Hi! I'm Jiasi Chen, an assistant professor in the Department of Computer Science and Engineering at the University of California, Riverside. I received my Ph.D. from Princeton University (advisor Mung Chiang) and my B.S. from Columbia University. My hometown is the lovely city of Halifax, Canada.

My research area is computer networks, wireless and mobile networks, and Internet video streaming. I'm also interested in network economics, sensor networks, and the newly emerging field of the Internet-of-things. My work involves optimization, algorithm design, and real implementation. See my CV for more details.

Office: Winston Chung Hall 308
Office hours: Thursday 1-3pm, or by appointment (Winter 2018)
Email: jiasi [at] cs [dot] ucr [dot] edu


Mobile Deep Learning: Deep learning shows great promise in providing more intelligence to augmented reality (AR) devices, but few AR apps use deep learning due to lack of infrastructure support. Deep learning algorithms are computationally intensive, and front-end devices such smartphones cannot deliver sufficient compute power for real-time processing. With Zhenming Liu (College of William & Mary), we propose a framework that ties together front-end devices with more powerful backend “helpers” (e.g., edge servers) that allow deep learning to be distributed across edge devices. We empirically measure the complex interactions between model compression, video quality, battery constraints, network data usage, and network conditions, and use this data to drive an optimization framework that maximizes user quality-of-experience. Paper to appear in Infocom 2018.

LTE multicast: With ever-increasing data traffic from mobile devices, but limited wireless resources, cellular service providers seek new ways to efficiently deliver content to users. In cases where there is spatial and temporal locality of content requests, multicasting the content using LTE's evolved multimedia broadcast multicast service (eMBMS) is a promising approach. Cellular network operators seek to understand how best to allocate scarce wireless resources, especially when there are users with heterogeneous channel conditions, and in the presence of a number of users consuming unicast traffic. We develop a resource allocation framework to efficiently and optimally partition multicast users into groups so that users with good signal strength do not suffer by being grouped together with users of poor signal strength. We analyze the interaction between the proposed globally fair solution and individual user's desire to maximize her rate, and show that the number of selfish users is bounded. [paper] [slides] [video]

AVIS: Scheduling for adaptive videos over cellular networks: As the growth of mobile video traffic outpaces that of cellular network speed, industry is adopting HTTP-based adaptive video streaming technology which enables dynamic adaptation of video bit-rates to match changing network conditions. However, recent measurement studies have observed problems in fairness, stability, and efficiency of resource utilization when multiple adaptive video flows compete for bandwidth on a shared wired link. Through experiments and simulations, we confirm that such undesirable behavior manifests itself in cellular networks as well. To overcome these problems, we design an in-network resource management framework, AVIS, that schedules HTTP-based adaptive video flows on cellular networks. AVIS effectively manages the resources of a cellular base station across adaptive video flows. We implement a prototype system of AVIS and evaluate it on both a WiMAX network testbed and a LTE system simulator to show its efficacy and scalability. [paper][slides]

QAVA: Quota-aware video adaptation: Two emerging trends of Internet applications, video traffic becoming dominant and usage-based pricing plans becoming prevalent, are at odds with each other. On one hand, videos, especially on high-resolution devices (e.g., iPhone 5, iPad, Android tablets), consume much more data than other types of traffic; for instance, 15 min of low bitrate YouTube videos per day uses 1 GB a month. On the other hand, gone are the days of unlimited data plans; instead, wireless ISPs such as AT&T and Verizon are imposing data caps on consumers. Given this conflict, a natural question to ask is: Can the consumer stay within her monthly data quota without suffering a noticeable drop in video quality? My research in this area focuses on designing algorithms to maximize the user's quality of experience and stay under the data quota, by leveraging the user's past data consumption profile and video preferences.[paper][slides][video]



Samet Oymak, Mehrdad Madavi, Jiasi Chen, "Learning Feature Nonlinearities with Non-Convex Regularized Binned Regression", under submission. [link]

Shahryar Afzal, Jiasi Chen, K.K. Ramakrishnan, "Characterization of 360-degree videos", ACM SIGCOMM Workshop on Virtual and Augmented Reality Network, 2017. [pdf] [slides]

Xukan Ran, Haoliang Chen, Zhenming Liu, Jiasi Chen, "Delivering deep learning to mobile devices via offloading", ACM SIGCOMM Workshop on Virtual and Augmented Reality Network, 2017. [pdf][slides]

Suzan Bayhan, Liang Zheng, Jiasi Chen, Mario Di Francesco, Jussi Kangasharju, Mung Chiang, "Improving Cellular Capacity with White Space Offloading", WiOpt, 2017. [pdf]

Liang Zheng, Jiasi Chen, Carlee Joe-Wong, Chee Wei Tan, Mung Chiang, "An Economic Analysis of Wireless Network Infrastructure Sharing", WiOpt, 2017. [pdf]

