I am an Assistant Professor in the Computer Science Department at Carnegie Mellon University. My research lies in the broad area of applied probability and stochastic systems, with applications in resource orchestration in large computing systems, data centers, and privacy-preserving data analytics. Such applications are the backbone of many ever-developing technologies, especially the emerging big-data technology. Enormous challenges are presented by these new technologies, including scalability to large sizes, coordination of data and computation, ultra-low latency, economic efficiency, etc. The goal of my research is to address these challenges, provide a clear understanding of fundamental limits of systems, and build theoretical foundations for designing new architectures and algorithms.
I joined the Computer Science Department at Carnegie Mellon University in Fall 2018 as an Assistant Professor. Previously, I was a postdoc at the University of Illinois at Urbana-Champaign and Arizona State University, working with Prof. R. Srikant and Prof. Lei Ying. I received my Ph.D. degree in electrical engineering from Arizona State University in 2016, advised by Prof. Lei Ying and Prof. Junshan Zhang. I received my Bachelor’s degree from the Department of Electronic Engineering at Tsinghua University in 2009. My dissertation received the Dean’s Dissertation Award in the Ira A. Fulton Schools of Engineering at Arizona State University in 2016 (news article). I received the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS 2016 and the Best Paper Award at ACM MobiHoc 2022. I received an NSF CAREER Award in 2022.
I co-organized the SNAPP (Stochastic Networks, Applied Probability, and Performance) seminar series. Check it out!
I am interested in characterizing fundamental limits of large-scale computing systems that address emerging demands from big-data analytics, and designing algorithms with optimality on throughput and latency performance. My research in this area focuses on the following aspects.
Latency characterization. Latency is a crucial performance metric in technologies nowadays, especially in interactive applications such as real-time machine learning, online transactions, and video streaming. Amazon has calculated that a page load slowdown of just one second can cost $1.6 billion in sales each year. My research in this area provides tight characterizations of latency in large-scale computing systems, enabling identification of performance bottlenecks and informing optimal designs of scheduling and resource allocation algorithms.
Coordination of data and computation. Big-data analytics has led to a paradigm shift from computation-centric computing systems to systems where data plays an equally, if not more, important role. Data and computation are closely correlated in present computing systems. For example, data-parallel frameworks such as MapReduce/Hadoop and Spark associate a computing task with a chunk of data, where the computation can be executed only when both the associated data and a computation slot on servers are available. My research takes the lead in addressing the coordination of data and computation, and designs scheduling algorithms with rigorous theoretical guarantees on performance optimality.
Large-scale regimes. To accommodate the growing computational demand, modern data centers are scaled up in size, consisting of tens of thousands of servers. Such large-scale systems have attracted great attention both from practice and from theoretical research. My research investigates the scaling behavior of performance with respect to the size of system infrastructures, including the number of servers, the number of communication links, etc. My research further explores new large-scale regimes to capture the large volume of data. In particular, I study jobs whose sizes expand as the system scales, which models the trend that computational jobs are processing larger and larger volumes of data.
I work on data privacy and its intersection with other areas including game theory, information theory and statistics. Our recent focus is a new, market model that we envisage for collecting private data where data subjects (individuals) retain full control of their privacy. I aim to understand the economic fundamentals of collecting private data and design optimal incentive mechanisms.
[Stochastic systems] [Data privacy] [Graph statistics] [Systems]
The M/M/k with deterministic setup times
Jalani K. Williams, Mor Harchol-Balter, Weina Wang
ACM SIGMETRICS, 2023.
[pdf]
Maximizing utilization under time-varying resource requirements
Yige Hong, Qiaomin Xie, Weina Wang
Preprint, 2022.
[arXiv]
Sharp waiting-time bounds for multiserver jobs
Yige Hong, Weina Wang
ACM MobiHoc, 2022.
