Khushi Chelani

I’m Khushi. I recently graduated in Computer Science from VIT University with an 8 CGPA. Over the last few years, I’ve worked on a mix of analytics, research, and product-oriented projects through internships and academic work. I’ve explored areas like deepfake learning, data analysis, and business problem-solving, which made me realize I enjoy roles where technology and strategy come together.

Apart from academics, I enjoy taking initiative, learning quickly, and working closely with people to solve problems. I’m particularly interested in analytics, product, and growth-focused roles because I like understanding user behavior, identifying opportunities, and turning insights into action. Right now, I’m looking for an opportunity where I can contribute, learn fast, and grow in a high-impact environment.

Business analysis Intern at Mindgate Solutions (2026-02 – 2026-05)

• Facilitated requirement gathering sessions with Tier-1 banks to define project scopes for UPI payment switches and digital banking portals.
• Authored comprehensive BRDs and FSDs, translating complex banking needs into technical specifications for the development team to ensure seamless NPCI integration.
• Bridged the gap between stakeholders and engineers by conducting gap analysis and troubleshooting operational issues to deliver customized fintech solutions.
• Executed User Acceptance Testing (UAT) and data validation for banking portals, ensuring that real-time transaction reports were accurate and 100% compliant with NPCI regulatory standards.

Research Intern at VNIT Nagpur (Siemens CoE) (2025-06 – 2025-07)

• Programmed industrial PLCs using Ladder Logic (LAD), FBD, and SFC within Siemens TIA Portal to automate workflows.
• Developed SCADA and HMI systems for real-time monitoring and control of sensors, actuators, and industrial instrumentation.
• Validated 5+ automation workflows using PLC and Arduino, reducing logic errors and improving operational safety in simulations.

Analytics Intern at Nirjai Technologies (2025-05 – 2025-06)

• Developed a deepfake detection system using ResNeXt and MobileNet architectures, achieving 95%+ accuracy on the CASIA-FASD dataset.
• Analyzed 10,000+ biometric samples to identify facial spoofing patterns, optimizing performance and reducing false acceptance rates.
• Implemented real-time security workflows to mitigate identity fraud, bridging the gap between advanced deep learning and practical cybersecurity.

Bachelors in Technology in Computer Science – Vellore Institute of Technology (2022-09 – 2026-05)

Class XII in PCM [CBSE Board] – Center Point School (2020-07 – 2022-07)

Class X in CBSE Board – Delhi Public School (2020-07 – 2020-07)