CV

Rito Ghosh

(he/him)

Google Scholar 🎓 | Visit 📄 Blog | Visit LinkedIn for a more updated version | ✖️/Twitter | 😸 GitHub | 🐘 Mastodon | ✉️ [gh_uname].ml@gmail.com | 📃 CV

🥼 Research Interests: Computer Vision, Scientific Machine Learning, Artificial Life, Edge AI/TinyML, Computational Neuroscience, Efficient NLP.

🗝️ Key Skills: Python, Deep Learning, Machine Learning, Computer Vision, PyTorch, Mathematics, Programming, Team Work, Communication.

👨‍💻 Experience

🟦 Deep Learning Research Consultant - Akbar Brothers

🗓️ Mar 2021 - Jun 2023 | 🗺️ Remote
  • Worked in single-label classification with many labels.
  • Cleaned, wrangled a large amount of data (>60 GB).
  • Set up end-to-end Deep Learning training pipeline for single-label image classification.
  • Experimented and worked with Deep Learning architectures such as DenseNet, ResNet, EfficientNet
  • Used improvement techniques such as image data augmentation, oversampling and undersampling, gradual freezing, discriminative learning rates, ensemble methods, etc.
  • Achieved accuracy of >91% in the classification task, where this has never been done before.
  • Worked with tabular data of ~200k rows involving features to predict numerical data with XGBoost. Achieved ~98% accuracy.
  • Worked closely with business stakeholders to understand and solve problems.
  • Stack: Python, PyTorch, fast.ai, Scikit-Learn, XGBoost, Pandas, Python, Colab Pro.

Skills: Machine Learning, Deep Learning, Python (Programming Language), Data Science, Programming, Computer Vision, Linux, Jupyter Notebook, PyTorch, Written Communication, Convolutional Neural Networks (CNN)

🟦 Deep Learning Research Consultant - Okkular.io

🗓️ Sep 2021 - Oct 2021 | 🗺️ Remote
  • Wrote the patent filing for a novel architecture developed inside the company (filing process ongoing)
  • Briefly worked on Active Learning techniques in Computer Vision to automate the training process and improve accuracy and maintenance.
  • Stack: Python, PyTorch, fast.ai, AWS SageMaker, AWS S3, Pandas.

Skills: Deep Learning, Linux, AWS SageMaker, Team Work

🟦 Technical Reviewer - Packt

🗓️ Sep 2022 - Present | 🗺️ Remote
  • Reviewing a book on Advanced PyTorch. Chapters include: multimodal AI, Deep Reinforcement Learning, etc.
  • Working with co-ordinators and other editors.

Skills: PyTorch, Computer Vision, NLP, Reinforcement Learning, Team Work

🟦 Section Leader (Teaching Assistant) of Code in Place Programme - Stanford University

🗓️ Apr 2021 - Jun 2021| 🗺️ Remote

  • This online course was offered by Stanford University during the COVID-19 pandemic. It brought together 12,000 students and 1,100 volunteer teachers participating from around the world. The course is a 6-week introduction to Python programming using materials from the first half of Stanford’s CS106A course.

  • As a volunteer section leader, I prepared and taught a weekly discussion section of 8-10 students to supplement professors’ lectures.

  • Received direct training from Stanford Professors Mehran Sahami, Chris Piech, and Julie Zelenski. Received further training from Stanford-appointed teacher mentors.

  • Taught introductory Python and CS to about 12 students from India, Bangladesh, and the USA. Students were of various ages and came from diverse backgrounds including from Fine Arts backgrounds. For many students, it was the first introduction to programming and CS ever.

  • Taught five sessions of my own (lasting 1.5 h on average) and a backup session for another.

  • Taught programming, problem-solving, and CS concepts.

Skills: Python, Programming, Teaching, Team Work

👨‍🏫 Education

🟩 Master of Science (Computer Science) [M.Sc. (CS)]

🏛️ Techno College Hooghly (under MAKAUT) | 🗓️ Jul 2021 - Jul 2023 | 🗺️ Kolkata

CGPA: 9.04/10 Notable courses: Advanced algorithms, Soft Computing, Artificial Intelligence

🟩 Bachelor of Science (Physics, Mathematics, Computer Science) [B.Sc.]

