Research Scientist @ Huawei London
My research draws on topics of Generative AI, Multimodal Representation Learning, Computational Linguistics (NLP), and Quantum Information Theory (specifically for deploying algorithms).
About Me
I am, Dimitris Gkoumas
My research has drawn on topics including Generative AI, Multimodal Representation Learning, Computational Linguistics (NLP), and Quantum Information Theory, specifically for representation learning. I have worked on a variety of applied AI and NLP tasks such as alignment between LLMs and humans, self-evaluation ecosystems for LLMs, cross-linguistics for molecule representation learning, multimodal learning for linguistic+visual+acoustic modalities, discourse coherence, automatic labeling, text-to-concept generation, and semantics. Additionally, I utilized computational models and incorporated the mathematical formalism of Quantum Theory to model cognition in human language understanding.
My research interests lie in the fields of human-centric, geometric, and evolutionary representation learning, human-preference LLM-judge evaluation ecosystems, as well as unaligned multimodal representation learning.
The overarching goal of my research has been to discover inventive scientific solutions that are scalable, flexible, and inexpensive, particularly to tackle unprecedented challenges in areas such as climate change, healthcare, and pandemics. To address these challenges, integration of AI with modules to enhance the existing capabilities of LLMs and overcome fallacies when exposed to real-world settings has been crucial.
My Research Interests
Generative AI
Aligning Large Language Models (LLMs) with human preferences and values for enhanced integration into real-world AI applications.
Evaluation
Formulating assessment frameworks for generative AI.
AI & Life Sciences
Exploring the potential of generative AI in Life Sciences to address the most formidable health challenges of our generation and contribute to the treatment of incurable diseases.
Multimodal Representation Learning
Research LLMs for multimodal representation learning to solve tasks spanning from agents for decision-making to medicine and robotics.
Geometric Representation Learning
Researching the mathematical frameworks for developing new computational learning algorithms.
Research Projects
Creating time sensitive sensors from language & heterogeneous user generated content | Jan 2023 - |
---|---|
Memory Safe Trustworthy Programming Languages | Jun 2021 - Dec 2023 |
---|---|
Quantum Information Access and Retrieval Theory (QUARTZ) | Oct 2017- Sep 2020 |
---|---|
EDUWORKS: An EU-wide investigation of labour market matching processes | Jan 2016- Sep 2017 |
---|---|
Research Experience
Queen Mary University of London, London, UK | Jan 2021 - Oct 2024 |
---|---|
Huawei Ireland Research Center, Dublin, Ireland | Jun 2021 - Dec 2023 |
---|---|
University of Montreal, Montreal, Canada | Jan 2020 - Apr 2020 |
---|---|
University of Copenhagen , Copenhagen, Denmark | Aug 2019 - Dec 2020 |
---|---|
University of Padua , Padua, Italy | Jan 2019 - Mar 2019 |
---|---|
Tianjin University , Tianjin, China | Oct 2017 - Dec 2019 |
---|---|
The Open University, Milton Keynes, UK | Oct 2017 - Mar 2021 |
---|---|
Corvinno Technology Transfer Centre, Budapest, Hungary | Jan 2016 - Sep 2017 |
---|---|
Selected Publications
NEW |
|
|
|
|
|
Blog
Language Models: Exploring the AI Horizon in the Era of Nothing but Blue Skies
Dec 2023 - 4 MIN READ DimitrisNavigating the Challenges of Large Language Models: Tackling Hallucination and Ensuring Faithfulness in NLP
Nov 2023 - 3 MIN READ DimitrisLet's Keep in Touch
Google Scholar
- Find me on Google Scholar