Summary
Luca Miglietta is a Data Scientist holding a Ph.D. from the Electrical and Electronic Engineering Department and Infectious Disease Department (NHS imperial) at Imperial College London. He is the author of over 15 peer-review articles and 4 international patents. He received the Imperial EEE Collaboration Award in 2020, as a key worker during the COVID-19 pandemic, and the 3-minute-thesis Award in 2022.
His interest is in applying novel data-driven methodologies to healthcare and Point-of-Care (POC) instruments. He has computing skills as Bioinformatician for in-silico assay design and data analysis.
He has over 8 years of industry experience in Data Science, Molecular Biology, Bioinformatics, and product development for diagnostics. He has developed several genetic test for the detection and differentiation of SNPs (currently on the market), and several diagnostics panels, such as COVID-19 and Respiratory tract infectious (currently on the market).
His main research focus is on the development of novel data-driven methodologies to analyse information encoded in biological signals with the support of Artificial Intelligence and Machine learning algorithms.
Publications
Journals
Mao Y, Miglietta L, Kreitmann L, et al., 2023, Deep domain adaptation enhances Amplification Curve Analysis for single-channel multiplexing in real-time PCR, Ieee Journal of Biomedical and Health Informatics, Vol:27, ISSN:2168-2208, Pages:3093-3103
Kreitmann L, Miglietta L, Xu K, et al., 2023, Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR, Trac Trends in Analytical Chemistry, Vol:160, ISSN:0165-9936, Pages:1-11
Pennisi I, Moniri A, Miscourides N, et al., 2022, Discrimination of bacterial and viral infection using host-RNA signatures integrated in a lab-on-chip platform, Biosensors & Bioelectronics, Vol:216, ISSN:0956-5663
Miglietta L, Xu K, Chhaya P, et al., 2022, Adaptive filtering framework to remove nonspecific and low-efficiency reactions in multiplex digital PCR based on sigmoidal trends., Analytical Chemistry, Vol:94, ISSN:0003-2700, Pages:14159-14168
Moser N, Yu L-S, Rodriguez Manzano J, et al., 2022, Quantitative detection of dengue serotypes using a smartphone-connected handheld Lab-on-Chip platform, Frontiers in Bioengineering and Biotechnology, Vol:10, ISSN:2296-4185, Pages:1-14