🏛️ University of Calcutta | 🗓️ Jul 2017 - Apr 2021 | 🗺️ Kolkata

Grade: First Class Notable courses: Mathematical Methods for Physics I, II, Quantum Mechanics, Statistical Mechanics, Special Theory of Relativity

🟩 Summer School (Computational Neuroscience)

🏛️ Neuromatch Academy | 🗓️ Jun 2022 - Jul 2022 | 🗺️ Remote

📦️ Projects

🟧 Functional connectivity differences between ECoG signals during motor movement vs. motor imagery

🏛️ Neuromatch Academy Summer School in Computational Neuroscience

🗓️ Jun 2022 - Jul 2022 | 🔗 Link ➡️
  • Human brain is responsible for both motor tasks, i.e. when actual movement of some body parts happen, and also for imagery tasks: when the person imagines a movement. This is a project that aims to find functional connectivity among brain areas in both imagined and real motor movements. This project also aims to find the similarity (or lack thereof) between real and imaginary movement in lower dimensional latent space of brain signals using Principal Component Analysis.

Skills: Python, PyTorch, Deep Learning, Computational Neuroscience, Team Work

🟧 Learn JAX: From Linear Regression to Neural Networks

🏛️ Personal Project

🗓️ Dec 2021 - Jan 2022 | 🔗 Link ➡️

I introduce the JAX library for solving basic problems in Machine Learning. I explain everything so that a beginner in this framework will find it helpful. Introduced the functional programming paradigm for Deep Learning.

Won the Google Open Source Software Expert Prize

Skills: JAX, Machine Learning, Deep Learning, Written Communication

🟧 CLIP for Satellite Images

🏛️ Hugging Face x Google Cloud Hackathon

🗓️ Jul 2021 - Sep 2021 | 🔗 Link ➡️

  • This project is an Open-Source Deep Learning architecture that generates captions for satellite imagery.

  • We used transfer-learning to fine-tune OpenAI CLIP for satellite data.

  • Training was done for multiple hours on a TPUv3-8 enabled virtual machine provided by Google Cloud.

  • We used JAX/Flax and PyTorch for this model.

  • GitHub: https://github.com/arampacha/CLIP-rsicd

  • Demo: https://huggingface.co/spaces/sujitpal/clip-rsicd-demo

  • I worked on researching and applying different augmentation techniques that led to the improvement of the model.This project is an Open-Source Deep Learning architecture that generates captions for satellite imagery. We used transfer-learning to fine-tune OpenAI CLIP for satellite data. Training was done for multiple hours on a TPUv3-8 enabled virtual machine provided by Google Cloud. We used JAX/Flax and PyTorch for this model.

Skills: Transfer Learning, Deep Learning, Computer Vision, Linux, Jupyter Notebook

🟧 DALL·E Mini

🏛️ Hugging Face x Google Cloud Hackathon

🗓️ Jul 2021 - Sep 2021 | 🔗 Link ➡️

  • This project is an Open-Source Deep Learning architecture that generates images from a given text prompt. This implements the functionality of OpenAI’s DALL-E architecture, but with a 27x smaller model.

  • A BART autoregressive model was used to process and generate text, and a VQGAN to generate the images. Then we used a pre-trained CLIP model from OpenAI to rank the generated images.

  • Training was done for 70 hours on a TPUv3-8 enabled virtual machine provided by Google Cloud.

  • We used JAX/Flax and PyTorch for this model.

  • GitHub: https://github.com/borisdayma/dalle-mini

  • Demo: https://huggingface.co/spaces/flax-community/dalle-mini

Skills: Deep Learning, Linux, Google Cloud Platform, JAX, Flax, Team Work

🟧 Folk.AI: Classifying Between Two Folk Dance Forms of India Using Deep Learning

🏛️ Personal Project during taking the fast.ai MOOC

🗓️ Jul 2020 - Aug 2020 | 🔗 Link ➡️

  • Trained a Convolutional Neural Network with ResNet-34 leveraging transfer learning. Deployed the model as a web app for inference.

  • This app distinguishes between Kathakali and Chhau- two classical dance forms of India.Trained a Convolutional Neural Network with ResNet-34 leveraging transfer learning. Deployed the model as a web app for inference. GitHub- https://github.com/ghosh-r/folkAI This app distinguishes between Kathakali and Chhau- two classical dance forms of India.

