Akshay Raman
MSc in Computer Science | New York University, Courant Institute
Hi! I’m Akshay! I am a second-year master’s student studying computer science at NYU Courant. My research interests broadly span reinforcement learning, efficient DNN computing, and ML in healthcare.
Currently, I am working on multimodality and data curation methods as a researcher at the Data, Intelligence, Computation in Engineering (DICE) lab @ NYU Tandon, advised by Prof. Chinmay Hegde.
In my spare time, I enjoy music, running, and reading.
You can find my CV here.
Contact: ramanakshay112 AT gmail.com
News
| Aug 18, 2023 | 🎓 Graduated from Vellore Institute of Technology with a bachelor’s degree in computer science and engineering. |
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| Dec 20, 2022 |  Launched my personal website! |
Selected Projects
Canvas - A PyTorch Template for Deep Learning Projects
Designed a flexible deep learning project template using pytorch and hydra. The template is based on the agent-environment interface and supports a variety of machine learning tasks.
Hierarchical CLIP-based Image Geolocation Prediction
Trained a CLIP-inspired image geolocation model that predicts the precise location of an image taken anywhere on earth. Designed a novel inference approach based on hierarchical feature clustering which achieves comparable performance while being ~100x more efficient than previous methods.
Continual Learning for Policy Gradient Methods
Masters Capstone Project
Developed novel incremental learning algorithms to train reinforcment learning agents on a variety of real-world environments. Modified batch-wise policy gradient methods using eligibility traces to eliminate data buffers, particularly for long horizon tasks.
Solving Optimal Transport using Deep Neural Networks
Project under Prof. Augusto Gerolin, Undergraduate Thesis
Developed gradient-based DNN appoximators to solve the optimal transport problem for high-dimensional data. Aimed to study application of OT in Density Functional Theory (DFT) to study dissociation of atoms.
Diabetic Retinopathy Detection
Trained large-scale CNNs to predict diabetic retinopathy (an eye disease) from a noisy dataset of retinal images. Generated heatmaps using Grad-CAM to identify parts of the image which had the most impact on model prediction.