Purpose: As an undergraduate at the University of Utah, from 1993-1997, I received significant financial support from the university and from private individuals who provided scholarships for students. I am delighted to now be in a position to help out current students in a similar way.
Details: This scholarship, founded in 2004, provides $1500 each year to one undergraduate student at the University of Utah (lately the College of Engineering has been providing matching funds as well). This student is selected by the Artificial Intelligence faculty in the Computer Science department on the basis of GPA, a focus on AI or Machine Learning, and financial need. To apply, contact the CS department.Past Recipients:
I was born and raised here in the Salt Lake Valley. Both my mom and dad came to United States from Guatemala, in search of a better life for their future family. My family has always had big dreams for me and my brother. That is why I am doing all I can to make those dreams come true.
As long as I can remember I have been fascinated with computers and how they work. Little me assumed computer worked through magic, because I could not wrap my head around how they could work. Now that I am 2 years into the degree, I know how some of those magic tricks work. Knowing how some of the tricks work does not make them any less magical to me. Therefore, I look to the future with big dreams, I hope to one day have amaze the world with Artificial Intelligence since AI and machine learning is the most mysterious tricks there is!
As a computer science student at The University of Utah, I am primarily interested in the application of machine learning and artificial intelligence techniques to practical problems. I ultimately envision myself at the crossroads of computation - a field which is rapidly enabling new insights into problems where human ingenuity alone would never suffice - and biology - a field which is constantly producing more data than can realistically be analyzed at this point in time, let alone fully utilized to make personalized genomic recommendations, accurately model protein folding in search of a cure for cancer, or unravel the bewilderingly complex biological processes that enable human cognition.
To this end, I plan to study bioinformatics or computational biology in graduate school after completing my undergraduate work, ideally focusing on increasing our understanding of mental illness and hopefully contributing to more effective treatments of disorders such as depression. Utilizing my experience and skillset related to computing and engineering, and applying that to problems that could change health outcomes for millions of people both excites me and motivates me to push myself academically and individually on a daily basis.
The purpose of computing is insight, not numbers. The above statement by Dr. Richard Hamming has always resonated with me, and much of my interest in computer science stem from an aspirating to discover new knowledge from existing information. My interest deepened as I became exposed to the intertwining parts of data analysis, including data mining, machine learning and visualization. I hope to have a career in research and to contribute something meaningful to the field.
I am Victor Marrufo, originally from Venezuela, I recently graduated with a bachelors' degree in CS. What attracts me the most about computer science is how we have in the past, and will continue to improve nearly every aspect of our lives with technology, all the possibilities to create or improve on something that can makes people's lives better in any way is what excites me the most about joining this field.
My main interests are web and mobile applications development, which is the reason why I decided to work on "Therapy Connect" as my final school project, a system designed to improve communication and data access for patients and doctors. My plans for the future are to keep learning and continue to work on meaningful projects that many people can find useful.
My academic interests are to study computer intelligence in the fields of AI, ML, and NLP; as well as studying topics in math such as stochastic processes and probability, and to learn some introductory cognitive science. My career aspirations are to work in the field of computer intelligence in either software development or corporate research. I aim to find a career which ties together computer science, math, and cognitive science.
I didn't have an amazing experience that lead me to choose computer science as my major. The original reason was that my math score was pretty good and I want to use the math I learn into real life, then I choose computer science and mathematics double major. During four years studying, I had communicated with many people around me, learnt a lot of brilliant ideas and stories about changing people's life by programming and all those increase my passion on this major and I also want to use what I learn to change people's life.
When I first used a computer, I tried to tell it to do something by speaking to it, through the microphone. It didn't bother to listen. How rude!
Computers are only usable to the extent that we can successfully communicate with them. Now that they have become central to the daily work and livelihood of the general public of every developed nation on Earth, it is more important than ever that that communication take place intelligently and with ease. If it doesn't, bad things happen. Some begin to dread their work, because they must fight with their tools every day. Businesses suffer losses when clientele cannot use their products. Medical devices become useless and even harmful.
As long as usability bugs exist, there is usability research to be done. It is fortunate that Natural Language Processing (NLP) and Machine Learning (ML) have emerged as disciplines. We can use NLP to facilitate communication with computers. And we can use ML to track habits and deal with information more intelligently. These paradigms are exactly what we need to solve many pressing usability problems.
That's just something that really drives me.
A 2006 IDC study showed that in a given year people will generate more information than they have in all years in history combined. This represents a significant infrastructure problem for efficient information consumption, which we only really need to imagine a world without Google to fully appreciate. Ideally, we'd like for computers to just automatically figure out things like which websites are most important, and what we really mean when we enter some search query, but the reality is that we have to help them along in this effort. There are many challenges here, too, not the least of which is the fact that we arguably know very little about how to handle data at such a huge scale, and as a (hopefully!) PhD-bound researcher, I'm very eager to help to point the ship on this front.
