who's hiring fast? i am back on the job market. interested in research, software development, product management, marketing, or data analysis. junior level. i am DYING to be back in grind mode in the next 2-3 weeks. im about to hard sell myself under this tweet:

getting this out of the way but if you don't care about credentials just skip to next tweet.

academic:
-B.S. in Computer Science, 4.0 GPA
-B.S. in Mathematics, 4.0 GPA
-Class of 2022
-Awarded a full ride scholarship for academic leadership

research:
-i was a materials science researcher before i started coding
-vanderbilt: first place award at summer academy of science for my work w graphene. also received an award from the national science foundation.
-uark: published research in blood driven biopower cells

programming: 
-MIT for 2 years doing deep learning research
-coded a toolkit for unsupervised learning with audio data in pytorch--still used by lab
-built, trained, and analyzed lots of different types of deep learning models. 
-find me here: 
http://mcdermottlab.mit.edu/people.html

anecdote: 
i went in to MIT having only taken introductory coding courses. i taught myself deep learning, pytorch, and tensorflow while IN THE PROGRAM. i was only supposed to be there for 3 months but my work was so good they kept me for 2 years and i did my thesis there.

here is some of the code i wrote from my last research project, but the entirety of the repo is still private.
https://drive.google.com/drive/folders/1ajgrKMjakIMeqz7xNjGztzYBVT5MT0_N?usp=sharing

more programming: 
-completed projects in python, c++, java
-have used javascript, solidity, and sql briefly
-fabulous fundamentals --> can learn new languages and frameworks super fast
-some side projects: sprite style 2D video games, editing software, data analysis tools

technical writing:
-click the link in my bio for a sample but here's some screenshots of my writing
-fluent in LaTeX

social media:
-i create fast organic growth on 4 major platforms
-twitter has been my recent experiment and i went from 800 followers and no engagement to 28,000 followers and 15,000,000 impressions per month in <3 months
-over 100,000,000 views across platforms in <1.5 years

public speaking: 
-i wrote and produced a viral video on how climate change negatively impacts women in certain nations and won the first place prize in planet forwards storyfest
-traveled to DC to speak with US congressmen and senators about women's issues

im young so lack of experience has been a barrier in my search, but rlly i just need a team that believes in me. im looking to work in LA or remote. if remote, im down to travel to the office every month to keep in sync. i prioritize good company culture over everything.

job search

gradient descent is a popular optimization algorithm used in machine learning and deep learning at the moment, and its surprisingly easy to understand. its important to get a good grasp on the concepts driving modern tech so that you don't get left in the dust! free knowledge:

FIRST you need to know what the loss function is. a loss function is a method of evaluating how well your model does at predicting the expected outcome. if the model predictions deviate too much from the actual results, the loss function would produce a very large number.

so basically, we want the loss function to produce the smallest number possible. gradient descent is the algorithm that is used to minimize the loss function by finding the most optimal values of the parameters of the function from a large parameter space. bear with me.

example: if we have loss function f(x, y) = x*x + 2y*y, then the optimal values for parameters is x=0 and y=0, because that is where the function is at a minimum. this function is simple, but in higher dimensions it may be incredibly difficult to solve an equation for zero. 

approximation by using gradient descent may be much faster than trying to solve the problem by hand or using a computer. okay so what's a gradient? the gradient of a function at any point is the direction of steepest increase of the function. here's a quick exercise:


once the gradient of the function at any point is calculated, the direction of steepest DESCENT of the function at that point can be calculated by multiplying the gradient with -1. eezypeezy. 

-1 * gradient = direction of steepest descent

now imagine you're standing at the top of a mountain and you want to descend. also assume its EXTREMELY foggy so you can't really see where to go. you only have a tool that tells you the direction of steepest descent (how convenient?!). what do you do? 

you'll probably take a step in the direction of steepest descent, use the tool and find the new direction of steepest descent from your new location, take another step in that direction, and keep doing this until you reach the bottom. 

in this analogy, you are the algorithm, and the path taken down the mountain represents the sequence of parameter settings that the algorithm will explore. the tool used to measure steepness is differentiation. the direction you choose to travel in aligns with the gradient.

this diagram represents how parameter θ gets updated with the value of gradient in the opposite direction while taking small steps. this formula tells us our next position. in order to estimate the optimal parameter values, this process is repeated until convergence.

repeating until convergence just means keep going until you reach a minimum, and that's where the algorithm stops. this could be a local or global minimum which can lowkey cause problems but for simplicity i wont get into that right now. (look it up though frfr)

now imagine you have a machine learning problem and want to train your algorithm with gradient descent to minimize your loss function J(w, b) and reach its local minimum by tweaking its parameters (w and b). we know we want to find the values of w and b marked by the red dot.

to start finding the right values, we initialize w and b with some random numbers. gd then starts at that point (somewhere around the top), and it takes one step after another in the steepest downside direction until it reaches the point where the loss function is minimized.

how big the steps are is determined by the learning rate, which decides how slow or fast we will move toward the optimal weights. for gradient descent to reach the local minimum we must set the learning rate to an appropriate value, neither too low nor too high.

if the steps it takes are too big, it may not reach the local minimum because it bounces back and forth between the convex function of gradient descent (left image above). if we set the learning rate to a very small value, the optimization can be super slow. (right image)

alright im tired but those are some basics. i hope this helps people. its a pretty cool algorithm and it really shouldn't intimidate you at all.