Skills: Transfer Learning, Deep Learning, Computer Vision

🟧 Investing in A New Eatery in California: A Data Driven Approach

🏛️ Personal Project for IBM Data Science Professional Certificate

🗓️ Jul 2020 - Jul 2020 | 🔗 Link ➡️

  • Applied KMeans Clustering algorithm to cluster counties of California, to look for and recommend new investment opportunities for investors looking to fund new eateries in California City.

  • Evaluated feature correlation through the Pearson Coefficient, and evaluated model through elbow method.

  • Final Report Link: https://github.com/ghosh-r/California_Eatery/blob/master/final_deliverables/capstone_project_final_report.pdf

Skills: Machine Learning, Data Science, Python, Scikit-Learn, Written Communication

🟧 Free Bengali Poetry (Dataset project)

🏛️ Personal Project

🟧 Bengali-MNIST (Dataset Project)

🏛️ Personal Project

🏅 Honors and Awards

🏆️ Winner of Google Open Source Software Expert Prize

🏛️ Google/Kaggle

🏆️ 1st Place in JAX/Flax Deep Learning Community Event

🏛️ Hugging Face x Google Cloud

🏆️ 3rd Place in JAX/Flax Deep Learning Community Event Organized by Hugging Face and Google Cloud

🏛️ Hugging Face x Google Cloud

🏆️ “Best Speaker of The Legislative Assembly” in Youth Parliament Competition, District Level

🏛️ Department of Parliamentary Affairs, Government of West Bengal

🏆️ 3rd Place in District Level Student-Youth Science Fair

🏛️ Department of Youth Services, Government of West Bengal

📜 Publications

📜 Complete Introductory Guide to Speech to Text with Transformers

🏛️ Analytics Vidhya

📜 Non-Linear Dynamics Through Linear Algebraic Lenses: Attempting to Learn the Trajectories of the Logistic Map with Artificial Neural Networks

🏛️ Master’s Thesis published in Zenodo

📜 Best Podcasts for Machine Learning

🏛️ KDNuggets

📜 TAKING JULIA FOR A TEST DRIVE: My First Encounter With Julia

🏛️ Towards Data Science

📜 How Much Math Do You Need to Know to Get Started with Data Science? A Clear, Straightforward Answer

🏛️ Towards Data Science

Volunteering Experiences

🌱 Data Contributor

🏛️ Mozilla | 🗓️ Mar 2021 - Mar 2022

I recorded my voice and validates others’ for creating the Mozilla Common Voice Dataset. I was involved in five languages- Bengali, English, Spanish, Hindi, and German.

🌱 Mentor

🏛️ Code School by Giles Mcmullen | 🗓️ Jul 2021 - Present

  • I help answer questions from beginners getting into programming and learning Data Science. I also guide beginners about programming languages, problem solving skills, tools, and other related things about programming and Data Science.

🌱 Volunteer

🏛️ Porichoy | 🗓️ Jan 2015 - Jan 2017

  • Served as a volunteer in Porichoy, a local group of volunteers aimed to serve the less fortunate after natural disasters and helping in education of children from economically challenged families. I mainly worked in rehabilitation after flood as a part of a team and organised cultural events.

🌱 Mentor of Youth Parliament Competition

🏛️ Dainhat High School | 🗓️ Jul 2022 - Sep 2022

  • Trained a group of fifteen high school students in Youth Parliament Competition.

  • Taught them about government, state-level polity, and conducts of Vidhan Sabha (State Legisslative Assembly)

  • Helped train them in speech delivery, acting, etc.

  • Helped write the script of 45 minute run- many additions, full editing, and wrote all English dialogues.

  • In the first competition, held at the level of Municipality, they won the First place beating three other schools.

  • In the next level, they won the Third place competing with 29 other schools in the District Level.

🌱 Language and Culture Volunteer

🏛️ Project Bhasha | 🗓️ Jan 2019 - Feb 2021

  • Project Bhasha was aimed at creating a website and a community around it to teach Bengali to foreigners. We took a broader approach to teach culture as well as the language. My role was to write articles on Bengali art, culture, language, and literature. I also oversaw some aspects of the overall strategy, pedagogy, etc.

🤸‍♂️ Hobbies

Reading books, swimming, playing the piano, narrating stories, etc.

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