations for neural networks and AI applications Analysis of Professor Reichenbach's recent publications (2023-2025) reveals a strong focus on memory computing technologies, particularly RRAM-based systems. His work demonstrates expertise across multiple dimensions of computer architecture including ASIC design, FPGA acceleration, and novel memory systems. The publications show a clear trajectory toward implementing AI and machine learning capabilities directly in hardware, with applications ranging from edge computing to satellite systems. A significant portion of his recent work addresses the challenges of implementing neural networks using emerging memory technologies, focusing on efficiency, reliability, and performance optimization. Professor Reichenbach teaches several advanced courses including: Computer architectures for deep learning applications Project seminar Embedded Systems Advanced VLSI Design (Advanced CPU Design) His research group appears to be actively engaged in several cutting-edge projects related to hardware acceleration for AI applications, memory computing, and embedded systems design. The group collaborates on projects involving digital twins for hardware systems, real-time operating systems for heterogeneous architectures, and specialized computing systems for various applications from medical devices to drone technology.

Kostas Magoutisمشاهده پروفایل

استاد

• Distributed computer systems
• Cloud computing
• Scalable data stores and stream processing
• +۳ مورد دیگر

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete , and a collaborating researcher at FORTH-ICS . His research focuses on scalable distributed systems, cloud computing, IoT, and quantum-enhanced control. Education and Career: Ph.D. in Computer Science, Harvard University (2003) Research Staff Member, IBM T. J. Watson Research Center (2003-2009) Assistant Professor (2014-2019, tenured 2017) and Associate Professor (2020-2024), University of Crete Professor and Chair, University of Crete (2024-present) Research Interests: His work spans distributed computer systems , cloud computing , scalable data stores and stream-processing engines , Internet of Things , and the emerging area of quantum-enhanced control . Representative projects include the H.F.R.I.-funded QUADS (2025-2028) on quantum-enhanced adaptive systems, STREAMSTORE (2020-2023) on elastic stream processing, SmartCityBus on IoT-driven public transport, and the EU FP7 PaaSage project on model-based cloud lifecycle management. Awards and Honors: Best Paper Awards: USENIX ATC 2002, USENIX BSDCon 2002, IEEE SRDS 2014 (Best Student Paper), IoT 2024 (Runner-up) Grand Challenge Audience Award, ACM DEBS 2022 Best Poster Award, ACM EuroSys 2022 EU Marie Curie IEF Fellow (2009-2011) Alexander S. Onassis Fellow (1994-1995) and J. William Fulbright Scholar (1993-1994) Students and Mentoring: He has supervised or co-supervised more than 30 Ph.D., M.Sc. and undergraduate students, including Antonis Papaioannou (Ph.D. 2021), Efthimios Papageorgiou (current Ph.D.), and numerous M.Sc. graduates now in industry and academia. Labs and Teams: At FORTH-ICS he leads activities within the Distributed Systems and Storage Laboratory, coordinating research on scalable storage, stream processing, and IoT data management. The lab collaborates closely with European and national initiatives, hosting visiting researchers and industry partners.

Adriana Iamnitchiمشاهده پروفایل

استاد

• Computational Social Science
• Social Media Analysis
• Misinformation Detection
• +۳ مورد دیگر

Adriana Iamnitchi is a Full Professor and Key Domain Chair for Computational Science at Maastricht University's Faculty of Science and Engineering, affiliated with the Department of Advanced Computing Sciences. Her research focuses on computational social science, social media dynamics, and misinformation detection. Her primary research interests include: Analysis of coordinated information campaigns across social platforms Development of LLM-based synthetic data generation for social media research Polarization quantification in multi-community networks Policy compliance frameworks for digital regulation (e.g., EU's Digital Services Act) Ethical AI applications for content moderation and transparency Her recent publications (2023-2025) demonstrate strong focus on: Cross-platform disinformation detection using multimodal embeddings Generative AI for synthetic social media datasets Quantitative analysis of toxicity monetization in creator economies Regulatory compliance automation for content transparency

