I am an Assistant Professor in Data Science at the Department of Security and Crime Science and the Dawes Centre for Future Crime at University College London. I also hold a guest affiliation at the University of Amsterdam’s Department of Psychology.
I’m interested in solving and understanding (future) crime and security problems with computational techniques. An experimental behavioural scientists by training, I use a mix of open and closed-source data, experiments, and analytical and methodological approaches from natural language processing, machine learning and simulation studies. In general, my main research area revolves around drawing inferences on human behaviour from text data. Most of my research is aimed at solving problems in the real world.
Current research questions include:
Methodological topics:
All publications can be found on my Google Scholar page.
Example:
var input = "Max and Ben spent more than 1000 hours on writing the software. They started in August 2016 in Amsterdam.";
netanos.anon(input, function(output) {
console.log(output);
});
/*
"[PERSON_1] and [PERSON_2] spent more than [DATE/TIME_1] on writing the software. They started in [DATE/TIME_2] in [LOCATION_1]."
*/
From the README:
#3. USAGE EXAMPLE
##for texts from source, use the function: https://github.com/ben-aaron188/r_helper_functions/blob/master/txt_df_from_dir.R
# data = data.frame('text' = character(3)
# , 'text_id' = character(3))
# data$text = c('this is a super, great positive sentence and I just love doing this. Now this will be very negative and with disgusting words and ugly phrases'
# , 'here we begin in a bad, bad, and ugly way but quickly become overly positive for all the great things this exciting code can do'
# , "I haven't been sad in a long time. I am extremely happy today. It's a good day. But suddenly I'm only a little bit happy. Then I'm not happy at all. In fact, I am now the least happy person on the planet. There is no happiness left in me. Wait, it's returned! I don't feel so bad after all!")
# data$text_id = c('text1', 'text2', 'text3')
#
# ncs_full(txt_input_col = data$text
# , txt_id_col = data$text_id
# , low_pass_filter_size = 5
# , transform_values = T
# , normalize_values = F
# , min_tokens = 10
# , cluster_lower = 2
# , cluster_upper = 2
# )
### END
Possible topics for (UG & PG) dissertations and internships 2019/2020:
CURRENT MODULES
Past modules