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Schedule Interview NowAt Softaims, I have been fortunate to work in an environment that values creativity, precision, and long-term thinking. Each project presents a unique opportunity to transform abstract ideas into meaningful digital experiences that create real impact. I approach every challenge with curiosity and commitment, ensuring that every solution I design aligns not just with technical requirements, but also with human needs and business objectives. One of the most rewarding aspects of my journey here has been learning how to bridge the gap between innovation and practicality. I believe technology should simplify complexity, enhance efficiency, and empower people to do more with less friction. Whether building internal systems, optimizing workflows, or helping bring client visions to life, my focus remains on developing solutions that stand the test of time. Softaims has encouraged me to grow beyond coding—to think about design, communication, and sustainability in technology. I see every project as part of a larger ecosystem, where small details contribute to long-lasting results. My daily motivation comes from collaborating with people who share the same passion for doing meaningful work, and from seeing the tangible difference our efforts make for clients around the world. More than anything, I value the culture of learning and improvement that defines Softaims. It’s a place where ideas evolve through teamwork and constructive feedback. My goal is to continue refining my craft, exploring new approaches, and contributing to solutions that are not only efficient but also elegant in their simplicity.
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This project uses GPT API for mental health counseling by simulating expert opinions from various therapy techniques like CBT, Psychodynamic, and Mindfulness Therapy. Dynamic prompting incorporates topic inputs, few-shot examples, and social determinants of health (SDoH) to generate personalized advice. Counseling responses are evaluated on psychotherapy factors like empathy, engagement, and alliance capacity, ensuring high-quality guidance. This approach combines AI with psychotherapy expertise to deliver tailored mental health support.
In this project, I developed machine learning models to forecast sales using a variety of algorithms, including Linear Regression, Random Forest Regressor, XGBoost Regressor, and LSTM-based reinforcement learning. Trained and evaluated these models using sales data from 10 retail stores, assessing performance with metrics such as mean squared error, mean absolute error, and R2 score. This analysis helps businesses forecast sales trends and make strategic budget decisions while offering flexibility for further optimization.
In this project, I conducted a comprehensive data analysis and visualization of a video game sales dataset. The analysis involved exploring sales trends across various platforms, genres, and regions using Python libraries such as pandas, NumPy, and Matplotlib. The insights gained from this analysis help game publishers and platform developers make informed decisions, particularly highlighting the dominance of North America and Europe in sales and the potential of specific genres in Japan. This project also emphasized the importance of data-driven strategies in the gaming industry.
Fine-tuned LLaMA for Diagnosis-Related Group (DRG) prediction using sequence classification on hospital discharge summaries. The model processes MIMIC-IV clinical data, extracting brief hospital courses to classify them into standardized DRG codes. Implemented LoRA-based fine-tuning on LLaMA-7B, optimizing training efficiency. Integrated a Gradio interface for live inference, enabling seamless DRG prediction. Used preprocessing pipelines to map multi-year DRG data into a unified version.
I developed a multi model NLP pipeline to classify chatbot queries by sentiment, emotion, intent, and topic for an educational platform. And deployed on AWS SageMaker with Airflow triggers, it processes 200+ queries daily in GPU batches and stores results in RDS. I used RoBERTa for sentiment and DeBERTa for zero shot classification. Automated with lifecycle scripts for cost efficiency $5/day, the system provides structured insights for downstream analytics and personalization, with future ready support for real-time and multilingual expansion.
in
2013-01-01-2017-01-01
in
2011-01-01-2013-01-01
in
2003-01-01-2011-01-01
Master's degree in Data Science
2023-01-01-2025-01-01