Professor Adil Khan

Professor Adil Khan

Professor

Faculty and Department

  • Faculty of Science and Engineering
  • School of Computer Science

Summary

Adil Khan is a Professor of Machine Learning (ML) and Artificial Intelligence (AI). He has more than sixteen years of research, development, and teaching experience in AI and ML. His work comprises both traditional machine learning methods and deep learning techniques.

He has developed industrial solutions for Action and Expression Recognition, Remote Sensing, Medical Image Analysis, Natural Language Processing, Crime Detection, and Accident Detection problems. As for theoretical research, his work aims to help ML find answers to some of the most critical questions. For example, how to train ML models in the absence of large amounts of training data? How to improve the generalization of deep neural networks? How to enable ML models to adapt and generalize to new target domains? How to protect such models from adversarial attacks? How to ensure that these models would make fair decisions? What causes catastrophic forgetting in deep neural networks, and how can we overcome it?

Exemplar Projects:

Attention Sparsity for Efficient Document Ranking, 2022 - 2023

Goal: Designing novel sparse attention mechanisms to improve the efficiency and relevancy of the search engine result page (SERP).

Role: Principal Investigator, Amount: $150K, Funding Body: Huawei

Multi-modal Deep Rank for Efficient Document Ranking, 2021 - 2022

Goal: Designing deep ranking networks to improve the relevancy of the search engine result page (SERP) via incorporating alternative modalities into the Semantic Vector Space.

Role: Principal Investigator, Amount: $150K, Funding Body: Huawei

Fair, Robust and Life-long Machine Learning, 2021 - 2023

Goal: Designing new representation learning techniques to ensure fair and robust machine learning models that are capable of incremental learning without falling victim to catastrophic forgetting.

Role: Lead Scientist, Amount: $10 Million, Funding Body: The Analytical Center of Artificial Intelligence of Innopolis University

Robust Data Augmentation for Deep Networks, 2019 - 2020

Goal: Designing and building a new data augmentation methods to fine-tune trained deep neural networks to improve their generalization performance for financial markets.

Role: Principal Investigator, Amount: $100K, Funding Body: Sermaya Financial

551458: Artificial Intelligence

662086: Machine Learning

771948: Machine Learning & Deep Learning

Recent outputs

View more outputs

Journal Article

Improving Generalization for Hyperspectral Image Classification: The Impact of Disjoint Sampling on Deep Models

Ahmad, M., Mazzara, M., Distefano, S., Khan, A. M., & Altuwaijri, H. A. (2024). Improving Generalization for Hyperspectral Image Classification: The Impact of Disjoint Sampling on Deep Models. Computers, Materials & Continua, 81(1), 503-532. https://doi.org/10.32604/cmc.2024.056318

Leveraging Deep Reinforcement Learning and Healthcare Devices for Active Travelling in Smart Cities

Kazmi, S. M. A., Khan, Z., Khan, A., Mazzara, M., & Khattak, A. M. (online). Leveraging Deep Reinforcement Learning and Healthcare Devices for Active Travelling in Smart Cities. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2024.3470978

A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study

Azam, M. M. B., Anwaar, F., Khan, A. M., Anwar, M., Ghani, H. B. A., Eisa, T. A. E., & Abdelmaboud, A. (2024). A hybrid contextual framework to predict severity of infectious disease: COVID-19 case study. Egyptian Informatics Journal, 27, Article 100508. https://doi.org/10.1016/j.eij.2024.100508

Presentation / Conference Contribution

Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE

Raza, M., Khattak, A., Abbas, W., & Khan, A. (2024, June). Global Knowledge, Local Impact: Domain Adaptation and Classification for Obesity in the UAE. Presented at 37th IEEE International Symposium on Computer-Based Medical Systems (CBMS), Guadalajara, Mexico

LLM-guided Instance-level Image Manipulation with Diffusion U-Net Cross-Attention Maps

Palaev, A., Khan, A., & Kazmi, A. (2024, November). LLM-guided Instance-level Image Manipulation with Diffusion U-Net Cross-Attention Maps. Paper presented at The 35th British Machine Vision Conference, Glasgow

Research interests

Theory of Machine Learning (ML)

ML Robustness

ML Fairness

Domain Adaptation

Explainablity by Design in ML

Co-investigator

Project

Funder

Grant

Started

Status

Project

Office for Students PG Conversion Course Funding

Funder

Office for Students

Grant

£500,000.00

Started

1 September 2023

Status

Ongoing

Project

Wolfson Equipment bid 2023 - Confocal Imaging ZEISS Elyra 7 with Lattice SIM²

Funder

Wolfson Foundation

Grant

£500,000.00

Started

1 October 2024

Status

Ongoing

Project

AKT TRUST-LLM: Tailored Responsible Use of Safe and Trustworthy LLM for Planning Law Institutional Memory Management

Funder

Innovate UK

Grant

£29,958.00

Started

25 March 2024

Status

Complete

External examiner role

External Examiner for London School of Innovation (LSI) MSc Applied AI and Machine Learning

2024

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