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

• Speaker at: Information and Networking Days on Horizon 2020 Big Data topics

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