Kittipat Apicharttrisorn, Ahmed Osama Fathy Atya, Jiasi Chen, Karthikeyan Sundaresan, and Srikanth V. Krishnamurthy, "Enhancing WiFi Throughput With PLC Extenders: A Measurement Study", Passive and Active Measurement Conference (PAM), 2017. [pdf]

Liang Zheng, Carlee Joe-Wong, Jiasi Chen, Christopher G. Brinton, Chee Wei Tan, Mung Chiang, "Economic Viability of a Virtual ISP", IEEE INFOCOM, 2017. (21% acceptance rate) [pdf]

Michael Wang, Jiasi Chen, Ehsan Aryafar, and Mung Chiang, "A Survey of Client-Controlled HetNets for 5G" (invited), IEEE Access, 2017. [pdf]


Tao Lin, Hongjia Li, Haiyong Xie, Jiasi Chen, Huajun Cui, Guoqiang Zhang, Wei An, Yang Li, "Performance and Implications of RAN Caching in LTE Mobile Networks: a Real Traffic Analysis", IEEE SECON, 2016. [pdf]

2015 and earlier

Jiasi Chen*, Mung Chiang, Jeffrey Erman, Guangzhi Li, KK Ramakrishnan, Rakesh Sinha, "Fair and Optimal Resource Allocation for LTE Multicast (eMBMS): Group Partitioning and Dynamics," IEEE INFOCOM, 2015. (19% acceptance rate) *The authors are in alphabetical order except for the 1st author. [pdf]

Xiaoli Wang, Jiasi Chen, Aveek Dutta, Mung Chiang, "Adaptive Video Streaming over Whitespace: SVC for 3-Tiered Spectrum Sharing," IEEE INFOCOM, 2015. (19% acceptance rate) [pdf]

Jiasi Chen, Amitabh Ghosh, Mung Chiang, "Mechanisms for Quota-Aware Video Adaptation," book chapter: Smart Data Pricing, ed. Soumya Sen, Carlee Joe-Wong, Sangtae Ha, Mung Chiang, John Wiley, 2014. [Amazon]

Jiasi Chen, Rajesh Mahindra, M. Amir Khojastepour, Sampath Rangarajan, Mung Chiang, "Scheduling Framework for Adaptive Video Delivery over Cellular Networks," ACM MobiCom, 2013. (14% acceptance rate) [pdf]

Jiasi Chen, Soumya Sen, David Dorsey, Mung Chiang, "A Framework for Energy-efficient Adaptive Jamming of Adversarial Communications," CISS, 2013. [pdf]

Jiasi Chen, Amitabh Ghosh, Josphat Magutt, Mung Chiang, "QAVA: Quota-Aware Video Adaptation," ACM CoNEXT, 2012. (18% acceptance rate) [pdf]

Maria Gorlatova, Zainab Noorbhaiwala, Abraham Skolnik, John Sarik, Michael Zapas, Martin Szczodrak, Jiasi Chen, Luca Carloni, Peter Kinget, Ioannis Kymissis, Dan Rubenstein, Gil Zussman, "Prototyping Energy Harvesting Active Networked Tags: Phase II MICA Mote-based Devices (demo)," ACM MobiCom, 2010. [pdf]

Maria Gorlatova, Tarun Sharma, Deep Shrestha, Enlin Xu, Jiasi Chen, Abraham Skolnik, Dongzhen Piao, Peter Kinget, Ioannis Kymissis, Dan Rubenstein, Gil Zussman, "Prototyping Energy Harvesting Active Networked Tags (EnHANTs) with MICA2 Motes (demo)," IEEE SECON, 2010 June. [pdf]

M. Ete Chan, Jiasi Chen, Victor Chiang, X. Sherry Liu, Andrew Baik, X. Lucas Lu, Bo Huo, X. Edward Guo, "A Novel 3D Coculture Trabecular Bone Explant Model for the Study of Bone Adaptation and Mechanotransduction," World Congress on Bioengineering, July.

M. Ete Chan, Jiasi Chen, Victor Chiang, X. Sherry Liu, Andrew Baik, X. Lucas Lu, Bo Huo, X. Edward Guo, "Roles of Mechanical Stimuli and Gap Junctional Communication in Long-Term Coculture of 3D Trabecular Bone Explants," Transactions of the Orthopaedic Research Society, vol. 34, paper #53, 2009.



  • Shahryar Afzal (co-advised with K.K. Ramakrishnan)
  • Kittipat (Patrick) Apicharttrisorn (co-advised with Srikanth Krishnamurthy)
  • Xukan Ran


A few of my other interests (outside of research!) from both past and present:

Last updated Dec. 7, 2017