Di Niu @ University of Alberta

Last updated: July 1, 2018


Short Bio

I’m an Associate Professor in the Department of Electrical and Computer Engineering at University of Alberta.  I did my PhD work in iQua Research Group, the Department of Electrical and Computer Engineering at University of Toronto, from 2008 to 2012, and was a M.A.Sc. student in the same department from 2006 to 2008. I received the B.Eng. degree in 2005 from the Department of Electronics and Communication Engineering, Sun Yat-sen (Zhongshan) University, Guangzhou, Guangdong, China. Now I’m leading a team of graduate students working on exciting topics in the cross-disciplinary areas of applied statistics, optimization, data mining, and networked/distributed systems.

Associate Professor

Department of Electrical and Computer Engineering, University of Alberta

Edmonton, Alberta, Canada

Research Areas: data mining and applied machine learning, cloud computing and big data analytics,  parallel and distributed systems, text mining and natural language processing. 

Methodologies: optimization, statistical machine learning, parallel and distributed algorithms, matrix learning, data mining, distributed systems implementation.

Contact me

Email: dniu@ualberta.ca

Office: D-ICE, 11-361

Curriculum Vitae

Can be provided upon request.

To Prospective Students

I encourage highly motivated students that are interested in working with me to contact me through email 2-3 months before you plan to submit your application to University of Alberta.

Ongoing Research Projects

Currently, Di is actively leading his research team working on the following topics, in collaboration with a few industrial partners (mainly including Tencent and Wedge Networks) and other academic institutes (mainly including University of Toronto):

-Distributed machine learning and data analytics processing platforms, with a focus on both algorithm design and innovative system development. The specific problems we look at include distributed algorithms for training machine learning models, such as deep neural networks and matrix factorization, and distributed architectures for streaming analytics and batch job processing.

-News article understanding and recommendation, event discovery and summarization, storyline generation and natural language processing in general.

-Social-economic computing and big data analytics for Internet, social networks and business intelligence. The particular data we are dealing with include video browsing data, e-commerce traces, spatial-temporal and geographical information data, network quality-of-service traces, and crowdsourced data from mobile devices. And the specific topics we are working on range from recommendation and ranking algorithms to estimation and forecast.


Paper accepted to KDD 2018 (on natural language sentence matching)
Paper accepted to WWW 2018 (on natural language sentence matching)

Won the Extraordinary Achievement Award (top 1 out of 18 grant holders) for 2016-2017 CCF-Tencent Rhino Bird Open Grant, on CNCC, Oct 27, Fuzhou China.

Paper accepted to IEEE ICDM 2017 (on cellular activity prediction and spatial data mining )

Paper accepted to ACM CIKM 2017 (on story generation from text documents)

Paper accepted to ACM SoCC 2017 (on latency optimization in cloud storage)

Papers accepted to IEEE INFOCOM 2017 (on web service latency modeling and OvS multicast)

Papers accepted to ACM TOMPECS (on cloud computing, another on video storage)

Paper accepted to AAAI 2017 (on quantile regression in matrix factorization)
Paper accepted to IEEE ICDM 2016 (on house price modeling)

Paper accepted to IEEE Trans. on Parallel and Distributed Systems (on data transfer in clusters)

Paper accepted to IEEE/ACM Trans. on Networking (on mobile network latency modeling)

Paper accepted to ACM CIKM 2016 (on large-scale video recommendation)

Papers accepted to Trans. on Multimedia (on stream processing and interactive video streaming)

Paper accepted to IEEE INFOCOM 2016 (on cloud storage)

Paper accepted to IEEE/ACM Trans. on Networking (on large-scale optimization methods)