Laboratory Courses
In the PGSS lab courses students gain hands-on experience at the bench or computer terminal in one of the five core subject areas. Each student takes one lab course which meets for 3 hours a day twice a week with a focus on completing small proejcts. In addition to the research skills gained, student develop their scientific communication techniques through lab reports and small group presentations of their data.
2024 Lab Course Descriptions
Biological Techniques Laboratory | The biology lab covers a range of experimental work that illustrates the methods and technology used for modern research into biochemistry, cellular and molecular biology. You will develop an understanding of the theory behind the techniques you use and will learn the process of experimental design. You will also learn the biological concepts behind the experiments that you perform and will be able to communicate your results. Course Description: You will learn the principles of the techniques used in modern biology. The course will have a final project. You will improve your problem solving and analytical skills and will learn how to keep and maintain a lab notebook.
Introductory Organic Chemistry Laboratory | The laboratory exercises provide hands-on experiences which validate the reactions and theories discussed in lecture. The purpose of this course is to introduce methodology and processes of experimental organic chemistry problem-solving, essential laboratory procedures required for synthesis and analysis of organic compounds, and skills for scientific communication. Students will acquire laboratory skills relevant to synthesis and purification of organic compounds, as well as the practical use of chromatography and spectroscopy. In addition to learning laboratory techniques for organic synthesis and analysis, this lab course will emphasize the following skills: careful and thorough recording of data and observations, effective time management, problem-solving, lab safety, and proper waste disposal.
Computer Science Workshop | This workshop is structured into two pivotal components, meticulously designed to equip students with both foundational and advanced insights into scientific computation and machine learning. Enhanced with the latest advancements in AI tools and methodologies, the workshop offers a unique blend of theoretical knowledge and practical application.
- Introduction to Scientific Computation: This segment delves into scientific programming with hands-on exercises aimed at setting up language-specific development environments for R and Python. Students will tackle typical tabular data analytics and basic signal/image analysis, gaining exposure to AI-driven tools such as GitHub Copilot. This integration offers an augmented coding experience by assisting in code generation and documentation, streamlining the development process for scientific
- Introduction to Machine Learning: Designed as a fast-paced primer on machine learning, this section covers the principal algorithms and applications with a focus on modeling both categorical and continuous response variables. Incorporating lectures for conceptual understanding and daily hands-on exercises for real-world application, the curriculum emphasizes the use of R markdowns and Python notebooks. Through autoML toolboxes, students will experience simplified model creation, training, and testing processes, ranging from generalized linear models to deep neural networks, without the intricacy of complex coding. This component not only addresses the technical aspects of machine learning but also hones in on interpreting model results, fostering a deeper comprehension of the data insights
The workshop will also introduce students to Large Language Models (LLMs) and their burgeoning role in programming and data science, highlighting how these models can automate analysis, enhance decision-making, and support the development of sophisticated machine learning models. Additionally, students will explore the potential of vision-language models, which marry image understanding with natural language processing, showcasing the cutting-edge applications in bioengineering and beyond.
No prior knowledge in machine learning or programming is required, making this workshop accessible to a diverse audience. The only prerequisite is a familiarity with introductory algebra, including variables, linear equations, and basic graphing techniques. This workshop aims to arm students with not only the technical prowess but also the analytical acumen to interpret and leverage machine learning outcomes effectively.
Techniques of Experimental Physics | The physics laboratory course will provide direct experience with topics in classical and modern physics, such as harmonic oscillations, the nature of sound, superconductivity, and radioactivity. No prior physics course is necessary. Students who choose to do a team research project in physics are encouraged but not required to take this laboratory course.
Mathematics Laboratory | Neil: Hey ChatGPT, can you write me a paragraph description of operations research that would make it sound exciting to high-powered high school students?
ChatGPT: Sure! Operations research (OR) is an exciting field that uses advanced mathematical and analytical methods to help organizations make better decisions. It's all about finding the most efficient and effective ways to solve complex problems, from designing transportation networks to optimizing supply chains to improving healthcare systems. OR experts use sophisticated models and algorithms to analyze data and simulate different scenarios, allowing them to make informed decisions that can have a huge impact on businesses, governments, and society as a whole. If you're interested in tackling big challenges, using cutting-edge technology, and making a real difference in the world, then operations research might just be the field for you!
Neil: No prior mathematics experience is necessary, as I will be going over the basics of inequalities, matrix algebra, and the simplex method during the lab period.