جست‌وجوی استاد و پژوهشگر

Suhas Diggaviمشاهده پروفایل

استاد

• Information Theory
• Machine Learning
• Differential Privacy
• +۹ مورد دیگر

Suhas Diggavi is a Professor in the Department of Electrical and Computer Engineering at the University of California, Los Angeles, within the Henry Samueli School of Engineering and Applied Science. His primary research area is Signals and Systems, with a strong focus on information theory and its interdisciplinary applications. His research interests span Information Theory , Machine Learning , Differential Privacy , Federated Learning , Cyber-Physical Systems , and Bio-informatics . He investigates fundamental limits and practical algorithms for secure, efficient, and robust data processing in distributed and networked environments. The recent publications highlight a strong trend in privacy-preserving machine learning, particularly in the shuffled model of differential privacy , communication-efficient distributed SGD , and robust optimization . His work bridges theoretical information-theoretic foundations with real-world applications in federated learning, wireless networks, and genomic data analysis. Notable scientific awards include: Guggenheim Foundation Fellow (2021) ACM CCS Best Paper Award (2021) IEEE Fellow (2013) IEEE Donald G. Fink Prize Paper Award (2006) Multiple Google, Amazon, and Facebook Research Awards Suhas Diggavi actively advises graduate students and leads a research group focused on learning, information, and optimization. His work is supported by major industry grants and collaborations, particularly in privacy and distributed learning. He has made significant contributions to information-theoretic models in bio-sequencing and wireless security. He leads the LIOS (Learning, Information, Optimization, and Stochastic Systems) research group at UCLA, where his team develops theoretical frameworks and practical algorithms for next-generation data-driven systems.

• Databases
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Ming-Syan Chen is a distinguished academic holding dual roles as a Distinguished Research Fellow and Director of the Research Center for Information Technology Innovation (CITI) at Academia Sinica, Taiwan, and a Distinguished Professor jointly appointed across multiple departments at National Taiwan University (NTU), including Electrical Engineering (EE), Computer Science and Information Engineering (CSIE), and the Graduate Institute of Communication Engineering (GICE). His career spans academia and industry, with prior roles as a research staff member at IBM Watson Research Center and leadership positions in Taiwan's technology sector. Education: He earned a B.S. in Electrical Engineering from National Taiwan University, followed by M.S. and Ph.D. degrees in Computer, Information, and Control Engineering from the University of Michigan, Ann Arbor. Research Interests: Chen's work focuses on databases, data mining, machine learning, multimedia networking, and cloud computing. He has authored over 350 papers and holds numerous patents, contributing to foundational advancements in query processing, data management, and networked systems. Award Highlights: Recipient of ACM and IEEE Fellowships, National Chair Professorship (lifetime honor), Teco Award, Pan Wen Yuan Distinguished Research Award, and IBM's Outstanding Innovation Award. His contributions span research, teaching, and technology commercialization. Leadership & Service: Former Dean of NTU's College of Electrical Engineering and Computer Science, CEO of Taiwan's Networked Communication Program, and Editor-in-Chief of the International Journal of Electrical Engineering. He has chaired international conferences and served on editorial boards of journals like IEEE TKDE and VLDB. Labs & Teams: Leads the Network Database Laboratory and collaborates on national initiatives in information and communication technologies. His research groups focus on data science, distributed systems, and social network analysis.

Rashmi Vinayakمشاهده پروفایل

دانشیار

• Computer Systems
• Networked Systems
• Information Theory
• +۵ مورد دیگر

Rashmi Vinayak is an Associate Professor in the Computer Science Department at Carnegie Mellon University, with a courtesy appointment in the Electrical and Computer Engineering Department. She is a member of both the Systems group and Theory group at CMU and leads TheSys research group. She is also affiliated with the Parallel Data Lab (PDL). Her educational background includes a Ph.D. from UC Berkeley in 2016, followed by postdoctoral studies at the same institution. Rashmi's research spans the intersection of computer/networked systems and information/coding theory. Her current focus is on robustness and resource efficiency in data systems across storage, communication, and computation. Key thrusts include storage systems, caching systems, and systems for machine learning. Her work on SIEVE, a cache eviction algorithm, has been widely adopted by industry including VMware, Google, Redpanda, and numerous open source libraries. Her recent publications demonstrate a strong trend toward practical systems research with theoretical foundations, particularly in caching algorithms, storage systems, and machine learning infrastructure. Many of her papers have received best paper awards and industry adoption. Notable awards include: Sloan Research Fellowship (2023) IEEE Information Theory Society Goldsmith Lecturer (2023) NSF CAREER Award (2020) Multiple USENIX NSDI Community (Best Paper) Awards VMware Systems Research Award (2021) Facebook and Google Research Awards Rashmi has supervised numerous PhD, Master's, and undergraduate students, many of whom have gone on to prestigious positions at Harvard, Google, Meta, and other leading institutions. Her research has been generously funded by NSF, Sloan Foundation, Open Compute Project, Google, Facebook/Meta, VMware, and Amazon Web Services. She actively collaborates with industry partners including Google, Microsoft, NetApp, Facebook, Cisco, Intel and Cloudera. She leads TheSys research group which focuses on designing next-generation data systems that are robust, efficient, and performant. The group takes a multi-disciplinary approach spanning computer systems, information theory, and machine learning.

