- Nov, 2018: Our work – Angel-i, which is a software and CCTV combined technology developed to monitor, detect and alert when people may be at risk is called out by CEO and Chairman in Boral’s AGM speech LINK. It is generating signifcant business impact in the first 10 sites we roll out.
- June, 2018: I spent one week at Tata Consultancy Service in Chennai, see my experience here.
- Jan, 2018: During my stay in Telefonica, I worked on research problem of mining human activity pattern and clustering users from 10TB/day Dataset, then leverage the outcome to solve business problem.
- Sep, 2017: Our project uStash is the winner of 2017 NASSCOM IT Technical Innovation Award. Find Judge’s citation Here.
- Aug, 2017: Paper accepted in DSAA’17. With data collected during Kumamoto earthquake, we show how a spatio-temporal LSTM deep neural network outperforms existing regressive modes(i.e. ARIMA & SVR) for crowd density estimation in a highly dynamic and disruptive scenario. The result could help improve disaster management and relief.
- Apr, 2017: I serve as TPC member of Ubicomm’17, ICT-DM’17 and ICSNC’17 . I am attending PAM’17 held in Sydney and WWW’17 in Perth. Our paper in IWSC is 1 of Three Selected Papers for WWW Journal Recommendation.
- Feb, 2017: We have two papers accepted in Elsevier Computer Communication (comcom) and Social Computing workshop (co-located with WWW’17).
- Dec, 2016: My paper in collaboration with NII received the best paper award in ICT-DM. The acceptance ratio is 35%, and we are nominated from all 26 accepted papers. http://ict-dm2016.ait.ac.at/
I am a data scientist at Data & AI team in Boral digital solution. Our team is currently working towards solving many exciting data science problems to improve productivity, safety and our customer experience;
- Refine and expose insights effectively across the business.
- Apply Computer Vision on survilliance system to improve Safety
- Apply NLP to improve customer service
- Enable machine failure prediction through industrial IoT
- Digitalization of sites, various operational mobile (trucks) and fixed assets (machinery)
- Supply chain optimization leveraging ML models and predictions
I completed my Ph.D in the area of mobile computing, data mining and applied machine learning at UNSW, and I received B.E. (Hons.I) from the USYD in 2014. Experience in handling and analyzing large datasets using advanced data analysis and data mining tools. I am also experienced in applying machine learning, deep learning models on real-world problems, and solving business cases by translating them into analytics agenda. During my Ph.D, I was affiliated with CSIRO’s Data61, and worked closely within mobile system research group. Moreover, I was a research intern in NII, Japan, and worked as Ph.D fellow in Telefonica, Spain.
- Rich Experience in academic/industry research labs in Europe, Japan and Australia;
- Telefonica R&D, Spain (Sep-Dec, 2017) (advised by Dr. Alessandro Finamore)
- Mobile traffic/user behaviour characterization and modelling
- National Institute of Informatics, Japan (Jan-July, 2016) (advised by Prof. Yusheng Ji)
- Data-driven projects in mobile content distribution and disaster preparation and response.
- National ICT Australia/DATA61, CSIRO (Oct 2013-2018), Australia. (with Prof Aruna Seneviratne, Prof Mahbub Hassan, Prof. Dali Kaafar)
- Mobile Computing
- Applied Data Mining and Machine Learning
- Mobile System and Architecture
- Telefonica R&D, Spain (Sep-Dec, 2017) (advised by Dr. Alessandro Finamore)
- Experience working with large, often messy datasets
- Experience in visualising, presenting data and experiment results
- Experience in Python, Linux, Cloud Computing (GCP, AWS)
- Proven Experience on taking projects from concept to production
- Hands on experience with Computer Vision, NLP and other ML based products.
- Familiar with Scrum/Agile Frameworks and version control, i.e. Git, svn
I also enjoy travelling and photography, if your are interested in the photos I have taken Link.
Awards & Honors:
- 2017 · Telefonica R&D PhD fellowship Award.
- 2017 · Winner of NASSCOM IT Technical Innovation Award (1 Australia wide)
- 2016 · Best Paper Award ICT-DM’16
- 2016-2017 · National Institute of Informatics (NII) Intern Award (2 recipients)
- 2015-2016 · UNSW PRSS Travel Grant
- 2015-2018 · RTP scholarship
- 2015-2018 · NICTA (Data61) enhanced Ph.D. scholarship (<5% success rate)
- 2013-2014 · NICTA summer scholarship
- 2013 · NICTA summer intern Prize winner
- 2012-2015 · High Performance Certificate in 2nd/3rd/4th year Advanced Engineering & Advanced Engineering Certificate (USYD)
Ph.D in Telecommunications (2015-2018)
The University of New South Wales, Australia
- Field of Specialization:
- Mobile Content Distribution, Mobile Computing.
