Julio De Melo Borges
Karlsruhe Institute of Technology (KIT)
Campus Süd
Institute of Telematics
Chair for Pervasive Computing Systems / TECO
Vincenz-Prießnitz-Straße 1
76131 Karlsruhe
Germany
Building 07.07, Room 214
email: borges(at)teco.edu
LinkedIn: http://lnked.in/jborges
phone: +49 721 608-41708
fax: +49 721 608-41702
Short CV
- 2015-Now: PhD Student at TECO/KIT
- 2015: Graduation with Master (MSc) of Computer Science from Karlsruhe Institute of Technology (KIT)
- 2015: Conclusion of Software Campus Executive Training Program in cooperation with Software AG
- 2012: Graduation with Bachelor (BSc) of Computer Science from Karlsruhe Institute of Technology (KIT)
Projects
- 2014 – 2017: Data Scientist @ Smart Data Innovation Lab (SDIL) – Promotion of Cutting-Edge Smart Data Research
→ See our projects at: http://www.sdil.de/en/projects/ - 2014 – 2017: Data Scientist @ Smart Data Solution Center (SDSC) – Smart Data Analytics for Small and Medium Enterprises.
→ See our success stories at: http://sdsc-bw.de/erfolge - 2014 – 2015: Project Manager – ESTAData – Data driven decision recommendation for administrative bodies based on collective intelligence
Activities
2017
2016
- International Conference on IOT in Urban Space (Urb-IoT’16). Organizing Committee – Social Media Chair
- Speaker at ISC High Performance Computing Conference: Industry meets Research: Success-Stories of the Smart Data Solution Center Baden-Wuerttemberg
- Speaker at Bitkom Big Data Summit: Industry Meets Science: Erfolge des Smart Data Innovation Lab
- Speaker at Small Big Data Value Association Summit: Innovation Spaces (SDIL)
2015
- International Conference on Smart Grid Communications (SmartGridComm). Technical Program Committee
- Speaker at IHK Karlsruhe: Von Big Data zu Smart Data
Teaching
- 2017 – Praktikum Smart Data Analytics
- 2016 – Praktikum Kontextsensitive ubiquitäre Systeme (now: Smart Data Analytics): Techniques, methods and software for context acquisition and processing as the basis of Smart Data Analytics.
- Achievements: Top 1% in a Data Mining Competition on Kaggle
Supervised Theses
- Simon Sudrich: Anomaly Detection for Dynamic Heterogeneous Graphs (Praxis der Forschung)
- Qianqian Cao: Enhancing Traffic Flow Forecasting with Environmental Models (Master-Thesis)
- Daniel Ziehr: Leveraging Spatio-Temporal Features for Improving Predictive Policing (Master-Thesis).
- Wei Han: Association Rules Mining for Master Data (Master-Thesis)
Research Interests
- Data Mining
- Machine Learning
- Big Data Technologies
- IoT + Smart Cities
Peer-reviewed Publications
2017
(2017) A predictive policing application to support patrol planning in smart cities, 2017 International Smart Cities Conference, ISC2 2017, url, doi:10.1109/ISC2.2017.8090817
(2017) Graph-based Anomaly Detection for Smart Cities: A Survey, IEEE International Conference on Smart City Innovations (IEEE SCI 2017), url
(2017) Feature Engineering for Crime Hotspot Detection, IEEE International Conference on Smart City Innovations (IEEE SCI 2017), Best Paper Award, url
(2017) Event Detection for Smarter Cities, IEEE International Conference on Smart City Innovations (IEEE SCI 2017), url
(2017) Advances in Event Detection, The 3rd IEEE International Conference on Smart Data, url
(2017) Anomaly Detection in Evolving Heterogeneous Graphs, The 3rd IEEE International Conference on Smart Data, url
(2017) Predicting Target Events In Industrial Domains, Machine Learning and Data Mining in Pattern Recognition, Petra Perner (ed.), p. (To Appear), New York, NY, USA: Springer, url
(2017) Interestingness Classification of Association Rules for Master Data, 17th Industrial Conference on Data Mining (ICDM), url
2016
(2016) Analysis of highly variant, temporal data sets for condition-based maintenance, 1st Smart Data Innovation Conference (SDIC), url
(2016) Towards Two-Tier Citizen Sensing, 2nd IEEE International Smart Cities Conference (ISC2-2016), url, doi:10.1109/ISC2.2016.7580771
(2016) Urban Anomaly Detection: a Use-Case for Participatory Infra-Structure Monitoring, Proceedings of the Second International Conference on IoT in Urban Space - Urb-IoT'16, Best Note Award, url
(2016) EstaVis: A Real-World Interactive Platform for Crowdsourced Visual Urban Analytics, Proceedings of the Second International Conference on IoT in Urban Space - Urb-IoT'16, Best Paper Nominee, url
2015
(2015) Sequential Pattern Mining – a Study to Understand Daily Activity Patterns for Load Forecasting Enhancement, 1st IEEE International Smart Cities Conference (ISC2-2015), Guadalajara, Mexico: IEEE, url
2014
(2014) Leveraging Spatio-Temporal Clustering for Participatory Urban Infrastructure Monitoring, The First International Conference on IoT in Urban Space (UrbIoT'14), Best Paper Award, url
(2014) Improving Participatory Urban Infrastructure Monitoring through Spatio-Temporal Analytics, 3rd ACM SIGKDD International Workshop on Urban Computing (UrbComp’14) - Co-located with the ACM International Conference on Knowledge Discovery and Data Mining (KDD 2014), pdf