Summary
Rishika Mohanta is a doctoral student at The Rockefeller University with nine years of interdisciplinary research experience at the intersection of mechanistic neuroethology, sensory coding, and learning. She has built experimental systems (including a closed-loop fly-on-ball olfactory VR arena and a high-throughput olfactory Y-maze) and computational models linking locust and Drosophila olfactory circuits to behavior under value-learning frameworks. Her background blends hands-on assay engineering, GPU-accelerated neural simulation, and mentoring in deep learning and computational neuroscience, reflecting both experimental and theoretical fluency. Rishika has contributed to COVID transmission modeling and taught data science workshops, showing an ability to translate complex methods to applied problems and diverse audiences. Based in Kolkata, she brings relentless curiosity—summed up by “I love learning EVERYTHING”—to evolve comparative sensory neuroscience across ecologies and evolutionary contexts.
9 years of coding experience
8 years of employment as a software developer
Integrated BS-MS, Biology/Biological Sciences, General, 9.8/10, Integrated BS-MS, Biology/Biological Sciences, General, 9.8/10 at Indian Institute of Science Education and Research (IISER), Pune
High School, Science and Languages, High School, Science and Languages at Garden High School
English, Hindi, Bengali, German, Japanese