جست‌وجوی استاد و پژوهشگر

Brandon M. Luciaمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Programming Languages
• +۷ مورد دیگر

Brandon M. Lucia is the Kavčić-Moura Professor of Electrical and Computer Engineering at Carnegie Mellon University and CEO/co-founder of Efficient Computer Company. He leads the Abstract research group and focuses his work on the intersection of computer architecture, systems, and programming languages. Education: Ph.D. in Computer Science and Engineering, University of Washington (2013) — advised by Luis Ceze Research Focus: Brandon's research is broadly centered on intermittent computing , edge computing , and energy-efficient architectures . Two major thrusts define his current work: Intermittent Computing: Making battery-free, energy-harvesting devices programmable and reliable despite frequent power failures. Applications include medical implants, space systems, and large-scale sensing. Orbital Edge Computing: Designing nanosatellite constellations that perform on-orbit data processing, enabling low-latency, high-resolution sensing in space-constrained environments. Scientific Awards: NSF CAREER Award (2017) IEEE TCCA Young Computer Architect Award (2019) Sloan Foundation Fellowship (2021) ASPLOS 2020 Best Paper Award OOPSLA 2015 Distinguished Paper & Artifact Awards IEEE Micro Top Picks (2016, 2018 Honorable Mention) Advising & Grants: Brandon actively advises a strong cohort of Ph.D. students including Zhuo Cheng, Bradley Denby, Souradip Ghosh, Harsh Desai, Kiwan Maeng, Emily Ruppel, and others. His group is funded by the NSF (CAREER and SHF grants), the Sloan Foundation, and industry partnerships. Labs & Teams: He directs the Abstract research group at CMU ECE, which hosts interdisciplinary projects spanning hardware design, compiler construction, and system software for ultra-low-power and space-borne computing platforms.

Sarita V Adveمشاهده پروفایل

استاد

• Computer Architecture
• Extended Reality Systems
• Heterogeneous Computing
• +۴ مورد دیگر

Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.

Bahar Asgariمشاهده پروفایل

استادیار

• Computer Architecture
• Domain-Specific Architecture Design
• Near Memory Processing
• +۳ مورد دیگر

Bahar Asgari is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park. She holds appointments in CS, UMIACS, and ECE, and directs the Computer Architecture and Systems Lab (CASL). Her research focuses on domain-specific architecture design, near memory processing, and reconfigurable computing. Before joining UMD, she worked at Google, contributing to system optimization and memory utilization strategies. Education: Ph.D., Georgia Institute of Technology, 2021. Research Interests: Developing energy-efficient architectures for sparse problems, scientific computing acceleration, FPGA optimizations, and reconfigurable hardware systems. Her work addresses challenges in memory efficiency, parallelization, and hardware-software co-design across domains like machine learning and IoT. Key Awards: 2024 UMD Excellence in Teaching Award 2023 DOE Early Career Award Advising & Grants: Supervises 9 graduate students and leads projects sponsored by NSF, DOE, and industry partnerships. Her lab explores systolic arrays, near-data processing, and fault-tolerant computing. Labs/Teams: Directs CASL, which builds next-generation computing systems through hardware innovation and algorithm-architecture co-design.

• Computer Networks
• Network Security
• Machine Learning in Networking
• +۳ مورد دیگر

Mariano Scazzariello is a Lecturer at KTH Royal Institute of Technology, Sweden, affiliated with the School of Electrical Engineering and Computer Science and the Department of Network and Systems Engineering. He teaches the course 'Network Systems with Edge or Cloud Datacenters (IK2227)'. His research focuses on advanced networking topics including machine learning in networks, high-speed packet processing, network emulation, and software-defined networking innovations. His work spans contributions to network emulation tools like Kathará and Megalos, stateful packet processing at terabit scales, and leveraging large language models (LLMs) for network configuration and vulnerability detection. Recent research emphasizes low-latency protocols (e.g., SRv6/DetNet integration) and GPU-centric networking on commodity hardware. Mariano’s publications (2020–2025) highlight expertise in network function virtualization, ASIC-based switching, and optimizing network configurations through AI-driven approaches. He has pioneered frameworks for evaluating routing protocols and virtualizing large network scenarios at scale.

