ABOUT MY WORK
My academic career is interleaved with industrial work and spans from Speech and Natural Language Processing (NLP) to general and applied data science.
I am interested in real-world problems, large amounts of textual or other data and a novel or non-trivial application of deep-learning methods while identifying any resulting challenges that may be generalized as interesting research topics in the core data science disciplines.
Projects include: Deep learning models to assess quality traits in tomato fruits based on hyperspectral imaging. (Grant from ministry of Agriculture, joint work with colleagues from the Hebrew U. and Volcani Institute)
SELECTED PUBLICATIONS
1. Yaron Michael, David Helman, Oren Glickman, David Gabay, Steve Brenner and Itamar M. Lensky. Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series. Science of The Total Environment – 2020.
2. Ohad Gilbar, Rachel Dekel and Oren Glickman. Are ICD-11 PTSD and CPTSD Different from Depression and Anxiety? A Network Analysis PerspectiveAre ICD-11 PTSD and CPTSD Different from Depression and Anxiety? A Network Analysis Perspective. International Society for Traumatic Stress Studies 34th Annual Meeting · Oct 27, 2018
3. Oren Glickman. Applied textual entailment: A generic framework to capture shallow semantic inference. VDM Publishing 2009.
4. Ido Dagan, Oren Glickman, Alfio Gliozzo, Efrat Marmorshtein and Carlo Strapparava. Direct Word Sense Matching for Lexical Substitution, COLING-ACL. 2006.
5. PJ Moreno, CF Joerg, JM Van Thong and O Glickman. A recursive algorithm for the forced alignment of very long audio segments. ICSLP 1998
