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Researcher
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I make AI trustworthy. Homepage : www.satyapriyakrishna.com . Email: spkrishna@alumni.harvard.edu
Harvard John A. Paulson School of Engineering and Applied Sciences
Doctor of Philosophy (PhD) - Computer Science
N/A – Present
Carnegie Mellon University
Master of Science (M.S.) , Language Technology Institute, School of Computer Science
N/A – Present
Massachusetts Institute of Technology
(Cross registered), Computer Science
N/A – Present
Sesame
Researcher
February 1, 2026 – Present
San Francisco Bay Area · On-site
Amazon
Sr. Researcher - Amazon Nova Responsible AI
January 1, 2025 – March 1, 2026
Google DeepMind
Researcher
May 1, 2024 – September 1, 2024
New York, United States
Harvard AI Safety Team
Research Fellow
July 1, 2023 – August 1, 2023
Cambridge, Massachusetts, United States · On-site
Meta
Research Scientist Intern
May 1, 2022 – August 1, 2022
Menlo Park, California, United States
Digital, Data, and Design (D^3) Institute at Harvard
Researcher
September 1, 2021 – March 1, 2025
Cambridge, Massachusetts, United States · On-site
Harvard University
Researcher
August 1, 2021 – March 1, 2025
United States
Amazon
Sr. Researcher - Alexa AI
December 1, 2019 – August 1, 2021
United States
A9.com
Applied Scientist
September 1, 2018 – December 1, 2019
Palo Alto, California, United States
Amazon Web Services
Machine Learning Engineer - AWS Deep Learning
July 1, 2018 – September 1, 2018
Seattle, Washington, United States
Carnegie Mellon University
Graduate Teaching Assistant - Practical Data Science
January 1, 2018 – May 1, 2018
Greater Pittsburgh Region
Carnegie Mellon University
Graduate Teaching Assistant - Intro to Machine Learning (PhD)
January 1, 2018 – May 1, 2018
Greater Pittsburgh Region
Carnegie Mellon University
Graduate Research Assistant
January 1, 2018 – January 1, 2018
Greater Pittsburgh Region
Carnegie Mellon University
Graduate Teaching Assistant
October 1, 2017 – December 1, 2017
Greater Pittsburgh Region
Carnegie Mellon University
Graduate Teaching Assistant
August 1, 2017 – October 1, 2017
Greater Pittsburgh Region
Amazon
Machine Learning Engineer Intern
May 1, 2017 – August 1, 2017
Greater Seattle Area
Carnegie Mellon University
Graduate Research Assistant
August 1, 2016 – May 1, 2018
Greater Pittsburgh Region
LNMIIT
Teaching Assistant-Computer Networks Lab
January 1, 2016 – April 1, 2016
LNMIIT
Teaching Assistant - Database Management Lab
January 1, 2016 – April 1, 2016
LNMIIT
Teaching Assistant- Data Structure and Algorithms
July 1, 2015 – December 1, 2015
Indian Institute of Technology, Madras
Summer Intern
May 1, 2015 – August 1, 2015
Greater Chennai Area
LNMIIT
Teacher Assistant-Computer Architecture Lab
January 1, 2015 – May 1, 2015
Jaipur
NIT Rourkela
Summer Intern
May 1, 2014 – July 1, 2014
Grounding Complex Instructions Using Scene Graphs
January 1, 2018 – May 1, 2018
The target of this project was to train an agent to navigate with the help of a natural language instruction, in a 3D environment (vizdoom, in our case). For instance, the agent is trained to go to the "red skull" (target object) if we have an instruction such as "Go to the red skull next to the blue torch" where skull and torch are some of the objects in the doom environment.
Wit Chat
August 1, 2016 – Present
Smart AI Assistant with better language understanding.
Fuzzy Collaborative Book Recommendation System
January 1, 2016 – Present
Developed a book recommendation system for a library which recommends relevant books to students based on the similarity between two users in the system. For instance, if there are two students with higher similarity measure will be recommended similar genres of books to issue.
Fuzzy Collaborative Recommender System
January 1, 2016 – April 1, 2016
This is a model of recommender system in which features are fuzzyfied and then collaborative filtering is used to recommend movies to new users. Movielens 100k Dataset was used to train the model and it gives an accuracy of over 80%. Used packages such as pandas and numpy
Face Recognition Based Attendance System
September 1, 2015 – Present
This project used FR algorithms to mark the attendance of a class with appx. 80-100 students. Basically, the system takes the photograph of the class and then marks the attendance of every student it recognizes in the image feed.
Shadow Removal Using Texture Analysis
May 1, 2015 – Present
This was a project made for an elective course on Computer Vision where I implemented a paper "Shadow detection for moving objects based on texture analysis" by A. Leone and C. Distante. We used Gabor Filters to extract the textures and background subtraction for shadow detection.
Token Ring
August 1, 2013 – Present
This was a project based on some concepts related to Token Rings which we applied on applications which involve Inter-Process Communication(IPC) to find out if there is any improvement in the efficiency of the system. As a result, we found that many mathematical calculations such as Fibonacci numbers, Matrix Multiplication do get faster by using multiple processes and token ring for communication. We used POSIX threads for our analysis.
Introduction to R
DataCamp
June 24, 2026 – Present
Practical Machine Learning
Coursera
June 24, 2026 – Present
Programming For Everybody(Python)
Coursera
June 24, 2026 – Present
VTC Online Training on Computer Vision
Internshala
June 24, 2026 – Present
Computer Programming
Indian Institute of Technology, Bombay
June 24, 2026 – Present
Computer Science 101
Stanford University
June 24, 2026 – Present
TCP, HTTP and SPDY Deep Dive
Udemy
June 24, 2026 – Present
Scraping and Data Mining for Beginners and Pros
Udemy
June 24, 2026 – Present
Beginner PHP and MySQL Tutorial
Udemy
June 24, 2026 – Present
Design and Analysis of Algorithhms-MOOC
Microsoft
June 24, 2026 – Present
VTC Online Training on Web Development
Internshala
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background is heavily skewed towards advanced AI/ML research and development, with a strong academic foundation. While this demonstrates exceptional technical depth, the target role of 'Data Analyst' might not fully leverage their extensive research and PhD-level expertise. The projects and experience are highly specialized in AI/ML, which may indicate a preference for research-oriented roles rather than traditional data analysis. The breadth of skills is strong within AI/ML, but less explicit in core data analysis tools and methodologies (e.g., advanced SQL, BI tools, A/B testing design, specific data visualization libraries beyond basic pandas/numpy usage).
Soft Skills & Operational Fit
The candidate's extensive experience as a researcher and teaching assistant suggests strong analytical, problem-solving, and communication skills. Their involvement in founding teams and leading efforts (e.g., Amazon Nova Responsible AI) indicates leadership potential and initiative. The academic background implies a structured approach to problem-solving and a capacity for continuous learning. However, the provided data does not offer direct insights into stress handling or team collaboration beyond the general nature of research roles.