Ines Krissaane

PhD Student in Bayesian Statistics

University of Nottingham

I am interested in machine learning, with a particular focus on computational statistics, deep learning, and uncertainty quantification in the field of biology, genetics and medicine.

My doctoral research aim to understand and develop robust statistical inferential methods for handling model misspecification particularly for cardiac electrophysiology modelling.

Currently, I am pursuing my PhD at the University of Nottingham affiliated with Microsoft Research Cambridge. My PhD supervisors are Richard Wilkinson , Ted Meeds , Jeremy Oakley and Gary Mirams .

Interests

Artificial Intelligence
Deep Learning
Computational Statistics
Health Data
Genetics

Education

Current PhD in Statistics, 2019-2023

University of Nottingham
MSc in Statistics, 2018 (Highest honors)

Sorbonne Université
BSc in Mathematics, 2016

Pierre and Marie Curie University

Publications

Inès Krissaane, Carlos De Niz, Alba Gutiérrez-Sacristán, Gabor Korodi, Nneka Ede, Ranjay Kumar, Jessica Lyons, Arjun Manrai, Chirag Patel, Isaac Kohane, Paul Avillach (2020). Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services.. In Journal of the American Medical Informatics Association.

DOI Interview

Inès Krissaane, Kingsley Hampton, Jumanah Alshenaifi, Richard Wilkinson (2019). Anomaly Detection Semi-Supervised Framework for Sepsis Treatment.. In IEEE.

DOI

Emma Forst, Christophe Ambroise, Inès Krissaane, Isabelle Goldringer, Jérome Enjalbert, Stephane Robin, Tristan Mary-Huard, Vincent Allard (2019). A generalized statistical framework to assess mixing ability from incomplete mixing designs using binary or higher order variety mixtures and application to wheat.. In Field Crops Research.

PDF DOI

Experience

Research Associate

Harvard University, Biomedical Informatics Department

Sep 2018 – Feb 2019 Boston, USA

Scalability and cost-effectiveness analysis of whole genome wide association studies on Google Cloud Platform and Amazon Web Services.

Visiting Research Scholar in Data Science

Harvard University, Biomedical Informatics Department

Apr 2018 – Aug 2018 Boston, USA

Supervisor : Paul Avillach

Statistics Internship

AgroParisTech - INRA

Apr 2017 – Aug 2017 Paris, France

Supervisors : Stephane Robin and Christophe Ambroise

Recent & Upcoming Talks

Next Platform TV

Interview for the paper Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services.

Aug 13, 2020 Virtual

See

AI in health & Care Study group

Rare disease prediction using Machine Learning.

Jun 26, 2019 — Jun 28, 2019 Manchester, UK

PDF See

Genetics Analysis in the cloud workshop

Cloud set up for scalable genomics analysis on the cloud using HAIL

Jun 17, 2019 — Boston, USA

Code See

Projects

Data Science Africa

Image Processing for parasites detection in Uganda.

Hodgkin Huxley Model

The Hodgkin–Huxley model, or conductance-based model, is a mathematical model that describes how action potentials in neurons are initiated and propagated. It is a set of nonlinear differential equations that approximates the electrical characteristics of excitable cells such as neurons and cardiac myocytes.

PhysioNet/Computing in Cardiology Challenge 2019

Early detection of sepsis using physiological data

Challenge Data Societé Generale

Predict the Crude Oil production trend - Rank 12th position

Markov chains for detection of genetic motifs

Simulate and analyze genetic sequences using hidden Markov chains, implement the Expectation Maximization algorithm. (Supervisor Catherine Matias).

American Election Analysis (2016)

Analyze the vote in favor of the Republican or Democratic party during the American elections in Python (Supervisor Tabea Rebafka).

Spread of COVID-19

Predict the Global Spread of COVID-19

Conferences & Summer School

Sep 14, 2020

Gaussian Process Summer School

The Gaussian Process Summer Schools are a series of schools and workshops aimed at researchers who want to understand and use Gaussian process models, both in theory and practice. The main summer schools are held in Sheffield, UK.

Jul 13, 2020

Women in Machine Learning Un-Workshop

The Women in Machine Learning will be organizing the first “un-workshop” at ICML 2020. The un-workshop is based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants.

Sep 8, 2019

Computing in Cardiology 2019

Computing in clinical cardiology and cardiovascular physiology.

Jul 15, 2019

Machine Learning Summer School 2019

The Machine Learning Summer School (MLSS) is a 12-days event where participants take intensive courses on a variety of topics in machine learning, ranging from optimization and Bayesian inference to deep learning, reinforcement learning and Gaussian processes (see topics).

Jun 18, 2019