- Advisors: Prof. Aruna Seneviratne and Dr. Kanchana Thilakarathn.
B.E in EE (Telecommunications) First Class Honor (2011-2014)
The University of Sydney, Australia
Thesis with NICTA: User Stashy Performance
- Advisors: Prof. Aruna Seneviratne & A.Prof. Rafael Calvo
- TPC member of
- I have been reviewer for papers in
- Conference: IMC’16, ICC’16, PIMRC’16, DSAA’17, PAM’17, WoWMoM’18
- Journals: Transaction on Multimedia, Transaction on Mobile Computing, Internet Computing, Transactions on Internet and Information Systems.
- F. Jiang, K. Thilakarathna, S. Mrabet, M. Ali Kaafar, A. Seneviratne, “uStash: a Novel Mobile Content Delivery System for Improving User QoE in Public Transport,” Transaction on Mobile Computing, IEEE, 2018. (Impact Factor~3.822) [PDF]
- K. Thilakarathna, F. Jiang, S. Mrabet, M. Ali Kaafar, A. Seneviratne, G.Xie “Crowd- Cache: Leveraging on Spatio-Temporal Correlation in Content Popularity for Mobile Networking in Proximity,” Elsevier Computer Communication, 2017. (Impact Factor~2.099) [PDF]
- F. Jiang, E. Zarepour, M. Hassan, A. Seneviratne, and P. Mohapatra, “Type , Talk , or Swype : Characterizing and Comparing Energy Consumption of Mobile Input Modalities,” Pervasive and Mobile Computing, 2015. (Impact Factor~1.719) [PDF]
- F.Jiang, L.Zhong, K. Thilakarathna, A.Seneviratne, K.Takano, S.Yamada, Y.Ji, “Supercharging Crowd Dynamics Estimation in Disasters via Spatio-Temporal Deep Neural Network” in The 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA’17) [PDF]
- F.Jiang, K. Thilakarathna, M. Hassan, Y.Ji and A. Seneviratne. “Efficient Content Distribution in DOOH Advertising Networks Exploiting Urban Geo-Social Connectivity,”(IWSC17) in WWW ’17: 26th International World Wide Web Conference Companion. [PDF]
- F. Jiang, Z. Liu, K. Thilakarathna, Z. Li, Y. Ji, A. Seneviratne. “TransFetch: A Viewing Behavior Driven Video Distribution Framework in Public Transport,”in The 41th IEEE Conference on Local Computer Networks (LCN’16)(Dubai, UAE) (Core Rank A) [PDF]
- F. Jiang, K.Thilakarathna, S. Mrabet and M. A. Kaafar, “uDrop: Pushing Drop-Box to the Edge of Mobile Network,” in 2016 IEEE International Conference on Pervasive Computing and Communications Demonstrations. (Core Rank A*)[PDF]
- L.Zhong, K.Takano, F.Jiang, X.Wang, Y.Ji and S.Yamada”. “Spatio-Temporal Data-Driven Analysis of Mobile Network Availability During Natural Disasters,” in International Conference on Information and Communication Technologies for Disaster Management, IEEE (ICT-DM’16). [Best Paper Award] [Acceptance rate: 35%][PDF]
- F. Jiang, E. Zarepour, M. Hassan, A. Seneviratne, and P. Mohapatra, “When to type, talk, or swype: Characterizing energy consumption of mobile input modalities,” in Pervasive Computing and Communications (PerCom’15), 2015 IEEE International Conference on, pp. 114–122, March 2015. (Core Rank A*)( Acceptance rate: 8%)[PDF]
- F. Jiang, K. Thilakarathna, M. A. Kaafar, F. Rosenbaum, and A. Seneviratne, “A spatio-temporal analysis of mobile internet traffic in public transportation systems: A view of web browsing from the bus,” in Proceedings of the 10th ACM MobiCom Workshop on Challenged Networks, CHANTS ’15, (Paris, France), pp. 37–42, ACM, 2015. [PDF]
- S. Seneviratne, F. Jiang, M. Cunche, and A. Seneviratne, “SSIDs in the Wild: Extracting Semantic Information from WiFi SSIDs,” in The 40th IEEE Conference on Local Computer Networks (LCN’15), (Clearwater Beach, Florida, United States), Oct. 2015. (Core Rank A) [PDF]
- K. Thilakarathna, F. Jiang, S. Mrabet, M. A. Kaafar, A. Seneviratne, and P. Mohapatra, “Demo: Crowd-cache–popular content for free,” in Proceedings of the 12th annual international conference on Mobile systems, applications, and services, (Mobisys’14), pp. 358–359, ACM, 2014. [PDF]