Gradient Descent Explained

HELLO FRIENDS! im about to teach you how to read more math! today's lesson is set notation. these symbols are used a lot in mathematical writing because it helps us save space when describing sets, and you need to understand them when they appear. alright here we go:

symbol: { }
meaning: "set", "a collection of elements"
example: A = {3, 7, 9, 14}, B = {9, 14, 28}

symbol: A ∩ B
meaning: "intersection", "objects that belong to set A and set B"
example: A = {3, 7, 9, 14}, B = {9, 14, 28}, A ∩ B = {9, 14}

symbol: A ∪ B
meaning: "union", "objects that belong to set A or set B"
example: A = {3, 7, 9, 14}, B = {9, 14, 28}, A ∪ B = {3, 7, 9, 14, 28}

symbol: A ⊆ B
meaning: "subset", "A is a subset of B. set A is included in set B."
examples: {9, 14, 28} ⊆ {9, 14, 28} and {9, 14} ⊆ {9, 14, 28}

symbol: A ⊂ B
meaning: "proper subset", "A is a subset of B, but A is not equal to B."
example: {9, 14} ⊂ {9, 14, 28}
note: {9, 14, 28} ⊂ {9, 14, 28} would be FALSE because they are equal sets

symbol: A\B or A-B
meaning: "relative complement", "objects that belong to A and not to B"
example: A = {3, 9, 14}, B = {1, 2, 3}, A-B = {9, 14}

symbol: |A|
meaning: "cardinality", "the number of elements of set A"
example: A = {3, 9, 14}, |A| = 3

symbol: P(A)
meaning: "power set", "all subsets of A"
example: A = {1, 2}, P(A) = {{}, {1}, {2}, {1, 2}}


there are also sets of numbers that are used so often that they have special names and symbols

symbol: ℕ
common set: the natural numbers
description: the whole numbers from 1 upwards (or from 0 upwards in some fields of mathematics) aka the set {1, 2, 3, ...}

symbol: ℤ
common set: integers
description: the positive whole numbers, negative whole numbers, and 0 aka the set {..., -3, -2, -1, 0, 1, 2, 3, ...}

symbol: ℚ
common set: rationals
description: the set of all numbers you can make by dividing one integer by another (but not dividing by 0)

symbol: ℝ
common set: real numbers
description: ANY value on the number line, can be positive, negative, or zero.

symbol: ℂ
common set: complex numbers
description: a combination of a real and imaginary number in the form a+bi, where a and b are real, and i is imaginary

symbol: ∅
common set: the empty set
description: self explanatory. its an empty set aka { }.

Set Notation Explained

its super common to open a math, computer science, or physics textbook and have mathematical symbols thrown at you that you never even learned how to read. here's a list of logical symbols that can help you improve your math comprehension! 

symbol: A ⇒ B
meaning: “If A, then B.” In other words, “A implies B.” In other words, “If A is true, then B is true.” 
example: x = 2 ⇒ x + 3 = 5

symbol: A ⇔ B
meaning: “A if and only if B.” In other words, “If A is true, then B is true. If A is false then B is false.”
example: x = 2 ⇔ x + 3 = 5

symbol: ∵
meaning: “Because”
example: x + 3 = 5 ∵ x = 2

symbol: ∴
meaning: “Therefore”
example: x = 2 ∴ x + 3 = 5

symbol: ∀
meaning: “For all”/“For every”
example: ∀ x such that x is an integer, x + 2 is an integer

symbol: ∃
meaning: “There is at least one”
example: ∀ x where x is an integer, ∃ y where y is an integer so that y > x

symbol: ∃!
meaning: “There is only one”
example: ∀ x where x is an integer, ∃! y where y is an integer and x-y = 1

symbol: ∈
meaning: “Is in” or “in,” depending on context.
example: ∀ x ∈ R, ∃ y ∈ R  so that y > x (R is the set of real numbers)

symbol: A := B
meaning: “Let A equal B.” or “A, which has been defined to be equal to B,” depending on context.
example: A := R (The set A is defined to equal the set of real numbers)

symbol: s.t. or |
meaning: “Such that”/“So that”
example: ∀ x ∈ R, ∃ y ∈ R s.t. y > x

there are tons of other types symbols i can go over in the future like set and function notation if you guys are interested, but these logical symbols are crucial for reading proofs. 