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Kasia Kedzierska
I am a Computational Biologist and Machine Learning Researcher dedicated to advancing methods development and application within biology and healthcare.
Currently, I lead an R&D project focused on biological Foundation Models (FMs). During my internship with the bioML team at Microsoft Research New England, I evaluated FMs for single-cell biology applications. Previously, at Novo Nordisk Research Centre in Oxford, I used NLP techniques and knowledge graphs to enhance biomedical research. Additionally, with the Turing Data Study Group, I developed a framework using Transfer Learning and the YOLO model to identify sea pens from ocean floor video footage.
With my extensive expertise in Computational Biology, coupled with a robust background in Data Science and Machine Learning, I am enthusiastic about driving innovations at the intersection of these disciplines.
Selected Work Experience
Deep Learning Researcher @ DeepLife, Remote
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present - 2024
Leading an R&D project on Foundation Models in biology, including mentoring and supervising a Master’s student.
Intern @ Microsoft Research New England, Cambridge, Massachusetts, USA
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2023
During the summer I investigated the potential of the Foundation Models in the space of single cell biology. I was mentored by Alex Lu, Ava Amini, and Lorin Crawford.
Intern @ Novo Nordisk Research Centre Oxford, Oxford, United Kingdom
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2021
I worked with NLP and knowledge graphs to screen biomedical articles to identify and prioritise therapeutic targets. To increase the impact of the analysis and increase reach I built and deployed an interactive dashboard (using R Shiny) to allow colleagues within the company to investigate and visualise the results directly in real-time.
DPhil Researcher @ Wellcome Centre for Human Genetics, Big Data Institute, University of Oxford, UK
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2024 - 2018
In my PhD project I looked at how chromatin organization influences disease initiation and progression in uterine cancer using multimodal data. I was also working on building and refining ML models of cancer evolution, specifically identyfing evolutionary trajectories in the cancer of the uterus.
Education
DPhil in Genomic Medicine and Statistics @ Nuffield Department of Medicine, Brasenose College
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2024 - 2018
PhD fully funded by the Wellcome Trust Four-year PhD Studentships in Science
M. Sc. Eng., Biotechnology @ Warsaw University of Technology
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2018 - 2015
Master thesis Analysis of the mutational burden across gene sets in cancer awarded the title of The Best Master Thesis in Bioinformatics defended in 2018.
Selected Conference Presentations
Invited attendee @ Chan Zuckerberg Initiative AI in Single-Cell Biology Workshop, Redwood City, CA, USA
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2024
Chromatin modfiers in endometrial cancer, Poster @ Biology of Genomes 2023, Cold Spring Harbor, NY, USA
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2023
Systematic characterisation of chromatin modifiers in endometrial cancer, Poster @ European Association for Cancer Research 2022 Congress, Seville, Spain
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2022
Analysis of the mutational burden across gene sets in cancer, Invited talk @ Polish Bioinformatics Society Symposium, Cracow, Poland
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2019
Selected Awards and Honors
JXTX + CSHL 2023 Biology of Genomes Scholarship @ JXTX Foundation, Cold Spring Harbor Laboratory
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2023
Awarded to outstanding graduate students in genomics and data sciences.
Graduate Prize in the ‘Outstanding work outside degree’ category @ Nuffield Department of Medicine, University of Oxford
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2022
Each year Nuffield Department of Medicine, based on nominations, awards selected PhD students based on their performance within and outside of their degree.
Senior Hulme Scholarship @ Brasenose College, University of Oxford
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2023 - 2021
Senior Hulme Scholarship is awarded by Brasenose College, University of Oxford to DPhil students whose academic performance is deemed to be exceptional.
Selected Publications
Full list of publication is available through my Google Scholar profile scholar.google.com/citations?user=Yv6poTwAAAAJ.