Stefan Wildermannمشاهده پروفایل

استاد

• Reconfigurable Systems
• Edge Computing
• Organic Computing
• +۷ مورد دیگر

Stefan Wildermann is a Professor at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), where he leads the Reconfigurable Computing Group within the Chair of Computer Science 12 (Hardware-Software Co-Design) in the Department of Computer Science. He has maintained continuous research activity at FAU since 2006, progressing from researcher to his current leadership position. Dr. Wildermann earned his Diploma degree in Computer Science from FAU in 2006 and completed his doctorate (Dr.-Ing.) in Computer Science at the same institution in July 2012. His academic career has been entirely rooted at FAU, demonstrating a strong institutional commitment and progression through the ranks. His research spans multiple cutting-edge areas in computer science and engineering, with particular emphasis on reconfigurable systems and hardware-software co-design. Wildermann's work in edge computing explores efficient processing at the network periphery, while his research in organic computing investigates self-organizing systems that can adapt to changing environments. His expertise extends to optimization techniques for embedded systems, applying game theory principles and convex optimization methods to solve complex resource allocation problems. More recently, he has integrated reinforcement learning approaches to enhance system adaptability and performance. His teaching portfolio includes courses on event-driven systems, computer engineering fundamentals, embedded systems, and hardware-software co-design. Analysis of Wildermann's publication record from 2021-2025 reveals a strong focus on hardware acceleration, security, and embedded systems. His work demonstrates consistent evolution from foundational research in reconfigurable architectures toward practical applications in IoT, robotics, and secure computing. A significant portion of his recent work addresses near-data processing using FPGAs for database acceleration, while maintaining parallel research streams in side-channel security analysis and energy-efficient embedded systems design. His publications frequently appear in top-tier conferences including DATE, FPL, ASP-DAC, and HOST, reflecting strong recognition within the computer architecture and embedded systems communities. Wildermann has held significant leadership roles including Head of the Reconfigurable Computing Group since 2015 and previously served as Head of the Self-organizing Systems Group (2012-2015) and Lab Leader of the Automotive Lab within the Embedded Systems Initiative (2016-2020). His research has been consistently funded through multiple projects investigating invasive computing, reconfigurable architectures, and embedded systems design methodologies. Currently based in Room 02.116 at Cauerstr. 11, 91058 Erlangen, Wildermann continues to lead active research in the Hardware-Software Co-Design group, supervising projects that bridge theoretical computer science with practical hardware implementation challenges.

Dharanidhar Dangمشاهده پروفایل

استادیار

• Photonic Computing
• AI Hardware Acceleration
• Memristor-based Computing
• +۳ مورد دیگر

Dharanidhar Dang serves as Assistant Professor in the Department of Computer Engineering at the College of AI, Cyber and Computing, The University of Texas at San Antonio (UTSA), where he advances hardware-centric artificial intelligence solutions through photonic and memristor technologies. Education Ph.D., Texas A&M University His research program bridges hardware innovation and biomedical applications, with primary focus on photonic computing architectures for real-time AI acceleration and memristor-based neuromorphic systems. He investigates critical challenges in hardware reliability (particularly degradation in memristor crossbars), energy efficiency in photonic accelerators, and co-design methodologies that optimize both algorithms and physical implementations. His biomedical work applies machine learning to macrophage biology, identifying predictive signatures for inflammatory diseases through computational immunology approaches. Analysis of his 2020-2025 publications reveals three dominant research trajectories: 1) Silicon photonic accelerators (P-ReTI, P-ReTiNA, SOFTONIC) targeting real-time and energy-efficient AI, 2) Memristor reliability frameworks addressing aging effects in deep learning hardware, and 3) Translational biomedical applications where machine learning deciphers macrophage behavior in inflammatory bowel disease and preterm infant lung conditions. This tripartite focus demonstrates exceptional versatility across hardware engineering and life sciences.

