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Dr. Ahmad Aghaebrahimian

Dr. Ahmad Aghaebrahimian

Dr. Ahmad Aghaebrahimian

ZHAW Life Sciences und Facility Management
FG Biomedical String Analysis
Schloss 1
8820 Wädenswil

+41 (0) 58 934 45 04
ahmad.aghaebrahimian@zhaw.ch

Persönliches Profil

Tätigkeit an der ZHAW

www.zhaw.ch/icls/bioinformatics/en/

Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse

Deep Learning
Machine Learning
Natural Language Processing
Bio-text analytics
Semantic Web

Aus- und Fortbildung

2021 – present Senior Postdoc, Biomedical Text analytics, ZHAW School of Life Sciences and Facility Management
2018 – 2021: Postdoc, Text Analytics, and Deep Learning, ZHAW School of Engineering
2017 – 2018: Research Stay, Text mining and Deep Learning, University of Innsbruck, Austria
2015 – 2019: Ph.D. Computer Science focusing on Mathematical Linguistics, Charles University, Czech Republic
2011 – 2013: M.A. Applied Linguistics, Allame Tabataba’i University, with honors (summa cum laude), Iran
2007 – 2011: B.Sc. Computer Engineering (Double major), Iran University of Science and Technology (IUST) with honors (summa cum laude), Iran
2006 –2010: B.A. English Translation Studies, Iran

Beruflicher Werdegang

2021 – present: Senior researcher, Literature-Based Discovery, ZHAW School of Life Sciences and Facility Management
2018 – 2021: Deputy Leader and senior researcher, Innosuiss project, ZHAW School of Engineering
2017 – 2018: Researher, Transbank, University of Innsbruck, Austria
2015 – 2019: Ph.D., PI Hybrid Deep Question Answering, Charles University, Czech

Mitglied in Netzwerken

Swiss Institute of Bioinformatics

Projekte

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed
Konferenzbeiträge, peer-reviewed

Publikationen vor Tätigkeit an der ZHAW

2020
Aghaebrahimian, A., Stauder, A and Ustaszewski, M(2019), Testing the validity of Wikipedia categories for subject matter labelling of open-domain corpus data, Journal of Information Science, Sage Publications

2019
Aghaebrahimian, A., Ustaszewski M. and Stauder, A(2019), Highly Parallel Texts Enriched with Highly Useful Metadata? A Wikipedia Case Study Combining Machine Learning and Social Technology, Journal of Digital Scholarship in the Humanities, Oxford University Press
Aghaebrahimian, A., Ustaszewski M. and Stauder, A (2019), The TransBank Aligner: Cross-Sentence Alignment with Deep Neural Networks, In Proceedings of the 22nd International Conference on Text, Speech, and Dialogue (TSD), Ljubljana, Slovenia

2018
Aghaebrahimian, A., (2018), Deep Hybrid Question Answering, Ph.D. dissertation, Charles University, Faculty of Physics and Mathematics, Prague, Czech Republic
Aghaebrahimian, A., (2018), Deep Neural Networks at the Service of Multilingual Parallel Sentence Extraction, In Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, New-Mexico, USA
Aghaebrahimian, A., (2018), Linguistically-Based Deep Unstructured Question Answering, In Proceedings of the Conference on Computational Natural Language Learning (CoNLL 2018), Brussels, Belgium

2017
Aghaebrahimian, A., (2017), Hybrid Deep Open-Domain Question Answering, In Proceedings of the 8th Language and Technology Conference (LTC), Poznan, Poland
Aghaebrahimian, A., (2017), Constrained Deep Answer Sentence Selection. 20th International Conference on Text, Speech, and Dialogue (TSD), Prague, Czech Republic
Aghaebrahimian, A., (2017), Quora Question Answer Dataset, 20th International Conference on Text, Speech, and Dialogue(TSD), Prague, Czech Republic

2016
Aghaebrahimian, A. and Jurcicek, F., (2016), Open-domain Factoid Question Answering via Knowledge Graph Search, In Proceedings of the Workshop on Human-Computer Question Answering at North American chapter of Association of Computational Linguistics (NAACL), San Diego, CA, USA
Aghaebrahimian, A. and Jurcicek, F., (2016), Constraint-Based Open-Domain Question Answering Using Knowledge Graph Search, 19th International Conference on Text, Speech, and Dialogue (TSD), LNAI 9924

2015
Aghaebrahimian A. and Jurcicek, F., (2015), Machine Learning for Semantic Parsing in Review, In proceeding of 7th Language and Technology Conference (LTC), Poznan, Poland
Aghaebrahimian, A. and Jurcicek, F.(2015), Constraint-based Statistical Spoken Dialogue Systems. Young Round table of Researchers of Spoken Dialogue Systems (YRRSDS)
Aghaebrahimian, A. (2015) Constraint-based Semantic Parsing, In Proceedings of the Week of Doctoral Studies, Charles University, Faculty of Mathematics and Physics

2014
Aghaebrahimian, A., Rahimirad, M., Ahmadi, A. & Khalilpour Alamdari, j.(2014), Dynamic Assessment of Writing Skill in Advanced EFL Iranian Learners, Procedia- Social and Behavioral Sciences

2013
Seyed Erfani, S. and Aghaebrahimian, A.,(2013), Web 2.0 Incorporated Dynamic Assessment to Assess Writing Ability of Iranian EFL Learners, Global Journal of Human Social Science, Volume XIII Issue XIV(2013), ISSN,2249-460X
Aghaebrahimian. A, (2013), Use of Different Cohesive Devices as a Discursive Mechanism in Different Genres, A Corpus-based Study, The first international conference on language learning and teaching, an interdisciplinary approach, Ferdowsi University, Mashhad, Iran

2012
Aghaebrahimian. A, (2012), Corpus-based Analysis of Lexical Cohesive Devices in ESP (computer) Texts, Allame Tabataba’i University, Tehran, Iran
Aghaebrahimian. A, (2012), Age, social class and expression of condolence, Allame Tabataba’i University, Tehran, Iran
Aghaebrahimian. A, (2012), CALL and Teaching Writing, Allame Tabataba’i University, Tehran, Iran.

Weitere Publikationen

Teaching activity:

2021
Biomedical Natural Language Processing module 1
Bioinformatics: Semantic web lecture in University of Zurich (https://compbiozurich.org/UZH-BIO390/course-days/2021-11-23-Ahmad-Aghaebrahimian/)
Digital Health: Biomedical Text analytics in ZHAW department of engineering

2020
CAS Machine Intelligence: Machine Learning, ZHAW department of engineering


MSc. Thesis supervisor
Lirui Zhang Biomedical Relation Extraction with syntactic trees powered by Deep Learning: Ongoing
Tobias Fierz Webpage classification based on Machine Learning, Defended with a Perfect score

Workshops:

2021
Sentence End and Punctuation Prediction in NLG Text (SEPP)- Co-organizer, ZHAW: department of engineering (https://sites.google.com/view/sentence-segmentation/)