Hrithik Nambiar
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I am a Undergraduate from BITS Pilani, India with a major in Computer Science and a minor in Data Science.

My research interests lie in Artificial Intelligence specifically in Machine Learning, Deep Learning, Neuroscience and their applications in Computer Vision. I try to understand human learning and take insights from it to make incremental progress toward building a more generalizable and efficient learning algorithm.

Currently, I am a researcher in Serre Lab at Brown University. At BITS, I was affiliated with APPCAIR where I worked in collaboration with TCS Research. I also spent a summer at IBM, where I worked in collaboration with IBM Research to extend Watson OpenScale.

As a core member of the Society for Artificial Intelligence and Deep Learning, I have been involved in various projects and conducted and taught some student-run courses related to AI.

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May '24

Our work "Self-SLAM: A Self Supervised Learning Based Annotation Method to reduce labelling overhead" has been accepted at ECML PKDD 2024.

Aug '23

Joined Standard Chartered Bank as a Software Engineer.

Jul '23

Graduated with a major in Computer Science and minor in Data Science.

May '23

Defended my bachelor's thesis titled "Tackling Drift in Neural Responses in the Spinomotor Pathway".

Jan '23

Selected for and participated in the Google Research Week 2023.

Oct '22

Our work "DetAIL: A Tool to Automatically Detect and Analyze Drift In Language" has been accepted at AAAI '23 (IAAI conference).

Sep '22

Organizing our annual AI Summer Symposium 2022. Please do check it out here!

Aug '22

I'll be a TA for the course CS F320: Foundations of Data Science at BITS Goa.

Aug '22

Joined Serre Lab at Brown University as a Research Intern.

June '21

Selected to attend Amazon ML Summer School 2022.

May '21

Interning at IBM during the summer. Working in collaboration with IBM Research, to extend Watson OpenScale.

Jan '21

Excited to be joining APPCAIR, I will be working on a project in collaboration with TCS Research

Jan '21

Check out the SAiDL spring Assignment 2022!

Oct '21

Abstract of my research work at CNRL- BITS,Goa accepted at Frontiers in Aging Neuroscience.

Sep '21

Organising the Summer Symposium on AI Research,inviting top AI researchers as speakers.

Aug '21

I'll be a TA for the course CS F214: Logic in Computer Science at BITS Goa.

Aug '21

Completed the Deep Learning summer school by Neuromatch Academy. Presented our work Visual Hierarchy based fMRI decoding.

Jul '21

I'll be an instructor for this summer's Deep Learning QSTP course.

Jun '20

Excited to be selected to attend the Neuroscience and Deep Learning Summer School 2021 by Neuromatch Academy!

Jan '21

I'll be an instructor for this semester's Introduction to ML and DL, CTE course.

Aug '23 - Present

Standard Chartered Global Business Services

Aug '22 - Present

Serre Lab

May '22 - Aug '22


Jan '22 - May '22

APPCAIR & TCS Research.

Aug '21 - Dec '21

Cognitive Neuroscience Lab, BITS Goa

May '21 - Jul '21

JSW Steel

Fall '22

CS F320: Foundations of Data Science - Teaching Assistant [Undergraduate course @ BITS Goa.]

Fall '21

CS F214: Logic in CS - Teaching Assistant [Undergraduate course @ BITS Goa.]

Summer '21

Quark-QSTP: Introduction to Deep Learning - Instructor.

Spring '21

Introduction to Machine Learning and Deep Learning. - Instructor.

Self-SLAM: A Self Supervised Learning Based Annotation Method to reduce labelling overhead
Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024)
Supervisors: Snehanshu Saha, Surjya Ghosh
[ Paper ]

SSLAM is a self-supervised deep learning framework designed to generate labels while minimizing the overhead associated with tabular data annotation. SSLAM learns valuable representations from unlabeled data that are applied to the downstream task of label generation generation by utilizing two pretext tasks with a novel log-cosh loss function.

Tackling Drift in Neural Responses in the Spinomotor Pathway
Bachelors Thesis
Supervisor: Dr. Thomas Serre
[ Thesis ]

We aim to implement a robust and efficient machine learning-based solution for restoring motor functions in patients with spinal cord injury using Epidural Electrical Stimulation. This thesis examines the problem of neural drift and the possibility of using meta-learning to build an adaptive algorithm to tackle this drift.

DetAIL: A Tool to Automatically Detect and Analyze Drift In Language
Accepted at the Annual Conference on Innovative Applications of Artificial Intelligence, Collocated with AAAI '23.
Supervisors: Nishtha Madaan, Harivansh Kumar
[ Paper ]

We propose to measure the data drift that takes place when new data kicks in so that one can adaptively re-train the models whenever re-training is required. In addition to that, we generate various sentence and dataset-level explanations to capture why a given payload text has drifted.

Abstraction and Reasoning Corpus
Supervisors: Dr. Gautam Shroff , Dr. Tirtharaj Dash, Prof. Ashwin Srinivasan

The Abstraction and Reasoning Corpus (ARC) was introduced by François Chollet as a benchmark to measure AI skill acquisition on unknown tasks, with the constraint that only a handful of demonstrations are shown to learn a complex task. We show how modern neural networks fail ARC and test the idea of using meta-learning to learn priors useful to solve ARC.

Alzheimer’s Dementia detection
Superivisor: Dr. Veeky Baths
Research Abstract accepted at Frontiers in Aging Neuroscience.

This work explores the contexts associated with errors in the automatic transcription of spontaneous speech and its effects on current State-of-the-art methods for dementia detection. We attempt to build a purely acoustic solution based on Vision Transformers for dementia detection.

Visual Hierarchy based fMRI decoding
[ Abstract ]

We develop a model miming the Visual Hierarchy observed in animals for decoding fMRI to classify images. We show that hierarchical processing of fMRI data from different parts of the animal visual system aids classification.

Out-of-Distribution Detection for Skin Lesion Classification
[ Code ]

In this work, we study the problems dealing with OOD data for skin cancer classification. We compare the performance of Virtual Outlier Synthesis, a recent work published at ICLR 2022, with other State-of-the-art OOD detection methods. We further show how using another inference method help achieve better results than VOS. We also study the effects of different types of OOD data on our method.

Covid cough detection
[ Code ]

An SVM-based model which can recognize COVID coughs using short audio cough recordings. An attempt at the INTERSPEECH 2021 Computational Paralinguistics Sub-Challenge .

Facial Keypoint Detection
[ Code ]

A ResNet model which detects important facial key-points in images. Facial key-point detection plays a pivotal role in tasks such as Emotion Analysis.

Reddit flair classification
[ Code ]

An LSTM-based model which classifies Reddit posts to their appropriate flairs. Used GloVe for word embedding.

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