Anika Thapar

Automated research workflows using Zapier and Clay, significantly reducing manual work for the investment team.

• Automated research workflows using Zapier and Clay, reducing manual effort by 80% for the investment team.
• Utilized BeautifulSoup to extract critical data on emerging startups from accelerators, deal sites, and curated databases.
• Leveraged AI tools (Gemini, ChatGPT) with advanced prompt and context engineering to streamline workflows, accelerate analysis, and enrich sourcing pipelines.

Developed and deployed a Python script to automate core dump detection and email notifications, significantly increasing detection rates.

• Developed and deployed a Python script with pexpect and multi-threading to automate core dump detection, increasing detection rates by 500% and streamlining engineering team notifications.
• Engineered 4 Jinja2 templates to standardize and streamline configuration testing across diverse networking protocols.

Directed a 6-member robotics team, managing workflows and strategies for real-time competition success.

• Directed a 6-member robotics team, assigning roles and developing real-time competition strategies for optimal performance.
• Programmed 8 VEX robots in C, integrating aluminum frameworks, pneumatics, and gear systems to achieve competitive objectives.

Collaborates with the Computer Science department to manage logistics for large-scale student events.

• Collaborated closely with the Computer Science department to manage logistics for 5+ annual events, serving over 3,500 students.
• Led a 6-member team in orchestrating various Computer Science Department events, ensuring successful execution and attendee satisfaction.

Presented on robotics trade-offs and mentored young girls in building and coding.

• Educated over 500 girls on the critical trade-offs between torque and speed in robotics design and execution.
• Mentored 6 teams of 4 girls each (24 total), providing comprehensive guidance on building, coding, and strategic planning.

Web-based application for analyzing food ingredients based on dietary preferences.

Hackathon winning application that uses image scanning to detect harmful ingredients in feminine products.