We can organize an interview with Aldin or any of our 25,000 available candidates within 48 hours. How would you like to proceed?
Schedule Interview NowMy name is Manasi C. and I have over 4 years of experience in the tech industry. I specialize in the following technologies: Natural Language Processing, Data Science, Deep Learning, Machine Learning, Python, etc.. I hold a degree in Bachelor of Technology (BTech), Master of Science (MS), Doctor of Philosophy (PhD). Some of the notable projects I’ve worked on include: Voice-Controlled TurtleBot for Speaker-Aware Commands, Audio Deepfake Detection Using Explainable ML, ChatGPT-Powered Custom Search Engine for Client Data, Reinforcement Learning Agent for Self-Play in Tic-Tac-Toe, COVID-19 Cough Audio Classification with Neural Networks, etc.. I am based in Noida, India. I've successfully completed 6 projects while developing at Softaims.
I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.
I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.
I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.
Main technologies
4 years
3 Years
3 Years
3 Years
Potentially possible
IBM India
Built a voice-responsive robot using ECAPA-TDNN for speaker identification and ROS for command execution. The bot could recognize exclusive speakers and act only on their voice inputs, enhancing security and user control. Streamlit was used for the user interface.
Developed an interpretable deep learning framework for detecting spoofed speech. The system replaces black-box models with explainable probabilistic features, enabling both detection and attribution of audio attacks. Achieved competitive performance while improving model transparency. This research was accepted at a major international conference, ICASSP 2025.
Developed a Streamlit-based search engine using the ChatGPT-3.5 API. The model was custom-trained on company-specific data, enabling accurate, context-aware responses tailored to internal needs and improving support and productivity.
Developed a reinforcement learning agent that trained through self-play and performed strategic gameplay against both random and DQN-based opponents. Highlighted the impact of self-play in improving decision-making.
Implemented and evaluated models to classify COVID-19 cough audio samples using Kaggle and DICOVA datasets. Achieved up to 98.9% accuracy. Cross-dataset testing provided insights into generalizability of audio diagnosis models.
Bachelor of Technology (BTech) in Artificial Intelligence
2019-01-01-2023-01-01
Master of Science (MS) in Computer science
2023-01-01-2024-01-01
Doctor of Philosophy (PhD) in Audio Deepfake Detection | Explainable AI
2024-01-01-2027-01-01