Satyapriya Krishna
Fair | Private | Robust | Interpretable

Satyapriya Krishna
Trustworthy ML @ Alexa AI
Hi! I am a Scientist at Amazon Alexa, Boston solving problems related to fairness, privacy, interpretability and robustness. Prior to joining Alexa, I worked at Amazon Search (A9.com) where I worked with query understanding and knowledge graph teams.
Before joining the industry, I was a grad. student in Language Technology Institute @ Carnegie Mellon University. As part of my master research, I worked on dialog systems with Prof. Anatole Gershman (LTI, SCS).
Work Experience
-
Student Research Intern - VP Lab,IIT Madras
May 2015-July 2015I worked at Visualization and Perception Lab(VP Lab) of IIT Madras on Face Recognition under the supervision of Prof. Sukhendu Das. My project contributions were the following:-
- Developed a Online Face Recognition Software using basic algorithms such as Eigenfaces and Fisherfaces
- Software was developed over .NET framework
- Maximum Reliability and modularity
-
Summer Research Intern- NIT,Rourkela
May - July 2014I worked as an intern at the Computer Vision Lab of NIT Rourkela for the duration May,2014-July,2014. My target in this project were broadly the following:-
- Developed a highly accurate shadow removal system, a method to remove shadows from images and videos
- Explored many classification algorithms and recorded their accuracy over 5 different UCI datasets
-
PHP Developer - NIC,Govt. of India
Nov - Dec 2013 & Nov-Dec 2014I worked at NIC as a PHP programmer to develop a Project Information Tracking System. My major responsibilites were:-
- Developed a back-end model to track project information
- Maximum Modularity, minimum Ambiguity and improved Maintainability
-
Explorer
1994-Till Death- Regular programmer over several online programming websites such as Codechef.com and SPOJ
- In my non-academic sessions of the year, I visit different schools to give tutorials on basic Computer Science topics
- Currently involved in Data Visualization using R
- A regular Open Source Contributor
Education
-
Carnegie Mellon University
AUG 2016-- MS Biotechnology Innovation & Computation
- School of Computer Science
-
The LNM Institute of Information Technology
AUG 2012-JUN 2016- B.Tech (Major: Computer Science Engineering)
- CGPA : 9.29 (on a scale of 10.00)
-
Sir Padampat Sighania School,Kota
2010-2012- Class XII (CBSE) with 82.60% marks.
-
Army Public School,Kirkee,Pune
2005-2010- Class X (CBSE) with 9.8 CGPA(on the scale of 10).
-
1. Recommender's System
-
2. Computer Vision
-
3. Interview Stuff
-
4. Dynamic Programming
-
Awesome YouTube Channels
-
5. Trees and Graphs
-
6. JavaScript
-
7. Python
-
8. Natural Language Processing
-
9. Machine Learning: Techincal Paradigm Change
-
10. Logical Thinking: Most important skill
-
11. Deep Learning
-
- Stat 212b : Topics in Deep Learning
- Deep Learning by CSAIL, MIT
- Deep Learning in NLP
- Deep Learning in NLP
- Convolutional Neural Networks
- Machine Learning in Oxford U
- Optimization Algorithms - Overview
- Hackers Guide to NN
- Convolutional Neural Networks for Visual Recognition
- Karpathy's Blog
- Yoshua Bengio
- Best Guys in Machine Learning
- Stanford Tutorials
- Uber Chief Scientist - Rockstar
- Ian Goodfellow recommended
- Restricted Boltzman Machines
- Hugo Deep Learning Tutorials
- Hinton on BMs
- Cheat Sheet for DL by YB,IG
- Introduction to Boltzmann Machine
- ELI5 : Its simply awesome .
- Long Short Term Memory Great Resource to start
-
12. Big Data Analytics
-
13. Design
-
Bitcoins - Emerging Disruption
-
Things to know:-
-
Legacy NLP & Stat Resources
DL NLP Papers
-
Year 2015
-
Year 2016
-
Year 2017
-
Year 2018
- [SOTA] BERT
- Phrase-Based & Neural Unsupervised Machine Translation
- [DeepMind] Conditional Neural Processes
- [DeepMind] Fast Parametric Learning with Activation Memorization
- [DeepMind] Automatic Goal Generation for Reinforcement Learning Agents
- [DeepMind] Successor Features for Transfer in Reinforcement Learning
- [DeepMind] Measuring abstract reasoning in neural networks
April 4 2019
Burning Hot Ones
My research focus : Language Modelling
-
- [SOTA] BERT
- ULM FiT
- ELMo
- AWD LSTM
- Multi-Task Learning
- Attention is all you need
- Bi-DAF
- GLoMo
- QANet
- QRNN
- Char-CNN
- Morphological Embeddings
- Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings
- DiSAN
- Character-Level Language Modeling with Deeper Self-Attention
- A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
- LOOKING FOR ELMO’S FRIENDS: SENTENCE-LEVEL PRETRAINING BEYOND LANGUAGE MODELING
- TRANSFORMER-XL: ATTENTIVE LANGUAGE MODELS BEYOND A FIXED-LENGTH CONTEXT
- On the State of the Art of Evaluation in Neural Language Models
- Assessing BERT’s Syntactic Abilities
Great Blogs
-
- A3C RL Policy Gradient Medium Article
- BERT, ELMo and Co.
- BERT Google Blog
- ULMFiT Analitics Vidya Blog
- Reddit - Link to BERT resources
- BERT explained
- Allen Ins. tutorials on Elmo
- Rudder's [Amazing Guy for NLP] Intro to Multi-task Learning
- CMU ML Blog on Parallel HPO
- Progress in NLP
- Optimization algorithms : Overview
- Best Practices of DL in NLP
- Selecting data for Transfer Learning
- Approximating Softmax in Word Embeddings
- [GAN] PAPER READING ROAD MAP
Great Githubs and Data Sources
-
- Datasets by Percy Liang
- SQUAD
- SNLI Dataset
- WMT
- WordNet
- Pre-trained Word Embeddings : Glove Stanford , Spacy Large , Google wrd2vec, Fast Text
- Stanford NLP Reading Group
- Awesome deep Learning Papers
- Deep Learning Papers Git 2
- NLP Deep Learning Papers Git 1
- GAN Zoo
- [Google] Attention and Augmented Recurrent Neural Networks
- [Google] Sequence Modelling with CTC
- [SEAS] The Annotated Transformer
- Generalized Language Models
Things to remember
Mail Me!
Email Address
Satyapriya Krishna
LinkedIn Profile
Copyright © 2018 by Satyapriya Krishna. All rights reserved.