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madhusakth Goto Github PK

followers: 2.0 following: 1.0 repos: 21.0 gists: 1.0

Type: User

Bio: Graduate student @ UT Austin Object Detection, Model Compression, RADAR acquisition for AV

Hi there 👋

I currently hold the position of Senior Video Research Engineer at Qualcomm, where I focus on developing deep learning models to estimate depth and optical flow for autonomous driving applications.

Prior to this role, I completed my Ph.D. in Electrical and Computer Engineering at The University of Texas at Austin. Under the guidance of Prof. Ahmed Tewfik, my doctoral research was centered around developing novel algorithms for efficient radar data acquisition for automotive scenarios and deep learning model compression.

madhusakth's Projects

adaptive-radar-acquisition

Adaptive automotive radar data acquisition

bci-sentence-silence-spotter

This is the code to our Sentence spotter, Phoneme vs. Silence and Audio vs. Audio-visual models submitted to NER 2021 conference.

boosting

Boosting algorithm on regression and classification

boostingspeech

btq-k-means-ewgs

centerfusion

CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

deepspeech

A TensorFlow implementation of Baidu's DeepSpeech architecture

dm_pro

face-to-year-prediction

madhusakth

Collection of projects

model-compression-bq

models

Models and examples built with TensorFlow

nlp-code-switching

Sentiment Analysis of Code-Switched data

nlp-neural-networks-for-sentiment-analysis

Neural Networks for Sentiment Analysis

nlp-project-2-sentiment-parsing

Semantic Parsing with Encoder-Decoder Models

nlp-project1-sequential-crf-for-ner

Sequential CRF for NER

radiate-adaptive-cs

Code

radiate_sdk

SDK developed to access data from RADIATE dataset

recurrent-neural-network-introduction-applications

A brief introduction about Recurrent Neural Networks

reproducible-radar-object-detection

A collection of reproducible radar object detection source code with the pdf link

tensorflow

An Open Source Machine Learning Framework for Everyone

1

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