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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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<title>Sriram Mandalika</title>
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<font size="6">Annapoorna Sai Sriram Mandalika</font>
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<p>I am a final-year Undergraduate student at <a href="https://www.srmist.edu.in/">SRM Institute of Science and Technology, Chennai</a> advised by Dr. <a href="https://www.srmist.edu.in/faculty/dr-athira-m-nambiar/">Athira M. Nambiar</a>, currently working on improving memory retention in DL models using continual learning.
</p>
<p>
I have had the opportunity to intern at the Indian Institute of Technology, Hyderabad (2022, 2023, 2024) with <a href="https://people.iith.ac.in/ckm/">Prof. C. Krishna Mohan</a>. I have spent 3 summers working as a Research Intern and a collaborator at <a href="https://ckmvigil.in">VIGI Lab</a> at IIT Hyderabad. I am also an active member at <a href="https://ckmvigil.in">IEEE SRM Student Branch</a>, currently serving as the Director of the organization.
</p>
<p>
In my free time, I enjoy travelling and spending time with friends and family. Feel free to email me to chat about research. I am always looking for opportunities to work on building AI systems!
</p>
<p align="center"><a>
</a><a href="Data/CV.pdf">CV</a> | </a><a href=mailto:mc9991@srmist.edu.in>E-Mail</a> | <a href="https://www.linkedin.com/in/srirammandalika/">LinkedIn</a> | <a href="https://github.com/srirammandalika">Github</a> | <a href="https://drive.google.com/file/d/1dUP0op6rBkNMSsvQAs_xaFwAqK08HITa/view?usp=sharing"> Twitter</a>
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<a href="https://www.srmist.edu.in" target="_blank">
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<a href="https://www.iith.ac.in" target="_blank">
<img style="width:80px" src="images/IIT_H.png"></a>   
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<a href="https://research.fb.com/category/facebook-ai-research/" target="_blank">
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<td width="22%" align="center"><font size="2">SRM IST<br>2021-2025</font></td>
<td width="22%" align="center"><font size="2">IIT Hyderabad<br>Summer 2022, 2023, Winter 2023</font></td>
<!-- <td width="22%" align="center"><font size="2">UIUC <br>2016-2022</font></td>
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<a href="https://www.iith.ac.in" target="_blank">
<img style="width:100px" src="images/IIT_H.png"></a>   
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<a href="https://xulabs.github.io/" target="_blank">
<img style="width:60px" src="images/CMU-logo.png"></a>   
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<a href="https://www.nrsc.gov.in/" target="_blank">
<img style="width:60px" src="images/ISRO.png"></a>   
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<a href="http://deepmind.com/" target="_blank">
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<td width="24%" align="center"><font size="2">IIT Hyderabad<br>Summer 2022, 2023, Winter 2023</font></td>
<td width="24%" align="center"><font size="2">Carnegie Mellon University<br>Dec 2023 - Feb 2024</font></td>
<td width="24%" align="center"><font size="2">Indian Space Research Organisation (ISRO), Hyderabad<br>Aug 2024 - Present</font></td>
<!-- <td width="24%" align="center"><font size="2">FAIR (collab w/ UT Austin)<br>Summer 2019, FA19, SP20</font></td>
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<td valign="top" align="center"><strong>Aug 2024</strong></td>
<td>Started working at National Remote Sensing Centre, ISRO in Hyderabad under Urban Hydrological Studies Divison.</a></td>
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<td valign="top" align="center"><strong>Aug 2024</strong></td>
