
Vellore Institute of Technology, Chennai
MTech Integrated in Software Engineering
(5 years program)
CGPA: 9.32/10.0
About me
I'm a Data Scientist and computer vision engineer based in Bangalore, India, where I am currently working with search and recommendations team at Nykaa.
Previously, I worked as a Machine Learning Engineer at Capillary Technologies Pvt Ltd. part of Smart Store+ division. Earlier to this, I was a Computer Vision Intern at Capillary Technologies and at an early stage startup SDTECH (Formely known as Studio Diseno).
I also maintain a blog "Machine Learning Cognitio" to document my understanding of computer vision and machine learning while sharing my knowledge with others.
Below are my skillset, and I'am always looking to learn more.
Feb 2022 - Present
Python, PySpark, PyTorch and Pandas
April 2021 - Feb 2022
TensorFlow, AirFlow, OpenCV, Python, PySpark
June 2019 - March 2021
TensorFlow, OpenCV, Python, Scikit-learn, Scipy
January 2019 - May 2019
OpenCV, TensorFlow, Python, C++
Journal: Neural Processing Letters | Springer | Status: Paper Accepted
Transient Evoked Otoacoustic Emissions (TEOAE) are a class of otoacoustic emissions that are generated by the cochlea in response to an external stimulus. The TEOAE signals exhibit characteristics unique to an individual, and are therefore considered as a potential biometric modality. Unlike conventional modalities, TEOAE is immune to replay and falsification attacks due to its implicit liveliness detection feature. In this paper, we propose an efficient deep neural network architecture, EarNet, to learn the appropriate filters for non-stationary (TEOAE) signals, which can reveal individual uniqueness and long-term reproducibility. EarNet is inspired by Google’s FaceNet. Furthermore, the embeddings generated by EarNet, in the Euclidean space, are such that they reduce intrasubject variability while capturing inter-subject variability, as visualized using t-SNE. The embeddings from EarNet are used for identification and verification tasks. The K-Nearest Neighbour classifier gives identification accuracies of 99.21% and 99.42% for the left and right ear, respectively, which are highest among the machine learning algorithms explored in this work. The verification using Pearson correlation on the embeddings performs with an EER of 0.581% and 0.057% for the left and right ear, respectively, scoring better than all other techniques. Fusion strategy yields an improved identification accuracy of 99.92%. The embeddings generalize well on subjects that are not part of the training, and hence EarNet is scalable on any new larger dataset.
Conference: International Conference on Advanced Computing and Communications (ADCOM 2018)
The paper proposes a device capable of making the lives of visually impaired easier. The device encompasses an image recognition using deep learning (convolutional neural network) unit coupled with the novel idea of the bone conduction system, which can be mounted on the sunglasses of the visually impaired. The whole process allows two-channel hearing enabling people to hear regular as well as the intended audio. Instead of air, the sound is propagated through the bone in the form of vibration and is sent to cochlea through a membrane. The proposed system takes an image from a mounted camera, classifies it with a dedicated processor and sends the audio signal through a Bluetooth channel to the bone conduction transducer so that the user can hear through the system what is in front of him. The system is able to recognize the input image using deep learning and give an audio output directly to the eardrum of the user.
MTech Integrated in Software Engineering
(5 years program)
CGPA: 9.32/10.0
My education in Software Engineering focused on strong software engineering principles such as SDLC, Software Requirement Specificatin (SRS), Requirement Specification Document etc. to good programming practices in Python, C++, JAVA, .NET and Computer Networks. Furthermore, we had vigrous courses on Data Structures & Algorithms, Linear Algebra, Probability & Statistics and Calculus.
Was part of Technocrats Robotics Team for two years which participated in various robotics competitions across the nation. This team had approximately 25 students from Mechanical, Electrical & Electronics,
Computer Science and Management. I was heading the computer science team for the year 2017 and all the divisions in 2018. My core contributions are given below:
A few of my notable achievements are given below:
I achieved rank 6th in the class of 221 students for the batch of 2014-2019 M.Tech Software Engineering (5 years Integrated) program with the CGPA of 9.32/10.0. I was honoured to receive the award from Ms. Smriti Irani, Honourable Minister of Women and Child Development, Government of India.
Me and my teammate won 1st place among 73 teams in the Data Science Hackathon conducted by Vellore Institutue of Technology, Chennai. The competition was to predict the taxi fare for a given dataset. We built am XGBoost based model to arrive at the lowest RMSE in the competition.
Ideated and developed the product "Just in Time" which is an IOT based product for real time accident detection and notifying the helping authorities. The accident detection was done using real data collected from accelerometer and then classifying them using Dynamic Time Wrapping (DTW) Algorithm.
I had a liking for Tennis since elementary school days. Knocking tennis balls on the walls inside the house was my favourite passtime. In the process broke few of mom's favourite crockery, followed by being reprimanded for the same (But, I never stopped though! 😜) . My Tennis idol growing up and still date has been the one and only Roger Federer. I played Tennis regularly during my ears in school and University. Even now playing tennis is my main recreational activity. I have won till date two minor competitions in Tennis, at school level.
Being a Chess player's son (yep, my dad, Varugeese Koshy is an Iternational Master (IM) with a highest ever FIDE Rating of 2430). I learnt the basics of chess from my father although I never developed a passion for it like Tennis. I Played many rated open tournaments and ended up getting a FIDE rating of 1335. These day's I don't play open tournaments but I do play Chess Online time to time. For Accomplishments in Chess, I had decent placings in school zonals and some state level competitions.