Jonathan L. Auerbachمشاهده پروفایل

استادیار

• Data Science
• Causal Inference
• Survey Methods
• +۶ مورد دیگر

Jonathan L. Auerbach is an Assistant Professor in the Department of Statistics at George Mason University . His work bridges statistics and public policy , focusing on causal inference , longitudinal data analysis , and official statistics . He has contributed to understanding urban myths (e.g., New York City rat populations, building height trends), election fraud , and policy evaluation for initiatives like Vision Zero. Education : PhD in Statistics (Columbia University, 2020), BA in Economics (Cornell University, 2010) Research Interests include data science , causal inference , survey methodology , and urban analytics . His recent work addresses climate change impacts on cherry blossom seasons, federal data security , and street vendor demographics in New York City. Scientific Awards include the 2024-2025 President of Washington Statistical Society , 2020-2021 American Statistical Association Science Policy Fellow , and 2019 Howard Levene Outstanding Teaching Award . He has served as an instructor for courses like Capstone in Statistics and Causal Inference , emphasizing technical communication and data ethics .

• Optical Packet Switching
• High-Speed Optical Signal Processing
• Wireless Optical Access Networks
• +۱۱ مورد دیگر

Nikos Pleros serves as a Professor in the Department of Informatics at Aristotle University of Thessaloniki (AUTH), where he leads the Wireless and Photonic Systems and Networks (WinPhoS) research group established in 2016 at AUTH’s Center for Interdisciplinary Research and Innovation (C.I.R.I.). This interdisciplinary initiative bridges the Departments of Physics, Informatics, and Electrical and Computer Engineering, focusing on advanced research in communication systems and networks. His research spans critical domains in photonics and optical networking, including Optical Packet Switching, High-Speed Optical Signal Processing, Wireless Optical Access Networks, and High-Speed Optical Telephony Switching Systems. Additional expertise encompasses Integrated Photonics, Plasmonics, Optical Interconnects for Data Centers, Optical Computing architectures, Neuromorphic Photonics, and 5G mmWave fiber-wireless integration, addressing next-generation computing and telecommunications challenges. WinPhoS operates world-class laboratories featuring high-speed optical interconnect testbeds, 5G mmWave platforms, and Photonic Integrated Circuit characterization facilities. With over 30 FP7 and Horizon EU research projects coordinated in the past 15 years, the group maintains global collaborations across Greece, Europe, the USA, and Japan. Current initiatives include two active PhD openings in Photonic Neural Networks and Photonic AI processors, reflecting ongoing innovation in neuromorphic computing hardware.

Tyson Govan Phillipsمشاهده پروفایل

مدرس ارشد

• Autonomous Systems
• Robotics
• Mining Automation
• +۴ مورد دیگر

Dr. Tyson Phillips serves as Senior Lecturer and Director of Teaching and Learning at The University of Queensland's School of Mechanical and Mining Engineering within the Faculty of Engineering, Architecture and Information Technology. He is an active Affiliate of the Future Autonomous Systems and Technologies research group, focusing on translating robotics innovations into practical mining applications. His academic leadership includes curriculum development for engineering programs and direct industry engagement with major mining equipment manufacturers. He earned his Doctor of Philosophy (PhD) from The University of Queensland in 2016, with thesis research centered on LiDAR-based perception systems for autonomous excavators. His doctoral work established foundational methods for object pose verification in mining contexts. Phillips' research specializes in robotics perception for extreme mining environments, developing LiDAR-centric solutions for autonomous equipment operation amid dust, fog, and unstructured terrain. Key contributions include evidential reasoning frameworks for uncertainty management, real-time pose estimation algorithms, and sensor fusion techniques for excavators and bulldozers. His work bridges theoretical computer vision with industrial deployment, targeting operational safety and efficiency in mineral extraction. Publication analysis reveals consistent focus on mining robotics since 2012, with recent works (2021-2024) emphasizing minimal-sensor configurations, probabilistic terrain mapping, and vibration-assisted gripper technology. His 14 scholarly outputs demonstrate evolution from sensor evaluation (2012-2015) toward integrated autonomy systems (2018-2024), predominantly in Journal of Field Robotics and Sensors . He actively supervises graduate researchers as Principal Advisor for a PhD on multimodal perception mapping and Associate Advisor for two PhD projects involving spreader systems and physics-informed neural networks. Completed supervision includes a 2024 PhD on bulldozer terrain mapping and a 2021 Master's on shovel/hopper interaction strategies. Research funding spans 14 projects from 2012-2026, including current Australian Coal Association Research Program support (2025-2026) and major Caterpillar Inc. collaborations for ERS self-protection and articulated truck automation. Phillips operates within The University of Queensland's Future Autonomous Systems and Technologies group, which develops field-deployable autonomy solutions for mining partners. This team conducts real-world testing of perception systems using Caterpillar and FMG operational sites as validation environments.

