Nishit Agarwal

Nishit Agarwal is a Senior Data Science Engineer at Medidata Solutions, specializing in medical AI, wearable technology, and machine learning for healthcare. With a strong background in advanced medical signal processing and biostatistics, he leverages digital biomarkers to enhance clinical trials and patient care. Nishit has a Master's in Bioengineering from Northeastern University and a Bachelor's in Electrical Engineering from Mahindra University. He actively shares insights on wearable technology's transformative role in clinical research and has spoken at prominent conferences, including the Mobile in Clinical Trials Conference and the Graybill Conference on rare disease drug development. Nishit Agarwal is a highly skilled data science engineer with expertise in bioengineering, machine learning, and advanced signal processing, bringing a strong foundation in both electrical and electronics engineering from Mahindra Ecole Centrale and a Master of Science in Bioengineering from Northeastern University. With extensive experience in programming languages such as Python, SQL, and MATLAB, along with proficiency in deep learning frameworks like TensorFlow and PyTorch, he specializes in developing innovative AI-driven solutions for healthcare and neuroscience applications. Currently working as a Senior Data Science Engineer at Medidata Solutions, he has led groundbreaking biomarker development projects, integrating accelerometer and ECG data to enhance diagnostic reliability while spearheading machine learning applications for spinal cord injury research, focusing on EMG and accelerometer data analysis. His role involves end-to-end data pipeline management, collaboration with software teams for scalable data engineering solutions, and pioneering chatbot-based data review improvements using large language models (LLMs). Previously, as a Computational Neuroscience Co-op at Neurable Inc., he developed a Python package for real-time EEG signal quality assessment, implementing artifact detection and correction techniques through blind source separation. His contributions included co-authoring a white paper validating proprietary EEG-based focus estimation algorithms, gaining hands-on experience with Neurable’s dry EEG headphones and analytics platform. Earlier, as Lead Digital Signal Processing & ML Engineer at Nadipulse Prognostics, he applied deep learning techniques to classify diabetic patients using wrist-mounted PPG sensors, integrating convolutional neural networks to detect and filter motion artifacts, achieving an 82% accuracy rate validated through clinical trials under the guidance of Dr. Vasant Lad. With extensive expertise in brain-computer interfaces, wearable sensors, feature engineering, and MLOps, Nishit has a deep understanding of time-frequency domain analysis, statistical modeling, and unsupervised learning, making significant contributions to cutting-edge biomedical and AI research. His ability to translate complex neuroscience data into actionable insights has been instrumental in shaping real-world AI applications in medical and healthcare technology. Adept in working with Agile methodologies and collaborating across multidisciplinary teams, he has consistently delivered scalable, high-quality solutions across global organizations. Nishit has also played a key role in integrating AI-driven insights into regulatory and patent documentation, ensuring alignment with FDA requirements. His knowledge spans multiple technical domains, including cloud computing with AWS, API development, and data engineering, making him a valuable asset in the intersection of AI, healthcare, and biomedical research. Throughout his career, he has demonstrated a strong ability to bridge the gap between machine learning and real-world healthcare applications, pushing the boundaries of technology in medical diagnostics and rehabilitation sciences.