Pinkesh Badjatiya

pinkeshbadjatiya [at] gmail [dot] com | +91-720-774-6433

Resume | Google Scholar | dblp | LinkedIn | Github

I am currently working as an Applied Scientist 2 with the Search Technology Center India (STCI) AI Products team at Microsoft (R&D) working on relevance and ranking systems for the PeopleAlsoAsk Experience. I work on large-scale NLP recommendation systems that show relevant questions to Bing users while maximizing user engagement & experience. I work on mining signals from massive interaction data at Bing scale, building large-scale ML models, utilizing Machine Learning to distill information, and deploying them with Web-ready latency (<10ms).

Prior to this, I worked with the Media and Data Science Research (MDSR) Lab - a BU embedded research group in Digital Marketing at Adobe as a Machine Learning Researcher and Engineer 2, where I mainly focused on Computer Vision and Reinforcement Learning.

I am interested in artificial intelligence, natural language processing, machine learning, and computer vision. Much of my research is in Deep Reinforcement Learning (Deep-RL), Natural Language Processing (NLP), and training Deep Neural Networks to solve complex social problems. I am also interested in bringing these recent developments in AI to production systems.

I finished my MS by Research in Machine Learning with a focus in the domain of Information Retrieval and Extraction (IRE) from IIIT Hyderabad under the guidance of Prof. Vasudeva Varma (former Dean R&D, IIIT Hyderabad) and Prof. Manish Gupta (Microsoft, India). My thesis was encompassing the realm of Hate Speech Detection and Bias Mitigation in neural networks. Insights from my research work subsequently resulted in the formation of Project ANGEL which later garnered the Google AI/ML Research Award 2020. Other projects I worked on during my studies include Trust Inference in Social Network, Document Segmentation, Text Summarization and Project Sexism.

I also completed my B.Tech with Honours in Computer Science and Engineering from IIIT Hyderabad.

I feel fortunate to have worked with some of the amazing folks from both Academia and Machine Learning Research. Just to thank a few folks; Manish Gupta, Georgios Theocharous, Balaji Krishnamurthy, Jayakumar Subramanian, Mausoom Sarkar, Niyati Chhaya, and Vasudeva Verma.

I spend most of my time reading books. I also spend my spare time pursuing my childhood passion; following Astronomy, General Relativity, and Quantum Mechanics. Occasionally, I play piano (Youtube), listen to music, eat, drink, and sleep.

Work Experience

	Applied Scientist 2 October 2021 - Current Microsoft I am primarily working on building innovative NLP and Multi-Modal solutions for BING search using Machine Learning/Deep Learning techniques.
	Machine Learning Researcher & Engineer 2 June 2019 - October 2021 (2 years, 4 months) Adobe I primarily work with Data Science, NLP, and Reinforcement Learning research. I also work towards building ML algorithms for production.
	Machine Learning Research Intern Jan 2019 - April 2019 (4 months) Adobe Working towards solving social dilemmas for Multi-agent setting using Deep Reinforcement Learning using Model-based Value functions. Extended a full-time offer.
	Computer Vision Engineer ◉ Remote ◉ Freelance December 2019 - April 2021 Fujitsu Labs
	Software Engineer Intern May 2018 - July 2018 (3 months) Goldman Sachs Designed an algorithmic solution using constraint optimization in java and scala. Used TDD with OOP concepts and Design patterns. Performed error analysis and deployed to production. Extended a full-time offer.
	Data Analytics/Site Reliability Engineer Intern Sep 2017 - Mar 2018 (7 months) T-Hub Homepage Developed analytics from the reports produced by ~280 co-working startups incubated at T-Hub. Analyzed the user traffic flows to improve the site experience and generate weekly statistics for internal-analysis using Google Analytics and Python
	Google Code-In Mentor Nov 2016 - Jan 2017 (3 months) Google Code-In / MetaBrainz Homepage Mentored students working on the ListenBrainz project. Tasks involved creating tasks of varying difficulty levels and evaluating their submissions.
	System Administrator and Organizer Jul 2016 - Jun 2017 (11 months) Felicity Website Felicity Threads is the annual technical fest of IIIT Hyderabad. I was part of the team responsible for maintaing the contest portals and the website for the fest. I was also a problem setter for the event Break-In CTF and
	Google Summer Of Code (GSoC) Intern April 2016 - August 2016 (4 months) Google Summer of Code / Blog@MetaBrainz Created a proxy submission Flask API compatible with Last.fm scrobblers. Added scrobbling support for desktop clients with support for tracking currently playing song in Redis.

