Research Interest
Trustworthiness Artificial Intelligence. I work on Deep Learning and Machine Learning. My primary research goals are to work on Deep Neural Networks towards the Security, Explainability, and Trustworthiness of AI. Engineering and Science Applications of AI. I work on Artificial Intelligence Algorithms to solve complex Engineering and Science problems. For example, I have collaborated with researchers from various disciplines, including Engineering, Climate Science, Hydrology, Environmental Science, and Biology, to innovate and apply AI in solving problems across these fields.
Funded Project
- EPSRC | National Edge AI Hub (I am AI Theme Lead). ~12 million GBP.
- EPSRC | Federated Edge–HPC Architectures for AI Workflows £200,000
- EU Horizon Europe | 6G Path- RESCUE project £60,000
- Industry-funded projects | with multiple companies ~£178,000
- Innovate UK | AI for focasting EV Charging £116,007
- Innovate UK | AI for Energy Usage Optimization £113,740
- EPSRC PhD | Adversarial Attacks on Deep Leanring ~ £70,000
- EPSRC PhD | AI for Climate/Dynamical Systems~ £70,000
Deep Learning
(Security, Explainability and Trustworthiness of AI)
My original works have been about developing algorithms like Backpropagation Neural Tree with a long-term goal of developing neuroscience-informed new neural network algorithm design. The image below shows the computation of this neural tree algorithm and other computational models from the literature.
Figure. Biologically plausible neural computation of dendritic trees. The red circle represents a neuron (soma), black lines are dendrites, and the numbers indicate inputs. Left is a biological neuron model (Travis et al., 2005), the middle is a single neuron dendritic tree (Jones and Kording, 2021), and the right is the proposed backpropagation neural tree ( Ojha et al. 2022 ). Check previous related work on trees (Ojha et al 2016 and Ojha et al 2017). Students interested in the topics of neural networks and deep learning network architecture analysis (e.g., using adversarial robustness) and exploring neuroscience, information theory, or complex network literature for PhD research are welcome!
Adversarial Deep Learning
Figure. Adversarial attacks on deep learning: explainability, compactness, adversarial robustness (Pravin et al. 2023). Also, check our previous work on the attacks on fragile neurons of deep neural networks (Pravin et al. 2021).
Deep Learning and Computer Vision
(Image Segmentation, Object Detection, Transfer Learning)
In my recent Computer Vision work, we investigated how Transfer Learning and Semantic Segmentation can be applied to a time series of images (see Fig below) to predict flood events on the rivers (Severn and Avon, UK). Our to-date results have successfully demonstrated use to Transfer Learning with an accuracy above 91% for these flood events (check out our research: Vandaele et al. 2020 and Vandaele et al. 2021).
Figure. Time series of river camera images. Water (blue) and non-water pixel predictions (Vandaele et al. 2021).
Interdisciplinary Research
I enjoy working on interdisciplinary research and data science projects. See Publications. For example, I work with Civil Engineering researchers on the following problem. A Civil Engineering problem was solved by applying AI algorithms. In structural engineering, large steel constructions, such as bridges and domes, are built, whose structural response is characterised by large displacements and are vulnerable to progressive collapses due to buckling effects, leading to sudden failures. A thorough analysis of such buckling was done using optimisation algorithms.
Figure. Twenty-Four Member Shallow Truss Structure. Left: Top View and Side View of the structure. The central node of the structure is indicated by a lighter colour dot and arrow. The arrow indicates a vertical downward external force. Right: The results of the adaptive search space decomposition method (Center) applied on this benchmark ( Ojha et al. 2022 ).
