Hussein S. Al-Olimat, PhD

Hussein S. Al-Olimat obtained his Ph.D. in Computer Science from Wright State University in 2019. He worked at the Kno.e.sis research center at Wright State University since 2015. Pursuing multifaceted syntactic, semantic, and pragmatic information extraction techniques, his dissertation titled “Knowledge-enabled Entity Extraction” has resulted in several publications in the areas of IE, NLP, GIS, and decision support. As an advocate and a member of the Open Source Initiative, Hussein’s research is implemented as open-source projects and has lead to collaborations with humanitarian and social development organizations.

In his research, Hussein studies faster, cheaper, and more reliable IE and NLP methods in search of practical and impactful solutions to real-world problems with fundamental, generalizable research. His research was recognized by the Computing Community Consortium (CCC) as a Blue Sky vision idea and patented in the USA.

Updates

• I joined Tempus, a biotech company, as an NLP Scientist in January 2020.
• I successfully defended my Ph.D. dissertation on Nov 2019.
• 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.
• 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 23rd NIMH Conference on Mental Health Services Research, MHSR 2016: Harnessing Science to Strengthen the Public Health Impact.