Hussein S. Al-Olimat

Currently pursuing PhD in Computer Science and Engineering at Wright State University working as part of the research team at The Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis). My dissertation titled “Knowledge-enabled Entity Extraction” is advised by Dr. Krishnaprasad Thirunarayan.

Kno.e.sis performs world-class research on translating information to meaning, towards the vision of Computing for Human Experience. My research is focused on Natural Language Processing, Information and Knowledge Retrieval and Extraction, and Machine Learning.

Previously, received MSc. in Computer Science and Engineering from the University of Toledo. My research and thesis work was focused on the development of multi-level and multi-objective optimization techniques through the development of Metaheuristics (mainly using Bio-Inspired Computing).

Before joining the master’s program at UT, I received my bachelor’s degree in Computer Science from the German Jordanian University, Amman, Jordan.

Updates

• Our paper “Towards Geocoding Spatial Expressions” won a Blue Sky Idea prize by the The Computing Community Consortium - CCC.
• Our vision paper “Towards Geocoding Spatial Expressions” was accepted for publication in SIGSPATIAL 2019.
• Excited to be a program committee member of both ACL and EMNLP this year, 2019.
• In May 2019, I got the “Outstanding Student Award” from the CES Department at Wright State University.
• In April 2019, our demo paper was accepted at SIGIR 2019. To appear.
• In March 2019, our US. patent titled “Machine Assisted Learning of Entities” was granted.
• Our DisasterRecord paper was accepted at ARIC workshop at SIGSPATIAL 2018.
• I presented our two papers LNEx and SpExtor at COLING 2018 in Santa Fe, NM.
• I will be presenting with Amir Yazdavar our tutorial on Location Extraction @ ISCRAM - 2018 in May 2018 in Rochester, NY. Tutorial Website
• I presented our depression detection paper in ASONAM 2017 in Sydney, Australia on August 2017. Slides
• Our paper titled “Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media” was accepted in ASONAM 2017.
• In June 2016, our poster titled “Analyzing Depressive Symptoms in Twitter” was accepted and will be presented in the the 23rd NIMH Conference on Mental Health Services Research, MHSR 2016: Harnessing Science to Strengthen the Public Health Impact.