Manasi Malik
Ph.D. Student at The Johns Hopkins University
Baltimore, Maryland, United States
Summary
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Senior🎓
Top SchoolManasi Malik is a computational cognitive neuroscientist and Ph.D. student at Johns Hopkins University with a decade of experience spanning deep learning, graph theory, signal processing, and data-driven behavioral modeling. She investigates social intelligence in humans and machines, blending rigorous experimental neuroscience with machine learning methods to probe social perception and networked brain dynamics. Prior industry experience at TCS Research & Innovation applied deep-learning to user behavior and advertising effectiveness, while earlier research roles explored multisensory perception, brain networks, and computational gastronomy. Comfortable at the intersection of theory and application, she brings hands-on skills in building and evaluating models grounded in real-world data. Based in Baltimore, she combines strong academic training from IIIT-Delhi with interdisciplinary collaborations and selective participation in programs like Brains, Minds, and Machines. An underlying thread in her work is leveraging graph- and signal-based approaches to make social cognition interpretable for both neuroscientific insight and machine intelligence.
10 years of coding experience
3 years of employment as a software developer
School, Science, CGPA : 10/10 in CBSE Class X, School, Science, CGPA : 10/10 in CBSE Class X at Apeejay SchoolHigh School, Science, 95.6% in Class 12 CBSE Board, High School, Science, 95.6% in Class 12 CBSE Board at Modern School BarakhambaB.Tech with Honors, Major: Electronics and Communications Engineering & Minor: Computational Biology, 8.61/10, B.Tech with Honors, Major: Electronics and Communications Engineering & Minor: Computational Biology, 8.61/10 at Indraprastha Institute of Information Technology, DelhiBrains, Minds, and Machines Summer School
English, Hindi
Github Skills (24)
value-iteration9
iteration7
meta-analysis7
systems-biology6
grid6
grid-world6
q-learning5
matlab5
computational-neuroscience4
electrophysiology4
dynamical-systems4
neuroscience3
laravel2
youtube2
youtube-api2
Programming languages (6)
Github contributions (5)
adityajain07/ReinforcementLearning-Gridworld
Jan 2018 - Oct 2020
This project solves the classical grid world problem first with DP methods of RL like Policy Iteration and Value Iteration. Q learning is implemented too. Q learning is then implemented with changing positions of obstacles in the grid.
Contributions:14 commits, 2 PRs, 13 pushes in 2 years 9 months
grid-worldpolicyobstaclespositionsvalue-iteration
Isik-lab/SocialGNN
Nov 2022 - Oct 2023
Contributions:3 releases, 176 pushes, 5 tags in 11 months
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