Awards/Honors/Mentions

2021	10 patents being filed in US and other countries
2021	Promoted to ML Engineer 2 at Adobe (in 12 months)
2020	Featured in Adobe Tech Blog! -- Blog
2020	Promoted to SDE-2 at Adobe (in 6 months)
2020	Project ANGEL \| Google AI/ML Research Award 2020 Insights from my undergraduate research work subsequently resulted in the formation of Project ANGEL at IIIT Hyderabad which later garnered the Google AI/ML Research Award 2020 to my advisor.
2019	TheWebConf Student travel grant for attending TheWebConf ’19 conference in US.
2019	Microsoft Research (MSR) Travel Grant for attending TheWebConf ’19 conference in US
2018	Dean’s Research Award Awarded in semesters Monsoon 17 & Monsoon 18 for obtaining research publications during undergraduate studies.
2018	Dean’s Merit List for academic excellence Selected in Dean’s List in semesters Monsoon 15, Monsoon 17 & Spring 18. Awarded to Top 5% of the batch.
2018	ECIR-Grenoble Travel Grant for attending ECIR’18 conference in France.
2017	Ranked 3^rd in the Big Data Challenge Department of Higher & Technical Education Government of Rajasthan, India 1741 teams participated from all across India. Awarded prize by the Vasundhara Raje, Chief Minister of Rajasthan
2017	Our work on HateSpeech featured in 10+ media outlets! -- Blog
2017	Best Poster Presentation Award at WWW'17 (Perth, Australia)
2016	Featured in MetaBrainz Blog!! -- Blog
2016	ACM-ICPC Chennai Regionals 2016 My team SourceCode ranked 80^th in the 2016 ACM-ICPC Asia Chennai Regional Contest, 67^th in the Asia-Chennai '16 Online Round and 107^th in the Asia Amritapuri '16 Online Round.
2015	Ranked 2^nd in Microsoft Code.Fun.Do Hackathon
2014	AIR 3313/1.4 million (All India Rank) in JEE-Advance
2014	AIR 7044/1.4 million (All India Rank) in JEE-Mains (State Rank-258)