[arXiv]
Tackling heterogeneous traffic in multi-access systems via erasure coded servers
Tuhinangshu Choudhury, Weina Wang, Gauri Joshi
ACM MobiHoc, 2022.
[arXiv]
On low-complexity quickest intervention of mutated diffusion processes through local approximation
Qining Zhang, Honghao Wei, Weina Wang, Lei Ying
ACM MobiHoc, 2022.
[arXiv]
Beyond response time: scheduling to speed up convergence in machine learning
Weina Wang
‘100 views on queues’, Volume 100 of Queueing Systems, 2022.
[DOI]
Probing to minimize
Weina Wang, Anupam Gupta, Jalani K. Williams (random author order)
ITCS, 2022. [DOI]
[pdf]
The case for phase-aware scheduling of parallelizable jobs
Benjamin Berg, Justin
Whitehouse, Benjamin Moseley, Weina Wang, Mor Harchol-Balter
IFIP Performance, 2021.
[pdf]
Job dispatching policies for queueing systems with unknown service rates
Tuhinangshu Choudhury, Gauri Joshi, Weina Wang, Sanjay Shakkottai
ACM MobiHoc, Jul. 2021. [DOI]
[arXiv]
Zero queueing for multi-server jobs
Weina Wang, Qiaomin Xie, Mor Harchol-Balter
ACM SIGMETRICS, Jun. 2021. [DOI]
[arXiv]
Achieving zero asymptotic queueing delay for parallel jobs
Wentao Weng, Weina Wang
ACM SIGMETRICS, Jun. 2021. [DOI]
[arXiv]
Optimal resource allocation for elastic and inelastic jobs
Benjamin Berg, Mor Harchol-Balter, Benjamin Moseley, Weina Wang, Justin
Whitehouse
ACM SPAA, Jul. 2020. [DOI]
[pdf]
QuickStop: A Markov optimal stopping approach for quickest misinformation detection
Honghao Wei, Xiaohan Kang, Weina Wang, Lei Ying
ACM SIGMETRICS, Phoenix, AZ, Jun. 2019. [DOI]
[pdf]
Delay asymptotics and bounds for multi-task parallel jobs
Weina Wang, Mor Harchol-Balter, Haotian Jiang, Alan Scheller-Wolf, R. Srikant
Queueing Systems Jan. 2019. [DOI] [QUESTA pdf]
International Symposium on Computer Performance, Modeling, Measurements and Evaluation (IFIP Performance), Toulouse, France, Dec. 2018. [DOI]
Heavy-traffic insensitive bounds for weighted proportionally fair bandwidth sharing policies
Weina Wang, Siva Theja Maguluri, R. Srikant, Lei Ying
Mathematics of Operations Research, 2022. [DOI][arXiv]
Heavy-traffic delay insensitivity in connection-level models of data transfer with proportionally fair bandwidth sharing
Weina Wang, Siva Theja Maguluri, R. Srikant, Lei Ying
International Symposium on Computer Performance, Modeling, Measurements and Evaluation (IFIP Performance), New York City, NY, Dec. 2017. [DOI]
Resource allocation for data-parallel computing in networks with data locality
Weina Wang, Lei Ying
Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2016. [DOI] [Allerton pdf]
Decentralized scheduling with data locality for data-parallel computation on Peer-to-Peer networks
Weina Wang, Matthew Barnard, Lei Ying
Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2015. [DOI] [Allerton pdf]
Data locality in MapReduce: A network perspective
Weina Wang, Lei Ying
Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2014. [DOI]
Performance Evaluation, Feb. 2016. [DOI] [PEVA pdf]
[technical report]
MapTask scheduling in MapReduce with data locality: throughput and heavy-traffic optimality
Weina Wang, Kai Zhu, Lei Ying, Jian Tan, Li Zhang
IEEE/ACM Transactions on Networking, Feb. 2016. [DOI] [ToN pdf]
Map task scheduling in MapReduce with data locality: throughput and heavy-traffic optimality
