momentum

January always sneaks up on me. Every year, I look up and realize the month is almost over, and I’m not entirely sure where the days went. I’ve been trying to be more intentional with my time this year. Trying is the keyword.  Spring 2026 Spring 2026 is officially underway (I'll be graduating in May, which still feels surreal to say). This semester, I'm taking INFO 6500: Information Science Seminar Series . It’s a one-credit course, but the intent behind it feels expansive. The focus is less on workload and more on intellectual exploration: learning about research outside my immediate interests, practicing critical writing, and actively contributing to an intellectual community. So far, the speakers have been incredible. One of the early sessions explored  information history  through the lens of  Thomas Kuhn  and the evolution of the field itself. The idea that information history is not synonymous with the Information Age. It’s older and deeply embedded in ...

Happy New Year!! Last year began with a lot of uncertainty. Balancing graduate coursework, on-campus work, the internship search, and personal growth required constant adjustment. Being persistent was definitely the key. The first half of the year was a steep learning curve. I deepened my understanding of big data architecture, statistics, and machine learning, and applied these concepts through hands-on projects such as ResearchPod , BankIntel , and DoctorVisits . One aspect of education here that I truly value is its emphasis on practice, translating theory into real, end-to-end projects that mirror how data work happens outside the classroom. Over the summer, I spent time back home in India. Returning to a familiar environment helped me reset, reflect, and regain clarity before coming back to continue building. I completed my role as a Data Engineer with CU Libraries , where I worked on the digitization and metadata extraction of historical tsunami marigram scans from the 1800s. Th...

Coursework Neural Networks and Deep Learning: This week was basically a deep learning bootcamp. We started with the fundamentals in TensorFlow/Keras. How the Sequential API works, how layers stack, and why activation functions matter. I built an ANN on MNIST with exactly three dense layers, a Flatten layer, and dropout. Then I visualized everything: raw images, training/testing splits, the first and last epochs, loss/accuracy curves, and a final confusion matrix to see where the model stumbled. Check out the code I wrote here ! After that, we moved into CNNs. This part really forced me to understand image shapes, filters, cross-correlation, max pooling, and flattening by actually illustrating every step. It made the CNN architecture much easier to understand. Check out the code I wrote  here ! Next came RNNs and LSTMs. I trained both on a sequential/text dataset and compared how they handled dependencies. The LSTM’s ability to remember longer patterns felt very real once I saw the ...