Deian Stefanمشاهده پروفایل

دانشیار

• security
• programming languages
• systems
• +۷ مورد دیگر

Deian Stefan is an Associate Professor at the University of California San Diego (UCSD) in the Department of Computer Science and Engineering . His research spans security , programming languages , and systems , with a focus on building principled and practical secure systems. He has served as a co-founder and Chief Scientist at Intrinsic (acquired by VMWare) and contributed to standards bodies like the W3C WebAppSec and Node.js Security Working Groups . His research interests include: Secure Systems : Web frameworks, browser designs, sandboxing, runtime systems Language-Based Security : Constant-time programming, memory safety, information flow control Verification : Security verification, static/symbolic analysis tools WebAssembly and JavaScript JITs security Deian Stefan has received multiple scientific awards , including several Distinguished Paper Awards at venues like POPL, ICFP, and USENIX Security, as well as the IEEE Cybersecurity Award for Practice (2022) and CSAW 2020 First Place for Applied Research. He has taught courses on Computer Security (CSE 127, CSE 227) and advanced topics in Building Secure Systems (CSE 291) using Rust, WebAssembly, and blockchain security. His work has been supported by collaborations with industry and academia, including projects like RLBox and COWL .

• Wireless Communications
• Coding Theory
• Quantum Information Processing
• +۵ مورد دیگر

Olav Tirkkonen serves as a Full Professor in the Department of Communications and Networking at Aalto University, Finland, a position he has held since August 2006. He leads the Communication Theory research group, driving innovation in wireless communication systems. His academic journey includes a distinguished career spanning industry and academia, with significant contributions to 3G, 4G, and 5G technologies. His educational qualifications are: Doctor of Science (Ph.D.) in Theoretical Physics, Helsinki University of Technology, 1994 Master of Science (M.Sc.) in Theoretical Physics, Helsinki University of Technology, 1990 Professor Tirkkonen's research interests are centered on wireless communications, with a focus on physical layer processing, coding theory, and quantum information processing. His group explores advanced topics including 5G and beyond wireless networks (spectrum management, large-scale MIMO, ultra-reliable low-latency communication), network-level interference coordination, collaborative caching, machine learning applications for wireless channel geography, coding on manifolds, and quantum communication systems. This research bridges fundamental theory with practical implementation in next-generation wireless networks. Analysis of his recent publications (2024-2025) indicates a predominant focus on machine learning techniques for wireless channel modeling (channel charting), pilot allocation in MIMO systems, and quantum error correction. His work is instrumental in addressing key challenges in 5G/6G networks, particularly in scenarios demanding ultra-reliability, low latency, and efficient resource utilization. His scientific contributions include: Co-inventor of approximately 80 families of patents and patent applications Co-author of the book "Multiantenna transceiver techniques for 3G and beyond" Throughout his career, Professor Tirkkonen has mentored numerous graduate students and secured substantial research funding from various sources. His industry experience at Nokia Research Center (1999-2010) and visiting position at Cornell University (2016-2017) have enriched his research perspective and fostered strong industry-academia collaborations. The Communication Theory group, under his leadership, maintains active collaborations with leading institutions and companies worldwide, positioning Aalto University at the forefront of wireless communications research.

