Karan Dwivedi

Operations Associate

New Delhi, Delhi, India5 yrs 2 mos experience
Highly StableAI ML Practitioner

Key Highlights

  • Co-Founder of a health-focused food startup.
  • Expert in Deep Learning and Computer Vision.
  • Developed AI solutions for HR and recruiting.
Stackforce AI infers this person is a Deep Learning and Computer Vision expert in the Healthcare and FoodTech industries.

Contact

Skills

Core Skills

OperationsEntrepreneurshipDeep LearningComputer Vision

Other Skills

AI agentsDevOpsPython (Programming Language)TypeScriptproject management

About

Founder's Office, Memorang Co-Founder, Basil Volunteer, Govt of NCT of Delhi BTech Computer Science, IIT Delhi

Experience

Memorang

Operations Associate

Jan 2024Aug 2025 · 1 yr 7 mos · California, United States · Remote

  • Supported founder and teams in India & US with recruiting, project management, and operations.
  • Set up AI agents to streamline HR and recruiting.
project managementoperationsAI agents

Basil

Co-Founder

Apr 2020Nov 2023 · 3 yrs 7 mos · Delhi, India

  • Basil was born from a passion to make clean, unprocessed food accessible to everyone. Recognizing the dilemma of choosing between oily cheap options and expensive diet food while on the go, the founders aimed to be a trusted brand offering affordable, preservative-free daily needs, revolutionizing convenient and healthy eating.
entrepreneurship

Harvard university

Post Bacc Fellow

Mar 2018Jul 2018 · 4 mos · Boston, Massachusetts, United States

  • Meta-learning in Deep Convolutional Neural Networks
  • Worked on Neural Architecture Search and Metamodelling in deep computer vision.
  • Worked on developing deep neural networks robust to adversarial attacks for image classification.
Python (Programming Language)Computer VisionDeep Learning

Qure.ai

Research Intern

May 2017Jul 2017 · 2 mos · Mumbai, Maharashtra, India

  • Attention Models for High-resolution Chest XRay Diagnosis
  • Developed attention models inspired by early spatial attention in the human visual system, so as to achieve interpretable results and be able to process high-resolution images without losing important information.
  • Combined spatial attention model with object-level attention to model the feedback connections in the human visual system.
  • Improved on the baseline methods from 76% to 82% binary classification accuracy for cardiomegaly detection on a private dataset using only spatial attention.
Python (Programming Language)Computer VisionDeep Learning

École des ponts paristech

Research Intern

Jun 2016Nov 2016 · 5 mos · Paris, Île-de-France, France

  • Instance Matching and Object Discovery in Artwork
  • Discovered single-view instance matches for small objects (e.g. huts, boats) using Fast-RCNN in an unannotated dataset of artworks from Jan Brueghel's workshop. Studied the effect of network architecture, scaling, bounding box proposal algorithms, and preprocessing on matching quality.
  • Applied adversarial learning with synthetic artistic styles to generate style-invariant features for matching artworks across different styles.
  • Combined adversarial training with multi-task training in a hybrid CNN architecture to obtain more robust feature representations.
  • Achieved state of the art on Artist, Genre, and Style classification on Wikipaintings and Rijksmuseum
  • datasets using CNNs trained for content and style multi-task learning.
Python (Programming Language)Computer VisionDeep Learning

Omnipresent robot tech

Engineer Intern

Dec 2015Jan 2016 · 1 mo · New Delhi, Delhi, India

  • Created an aerial mapping software for Unmanned Aerial Vehicles using OpenCV in Python, with video mosaicing, deblurring and image super-resolution.
  • Implemented superior real time video stabilization by improving Youtube's video stabilization algorithm.
Python (Programming Language)Computer VisionDevOpsDeep Learning

Education

Indian Institute of Technology, Delhi

Bachelor of Technology — Computer Science and Engineering

Jan 2014Jan 2019

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