Full-stack work across AI, data, and product

Architected a multi-agent system that connects Outplay's lead research, sequencing, email, and scheduling modules into one autonomous SDR assistant.

• Mapped end-to-end workflows so an AI agent can progress prospects from research to booked meetings.
• Defined dual autopilot and co-pilot modes to balance automation with human approvals.

Shipped an LLM email composer that tailors tone, sequencing, and data cues for every SDR persona.

• Captured product intel, objections, and personas to personalize outbound email content.
• Boosted positive reply rates by 80% across 40 pilot customers.

Delivered a copilot that classifies objections in sales conversations and surfaces context-aware rebuttals.

• Trained intent and NER models that tag objections with 95% accuracy.
• Linked rebuttal playbooks to deal stage, persona, and sentiment cues.

Designed a playbook system using NLP to match prospect objections with the best handling messages and auto-generate playbooks for new users.

• Used NLP techniques to match objections with optimal handling messages, increasing response rates.
• Created a workflow to auto-generate personalized playbooks from onboarding details.

Created a generative engine that assembles personalized multi-channel outreach sequences in minutes instead of hours.

• Cut sequence build time by 90% with persona-aware prompts and context ingestion.
• Streamed each step to the UI as soon as it was ready so SDRs could review progressively.

Built multi-output classification models to predict ADHD diagnosis and participant gender from behavioral assessment data.

• Benchmarked Logistic Regression, kNN, and XGBoost with feature engineering and evaluation techniques.
• Applied SHAP-style explanations to make model outputs interpretable for clinical researchers.

Analyzed time series sEMG data and developed deep learning and ensemble models to predict 10 hand gestures.

• Extracted 96 time- and frequency-domain features per segment from raw biosignals.
• Achieved 82% multi-class average accuracy using XGBoost.

Developed predictive models for multi-class galaxy classification using regression techniques.

• Achieved 88.78% accuracy with a multinomial logistic regression model.
• Manually built multinomial regression from individual logistic regression base models to validate the approach.

Developed a phishing website detection tool using ML classifiers to check if a given website URL is legitimate.

• Achieved 97% accuracy with Random Forest and 92% with KNN.
• Built a Flask web app for real-time URL legitimacy predictions.

Conducted a survey of 1200 students and built a classifier model to analyze sentiments towards the university.

• Surveyed 1200 students to gather sentiment data about the university.
• Developed an NLP classifier to categorize student sentiments from free-text responses.

Built a Streamlit platform that guides beginners from raw datasets to trained models with side-by-side insight panels.

• Launched a guided workflow covering upload, cleaning, visualization, modeling, and export.
• Implemented dual-panel previews so users see how preprocessing changes affect plots and tables.

Engineered an AI-powered analysis suite that scores sales calls, pinpoints objections, and surfaces next steps.

• Achieved 100% speaker identification accuracy using SpeechBrain embeddings.
• Built a call scoring model spanning tone, action items, and objection handling metrics.

Researched and prototyped automation that records Zoom calls and streams highlights without manual effort.

• Evaluated Zoom SDK pathways to automate recording and live streaming reliably.
• Developed bots that join meetings, capture audio/video, and push via WebSockets for live analytics.

Developed multiple IoT projects as part of academics using electronics, embedded C, sensors, and Arduino.

• Built temperature and humidity monitors to track growing conditions of plants.
• Developed a prototype car using IR and ultrasonic sensors for obstacle avoidance.