SCISYNTH: A JOURNEY INTO SCIENTIFIC COMPUTING
2025 ARCTIC Summer Camp, June 2-6, 2025
Welcome to our summer camp dedicated to scientific computing, offering a valuable opportunity for high school students and undergraduates to explore the practical applications of computational skills. In today's dynamic job market, proficiency in scientific computing is a versatile asset applicable across a wide range of fields. Participants in our program will not only develop a solid foundation in coding, data analysis, and computational modeling but will also gain insights into how these skills are applied in real-world scenarios. Scientific computing plays a pivotal role in diverse fields such as weather prediction, drug discovery, stock market predictions, and aerospace simulations. Whether you're interested in unraveling the complexities of climate patterns, contributing to groundbreaking medical advancements, making informed financial decisions, or simulating aerospace scenarios, mastering scientific computing opens doors to a spectrum of exciting career possibilities.
Participants in the summer camp will also engage in a collaborative group project, starting by researching relevant literature to identify a real-world problem ripe for computational solutions. With this foundation, they'll delve into the practical aspects, writing computer programs tailored to tackle the identified problem and iteratively refining them for optimal performance. Through this hands-on process, participants will enhance their problem-solving abilities, deepen their understanding of computational methodologies, and cultivate essential programming skills. Throughout the journey, workshop staff will provide invaluable guidance and mentorship, offering expertise and support to empower participants in navigating challenges and realizing their potential as computational researchers.
Furthermore, the workshop environment fosters a culture of collaboration and innovation, where participants learn not only from the expertise of workshop staff but also from each other's diverse perspectives and experiences. Through collaborative brainstorming sessions, peer reviews, and constructive feedback, participants will benefit from a rich exchange of ideas and insights, fostering creativity and sparking new avenues of exploration. Together, participants and workshop staff will create a dynamic learning community, where curiosity thrives, skills flourish, and transformative discoveries await.'
Join us to acquire practical skills that will not only enrich your academic experience but also empower you in the ever-evolving landscape of professional opportunities.
Dates: June 2 - June 6, 2025, 9 a.m. - 5.30 p.m. (Eastern time)
Application deadline April 15th, 2025 midnight, (Eastern time)
Location: Georgia State University, Atlanta Campus, room: GSU Library North, Classroom 1
Cost $200, Limited support available
Previous summer camps: 2023 - Build a self-driving car , 2024- SciSynth
Agenda
9:00 AM – 9:30 AM: Introduction & Logistics
Kick off the summer camp with a warm welcome! This session will cover an overview of the program, objectives, and expectations for the week. Participants will also receive any necessary materials, discuss the schedule, and go over logistical details such as Wi-Fi access, software setup, and communication channels.
9:30 AM – 10:30 AM: Python Basics
An introduction to Python programming, covering fundamental concepts like variables, data types, loops, and conditionals. Participants will write simple scripts and gain hands-on experience with basic syntax and structure.
10:30 AM – 11:30 AM: Solution to Linear Systems Using Numerical Methods
This session explores numerical techniques for solving linear systems, including Gaussian elimination, LU decomposition, and iterative methods like Jacobi and Gauss-Seidel. Attendees will implement these methods in Python and analyze their efficiency.
11:30 AM – 1:00 PM: Lunch
A break to relax and network with fellow participants.
1:00 PM – 2:00 PM: Python Library (NumPy)
A deep dive into NumPy, a fundamental library for numerical computing in Python. Topics include array manipulation, vectorized operations, broadcasting, and key mathematical functions. Hands-on exercises will reinforce the concepts.
2:00 PM – 2:30 PM: Project Introduction
Participants will be introduced to a real-world project that they will work on throughout the camp. This session outlines the objectives, scope, and expectations for the project, setting the stage for collaborative problem-solving.
2:30 PM – 5:00 PM: Project Hands-On Time
Participants will dedicate this time to working on their projects, applying the skills learned in the workshop. Mentors will be available to guide them through challenges and help refine their approach to data analysis, modeling, or autonomous vehicle development.
9:00 AM – 9:30 AM: Previous Day Review and Q&A
A recap of key concepts from Day 1, addressing any questions or challenges participants faced. This interactive session ensures a solid understanding before moving forward.
9:30 AM – 10:30 AM: Python Library (Pandas)
An introduction to Pandas, a powerful data manipulation library in Python. Participants will learn how to load, clean, and transform datasets using Pandas' DataFrames and Series, essential for efficient data analysis.
10:30 AM – 11:30 AM: Numerical Solutions to Ordinary Differential Equations (ODEs)
This session covers numerical methods for solving ODEs, including Euler’s method, Runge-Kutta methods, and stability analysis. Participants will implement these techniques in Python to model real-world systems.
11:30 AM – 1:00 PM: Lunch
A break to recharge and network with fellow participants.
1:00 PM – 2:00 PM: Data Preprocessing Techniques
A deep dive into data preprocessing, a crucial step in machine learning and data science. Topics include handling missing values, outlier detection, feature scaling, encoding categorical variables, and data normalization. Participants will apply these techniques to real datasets.
