Adrianna Janik

Researcher - CONTACT ME ↓

   ada.janik@gmail.com

Researcher with 6+ years of experience in R&D. Currently working in Accenture Labs, Dublin on Explainable AI and Graph ML for Life Sciences. B.E. in Control Engineering and Robotics, double M.S. in Data Science with minor in Entrepreneurship via EIT Digital. Inspired by new challenges and cross-disciplinary interactions. Gaining business acumen as EMBA Candidate at Trinity College Dublin. Publications • Experience • Education • Volunteering • Interests • Blog• Misc

Publications

JCO CCI Machine Learning–Assisted Recurrence Prediction for Patients With Early-Stage Non–Small-Cell Lung Cancer Adrianna Janik, Maria Torrente, Luca Costabello, Virginia Calvo, Brian Walsh, Carlos Camps, Sameh K Mohamed, Ana L Ortega, Vít Nováček, Bartomeu Massutí, Pasquale Minervini, M Rosario Garcia Campelo, Edel Del Barco, Joaquim Bosch-Barrera, Ernestina Menasalvas, Mohan Timilsina, Mariano Provencio Tumor Recurrence Prediction, Graph ML, XAI
PLOS ONE Examining explainable clinical decision support systems with think aloud protocols Sabrina G. Anjara, Adrianna Janik, Amy Dunford-Stenger, Kenneth Mc Kenzie, Ana Collazo-Lorduy, Maria Torrente, Luca Costabello, Mariano Provencio Tumor Recurrence Prediction, Graph ML, XAI, Think Aloud Protocol
Expert Systems with Applications Machine learning estimated probability of relapse in early-stage non-small-cell lung cancer patients with aneuploidy imputation scores and knowledge graph embeddings Samuele Buosi, Mohan Timilsina, Adrianna Janik, Luca Costabello, Maria Torrente, Mariano Provencio, Dirk Fey, Vít Nováček Tumor Recurrence Prediction, ML
IJCNN '23 Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient Mohan Timilsina, Samuele Buosi, Adrianna Janik, Pasquale Minervini, Luca Costabello, Maria Torrente, Mariano Provencio, Virginia Calvo, Carlos Camps, Ana L Ortega, Bartomeu Massutí, M Rosario Garcia Campelo, Edel del Barco, Joaquim Bosch-Barrera, Vit Novacek Tumor Recurrence Prediction, ML
Journal of Biomedical Informatics Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer Mohan Timilsina, Dirk Fey, Samuele Buosi, Adrianna Janik, Luca Costabello, Enric Carcereny, Delvys Rodrıguez Abreu, Manuel Cobo, Rafael López Castro, Reyes Bernabé, Pasquale Minervini, Maria Torrente, Mariano Provencio, Vít Nováček Tumor Recurrence Prediction, ML
AMIA '22 Integration of Clinical Information and Imputed Aneuploidy Scores to Enhance Relapse Prediction in Early Stage Lung Cancer Patients Mohan Timilsina, Samuele Bousi, Dirk Fey, Adrianna Janik, Maria Torrente, Mariano Provencio, Alberto Bermúdez, Enric Carcereny, Luca Costabello, Delvys Abreu, Manuel Cobo, Rafael Castro, Reyes Bernabé, Maria Guirado, Pasquale Minervini, Vít Nováček Tumor Recurrence Prediction, ML
pre-print Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples Adrianna Janik, Luca Costabello Graph ML, XAI
Web Conference '22 Unsupervised Customer Segmentation with Knowledge Graph Embeddings Sumit Pai, Fiona Brennan, Adrianna Janik, Teutly Correia, Luca Costabello Graph ML
AMIA '21 On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer Sameh K. Mohamed, Brian Walsh, Mohan Timilsina, Maria Torrente, Fabio Franco, Mariano Provencio, Adrianna Janik, Luca Costabello, Pasquale Minervini, Pontus Stenetorp, Vít Novácek XAI
SPIE Medical Imaging '21 Interpretability of a deep learning model in the application of cardiac MRI segmentation with an ACDC challenge dataset Adrianna Janik, Jonathan Dodd, Georgiana Ifrim, Kris Sankaran, Kathleen CurranXAI
Master's Thesis '19 Interpretability of a Deep Learning Model for Semantic Segmentation: Example of Remote Sensing Application Adrianna Janik XAI
KDD '19, XAI Discovering Concepts in Learned Representations using Statistical Inference and Interactive Visualization Adrianna Janik, Kris Sankaran XAI
EuroVis '19, MLVis Interpreting Black-Box Semantic Segmentation Models in Remote Sensing Applications Adrianna Janik, Kris Sankaran, Anthony Ortiz MLVis
LTC '17 Can word embeddings be used in an application of morphosyntactic disambiguation task? Adrianna Janik PolEval
'16 Estimation of Travel Time in the City Based on Intelligent Transportation System Traffic Data with the Use of Neural Networks Piotr Ciskowski, Adrianna Janik, Marek Bazan, Krzysztof Halawa, Tomasz Janiczek, Andrzej Rusiecki

Experience

GO UP ↑

Researcher

Interpretability in Knowledge Graph Embedding Models. Training and testing machine learning models. Designing and running experiments. Developing proof of technologies and concepts. I was a part of a CLARIFY project developing predictive models for early-stage lung cancer relapse prediction. You can read more about woman scientists in the project in CLARIFY International Day of Women and Girls in Science where I featured. I am also an active contributor of Accenture's graph machine learning open-source library AmpliGraph. I was also one of the organizers of two graph ML tutorials Knowledge Graph Embeddings for NLP: From Theory to Practice during COLING 2022 and Knowledge Graph Embeddings Tutorial: From Theory to Practice during ECAI 2020

since May 2020
Accenture Labs, Dublin
Doctoral Student

During my time as a PhD student I was working as a Teaching Assistant for Advanced Machine Learning course led by prof. Brian McNamee for Masters Program students and also as a demonstrator during Software Development course and Data Visualization course.

