Manish Kumar Singh

Software Engineer

Mountain View, California, United States6 yrs 8 mos experience

Highly Stable

Key Highlights

Expert in machine learning and deep learning.
Published research in top-tier conferences.
Developed large-scale intelligent systems.

Stackforce AI infers this person is a Machine Learning Engineer with expertise in AI research and development.

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Skills

Core Skills

Machine LearningDeep Learning

Other Skills

Stochastic Multi-Armed BanditPyTorchNeural Relational InferenceTensorFlowC++AlgorithmsApplied Machine LearningPython (Programming Language)

About

I am currently a Software Engineer in the Google Augmented Reality team. Previously, I was a Senior Research Engineer in the Perception team at Qualcomm AI Research led by Dr. Fatih Porikli, where I focussed on developing foundational models and techniques to enhance the efficiency of existing models, with applications in Generative AI and 3D vision. During my time there, I published research in top-tier computer vision and machine learning conferences and workshops. I hold an MS in Computer Science from UC San Diego, with a specialization in Machine Learning. During my graduate studies, I completed advanced coursework across various areas of computer science and contributed to theoretical research in machine learning. In the past, I worked as a Research Fellow at Microsoft Research and completed my B.Tech. in Computer Science from IIT BHU. With a strong foundation in computer science, I excel in problem-solving, software development, and transforming research into practical solutions. My passion lies in machine learning and deep learning, and I have substantial experience designing and building large-scale intelligent systems. Based on my professional and academic contributions, I have also been granted the Individuals of Extraordinary Achievement classification by the United States Citizenship and Immigration Services.

Experience

6 yrs 8 mos

Total Experience

2 yrs

Average Tenure

1 yr 4 mos

Current Experience

Google

Research Engineer

Jan 2025 – Present · 1 yr 4 mos

Qualcomm ai research

Senior Deep Learning Research Engineer

Jan 2021 – Jan 2024 · 3 yrs

Perception team

Uc san diego

2 roles

Graduate Student Researcher

Promoted

Jan 2020 – Oct 2021 · 1 yr 9 mos

Online Learning for healthcare robotics: Worked with Prof. Kamalika Chaudhuri on an interesting application of the stochastic multi-armed bandit(MAB) model to healthcare robotics. The goal of this project is to model the sequential decision-making of one or more robotic agents interacting with People with Dementia(PwD) using the stochastic MAB model where each aspect of a robot’s interaction with a PwD is considered to be an arm of the MAB and the goal is to build online algorithms to make choices in order to maximize the expected reward. Studied a lot of theory of online learning algorithms, and worked on implementing a novel algorithm to transfer experiences in a multi-player scenario where different players can learn from each other. Work accepted for publication at Workshop on Real World Experiment Design and Active Learning, International Conference on Machine Learning (ICML), 2020
Dynamic relational inference in multi-agent systems: Currently working with Prof. Rose Yu on the problem of inferring relations in an unsupervised fashion from the trajectories of agents in a multi-agent system. Developing a Neural Relational Inference based model in PyTorch to infer relations and predict future trajectories of agents in a completely unsupervised fashion.

Machine LearningStochastic Multi-Armed BanditPyTorchDeep Learning

Teaching Assistant

Sep 2019 – Oct 2021 · 2 yrs 1 mo

CSE-105: Theory of Computation(Fall, 19)
CSE-151A: Introduction to Machine Learning(Winter, 20 and Spring, 20)
DSE-200: Python for Data Analysis(Fall,20)
Responsibilities included preparing and grading assignments, holding office hours and discussion sections.

Microsoft

Research Fellow

Jul 2018 – Jul 2019 · 1 yr · Bangalore

Research Fellow at Microsoft Research working in the Applied Machine Learning group
Built from scratch an embedding learning system using TensorFlow and C++ to implicitly factorize the HeteSim similarity matrix for node to node recommendation among heterogeneous entities for Microsoft Teams
Tested the embedding learning system on multiple benchmark datasets like MovieLens, Yelp and Douban Movie and reported improved performance on classification, clustering and ranking tasks
Implemented an explicit loss formulation of the HeteSim scoring model to train the embeddings using Tensorflow

TensorFlowC++Machine Learning