2:00 PM – 5:00 PM: Project Hands-On Time
Participants will dedicate this time to working on their projects, applying the skills learned in the workshop. Mentors will be available to guide them through challenges and help refine their approach to data analysis, modeling, or autonomous vehicle development.
9:00 AM – 9:30 AM: Previous Day Review and Q&A
A brief review of key concepts from Day 2, providing an opportunity for participants to ask questions and clarify any challenges they encountered. This ensures everyone is on the same page before diving into new topics.
9:30 AM – 10:30 AM: Python Library (Matplotlib)
An introduction to Matplotlib, one of the most widely used Python libraries for data visualization. Participants will learn how to create various types of plots, customize visual elements, and effectively communicate data insights through visual storytelling.
10:30 AM – 11:30 AM: Transformations Using Matrix Algebra
This session covers the fundamental role of matrices in transformations, including scaling, rotation, translation, and reflection. Participants will apply these concepts in Python using NumPy and Matplotlib to visualize transformations in 2D and 3D space.
11:30 AM – 1:00 PM: Lunch
A break to relax, recharge, and network with fellow participants.
1:00 PM – 2:00 PM: Introduction to Machine Learning
A beginner-friendly introduction to machine learning, covering core concepts such as supervised vs. unsupervised learning, regression, classification, and model evaluation. Participants will explore real-world applications and implement a simple machine learning model using Scikit-learn.
2:00 PM – 5:00 PM: Project Hands-On Time
Dedicated time for participants to work on their projects, integrating the skills learned throughout the workshop. Mentors will be available to assist with debugging, fine-tuning models, and optimizing project implementations.
9:00 AM – 9:30 AM: Previous Day Review and Q&A
A recap of key concepts from Day 3, addressing any questions or challenges participants faced. This interactive session ensures a strong foundation before moving into more advanced topics.
9:30 AM – 10:30 AM: Introduction to Computer Hardware, Distributed, and Parallel Computing
Participants will explore the fundamental components of computer hardware, from processors to memory and storage. The session will also introduce distributed and parallel computing, explaining how large-scale scientific problems are tackled using multi-core processors, clusters, and cloud-based infrastructures.
10:30 AM – 11:30 AM: Scientific Software Best Practices & Working with a Software Development Team
An introduction to best practices in scientific software development, covering version control (Git), code documentation, testing, and collaboration techniques. Participants will also learn how software development teams work together in research environments.
11:30 AM – 1:00 PM: Lunch
A break to relax and network with fellow participants.
1:00 PM – 1:30 PM: Introduction to the Current Scientific Computing Ecosystem
This session provides an overview of modern scientific computing, highlighting the role of computer simulations, data analytics, artificial intelligence (AI), and high-performance computing (HPC) in solving complex real-world problems. Participants will gain insight into cutting-edge tools and methodologies shaping research today.
1:30 PM – 2:30 PM: Making Your Findings Available – Introduction to Web Development & Science Gateways
This session introduces participants to the importance of making research findings accessible to the broader community through web-based platforms. It covers the basics of web development, including HTML, CSS, and JavaScript, along with techniques for hosting and publishing data-driven applications. Participants will also learn about science gateways, which provide collaborative platforms for researchers to share tools, data, and computational resources.
2:30 PM – 5:30 PM: Project Hands-On Time
Participants will dedicate this time to working on their projects, applying the skills learned in the workshop. Mentors will be available to guide them through challenges and help refine their approach to data analysis, modeling, or autonomous vehicle development.
Summer camp projects
Hands-on projects are a crucial part of this summer camp, designed to provide practical experience in solving real-world problems using computational and engineering techniques. Throughout these sessions, participants will work with cutting-edge tools, programming languages, and scientific methods to explore topics ranging from data analytics and artificial intelligence to robotics, numerical simulations, and autonomous systems.
Each project is structured to bridge theory with application, allowing participants to engage in problem-solving, coding, experimentation, and analysis. By tackling challenges in heat transfer, machine learning, robotics, and data science, students will not only gain technical skills but also develop critical thinking, collaboration, and creativity—essential traits for future scientists and engineers.
By the end of the program, participants will have built functional projects, worked with real-world datasets, simulated physical phenomena, and implemented intelligent systems. These experiences will equip them with the confidence and skills to pursue further studies and careers in STEM fields.
In this hands-on session, participants will explore how heat transfers between different materials and how scientists and engineers use numerical techniques to model and simulate these processes. Heat transfer is a fundamental concept in physics and engineering, affecting everything from electronics cooling and building insulation to space exploration and industrial manufacturing.
Key Learning Objectives:
- Understanding Heat Transfer – Learn about the three main modes of heat transfer: conduction, convection, and radiation, with a focus on how heat moves across material boundaries.
- Formulating the Problem Using Physics Equations – Explore how real-world heat transfer scenarios are described mathematically using equations like the heat conduction equation (Fourier’s Law) and boundary conditions that define how heat flows between materials.
