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Smit Chandi

Smit Chandi

Master's in Data Science student and AI developer

Boston, Massachusetts
yocket+1
Joined February 2026
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Summary

Machine Learning practitioner with strong foundations in Python, algorithms, and data-driven problem-solving; experienced in building end-to-end systems such as RAG pipelines for financial documents, an Airbnb property recommendation bot, and multi-agent risk platforms. Passionate about applying AI to real-world problems and continuously expanding practical engineering skills. linkedin+1
Research contributor in multimodal emotion recognition: coauthor on multiple academic works examining multimodal emotion-detection techniques and architectures (vision, audio, text), indicating active involvement in applied emotion-recognition research. gov+2
Graduate-level data science training: enrolled in Northeastern University's Master's in Data Science program at Khoury College of Computer Sciences, Boston, Massachusetts, United States, indicating continued academic development in data science and applied machine learning. yocket+1
Applied AI practitioner with industry experience: listed as an AI Developer at Intelligent DataWorks, contributing to AI/ML and data engineering efforts for AssistX and other applied AI products. intelligentdataworks
Active in the data science community: maintains a Kaggle profile (username: smitchandi), suggesting engagement with data-science competitions, notebooks, or community contributions. kaggle

Work

Education

Projects

Writing

A Survey on Multi-modal Emotion Detection Techniques

Survey reviewing recent advancements in multimodal emotion recognition, covering fusion techniques, feature engineering, and classification architectures that leverage vision, audio, and text modalities.

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A Multi-Modal Emotion Recognition System Based on CNN-Transformer Deep Learning Technique

Paper describing a multimodal emotion recognition system that integrates CNN and Transformer architectures for improved emotion recognition from multiple input modalities.

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Innovative Method for Face Emotion Recognition using Hybrid Deep Neural Networks

Work presenting a hybrid deep neural network approach for face emotion recognition, applied to social media text and visual data extraction in emotion analysis tasks.

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Multimodal Emotion Recognition using Deep Learning Architectures

Study on multimodal emotion recognition exploring deep learning architectures and their application to emotion detection across different sensory streams.

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