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---
layout: default
---
<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:63%;vertical-align:top">
</p>
<p> My name is Vishal Sharma.
In my work, I build AI technologies like LLMs, rankers, retrievers, data-pipelines for (extremely) large scale data. During my PhD at Indian Institute of Technology Delhi, I did research in "Generalized AI in planning, RL and Probabilistic Graphical Models" under the guidance of <a href="https://www.cse.iitd.ac.in/~parags">Prof. Parag Singla</a> and <a href="https://www.cse.iitd.ac.in/~mausam">Prof. Mausam</a>.
</b></br>
</p>
<p style="text-align:left">
<!-- <h2>Vishal Sharma</h2> -->
<!-- [email protected] -->
<p style="text-align:left">
<!-- <a href="https://www.cse.iitd.ac.in/~vsharma/Vishal_CV_Mar_2024.pdf">CV</a> | -->
<a href="mailto:[email protected]">Email</a> |
<!-- <a href="data/Vishal_CV_2023.pdf">CV</a> | -->
<a href="https://scholar.google.com/citations?user=HBxIco0AAAAJ&hl=en">Google Scholar</a> |
<a href="https://www.linkedin.com/in/vishal-sharma-2a985a29/">LinkedIn</a> |
<a href="https://github.com/vshlroot/">Github</a>
<!-- <p style="text-align: center; color:red;">I am in the job market this year (starting May 2023).If interested, please checkout my <a href="">research statement</a>.</p> -->
<p style="text-align: center; color:red;">I am in the job market this year</p>
<!-- <u><p style="text-align: center; color:red;">I am in the job market this year (starting May 2023).</br>If interested, please checkout my <a href="">research statement</a>.</p></u> -->
</p>
<h3>Research</h3>
<p>
<!-- During my PhD, I have collaborated with <a href="https://www.cse.iitd.ac.in/~mausam">Prof. Mausam</a> on learning generalized object-centric neural policies for reinforcement learning (RL) and relational planning -- policies that can be applied to an unseen variation of a given environment. This is unlike the typical RL (and planning) setting, where the policy is learned for a single environment. -->
In my PhD, I try to answer a simple question -- Can we teach machines to transfer solutions from a small-size problem(s) to larger unseen (but related) problems in structured worlds? (Here, size implies the number of objects in a problem).
</br>
The work primarily focuses on learning generalized object-centric neural policies for Reinforcement Learning (RL) and Relational Planning -- policies that can be applied to an unseen variation of a given environment. This is unlike the typical RL (and planning) setting, where the policy is learned for a single environment.
This work is done under the joint supervision of <a href="https://www.cse.iitd.ac.in/~mausam">Mausam</a>, IIT Delhi.
</p>
<p>
In the past, I have also worked on developing transferable lifted inference approaches for Probabilistic Graphical Models in collaboration with <a href="https://personal.utdallas.edu/~vibhav.gogate/">Vibhav Gogate</a>, UT Dallas. I have also worked on learning object-centric video prediction models while collaborating with <a href="https://web.cs.ucla.edu/~guyvdb/">Guy Van den Broeck</a>, UCLA.
</p>
<p>
Prior to joining PhD, I worked at Oracle India for two years as a software engineer.
