Strong focus on Computer Vision, Machine Learning, Deep Learning, and Causal Inference. My passion lies in exploring complex problems at the intersection of perception, data, and decision-making— and translating research…
Strong focus on Computer Vision, Machine Learning, Deep Learning, and Causal Inference. My passion lies in exploring complex problems at the intersection of perception, data, and decision-making— and translating research into practical, impactful solutions.
I thrive on diving deep into academic literature and turning cutting-edge ideas into intuitive, accessible insights for both researchers and beginners alike. My work spans across implementing novel deep learning architectures, exploring causal reasoning in AI systems, and contributing to real-world projects that challenge the boundaries of what machines can learn and infer.
Areas of Interest:
• Vision Transformers, CNNs, and GANs
• Causal Inference & Probabilistic Graphical Models
• Representation Learning & Self-Supervised Methods
• Model Interpretability & Fair AI
What drives me?
A relentless curiosity for emerging technologies and a desire to collaborate with forward-thinking teams. I’m always on the lookout for opportunities that push me to innovate, build, and share knowledge.
Contributing to an Applied AI project focusing on end-to-end data lifecycle management.
Responsible for data collection, annotation, and preparation for model training using Label Studio.
Collaborating with the ML team to preprocess data and support model deployment pipelines.
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