CV

Education

• B.S. in Computer Science, PES University, 2019
• M.S. in Psychology (Cognition and Cognitive Neuroscience),University of Wisconsin - Madison, 2023(expected)
• M.S. in Computer Science, University of Wisconsin - Madison, 2024(expected)
• Ph.D in Psychology (Cognition and Cognitive Neuroscience), University of Wisconsin - Madison, 2026 (expected)

Academic research experience

• 2021 : Graduate Research Assistant, Univeristy of Wisconsin - Madison

• 2019-2021: Research Specialist, University of Wisconsin - Madison,
  • Supervisor: Dr Emily Ward, Visual Cognition Lab
  • Working on finding failures in visual awareness in Deep Neural Networks
  • Worked on finding the presence of ensemble properties in Deep Neural Networks
  • Worked on implementing the Neural Causation Coefficient introduced in the paper titled ‘Discovering Causal Signals in Images’ to find the causal direction in observational data.
  • Became proficient in MATLAB and Psychopy.
  • Worked on programming perception-related experiments.
  • Worked on analysing fMRI data from the BOLD 5000 dataset.
• Summer 2018: Research Intern, Brown University
  • Supervisor: Dr Thomas Serre, Serre Lab
  • Worked on comparing the performance of various action recognition models on an in-house dataset.
  • Closely worked on implementing the two-stream architecture for the same.
  • Compiled a dataset which was used to analyse the neural spikes from the V4 neurons in a monkey brain.
  • Worked on the electrophysiological data from the monkey brain to analyse the receptive field of V4 neurons using both deep and natural features.
  • Integrated an FCN for segmentation with the current pipeline used at Serre Lab for behavioural analysis of mice.
• 2018-2019: Research Assistant, PES University
  • Supervisor: Dr S NatarajanWorked on hierarchical embeddings in Knowledge Graphs which resulted in a publication at ACM SAC 2020.
  • Worked on building a domain agnostic question answering system for unstructured text(textbooks) by trying to utilise the inherent hierarchy which is present in Textbooks. The hierarchy helps us in coming up with appropriate embeddings for building a knowledge graph.
  • Worked on solving the problem of Night-time object detection using Generative Adversarial Networks

Industry experience

• Spring 2019: Research and Development Intern, VMWARE
  • Developed the authentication model for VMware’s Tanzu Mission control.
  • Added commands to the Command Line Interface tool of Vmware Tanzu.
  • Worked on cluster health remediation in Vmware Tanzu.
  • Contributed to upstream Kubernetes project.

Skills

• Skill 1
• Skill 2
  • Sub-skill 2.1
  • Sub-skill 2.2
  • Sub-skill 2.3
• Skill 3

Publications

GrCluster: a score function to model hierarchy in knowledge graph embeddings

Ranganathan, V., Suresh, S., Mathur, Y., Subramanyam, N., & Barbosa, D. (2020, March). Grcluster: a score function to model hierarchy in knowledge graph embeddings. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 964-971).

Visual ensemble representations in Deep Neural Networks trained for natural object recognition

Suresh, S., & Ward, E. J. (2021). Visual ensemble representations in Deep Neural Networks trained for natural object recognition. Journal of Vision, 21(9), 2677-2677.

Behavioral estimates of conceptual structure are robust across tasks in humans but not large language models

Ranganathan, V., Suresh, S., Mathur, Y., Subramanyam, N., & Barbosa, D. (2020, March). Grcluster: a score function to model hierarchy in knowledge graph embeddings. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (pp. 964-971).

Talks

Visual ensemble representations emerge in a Deep Neural Network trained on natural images

October 26, 2020

Talk at University of Wisonsin - Madison, HAMLET seminar, Madison WI, USA

Visual ensemble representations emerge in a Deep Neural Network trained on natural images

November 06, 2020

Talk at University of Wisconsin - Madison, HAMLET seminar, Madison WI, USA

Visual ensemble representations in Deep Neural Networks trained for natural object recognition

May 25, 2021

Talk at Online, St. Pete Beach, FL

Visual ensemble representations in Deep Neural Networks trained for natural object recognition

May 25, 2021

Talk at Online, Madison WI, USA

Visual memory for causal and coincidental events

May 14, 2022

Poster at Trade Winds, St. Pete Beach, FL

Tutorial on using Deep Learning Models to run psychology experiments

July 13, 2022

Tutorial at University of Wisconsin - Madison, Madison WI, USA

Teaching

Lead Teaching Assistant

Service and leadership

• Volunteered at Neuromatch Conference 3.0 (2020) to be a backend host. I was responsible for starting, recording and live streaming each session. I also contributed to the post-production of individual talks after the conference.
• Volunteered to be the Production editor for NMA in 2021. I was responsible to review the course content of 5 days of content for the NMA computational neuroscience summer school.