Emily Wallمشاهده پروفایل

استادیار

• Data Visualization
• Visual Analytics
• Cognitive Psychology
• +۳ مورد دیگر

Dr. Emily Wall is an Assistant Professor in the Department of Computer Science at Emory University, leading the CAV Lab (Cognition and Visualization). She earned her Ph.D. in Computer Science from Georgia Tech (2020) and completed a postdoctoral fellowship at Northwestern University before joining Emory. Ph.D., Computer Science, Georgia Tech (2020) Postdoctoral Researcher, Northwestern University Current: Assistant Professor, Emory University Her research focuses on enhancing human decision-making through data visualization and visual analytics by addressing cognitive limitations and biases. Key areas include metacognition promotion, computational strategies for bias mitigation, and human-AI collaboration in data analysis. She combines cognitive psychology principles with technical implementation using tools like D3.js and Tableau. Recent publications highlight her work on LLM applications in visual analytics, cognitive bias interventions, and trust calibration in AI systems. Current students like Mengyu Chen, Shiyao Li, and Zhongzheng Xu are presenting at top conferences such as CHI and IEEE VIS. She also explores educational applications through interactive visualization pedagogy and innovative assessment methods. Dr. Wall actively engages in academic service through graduate mentorship, conference judging, and diversity initiatives. Her lab emphasizes both technical proficiency and social justice implications in visualization design, reflected in course projects that merge artistic expression with data-driven storytelling.

Tulika Mitraمشاهده پروفایل

استاد

• Hardware-Software Co-design
• Real-Time Embedded Systems
• Energy-Efficient Computing
• +۷ مورد دیگر

Professor Tulika Mitra is the Dean of the School of Computing and Vice Provost (Special Projects) at the National University of Singapore (NUS). She holds the Provost’s Chair Professor in the Department of Computer Science and has been instrumental in shaping academic policies and strategic initiatives at NUS since joining in 2001. PhD in Computer Science, Stony Brook University (2000) M.E. in Computer Science, Indian Institute of Science (1997) B.E. in Computer Science, Jadavpur University (1995) Her research focuses on hardware-software co-design for energy-efficient computing systems, particularly in real-time embedded systems, heterogeneous architectures, and AI accelerators. She leads major research programs such as the NRF Competitive Research Programme on Low-Power Edge Accelerators and the MOE Tier-3 Programme on Green AI , collaborating with industry leaders like ARM, AMD, and Meta. Her recent publications highlight innovations in CGRA optimization , sparse attention mechanisms , photonic-digital hybrid architectures , and low-power ML inference . These works often integrate compiler techniques, architectural design, and real-time constraints for edge computing applications. Scientific Awards : ESWEEK Test-of-Time Award (2022), ACM SIGDA Distinguished Service Award, IEEE CEDA Outstanding Service Recognition Award, Teaching Excellence Award (2006), and multiple best paper recognitions. Education Leadership : Spearheaded the Computer Engineering (CEG) Programme at NUS, a joint initiative between Engineering and Computing. As a mentor , she has supervised over 25 PhD students , many now in prominent academic or industrial roles. Her research group eCO Lab focuses on embedded computing challenges, while her grant collaborations include projects on 5G base stations, reconfigurable architectures, and IoT-optimized SoCs.