<td>One of my recent works <a href="https://arxiv.org/abs/2408.04482">Arxiv</a>, got accepted at the 27th ICPR - 2024.</td>
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<td valign="top" align="center"><strong>Jun 2024</strong></td>
<td>I will be joining the Indian Space Research Organisation (ISRO), Hyderabad as a Research Engineer Intern.</td>
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<td valign="top" align="center"><strong>May 2024</strong></td>
<td>Published my Book Chapter titled <a href="https://www.igi-global.com/chapter/deep-learning-based-speech-emotional-analysis-using-convolution-neural-network/347293">
Deep Learning-Based Speech Emotional Analysis Using Convolution Neural Network: Bi-Directional Long Short-Term Memory</a>.</td>
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<td valign="top" align="center"><strong>July 2023</strong></td>
<td>Wll start working as a Research Collaborator at IIT Hyderabad.</td>
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<td valign="top" align="center"><strong>Mar 2023</strong></td>
<td>Started working as an Undergraduate Researcher at AI Robotics Lab, SRM Institute of Science and Technology, Kattankulathur.</td>
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<td valign="top" align="center"><strong>Dec 2022</strong></td>
<td>I have started working as an Undergraduate Researcher at SRM Institute of Science and Technology, Kattankulathur.</td>
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<hr>
<p>
I’ve worked with some amazing people, especially (in no particular order) <a href="https://nambiarathira.blogspot.com/search/label/Home/">Dr. Athira Nambiar</a>, <a href="https://people.iith.ac.in/ckm/">Prof. C. Krishna Mohan</a>, <a href="https://www.linkedin.com/in/mrinmay-sen-ab8ba1176/?originalSubdomain=in">Mr. Mrinmay Sen</a>, <a href="https://www.linkedin.com/in/rahul-biswas-5a2b1a147/">Mr. Rahul Biswas</a>, <a href="https://www.linkedin.com/in/saiveena-suresh-a37705277/?originalSubdomain=in">Dr. Sai Veena Suresh</a>, <a href="https://www.linkedin.com/in/sampath-kumar-a23319251/">Dr. Sampath Kumar</a>, and <a href="https://www.linkedin.com/in/shilpi-garg-399180155/">Ms. Shilpi Garg</a> for supporting my research journey.
</p>
<hr>
<!-- <a href="javascript:toggleblock('news')">+ Older News</a> -->
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<td valign="top" align="center"><strong>Oct 2021</strong></td>
<td>Nov 2022, I actively seek research opportunities to work on computer vision problems involving self-supervised learning-based approaches.</td>
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<td valign="top" align="center"><strong>May 2020</strong></td>
<td> Summer #2 at <a href="http://allenai.org/">Allen Institute for Artificial Intelligence</a>, Seattle.</td>
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<td valign="top" align="center"><strong>Dec 2019</strong></td>
<td>Released <a href="https://arxiv.org/pdf/1912.11474.pdf"> AV Embodied Navigation preprint</a> (intern work at Facebook AI Research).</td>
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<td valign="top" align="center"><strong>Nov 2019</strong></td>
<td> NeurIPS camera ready, code, and slides for <strong>TAB-VCR</strong> are up on <a href="https://deanplayerljx.github.io/tabvcr/">project page</a>!</td>
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<heading>Research</heading>
<p>
I'm interested in building agents that can independently explores environment and act in the world by learning from data and self-intuitive interactions. Building such systems raises many facinating learning questions that I am interested in solving problems including perception, generalization, memory retention and explainability.
</p>
<p>
<img src="images/note.png" alt="note" width="4%" style="border-style: none">* = Manuscript under review.