Patents

	Optimal sequential decision making with changing action space Pinkesh Badjatiya, Tanay Anand, Jayakumar Subramaniam, Sriyash Poddar, Georgios Theocharous, Balaji Krishnamurthy, (Submitted)
	Status-Quo Policy Gradient for Multi-agent Reinforcement Learning Pinkesh Badjatiya, Mausoom Sarkar, Jayakumar Subramaniam, Abhishek Sinha Nikaash Puri, Balaji Krishnamurthy, (Submitted)
	High Utility Sequential Pattern Mining using Reinforcement Learning Tanay Anand, Piyush Gupta, Pinkesh Badjatiya, Nikaash Puri, Jayakumar Subramaniam, Balaji Vasan Srinivasan, Chirag Singla, Rachit Bansal, Anil Singh Parihar (Submitted)
	Dynamic Non-Linear Interpolation of Latent Vectors for Semantic Face Editing Pinkesh Badjatiya, Parth Patel US Patent 17/454,645 (Filed)
	Semantics-Aware Hybrid Encoder For Improved Related Conversations Pinkesh Badjatiya, Tanay Anand, Simra Shahid, Nikaash Puri, Milan Aggarwal, S Sejal Naidu, Sharat Chandra Racha, Ganesh Palwe, Balaji Krishnamurthy (Submitted)
	SHERLock: Self-Supervised Hierarchical Event Representation Learning Sumegh Roychowdhury, Sumedh A Sontakke, Nikaash Puri, Mausoom Sarkar, Milan Aggarwal, Pinkesh Badjatiya, Balaji Krishnamurthy, Laurent Itti (Submitted)
	TAN-NTM: Topic Attention Networks for Neural Topic Modeling Shashank Shailabh, Madhur Panwar, Milan Aggarwal, Pinkesh Badjatiya, Simra Shahid, Nikaash Puri, Sejal Naidu, Sharat Chandra, Balaji Krishnamurthy, Ganesh Palwe (Submitted)
	Entropy Based Synthetic Data Generation For Augmenting Classification System Training Data Pinkesh Badjatiya, Nikaash Puri, Ayush Chopra, Anubha Kabra US Patent 16/659,147 (Published)
	Text Conditioned Image Search Based On Dual-disentangled Feature Composition Pinkesh Badjatiya, Surgan Jandial, Pranit Chawla, Mausoom Sarkar, Ayush Chopra US Patent 17/160,862 (Filed)
	Text-conditioned Image Search Based On Transformation, Aggregation, And Composition Of Visio-linguistic Features Pinkesh Badjatiya, Surgan Jandial, Pranit Chawla, Mausoom Sarkar, Ayush Chopra US Patent 17/160,893 (Filed)

Research

I'm interested in computer vision, machine learning and its applications, and natural language processing (NLP). Representative papers are highlighted.

	Understanding Bias in Hate Speech Pinkesh Badjatiya Tutorial Talk @ CODS-COMAD, 2022 CODS-COMAD 2022 Website / Slides
	Status-Quo Policy Gradient in Multi-agent Reinforcement Learning Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri, Jayakumar Subramanian, Abhishek Sinha, Siddharth Singh, Balaji Krishnamurthy Published at Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM2022) 2022 Published at DeepRL Workshop, NeurIPS 2021 paper
	Combating Online Hate Speech Roles of Content, Networks, Psychology, User Behavior and Others Sarah Masud, Pinkesh Badjatiya, Dr. Amitava Das, Dr. Manish Gupta, Dr. Vasudeva Varma, Dr. Tanmoy Chakraborty Tutorial Presented at WSDM, 2022 Tutorial Presented at ECML-PKDD, 2021 ECML-PKDD 2021 Tutorial / Tutorial-Website
	Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss Pinkesh Badjatiya, Mausoom Sarkar, Nikaash Puri, Jayakumar Subramanian, Abhishek Sinha, Siddharth Singh, Balaji Krishnamurthy Accepted as Extended Abstract in AAMAS 2022 paper
	SHERLock: Self-Supervised Hierarchical Event Representation Learning Sumegh Roychowdhury, Sumedh A Sontakke, Mausoom Sarkar, Pinkesh Badjatiya, Milan Aggarwal, Nikaash Puri, Balaji Krishnamurthy, Laurent Itti Published at International Conference on Pattern Recognition (ICPR) 2022 paper
	SAC: Semantic Attention Composition for Text-Conditioned Image Retrieval Surgan Jandial, Pinkesh Badjatiya, Pranit Chawla, Ayush Chopra, Mausoom Sarkar, Balaji Krishnamurthy * denotes equal contribution Accepted at WACV, 2022 paper
	Leveraging Style and Content features for Text Conditioned Image Retrieval Pranit Chawla, Surgan Jandial, Pinkesh Badjatiya, Ayush Chopra, Mausoom Sarkar, Balaji Krishnamurthy Accepted at CVFAD - CVPR 2021 paper
	MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance Anubha Kabra, Ayush Chopra, Nikaash Puri, Pinkesh Badjatiya, Sukriti Verma, Piyush Gupta, Balaji Krishnamurthy Published at IEEE International Conference on Data Mining (ICDM), 2020 paper
	Towards Identification, Classification and Analysis of Hate Speech on Social Media Pinkesh Badjatiya IIIT Hyderabad, Master's Thesis, 2019 thesis
	Multi-label Categorization of Accounts of Sexism using a Neural Framework Pulkit Parikh, Harika Abburi, Pinkesh Badjatiya, Radhika Krishnan, Niyati Chhaya, Manish Gupta, Vasudeva Varma Published at Empirical Methods in Natural Language Processing (EMNLP), 2019 paper
	Stereotypical bias removal for hate speech detection task using knowledge-based generalizations Pinkesh Badjatiya, Manish Gupta, Vasudeva Varma Published at The Web Conference (TheWebConf), 2019 paper
	Attention-based neural text segmentation Pinkesh Badjatiya, Litton J Kurisinkel, Manish Gupta, Vasudeva Varma Published at ECIR, 2018 paper / code
	Deep learning for hate speech detection in tweets Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, Vasudeva Varma * denotes equal contribution Published at WWW, 2017 (Best Poster Award) paper / Media Coverage