Willis Langمشاهده پروفایل

پژوهشگر

• Database Systems
• Cloud Computing
• Data Management
• +۳ مورد دیگر

Willis Lang is a Researcher at Microsoft , focusing on Database Systems , Cloud Computing , and Data Management . His work bridges academic research with industrial applications in cloud databases. Education: PhD in Computer Sciences - Databases (University of Wisconsin-Madison, 2012) MS in Computer Science and Engineering - Databases (University of Michigan, 2008) BMath in Honours Computer Science - Bioinformatics (University of Waterloo, 2006) Research Interests: Willis’s research spans Database Systems , Cloud Computing , and Energy Efficiency , with a focus on scalability, tenant management, and predictive provisioning. His work addresses real-world challenges in cloud database optimization, multi-tenancy, and power-aware systems. Recent Publications highlight trends in Cloud Database Efficiency , including auto-scaling, tenant placement, and energy-conscious cluster design. His contributions often involve collaboration with industry leaders like Microsoft and Jignesh M. Patel. Scientific Awards: Best Paper Award, DaMoN 2010 Best Presented Award, Midwest Database Research Symposium 2007 Service: Willis has served as a reviewer for conferences like SIGMOD, VLDB, and journals including VLDBJ and JPDC. His expertise is sought in cloud and database research communities.

Dong Liمشاهده پروفایل

دانشیار

• High performance computing
• Non-volatile memory
• Memory heterogeneity
• +۲ مورد دیگر

Dong Li is an Associate Professor at the University of California, Merced , where he directs the Parallel Architecture, System, and Algorithm Lab (PASA) and co-directs the High Performance Computing Systems and Architecture Group . He co-founded Yotta Labs Inc. and previously held research roles at Oak Ridge National Laboratory (2011-2014) and a PhD from Virginia Tech. Research Interests: Dong's work focuses on High performance computing (HPC) Memory heterogeneity and non-volatile memory Systems for machine learning and AI Fault tolerance in large-scale systems His innovations include heterogeneous memory optimization for recommendation models and GNNs, CXL memory integration, and persistent memory debugging tools. Recent Publications highlight advancements in CXL-based inter-node communication Memory tiering for laminography reconstruction ML-guided memory optimization for DLRM and GNN Fault tolerance benchmarks and error analysis Awards & Recognition: NSF CAREER Award (2016) Oracle Research Award (2022) ASPLOS Distinguished Artifact Award (2021) Virginia Tech Early Career Alumni Award (2023) Advising & Funding: Dong has mentored 22 students (8 PhD, 6 Master’s, 8 undergraduates) and secured grants from NSF, NVIDIA, Meta, and national labs (Argonne, Lawrence Berkeley, Lawrence Livermore). Collaborations include Microsoft (DeepSpeed, Intel PMDK), AMD, SK Hynix, and Intel/MICRON hardware donations.