Peter Druschelمشاهده پروفایل

استاد

• Distributed Systems
• Operating Systems
• Network Security
• +۳ مورد دیگر

Peter Druschel is a Professor and founding Director of the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany. He holds adjunct professorships at Saarland University and the University of Maryland. His research focuses on distributed systems, operating systems, and privacy-preserving technologies. He earned his Ph.D. from the University of Arizona in 1994 and has held roles at Rice University since 1994, including Professor of Computer Science (2002–2005). Education: Ph.D. in Computer Science, University of Arizona (1994) Research Interests: Distributed systems, operating systems, network security, accountable computing, and privacy technologies. Current projects include privacy compliance in data systems (Thoth), secure communication (EbN), and privacy-aware image capture (I-Pic). Awards: SIGOPS Mark Weiser Award (2008) NSF CAREER Award (1995) Member of Academia Europaea and German Academy of Sciences Leopoldina Grants & Leadership: Leads the ERC Synergy Project imPACT, chairs the Max Planck Society’s Chemistry, Physics, and Technology Section, and collaborates with institutions like Cornell and Google. Advises on policy issues related to technology and privacy. Labs/Teams: Distributed Systems Group at MPI-SWS, collaborations with Microsoft Research and MIT. Current team includes students and postdocs working on privacy, security, and distributed systems.

Michael J. Freedmanمشاهده پروفایل

استاد

• Distributed Systems
• Networking
• Security
• +۴ مورد دیگر

Michael J. Freedman is the Robert E. Kahn Professor of Computer Science at Princeton University and co-founder/CTO of Timescale. He received his Ph.D. from NYU’s Courant Institute and degrees from MIT. Current roles: Professor, Co-founder & CTO Affiliations: Princeton University, SNS Group, CITP Associate Education: Ph.D. (NYU), S.B./M.Eng. (MIT) His research spans distributed systems, networking, and security, with innovations like CoralCDN, DONAR, and Ethane. His work impacts decentralized content delivery, software-defined networking, and privacy-enhancing technologies. His recent publications address scalable fusion algorithms, GPU acceleration for data systems, and distributed GPU resource management. These works intersect with cloud infrastructure, network optimization, and security. Scientific honors include: Presidential Early Career Award for Scientists and Engineers (PECASE) Sloan Fellowship NSF CAREER Award Office of Naval Research Young Investigator Award Test of Time Award (Theory of Crypto Conference) ACM SIGOPS Mark Weiser Award He advises graduate students like Sam Ginzburg and Ashwini Raina, who joined Meta AI and Timescale post-PhD. His projects have secured substantial grants, including $110M Series C funding for Timescale. Key labs/teams: Princeton SNS Group Co-founder, Timescale (enterprise data platform) Co-founder, iobeam (IoT analytics, acquired by Timescale) Collaboration with FCC on Consumer Broadband Test Contributions to OpenFlow/SDN standardization

• Cloud Computing
• Operating Systems
• File/Storage Systems
• +۳ مورد دیگر

Haryadi S. Gunawi is a Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). His work focuses on improving the dependability of storage and cloud computing systems, with a particular emphasis on addressing performance stability, reliability, and scalability challenges in modern computing environments. Dr. Gunawi received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. Following his doctoral studies, he was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012 before joining the University of Chicago faculty. His research focuses on three main areas: (1) performance stability, where he builds storage and distributed systems robust to latency tails and "limping" hardware; (2) reliability and scalability, where he addresses concurrency and scalability bugs in cloud-scale distributed systems; and (3) the intersection of machine learning and systems, exploring how machine learning techniques can solve operating and storage system problems. His work often combines theoretical insights with practical system implementations that address real-world challenges in cloud and storage infrastructure. Dr. Gunawi's publication record shows a consistent focus on storage and cloud system reliability, with recent work increasingly incorporating machine learning techniques to address traditional systems challenges. His research spans the full stack from hardware interfaces to distributed system design, with a strong emphasis on practical solutions that can be deployed in production environments. His work often involves close collaboration with industry partners to ensure real-world relevance and impact. Dr. Gunawi has received numerous prestigious awards including the NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, multiple NetApp Faculty Fellowships, and an Honorable Mention for the 2009 ACM Doctoral Dissertation Award. He has also received the Provost's Global Faculty Award and Facebook Faculty Research Award, highlighting the broad recognition of his contributions to the field. As an advisor, Dr. Gunawi has mentored several PhD students including Ruidan Li, Ray Andrew, Rani Ayu Putri, and William Nixon. His research has been supported by major grants from NSF, Google, Facebook, and NetApp, enabling his team to pursue ambitious research projects at the intersection of systems, storage, and machine learning. Dr. Gunawi leads the UCARE research group at UChicago, which focuses on improving the dependability of storage and cloud-scale distributed systems. He is also involved with the Chameleon cloud research infrastructure project and the broader Systems Group at UChicago, contributing to a vibrant research community focused on systems, programming languages, and software engineering.

