--- title: "Hany Hassan Awadalla — author profile and papers" description: "Hany Hassan Awadalla, Meta: 2 papers on Elamur Research (2024–2025), h-index 9 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/hany-hassan-awadalla/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/hany-hassan-awadalla/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Hany Hassan Awadalla # Hany Hassan Awadalla Hany Hassan Awadalla's papers here focus on small language models. They ask how far a compact model can go when the effort goes into the quality of its training data rather than into more parameters, and how such a model can be extended to handle images and speech. The work falls under [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/). It takes the form of technical reports that cover data curation, staged training, alignment and evaluation. The most cited of these is the [Phi-3 Technical Report](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/) (2024). It presents phi-3-mini, a 3.8B-parameter model pretrained in two stages. The first stage used broad filtered web text. The second used a stricter slice of that text, combined with synthetic material produced by larger models. After pretraining, the model was tuned for conversation, robustness and safety through supervised training and DPO. The authors describe this as a "data optimal" approach, in which web content is kept according to what it teaches and how much reasoning it supports. The more recent [Phi-4-Mini Technical Report](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/) (2025) stays at the same 3.8B scale, with training data weighted toward math and code. It also introduces Phi-4-Multimodal, which keeps the language backbone frozen. Each modality gets its own encoder, projection layer and LoRA module. This "Mixture-of-LoRAs" design lets one checkpoint accept text, images, audio or combinations of them without degrading the model's text abilities. Hany Hassan Awadalla is currently at [Meta](https://md.research.elamur.ai/free/en/companies/meta-united-kingdom/) in London. Bylines from 2015 to 2025 list [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) in Redmond. Co-authors on these reports include [Alon Benhaim](https://md.research.elamur.ai/free/en/authors/alon-benhaim/), [Jianmin Bao](https://md.research.elamur.ai/free/en/authors/jianmin-bao/) and [Nguyen Bach](https://md.research.elamur.ai/free/en/authors/nguyen-bach/), along with [Marah Abdin](https://md.research.elamur.ai/free/en/authors/marah-abdin/) and others. Hany Hassan Awadalla has 2 papers in our library, in 2024–2025, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/); often with [Alon Benhaim](https://md.research.elamur.ai/free/en/authors/alon-benhaim/), [Jianmin Bao](https://md.research.elamur.ai/free/en/authors/jianmin-bao/), [Nguyen Bach](https://md.research.elamur.ai/free/en/authors/nguyen-bach/). 2 papers in our library · 2024–2025 current affiliation [Meta](https://md.research.elamur.ai/free/en/companies/meta-united-kingdom/), London, United Kingdom [OpenAlex](https://openalex.org/A5030937723) · [**h-index 9**](https://openalex.org/authors/A5030937723) · [36 works](https://openalex.org/works?filter=author.id:A5030937723) · [480 citations](https://openalex.org/works?filter=author.id:A5030937723&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [2 works in our library](https://md.research.elamur.ai/free/en/authors/hany-hassan-awadalla/#papers) · [1 507 citations in our library](https://md.research.elamur.ai/free/en/authors/hany-hassan-awadalla/cited-by/) · 5 more works in our library · [⭐ Paper Star rank #1694](https://md.research.elamur.ai/free/en/paper-stars/page/17/#star-1694) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Hany+Hassan+Awadalla) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Hany+Hassan+Awadalla) · [dblp](https://dblp.org/search?q=Hany+Hassan+Awadalla) · [ACL Anthology](https://aclanthology.org/search/?q=Hany+Hassan+Awadalla) ## Affiliations [🏢**Meta** London · United Kingdom 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/companies/meta-united-kingdom/) · [🏢**Microsoft** Redmond · United States 2015–2025 · 14 papers](https://md.research.elamur.ai/free/en/companies/microsoft/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Topics [Language models & pretraining 2](https://md.research.elamur.ai/free/en/topics/language-models/) · [Vision & language 1](https://md.research.elamur.ai/free/en/topics/multimodal/) ## Works by year ### 2020s [**2025** 1 paper](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/) · [**2024** 1 paper](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/) ## Papers The most cited first (OpenAlex and our library). 1. **[Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/)** *(2024 · 1 113 citations)* Phi-3 asks whether a small language model can match much larger systems by improving its training data rather than adding parameters. 2. **[Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/)** *(2025 · 145 citations)* Small language models usually trail larger ones at reasoning. Adding vision or audio to them typically means fine-tuning the backbone, which weakens its text skills and forces teams to run several separate models. In this report, Microsoft presents Phi-4-Mini, a 3.8B-parameter decoder-only model. It was trained on… ## Also in our library 5 more works of theirs are in our library without a page here yet; they will appear as we publish them. ## Co-authors here 10 co-authors with a page, the most papers together first. - [Alon Benhaim](https://md.research.elamur.ai/free/en/authors/alon-benhaim/) ×2 - [Jianmin Bao](https://md.research.elamur.ai/free/en/authors/jianmin-bao/) ×2 - [Nguyen Bach](https://md.research.elamur.ai/free/en/authors/nguyen-bach/) ×2 - [Ahmed Awadallah](https://md.research.elamur.ai/free/en/authors/ahmed-awadallah/) - [Ammar Ahmad Awan](https://md.research.elamur.ai/free/en/authors/ammar-ahmad-awan/) - [Atabak Ashfaq](https://md.research.elamur.ai/free/en/authors/atabak-ashfaq/) - [Harkirat Behl](https://md.research.elamur.ai/free/en/authors/harkirat-behl/) - [Jyoti Aneja](https://md.research.elamur.ai/free/en/authors/jyoti-aneja/) - [Marah Abdin](https://md.research.elamur.ai/free/en/authors/marah-abdin/) - [Vishrav Chaudhary](https://md.research.elamur.ai/free/en/authors/vishrav-chaudhary/) ## Cited by Papers of our library cite their works 1 507 times (of the 140 884 papers whose bibliographies we read); [94 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/hany-hassan-awadalla/cited-by/).