Envisagenics’ Post

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

We’re thrilled to welcome LI SHI, a PhD student from the University of Texas, Austin, as our #DAAD intern. 🎓📊 Shi Li’s research uses neuro-physiological methods to explore human behaviorial information and the interactions with information systems. She finds her #internship in data science department fulfilling, with supportive colleagues and ample opportunities for learning and applying her skills in data analysis and visualization and management. 🤝 Welcome, Shi Li! We’re excited for your contributions. 🌟 #DataScience #AI

I am pleased to announce the successful completion of my tasks at InternJunction, where I engaged in several machine learning and data science projects. Throughout the internship, I developed and honed my skills in data preprocessing, model development, and predictive analysis. The key assignments I completed include: Breast Cancer Prediction: Built and evaluated classification models to predict the malignancy of tumors using the Breast Cancer dataset. House Price Prediction: Developed a RandomForestRegressor model to predict house prices based on a comprehensive dataset of 1460 samples with 80 features. Stock Price Prediction: Implemented and tested various models to forecast stock prices, incorporating technical indicators and historical data. These projects provided me with valuable experience in handling real-world datasets, refining machine learning models, and applying advanced analytical techniques. I am grateful for the opportunity to contribute to meaningful work and further my professional development in data science. #internship InternJunction

Here's my Task 5 of Data Science internship from Main Flow Services and Technologies that is about feature engineering and selection, which are essential in building efficient models. Mini Project on Heart Disease Analysis. #mainflow #internship

Intern Spotlight: Meet Reese Richardson Join us this summer as we learn more about the dedicated interns working at Envisagenics. Today, we are getting to know Computational Biology Intern Reese Richardson! Hometown: Chicago, IL School: Northwestern University Degree: PhD Candidate Why intern at Envisagenics: "I have been interested in the detection of alternative splicing events since I started my PhD and I know that Envisagenics does great work in this field." What are some of your interests outside of work: "I enjoy lapidary, hobbyist microcomputing, 3D-printing, and flags." What are your plans after completing this internship: "Finishing up my PhD!"

Task-3 : Leaf Disease Detection Leaf disease detection using images involves creating a system that can automatically identify and classify diseases affecting plant leaves based on visual information. Data Collection: Gather a dataset of leaf images, each labeled with the disease type (or "healthy" if no disease is present). Public datasets are available online. Data Preprocessing: Prepare the images for input into a deep learning model by resizing, normalizing, and augmenting them to increase the model's robustness. Model Building: Build a model for image classification tasks. Model Training: Train the model using the preprocessed images and their corresponding labels. Model Evaluation: Evaluate the model's performance on a separate validation set to ensure it generalizes well to unseen data. Model Deployment: Use the trained model to make predictions on new leaf images. GitHub:https://lnkd.in/gZt-FKgm #internship #CodeAlpha CodeAlpha

I'm thrilled to share that I have successfully completed task 2 in my one-month Machine Learning Internship at Code Alpha! 2️⃣ Disease Prediction: In Task 2, I focused on disease prediction from medical data, specifically working on heart disease prediction. This allowed me to apply advanced machine learning techniques to analyze complex medical datasets, aiming to improve early detection and treatment strategies. #MachineLearning #Internship #CodeAlpha #CreditScoring #DiseasePrediction #HeartDisease #DataScience #AI #CareerGrowth

Intern Spotlight: Meet Ted Kang! Join us this summer as we learn more about the dedicated interns working at Envisagenics. Today we are getting to know Software Engineer Intern Ted Kang! Hometown: California School: Princeton University Degree: Computer Science Class: 2024 Why intern at Envisagenics: "I am excited about the opportunity to intern at Envisagenics because it represents a place where I can leverage my computer science skills to make a meaningful contribution to the larger community. I am interested in computational biology, and I also appreciate the more collaborative environment of a smaller organization like Envisagenics." What are some of your interests outside of work: "Outside of work, I spend a lot of time playing basketball and staying active. I also have gotten into cooking more recently, and I like to watch movies and tv shows with my dad." Fun Fact: My favorite movies are La La Land, Burning, and Mad Max: Fury Road.

During my data science internship at Innomatics Research Labs, I successfully completed a substantial project. Specifically, I focused on debugging and enhancing a Note Taking Application as part of my internship responsibilities. Through systematic debugging and strategic problem-solving, I addressed critical issues within the application, including the Empty String Submission as None and Resubmission Bug, Each of these challenges provided an opportunity for technical growth and innovation. The Note Taking Application is built on the Flask framework, and the codebase is hosted on GitHub at the following link: https://lnkd.in/g5n8Xk3m

With an MSC in Applied Mathematics and pursuing an MTech in Data Science, I'm on a heartfelt quest for understanding and enlightenment. Seeking a funded research internship to dive deep into the realms of discovery. Let's embark on this odyssey together! #ResearchInternship #DataScience #Mathematics #AcademicJourney #HeartfeltQuest