- Alex's paper won the SIGIR 2017 Best Paper Award ("BitFunnel: Revisiting Signatures for Search")
I've always been interested in computing since a young age. However I did not get interested in AI until I took the AI class from Hal Daume at the University of Utah. I ended up loving the class and taking, and loving, Machine Learning the following semester. I am not sure if I will pursue ML in a career or grad school, but it is definitely one of the subjects I am most interested in. Regardless of what I end up studying I will keep learning about Machine Learning because I find it so fascinating.
I've been interested in computers as long as I can remember, but I never knew how interested I was in AI until I took the introductory course here at the University of Utah. Of course, I've always been interest in robots and things like that. I'm now taking a course machine learning, which I find both challenging and rewarding. I've long had an interest in natural languages as well, although I haven't studied any seriously yet. As I go along, I plan to study natural language processing, which would allow me to combine my interest in computer science and linguistics. I'm not sure yet what path my career will take, but I find the AI the most interesting of any specialty I've looked at, and I plan to gain as solid a foundation in the field as possible.
I was born in Beijing and immigrated to Utah with my parents when I was seven years old. I was eleven when my father first brought home a brand new, top of a line 75mhz Pentium computer. I sat down and since that day I have been in front of a monitor pretty much continuously with only a few brief interruptions to use the bathroom and see if a pie has manifest in my fridge since last I checked. I graduated high school in 2001 and after a few years of various bohemian wanderings, I settled into a respectable life as an undergraduate CS student here at the U of U. I would say my favorite course here so far would have to be algorithms as I enjoy the more abstract aspects of computer science. Currently, I plan on also getting bachelors in mathematics and I will probably end up doing something in finance, possibly after some ill advised jaunts into entrepreneurship.
I was born and raised here in Salt Lake City. I am the middle child in a family of seven children. My inspiration to enter computer science probably comes primarily from a very logical father, who has always felt the need to explain the reason behind everything to his children, and a perceptive grandfather, who provided my family with computers in the mid 80's when they were just becoming popular -- I was hooked at an early age. From 2001 - 2003 I was in Japan serving as an LDS missionary; I remember walking down the streets of Akihabara (Tokyo's electronic's district) and being fascinated at the miriad robotic devices the Japanese were developing. This may be where my interest in machine learning first developed. Since returning from Japan, I have been studying CS here at the University of Utah, and will have my Bachelor's degree by the end of the school year. It is likely that I will then enter into grad school, and continue my studies. I have taken AI, machine learning, and NLP and loved all of these courses. I still don't have concrete plans for what I would like to do for a career, but I definitely would be thrilled if I could do something working with machine learning.
Even though I knew I wanted to work with computers from a young age, I only became interested in Artificial Intelligence during my Jr. High years. Going through Jr. High and High School, I was eager to start a Computer Science degree so I would be able to learn more about AI. As I finish my degree, I plan on continuing to study AI. I am currently taking a Natural Language processing class, and will take a Machine Learning class in the Spring. My other interests involve Linguistics, and I would be thrilled to find a job involving NLP so I could work in both field. I have studied some Latin and Japanese, and would like to study Russian in the future. While I'm only fluent in English, I enjoy learning about different languages. I plan on graduating in 2007, and if I don't remain in Utah, I'd like to move back to Texas to be near my family.
I am finishing my BS in CS this semester. In the last year I have been working on my biggest interest: applying the principals of NLP to the field of law. The language of the law and its practitioners is fairly homogenous with limited and specific word usage - making it a good candidate for machine understanding. Initially I considered the possibilities of "patent understanding" to aid inventors in their search for prior art before filing a patent. I soon moved on to a more tractable problem: "license understanding." I recently presented a proposal to Professor Lee Hollaar on the subject. The proposed system would improve the usefulness of now-ubiquitous "click-wrap" licenses. I will soon present my work to David Bean, a former PhD student of Ellen Riloff and co-founder of Attensity, who has offered to make his commercial tools available in developing my system. There are a myriad of possibilities where these two fields intersect: using NLP to understand legal documents and using the law (i.e. patents) to protect inventions in NLP and AI related fields. I hope to apply my efforts in both directions. This fall I start law school at George Washington - the top intellectual property law school on the East Coast. David Bean's company maintains an office in DC, where I would hope to keep contact with the industry while I complete law school.
My name is Jacob Quist, born and raised in Salt Lake City, Utah. I have two parents and one older sister who haunted my childhood with relentless teasing and taunting. Growing up, I was the obscure but overly witty computer geek who was always called by friend's parents to fix their computers. My interests are not limited to computers alone. My aspirations also include driving a Harley on every continent. I'm also very interested in the Japanese Language & Culture. I'm doing the BS/MS program and plan to graduate 2006 (maybe?). I hope to work with AI and break the current AI barriers.