- Making Assumptions in Scientific Models – Discuss the assumptions made when formulating heat transfer equations, such as ignoring certain effects to simplify calculations while still obtaining useful insights.
- Solving Physics Equations Using Numerical Methods – Learn how to discretize differential equations and use numerical techniques like the Finite Difference Method (FDM) to approximate solutions.
- Simulating Heat Transfer on a Computer – Implement these numerical methods in Python to simulate heat transfer, visualize temperature changes over time, and analyze how different materials affect heat flow.
- Interpreting and Understanding Results – Learn how to analyze simulation results, recognize patterns, and draw conclusions about material properties and heat dissipation efficiency.
Hands-On Activities:
- Building a Simple Heat Transfer Model – Define a heat transfer problem and apply physics-based equations.
- Numerical Approximation – Use computational techniques to solve equations when exact solutions are not possible.
- Programming & Simulation – Implement a simulation in Python to model heat flow across different materials.
- Visualization & Analysis – Plot temperature distributions and understand how changing parameters affects results.
By the end of the session, participants will have a deeper understanding of how heat transfer works, how numerical simulations help predict real-world behavior, and how scientists and engineers use computational tools to solve complex physical problems. This session is designed to be engaging, practical, and accessible, even for those new to computational modeling.
In this exciting hands-on session, participants will explore the core technologies behind autonomous vehicles and the real-world challenges of building intelligent robotic systems. This workshop provides an interdisciplinary approach, blending embedded systems, computer vision, and machine learning to develop a functional autonomous vehicle prototype.
Key Learning Objectives:
🔹 Understanding Autonomous Systems – Learn about the essential components that enable self-driving technology, including sensors, controllers, and real-time decision-making.
🔹 Embedded Programming with C and Arduino – Work directly with Arduino controllers, using C programming to interface with sensors, motors, and actuators.
🔹 Serial Communication – Explore how data is transmitted between microcontrollers and external devices, a critical aspect of robotics and automation.
🔹 Computer Vision for Autonomous Navigation – Capture and process images from onboard cameras to detect lane markings and obstacles.
🔹 Control Loops & Real-Time Decision-Making – Implement closed-loop control systems to enable the vehicle to react dynamically to its environment.
🔹 Machine Learning for Lane Following – Train a machine learning model using collected images to detect and follow lanes, a fundamental aspect of self-driving technology.
Hands-On Activities:
✔ Building & Programming a Mini Autonomous Vehicle – Configure an Arduino-based vehicle, integrating sensors and actuators.
✔ Image Collection & Data Processing – Capture real-world driving scenarios and preprocess images for machine learning.
✔ Developing & Testing a Lane-Following Algorithm – Train and deploy a model that enables the vehicle to autonomously follow lanes using computer vision.
✔ Experimentation & Optimization – Fine-tune parameters, adjust control algorithms, and improve real-time performance.
By the end of this session, participants will have practical experience with robotics, autonomous navigation, and artificial intelligence, gaining the skills needed to tackle real-world engineering challenges in self-driving technology. Whether you're a beginner or have some prior experience, this workshop will equip you with the fundamentals of autonomous vehicle development in an engaging, hands-on way.
In this interactive session, participants will dive into real-world datasets to develop essential data analysis skills. Using publicly available datasets from the U.S. Census Bureau’s Data Portal (data.census.gov) and the FBI’s Crime Data Explorer (CDE) (Crime Data Explorer), we will explore key societal trends, including demographics, economic indicators, housing patterns, and crime statistics.
Key Learning Objectives:
🔹 Data Acquisition & Preparation – Learn how to find, access, and clean large-scale datasets for analysis.
🔹 Data Cleaning & Preprocessing – Handle missing values, standardize formats, and prepare datasets for deeper exploration.
🔹 Exploratory Data Analysis (EDA) – Use statistical summaries and visualizations to identify trends, correlations, and anomalies.
🔹 Data Visualization – Learn best practices for creating meaningful charts, graphs, and dashboards to effectively communicate insights.
🔹 Basic Statistical Modeling – Apply foundational statistical techniques to analyze trends and make data-driven predictions.
🔹 Data Storytelling & Communication – Learn how to present findings clearly and persuasively to different audiences.
Hands-On Activities:
✔ Working with Real Datasets – Explore structured and unstructured data from government sources.
✔ Cleaning & Transforming Data – Perform preprocessing tasks to prepare datasets for analysis.
✔ Performing EDA & Statistical Analysis – Identify patterns and insights using Python and relevant libraries.
✔ Building Visual Dashboards – Use Matplotlib, Seaborn, and Plotly to create compelling data visualizations.
✔ Interpreting & Presenting Insights – Develop a data-driven narrative and communicate findings effectively.
By the end of the workshop, participants will have practical experience working with large datasets, extracting meaningful insights, and presenting findings through data storytelling. This session is ideal for anyone looking to strengthen their analytical skills and apply them to real-world challenges.