Imperial College London - United Kingdom
Author Profile
GOOGLE SCHOLAR
🎓 Early academic pursuits
Mohsin Masood began his academic journey with a strong foundation in computer science, securing a merit-based msc funding grant from the university of east london (2011–2012). his early academic achievements laid the groundwork for advanced training in health data science, culminating in a phd in statistical modelling from the university of strathclyde, funded by a competitive grant (2014–2017). his scholarly curiosity extended internationally through the erasmus exchange research grant at vsb-technical university of ostrava (2015–2018), where he deepened his expertise in predictive modelling and bioinformatics. during this phase, his interest in power electronics and algorithmic optimization in medical diagnostics emerged as an interdisciplinary focus.
💼 Professional endeavors
With over a decade of experience, mohsin has held impactful roles across academia and research institutions. as an assistant professor in computer science at abasyn university (2018–2022), he designed and taught advanced courses in ai, ml, and data science, incorporating real-world applications in epidemiology and digital health. later, as a senior research fellow at the university of leeds (2022–2023), he led data engineering for a large-scale 2.9 million patient cohort, employing advanced machine learning and nlp tools to analyze multimorbidity and Data Scientist cardiovascular risks. currently, he serves as a research associate at imperial college london (2023–present), where he leads predictive modelling projects on population health, integrating datasets like uk biobank, sail databank, and triphic. he also fosters collaborations with institutions such as sheffield university and a* university of singapore.
🧠 Contributions and research focus
Mohsin’s core research focus lies in multimorbidity, disease progression modelling, and epidemiological risk assessment, particularly within cardiovascular and pulmonary health Data Scientist domains. he applies deep learning, survival analysis, and multistate modelling to interpret complex patient trajectories and disease clustering. his work with electronic health records (ehrs) like hospital episode statistics (hes) reflects a commitment to improving precision medicine. integrating technologies from power electronics into patient monitoring systems has been a unique dimension of his research, promoting real-time diagnostics and health alerts. he has also contributed to educational leadership by mentoring bsc and msc students in ai-based health science projects.
🌍 Impact and influence
Mohsin’s influence extends beyond academia through his involvement in cross-institutional research, digital outreach, and public health strategy. at imperial’s national heart and lung institute (nhli), he manages social media communications to boost public engagement with medical data science. his high-impact publications have contributed to the growing intersection of Data Scientist computational modelling and health outcomes, often cited in cardiovascular and cancer research communities. he is recognized for bridging the gap between statistical modelling and power electronics, showing how smart analytics can optimize hardware for healthcare delivery.
📚 Academic cites
His academic influence is reflected in high citation counts across journals focusing on biostatistics, machine learning in medicine, and epidemiology. notable contributions include projects funded by imperial nhli’s pilot project award (2023–2024) and royan pharmaceutical’s breast cancer research grant (2020–2021). his statistical toolkits and predictive algorithms are widely referenced in studies involving survival analysis, deep learning applications in ehrs, and risk prediction modelling.
🧬 Legacy and future contributions
Mohsin’s legacy lies in fostering a data-driven healthcare ecosystem where interdisciplinary tools empower clinicians and researchers alike. he aims to develop scalable, explainable ai systems for public health, enabling real-time decision-making in hospitals and remote settings. leveraging power electronics, he envisions the integration of wearable technologies with ai for early disease detection and patient monitoring. his commitment to mentoring the next generation of health data scientists ensures a lasting academic and societal impact.
Notable Publications
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Title: Emotion classification and crowd source sensing; a lexicon based approach
Authors: R. Kamal, M.A. Shah, C. Maple, M. Masood, A. Wahid, A. Mehmood
Journal: IEEE Access
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Title: An improved particle swarm algorithm for multi-objectives based optimization in MPLS/GMPLS networks
Authors: M. Masood, M.M. Fouad, R. Kamal, I. Glesk, I.U. Khan
Journal: IEEE Access
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Title: Energy efficient software defined networking algorithm for wireless sensor networks
Authors: M. Masood, M.M. Fouad, S. Seyedzadeh, I. Glesk
Journal: Transportation Research Procedia
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Title: Proposing bat inspired heuristic algorithm for the optimization of GMPLS networks
Authors: M. Masood, M.M. Fouad, I. Glesk
Journal: 2017 25th Telecommunication Forum (TELFOR)
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Title: Analysis of artificial intelligence-based metaheuristic algorithm for MPLS network optimization
Authors: M. Masood, M.M. Fouad, I. Glesk
Journal: 2018 20th International Conference on Transparent Optical Networks (ICTON)