Sept 2019 - May 2020 (discontinued)
University College Dublin ML-Labs
Visiting Researcher

Humanitarian AI for disasters mapping. Computer Vision, Interpretability in Deep Learning. Training and testing deep neural nets on the cluster with GPUs for image segmentation, developing interpretability methods for deep learning, writing scientific articles, developing demos U-Net Vis, Concept Vis, interacting with industrial and NGOs collaborators. (Python, PyTorch, d3js, SLURM, Singularity)

U-Net Vis Concepts Vis
Feb 2019 - Sep 2019
University of Montreal, Montreal Institute for Learning Algorithms
Data Scientist - Freelancer

Temporal expressions detection with standard Time-ML - training the initial model that recognizes the expression and time relationships between time points and situations. Project was a part of CLARIN ERIC — European Research Infrastructure Consortium. I was developing an initial module under CLARIN-PL.

Dec 2017 - Jan 2018
Polish Academy of Science
R&D Embedded Python Software Developer

Work in a team responsible for development of the component test framework for 5G components.

Jul 2017 - Sep 2017
Nokia Networks 5G
Machine Learning Intern

Machine learning project concerning protein pockets detection in new medicine discovery process for virtual screening. Organized by Estymator scientific circle in PWR.

Jul 2017 - Jul 2017
Wroclaw University of Technology
R&D Embedded Software C++ Engineer

Work in a team responsible for time synchronization in Base Transceiver Station. My main task: development and maintenance of a web application for analyzing and visualizing BTS logs with machine learning support in Python hosted in a cloud. Other tasks: writing new features in C++14/17, fixing software bugs, unit-testing, documentation.

Nov 2016 - Jul 2017
Nokia Networks MCUHWAPI
R&D Embedded Software Engineer Intern

Writing new features in C++14, fixing software bugs, refactorization, unit-testing, component tests in robot framework and google test, writing documentation. One of my task was also to refactor Holdover algorithm part of the code, especially logger part, reimplementation was done with the usage EBNF notation of format and I was responsible for writing reader and writer for this configuration as a checker of configuration context-free grammar

Mar 2015 - Oct 2016
Nokia Networks MCUHWAPI
Robotics Intern

Developing application for robotic arm: Epson SCARA G3 351S to test pins in electronic device using state machine combining LabVIEW software and Spel language. Interfacing Astraada HMI 10' for robotic arm through TCP/IP using Astraada CFG and through RS232. Basic programming of PLC Versa Max Micro controllers using Proficy Machine Edition. Designing and simulating robotized production cells with Kawasaki in K - Roset Programming Tool and Blender graphical environment: Robot welding station in few configurations (single-axis positioner,rotary table, rotary table and positioners) and CNC robotized production cell.

Feb 2014 - Jul 2014
Astor

Education

GO UP ↑

Jul-Aug 2019
Summer School
Deep Learning and Reinforcement Learning Summer School

At this summer school hosted by CIFAR and AMII I extended further my knowledge in AI especiallyinteresting for me was the reinforcement learning part which connects well with my previous education in control engineering and robotics. I had an opportunity to listen and meet researchers both from industry and academia e.g. from DeepMind, Borealis, AMII, CIFAR, Vector Institute and of course MILA. Networking with other students from around the world was an important part of the summer school and I made a lots of meaningful research connections.

DLRLSS, Edmonton, Canada
Aug 2018 - Feb 2020
Master year 2
DATA SCIENCE
KTH Royal Institute of Technology

Specialization: Distributed Systems and Data Mining for Really Big Data

EIT Digital Master Program, Sweden
Aug 2018
Summer School
YALP Yerevan Academy of Linguistics and Philosophy

I took a linguistics track with the goal of extending my knowledge about language to be able to write better algorithms for natural language processing

YALP, Yerevan, Armenia
Jul 2018
Summer School
TMLSS Transylvanian Machine Learning Summer School

TMLSS, Cluj-Napoca, Romania
Jul 2018
Summer School
Cybersecurity and Privacy

EIT Digital Trento, Italy
Sep 2017 - Jul 2018
Master year 1
DATA SCIENCE
UNS Nice Sophia Antipolis

During this year I had an opportunity to work in Inria team - BIOVISION on computerized generation of MNREAD reading charts with the usage of Deep Recurrent Neural Networks, I also learned a lot about semantic web

EIT Digital Master Program France
Jan 2017 - Nov 2017
Course
Data Science Bootcamp

I submitted my final project for PolEval competition: morphosynthactic disambiguation for Polish language and my idea was accepted for Language & Technology Conference 2017 where I presented it as a poster

Sages, Warsaw, Poland
Jul 2016
Summer School
Future Cloud and Big Data Summer School

EIT Digital Stockholm, Sweden
Oct 2012 - Feb 2016
Bachelor of Engineering
Control Engineering and Robotics

Specialization: Information Technologies in Control Systems Research thesis: Travel time prediction with neural nets based on Intelligent Transportation System data, outcomes published in a chapter: Estimation of Travel Time in the City Based on Intelligent Transportation System Traffic Data with the Use of Neural Networks of Dependability Engineering and Complex Systems by Springer, article can be found here

Wroclaw University of Science and Technology, Poland
Sep 2014- Jan 2015
Erasmus + Exchange
Control Engineering and Robotics

Cork Institute of Technology, Ireland

Volunteering

GO UP ↑

Interests

GO UP ↑

Blog

Posts that I have published on medium - friend links

Tutorials

GO UP ↑