Ashudeep Singh

AI Researcher

San Francisco, California, United States4 yrs 7 mos experience

Highly StableAI Enabled

Key Highlights

Expert in AI and machine learning systems.
PhD research on fairness in recommendation systems.
Best paper award at ACM SIGIR 2020.

Stackforce AI infers this person is a leading expert in AI and machine learning for social media and user interaction systems.

Contact

ashudeep21@gmail.com LinkedIn

Skills

Core Skills

Artificial Intelligence (ai)Machine LearningRecommender SystemsNatural Language Processing

Other Skills

Algorithm AnalysisAlgorithm DesignAlgorithm DevelopmentAlgorithmsApache SparkArtificial IntelligenceArtificial Neural NetworksBashCC++CSSCausal InferenceCommunicationComputer VisionData Analysis

About

At Microsoft, I conduct applied research at Microsoft AI on search, recommendation and chat-based user interaction systems, working on multimodal deep neural networks that learn from human interactions, along with leveraging LLMs and Generative AI. In my previous role as an Applied Scientist at Pinterest Labs, I built machine learning systems that ensure safety, algorithmic fairness, diversity, and inclusive system design for personalized discovery. I leveraged my expertise in deep learning, ranking and recommender systems, and large language models to ensure that Pinterest is useful to a diverse set of users, creators, and merchants. I completed my PhD in Computer Science at Cornell University, where I introduced the idea of fairness of exposure in ranking and recommender systems and developed algorithms that ensure a fair distribution of opportunity for multiple stakeholders. My research has been published in prestigious conferences such as NeurIPS, ICML, SIGIR, and KDD, and received the best paper award at ACM SIGIR 2020. I also have multiple internships at Google, Facebook, and Microsoft Research, where I worked on various aspects of machine learning and natural language processing. I hold a dual degree in Computer Science and Engineering from IIT Kanpur.

Experience

Microsoft

Principal Applied Scientist

Aug 2024 – Present · 1 yr 7 mos · Mountain View, California, United States · On-site

Agentic search, Multimodal retrieval at the web scale with complex user intents and queries using Multimodal LLMs and SLMs, User Personalization. Also, focusing on AI Safety & Alignment in Human-AI interaction scenarios.

Large Language Models (LLM)Machine LearningArtificial Intelligence (AI)

Applied Scientist

Aug 2021 – Aug 2024 · 3 yrs · Palo Alto, California, United States · Hybrid

Retrieval, Ranking, and Personalization: Developing and benchmarking state-of-the-art machine learning algorithms that learn ranking and retrieval models and embeddings from sequential human feedback data spanning Graph ML, Reinforcement Learning, and sequential models such as Transformers.
Developing recommendation systems that ensure safety, algorithmic fairness, and diversity to users and creators. Working towards inclusive system design in production systems for personalized discovery.
[Link: https://www.pinterestlabs.com/]

Large Language Models (LLM)Machine LearningPattern RecognitionRecommender Systems

Google

Research Intern

Jan 2020 – May 2020 · 4 mos · Greater New York City Area

Safe Reinforcement Learning

ResearchMachine LearningReinforcement Learning

Microsoft

Research Intern

May 2019 – Aug 2019 · 3 mos · Montreal, Canada Area

Feedback Loops in Interactive Learning Systems

CommunicationMachine Learning

Facebook

Research Intern

May 2017 – Aug 2017 · 3 mos · San Francisco Bay Area

Newsfeed integrity: Active Learning for Multilabel classification on Facebook Newsfeed.

CommunicationMachine LearningPattern Recognition

Microsoft

Research Intern

May 2016 – Aug 2016 · 3 mos · Greater New York City Area

Contextual Bandits for Personalization

Machine Learning

Cornell university

2 roles

Teaching Assistant

Aug 2015 – Dec 2015 · 4 mos · Ithaca, New York, United States

Research Intern

May 2014 – Jul 2014 · 2 mos

Carnegie mellon university

Research Intern at Language Technologies Institute

May 2013 – Jul 2013 · 2 mos · Greater Pittsburgh Area

As an intern worked in the field of Computer Supported Collaborative Learning, using Machine Learning techniques on data comprising of student interactions with an intelligent education assistant.

Large Language Models (LLM)Natural LanguageNatural Language Processing