About Me

I am a first-year second-year MSML student at Carnegie Mellon University (CMU), advised by . Previously, I was a Predoctoral Fellow at the Vision and AI Lab (VAL), Indian Institute of Science, where I was advised by and worked with his Ph.D. students and . I also collaborated with from the Visual Computing Lab (VCL), Indian Institute of Science.

I completed my B.Tech in Computer Science and Engineering (2018-2022) from , Bangalore where I was advised by for my Capstone Project.

CV Google Scholar ashish.ramayee at gmail dot com

Education

Carnegie Mellon University
Master's of Science in Machine Learning (MSML)
Aug. 2024 - Present
Relevant coursework: Advanced Into. to ML (PhD), Convex Optimization (PhD), Probabilistic Graph Models (PhD), Deep Reinforcement Learning (PhD), Probability and Statistics
PES University
B.Tech (Honors) in Computer Science and Engineering (CSE)
Aug. 2018 - May. 2022
Relevant coursework: Linear Algebra, Intro. to ML, Topics in Deep Learning, Information Retrieval

Recent Experience

Carnegie Mellon University
Graduate Researcher | Advisor:
Collaborators: Raviteja Vemulapalli, Nicholas Apostoloff, Oncel Tuzel
Sept. 2024 - Present
Research areas: Diagnosing failures in Foundation Models
Indian Institute of Science
Predoctoral Fellow | Advisor -
May 2022 - Jul. 2024
Research areas: Distribution Shift, Foundation Models, Long-Tail Learning, Federated Learning
Intel Corporation
Research Intern | Supervisor -
Aug. 2021 - Dec. 2021
Research areas: Continual Learning, Federated Learning, Neural Network Pruning

News

Aug 26, 2024 Started my Master's in Machine Learning (MSML) at Carnegie Mellon University (CMU)
Feb. 27, 2024 Two papers accepted at ! The first on Generalization with VLMs and the second on Long-tail learning with ViTs!
Feb. 9, 2024 Serving as a reviewer for .
Nov. 20, 2023 Presented DSiT (ICCV 2023) at the Adobe Research Workshop at IISc.
Nov. 1, 2023 Awarded the Kotak IISc AI-ML Pre-Doctoral Fellowship!
Oct. 31, 2023 Serving as a reviewer for .
Oct. 28, 2023 Our paper on Source-Free Domain Adaptation is accepted at !
Sept. 23, 2023 Serving as a reviewer for .
Jul. 18, 2023 Our paper on Source-Free Domain Adaptation is accepted at !
May 4, 2023 Serving on the Program Committee for .
Apr. 2, 2023 Our paper on Domain Generalization is accepted at the ODRUM Workshop at !
May. 16, 2022 Joined the as a Predoctoral Fellow.

Recent Publications

* indicates equal contribution.


Leveraging Vision-Language Models for Improving Domain Generalization in Image Classification
Sravanti Addepalli*, *, Lakshay Sharma, R. Venkatesh Babu

DeiT-LT: Distillation stikes back for Vision Transformer training on Long-tailed datasets
Harsh Rangwani, Pradipto Mondal, Mayank Mishra, , R. Venkatesh Babu

Domain-Specificity-inducing Transformers for Source-Free Domain Adaptation
Sunandini Sanyal*, *, Suvaansh Bhambri*, Akshay Kulkarni, Jogendra Nath Kundu, R. Venkatesh Babu

Aligning Non-Causal Factors for Transformer-based Source-Free Domain Adaptation
Sunandini Sanyal*, *, Suvaansh Bhambri, Pradyumna YM, Akshay Kulkarni, Jogendra Nath Kundu, R. Venkatesh Babu

Distilling from Vision-Language Models for Improved OOD Generalization in Vision Tasks
Sravanti Addepalli*, *, Lakshay Sharma, R. Venkatesh Babu
ODRUM Workshop,

Interpretability for Multimodal Emotion Recognition using Concept Activation Vectors
, K. Nidarshan, Anirudh V Ragam, Shylaja S S,

Side Work

Course and Personal Projects


Fourier Feature Mapping Networks
My implementation of the paper "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains" by Tancik et al using Tensorflow 2.0

Semantic Segmentation
My implementation of the paper "ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation" by Paszke et al using Tensorflow 2.0

Big Data
My assignments and final project for a course on Big Data at . My final project involved Premier League Analytics using Streaming Spark.

Gait Recognition
Gait Recognition for infrared videos using Point Light Animation implemented using OpenCV. This project was carried out at the Center for Data Science and Applied Machine Learning (CDSAML) at .

This Website
A simple, responsive webpage for academics. Thanks to for this template based on .

Academic Service

Conference Reviewer

CVPR: 2024, 2025
ECCV / ICCV: 2023, 2024
NeurIPS: 2023, 2024, 2025
ICLR: 2024, 2025
ICML: 2024, 2025
AAAI: 2024

Program Committee

,

Awards

  • Outstanding Reviewer Award : Recognised among the Top 2% of Reviewers at CVPR 2024 and ECCV 2024
  • Kotak IISc AI-ML Predoctoral Fellowship : Competitive Research Fellowship at the Indian Institute of Science
  • Prof. CNR Rao Merit Scholarship : Awarded at PES University for being among the Top 2% of the batch
  • Prof. MRD Merit Scholarship : Awarded at PES University for being among the Top 20% of the batch