From BERT to Mamba: Evaluating Deep Learning for Efficient QA Systems
This work explores the trade-offs between accuracy and computational efficiency in question-answering (QA) systems comprising of LLMs
Posts by Collection
portfolio
Distributed Financial Risk Assessment
This project aims to implement a distributed framework for financial risk assessment using Monte Carlo simulations to estimate Value at Risk (VaR) and Conditional Value at Risk (CVaR) by leveraging Spark’s distributed computing capabilities.
Enhancing Emergency Response Efficiency in San Francisco
In this project, we analyze and gain actionable insights into the efficiency of emergency response times and patterns related to fire incidents and emergency medical services (EMS) in San Francisco.
Future Video Frame Segmentation Prediction
Project to predict the segmentation mask of the 22nd frame of a video using the first 11 frames
Variable Sized-Batch General Multiplication (Variable GEMM)
This repo improves Mixture-of-Experts (MoE) models by addressing load-imbalance during dynamic routing, enhancing inference performance on hardware accelerators. It integrates CuBLAS and CuSparse, optimizing batched GEMM tasks for variable-sized inputs, resulting in significant efficiency gains across different model sizes.
Online Speaker Diarization with Interactive Learning
We formulate the online speaker diarization as a contextual-bandit problem similar to the online semi-supervised learning method
Dynamo Key-Value Store
Implementation and Analysis of a Weakly Consistent Key-Value Store - Dynamo
Twitter Sentiment Mining
Project concerning Mining and Classifying tweets based on sentiment expressed in them.
Skin Track
Project architecture which enables continuous touch-tracking for detection of gestures so as to send commands to the wearable watch.
Communication Architecture For UAV Swarms
Project to execute the decision making process for communication of a Unmanned Aerial Vehicle 
publications
A Semi-Supervised Approach for Multi-Domain Classification
Published in ICEET, 2023
This research presents an innovative approach for addressing the classification of data originating from multiple domains when there is a scarcity of labeled data.
Recommended citation: Anirudh Garg, Kartikey Singh, Radhika Mundra. (2023). " A Semi-Supervised Approach for Multi-Domain Classification." IEEE Xplore Digital Library . https://ieeexplore.ieee.org/document/10525791
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Electronics Club
Workshop, Indian Institute of Technology, Kanpur, Science & Technology Council, 2018
I delivered lectures on IOT, communication and Logic Circuits during the winter workshop in December’18 held by the Electronics Club under the aegis of the Science & Technology Council, IIT Kanpur. I, along with my team, also conducted electronics events of the inter-hall technical competition TAKNEEK’18 with a team of 20 members.
Basic Algorithms
Undergraduate course, New York University, Computer Science Department, 2023
My job as a teaching assistant for Prof. Chee Yap involved assessing student code for accuracy, efficiency, and style, evaluating written components of exams and assignments, and ensuring fairness and consistency. Furthermore, I also offered detailed feedback to guide student growth and understanding and liaised with the course instructor about students’ progress, challenges, and any potential issues.
Fundamental Algorithms
Graduate course, New York University, Computer Science Department, 2024
As a teaching assistant for Prof. Chee Yap, I assessed exams and assignments to maintain fairness and consistency, providing detailed feedback to support student development. I also communicated regularly with the instructor to discuss student progress and address any concerns.