Academic Service

PC Chair	CODS-COMAD 2022 WOAH-EMNLP 2021 AAAI 2021 ICON 2019
Reviewer	ICCV 2021 CVPR 2021 AAAI 2021 CODS-COMAD 2021 WOAH-EMNLP 2020 ICWSM 2019 ICON 2019 ALW 2019 EMNLP 2018

Graduate Teaching Assistant

Information Retrieval & Extraction (Monsoon 2018, Instructor: Prof. Vasudeva Varma)
NLP Applications (Spring 2018, Instructor: Prof. Manish Shrivastava)
Statistical Methods in Artificial Intelligence (Monsoon 2017, Instructor: Prof. Vineet Gandhi)
Data Structures (Spring 2017, Prof. Lini Teresa Thomas)
ITWS-1 (Monsoon 2016, Prof. Shatrunjay Rawat)

Website template borrowed from here.
If you wish to use my version of the template, please provide credits.

Pinkesh Badjatiya

Work Experience

Applied Scientist 2

Machine Learning Researcher & Engineer 2

Machine Learning Research Intern

Computer Vision Engineer

Software Engineer Intern

Data Analytics/Site Reliability Engineer Intern

Google Code-In Mentor

System Administrator and Organizer

Google Summer Of Code (GSoC) Intern

Awards/Honors/Mentions

Patents

Optimal sequential decision making with changing action space

Status-Quo Policy Gradient for Multi-agent Reinforcement Learning

High Utility Sequential Pattern Mining using Reinforcement Learning

Dynamic Non-Linear Interpolation of Latent Vectors for Semantic Face Editing

Semantics-Aware Hybrid Encoder For Improved Related Conversations

SHERLock: Self-Supervised Hierarchical Event Representation Learning

TAN-NTM: Topic Attention Networks for Neural Topic Modeling

Entropy Based Synthetic Data Generation For Augmenting Classification System Training Data

Text Conditioned Image Search Based On Dual-disentangled Feature Composition

Text-conditioned Image Search Based On Transformation, Aggregation, And Composition Of Visio-linguistic Features

Research

Understanding Bias in Hate Speech

Status-Quo Policy Gradient in Multi-agent Reinforcement Learning

Combating Online Hate Speech Roles of Content, Networks, Psychology, User Behavior and Others

Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss

SHERLock: Self-Supervised Hierarchical Event Representation Learning

SAC: Semantic Attention Composition for Text-Conditioned Image Retrieval

Leveraging Style and Content features for Text Conditioned Image Retrieval

MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance

Towards Identification, Classification and Analysis of Hate Speech on Social Media

Multi-label Categorization of Accounts of Sexism using a Neural Framework

Stereotypical bias removal for hate speech detection task using knowledge-based generalizations

Attention-based neural text segmentation

Deep learning for hate speech detection in tweets

Academic Service

PC Chair

Reviewer

Graduate Teaching Assistant