Purnima P. — University of Chicago, US: case study

Purnima came to UnivAdmitHelp as a Research and Development Engineer at Synopsys, where she worked on virtual prototyping for automotive systems. She held a B.Tech in Information Technology.

Her experience included internships at Morgan Stanley and Microsoft, along with research projects in plant disease detection using computer vision and crop pattern analysis using satellite imagery. At Synopsys, she had won the SEEDS 3.0 award and was invited to present her research at an engineering conference in California. She wanted to pursue a Master's in Computer Science, focusing on machine learning and computer vision.

Purnima had experience in software engineering, virtual prototyping and machine learning. However, her professional work at Synopsys focused on automotive hardware systems, while her academic interests lay in machine learning and computer vision.

She had studied these subjects during her undergraduate degree but wanted to develop her knowledge through graduate education. Her attempts at self-learning had been difficult to sustain alongside work.

The challenge was to explain why she wanted to move towards machine learning without making her existing engineering experience appear unrelated.

Her application needed to connect her undergraduate research, professional projects and future goals. We also needed to establish why a Master's in Computer Science was the right choice for this transition.

We began with a detailed review of Purnima's academic projects, internships and work at Synopsys. Through discussions and storyboarding, her UAH mentor helped her identify experiences that explained how her interest in machine learning had developed.

Her computer vision coursework became the starting point. We explored her plant disease detection project, where she worked with thousands of images across many classes and improved model accuracy through testing and refinement. Her final-year project on satellite imagery added another dimension. When the initial ARIMA model failed to capture crop patterns, she examined the seasonal nature of the data and moved to SARIMA.

Purnima and her mentor worked through multiple essay drafts. They refined her academic motivation, career goals and reasons for choosing each programme while retaining her own experiences and voice.

For recommendation letters, identifying examples for her professors and manager could be used to discuss her research skills, problem-solving and professional growth was very helpful for recommenders. Support also covered university selection, application planning and interview preparation.

After admission decisions, mentoring extended to comparing university offers and choosing a programme based on curriculum, career goals and long-term fit.

Purnima secured admission to the University of Chicago's MS in Computer Science programme, Class of 2027.

Her application connected her experience in automotive virtual prototyping with her interest in machine learning and computer vision. It showed how her undergraduate projects had shaped her interests and how her work at Synopsys had helped her identify areas for further study.

The final essays and recommendation letters presented different aspects of her academic and professional journey without repeating the same achievements.