Yujie Tangمشاهده پروفایل

استادیار

• Internet of Vehicles
• Resource Management in Heterogeneous Networks
• Machine Learning
• +۷ مورد دیگر

Dr. Yujie Tang is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Canada, where she has been serving since September 2022. Prior to this, she was an Assistant Professor at Algoma University (2019–2022) and a Post-Doctoral Fellow at the University of Waterloo (2017–2019). Her academic journey includes a PhD from the University of Waterloo and earlier degrees from Harbin Institute of Technology and Lanzhou Jiaotong University. PhD – University of Waterloo (2017) M.E. – Harbin Institute of Technology, Shenzhen, China B.E. – Lanzhou Jiaotong University, Lanzhou, China Her research focuses on intelligent networking and computing technologies for future IoT and 5G/6G systems. Key areas include Internet of Vehicles (IoV), AI-empowered edge computing, resource management in heterogeneous networks, software-defined networking, and UAV-assisted communications. She employs machine learning and optimization techniques to design energy-efficient and high-performance network protocols. The most recent publications reflect a strong trend in applying AI and machine learning to solve complex problems in vehicular networks, edge caching, and spectrum management. Her work spans top-tier IEEE journals such as IEEE Transactions on Vehicular Technology , IEEE Internet of Things Journal , and IEEE JSAC , with a clear emphasis on real-world deployable solutions for next-generation wireless systems. Faculty Research Startup Fund, Dalhousie University, 2022 NSERC Discovery Grant, 2021–2026 Algoma University Research Fund, 2021 Faculty Research Startup Fund, Algoma University, 2019 Best Speaker Award, University of Waterloo, 2017 Faculty of Engineering Award (4 times), University of Waterloo, 2013–2017 University of Waterloo Graduate Scholarship, 2013–2015 International Doctoral Student Award, 2012–2016 Graduate Research Studentship (twice), 2011–2012 Provost Doctoral Entrance Award for Women, 2011 Dr. Tang actively supervises graduate and undergraduate students and has secured competitive research grants, including the NSERC Discovery Grant. She serves on the technical program committees of major IEEE conferences such as INFOCOM, GLOBECOM, and ICC, and regularly reviews for top journals like IEEE JSAC , IEEE TWC , and IEEE TVT . She currently leads a research group focusing on B5G/6G networks, IoV, and edge computing, and she is actively recruiting new students and visiting scholars. Her research group operates within the Faculty of Computer Science at Dalhousie University, where she leads projects in intelligent resource management, AI-driven networking, and integration of space-air-ground networks. She is a member of IEEE, IEEE Communications Society, and IEEE Vehicular Technology Society.

Subir Biswasمشاهده پروفایل

استاد

• Wireless Data Networking
• Low-Power Network Protocols
• Vehicular Networks
• +۶ مورد دیگر

Subir Biswas is a Professor in the Department of Electrical and Computer Engineering (ECE) at Michigan State University (MSU), part of the College of Engineering. His research focuses on wireless networking, low-power protocols, vehicular networks, and health monitoring systems, conducted through the NeEWS laboratory. He holds a Ph.D. from the University of Cambridge and degrees from Jadavpur University. Education: Ph.D., Computer Engineering, University of Cambridge (1994) M.S., Electrical Engineering, Jadavpur University (1989) B.S., Electrical Engineering, Jadavpur University (1987) Professional Experience: Professor, MSU ECE (2011–present) Associate Professor, MSU ECE (2003–2010) Research interests include traffic analysis firewalls, pricing calculus for social wireless networks, low-energy green networking, and ultrasound-based sensor systems . He leads the NeEWS lab, exploring embedded systems and network middleware. His work spans IoT, cybersecurity, and biomedical applications like wearable sensors for autism monitoring and hydration tracking. Publications emphasize innovative protocols (e.g., pulse position coding) and machine learning for network optimization. Collaborations include NSF-funded projects and industry partnerships (e.g., Tellium Optical Systems).

