RESUME

I am interested in designing intelligent systems that can learn concepts from visual data. Visual data are images or videos coming from either existing available databases, or directly from the Web.

My PhD thesis was focused on the attribute-based object recognition task and centred around the framework of learning using privileged information. My current research interests include deep learning methods for understanding human behaviour (actions and emotions) from facial and bodily cues. Also I am interested in the topic of algorithmic fairness and in designing machine learning models that can overcome human and dataset collection biases. 

Professional ​
info​​
 
Services

Area Chair CVPR2021, ICLR2019, ICLR2020

Co-chair WiCV2018, BeyondLabeler

Reviewer CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, IJCAI, TPAMI, IJCV

Awards
Education
2010-2015, PhD in Computer Vision and Machine Learning

I received my PhD from the Institute of Science and Technology Austria (IST Austria). With my supervisor, Prof. Christoph Lampert, we worked on the topic "Learning with attributes for object recognition: parametric and non-parametric views".

2003-2009, MSc in Applied Mathematics

I got my BSc and MSc in Applied Mathematics, summa cum laude, from the Taras Shevchenko National University of Kyiv, Ukraine. With my supervisor, Prof. Bogdan Rublyov, we explored the area of computational geometry.

Skills

Python

PyTorch

C++

Amazon MTurk

Matlab

Research​
experience​

2017, Imperial College London, UK

From October 2017, I am working as an Imperial College Research Fellow at the Department of Computing at Imperial College London. My fellowship project is entitled "Deep Understanding of Human Behaviour from Video Data: Action and Emotion Approach".  I am collaborating with Dr Stefanos Zafeiriou and Facesoft on the topics of facial behaviour analysis. 

2015, University of Sussex, UK

From June 2015, I am a visiting research fellow at the Department of Informatics at the University of Sussex, UK. Together with Dr. Novi Quadrianto and PAL, we are working on crossmodal learning with privileged information and algorithmic fairness.

 

2013, NICTA, Australia

In December 2013 I was visiting Prof. Tiberio Caetano and Machine Learning Group at NICTA Sydney Research Laboratory, Australia.

 

2013, UT Austin, USA

I spent around three months visiting Prof. Kristen Grauman and Computer Vision Group at the University of Texas at Austin, USA, working on object recognition in images.

 

2012-2013, Cambridge, UK

I was visiting Machine Learning Group at the University of Cambridge, UK. Together with Dr. Novi Quadrianto and Prof. Zoubin Ghahramani we worked on infinite latent variable models.

Teaching​
experience​

Imperial College London, UK

In spring term 2020, I delivered lectures on algorithmic fairness methods in machine learning in the Ethics, Privacy, AI in Society module for postgraduate students at Imperial College London. 

Slides

Since 2018, I am organising the machine learning tutorials at Imperial College London as part of the Machine Learning initiative. 

Lviv Data Science Summer School, Ukraine

In summer 2018, I was invited as a lecturer for two courses:  Introduction to Computer Vision, Computer Vision for video understanding and Interpretability of automated decisions at UCU.

University of Sussex, UK

In spring 2016 - 2019, I delivered invited lectures on Deep learning models in the Computer Vision, Machine Learning, and Neural Networks courses for undergraduate and postgraduate students at the University of Sussex.

Slides

 

2013-2014, IST Austria

I was teaching assistant for the Image Processing and Statistical Machine Learning courses at IST Austria.

 

Contact me

lastname.v (at) gmail.com

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Copyright © 2015 

by Viktoriia Sharmanska

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