Anjishnu Kumar

Applied Research Scientist // Machine Learning Engineer


Hi, I'm Anjishnu, I work as an applied machine learning scientist on Alexa, Amazon's virtual assistant platform. I current work in the Amazon's Cambridge office as an Applied Machine Learning Scientist where my focus is on improving Alexa's ability to answer arbitrary questions in generic ways leveraging deep learning.

I enjoy building bleeding edge consumer facing products that are made possible by new advances in machine learning. I consider myself both a scientist and an engineer, and do not shy away from either invention or implementation. On the research side, my interest lies mainly in the intersection of representation learning and low resource machine learning - on how to design algorithms that can better deal with noisy or sparse datasets, or more generally, on constrained problem settings that are more likely be encountered in real life scenarios - this has led me to work on projects in model compression and acceleration, zero-shot learning and weak supervision techniques.

On the engineering side, production code that I have written currently powers online ML model building for two different services spanning Alexa and Amazon Web Services - namely, the Alexa Skills Kit (ASK) and AWS's Amazon Lex spoken language SDKs. I have been lucky to have the chance to work on state of the ML research as well as software architecture, and have over half a dozen patent applications pending with the USPTO in fields such as deep learning, NLP, question answering, spoken language technology and recommender systems. Resume (last update: 19 October 2017)

In a Nutshell

September 2017 Onwards Alexa Knowledge (Evi) Applied Scientist
Understanding Team
Feb 2015 - June 2017 Alexa Machine Learning Machine Learning Scientist
Alexa Brain, ASK ML teams.
Aug 2013 - Dec 2015 Columbia University in the City of New York Master of Science, Computer Science
Advisors: Michael Collins and Peter Bellhumeur.

Research Publications

  • “Zero Shot Learning Across Heterogenous Overlapping Domains”

    International Conference on Spoken Language Processing (INTERSPEECH) 2017
    Anjishnu Kumar, Pavankumar Muddireddy, Markus Dreyer and Bjorn Hoffmeister

  • "Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding"

    1st Workshop on Conversational AI at the Conference on Advances in Neural Information Processing Systems (NIPS) 2017
    Anjishnu Kumar, Arpit Gupta, Julian Chan, Sam Tucker, Markus Dreyer, Bjorn Hoffmeister, Ankur Gandhe, Ariya Rastrow, Denis Filmonov, Stanislav Peshterliev, Christian Monson, Agnika Kumar

  • "Efficient Large-Scale Domain Classification with Personalized Attention"

    Accepted for publication at 56th Annual Meeting of the Association for Computational Linguistics (ACL) 2018
    Young-Bum Kim, Dongchan Kim, Anjishnu Kumar, Ruhi Sarikaya

Industrial Projects

  • Natural Skill Interaction

    Designed, prototyped, and helped productionize system that enables customers to talk to Alexa skills without needing to remember the name of the skill. Watch Alexa Science VP Rohit Prasad describe the feature at AWS Re:invent 2017.

  • Conditional Random Field Model Compression

    As an intern on the Amazon Echo team, I used a combination of voronoi iteration, bloom filters, feature hashing, elastic net regularization and weight quantization to reduce model sizes by 95% and increase inference speed by 25%, without any statistically significant loss in accuracy.



    Patent approved by USPTO, Publication Date - Februrary 2018

    Inventors: Anjishnu Kumar and Markus Dreyer

    Note: 8 more patent applications are currently pending with the USPTO: Watch this space.

Open Source

  • ASK Alexa PyKit

    A minimalist Python library to build Alexa Skills using AWS Lambda. This library used to teach CIS 700 Deep Learning for Automated Discourse at the University of Pennsylvania.

  • Ask Alexa : Twitter

    An Unofficial Twitter Alexa Skill written using ask-alexa-pykit.
    I implemented an Alexa Skill for Twitter, after pitching it to their business development team - it was adopted by a software engineering team as Twitter's official skill for Alexa.

  • Surreal

    SUpeR REsolution, (A) Library. Implemented using Deep Convolutional Generative Adversarial Networks (DCGANs) in Keras.

  • MXNet Gluon

    Contributions to MxNet's Gluon API, including advanced activation functions and examples.

  • Seekr

    A local social network Android App with Machine Learning powered event mining from Twitter.

  • Galleria

    A machine learning augmented image gallery for Android for semantic visual search. Basically Google Photo's image search implemented using a cloud-side cascade of Support Vector Machines, a year before Google launched the feature.

  • Crackr: Supervised Keyphrase Extraction

    A supervised keyword extraction system using a combination of Brown Clustering and RAKE. Given a small training sample, can learn to extract similar keyphrases. Developed for CollegeFeed to extract valid phrases from job listings. E.g. recognize Rust, Scala as being important keywords given Python and C in training data.


Industrial Exposure

  • Machine Learning Scientist, Amazon Alexa Knowledge

    Cambridge UK, September 2017 onwards

    Semantic Parsing, Knowledge Bases, Semantic Search.

  • Machine Learning Scientist, Amazon Alexa Machine Learning

    Seattle, USA

    Worked on the Alexa Skills Kit and Alexa Brain initiatives.


  • Columbia University

    Master of Science in Computer Science, (Applied Machine Learning)

  • University of California, Berkeley

    Electrical Engineering and Computer Science (Exchange Student)

  • Birla Institute of Technology and Science (BITS), Pilani

    Bachelor of Engineering (With Honours), Chemical Engineering

Internships and other Exposure

  • Huffington Post Data Science Intern, Opinion and Sentiment Mining

    Built a prototype opinion mining system using Recursive Neural Networks.

  • Machine Learning Scientist Intern, Amazon Echo Cambridge, Mass., USA

    Used machine learning and probabilistic data structures to compress a ML model by 95% and reduce inference time by 25% with no significant loss in accuracy.

  • The Earth Institute, Data Engineering Intern, Project SharedSolar, Sustainable Innovations Lab

    I worked on data engineering tasks for a distributed solar array in northern Africa, working on anomaly detection in sensor readings, with an aim to develop dynamic pricing strategies in the future.

  • Electronic Arts, Software Engineering Intern, Digital Advertising Platform

    As a server side engineering intern, I migrated EA's push notification serving architecture from a monolithic to a distributed software architecture. I also built the components that serve push notifications to Amazon's Kindle line of devices.

  • Deccan Chronicle

    Weekly Columnist, wrote a column on Software and Technology for 3 years.

  • Collegefeed, Consultant, Head of India Business Development, Part-time NLP Software Engineer

    Collegefeed was an early stage startup specializing in recruitments targeting college student. I donned several hats there, drafted growth strategies to penetrate the Indian market and executed customer acquisition campaigns in top tier Indian and US colleges. I also invented/implemented a natural language processing library that automatically discovers keywords from an employee's resume that are relevant to any given job listing.

  • PricewaterhouseCoopers Management Consulting Intern, India e-Governance Advisory ,

    I worked as part of PwC's advisory division, helping draft policy and technology strategy for the Government of India's GST (Goods and Sales Tax) project, the largest tax reform in the world.

  • National Thermal Power Corporation of India, Process Engineering Intern ,

    As a process engineering intern in the Coal Processing division, I worked on regression models to project possible monetary savings and efficiency gains in a coal waste re-use project.

  • O-Zone Networks Business Analyst Intern, Business Development / Strategy.

    Created an assessment report projected the growth of wireless internet technologies in India through 2020, the report was used to pitch to the head of Apple India.,

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