• Cybersecurity
• Privacy-Preserving Technologies
• Internet of Things (IoT)
• +۴ مورد دیگر

Dr. Kanchana Thilakarathna is a Senior Lecturer in Distributed Computing at the University of Sydney's School of Computer Science, and a member of the Centre for Distributed and High Performance Computing. They hold a PhD from the University of New South Wales (UNSW) and a B.Sc. Eng (Hons) from the University of Moratuwa, Sri Lanka. Prior to academia, they worked as a Research Scientist at CSIRO/Data61 and had industry experience as a Mobile Radio Network Engineer. Research Interests : Dr. Thilakarathna focuses on cybersecurity, privacy in mobile and IoT systems, mixed reality privacy, and distributed computing platforms. Their work emphasizes user-centric solutions like the Yalut social media app, which enables decentralized data sharing. Key themes include privacy-preserving techniques, edge computing, and secure federated learning frameworks. Recent Work : Recent articles (2023–2025) explore machine unlearning for large language models, federated learning security, and IoT network slicing using P4 programmability. Their work on synthetic video traffic generation (VideoTrain++) and drone detection (DronePrint) demonstrates cross-disciplinary innovation. Awards : Malcolm Chaikin Prize (2015), Meta Research Awards (2020/2022), and Heidelberg Laureate Fellowship (2019). Grants : ARC Research Hub for Future Digital Manufacturing (2024), NSW Defence Innovation Network Projects (2024/2021), and Facebook Research Awards (2022/2020). Students : Advising 4 current PhD students on topics like wireless trust establishment and machine unlearning. Labs/Teams : Part of the Centre for Distributed and High Performance Computing and Sydney Nano Institute.

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.

Oliver Hohlfeldمشاهده پروفایل

استاد

• Network Security
• Network Measurements
• Internet Architecture
• +۹ مورد دیگر

Oliver Hohlfeld is a Professor at the University of Kassel, where he leads the Distributed Systems group. He previously held academic positions at Brandenburg University of Technology and RWTH Aachen University, and was a visiting scholar at the University of Wisconsin–Madison. His research focuses on network security, Internet measurements, and Quality of Experience (QoE). He completed his Ph.D. in Computer Science at TU Berlin under Anja Feldmann and holds a B.Sc. and M.Sc. from Darmstadt University of Technology. He also worked at Fraunhofer IGD on telemedical network architectures. His research adopts a data-driven approach, combining large-scale Internet measurements, user studies, and machine learning to understand and improve Internet performance and security. Key areas include DDoS detection, QUIC, HTTP/2, TLS deployment, and BGP analysis. He investigates how protocols like QUIC and HTTP/2 impact user experience and network efficiency, often through empirical studies of real-world deployments. His recent publications highlight a strong trend in network security and protocol analysis, with significant work on DDoS attacks, traffic ingress detection, and web consolidation. He also explores social media dynamics and censorship circumvention through user reviews. 2022 IETF/IRTF Applied Networking Research Prize Best of CCR (2021) for TLS 1.3 deployment study ACM Senior Member (2020) IEEE QoMEX 2019 Best Reviewer Award ACM IMC Community Contribution Award (2018) He has advised numerous Master’s and Bachelor’s students, primarily at RWTH Aachen, and has been a principal investigator in major projects such as AIDOS (AI-based DDoS mitigation), DFG SFB MAKI, COMTEX, and the EU-funded SSICLOPS. He actively serves on the technical program committees of top-tier conferences including SIGCOMM, NSDI, IMC, and CoNEXT, and is a frequent reviewer for leading journals in networking and systems. He leads the Distributed Systems group at the University of Kassel, which conducts research on Internet observability, secure infrastructures, and scalable networking solutions.

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

Shirin Saeedi Bidokhtiمشاهده پروفایل

استادیار

• Information Theory
• Networking
• Data Compression
• +۲ مورد دیگر

Shirin Saeedi Bidokhti is an Assistant Professor at the University of Pennsylvania's School of Engineering and Applied Science with primary appointment in Electrical and Systems Engineering and secondary appointment in Computer and Information Science. She is affiliated with the Warren Center for Network and Data Sciences. She holds M.Sc. and Ph.D. degrees from EPFL and completed postdoctoral work at Stanford and Technical University of Munich. Her research focuses on information theory, networking, data compression, and machine learning. Her recent publications demonstrate strong emphasis on neural compression algorithms, network optimization during the COVID-19 pandemic, and age-of-information theory. Awards include: 2023 IEEE Communications Society & Information Theory Society Joint Paper Award 2021 NSF CAREER Award 2019 NSF-CRII Award Swiss National Science Foundation Fellowships She advises PhD students including Xingran Chen. Current research involves developing data compression algorithms for IoT applications and network strategies for pandemic response.