Hongsheng Dai | Pharmacogneomics and Personalized medicine | Best Researcher Award 

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Prof Hongsheng Dai | Pharmacogneomics and personalized medicine | Best Researcher Award 

Newcastle University | United Kingdom

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Early Academic Pursuits

Hongsheng Dai pursued his academic journey with a B.Sc. in Applied Mathematics from Tianjin University, followed by an M.Sc. in Statistics from Beijing University, where he focused on survival analysis. He furthered his studies with a D.Phil. in Statistics from the University of Oxford, specializing in perfect Monte Carlo simulation techniques.

Professional Endeavors

Dai's professional journey showcases a progression from lecturer roles to professorships, demonstrating his dedication and expertise in the field of statistics. He held positions at various prestigious institutions, including Lancaster University, Brighton University, and the University of Essex, before assuming his current role as a Professor in Statistics at Newcastle University.

Contributions and Research Focus on Pharmacogenomics and personalized medicine

Dai's research interests encompass a broad spectrum of statistical methodologies, including exact Monte Carlo simulations, Bayesian computational methods, graphical models, mixture models, and survival analysis. His notable contributions include advancements in coupling from the past, path-space rejection sampling, Bayesian fusion, and nonparametric survival analysis for both censored and truncated bivariate data.

Impact and Influence

Dai's research has garnered significant recognition, as evidenced by the grants he has secured, such as the ERC Synergy Grant and EPSRC funding. His work on responsible AI for gender and ethnic labor market equality reflects his commitment to addressing societal challenges through statistical methodologies.

Academic Citations

Dai's work has likely garnered citations in various academic publications, particularly in the fields of statistics, computational methods, and applied mathematics, given the breadth and depth of his research contributions.

Legacy and Future Contributions

Dai's legacy lies in his substantial contributions to statistical methodologies, particularly in exact Monte Carlo simulations and survival analysis. His interdisciplinary collaborations and innovative approaches have positioned him as a leader in the field. Looking forward, Dai is poised to continue making significant contributions to statistical research, with potential applications in diverse domains.

Notable Publications

Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation 2022(7)

The role of insulators and transcription in 3D chromatin organization of flies 2022(18)

Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving 2022(15)

ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data 2022(4)

Efficient empirical likelihood inference for recovery rate of COVID19 under double-censoring 2022 (1)

Muhammed Dawood | Pharmacogenomics and Personalized Medicine | Best Researcher Award

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Mr. Muhammed Dawood | Pharmacogenomics and personalized medicine | Best Researcher Award

Computer Science Department | United Kingdom

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EARLY ACADEMIC PURSUITS

Muhammad Dawood's academic journey began with a Bachelor's in Computer Software Engineering from the University of Engineering and Technology (UET) Peshawar, Pakistan, where his thesis explored a novel approach for Quran learning using Neuro-Linguistic Programming (NLP) and Total Physical Interaction (TPI) in handheld devices. He continued his studies with a Master's in Computer Science from the Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, focusing on the analysis of hurricane satellite imagery.

PROFESSIONAL ENDEAVORS

Following his master's, Dawood gained teaching and industrial experience. As a Lecturer at the Department of Software Engineering, University of Science & Technology Bannu, Pakistan, he taught various courses, supervised undergraduate final year projects, and served as a coordinator. Additionally, he worked as a Machine Learning Engineer at INTAQSOL Islamabad, Pakistan, before embarking on his Ph.D. journey at the TIA Research Centre, Computer Science Department, University of Warwick.

CONTRIBUTIONS AND RESEARCH FOCUS ON PHARMACOGENOMICS AND PERSONALIZED MEDICINE

Dawood's contributions span a diverse range of projects, reflecting his expertise in computational pathology, genomic data science, machine learning, and software engineering. Notable projects include gene expression prediction, cellular composition prediction, and drug sensitivity prediction from histology images. His research also delves into hurricane intensity prediction, personalized aesthetic-based apparel recommendation, and sentiment analysis of Twitter tweets.

IMPACT AND INFLUENCE

Muhammad Dawood's impactful work is underscored by numerous achievements. He secured a fully funded studentship for his Ph.D. studies from GlaxoSmithKline and received the Best Paper Award at ICCV CDpath 2021. His teaching contributions as a Graduate Teaching Assistant at the University of Warwick further showcase his commitment to academic excellence.

ACADEMIC CITES

Dawood's research, particularly in the field of computational pathology and genomics, has likely garnered attention within the academic community. While specific citation metrics are not provided, his Best Paper Award at ICCV CDpath 2021 suggests recognition and appreciation for the quality of his work.

LEGACY AND FUTURE CONTRIBUTIONS

As a fourth-year Ph.D. student, Dawood's legacy includes a diverse portfolio of projects that contribute to the understanding of cellular processes, cancer therapy personalization, and drug design. His future contributions are anticipated to further advance the fields of machine learning, computational pathology, and genomic data science, leaving a lasting impact on personalized medicine and beyond.

Notable Publications

SlideGraph+: Whole slide image level graphs to predict HER2 status in breast cancer 2022(47)

Deep-PHURIE: deep learning based hurricane intensity estimation from infrared satellite imager  2020(37)

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting 2023(5)