Student Projects

2019/20

Individual Projects

  • Michelangelo Conserva (UCL)
  • Neil Leiser (UCL)
  • Carlos Xu (UCL)

2018/19

Individual Projects

  • Mergahney Mohammed (AIMS Rwanda): Deep Convolutional Gaussian Process Residual Learning for Image Recognition
  • Jonas Ngnawé (AIMS Rwanda): Scalable Inference with the Wasserstein Barycenter of Distributed GPs
  • Jean Kaddour: Active Learning of Task Space
  • Alexandre Maraval: Robust MPC with Learned Gaussian Process Dynamics Models
  • Samuel Ogunmola: Likelihood-Free Variational Inference and Model-Based Trajectory Matching

2017/18

Individual Projects

  • Zhe Dong: Distributional Robust Adversarial Training
  • Mike Scott: Neural Network Transparency through Comparison to Regression Models

2016/17

Individual Projects

  • Karl Taylor: Symposter: A Minimally Intrusive Application for Enhancing Poster Session Effectiveness
  • James Gartland
  • Riccardo de Lutio
  • Georg Grob: Predicting when customers return: a recurrent neural network-based survival model
  • Pavan Pinnaka: Robust Grasping & Projectile Catching
  • Samuel Coope: Arbitrary Program Generation Using Deep Learning
  • George Ivanov: Deep Generative Models for Musical Notation
  • Charles Avornyo (AIMS Senegal): Algorithm for Large-Scale Bayesian Optimization with Gaussian Processes

    Group Projects

  • Paul Vidal, Elyas Addo, Louis Blin, Florian Emile, Corentin Herbinet, Saturnin Pugnet: House Price Predictions in London

2015/16

Individual Projects

  • Rajkumar Conjeevaram Mohan: Speech Recognition using Deep Learning
  • Ahmed Osman: Data Efficient Learning and Control in Partially Observable Markov Decision Processes
  • Ryutaro Oikawa: Variational Inference and Expectation Propagation for State-Space Estimation
  • Steven Kingaby: Postr: The Poster Competition Voting System
  • Bryan Liu: On Overlapping Community-based Networks: Generation, Detection, and their Applications
  • Ross Baker: Unsupervised Learning of Low-Dimensional Representations with Autoencoders
  • Katsushi Minamizono: A Survey of Anomaly Detection Methods using Machine Learning
  • Sanket Kamthe: EEG Data Modelling
  • Simon Olofsson (Uppsala University): Probabilistic Feature Learning Using Gaussian Process Auto-Encoders
  • Mawulolo K. Ameko (AIMS Senegal): Human Motion based Classification of Friedreich’s Ataxia Disease

Group Projects

  • Radu Gheorman, Adela Baciu, Christopher Lockwood, Suryansh Rastogi, Alfonso White: Delta: A London House Price Prediction App
  • Michaelbrian Cheung, Chun Chan, Yuliya Gitlina, Chun Ho, Artem Kalikin, Samuel Wong: Guess my Social Age (Project with Starcount, supervised by Ben Chamberlain)
  • Steven Kingaby, Ilie-Cosmin Paunel, James Stewart, Dharmesh Tailor, Karl Taylor: Parallel Ninja: Practical Topic Modelling on Domains
  • Dragos Dumitrache, Tudor Cosmiuc, Daniel Hernandez, Claudia Mihai, Madalina-Ioana Sas, Alvaro Sevilla:
  • Seek: Topic Modelling and Semantic Interpretation on Unstructured Data

2014/15

Individual Projects

  • Aaron Ng: Machine Learning for a London Housing Price Prediction Mobile Application
  • Pete Turnbull: Predicting the Outcome of Online eBay Auctions using Techniques from Machine Learning
  • John Assael: From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Networks
  • Doniyor Ulmasov: Fast Bayesian Optimization with Dimension Scheduling
  • Adrien Payan: Predicting Car Tyre Degradation in Formula One Races
  • Ioannis Kasidakis: Pit Stop Predictions in Formula One with Machine Learning
  • John Gingell: Data Analysis in the Context of Formula One Racing: State Estimations from Race Car GPS
  • Graham Walker: Race Car Lap Time Prediction from GPS
  • Ian Walker: Deep Convolutional Neural Networks for Brain Computer Interface using Motor Imagery
  • Johan Kestenare: Wearable Sensors for Skier Movement Analysis (with Paul Ginzberg and MotionMetrics)
  • Adrian Millea: Information Geometry for Machine Learning

Group Projects

  • Vitalii Protsenko, Michael Douglas, Sangwon Lee, Ben Magistris, James Rodden, Yeona Kim: Insight — London Housing Price Prediction App, 2015
  • Christophe Steininger, Finlay Curran, Jaime Lennox, Ben Lindsey, Louis Mackie, Thomas Kaplan: AI Racing Challenge, 2015 (Project with G-Research)

2013/14

Individual Projects

  • Jun Wei Ng: Hierarchical Gaussian Processes for Large-Scale Bayesian Regression
  • Yuanruo Liang: Model-based Apprenticeship Learning for Robotics in High-Dimensional Spaces
  • Pedro A Martínez Mediano: Data-Efficient Reinforcement Learning for Autonomous Helicopters

Group Projects

  • Ying Deng, Edward Khon, Yuanruo Liang, Terence Lim, Jun Wei Ng, Lixiaonan Yin: GPU Implementation of Gaussian Processes, 2014
  • Albert Busquets Armengol, Michele Lo Russo, Francesco Perrone: Practical State Representation in the Invasive Species Domain of the Reinforcement Learning Competition