Indir Jaganjac
Education: MSc.E.E., major computer engineering, 1995
Faculty of Electrical and Computer Engineering,
Zagreb, Croatia.
Personal Statement: A researcher with strong knowledge in big data, predictive modeling, technical systems diagnostics and prognostics.
Skills & Abilities:
• Software development life cycle
• Radio-frequency planning
• WAN, Cisco routers, switches
• Big data, predictive modeling
• Technical systems diagnostics and prognostics
Work Experience:
1990-1995 Participated at conferences: American Association for Artificial Intelligence (AAAI) 1990 in Boston, AAAI ’91 in Anaheim and AAAI ’92 in San Jose. Attended workshops on machine learning and computer-aided diagnostics of technical systems.
1995 – 1998
Malaysian Embassy, Zagreb, Croatia
• Assistant to Charge d’Affaires
• Business continuity planning
• Disaster recover planning
1999 – 2002
Independent Media Commission, Sarajevo
• Radio-frequency planning in ICS telecom on digital elevation model of 10-m resolution
.
2003 – 2007
Technical High School, Zenica
Professor for informatics, computer networks
2007 – 2009
Mittal Steel, Zenica
• SCADA, predictive maintenance
2009 – present
• Consulting, PHP, JavaScript, MySQL
• Consulting, ArcGIS, QGIS
• Kaggle data science competitions
Currently solving on Kaggle:
– DSTL Satellite Imagery Feature Detection
– The Nature Conservancy Fishery Monitoring
– Two Sigma Financial Modeling Challenge
– Participating in NIJ Real-Time Crime Forecasting Challenge
– Participating in computational biometrics science projects:
• Unconstrained facial recognition, latent fingerprint image super-resolution enhancement, voice stress analysis (prototypes in MATLAB).
Programming Languages and Applications:
• MATLAB, LabVIEW, R, Python
• WEKA data mining software in Java
• RapidMiner data mining software in Java
• SkyTree machine learning software for big data
• BayesiaLab belief networks modeling software in Java
• PHP, JavaScript, MySQL
• Python TensorFlow deep learning library for big images and big data
• NVIDIA GPUs deep learning in Linux Ubuntu 14.04, libraries: CUDA, cuDNN, DIGITS
Publications:
Computing with Cellular Automata, Reed-Muller workshop, Trier, Germany
http://www.lsi-cad.com/RM/RM2003_pro.html
Long-term prediction of nonlinear time series with recurrent least squares support
vector machines, ESTSP ’08, Helsinki, Finland
http://www.mafy.lut.fi/timeseries/ESTSP/
Radial Basis Function Networks for Classification and Prediction, ISP ’05, San Antonio, Texas, USA
http://www.forecastingprinciples.com/paperpdf/isf_2005_program.pdf
Automatic identification of causal knowledge and causal graphs in technical systems
of process ventilators
http://hrcak.srce.hr/index.php?show=clanak&id_clanak_jezik=231181&lang=en