Gaurav B
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If such data is not available, the star rating is calculated from the fraction of work sessions approved by their past clients versus the total tracked by the talent.
If such data is not available, the star rating defaults to the interview rating of the talent at the time of the vetting process.
Other data including reviews from interviews are available on the talents’ profiles.
If such data is not available, the star rating is calculated from the fraction of work sessions approved by their past clients versus the total tracked by the talent.
If such data is not available, the star rating defaults to the interview rating of the talent at the time of the vetting process.
Other data including reviews from interviews are available on the talents’ profiles.
Machine Learning Engineer Preferred Title
$31.25 /hr $ 80.0K /yr Hourly Rate and Yearly Salary
Overview
Basic Summary
Vetting Summary - Code Quality 60%
- Soft Skill Attributes 60%
- Expertise 2/3
- Coding Challenges 60%
- No. of Lifelong Learning Projects 2
- No. of Coding Challenges Completed 2 More details
- Software Engineering Process 5.0/5
- Technical Breadth 5.0/5
- Algorithmic Thinking 5.0/5
- Technical Strength 5.0/5
- Teamwork 4.0/5
- Intellectual Merit 5.0/5
- English Communication 5.0/5
- Desktop Mac
- Phone Android
- Tablet iPad
- Member Since Dec 12, 2022
- Profile Last Updated Dec 06, 2022
- Last Activity Jan. 1, 2023, 4:02 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: 7+ years
Deep learning (4E, 6Y)
4 experiences, across 6 yearsComputer vision (3E, 6Y)
3 experiences, across 6 yearsMachine learning (3E, 5Y)
3 experiences, across 5 yearsNatural language processing (2E, 4Y)
2 experiences, across 4 yearsPyTorch (3E, 3Y)
3 experiences, across 3 yearsTensorflow (1E, 3Y)
1 experience, across 3 yearskeras (1E, 3Y)
1 experience, across 3 yearsscikit-learn (1E, 3Y)
1 experience, across 3 yearsPython (2E, 2Y)
2 experiences, across 2 yearsProbabilistic graphical models (1E, 1Y)
1 experience, 1 yearSpeech recognition (1E, 1Y)
1 experience, 1 year
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
- Agile Development Process
- Productivity and Responsiveness
- Teamwork
- Software Engineering
- Logical Thinking
- Technical Strength
- Intellectual Merit
- English Communication
- Documentation
- System Design
- Coding Challenges60%
- Algorithms Score60%
- General Score60%
- Easy Algorithm
- Correctness60%
- Performance60%
- Medium Algorithm
- Correctness60%
- Performance60%
- Hard Algorithm
- Correctness60%
- Performance60%
- Expertise2/3
- Design Patterns and Architectures2/3
- Debugging2/3
- Stack Traces2/3
- Testing2/3
- System Administration2/3
- Soft Skill Attributes60%
- Entrepreneurial60%
- Whole Brained60%
- Divergent Thinking / Creativity60%
- Design Ability60%
- Empathy60%
- Project Management Ability60%
- Security60%
- Code Quality60%
- Complex Logic60%
- Models60%
- Controllers60%
- Templates60%
- APIs60%
- Training/Testing Data Models60%
- Code Readability60%
- Ongoing Evaluation
- Number of Lifelong Learning Project2
- Number of Coding Challenge Completed3
Experience
Student
Employment
Sep 2021 - Dec 2025
University of British Columbia Company
Research Industry
Project: Domain Translation with Deep Generative Models
- Computer vision
- Machine learning
- PyTorch
- Python
- Deep learning
- 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
- Computer vision
- Probabilistic graphical models
- Natural language processing
- PyTorch
- Deep 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
- Speech recognition
- Machine learning
- PyTorch
- Python
- Deep learning
- 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
- Computer vision
- Machine learning
- Natural language processing
- scikit-learn
- Tensorflow
- Deep learning
- keras
- 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
Machine Learning
Jan 2021 - Jun 2022
University of British Columbia
- Probabilistic graphical models
- Deep learning
- Artificial intelligence
- Machine learning
Probabilistic Programming
Sep 2021 - Dec 2021
University of British Columbia
- Probabilistic graphical models
- Deep learning
- Compilers
- Machine learning
Visual Geometry
Sep 2021 - Dec 2021
University of British Columbia
- Deep learning
- PyTorch
- Computer vision