I am a PhD student in Artificial Intelligence and Machine Learning at Imperial College London working on deep learning approaches for analytical chemistry, chemometrics and drug discovery under the supervision of Prof. Mauricio Barahona and Prof. Dame Molly Stevens.

Recent highlights

2024:

• July: Presented our work “Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders” at the AI4Science (oral) and ML4LMS workshops at ICML 2024 in Vienna.
• July: Presented RamanSPy at the AI4Science and ML4LMS workshops at ICML 2024 in Vienna.
• June: Presented our work “Accelerating Molecular Graph Neural Networks via Knowledge Distillation” at MoML 2024 in Montreal.
• June: Participated in the ML for Drug Discovery Summer School.
• May: Our work “RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis” has been accepted to Analytical Chemistry.
• March: Joined the University of Oxford as an Academic Visitor.
• March: Our preprint titled “Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders” is out.

2023:

• December: Presented our work “Accelerating Molecular Graph Neural Networks via Knowledge Distillation” at NeurIPS 2023 in New Orleans.
• November: Our work “Accelerating Molecular Graph Neural Networks via Knowledge Distillation” has been accepted (oral) to the Learning on Graphs (LoG) Conference 2023.
• September: Our work “Accelerating Molecular Graph Neural Networks via Knowledge Distillation” has been accepted to the Conference on Neural Information Processing Systems (NeurIPS) 2023.
• July: Our work “Accelerating Molecular Graph Neural Networks via Knowledge Distillation” has been accepted to the Synergy of Scientific and Machine Learning Modeling workshop at the International Conference on Machine Learning (ICML) 2023.
• July: Our preprint on RamanSPy is out.
• June: We introduce RamanSPy - an open-source Python package for integrative Raman spectroscopy data analysis.
• June: Our preprint titled “Accelerating Molecular Graph Neural Networks via Knowledge Distillation” is out.

Selected publications

Accelerating Molecular Graph Neural Networks via Knowledge Distillation Filip Ekström Kelvinius*, Dimitar Georgiev*, Artur Petrov Toshev*, Johannes Gasteiger *Equal contribution. Order was determined by rolling a dice. Conference on Neural Information Processing Systems (NeurIPS), 2023 Code This work has also been presented at: Synergy of Scientific and Machine Learning Modeling Workshop at International Conference on Machine Learning, 2023 Learning on Graphs Conference (oral), 2023 Molecular Machine Learning Conference, 2024

RamanSPy: An open-source Python package for integrative Raman spectroscopy data analysis Dimitar Georgiev, Simon Vilms Pedersen, Ruoxiao Xie, Álvaro Fernández-Galiana, Molly M. Stevens, Mauricio Barahona Analytical Chemistry, 2024 Code Star This work has also been presented at: AI for Science Workshop at International Conference on Machine Learning, 2024 Machine Learning for Life and Material Science Workshop at International Conference on Machine Learning, 2024 This work will also be presented at: BioMedEng24 Conference, 2024

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders Dimitar Georgiev, Álvaro Fernández-Galiana, Simon Vilms Pedersen, Georgios Papadopoulos, Ruoxiao Xie, Molly M. Stevens, Mauricio Barahona Under review, 2024 This work has also been presented at: AI for Science Workshop at International Conference on Machine Learning (oral), 2024 Machine Learning for Life and Material Science Workshop at International Conference on Machine Learning, 2024 This work will also be presented at: BioMedEng24 Conference (oral), 2024

My complete, up-to-date publication record is available on Google Scholar.