I am Niranjan Kumar Kishore Kumar, an AI/ML Engineer dedicated to solving real-world challenges through state-of-the-art technology. With a strong background in Biomedical Engineering and Artificial Intelligence, my mission is to develop innovative AI solutions in healthcare, focusing on diagnostics, predictive modeling, and personalized treatments.
I hold a Bachelor's degree in Biomedical Engineering and a Master's in Artificial Intelligence, combining expertise in healthcare and advanced AI methodologies. My experience includes research in computer vision, natural language processing (NLP), and generative AI, with a focus on enhancing healthcare outcomes through technology.
Here are some of the technical skills I have honed through research, industry experience, and hackathons:
Here are some of the key milestones and recognitions I've achieved through dedication, innovation, and collaboration.
Developed the Mental Health Support Assistant, winning 3rd place and a $1,000 cash prize at the Cloudera AI Impact Hackathon, Evolve NYC, October 2024.
Built a cloud-based app in just 15 minutes, showcasing expertise in rapid AI deployment, earning recognition at the UC Berkeley AI Summit.
Successfully earned the AWS Certified Machine Learning – Associate (MLA-C01) certification, demonstrating advanced ML proficiency.
Achieved the Oracle Gen AI Professional Certificate, showcasing expertise in developing AI solutions with Cohere and embedding models.
With a solid foundation in biomedical engineering, AI/ML, and data science, my journey has led me to work on innovative projects across various industries. Below, you'll find a timeline of my work experience and education, showcasing my growth and accomplishments.
Relevant Coursework: Predictive Modeling, Machine Learning, Reinforcement Learning, Neural Networks & Deep Learning, Natural Language Processing (NLP), Generative AI.
Relevant Coursework: Data Structures & Algorithm, Python Programming, C++, Linear Algebra, Probability & Calculus, Biology, Biochemistry.
Explore some of the projects I've worked on, showcasing my expertise in AI, machine learning, data science, and beyond. Each project reflects my commitment to solving complex problems through innovative and data-driven approaches.
In this project, I developed and improved a deep learning model using InceptionV3 to predict Vertebral Heart Size (VHS) from medical images, a critical marker for detecting cardiomegaly. I compared my model's performance with state-of-the-art architectures benchmark results like ResNet50 and EfficientNetB7, ultimately achieving better results with InceptionV3.
In this project, I developed a custom convolutional neural network (CNN) to enhance bird sound analysis by segmenting and denoising audio spectrograms. Unlike traditional approaches that rely on pretrained models, my encoder-decoder architecture is specifically tailored for this task. The model achieved a 62.5% Intersection over Union (IoU) on a bird sound dataset, demonstrating its effectiveness in improving the quality of spectrograms for more accurate classification and identification.
In this project, I developed a machine learning model to predict breast cancer types based on diagnostic data. The project involved data preprocessing, model training, and evaluation, with deployment steps for real-world applications. By leveraging Python libraries and following a structured machine learning pipeline, the model provides accurate predictions, aiding in early diagnosis and treatment planning.
In this project, I developed a machine learning model to predict the risk of stroke based on patient data, including factors like age, hypertension, and lifestyle. By leveraging various classification algorithms and feature engineering techniques, I optimized the model to achieve high accuracy in identifying individuals at risk. This project aims to aid early detection and prevention of strokes, enhancing patient outcomes through data-driven insights.
The project aims to build a predictive model for a recipe website that classifies food items into three nutritional categories: "Nourishing", "Indulgent", and "Balanced". This classification is based on various nutritional parameters such as protein, fiber, saturated fat, sugar, and caloric content. The objective is to leverage this classification to recommend healthier food choices to users.
In this project, I applied unsupervised machine learning techniques, including PCA and KNN, to detect fraudulent healthcare charges. Using Python libraries such as Numpy, Pandas, and Scikit-Learn, I analyzed a dataset to identify outliers and unusual patterns in billing data. The results provided valuable insights into detecting potential fraud, contributing to more secure and reliable healthcare billing processes.
In this project, I optimized the AudioLDM 2 model for music generation. This involved fine-tuning the model to enhance quality and diversity. Below are live demos comparing the pretrained model and the fine-tuned model for two different prompts.
Prompt: "A classical drum with guitar background"
Prompt: "A cheerful ukulele strumming in a beachside jam."
Developed a Mental Health Support Assistant using Generative AI within a 12-hour timeframe. The project leverages Retrieval-Augmented Generation (RAG) to provide mental health support through intelligent and context-aware interactions. Achieved 3rd place and a $1,000 cash prize at the Cloudera AI Impact Hackathon, Evolve NYC 2024.
As an expert in AI/ML and Data Science, I provide a wide range of services across various industries, not limited to biomedical data. From AI-driven solutions to data analytics and consultancy, I can help you leverage technology to innovate, optimize processes, and achieve your goals. Let's collaborate to turn your ideas into impactful solutions.
I specialize in creating AI and machine learning models that solve real-world problems. With experience in building predictive models and automating processes, I can help you harness the power of AI to drive innovation.
Leveraging my data science skills, I can assist with data cleaning, analysis, and visualization to help businesses make data-driven decisions. Whether it’s exploring data patterns or building insightful dashboards, I’m here to help.
With a background in Biomedical Engineering and experience in data analysis, I offer specialized services in analyzing biomedical data. From healthcare datasets to medical imaging, I bring a unique perspective to healthcare AI.
If you’re looking to start with AI/ML or need guidance on your projects, I can provide consultancy services. Whether it’s choosing the right tools or developing a strategy, I offer insights to help you get started.
Although I'm still growing my expertise in this area, I've successfully built chatbots and explored generative AI technologies. I’m eager to collaborate on projects that push the boundaries of what's possible with AI.
While I'm continually expanding my cloud expertise, I’ve worked on cloud-based projects and can help you integrate AI/ML solutions into your cloud infrastructure, ensuring scalability and performance.
I'm always excited to connect with like-minded professionals, potential collaborators, or anyone interested in my work. Feel free to reach out to discuss opportunities, share ideas, or just say hello. Let's start a conversation!
40 Newport Parkway
Jersey City, NJ
07310 US
nkniranjankumar27@gmail.com
nkishore@mail.yu.edu
Phone: (+1) 516 968 6031
