I've been working as an independent statistical consultant in fields of IT, Biomedicine, Economics, Traffic & Transport, etc. for 13 years now, applying different statistical and machine learning methods (designing experiments, classification, estimating parameters, etc.). In modern terms, I know something about data mining, data science and big data. You should check my CV for more details if you are interested.
Although I use different e-mail addresses (@pmfst.hr, @math.hr, @kcl.ac.uk, @genos.hr, ...) for different projects the easiest way to contact me is to send an e-mail to ivo+web@iugrina.com. If you use GPG my key is 4096R/A852545D.
Some additional services where you can find/contact me are LinkedIn and GitHub.
I simply love mathematics. I've been obsessed with it since my childhood and I have a formal (university) education in mathematics. I've been a teaching asistant on a number of mathematical courses and currently I give lectures in Mathematical Statistics, Probability and Stochastic processes at PMF-UNIST. Also, every now and then I try to prove a theorem or two since that's what (theoretical) mathematicians do. However, doing research in mathematics leads to a "monastic" career so, to improve my social life/intelligence, I switch between mathematics and other fields from time to time.
Statistics is so much mathematics but also something more. It encompasses decision theory, planing, philosophy and many other fields, pushing for constant intellectual exercise. A field that is hard to dislike and yet easy to hate. The field I chose for my PhD thesis.
For proper development of statistical methods understanding applications in other fields is of utter importance thus I spend a lot of time working as an applied statistician. Either as an independent statistical consultant or the head of a data analysis team.
Is there an easier way to do computations than to let a computer do it for you? Using computers also has an added value in a special beauty computer science holds, be it all the beautiful algorithms, optimizations or services. Therefore, investing time to study computer science can only be beneficial (intellectually and applications wise) and I take this task seriously. I've been a teaching assistant on a number of computer science courses at PMF-UNIZG, participant and speaker at different conferences and a member of some hackerspaces. Also, I am familiar with many programming languages (like R, Python, C, C++, etc.).
Software should be efficient and should give you the freedom to adapt it to your needs. Especially if you like to play with things. Therefore, Free and/or Open Source Software seems like an obvious choice and I use it as much as I can. I'm a big fan and supporter of Debian GNU/Linux distribution, (neo)VI(m) and R among others. However, I am not a F(L)OSS evangelist. I adapt to and use proprietary software if needed. Since I take a lot from the community I try to give back when possible by helping well established projects (R), designing and implementing topic tailored packages or just releasing code to the community.
Working in/for science, doing research, is one of the most fulfilling things one can do in his life. The idea that you are inventing things, things that no one else invented before is, well, unbelievable. I am (or have been) a part of some really awesome groups at PMF-UNIZG, PMF-UNIST, Genos and DTR-KCL and published a few papers. However, science is done by humans and that sometimes means bad business processes, slow transition of knowledge and individualistic approach. Since I'm a guy who likes being (more) efficient, I often switch between science and industry related projects.
A lot to say here, but I suppose not that interesting to general (WWW) public. In short, I love sports (running, swimming, tennis, basketball), animated movies, sci-fi and fantasy, blues and jazz, comics, etc.
Also, happily married (yes, my wife forced me to write this :D) with two small kids.