</p>
<td style="padding:2.5%;width:25%;max-width:25%;vertical-align:center">
<a href="https://www.cse.iitd.ac.in/~vsharma/V.jpeg"><img style="width:70%;max-width:70%;border-radius:15%" alt="profile photo" src="https://www.cse.iitd.ac.in/~vsharma/V.jpeg" class="hoverZoomLink"></a>
<!-- <p style="text-align:left">
<!-- <h2>Vishal Sharma</h2> -->
<!-- [email protected] -->
<!-- <p style="text-align:left">
<a href="mailto:[email protected]">Email</a> |
<a href="data/Vishal_CV_2023.pdf">CV</a> |
<a href="https://github.com/vshlroot/">Github</a> |
<a href="https://scholar.google.com/citations?user=HBxIco0AAAAJ&hl=en">Google Scholar</a>
</p> -->
</td>
</tr>
</tbody></table>
<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:63%;vertical-align:top">
<h3>Conference Publications</h3>
<ol>
<li> SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs </br>
<b>Vishal Sharma</b>*, Daman Arora*, Mausam, and Parag Singla.</br>
<i> The 39th Conference on Uncertainty in Artificial Intelligence <b>(UAI) 2023</b>. Pittsburgh, USA. </i></br>
<a href="https://proceedings.mlr.press/v216/sharma23c/sharma23c.pdf">Paper</a> | <a href="https://proceedings.mlr.press/v216/sharma23c/sharma23c-supp.pdf">Supplement</a> | <a href="https://github.com/dair-iitd/symnet3">Code</a>
</li>
<li> SymNet 2.0: Effectively handling Non-Fluents and Actions in Generalized Neural Policies for RDDL Relational MDPs </br>
<b>Vishal Sharma</b>, Daman Arora, Florian Geißer, Mausam, and Parag Singla.</br>
<i> The 38th Conference on Uncertainty in Artificial Intelligence <b>(UAI) 2022</b>. Eindhoven, Netherlands. </i></br>
<a href="https://proceedings.mlr.press/v180/sharma22a/sharma22a.pdf">Paper</a> | <a href="https://proceedings.mlr.press/v180/sharma22a/sharma22a-supp.pdf">Supplement</a> | <a href="https://github.com/dair-iitd/symnet2">Code</a>
</li>
<li> Lifted Marginal MAP Inference <b>(ORAL)</b></br>
<b>Vishal Sharma</b>, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, and Parag Singla.</br>
<i> The 34th Conference on Uncertainty in Artificial Intelligence <b>(UAI) 2018</b>. Monterey, California, USA. </i></br>
<a href="http://auai.org/uai2018/proceedings/papers/322.pdf">Paper</a> | <a href="http://auai.org/uai2018/proceedings/supplements/Supplementary-Paper322.pdf">Supplement</a>
</hr>
</li>
</ol>
</td>
</tr>
<tr style="padding:0px">
<td style="padding:2.5%;width:63%;vertical-align:top">
<h3>Workshop Publications</h3>
<ol>
<li> Object-Centric Learning of Neural Policies for Zero-shot Transfer over Domains with Varying Quantities of Interest </br>
<b>Vishal Sharma</b>, Vishal Sharma, Aniket Gupta, Prayushi Faldu, Rushil Gupta, Mausam, Parag Singla</br>
<i> Planning and Reinforcement Learning workshop, <b>PRL@IJCAI 2023</b></i></br>
</li>
<li> SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs </br>
<b>Vishal Sharma</b>*, Daman Arora*, Mausam, and Parag Singla.</br>
<i> Planning and Reinforcement Learning workshop, <b>PRL@IJCAI 2023</b></i></br>
</li>
<li> Towards an Interpretable Latent Space in Structured Models for Video Prediction </br>
Rushil Gupta*, <b>Vishal Sharma</b>*, Yash Jain, Yitao Liang, Guy Van den Broeck, and Parag Singla. </br>
<i> Workshop on Weakly Supervised Representation Learning <b>(WSRL) at IJCAI 2021</b>. Online. </i></br>
<a href="https://arxiv.org/pdf/2107.07713.pdf">Paper</a>
</li>
<li> Lifted Marginal MAP Inference </br>
<b>Vishal Sharma</b>, Noman Ahmed Sheikh, Happy Mittal, Vibhav Gogate, and Parag Singla.</br>
<i> IJCAI/ICML Workshop on Statistical Relational AI, <b>StaRAI@IJCAI/ICML 2018</b></i>
</li>
</ol>
</td>
</tr>
</tbody></table>
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