I am a professional working in an MNC as an AI Practitioner. Having topped in diverse hackathons ranging from college to international level and a handful of professional internships and fellowships, I aim to apply the knowledge gathered from these experiences in your projects and help develop some state-of-the-art solutions. I have three years of industry experience and have published multiple papers in top AI, ML, and NLP journals. I am proficient in oratory and management skills, which may further aid my work. I can work diligently and with perfection to contribute significantly and live up to your expectations if given a chance. I would be glad to send you any other information and welcome any suggestions on your part. I eagerly look forward to your response.
Portfolio: [login to view URL]
Brownian Motion of A-Particles Using Pre-LN Transformers
PyBMS:BMS using Sync-LSTM with Parallel Rectified Filtration
Rank Estimating Recommendation with RankNet & LambdaMart
SEC Filling Analyzer for SaaS Companies using EDGAR Data
Combining POS-tag with Context Embed for Causality Detection
AI-based Predictive Analysis of (SAGES) for Mines Safety
Ingen anbefalinger at se her!
Data Scientist (Customized Intelligence)
jul. 2022 - Nuværende
• Scrapped product review data up to 94% using BeautifulSoup, OAuth2, and REST API to make quality datasets
• Sorted text data based on relevancy using Sorensen-Dice Coeff, Twersky Index with Multihead-Siamese Nets,
and SMART-Roberta-Large and applied Text Clustering using G-BAT with 89% efficiency. Performed analytics
on Lexical Richness
AI Researcher Intern
okt. 2021 - jul. 2022 (9 måneder, 1 dag)
• Engineered a library for Battery Management Systems (BMS), enhancing functionality using IIoT, Neural Nets,
• Discovered a novel multi-channel driving pattern LSTM network, TV4DX-LSTM, based on real-time data from
SoC and Performance Index (PI), improving the SOTA MAPE score by 6.4% upon using the Kalman Filter
Larsen & Toubro
jan. 2021 - sep. 2021 (8 måneder, 1 dag)
• Built a semi-automatic annotation tool for labelling point cloud image data on a large scale relying on multi-
• Boosted with advanced pixel gradient algorithms to provide bounding box annotations on probable objects,
which include a pointwise strategy to guide the annotator and help manage the dataset with 85% less human
Bachelor of Technology (IIT Dhanbad)
Indian School of Mines, India 2019 - 2023
IBM Machine Learning Professional Certificate
The IBM Machine Learning Professional Certificate is an online training program by IBM that equips learners with skills in machine learning. It covers Python programming, data science, supervised and unsupervised learning, deep learning, reinforcement learning, and time series analysis. Learners gain hands-on experience with popular libraries and work on a capstone project. The certificate signifies proficiency in machine learning techniques.
DeepLearning.AI TensorFlow Developer Professional Certificate
TensorFlow - Google Brain
"DeepLearning.AI TensorFlow" is an online course that offers a practical introduction to deep learning using TensorFlow. It covers fundamental concepts, such as neural networks, CNNs, RNNs, and GANs. Learners gain hands-on experience building, training, and evaluating models using TensorFlow. The course emphasizes real-world applications, transfer learning, text data processing, and model deployment in practical scenarios.
Applied Data Science with Python
University of Michigan
The University of Michigan's course "Applied Data Science with Python" is a comprehensive introduction to data science using Python. It covers topics such as data manipulation, visualization, machine learning, text mining, social network analysis, and includes a capstone project. The course equips learners with the skills needed to analyze, visualize, and model data using Python.
POS tagging with Attention-based Contextual Representations for Identifying Causality in Documents
ACL, FinCausal 2021
Causality detection is important in NLP and linguistics research. It finds applications in information retrieval, event prediction, question answering, financial analysis, and market research. We explore methods for extracting cause-effect pairs in financial documents using transformers. Our approach combines POS tagging with the BIO scheme, integrated with modern transformers. Our best methodology achieves an F1-Score of 0.9551 and Exact Match Score of 0.8777 in the FinCausal-2021 Shared Task.
Artificial Intelligence-Assisted Efficient Analysis of SAGES for Improving Underground Mines Safety
Efficient analysis of SAGES in underground coal mines using historical data. Predictive machine learning automates after-effect analysis, aiding maintenance and safety. Linear estimators, clustering methods detect SAGES output. Fine-tuning approach for algorithms compared. Ensemble models used for target variables.
Forecasting Brownian Dynamics of Active Particles Using Pre-LN Transformers with L-LSTM Embeddings
This paper introduces a novel method for predicting active particle paths using time series analysis, LSTM networks, and Transformers. It outperforms other approaches in accuracy and robustness, especially in complex scenarios. The research provides insights into particle dynamics and contributes to more accurate predictions with broad scientific and engineering implications.
Battery Management System using Driving Pattern Synchronised LSTM with Parallel Rectified Filtration
Digital transformation drives innovations in transportation with ICT, AI, and IIoT. Our research focuses on developing a digital twin of BMS for lead acid batteries. PyBMS is proposed as an enhanced BMS digital twin using LSTM network and parallel rectified filtration for real-time SoC and Performance Index estimation. It dynamically identifies driving pattern and battery anomalies, integrating V-EKF algorithm as an enhancement to EKF.
Evaluation and Comparison of Mechanical Subsystems of Mobile Roof Support Systems with SAGES
This study objectifies the role of mobile roof support (MRS) systems in the area of underground mining operations to improve stability and worker safety while presenting a comprehensive comparison and evaluation of various mechanical subsystems and operations of mobile roof supports used worldwide with Self Advancing Goad Edge Supports (SAGES) taking in minor consideration of integration of AI and IoT-based sensors to develop appropriate and efficient load bearing architecture.