Nga Wun Liمشاهده پروفایل

مدرس ارشد

• Functional Garments
• Knitting Technology
• Smart Textiles
• +۳ مورد دیگر

Dr Nga Wun (Doris) Li is a Senior Lecturer at the University of Technology Sydney (UTS) within the Faculty of Design, Architecture and Building, Department of Fashion and Textiles. She leads research in seamless knitting technology, smart textiles, and sustainable fashion innovation. PhD in Fashion & Textile Design (HK PolyU, 2021) BA (Hons) in Fashion & Textiles (HK PolyU, 2012) Certifications in Wholegarment Machine (Shima Seiki, Japan) and Higher Education Pedagogy (UTS) Her research focuses on functional garments, knitting technology, and bio-informed textile design. Key projects include smart socks for DVT prevention , one-size sports bras , and buoyant swimwear for children . She combines knitting with machine learning and 3D printing for material innovation. Recent publications highlight her work on compression textiles (2025), wearable glucose sensors (2025), and knitted metasurfaces for acoustic comfort (2024). She has secured AU$52,000+ in grants across 5 funded projects. Fashion Design Award (Jeanswest, China) Outstanding Presentation & Research Paper Awards (2024) Dr Li supervises PhD and MPhil students in smart textiles and sustainable design while coordinating machine knitting courses. She collaborates with AiDLab (Hong Kong) and Powerhouse Museum (Australia).

• Cybersecurity and Privacy
• Data Management and Data Science
• Distributed Computing and Systems Software

Professor Jun Zhang is a leading academic in cybersecurity at Swinburne University, Australia, where he directs the Cybersecurity Lab. He has been honored as Australia's top cybersecurity researcher and instrumental in establishing Swinburne as a globally recognized cybersecurity research institution. His work includes high-impact papers and multi-million-dollar R&D projects, culminating in awards like the 2021 'Top Cybersecurity Research Institution' accolade. As course director of the Bachelor of Cyber Security, he pioneered an industry-driven teaching model with Deloitte and CSIRO, significantly boosting course enrollment. His collaborations extend to Adobe's Curriculum Innovation Program and the Australian P-TECH initiative, promoting STEM education and cybersecurity awareness. He supervises doctoral candidates and leads grants focused on AI-driven cybersecurity, smart home security, and blockchain-based edge computing. His research spans vulnerability detection, GAN forensics, IoT security, and privacy preservation in OSNs. Research interests include cybersecurity fundamentals, data science applications, and distributed systems. Notable achievements include the PTFix framework for Java vulnerabilities, the IoTFuzz smart home testing system, and CTI mining methodologies. Awards reflect his mid-career research excellence and industry partnerships. His grants with CSIRO and defense organizations emphasize real-world impact, addressing challenges from malware detection to adversarial machine learning. The Cybersecurity Lab and collaborative projects like Artchain demonstrate his commitment to bridging academia and industry. Professional activities include supervising over 20 HDR students and securing grants totaling millions. His work on blockchain-based edge storage (CSEdge) and SDCCP congestion control highlights innovation in networking. Future directions include advancing AI for design collaboration with CSIRO and enhancing privacy in smart energy technologies. His contributions span technical, educational, and community outreach domains, positioning him as a pivotal figure in cybersecurity's evolution.

Mohammad Mohammadi Amiriمشاهده پروفایل

استادیار

• Large Language Models
• Data Valuation
• Federated Learning
• +۵ مورد دیگر

Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.

Jia Zhangمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Information Retrieval
• +۱۰ مورد دیگر

Dr. Jia Zhang is the Inaugural Robert H. Dedman Jr. Endowed Department Chair and Professor of Computer Science at Southern Methodist University (SMU Lyle School of Engineering). She holds the Cruse C. and Marjorie F. Calahan Centennial Chair in Engineering and has a courtesy appointment in the Department of Operations Research and Engineering Management. Her research focuses on applying machine learning, natural language processing, and information retrieval to data science infrastructure, particularly scientific workflows, provenance mining, software discovery, knowledge graphs, cloud computing, immune AI, and applications in earth science and healthcare. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, Nanjing University B.S. in Computer Science, Nanjing University Dr. Zhang's work emphasizes data science infrastructure and machine learning for scientific workflows and knowledge graphs. Her recent publications highlight deep learning , graph neural networks , and optimization algorithms in cloud computing, cybersecurity, and environmental applications. Key trends include spatiotemporal modeling , hybrid neural architectures , and AI-driven service ecosystems . Scientific Awards: Best Paper Awards IEEE SCC (2011, 2017) Best Student Paper Awards IEEE ICWS (2014, 2018), IEEE ICCC (2018) Distinguished Paper Award ICSOC (2023) First Outstanding Service Award IEEE Technical Committee on Services Computing (2016) She has secured over $5 million in federal grants (as PI) and $11 million as PI/Co-PI from NSF, NASA, NIH, UTSW, Ericsson, SAP, and Google. Her lab (Caruth Hall 308) actively recruits research assistants. She previously served as a faculty member at Carnegie Mellon University, Northern Illinois University, and Nanjing University, and worked in industry as a software architect.

