Research output per year
Research output per year
Personal profile
Biography
Hello! I am a tenured assistant professor in the Department of Data Science and Artificial Intelligence of Monash, and an affiliated lecturer of University of Cambridge. Prior to this, I was an associate lecturer at the Department of Electrical and Electronic Engineering of UCL, and before that a postdoc in the Language Technology Lab at University of Cambridge. I have obtained my PhD in Computer Science from Monash University.
Research interests
- Deep generatinve models (e.g., generation and representation learning)
- Bayesian (nonparametric) learning and inference (e.g., application in low-resource NLP)
- Privacy and interpretability in representation learning and model prediction
- Utilising biomedical knowledge graphs (e.g., graph-based generation, entity linking)
Monash teaching commitment
Ehsan has experience as both the Chief Examiner and Lecturer for the following units in the Faculty of IT:
- FIT5217 Natural Language Processing
- FIT5212 Data Analysis for Semi-structured Data
University Service
- Early Career Academic (ECA) Committee (2022-Present)
Community service
Program Committee
- Association of Computational Linguistics (ACL)
- North American Chapter of Association of Computational Linguistics (NAACL)
- Empirical Methods in Natural Language Processing (EMNLP)
- Association for the Advancement of Artificial Intelligence (AAAI)
- The International Joint Conference on Artificial Intelligence (IJCAI)
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Computer Science, PhD, MONASH UNIVERSITY
Award Date: 18 Oct 2017
Computer Science, BSc, Shahid Beheshti University
Award Date: 1 Sep 2009
External positions
Affiliated Lecturer, University of Cambridge
1 Oct 2020 → 30 Sep 2022
Research area keywords
- Natural Language Processing
- Machine Learning
- Deep Generative Models
- Deep learning
- Bayesian Inference
Network
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Fire burns, sword cuts: commonsense inductive bias for exploration in text-based games
Ryu, D. K., Shareghi, E., Fang, M., Xu, Y., Pan, S. & Haffari, R., 2022, ACL 2022, The 60th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers). Croce, D., Cotterell, R. & Zhang, J. (eds.). Stroudsburg PA USA: Association for Computational Linguistics (ACL), p. 515–522 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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Introduction
Cherry, C., Fan, A., Foster, G., Haffari, R., Khadivi, S., Peng, N., Ren, X., Shareghi, E. & Swayamdipta, S., 2022, The 3rdWorkshop on Deep Learning Approaches for Low-Resource NLP, Proceedings of the DeepLoWorkshop. Cherry, C., Fan, A., Foster, G., Haffari, G., Khadivi, S., Peng, N., Ren, X., Shareghi, E. & Swayamdipta, S. (eds.). Stroudsburg PA USA: Association for Computational Linguistics (ACL), p. iv 1 p.Research output: Chapter in Book/Report/Conference proceeding › Foreword / Postscript › Other
Open AccessFile -
On the effect of isotropy on VAE representations of text
Zhang, L., Buntine, W. & Shareghi, E., 2022, ACL 2022, The 60th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 (Short Papers). Croce, D., Cotterell, R. & Zhang, J. (eds.). Stroudsburg PA USA: Association for Computational Linguistics (ACL), p. 694–701 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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Rewire-then-probe: a contrastive recipe for probing biomedical knowledge of pre-trained language models
Meng, Z., Liu, F., Shareghi, E., Su, Y., Collins, C. & Collier, N., 2022, ACL 2022, The 60th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 1 (Long Papers). Croce, D., Cotterell, R. & Zhang, J. (eds.). Stroudsburg PA USA: Association for Computational Linguistics (ACL), p. 4798–4810 13 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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TaCL: improving BERT pre-training with Token-aware Contrastive Learning
Su, Y., Liu, F., Meng, Z., Lan, T., Shu, L., Shareghi, E. & Collier, N., 2022, Findings of the Association for Computational Linguistics: NAACL 2022. Carpuat, M., de Marneffe, M-C. & Vladimir Meza Ruiz, I. (eds.). Stroudsburg PA USA: Association for Computational Linguistics (ACL), p. 2497–2507 11 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open AccessFile