Kathakoli Sengupta

Master of Science in Artificial Intelligence

GPA: 3.96/4.00 | Boston,MA | Sep 2022 - May 2024

Boston University (Graduate School of Arts and Sciences)

Relevant Coursework: Tools for Data Science, Intro to Machine Learning, Artificial Intelligence, Image and Video Computing, Robot Learning and Vision for Navigation, Geometric Processing, Neural Networks and Fuzzy Control, NLP

Bachelor of Technology in Electronics and Communication Engineering

GPA: 9.33/10.00 | Vellore,India | Jul 2018 - May 2022

Vellore Institute of Technology,Vellore

Relevant Coursework: Neural Networks and Fuzzy Control, Robotics and Automation, Sensors and Instrumentation

Computer Vision Scientist

July 2024 - Ongoing

Wicket LLC

• Improving Wicket's proprietary spoof detector.

Computer Vision Intern

Dec 2023 - Jan 2024

Wicket LLC

• Fine-tuned patch-based spoof detection algorithm to achieve 98.78% accuracy on six new spoof types.
• Built a robust two stage spoof classifier with gaussian blur, random fog augmentated face crops.
• Designed the semi-supervised spoof detector with contrastive learning and knowledge distillation and trained the classifier head by freezing the rest with first stage shared weights delivering a very generalised detector.

Data Science Team Lead (Patent Equity Project)

Sep 2022 - Dec 2022

BU Spark!

• Collaborated with Spark to analyse USPTO official data for finding issued patent breakdown and correlation (with regression analysis) on grouping across agent type, gender etc for faster understanding of patent scenario.
• Concluded whether switching firms or subject matter impact attorney success rate grouped by gender with support graphs and success rate measures that is expected to significantly affect firm trend .

Data Science Intern

Sep 2021

The Sparks Foundation, India

• Analysed Global Terrorism Database comprehending patterns of terrorism like hot zone of attack, frequent tools for attacks etc with heatmaps, contour plots, barcharts and pairplots in matplotlib, seaborn etc.

Data Science Engineer Intern

Aug 2021 - Sep 2021

Pianalytix, India

• Developed the first prototype for their College Placement Prediction Model by designing and comparing KNN, Gaussian Naive Bayes, Support Vector Machine and Random Forest classifiers and visualizing each stream demand, age impact.

Research Assistant - Prof. Eshed Ohn Bar (Guide)

Mar 2023 - Ongoing

Human to Anything Lab (H2X Lab) - Boston University College of Engineering

• Introduced novel text-motion data for blind individuals with high-level and low-level annotations by motion experts.
• Benchmarked fine-tuned text-to-motion LLMs like MotionGPT against language context integrated motion prediction models with visually-impaired user study data and introduced novel joint-based loss to imitate the subtle actions.
• Collaborated with RedHat research to develop a cloud-assisted agent for perception based navigation.
• Achieved SOTA results on latency, power, accuracy, uncertainty trade-off using PPO algorithm.
• Introduced a novel custom-designed CARLA Reinforcement Learning environment.

Teaching Assistant - EC-518 Robot Learning

Sep 2023 - Dec 2023

Boston University College of Engineering

• Course Topics: Advanced Robotics, Deep Imitation Learning, Semantic Scene Understanding, Object Detection and Tracking, Reinforcement Learning, Sim2Real, Human Robot Interaction.

Research Assistant - Prof. Bang Bon Koo (Guide)

Oct 2022 - Aug 2023

Bio-Imaging and Informatics Lab (BIL Lab) - Boston University School of Medicine

• Designed a dual-channel Global-Local Transformer (GLT) architecture to extract MRI intensity and ravens map features, enabling their fusion for accurate brain age prediction.
• Compared the performance of the architecture for lobe-wise segmented brain and whole brain age prediction.
• Implemented Transfer Learning for accurate brain age prediction of Gulf-war patients.

Machine Learning Intern - Dr. Rajarshi Gupta (Guide)

Sep 2021 - Nov 2021

University of Calcutta, Kolkata

• Segregated noisy ECG signals using a random forest classifier with features like Shannon- Entropy and Log Energy entropy.
• Increased the 9.2\% performance by balancing data and choosing proper feature space with Adaptive Synthetic Sampling (ADASYN) and Principal Component Analysis (PCA).

Data Science and Machine Learning Intern - Dr. Abhijit Majumdar (Guide)

Aug 2021 - Oct 2021

Indian Institute of Technology, Delhi

• Modelled a Random Forest Regressor with 92.88% accuracy score for determination of Ultraviolet Protection Factor Jute to measure fabric suitability.
• Designed a random forest classifier with 94.44% 3 fold cross-validation score for categorizing it that scaled fabric production rate and reduced time-consumption.