Student Projects

2023/24

Justin Koo (UCL): Robust Robotic Grasping Utilising Touch Sensing
Kaloyan Rusev (UCL): Safe Model-based RL with Neural Network Ensembles
Jeffery Wei (UCL): Simultaneous Unknown Object Shape Reconstruction and Pose Estimation During Active Non-prehensile Tactile Exploration
Max Norman (UCL): Gaussian Process Regression for Gridded Domains
Eirik Baekkelund (UCL): Probabilistic Solar PV Nowcasting
Rafael Anderka (UCL): Efficient Data Assimilation With Nonlinear Stochastic Partial Differential Equations Through Markov Structures

Ronald MacEachern (UCL): Sea Ice Freeboard Interpolation using Gaussian Process Regression
Sean Nassimiha (UCL): Modelling Solar Power Production withSpatio-Temporal Variational Gaussian Processes
William Bankes (UCL): AutoEncoding Normalising Flows Using Neural Ordinary Differential Equations
Rares-Ioan Jordan (UCL): On the Spectral Stability of DeepReinforcement Learning Algorithms
Bengt Lofgren (UCL): Boundary-aware Gaussian Processeses
Christopher Tan (UCL): Towards an Artificial Scientist
Maria Kapros (UCL): Analysis of state propagation and policy learning in model-based RL

Lucas Cosier (UCL)
Nanxi Zhang (UCL)
Zuka Murvanidze (UCL)
Alexander Norcliffe (UCL)
Chanel Sadrettin-Brown (UCL)
Eiki Shimizu (UCL)
Ilana Sebag (UCL)
Kai Biegun (UCL): Robustness through Online Action Correction for Model-Based Reinforcement Learning
Kamiylah Charles (UCL)
Piotr Tarasiewicz (UCL)
Ross Murphy (UCL)
Rui Li (UCL): Learning Input-Conditional Invariances via the Marginal Likelihood

Andreas Hochlehnert (UCL): A Contact-Aware Symplectic Integrator Network
Carlos Xu (UCL): Autoencoder Gaussian Process for High-Dimensional Bayesian Optimization
Lorenzo Minto (UCL): Gaussian Process Regression and Multi-task Learning for Commodity Spot Price Forecasting
Michelangelo Conserva (UCL): A Novel Kernel for Ranked Data
Neil Leiser (UCL): Nowcasting Solar Photovoltaics Output based on Satellite Images
Thomas French (Imperial College): Analyzing Fuel Load from GPS

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 (Imperial College): Active Learning of Task Space
Alexandre Maraval (Imperial College): Robust MPC with Learned Gaussian Process Dynamics Models
Samuel Ogunmola (Imperial College): Likelihood-Free Variational Inference and Model-Based Trajectory Matching

Zhe Dong (Imperial College): Distributional Robust Adversarial Training
Mike Scott (Imperial College): Neural Network Transparency through Comparison to Regression Models

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

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

Rajkumar Conjeevaram Mohan (Imperial College): Speech Recognition using Deep Learning
Ahmed Osman (Imperial College): Data Efficient Learning and Control in Partially Observable Markov Decision Processes
Ryutaro Oikawa (Imperial College): Variational Inference and Expectation Propagation for State-Space Estimation
Steven Kingaby (Imperial College): Postr: The Poster Competition Voting System
Bryan Liu (Imperial College): On Overlapping Community-based Networks: Generation, Detection, and their Applications
Ross Baker (Imperial College): Unsupervised Learning of Low-Dimensional Representations with Autoencoders
Katsushi Minamizono (Imperial College): A Survey of Anomaly Detection Methods using Machine Learning
Sanket Kamthe (Imperial College): 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

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

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

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

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

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