Deepak Ganesanمشاهده پروفایل

استاد

• Networking and Distributed Systems
• Mobile and Sensor Systems
• Human-Computer Interactions
• +۳ مورد دیگر

Deepak Ganesan is a Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. His research focuses on low-power sensing and communication, networked systems, and machine learning applied to pervasive health monitoring and societal challenges. PhD, Computer Science, University of California, Los Angeles (2004) MS, Computer Science, University of California, Los Angeles (2000) BTech, Computer Science, Indian Institute of Technology, Madras (1998) Ganesan's work bridges wireless sensor networks, smart textiles, and healthcare applications. He designs ultra-low-power wearable devices for tracking health signals like drug use, smoking, and cognitive performance, often integrating machine learning for robust detection. His research emphasizes societal impact, particularly in aging and Alzheimer's care through the Massachusetts AI and Technology Center for Connected Care (MassAITC) and the Center for Personalized Health Monitoring (CPHM). Recent publications highlight innovations in edge-cloud collaboration, fabric-based sensors, and longitudinal health analytics. His NIH-funded MD2K Center for Excellence and affiliations with the Center for Data Science and Computational Social Science Institute further underscore his interdisciplinary approach. ACM Fellow NSF CAREER Award (2006) IBM Faculty Award (2008) UMass Junior Faculty Fellow (2008) UMass Lilly Teaching Fellow (2009) Best Paper at CHI 2013 Best Paper Runner-up at Mobicom 2014 Honorable Mentions at Ubicomp 2013 Ganesan leads the SENSORS: Wireless Sensor Networks Group and contributes to global initiatives like the Internet of Battlefield Things. His work spans academic research, industry partnerships, and policy development in AgeTech and digital health.

Xuemin (Sherman) Shenمشاهده پروفایل

استاد

• Wireless communications and networking
• Resource allocation and mobility management
• Wireless network security and privacy preservation
• +۶ مورد دیگر

Xuemin (Sherman) Shen is a University Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Royal Society of Canada, the Canadian Academy of Engineering, and the Engineering Institute of Canada. Professor Shen serves as Editor-in-Chief of multiple prestigious journals including the IEEE Internet of Things Journal and Springer Peer-to-Peer Networking and Applications. Dr. Shen received his Doctorate in Electrical Engineering from Rutgers University in 1990, following a Master of Applied Science from the same institution in 1987. His undergraduate degree is a Bachelor of Applied Science in Electrical Engineering from Dalian Marine University, China (1982). Professor Shen's research spans wireless communications and networking, with particular expertise in resource allocation, mobility management, wireless network security, and privacy preservation. His work extends to IoT applications, connected and automated vehicles, network digital twins, and satellite-terrestrial networks. His research has been applied to vehicular networks, wireless body area networks, remote e-healthcare systems, and smart grid technologies, demonstrating both theoretical depth and practical impact across multiple domains. His recent publications reveal strong trends in AI-assisted networking, security and privacy preservation for IoT applications, and energy management in vehicular and smart grid systems. The research shows an increasing focus on integrating AI techniques with traditional networking approaches, addressing critical challenges in security, privacy, and resource management for next-generation wireless systems. R.A. Fessenden Award (2019) from IEEE Canada James Evans Avant Garde Award (2018) from the IEEE Vehicular Technology Society Joseph LoCicero Award (2015) from the IEEE Communications Society Education Award (2017) from the IEEE Communications Society West Lake Friendship Award from Zhejiang Province (2023) President's Excellence in Research from University of Waterloo (2022) Canadian Award for Telecommunications Research (2021) Professor Shen has mentored over 100 graduate students and postdoctoral fellows throughout his career, with many now holding prominent academic positions at top universities worldwide. His supervision has been recognized with multiple awards including the Award of Excellence in Graduate Supervision (2006) from the University of Waterloo. He has served in numerous leadership roles including Past President of the IEEE Communications Society and has chaired major international conferences including IEEE Globecom 2024.

