Hector Quintero
Software Developer
Austin, Texas, USA
software.developer.hector@gmail.com
512-621-4730
Languages
English
Java
C
C#
VB.NET
SQL
About me
Hi! My name is Hector Quintero. I recently graduated with a Bachelor of Science in Computer Science. I wanted to travel before starting my career, and so I visited Munich, Prague, Bratislava and Vienna. Now I am in Austin, Texas ready to start as a software developer. My passions do not actually lie in web development, instead I love interesting algorithms and data structures. One of my favorite projects in college was using a genetic algorithm to solve a version of the knapsack problem.
If you'd like to know more about my experiences,
click an icon above.
Software Development Intern
Dematic Corporation
Jun 2016 - Aug 2016
I modified an internal emulator to enhance testing capabilities. This allowed testing to be conducted without needing to run the entire material handling system.
Skills improved:
| C# | |
| Multi-threading | |
| Reflection | |
| JSON | |
| Asynchronous callbacks |
Programmer Analyst Intern
Infosoft Group Inc.
Sep 2015 - May 2016
I created, maintained and debugged over 300 web crawling programs that retrieved job information.
Skills improved:
| VB.NET | |
| SQL | |
| HTML | |
| XML | |
| CSS | |
| Javascript | |
| Regular expressions | |
| Version control |
Software Development Intern
Dematic Corporation
Jun 2015 - Aug 2015
I developed a process within a multi-process material handling system to interface with a customer printer.
Printer received commands in a special programming language that had little documentation and community.
To pass my knowledge on to my team past my summer internship, I created a how-to guide on how to create commands for printer, in case customer's needs changed.
Skills improved:
| C | |
| SQL | |
| Material handling systems | |
| End-user interaction | |
| Code documentation | |
| Version control |
Tutor
University of Wisconsin - Milwaukee
Sep 2014 - Dec 2016
I tutored individual students as well as in groups of up to 10 in computer science and chemistry.
Skills improved:
| Recursion | |
| Object Oriented Design | |
| Algorithms/Data Structures | |
| Communication | |
| Coaching |
Let's play a game, with a genetic algorithm.
You: Chief Pharmaceutical Engineer.
Job: Choose how many of each leaf below will go into your pill.
Pills should be as low in weight as possible while
still obtaining 100 in Magnesium, Calcium, Sodium and Lithium.
What is the lightest complete pill you can devise?
Could a genetic algorithm devise a lighter complete pill?
Weight
Magnesium
Calcium
Sodium
Lithium
Once you're done designing your pill, fire up the genetic algorithm below.
Human's pill:
Weight
Magnesium
Calcium
Sodium
Lithium
Weight
Magnesium
Calcium
Sodium
Lithium
Genetic algorithm's pill:
Weight
Magnesium
Calcium
Sodium
Lithium
Weight
Magnesium
Calcium
Sodium
Lithium
Mutation rate:
Mutation step:
Selection strength:
Random individuals:
Elite copy:
Mutation rate effects how often genes are mutated. 0 means never, 1 means always.
Mutation step effects how much a gene is mutated, when it is. 3 is a small change, 20 is a huge change.
Selection strength effects how likely more "fit" individuals are chosen to pass on genes. .125 means the most fit is chosen most often. 1 means all individuals have equal chances.
Random individuals effects how many random individuals are introduced each generation.
Elite copy effects how many of the most "fit" individuals are copied into the next generation.
To summarize, the genetic algorithm above is trying to find the lowest weight pill that
meets 100 in each of the other categories, Calcium, Magnesium, Lithium and Sodium.
It's method of success models natural selection and genetics.
A single line of leaves above represents 1 individual. An individual's genes can be interpreted
as the number of leaves to include of each type.
By combining individuals that meet the target, of lowest weight with at least 100 in each category,
the algorithm can iteratively find better individuals. By repeatedly combining individuals, with added mutations,
the algorithm is able to optimize to find a very low weight "complete" pill.
Education
University of Wisconsin - Milwaukee
Dec 2016
Bachelor of Science in Computer Science