I am an ex-SWE Intern at Apple and a Computer Science graduate student at North Carolina State University, Raleigh; with a Bachelor's degree in Electrical and Electronics Engineering from Sardar Patel Institute of Technology, Mumbai.
At Apple, I made a significant impact as part of the Layer 1 Controller SCV Subcomponent Verification team by developing an anomaly detection system using unsupervised machine learning. This system was crucial in identifying False Positives in Apple's latest modem software, ensuring the accuracy and reliability of continuous integration processes.
My work automated the detection of potential issues, drastically reducing the manual effort required and mitigating the risk of costly post-launch problems. I also introduced user-friendly features, including visualization tools and adjustable thresholds, to enhance the precision of test result analysis.
The success of this project led to widespread interest, with multiple teams seeking to implement similar pipelines, highlighting the scalability and value of my contributions to the software verification process at Apple.
I put my data science knowledge to the test at BARC, India's premier research institute BARC Website. I worked in the Radiopharmaceuticals division Radiopharmaceuticals Division under the supervision of B.G Avhad and Dr. Tapas Das. The main goal of my project was to automate the process of leak detection in heat exchangers and fault detection in distillation columns using data from radiotracers and gamma scans.
I devised an algorithm in Python to detect leakages using peak detection techniques for heat exchangers and utilized Discrete Fourier Transform and Cross-correlation to compare the data of multiple gamma detectors, finding the degree of overlap. This was further developed into a user-friendly GUI-based software, increasing reliability, removing human error, and optimizing time and effort, translating into substantial economic savings.
Two papers were published on this work, showcasing my commitment to impactful projects:
Certificate of completion: Link
The following year, I worked on another fascinating data science project. BARC manufactures its own I-131 capsules, and the workers in these labs may be exposed to radioiodine. This time, the task was to develop a Radiation Monitoring Software System to detect dangerously high levels of radiation on individuals working with Radio-iodine (Iodine-131) in the Radiopharmaceuticals division. A thyroid monitoring system was designed and developed to ensure I-131 presence in the thyroid gland below the safe limit. The system features a collimated thyroid probe with a NaI(Tl) detector for gamma. The project replaces the conventional radiation counter with a more sophisticated hardware and software solution, implementing better features like automatic alarms when the count rate is above the safe limit, selective counting for radioiodine I-131, and displaying the energy spectrum, gross counts, and count rate.
Certificate of completion: Link
Taking a deeper dive into Cloud Computing, Computer Networks and Database Management Systems, as apparent from my current projects on Github.
Let's build the future together! If you're as passionate about tech, data science, and Software Engineering as I am, let's connect and explore the possibilities.
I enjoy table tennis, skiing, ice skating, skateboarding, rock climbing, kayaking, and hiking.