Welcome to my website!
My name is Syed Faizan, and I am a Data Analyst and Machine Learning Engineer, as well as a trained physician with a deep passion for advancing healthcare and other sectors like finance, banking, agriculture, and education through technology. My expertise spans data analysis, data visualization, predictive modeling, and statistical analysis, with a strong focus on machine learning, deep learning, and natural language processing (NLP).
My primary research interests lie in Computational Radiology, particularly in Inverse Supervised Learning for medical imaging analysis and Multimodal Models for healthcare applications. I am fascinated by how cutting-edge artificial intelligence (AI) techniques can address disparities in healthcare delivery, improve diagnostic accuracy, and empower clinicians to make data-driven decisions. Leveraging my background in medicine and machine learning, I aspire to contribute to innovative solutions at the intersection of technology and healthcare.
My professional life has been an exciting journey from clinical practice in India to the world of data analytics and machine learning. After earning my Bachelor of Medicine and Bachelor of Surgery from Rajiv Gandhi University of Health Sciences, I spent over a decade as a physician, excelling in clinical research and patient care. I further honed my skills as a resident physician at clinics, hospitals, and nursing homes. During this period, I developed a nuanced understanding of healthcare systems and their challenges.
Currently, I am pursuing a Master of Professional Studies in Analytics with a concentration in Applied Machine Learning at Northeastern University (January 2024–December 2025). My academic achievements include maintaining a GPA of 4.0 and leading several projects involving ETL (Extract, Transform, Load) processes, SQL for data querying, and advanced Power BI dashboards. Notably, I spearheaded REPP, a Retrieval-Augmented Generation (RAG)-based platform using LangChain and FAISS, featured at the Northeastern University Showcase.
As a Team Lead and Data Analyst Consultant at United Way Greater Toronto (UWGT) (September 2024–December 2024), I led data analysis and visualization projects under Northeastern University’s Experiential Learning course. I developed dynamic Power BI dashboards, integrated datasets from Statistics Canada and NGOs, and utilized advanced DAX for predictive models on income inequality and housing needs in the Greater Toronto Area. This project earned me the first rank as Team Lead and was showcased at the Northeastern University Showcase. Additionally, I contributed to the development of a RAG-enhanced presentation platform (REPP), enhancing data storytelling and stakeholder engagement through interactive dashboards.
I also work as a Data Analyst and Scientific Advisor at Tarsal Education Technologies (March 2024–December 2024), where I lead NLP projects, extract insights from educational datasets, and advise on data-driven content strategies to optimize learning outcomes. My role involves applying advanced machine learning techniques using Python, R programming, and libraries such as Scikit-learn, Pandas, and Matplotlib to enhance educational technologies and improve data-driven decision-making.
Throughout my career, I have consistently blended medical expertise with analytical skills. As a Research Assistant (April 2022–January 2023), I contributed to public health studies, including the Government of Karnataka’s study on nutritional status and diabetes prevalence in urban slums. My work streamlined data collection, improved data entry accuracy by 20%, and utilized business intelligence tools to enhance reporting. I applied SQL for database management and Tableau for data visualization to support public health policy development.
In the realm of machine learning and AI, I have hands-on experience with frameworks like TensorFlow, PyTorch, and MONAI for developing deep learning models in medical imaging analysis. My technical toolkit also includes Azure, AWS, Git, and advanced Natural Language Processing (NLP) techniques for building AI-powered applications.
I have also collaborated in cross-functional teams as a Pharma Regulatory Editor at Cactus Communications (October 2021–March 2022), ensuring timely submission of critical documents with a 95% on-time success rate. My research contributions have been recognized internationally, including publications in esteemed journals like the Australian & New Zealand Journal of Psychiatry and BMC Proceedings.
Looking ahead, I am excited to explore opportunities in data analysis, data science, and machine learning that allow me to contribute to impactful projects. Whether it’s analyzing financial data, integrating AI into the banking sector, solving healthcare challenges with AI, or innovating new AI tools, I am eager to bring my unique blend of medical knowledge and data expertise to dynamic teams and forward-thinking organizations.