Assessing the limits of zero-shot foundation models in single-cell biology @ bioRxiv
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2023
K. Z. Kedzierska, L. Crawford, A. P. Amini, A. X. Lu ✨This work was featured in The New York Times✨
Data Study Group Final Report: CEFAS - Automated identification of sea pens using OpenCV and machine learning @ Zenodo
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2023
In alphabetical order: M. Asthana, R. Blackwell, S. Davis, A. Downie, J. Forsyth, K. Kedzierska, R. Mestre, Z. Reza, J. Ribeiro, P. Palola, Y. Said
Functional analysis reveals driver cooperativity and novel mechanisms in endometrial carcinogenesis @ EMBO Molecular Medicine
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2023
M. Brown, A. Leon, K. Kedzierska, C. Moore, H. L. Belnoue‐Davis, S. Flach, J. P. Lydon, F. J. DeMayo, A. Lewis, T. Bosse, I. Tomlinson, D. N. Church
Prognostic integrated image-based immune and molecular profiling in early-stage Endometrial Cancer @ Cancer Immunology Research
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2020
N. Horeweg, M. de Bruyn, R. A. Nout, E. Stelloo, K. Kedzierska, A. León-Castillo, A. Plat, K. D. Mertz, M. Osse, I. M. Jürgenliemk-Schulz, L. C.H.W. Lutgens, J. J. Jobsen, E. M. van der Steen-Banasik, V. T. Smit, C. L. Creutzberg, T. Bosse, H. W. Nijman, V. H. Koelzer and D. N. Church
Dynamics of cardiomyocyte transcriptome and chromatin landscape demarcates key events of heart development @ Genome Research
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2019
M. Pawlak, K. Z. Kedzierska, M. Migdal, K. A. Nahia, J. A. Ramilowski, L. Bugajski, K. Hashimoto, A. Marconi, K. Piwocka, P. Carninci and C. L. Winata
SONiCS: PCR stutter noise correction in genome-scale microsatellites @ Bioinformatics
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2018
K. Z. Kedzierska, L. Gerber, D. Cagnazzi, M. Krützen, A. Ratan, L. Kistler
Summer Schools & Hackathons
Sea pen identification from video footage challenge @ Turing Data Study Group @ The Alan Turing Institute, London, UK
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2022
Machine Learning Summer School @ Imperial College London, University College London, London, UK
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2019
Selected Teaching Experience
Unsupervised learning @ NGSchool2022: Machine Learning in Computational Biology, Jablonna, Poland
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2022
I co-led, with Kaspar Märtens, lecture and tutorial sessions on unsupervised learning and its use cases in computational biology. All materials are availble at github.com/kzkedzierska/ngs22_unsupervised.
Data visualization in bioinformatics - hackathon mentor @ Online hackathon NGSprint, Discord
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2021
I led the hackathon in data viusalisation with emphasis on computational biology. Teaching materials are available at github.com/kzkedzierska/NGSprint_data_viz.
Online tutorials: Python for Data Science and Introduction to Python @ NGSeminars, YouTube
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2020
I led two Python tutorials: Introduction to Python kasia.codes/talk/intro_to_python/ and Python for Data Science kasia.codes/talk/py4ds/.
Introduction to R @ Wellcome Centre for Human Genetics, Oxford, UK
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2019
8 week course in Introduction to R, Data Manipulation, Data Visualisation and RNA-seq data analysis.
Introduction to Managing Code with Git @ Wellcome Centre for Human Genetics, Oxford, UK
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2019
I led a 2-hour introduction to working with Git. Materials, including slides and exercises are available at kasia.codes/talk/into_to_git/.
Selected Grants
Visegrad Grant to organise NGSchool2022 @ Visegrad Fund
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2022 - 2020
32,190 EUR awarded towards organising affordable training and conference focusing on ML application in Computational Biology. During this project I managed an international team of volunteers and led the organisation of summer school, conference, online seminars and hackathon.
Scientific grant as part of Wellcome Trust funded DPhil @ Wellcome Trust
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2023 - 2018
30,000 GBP towards research expenses for the PhD project which allowed me to design and led pilot experiments, managing the grant for successful execution of research objectives.
Non-profit Work
President @ NGSchool Society
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2022 - 2018
The goal of the Society is to promote and support science, with emphasis on computational biology.