About
On top of my work at Nebula, I'm an avid contributor to the data science community:
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Articles by Jon
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A Data Science Approach to Maximizing Data Scientist Salary
A Data Science Approach to Maximizing Data Scientist Salary
By Jon Krohn
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How to Transition from Academia to Data Science
How to Transition from Academia to Data Science
By Jon Krohn
Activity
Experience
Education
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University of Oxford
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Activities and Societies: Founding Director of Oxford Entrepreneurs Incubation Centre, Social Secretary for Magdalen College Middle Common Room, Fullback for Magdalen College Football Club, Admiral of Magdalen's fleet of 16 recreational watercraft 🏴☠️
As a Wellcome Trust Doctoral Scholar, I developed and applied machine learning models to identify patterns and causal pathways within high-dimensional genomic, neuroscientific, and behavioural data.
Cited over a thousand times via authorship of 11 journal articles in high-impact publications, e.g., NeurIPS, Nature Genetics, Neurology, and PLOS ONE.
Held a diverse range of leadership roles, the most significant being the Founding Director of the Oxford Entrepreneurs Incubation…As a Wellcome Trust Doctoral Scholar, I developed and applied machine learning models to identify patterns and causal pathways within high-dimensional genomic, neuroscientific, and behavioural data.
Cited over a thousand times via authorship of 11 journal articles in high-impact publications, e.g., NeurIPS, Nature Genetics, Neurology, and PLOS ONE.
Held a diverse range of leadership roles, the most significant being the Founding Director of the Oxford Entrepreneurs Incubation Centre. During my time running it, the OEIC served as a launch pad for several successful tech start-ups, including PlinkArt, the first British firm acquired by Google.
In addition to generous funding from the Wellcome Trust, I am grateful to have received support as an Alexander Graham Bell Canada Graduate Scholar and as an Overseas Research Scholar. -
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GPA: 11.95 out of 12
Rank: 1st of 2261 students university-wide
Awarded a dozen scholarships for academics, leadership, and volunteerism, including the Governor General of Canada's Academic Medal and the university President's Scholarship.
During my time there, Wilfrid Laurier University was consistently ranked as one of the top undergraduate institutions in Canada.
Courses included:
• Linear Algebra
• Calculus
• Statistics I to IV
• Molecular Genetics
•…GPA: 11.95 out of 12
Rank: 1st of 2261 students university-wide
Awarded a dozen scholarships for academics, leadership, and volunteerism, including the Governor General of Canada's Academic Medal and the university President's Scholarship.
During my time there, Wilfrid Laurier University was consistently ranked as one of the top undergraduate institutions in Canada.
Courses included:
• Linear Algebra
• Calculus
• Statistics I to IV
• Molecular Genetics
• Genetic Analysis
• Advanced Computational Genomics
• Bioinformatics
• Molecular Evolution
• Sensory Processes & Perception
• Neurobiology
• Human Neuropsychology
• Cognition
• Reasoning & Argumentation
• Choral Chamber Ensemble
Publications
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NLP with ChatGPT (and other Large Language Models)
Pearson
Large Language Models (LLMs) such as GPT series architectures have dramatically accelerated the natural language processing (NLP) capabilities of machines in recent years. These capabilities, facilitated by LLMs’ hundreds of billions of model parameters, approach or exceed human-level performance on a staggeringly broad set of natural-language tasks — often without any task-specific training being required. In this event, leading subject-matter experts introduce LLMs and their associated…
Large Language Models (LLMs) such as GPT series architectures have dramatically accelerated the natural language processing (NLP) capabilities of machines in recent years. These capabilities, facilitated by LLMs’ hundreds of billions of model parameters, approach or exceed human-level performance on a staggeringly broad set of natural-language tasks — often without any task-specific training being required. In this event, leading subject-matter experts introduce LLMs and their associated concepts (e.g., Transformers, Attention), survey LLMs’ breadth of capabilities, and provide the best practices on how to leverage LLMs efficiently and confidently in order to supercharge your own natural-language applications.
Through this course, viewers:
• Discover the astounding state-of-the-art in Natural Language Processing (NLP) that is enabled by Large Language Models (LLMs) like ChatGPT and T5
• Understand Attention and Transformers, as well as how these essential modern NLP concepts relate to Deep Learning and LLMs
• Survey the staggeringly broad range of LLMs’ natural-language capabilities
• Learn how to use LLMs in practice, including how to train and deploy them into production NLP applicationsOther authorsSee publication -
Deep Learning Illustrated
Addison-Wesley
Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. The book was published in 2019 by Pearson’s Addison-Wesley…
Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. The book was published in 2019 by Pearson’s Addison-Wesley imprint. It became an instant #1 Bestseller in several Amazon categories.
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Genetic evidence of assortative mating in humans
Nature Human Behaviour
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Genetic Variation in the Social Environment Contributes to Health and Disease
PLOS Genetics (Volume 13)
Provides evidence that the genetics of those around you influences your own health. Was covered in the media by The Sun, The Daily Mail, the BBC, and many more news outlets.
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Predicting job application success with two-stage, Bayesian modeling of features extracted from candidate-role pairs
Proceedings of the Joint Statistical Meetings, Section for Statistical Learning and Data Science
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Factors influencing success of clinical genome sequencing across a broad spectrum of disorders
Nature Genetics (Volume 47)
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Association of UV radiation with multiple sclerosis prevalence and sex ratio in France
Neurology (Volume 76)
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Inference of causal relationships between biomarkers and outcomes in high dimensions
Journal of Systemics, Cybernetics and Informatics (Volume 9)
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Sparse Instrumental Variables: an integrative approach to biomarker validation
Journal of Epidemiology and Community Health (Volume 65)
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Sparse Instrumental Variables (SPIV) for genome-wide studies
Neural Information Processing Systems (Volume 23)
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Exposure to a context previously associated with nausea elicits conditioned gaping in rats: A model of anticipatory nausea
Behavioural Brain Research (Volume 187)
Patents
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Apparatus for Determining Role Fitness While Eliminating Unwanted Bias
Issued US20230222409A1
A multicore apparatus determines fitness of a candidate for a role. The apparatus includes a multicore system processing device, a plurality of parallel multicore graphics processing devices, a network interface device, a storage device, and a system interface bus. The network interface device provides remote connection to the multicore system processing device. The storage device stores training data including positive and negative examples. The positive examples represent candidates who would…
A multicore apparatus determines fitness of a candidate for a role. The apparatus includes a multicore system processing device, a plurality of parallel multicore graphics processing devices, a network interface device, a storage device, and a system interface bus. The network interface device provides remote connection to the multicore system processing device. The storage device stores training data including positive and negative examples. The positive examples represent candidates who would be invited to an interview, and the negative examples represent candidates who would not be invited to an interview. The positive and negative examples are used by the plurality of parallel multicore graphics processing devices to train a deep learning model, which is used by the multicore system processing device to determine fitness of the candidate for the role while eliminating unwanted bias.
Other inventorsSee patent
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