Hi, I'm Jelin Raphael Akkara, a Master's student in Physics of Data at the University of Padua. My interests span Natural Language Processing, Computer Vision, and their intersection in fields like Embodied AI. Below is a selection of projects, both major and minor, that have influenced my growth and shaped my interests. Feel free to reach out using the provided contact information.

Personal Site: https://jelinr.github.io/

These refer to projects that I have improved upon pre-existing models or methods using innovative approaches, often producing surprisingly good results.

• Lightweight CNN for Speech Keyword Detection: Developed a lightweight CNN model (31k parameters) to classify speech keywords with 90.27% categorical accuracy on the Google V12 Speech Commands Dataset. The model has a 39 ms average inference time and uses 90 KB memory, competing with SoTA models like TDNN (250k parameters, 94% accuracy).

• YOLOv8n Object Detection using Blob Enhancers: Improved YOLOv8n for small human (far away or occluded persons) detection by 1.1% by adding a pre-processing layer that enhances regions of interest (through blob detection) before fine-tuning the model. The preprocessing speed merely increased by 2 ms (from 7 ms to 9 ms).

• Fake News Recognition with Naive Bayes: Built an efficient text classifier in R for fake news detection using Multinomial Naive Bayes. A SQL-based approach enabled handling term-context matrices efficiently, training on 20,800 rows (average 4,544 words) in under 30 seconds.

• Dask Distributed Analysis with Big Data: Implemented anomaly detection on a large industrial dataset (~5GB) using a virtual cluster of 3 worker nodes. Efficiently utilized low-level map-reduce parallelization to process the big data.

These refer to projects that I use to get comfortable with a concept or tool. The focus of these projects is to familiarize myself with the tool, and it is less about deriving benchmark-worthy results.

• Learning Kant using LLM and RAG: Tested RAG's capability using Llama-2 as the chatbot LLM, FAISS as the vector store, and HuggingFace for the pipeline on four influential works of Immanuel Kant, optimizing prompts and parameters for best results.

• Audio Generation using Variational Autoencoders: Built a continuously varying latent space using VAE to generate speech keyword audio samples. This was done by optimizing the latent dimension and the encoder and decoder parameters, experimenting with assymetric structures.

• Predicting Plasma Crashes with Transformers: Conducted anomaly detection (magnetic crashes) on a highly imbalanced time series dataset (plasma evolution) using three architectures: 1-D CNN, DNN, and Transformer.

• Muon Pair Detection: Identified rare muon events using a signal selection strategy with linear interpolation and count thresholds on a four-layer detector, modeled after CMS drift tubes.