Splash Biography
VYASSA BARATHAM, Grad Student @ UC Berkeley in Physics/neuroscience
Major: Physics College/Employer: UC Berkeley Year of Graduation: G 

Brief Biographical Sketch:
Hi! I'm a graduate student in the physics department at UC Berkeley, though my current research is in the field of neuroscience. My undergrad degree is from Stony Brook University in Physics with a minor in computer science. I'm originally from the Boston area, where I attended MIT's Splash programs as a high school student, so teaching at Splash really brings me back. I love engaging with dedicated and curious students, and I'll do my best to foster some of that dedication and curiosity in my Splash classes! Hope to see some of you there! Past Classes(Clicking a class title will bring you to the course's section of the corresponding course catalog)M557: Edge Detection: How Computers See in Splash Spring 19 (Mar. 16, 2019)
Edge detection is a fundamental problem in computer vision which provides a good opportunity to learn some basics of image processing. In this course, we will talk about some of these basic tools (convolution, Gaussian smoothing, and spatial differentiation), then use this foundation to study the Canny Edge Detection algorithm and some of its optimizations. It is my goal for everyone in the class to understand the Canny edge detector well enough to implement it on their own, although we will not do this during the class. If time permits, we will also take a brief high level look at some more modern approaches to edge detection, including an overview of machine learning.
S559: A first dive into special relativity in Splash Spring 19 (Mar. 16, 2019)
Albert Einstein's theory of relativity is a bold and bizarre hypothesis about the way our universe works when things move very quickly. It predicts that objects shrink and expand and even age at different rates depending on how fast they're moving. And, as it turns out, these strange predictions are 100% correct!
While parts of the theory are complicated mathematically, its core physical ideas are straightforward to understand without any math. And there are mathematically simple applications of relativity which are nonetheless meaningful, counterintuitive, and inspiring.
In the course, we will briefly review the history of physics leading up to Einstein, then introduce the basic concepts that allow us to discuss special relativity (observers, relative motion, reference frames), present the postulates that special relativity is based on, develop some fundamental equations (time dilation, length contraction), and finally use this machinery to treat some problems of interest, hopefully including classics such as the "twin paradox" and how to fit a 1.0m pole into a 0.9m long barn.
M393: Edge Detection: How Computers See in Splash Fall 18 (Nov. 04, 2018)
Edge detection is a fundamental problem in computer vision which provides a good opportunity to learn some basics of image processing. In this course, we will talk about some of these basic tools (convolution, Gaussian smoothing, and spatial differentiation), then use this foundation to study the Canny Edge Detection algorithm and some of its optimizations. It is my goal for everyone in the class to understand the Canny edge detector well enough to implement it on their own, although we will not do this during the class. If time permits, we will also take a brief high level look at some more modern approaches to edge detection, including an overview of machine learning.
S394: A first dive into special relativity in Splash Fall 18 (Nov. 04, 2018)
Albert Einstein's theory of relativity is a bold hypothesis about the way our universe works when things move very quickly. It predicts that we live in a bizarre universe where objects shrink and expand and even age at different rates depending on how fast they're moving. And, as it turns out, these strange predictions are 100% correct!
While most of the theory is conceptually deep and mathematically complicated, one can readily build a surfacelevel understanding which is tractable (even for students who haven't studied calculus), but still meaningful, counterintuitive, and inspiring. In the course, we will briefly review the history of physics leading up to Einstein, then introduce the basic concepts that allow us to discuss special relativity (observers, relative motion, reference frames), present the postulates that special relativity is based on, develop some fundamental equations (time dilation, length contraction), and finally use this machinery to treat some problems of interest, hopefully including classics such as the "twin paradox" and how to fit a 1.0m pole into a 0.9m long barn.
M302: MapReduce, the Big Data Workhorse in Splash Spring 18 (Mar. 04, 2018)
An Intel Core i7 980 XE processor can run 100 billion floating point operations every second. But some data processing jobs require astronomically huge computing resources, which require tasks and data to be distributed over several machines. Often, this means using an algorithm called MapReduce, which deals with the fact that two pieces of data sent to two different machines may, in fact, depend on each other. In this class, we will explore some basics of distributed computing, and then talk about the MapReduce algorithm conceptually, before seeing a basic example and discussing some practical aspects of the algorithm and its open source implementation, Hadoop, and Amazon's MapReduce service, EMR.
M303: Edge Detection: How Computers See in Splash Spring 18 (Mar. 04, 2018)
Edge detection is a fundamental problem in computer vision which provides a good opportunity to learn some basics of image processing. In this course, we will talk about some of these basic tools (convolution, Gaussian smoothing, and spatial differentiation), then use this foundation to study the Canny Edge Detection algorithm and some of its optimizations. It is my goal for everyone in the class to be understand the Canny edge detector well enough to implement it on their own, although we will not do this during the class. If time permits, we will also take a brief high level look at some more modern approaches to edge detection, including an overview of machine learning.
