Data Science Engineer
at StatusNeo

Email:
utkarsh.lal.official1@gmail.com
utkarsh.lal@statusneo.com,
utkarsh.lal@learner.manipal.edu

Github

Utkarsh Lal

As a Data Science Consultant at StatusNeo, I am presently stationed at Reliance Jio's Artificial Intelligence Center of Excellence. My prior engagements include prominent roles in data science at American Express, Accenture AI, and Kimberly-Clark, where I worked in a diverse spectrum of domains, including sentiment analysis, time-series forecasting, e-retail click-based predictive analytics, credit risk assessment, and Data Engineering in Spark.

I'm captivated by the art of unveiling hidden narratives within raw data and translating them into actionable insights using AI. This passion propelled me into both academic research and practical business applications. From building customer-churn prediction models at Accenture to employing time series analysis techniques for neurodegenerative disease diagnostics, I have gained diverse exposure to data science. My ultimate objective is to leverage my learnings to generate meaningful contributions to the Tech industry, building equitable data-driven solutions that promote diversity and bring tangible impact. My long-term goal is to serve at the intersection of data science and business strategy, leading products that help make society more equitable and challenge its biases.

Research Interests

I'm currently conducting research in Computational Neuroscience. I'm interested in learning more about how AI tools can be leveraged to understand the neurological characteristics of debilitating disorders like Parkinson's and Alzheimer's. My recent research in this field utilizes Electroencephalography (EEG) and combines machine learning with statistical feature extraction measures such as fractal dimensions and entropies to capture the principal differentiating characteristics of such disorders. Furthermore, my recent research also delves into Sleep Stage Classification using Polysomnography, illustrating the significance of sophisticated feature extraction as a potentially more efficient avenue for sleep staging than complex deep neural networks.

Additionally, I'm also conducting research in Natural Language Processing. I'm interested in leveraging computational linguistic methods to understand the implicit social aspects of language better. My research on Negation Handling opens a new cost-effective avenue for increasing the accuracy of Sentiment Classification in projects requiring quick turnaround time. Currently, I am focusing on self-attention models and transformers such as BERT to continue advancing my knowledge in this field.

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My Certifications

Recent Updates

February, 2024: My paper titled 'Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG' was published in the Neural Computing and Applications journal (IF: 6.0). This research was conducted with the AI and Cognitive Load Lab of Technological University Dublin, supervised by Dr. Luca Longo.
August, 2023: Selected as an AI mentee in the KaggleX BIPOC Mentorship program with a grant of $1000. This program aims at increasing diversity and inclusivity in the data science industry.
August, 2023: My paper titled 'Temporal Feature Extraction and Machine Learning for Classification of Sleep Stages Using Telemetry Polysomnography' got published in the Brain Sciences journal.