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dil.html
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<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Data-Driven Intelligence & Learning Lab, IIT Hyderabad</title>
<meta name="author" content="Data-Driven Intelligence & Learning Lab">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
<link rel="icon" type="image/jpg" href="dil/DIL-logo.png">
</head>
<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:0px">
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr style="padding:0px">
<td style="padding:2.5%;width:100%;vertical-align:middle">
<p style="text-align:center">
<!-- <name>Machine Intelligence Group</name> -->
<a href="dil/DIL-logo.png"><img style="width:100%;max-width:85%" alt="profile photo" src="dil/DIL-logo.png" class="hoverZoomLink"></a>
</p>
<p>We are an enthusiastic research lab at the <a href='https://ai.iith.ac.in/'>Department of Artificial Intelligence</a>, <a href="https://iith.ac.in/">Indian Institute of Technology Hyderabad</a>,
led by <a href="https://krmopuri.github.io/">Dr. Konda Reddy Mopuri</a>.
The broad research interests of our group include Data Science & Engineering, Machine Learning (specifically Deep Learning), Artificial Intelligence,
Computer Vision, and Image/Signal Processing.
</p>
<p style="text-align:center">
<a href="publications.html">Publications</a>  / 
<!-- <a href="recognition.html">Recognition</a>  /  -->
<a href="openings.html">Openings</a>  / 
<a href="funding.html">Funding</a>
<!-- <a href="https://twitter.com/mkreddy48">Twitter</a> -->
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<!-- <a href="images/MIG-logo-title.png"><img style="width:100%;max-width:85%" alt="profile photo" src="images/MIG-logo-title.png" class="hoverZoomLink"></a> -->
</td>
</tr>
<!-- News section -->
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>News</heading>
<ul>
<li>Feb 2024: <mark>Summer internship opportunity</mark>: please visit <a href="https://iith.ac.in/research/SURE/" target="_blank">this page</a></li>
<li>Feb 2023: <a href="https://arxiv.org/abs/2302.14290">Paper</a> accepted at the <a href="https://cvpr2023.thecvf.com/">CVPR 2023</a> Conference (CORE A* ranked).</li>
</ul>
</td>
</tr>
</tbody></table>
<!-- People section -->
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>People </heading>
<ul>
<li>Saumyaranjan Mohanty - Ph.D. (External from DRDO)</li>
<li>Madhumitha V - Ph.D.</li>
<li>Harsh Udai - Ph.D.</li>
<li>Naveen George - M.Tech (3Y)</li>
<li>Sunayna Padhye - M.Tech (3Y)</li>
<li>Roshin Roy - M. Tech (2Y)</li>
<li>Rupa Kumari - M. Tech (2Y)</li>
<li>Anish Pawar - M. Tech (2Y)</li>
<li>Puneet Rajan - M. Tech (2Y)</li>
</ul>
Alumni can be found <a href='dil/alumni.html'>here</a>.
</td>
</tr>
</tbody></table>
<!-- Research section -->
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:20px;width:100%;vertical-align:middle">
<heading>Research</heading>
<p>
Following is an approximate clustering and labeling of the research (click on the label to find relevant research).
</p>
</td>
</tr>
</tbody></table>
<!-- Research 5: Long-Tailed Training Data -->
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/longtail.png' width="160">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="long-tail.html">
<papertitle>Long-Tailed Training Data</papertitle>
</a>
<br>
<p></p>
<p>Real-world datasets suffer skewed label frequency distribution, generally with a long-tail. Models trained on such data generalize poorly. We aim to
contribute effective solutions to alleviate the adverse effects casued by class imbalance in the training data.</p>
</td>
</tr>
</tbody></table>
<!-- Research 4: Data Engineering for Deep Learning -->
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/data-engineering.JPG' width="160">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="data-engineering.html">
<papertitle>Data Engineering for Deep Learning</papertitle>
</a>
<br>
<p></p>
<p>While the heaps of digital data surely serve the data hungry deep learning, it comes with a set of new challenges (e.g., data redundancy, complexity of training, etc.). We aim to investigate engineering solutions to these data and learning related challenges.</p>
</td>
</tr>
</tbody></table>
<!-- Research 1: Robust Deep Learning -->
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='robustness.jpg' width="160">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="robustness.html">
<papertitle>Robustness</papertitle>
</a>
<br>
<p></p>
<p>DNNs are vulnerable to adversarial samples that are a dangerous threat for deploying these models in practice. Therefore, the effect of adversarial perturbations warrants
the need for in depth analysis of this subject.</p>
</td>
</tr>
</tbody></table>
<!-- Research 2: Adaptable Deep Learning -->
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr onmouseout="ff_stop()" onmouseover="ff_start()">
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='adaptability.jpg' width="160">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="adaptability.html">
<papertitle>Adaptability</papertitle>
</a>
<br>
<p></p>
<p>Deep Learning has been data and resource intensive. However, real-world may challenge us with various constraints to apply these sophisticated tools.
Adapting deep learning techniques/models (e.g. knowledge transfer, domain adaptation) across tasks and to challenging environments such as low data
and data-free scenarios is of high importance.</p>
</td>
</tr>
</tbody></table>
<!-- Research 3: Interpretable Deep Learning -->
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='interpretability.JPG' width="160">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="interpretability.html">
<papertitle>Interpretability</papertitle>
</a>
<br>
<p></p>
<p>NNss are complex ML systems. Because of end-to-end nature of their learning, these models suffer from lesser
decomposability and hence many of us treat them as black-boxes. We study these models in order to make their inference more human-interpretable
and explainable, and devise useful inferences and tools.</p>
</td>
</tr>
</tbody></table>
<!-- Research 6: ML in diverse disciplines -->
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<td style="padding:20px;width:25%;vertical-align:middle">
<img src='images/ai-everywhere.jpg' width="160">
</td>
<td style="padding:20px;width:75%;vertical-align:middle">
<a href="appliedml.html">
<papertitle>ML/AI in diverse disciplines</papertitle>
</a>
<br>
<p></p>
<p>ML and AI are generic set of tools that can be applied to solve problems from diverse set of fields.
This is a list of projects that I had a chance to apply the ML/AI techniques in areas other than CV, NLP, and Speech. [Figure taken from teachingai.blog]</p>
</td>
</tr>
</tbody></table>
<!--Link to the source of the website-->
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:0px">
<br>
<p style="text-align:right;font-size:small;">
Source taken from <a href="https://jonbarron.info/">here</a>.
</p>
</td>
</tr>
</tbody></table>
</td>
</tr>
</table>
</body>
</html>