I am a highly skilled professional with expertise spanning various tools and technologies in data analysis, machine learning, and AI development. My skill set enables me to analyze complex datasets, build predictive models, and craft insightful visualizations, making data actionable and impactful. Here’s a glimpse of my core competencies:
Python is at the heart of my data analytics and machine learning toolkit. I am proficient in using libraries such as Pandas, NumPy, and Scikit-learn for data manipulation and predictive modeling. I specialize in deep learning frameworks like TensorFlow and PyTorch, which I leverage to develop and optimize neural networks for real-world applications. Additionally, I have experience with LangChain, which enables me to integrate large language models into workflows for advanced natural language processing tasks.
R is a powerful tool in my analytical arsenal for statistical computing and data visualization. I have a strong command of ggplot2, dplyr, and caret for data exploration and building robust statistical models. R enables me to perform in-depth exploratory data analysis (EDA) and generate publication-quality visualizations, which are essential for healthcare analytics and research projects.
I am proficient in Structured Query Language (SQL) for managing and querying relational databases. My expertise lies in optimizing complex queries, creating efficient database schemas, and extracting meaningful insights from large datasets. Whether it’s joining multiple datasets or ensuring data integrity, I use SQL to enable seamless data operations.
Excel is one of the most versatile tools in my skill set, where I use advanced features such as pivot tables, VLOOKUP, and macros to organize, analyze, and present data. I am also skilled in applying solver and data analysis add-ins to optimize decision-making models, a crucial skill for business analytics.
I am adept at creating interactive dashboards and visualizations using Power BI and Tableau. My work includes crafting dashboards that present complex data in intuitive ways, allowing stakeholders to make data-driven decisions. I excel in DAX calculations, custom visuals, and embedding real-time analytics into business intelligence platforms.
My expertise extends into the specialized field of computational radiology through MONAI, a PyTorch-based library designed for medical imaging. I utilize MONAI to develop AI models for segmentation, classification, and diagnosis, bringing innovative solutions to the healthcare domain.
I am skilled in using LangChain, a framework for developing applications that use large language models (LLMs). This expertise allows me to build conversational agents and deploy NLP solutions in healthcare and beyond, including tasks such as medical documentation and chatbot development.
As a passionate advocate of Generative AI, I have explored its applications across multiple domains. My experience includes building projects that utilize transformer models, GANs, and tools like OpenAI’s GPT to generate innovative solutions, particularly in the intersection of healthcare and technology.
Combining these tools and technologies, I bridge the gap between data and decision-making. My expertise enables me to solve complex problems, design impactful visualizations, and develop machine learning solutions that contribute to innovation and progress across industries, particularly in healthcare and AI.
At my pre-university college, I achieved a rare distinction by excelling in both the state engineering and state medical entrance examinations, securing an impressive Rank 261 in engineering and Rank 89 in medicine. I was also the only pre-med student from my institution to pass the prestigious IIT Screening Exam, a testament to my interdisciplinary aptitude and dedication.
My academic success earned me a full scholarship ("free seat") to Mysore Medical College and Research Institute, India’s second-oldest medical college, renowned for its academic rigor and clinical training. During my time there, I graduated with honors, receiving ‘Best Intern’ awards from both the Departments of Psychiatry and Community Medicine for my outstanding contributions to clinical research. My work culminated in several poster presentations and peer-reviewed publications, further solidifying my academic and research credentials.
After earning my Bachelor of Medicine and Bachelor of Surgery from Rajiv Gandhi University of Health Sciences, I spent over a decade as a physician, excelling in clinical research and patient care. I further honed my skills as a Duty Doctor at Cauvery Hospitals (August 2023–December 2023) and a Family Physician at Elite Nursing Home (2016–2020). During this period, I developed a nuanced understanding of healthcare systems and their challenges. I also worked at my alma mater on research projects and worked as Pharma-Regulatory Editor at Cactus communications.
Currently, I am pursuing a Master's in Data Analytics with a concentration in Applied Machine Learning at Northeastern University (January 2024–December 2025). I am also engaged as a Data Analyst and Scientific Advisor with Tarsal Education Technologies, where I apply AI-driven insights to medical education challenges, including preparing dashboards and working on NLP projects. My academic achievements include maintaining a perfect GPA of 4.0.
Data Analysis and AI in Health Care.
I am a brilliant communicator, an instinctive autodidact, and work well in a team.
I was trained and practiced for 8 years as a physician.
M.B.B.S (Bachelor of Medicine and Bachelor of Surgery) and M.P.S. (Master of Professional Studies) in Data Analysis and Applied Machine Intelligence.
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