Gaurav B
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The client satisfaction rating is the weighted average of client ratings, with weights based on reviewed work hours. When no client rating exists, the approval fraction (approved vs. reviewed work hours) determines it.
If the client satisfaction rating exceeds the overall rating, it becomes the star rating. In the absence of client ratings, if the average client interview rating is higher than the overall rating, it becomes the star rating. If no data is available, the star rating defaults to the internal interview score.
Machine Learning Engineer Preferred Title
$31.25 /hr $ 80.0K /yr Hourly Rate and Yearly Salary
Overview
Basic Summary
LD Talent History - Average Count of Messages/Day 0.6
- Project Completion Rate 2/4
- Interview Acceptance Rate 4/6
- Timezone Overlap 32h PST, 10h UTC
- Software Engineering Process 5.0/5
- Design Practice 3.0/5
- Design Theory 3.0/5
- Technical Breadth 5.0/5
- Logical Thinking 5.0/5
- Technical Strength 5.0/5
- System Design 3.0/5
- Productivity and Responsiveness 3.0/5
- Teamwork 4.0/5
- Agile Development Process 3.0/5
- Intellectual Merit 5.0/5
- English Communication 5.0/5
- Documentation 3.0/5
- Desktop Mac
- Phone Android
- Tablet iPad
- Member Since Dec 12, 2022
- Profile Last Updated Dec 06, 2022
- Last Activity March 11, 2024, 9:28 p.m. UTC
- Location Canada
Profile Summary
I am a deep-learning researcher with an experience in computer vision, natural language processing, and data sciences. Currently, I am working as a P.h.D. researcher in the Machine Learning and Computer Vision Lab at the University of British Columbia. Previously I worked as Research Scientist at Descript-Inc, and as a Research Assistant at IIT-Hyderabad. My experience ranges from data analysis and statistics to implementing and deploying various machine learning models on Slurm and AWS.
Skills
Total Experience: 9+ years
Deep learning (4E, 9Y, 3C)
4 experiences, across 9 years, with 3 coursesComputer vision (3E, 9Y, 1C)
3 experiences, across 9 years, with 1 courseMachine learning (3E, 9Y, 2C)
3 experiences, across 9 years, with 2 coursesPyTorch (3E, 6Y, 1C)
3 experiences, across 6 years, with 1 coursePython (2E, 6Y)
2 experiences, across 6 yearsNatural language processing (2E, 6Y)
2 experiences, across 6 yearsTensorflow (4Y)
4 years of experiencekeras (4Y)
4 years of experiencescikit-learn (4Y)
4 years of experienceProbabilistic graphical models (2Y, 2C)
2 years of experience, with 2 coursesSpeech recognition (2Y)
2 years of experienceArtificial intelligence (1C)
1 courseCompilers (1C)
1 course
Weekly Availability
Timezone Overlap with 06 - 21 per Week: 32h PST, 10h UTC
Day
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
UTC
16 - 22
00 - 05
00 - 05
00 - 05
00 - 05
00 - 05
16 - 22
PST
09 - 15
17 - 22
17 - 22
17 - 22
17 - 22
17 - 22
09 - 15
Vetting
- Interview Data
- Software Engineering Process
- Design Practice
- Design Theory
- Technical Breadth
- Logical Thinking
- Technical Strength
- System Design
- Productivity and Responsiveness
- Teamwork
- Agile Development Process
- Intellectual Merit
- English Communication
- Documentation
- Code Quality
- Code Readability
Experience
Student
Employment
Sep 2021 - Dec 2025
University of British Columbia Company
Research Industry
Project: Domain Translation with Deep Generative Models
- Developed a new deep generative model (a.k.a GAN) for solving domain translation problems in computer vision. The framework takes an image as input and generates a new image in another domain.
- This framework can be used to translate the image of a male to the image of a female. The machine learning model can be used to convert images taken in daylight to night or taken in summer to winter.
- The deep learning architecture is developed in PyTorch using Python. It is highly scalable and can handle multiple domains without adding more parameters. Thus the GPU requirement is very less.
- This work also resulted in a research paper that has been published in WACV'23.
- Publications - https://scholar.google.co.in/citations?user=PcmMT-4AAAAJ&hl=en
Developer
Employment
May 2020 - May 2021
Indian Institute of Technology Hyderabad Company
Research Industry
Project: Deep generative and probabilistic models for Seq2seq learning
- Created framework in Python using Pytorch that contains the implementation of deep probabilistic graphical models such as Variational Autoencoders (VAEs), Mixture Density Networks (MDN), and GANs.
- The framework consists of attention-based seq2seq deep learning models.
- The framework can be used for 3D sketch generation (computer vision) where the deep learning models complete an incomplete sketch automatically.
- The framework can also be used to convert text into handwriting. The handwriting is personalized to each person by fine-tuning the model on a small handwriting dataset of the person.
- It also supports machine translations in natural language processing which translates text from one language into another such as English-to-French, English-to-German, French-to-English, etc.
Developer
Employment
Jun 2019 - May 2020
Descript-Inc Company
Technology Industry
Project: Speech generation and recognition
- Created the framework for speech recognition and generation using Python, Keras, and Pytorch. The framework was also used in the deployment of machine learning and deep learning models.
- The framework consists of the implementation of deep learning models such as CNN, LSTMs, etc for audio synthesis, multi-class speech recognition, and tagging.
- Used Amazon AWS and Google GCP for implementation of the framework.
- This work resulted in a research paper on multi-classification and audio tagging in InterSpeech'18.
Developer
Passion Project
May 2016 - Jun 2019
Indian Institute of Technology Roorkee Company
Research Industry
Project: DeepLearn: Reproducing Deep Learning Papers on NLP and Data Sciences
- Created an open-source framework for Natural Language Processing and Computer Vision tasks. The framework is written in Python using Keras, Scikit-learn, and Tensorflow, and is available on GitHub.
- Implemented 15+ deep learning models from various research papers for many NLP and data sciences tasks. The deep learning models include attention-based CNN/LSTMs/Transformers.
- This repository supports a ranking-based question-answer system. For a text question, the machine learning system produces top-K relevant answers based on contextual similarity.
- It supports fake-news stance detection that can be used to classify whether a given news article is fake or not. It also supports finding documents written by a single author.
- This repository also contains implementations of my research papers published in WWW'18, Pattern Recognition'19, and ACPR'17. Papers - https://scholar.google.co.in/citations?user=PcmMT-4AAAAJ&hl=en
Qualifications
Education
University of British Columbia
Aug 2021 - Aug 2025
PhD (Computer Science)
Indian Institute of Technology Roorkee
Aug 2015 - Aug 2017
Mtech (Computer Science)
Courses