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Cătălina Cangea

PhD student in Machine Learning

University of Cambridge

Biography

I am a third-year PhD student at the Department of Computer Science and Technology, University of Cambridge, supervised by Pietro Liò and affiliated with King’s College. My research focuses on learning multimodal and graph-structured representations of the world.

My professional experience includes undergraduate Software Engineering internships at Google and Facebook, continuing with more research-oriented ones at Mila, X and DeepMind (starting July 2020).

I also have a great passion for teaching and am constantly involved in academic and departmental activities - supervising undergraduate courses, final year projects and master’s research projects (~180h and ~50 students), interviewing CS applicants, chairing women@CL and introducing professionals to ML concepts as a Cambridge Spark teaching fellow.

In my “free time”, I row with the Women’s First Boat in King’s College, play guitar/sing in a rock band and chase my favourite bands on tour. 🎼

Interests

  • Graph Representation Learning
  • Cross-Modal Learning
  • Visual Reasoning

Education

  • PhD in Machine Learning, 2021 (expected)

    University of Cambridge

  • MPhil in Advanced Computer Science, 2017

    University of Cambridge

  • BA in Computer Science, 2016

    University of Cambridge

News

Gave a spotlight talk at the NeurIPS ViGIL workshop on VideoNavQA!

Two (1, 2) papers accepted at NeurIPS workshops.

Received the departmental Wiseman Prize (only six recipients this year!)

Tea Talk at Mila on VideoNavQA, our new study and benchmark.

Supervising two undergraduate projects and one master’s project this year!

One paper published by IEEE Transactions on Neural Networks and Learning Systems.

One paper accepted at BMVC.

Released the VideoNavQA benchmark!

Started the AI Residency at X, the moonshot factory.

Two papers (1, 2) accepted at ICLR workshops.

Two papers (1, 2) accepted at NeurIPS workshops.

Co-supervising one undergraduate and two master’s projects this year.

Started the research internship at Mila, working with Aaron Courville.

Recent Publications

Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation

Scene graph generation (SGG) aims to predict graph-structured descriptions of input images, in the form of objects and relationships …

Deep Graph Mapper: Seeing Graphs through the Neural Lens

Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties …

The PlayStation Reinforcement Learning Environment (PSXLE)

We propose a new benchmark environment for evaluating Reinforcement Learning (RL) algorithms: the PlayStation Learning Environment …

XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification

In recent years, there have been numerous developments towards solving multimodal tasks, aiming to learn a stronger representation than …

VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering

Embodied Question Answering (EQA) is a recently proposed task, where an agent is placed in a rich 3D environment and must act based …

Experience

 
 
 
 
 

(Remote) Research Scientist Intern

DeepMind

Jul 2020 – Oct 2020 London, UK
Will be working with Piotr Mirowski and Raia Hadsell!
 
 
 
 
 

AI Resident

X, the moonshot factory

May 2019 – Aug 2019 Mountain View, California
Worked on a real-world challenging problem, using and adapting state-of-the-art ML techniques.
 
 
 
 
 

Reviewer

Conferences and workshops

Nov 2018 – Present
 
 
 
 
 

Research Intern

Mila

Jul 2018 – Sep 2018 Montréal, Canada
Collaboration with Aaron Courville on a visual reasoning project involving a novel benchmark and alternative perspective on EQA-style tasks.
 
 
 
 
 

Machine Learning Teaching Fellow

Cambridge Spark

May 2017 – Present Cambridge, UK
Teaching the Neural Networks module from the Applied Data Science London Bootcamp to industry professionals.
 
 
 
 
 

Admissions Interviewer

University of Cambridge

Dec 2016 – Dec 2017 Cambridge, UK
Undergraduate admissions interviews for the Computer Science Tripos, in Murray Edwards College (Dec 2016) and King’s College (Dec 2017).
 
 
 
 
 

Supervisor

University of Cambridge

Oct 2016 – Present Cambridge, UK

MPhil in Advanced Computer Science research projects: Representation Learning for Spatio-Temporal Graphs (Felix Opolka, 2018-19) (85100), Dynamic Temporal Analysis for Graph Structured Data (Aaron Solomon, 2018-19).

Computer Science Tripos Part II projects: Benchmarking Graph Neural Networks using Wikipedia (Péter Mernyei, 2019-20), The PlayStation Reinforcement Learning Environment (Carlos Purves, 2018-19) (80100), Deep Learning for Music Recommendation (Andrew Wells, 2017-18) (76100).

Undergraduate courses for Murray Edwards, King’s, and Queens’ Colleges: AI, Databases, Discrete Mathematics, Foundations of Computer Science, Logic and Proof, Machine Learning and Real-world Data.

 
 
 
 
 

Software Engineer Intern

Facebook

Jun 2016 – Sep 2016 London, UK
LogDevice team. I optimised client operations on a RocksDB database and implemented a new API required by another team in Facebook.
 
 
 
 
 

Software Engineer Intern

Facebook

Jul 2015 – Sep 2015 New York, USA
iOS Product Infrastructure Team. I worked towards delivering a better experience for users of the Facebook iOS app. My project aimed to reduce the time taken to load content close to the area currently being viewed on screen, by improving the prioritization system for network requests.
 
 
 
 
 

STEP Intern

Google

Jun 2014 – Sep 2014 Zurich, Switzerland
YouTube Uploads team. I added processing progress for video uploads on several YouTube pages, as the Upload page was the only one displaying this information.

Recent & Upcoming Talks

Deep Graph Mapper: Seeing Graphs through the Neural Lens (remote talk, with Cristian Bodnar)

A novel method based on topology and GNNs for graph visualisation and pooling.

Graph generation methods

A short intro to graph generation methods in recent ML research.

UCL CSML Talk

Challenges and approaches to QA in realistic environments; an introduction to VideoNavQA, an alternative take on EQA.

ViGIL Spotlight Talk @ NeurIPS 2019

Introducing the recently published VideoNavQA benchmark and study (BMVC 2019).

Question Answering in Realistic Visual Environments: Challenges and Approaches

An overview of challenging QA tasks and an alternative take on EQA.

Accomplishments

Wiseman Award

The award acknowledges those who make a commendable contribution to the work of the Department, going above and beyond the requirements of their course or project.

MPhil Graduation Prize

Awarded for obtaining a Distinction in the MPhil degree.

Master of Philosophy in Advanced Computer Science

Graduated with Distinction.

Bronze Medal

Won 3rd place at the Hack Cambridge MLH hackathon, as part of team facejack.

Rosemary Murray Scholarship

Awarded for obtaining a First Class result in Part II of the Computer Science Tripos.

Bachelor of Arts in Computer Science

First Class honours in final year.

Paula Browne Scholarship

Received the scholarship every year during my undergraduate degree. The scholarship was given to only one other student in my year.

Silver Medal

Awarded for obtaining 10th place at the National phase of the Olympiad during 10th grade.

Special Prize for Best Writing Style

Awarded for obtaining the highest score on the essay that was part of the written task.

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