Stephen W. Kecklerمشاهده پروفایل

استاد مدعو

• Computer Architecture
• Deep Learning Accelerators
• GPU Computing
• +۴ مورد دیگر

Stephen W. Keckler is an Adjunct Professor at the Department of Computer Science , The University of Texas at Austin , and serves as Vice President of Architecture Research at NVIDIA . He is an ACM Fellow , IEEE Fellow , and Sloan Foundation Research Fellow . Education: BS in Electrical Engineering, Stanford University (1990) SM in Computer Science, Massachusetts Institute of Technology (1992) PhD in Computer Science, MIT (1998) Research Interests focus on computer architecture for deep learning , GPU computing , and energy-efficient systems . His work explores memory compression , network-on-chip designs , and heterogeneous computing . Publication Trends highlight advancements in deep learning accelerators , GPU memory systems , and energy-efficient architectures . Notable themes include sparsity exploitation , multi-chip modules , and fault-tolerant GPU pipelines . Scientific Recognition : ACM Fellow IEEE Fellow Sloan Foundation Research Fellow Best Paper Awards at ASPLOS 2009 and ISPASS 2011 Laboratory Affiliations : Computer Architecture and Technology Laboratory (CART) TRIPS Project (Tera-Op Reliable Intelligently adaptive Processing System) NVIDIA Research

Andrew Warfieldمشاهده پروفایل

استاد مدعو

• Computer Systems Software
• Virtualization
• Distributed Storage
• +۳ مورد دیگر

Andrew Warfield is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Principal Engineer at Amazon. His research focuses on computer systems software, including virtualization, distributed storage, security, and high-availability systems. He previously held roles as Associate Professor at UBC, CTO at Coho Data, and Technical Director at Citrix Systems. His work has led to projects like Remus (high-availability replication), Tralfamadore (execution analysis), and secure hypervisor development with Xen. Warfield's education includes a PhD from the University of Cambridge's Computer Laboratory, where he researched I/O device virtualization under Steven Hand. He has held visiting roles at Intel Research Cambridge and internships at AT&T Research and Nortel Networks. Research Grants: Supported by Intel Research, NSERC, Network Appliance, and the Communications Security Establishment. Professional Activities: Technical Advisory Board Member at Teradici, and involvement in program committees for conferences like HotOS, EuroSys, and FAST. His research emphasizes practical systems, aiming to bridge the gap between theoretical computer science and real-world applications. Notable contributions include innovations in storage for virtualized environments, secure hypervisor architectures, and disaster-tolerant systems like SecondSite. Warfield is affiliated with UBC's Department of Computer Science and maintains active collaboration with industry partners. Though currently not actively recruiting students, his prior mentorship has influenced many in systems research.

Bo Chenمشاهده پروفایل

پژوهشگر ارشد

• Networking
• Machine Learning Systems
• Virtual Reality
• +۵ مورد دیگر

Bo Chen is a postdoctoral researcher at the Siebel School of Computing and Data Science and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. His work focuses on AI-system co-design for immersive computing, particularly in extended reality (XR) and multi-modal content delivery over wireless networks. Ph.D. in Computer Science (2022), advised by Klara Nahrstedt B.S. in Computer Science from Shanghai Jiao Tong University (2016) His research integrates AI techniques with system-level optimizations to address challenges in XR infrastructure , including multi-view video streaming , NeRF-based content delivery , and uncertainty management in video transmission . He has pioneered methods like Loose Frame Referencing for learned codecs and Context-Aware NeRF Serving for mobile XR applications. Bo Chen's recent publications span top venues like ACM MobiSys, ACM SenSys, and USENIX NSDI. Key themes include AI-driven compression , dynamic 3D rendering , and reliable streaming over mobile networks . He has received recognition including the Rising Star Best Presentation Award (ACM MobiSys 2025) and Best Student Paper Award (ACM MMSys 2022). Bayesian optimization for XR systems Multi-view video aggregation at edge networks 3D Gaussian Splatting for immersive media