◦ Agricultural LLM: Developed Large Language Model (LLM) for precise agricultural recommendations using expert-verified data on 90 species. Implemented Retrieval Augmented Generation (RAG) with 82% recall and 65% precision. Integrated multi-LLM support for enhanced performance. (AgLLMs.github.io)◦ LLM Evaluation: Developed 12-task agricultural benchmark to evaluate multimodal LLMs (Claude, GPT-4, Gemini, and LLaVA). Established baseline metrics, achieving up to 73.37% F1 score with few-shot learning. (AgLLMs.github.io/AgEval)◦ 3D Plant Modeling: Evaluated Neural Radiance Fields (NeRFs) for detailed 3D plant reconstruction in various environments. Achieved 74.6% accuracy in challenging outdoor scenarios, demonstrating NeRFs’ potential for complex modeling. Developed optimization technique reducing training time by 50% with minimal accuracy loss of 7.4%.

Deployed auto-scaling in AWS Fargate; stress-tested API to validate container duplication and optimized resource usage.• Constructed end-to-end pipeline for routine stress tests, utilizing JMeter for scripting and Blazemeter via Taurus for cloud execution.• Customized GitLab CI/CD pipeline to execute tests seamlessly, guaranteeing no disruption to AWS resources or other development work• Received formal recognition in two sprint retrospectives for establishing the baseline for comprehensive load tests.

Contributed to the execution of 5 automated program repair tools for an empirical study on SLURM-based GPU clusters• Reduced execution time by 16x by enabling parallel execution of tools on 40 GPU clusters• Publication received a Distinguished Paper Award at the 38th IEEE/ACM International Conference on Automated Software Engineering.

• Developed 12 ML models (including Artificial Neural Network, Deep Belief Network, Random Forest, and Light Gradient Boosting) using 50 million data records of 18 features to predict monthly electricity consumption in Dubai.• Assessed model performance through 10-fold cross-validation, resulting in R2 scores ranging from 64.2% to 92.5%. • Optimized training time from 420.4 ms to 45.2 ms by using the decision tree model.• Authored and published research paper (publication) and facilitated a team of 6 data scientists in analyzing model outcomes.

Worked as TA for three undergraduate CS courses-Software Engineering-Digital Logic Circuits-Introduction to Computer Science

-Developed and published a solar power prediction model using artificial neural networks at the ICEIC conference.-Trained model on six months of data, achieving an RMSE of 6.9% in maximum power prediction.-Model used time of day and cleaning intervals as inputs to predict the output of soiled panels and determine the cleaning schedule to prevent losses due to soiling.-Presented two models in paper.

Dr. Naveed Arshad (Professor of CS at LUMS) and I co-authored the research paper on utilizing the potential of Big Data in smart grids. The work was published in 9th International Conference on Smart Cities and Green ICT Systems, Greece.