Examiner of PhD Thesis
- Macquarie University, Australia
- Queensland University of Technology (QUT), Australia
- Durham University, UK
- University of Essex, UK
- University of Reading, UK
- Newcastle University, UK
- Northumbria University, UK
Research Student Projects (Supervised)
PhD Projects (completed and present)
Select Master/Bachelor Student Projects
- AI Safety and Security
- Adversarial attacks and defenses on autonomous vehicles, Vaicius I, (NCL, 2023, Report)
- Robustness Analysis Of Neural Networks (2022, Thesis)
- Deep learning for detecting attacks on LiDAR in autonomous vehicles
- Out-of-Distribution Detection under Lens Contamination
- Adversarial Attacks on Object Detection via Federated Learning
- Robust Training Pipelines using Adversarial Augmentations
- Computer Vision and Image Data Processing
- Self-supervised representation learning for medical image processing, Bhardwaj M (2023, Thesis)
- Ancient artifact (pottery) reconstruction based on deep learning, Li X (2024, Thesis)
- DL and box speed calibration for predicting rowing boat speed, Young R (UoR, 2021, Report)
- Convolutional neural network-based visual feature extraction for evaluation of the urban environment, Victor S (ETH Zurich, 2018, Thesis)
- Transfer learning for segmentation of waste bottles using Mask R-CNN, Jaikumar P (2020, Paper)
- Autoencoders and GANs for addressing imbalanced image data, Ashokan V (UoR, 2021, (Thesis)
- Using three GAN-based models to provide modelling inspiration (2022, Thesis)
- Monocular depth estimation for autonomous vehicles utilising a self-supervised learning, Murph C, (NCL, 2023, Report)
- Natural Language Processing / Text Data Processing
- Fake news detection with neural networks, Mehring C (UoR, 2021, Report)
- An analysis of NLP techniques applied to generating tweets, Rickard J (UoR, 2020, Report)
- Analysing and presenting the general public opinions of feature films through data mining from social media feeds and a chatbot, Braund T (UoR, 2019, Report)
- Audio/Speech Data Processing
- Time-series analysis and forecasting
- Using neural networks for time-series analysis of UK river flow data, Neele M (UoR, 2021, Report)
- A study into the effectiveness of RNNs for trading, Doidge KB (UoR, 2021, Report)
- Reading bus time prediction-A data science approach, Ford J (UoR, 2020, Report)
- Computing of local Lyapunov exponents using machine learning, Lau J (UoR, 2021, Thesis)
- Reconstruction and parameter estimation of dynamical systems using DNNs (2022, Thesis)
- Data Science Projects
- Study of diffusion of nano-particles in polymer and ferrofluids using ML, Desai SV (2021, Thesis)
- Non-spatial and spatial statistics for analysing humans’ perception of the built environment, Heidi S (ETH Zurich, 2018, Thesis)
- People’s perception of urban and architectural features, Charlotte S (ETH Zurich, 2017, Thesis)
- Machine Learning modelling of die filling for pharmaceutical powders, Peschiutta P (University of Padua, Padua, Italy, 2020) (external co-supervision with Prof C Wu) (Thesis)
Code Repository at GitHub (github.com/vojha-code)
I program in Java (mainly), Python (mostly these days for machine learning), MATLAB (occasionally when need be), and C++ (when I am looking to speed up my code). The following are a few examples of algorithms, tools, and software I have developed over the years.
- Adversarial Deep Learning (Code)
- Backpropagation Neural Tree (BNeuralT) (Code)
- Neural-Tree-Software (Code)
- Neural-Network-Predictor Software(Code)
- Conjugate-Gradient-Neural-Network (Code)
- Complex-Valued-Neural-Network (Code)
- Multiple-Linear-Regression (Code)
- Hierarchical-Fuzzy-Tree (Code)
- K-Nearest-Neighbor (Code)
- Support-Vector-Machine (Code)
- Self-Organizing-Map (Code)
- Anomaly-Detection (Code)
-
Humans Perception Through Physiological Response Code)
- Sensitivity Analysis of Evolutionary Algorithms Code)
- Search Space Decomposition Method (Code)
- Meta-heuristic-Optimizer Software(Code)
- Multilevel-Coordinate-Search (Code)
- Evolutionary-Computation (Java, Python)
-
Evolutionary-Ensemble (Code)
- Algorithm-Complexity (Code)
- Curse-of-Dimensionality (Code)
- Principal-Component-Analysis (Code)
- Kolmogorov-Smirnov-Test (Code)
- Block-Truncation-Coding (Code)
- Python-MATLAB-Communication (Code)
- Cython-Project-Hierarchy (Code)