May 2025, SimPPL alumni head to CERN, Columbia, NYU, Google, Cummins, and UMD
Six SimPPL alumni placements this month: CERN, Columbia MSDS, NYU MSCE, Google TPM internship, Cummins Data Science, and University of Maryland. Plus: Engraph.dev ships a developer tool for codebase understanding.
We're incredibly proud to announce that one of our former interns, Uday Sharma, has been accepted as a Summer Student at CERN, Switzerland. This is a tremendous achievement and a testament to their hard work and the foundational experience gained during their time with SimPPL.
Our former intern, Chaitya Shah, has been accepted into the highly competitive Master of Science in Data Science (MSDS) program at Columbia University. Their journey with us undoubtedly contributed to this remarkable achievement.
Viral Dalal has been accepted into the Master of Science in Computer Engineering (MSCE) program at New York University. This is a testament to their dedication and the practical experience gained at SimPPL.
Our alumna, Mrunmayi Parker, has joined Google as a Technical Program Manager Intern this summer. This incredible achievement reflects her dedication to bridging the gap between tech and management, a journey SimPPL was proud to support.
Nahush Patil has secured a Student Engineering Internship with the Data Science & Analytics team at Cummins Inc. this summer. Nahush will be contributing to projects focused on optimized diagnostics in engines, showcasing the practical skills and knowledge gained during his time [SUCCESS] at SimPPL.
Pankti Sheta has been accepted into the highly regarded Master of Science in Data Science program at the University of Maryland. This significant accomplishment highlights her commitment to advancing in the field and the strong foundation she built during her time [SUCCESS] at SimPPL.
Engraph is revolutionizing how organizations interact with complex codebases. We use AI to demystify code, allowing new developers to quickly onboard by getting precise, contextual answers to natural language queries. This significantly reduces the learning curve, transforming complicated code into understandable insights. We've already seen success in early pilots, reaching tens of developers.