Weina Wang, Kai Zhu, Lei Ying, Jian Tan, Li Zhang
IEEE International Conference on Computer Communications (INFOCOM), Turin, Italy, Apr. 2013. [DOI] [INFOCOM pdf]
A throughput optimal algorithm for map task scheduling in mapreduce with data locality
Weina Wang, Kai Zhu, Lei Ying, Jian Tan, Li Zhang
ACM SIGMETRICS Performance Evaluation Review, Mar. 2013. [DOI]
[poster]
On the performance of largest-deficit-first for scheduling real-time traffic in wireless networks
Xiaohan Kang, Weina Wang, Juan José Jaramillo, Lei Ying
IEEE/ACM Transactions on Networking, Feb. 2016. [DOI] [ToN pdf] [supplementary]
ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), Bangalore, India, Jul. 2013. [DOI] [MobiHoc pdf]
Privacy-utility tradeoffs in routing cryptocurrency over payment channel networks
Weizhao Tang, Weina Wang, Giulia Fanti, Sewoong Oh
ACM SIGMETRICS, Jun. 2020. [DOI]
[pdf]
Data collection from privacy-aware users in the presence of social learning
Abdullah Basar Akbay, Weina Wang, Junshan Zhang
Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2019. [DOI]
A winners-take-all incentive mechanism for crowd-powered systems
Pengfei Jiang, Weina Wang, Yao Zhu, Jingrui He, Lei Ying
Workshop on Economics of Networks, Systems and Computation (NetEcon), Jun. 2018. [DOI]
Buying data from privacy-aware individuals: The effect of negative payments
Weina Wang, Lei Ying, Junshan Zhang
Conference on Web and Internet Economics (WINE), Montreal, Canada, Dec. 2016. [DOI] [WINE pdf]
[technical report]
The value of privacy: Strategic data subjects, incentive mechanisms and fundamental limits
Weina Wang, Lei Ying, Junshan Zhang
ACM Transactions on Economics and Computation, Aug. 2018. [DOI] [TEAC pdf]
ACM SIGMETRICS, Antibes Juan-les-Pins, France, Jun. 2016. [DOI] [SIGMETRICS pdf]
Kenneth C. Sevcik Outstanding Student Paper Award
A game-theoretic approach to quality control for collecting privacy-preserving data
Weina Wang, Lei Ying, Junshan Zhang
Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2015. [DOI] [Allerton pdf]
[technical report]
A minimax distortion view of differentially private query release
Weina Wang, Lei Ying, Junshan Zhang
Asilomar Conference on Signals, Systems, and Computers (Asilomar), Pacific Grove, CA, Nov. 2015. [DOI] [Asilomar pdf] [technical report]
On the relation between identifiability, differential privacy, and mutual-information privacy
Weina Wang, Lei Ying, Junshan Zhang
IEEE Transactions on Information Theory, Sep. 2016. [DOI] [TIT pdf]
Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Sep. 2014. [DOI]
Poster presented at IEEE North American School of Information Theory, Toronto, Canada, Jun. 2014. [poster]
On the Feasible Region of Efficient Algorithms for Attributed Graph Alignment
Ziao Wang, Ning Zhang, Weina Wang, Lele Wang
preprint, 2022.
[arXiv]
Attributed graph alignment
Ning Zhang, Weina Wang, Lele Wang
IEEE ISIT, Jul. 2021.
[arXiv]
SurgeProtector: Mitigating temporal algorithmic complexity attacks using adversarial scheduling
Nirav Atre, Hugo Sadok, Erica Chiang, Weina Wang, Justine Sherry
*ACM SIGCOMM, Aug. 2022.
[DOI]
[pdf]
Caching with delayed hits
Nirav Atre, Justine Sherry, Weina Wang, Daniel Berger
ACM SIGCOMM, Aug. 2020. [DOI]
[pdf]