Vikesh Khanna

Vikesh Khanna

Stanford, California, United States
5K followers 500+ connections

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About

CTO & co-founder of Ambient.ai, a Series-B company backed by YC and a16z pushing the…

Activity

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Experience

  • Ambient.ai Graphic

    Ambient.ai

    San Francisco Bay Area

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    San Francisco Bay Area

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    Palo Alto, CA

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    Mountain View, CA

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Education

  • Stanford University Graphic

    Stanford University

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    MS, Computer Science (Depth: AI) with a focus on large-scale graph (networks) analysis and deep learning (especially its application to computer vision). I've trained massively deep neural networks and deployed them on a performance-critical scale-out infrastructure. I've also worked on several large-scale data and graph analysis problems at Stanford, particularly Graft (ACM SIGMOD 2015 paper published) and Ringo (part of SNAP, Stanford network analysis project).

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    Activities and Societies: Technology Head of Entrepreneurship Development Cell, Lead New Media Developer & Co-founder at ZignDog Media

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    Awarded NTSE scholarship (National Talent Search Examination) by NCERT (National Council for Education, Research and Training) in 2006. NTSE is an annual national level scholarship awarded to a small number of Indian students with a consistently high academic and leadership record.

Publications

  • Graft: A Debugging Tool For Apache Giraph

    ACM SIGMOD

    We address the problem of debugging programs written for Pregel-like systems. After interviewing Giraph and GPS users, we developed Graft. Graft supports the debugging cycle that users typically go through: (1) Users describe programmatically the set of vertices they are interested in inspecting. During execution, Graft captures the context information of these vertices across supersteps. (2) Using Graft's GUI, users visualize how the values and messages of the captured vertices change from…

    We address the problem of debugging programs written for Pregel-like systems. After interviewing Giraph and GPS users, we developed Graft. Graft supports the debugging cycle that users typically go through: (1) Users describe programmatically the set of vertices they are interested in inspecting. During execution, Graft captures the context information of these vertices across supersteps. (2) Using Graft's GUI, users visualize how the values and messages of the captured vertices change from superstep to superstep,narrowing in suspicious vertices and supersteps. (3) Users replay the exact lines of the code vertex.compute() function that executed for the suspicious vertices and supersteps, by copying code that Graft generates into their development environments' line-by-line debuggers. Graft also has features to construct end-to-end tests for Giraph programs. Graft is open-source and fully integrated into Apache Giraph's main code base.

    Other authors
    See publication

Courses

  • Advanced Project in Data Mining

    CS341

  • Convolutional Neural Networks for Visual Recognition

    CS231N

  • Data Structures

    EC-251

  • Deep Learning for Natural Language Processing

    CS224D

  • Design and Analysis of Algorithms

    EC-351

  • Independent Study (Ringo, Large Scale Graph Processing)

    CS399 (Jure Leskovec)

  • Introduction to Databases

    CS145

  • Introduction to Probability for Computer Scientists

    CS109

  • Machine Learning

    CS229

  • Mining Massive Datasets

    CS246

  • Social Networks and Information Analysis

    CS224W

Projects

  • Ultimate Tic-Tac-Toe

    Ultimate Tic-Tac-Toe is an enhanced version of the classical Tic-Tac-Toe game. Built the AI (using min-max strategy in Python) and wrote the UI (using D3.js and lots of jQuery) as a weekend project. The game collects data of every move made by the players in a mySQL DB which can be analyzed to compute the optimal strategy for the game. Over 50,000 games have been played.

    See project

  • AuctionBase

    Design and implementation of an auctioning site using real eBay data as part of Stanford's CS145 - Introduction to Databases. One of the six winners out of 300 students enrolled in class for the best functionality and user experience.

    See project

  • Link Prediction on GitHub

    Recommending potential collaborators on GitHub using biased random walks with restart.

    Other creators
    See project

  • Kris Gethin's 12 Week Workout - Windows Phone App

    Windows Phone App for people following Kris Gethin's 12 Week Daily Training program. Over 50,000 app downloads.

    See project

  • YouTurn

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    Chrome Extension to auto-repeat single YouTube videos. Minimal. Beautiful. Done right.

    See project

  • Audio Fingerprinting for song identification

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    Song identification using Open Fingerprint Architecture to correct ID3 tags, detect copyright infringement on P2P clients etc.

    See project

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