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As a Master's student in Computer Science at Virginia Commonwealth University, I'm excited to share insights from my recent summer research internship in the Department of Biostatistics. Key Contributions and Learning Outcomes: - Cell Segmentation Model Development: • Worked with diverse tissue sample data including H&E, PhenoCycler, and Xenium 5k images • Applied advanced computational techniques to analyze complex biological structures - Advanced Image Processing and Data Integration: • Utilized cutting-edge tools to develop sophisticated image registration techniques • Created innovative methods for aligning multi-section tissue images • Designed integrated pipelines combining spatial transcript data with segmentation results - Algorithm Performance Analysis: • Conducted comparative studies of various cell segmentation algorithms This internship presented a significant challenge as I entered the biostatistics field with no prior experience in medical research. The steep learning curve required me to rapidly adapt to complex biological concepts and their computational applications. This experience has not only enhanced my technical skills but also broadened my perspective on the critical role of data analysis in advancing medical research. I am profoundly grateful to Dr. Jinze Liu for providing this opportunity and for her invaluable mentorship. I would also like to express my sincere appreciation to Dr. Kevin Matthew Byrd, DDS, PhD for his support and guidance throughout the internship. Additionally, I'm thankful to Khoa Huynh, a PhD candidate in the department, for his assistance during the crucial first few weeks of my internship. Reflecting on my journey, from experiences in FinTech and eCommerce to now Biostatistics, it's been enlightening to discover the pivotal role software developers play across diverse sectors. Each field has offered unique learning opportunities and has expanded my understanding of how technology can drive innovation and progress. This experience has reinforced my passion for leveraging computational methods to solve complex problems across various industries. The opportunity to apply my software development skills in a new domain has broadened my perspective on the versatility and impact of technology. I look forward to continuing to explore diverse fields where data science and software engineering can drive innovation and make meaningful contributions. #biostatistics #summerinternship #computationalbiology #softwaredeveloper #vcu

Excited to announce the completion of my CodeClause internship project: 'Detection of Parkinson's Disease using Machine Learning Algorithms'! Through rigorous research and innovative methodologies, we've developed a robust model for early diagnosis of Parkinson's, aiming to improve patient outcomes. Our project signifies a significant step forward in medical technology, enabling quicker and more accurate detection. Grateful for the invaluable experience gained during this journey, and eager to continue contributing to advancements in healthcare. Stay tuned for more updates on our impactful work in the field of medical research! #parkinsonsdisease #healthcare #intershipopportunity #intership2023 #interships #machinelearningalgorithms #dataanalyticsjourney #MedicalResearch

Task 3 Completed: Disease Prediction from Medical Data In my third task at CodeAlpha, I developed a machine-learning model for Disease Prediction using a medical dataset from Kaggle. The project involved analyzing medical symptoms to predict potential diseases. I employed three models: Support Vector Classifier (SVC), Naive Bayes Classifier, and Random Forest Classifier, and used an ensemble approach that combined their predictions by selecting the mode of the outputs. This method improved accuracy and ensured robust performance. Confusion matrices were used to evaluate the models' reliability. This task strengthened my understanding of model selection, ensemble techniques, and the real-world applications of machine learning in healthcare for early disease detection. CodeAlpha #CodeAlpha #Internship #MachineLearning GitHub: https://lnkd.in/dCVQfsrp

Hey All.. ✋🏻 Reflecting my final year project completion certificate for Msc Data science and Analytics. Project Topic:- Fetal Health Classification using Ml and Deep learning (FCNN) Fetal health classification is the process of assessing a fetus's health using a cardiotocograph (CTG) to determine if it is normal, suspicious, or pathological. A CTG is a non-invasive and cost-effective tool that continuously monitors the fetus's health. #Data_Analytics #Internship #Project #Datascience

Proud to Share My Summer Internship Experience! This summer, I had the opportunity to intern with the Think Neuro Summer '24 Internship at UCLA and UC Berkeley, where I worked on a cutting-edge project titled Advancements in Alzheimer’s Disease Diagnosis: Integrating AI, Machine Learning, and R Programming for Biomarker Analysis. Our team conducted an in-depth Bibliometric Analysis utilizing the World of Science Database and advanced R programming techniques. We focused on extracting, analyzing, and interpreting global data on Alzheimer’s Disease (AD) biomarkers, particularly how cerebrospinal fluid (CSF) and neuroimaging biomarkers contribute to various treatment plans, including Disease-Modifying Therapies (DMTs). As a computer science engineering student, I played a key role in implementing AI and machine learning algorithms to process large-scale datasets, uncover patterns, and develop predictive models that could be used for early detection and treatment strategy optimization in AD research. Collaborating with a talented team, including Kamya Harper, CrystalJade Johnson, Christine Chung, Andrew Yang, Maceo Sweeney, Noor Lais, Likhita Yegireddi, and Kaylen Chan, we developed and presented our findings, which I’m excited to share through our research poster. #AI #MachineLearning #RProgramming #AlzheimersResearch #DataScience #ComputerScience #ThinkNeuro #UCLA #UCberkeley #HealthcareInnovation

Completed: Task 1: Diabetes Prediction: Use a dataset containing medical data on patients to predict if a person has diabetes or not. from the ML internship.

Task 3 Completion: Disease Prediction from Medical Data I have successfully completed Task 3 Completion: Disease Prediction from Medical Data of my machine learning internship at CodeAlpha!!🎉 I explored healthcare analytics, using machine learning to build a strong model for disease prediction using medical data. By tapping into advanced algorithms and diverse datasets, my goal was to equip healthcare professionals with tools for early detection and proactive care. Github:https://lnkd.in/gWdb27A2 #MachineLearning #CodeAlpha

Hello Connections!! I've Successfully completed Task 4 of Machine Learning Internship at CodeAlpha Company. Task 4:- Disease Prediction from Medical Data Objective:- Create a model to predict the likelihood of a disease based on medical data (e.g., symptoms, patient history). Use datasets with labeled medical records and apply classification algorithms. IDE:- Jupyter Notebook #codealpha #codealphainternship #machinelearninginternship