Suliana Manley

Courses taught by this person (8)

Before you step into the lab to do an experiment, you have a long list of questions: How do I design an experiment that will give a clear answer to my question? What model system should I use? What are my controls? What's an ideal sample size? How can I tell if the experiment worked?

BIOENG-603: Sharing your research

Giving a research talk (say, at a conference, to your department or in your research group meeting) is a really important part of a scientist's career. This course is designed for anyone who will be giving research-based scientific talks in the future.

BIOENG-604: Writing for science

The goal of the course is to develop effective writing skills for academic and professional contexts.

BIOENG-605: Planning your scientific journey (EDBB)

Planning Your Scientific Journey: Being successful as a scientist requires more than acquiring knowledge and developing experimental skills. It also requires: (1) asking a good scientific question, (2) establishing a clear plan of action, and (3) seeking advice along the way.

BIOENG-605(a): Planning your scientific journey (EDBB)

PHYS-301: Biophysics : physics of the cell

In this course we will study the cell (minimum unit of life) and its components. We will study several key cellular features: Membranes, genomes, channels and receptors. We will apply the laws of physics to develop models to make quantitative and predictive statements.

PHYS-466: Topics in biophysics and physical biology

This course provides exposure to research in biophysics and physical biology, with emphasis on the nature of scientific breakthroughs, and using critical reading of scientific literature. Each week, we will discuss the research of one recipient of the Max Delbruck Prize in Biological Physics.

PHYS-647: Image data science with Python and Napari

This course introduces students to the basics of image data science using Napari and Python. Students will learn image filtering, segmentation and feature extraction. Other topics include supervised and unsupervised machine learning techniques for object classification and clustering.