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<heading>SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios
</heading></a><br>
<strong>Sriram Mandalika</strong>, Athira Nambiar
<br>
<strong>27th International Conference on Pattern Recognition (ICPR)</strong>
<br>
A novel active learning mechanism for semantic segmentation for self-driving vehicles using the Cityscapes dataset. We leverage the iterative data-pipeline to improve the model accuracy using the least amount labelled images along with a blend of XAI and entropy analysis.<br>
<!-- <strong><a href="https://arxiv.org/abs/2408.04482">Arxiv</a></strong><br> -->
<a href="https://arxiv.org/abs/2408.04482">Arxiv</a> |
<a href="">Github</a>
<!-- <a href="">code</a>
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Media:
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<heading>Active Learning based Semi-Supervised Approach with Attention Mechanisms for Improved Fetal Arrhythmia Detection
</heading></a><br>
Bhoovi Chauhan*, <strong>Sriram Mandalika*</strong>, Abel Jaba Deva Krupa, Samiappan Dhanalakshmi
<br>
<strong>IEEE 21st India Council International Conference (INDICON)
<br>
This study focuses on optimizing fetal arrhythmia
detection eliminating the need for extensive, manually annotated
ECG datasets, which are particularly scarce for fetal ECG, by
employing sample selector and calibration probability techniques
to avoid class imbalance. A semi-supervised active learning
the framework is proposed to enhance the reliability of the FECG signal
processing that uses a minimal volume of annotated data.<br>
<!-- <strong>arXiv 2024 [Will be available soon]</strong><br> -->
<a href="">Arxiv</a> |
<a href="">Github</a>
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<heading>Deep Learning-Based Speech Emotional Analysis Using Convolution Neural Network: Bi-Directional Long Short-Term Memory
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S. Aruna, G. Usha, A. Saranya, M. Maheswari, <strong>M. Annapoorna Sai Sriram Mandalika</strong>
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This book chapter, titled "<a href="https://www.igi-global.com/book/machine-deep-learning-techniques-emotion/338317">Machine and Deep Learning Techniques for Emotion Detection</a>," delves into the application of deep learning (DL) as part of artificial intelligence (AI) to perform tasks requiring human intelligence. Specifically, it focuses on speech emotion recognition (SpEmRe), which identifies various emotions from audio samples. DL techniques, especially bi-directional long short-term memory (Bi-LSTM) models, are utilized to predict human emotions from speech, reflecting the growing interest.. <a href="https://www.igi-global.com/book/machine-deep-learning-techniques-emotion/338317">Read more</a>
<br>
<strong></strong><br>
<a href="https://www.igi-global.com/book/machine-deep-learning-techniques-emotion/338317">Book Chapter</a> |
<a href="https://github.com/srirammandalika/BiLSTM-Speech-Emotion-Recognition">Code</a>
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<heading><heading>Unified Spatial Attention Concept for Foundational Computer Vision Tasks
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This approach allows multiple edge devices or decentralized nodes to collaboratively train a shared model without sharing their raw data. Instead, each node independently computes updates on local data and then securely aggregates these updates to improve the global model.
The project focuses on implementing deep neural networks for image recognition tasks, leveraging federated learning to address privacy concerns by keeping data decentralized. It explores how these models can learn from distributed datasets while preserving data privacy, making them applicable in scenarios where data sharing or centralized training isn't feasible or desirable.<br>
<br>
<a href="https://github.com/srirammandalika/Unified-Attention-Concept-CV/blob/main/Documentation(s)/UnifiedAtt_Report_Paper.pdf">Project Documentation</a> |
<a href="https://github.com/srirammandalika/Unified-Attention-Concept-CV">Code(s)</a> |
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<heading>Decentralized Deep Learning Federated Approach for Image Classification
</heading></a><br>
This approach allows multiple edge devices or decentralized nodes to collaboratively train a shared model without sharing their raw data. Instead, each node independently computes updates on local data and then securely aggregates these updates to improve the global model.
The project focuses on implementing deep neural networks for image recognition tasks, leveraging federated learning to address privacy concerns by keeping data decentralized. It explores how these models can learn from distributed datasets while preserving data privacy, making them applicable in scenarios where data sharing or centralized training isn't feasible or desirable.<br>
<br>
<a href="https://github.com/srirammandalika/Decentralized-Deep-Learning-Federated-Approach-for-Image-Classification">Project Documentation</a> |
<a href="https://github.com/srirammandalika/Decentralized-Deep-Learning-Federated-Approach-for-Image-Classification">Code</a> |
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<heading>Grad-CAM assisted Object Detection with various supervision setting
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In Progress <br>
<strong></strong><br>
<a href="">Project Documentation</a> |
<a href="">Code</a>
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