Category: Student Life

Recently, we caught up with current MSDS student Sepi Dibay and recent MSDS alumna Deepthi Hegde on their successful COVID-19 Hackathon from summer 2020.

Bios:

Sepideh (Sepi) Dibay immigrated to the United States in 2009 and pursued her Master of Public Health and Ph.D. in Epidemiology. She did her postdoctoral research at Fred Hutchinson Cancer Research Center and currently is interning at Amazon as a Research Scientist. Sepi has extensive experience designing/conducting research and analyzing observational and experimental data. She is also pursuing a Master of Science in Data Science at UW to enhance proficiency, and expand domain versatility.

 

Deepthi Hegde is a data scientist who is passionate about building real-time products that are scalable. She is currently with Microsoft and is a recent graduate of the Master of Science in Data Science program at UW. While at UW, she did research internships at Google and Nike, focusing on deep learning applications in computer vision. Before that, she was a researcher at Carnegie Mellon University where she worked on various machine learning projects. In her free time, she loves to mentor students on interviewing and jobs in data science.

 

What motivated you to organize the COVID-19 Hackathon?

It was 2 months into the pandemic and the situation wasn’t getting any better. We were bored of staying home and were looking for meaningful ways to contribute towards the cause in whatever way we could. We tried different channels such as volunteering for the State of Washington Health Department but since everything was new and the spread of this deadly virus was happening quickly we could not find a meaningful way to contribute.  As data scientists, we believed in the power of data in combating the situation. We thought that by coming together as a community and combining research efforts and sharing insights, we could create more impact than each of us could individually. That’s when we decided to organize an online hackathon.

 

How many participants were there in total? 

100+ students joined the competition and 42 participants made a submission.

How many teams submitted projects?

We had 13 teams submit their projects. Each team had 3-5 members.

The event was virtual because of the COVID-10 pandemic. How did the fact that it was completely online impact the event? 

This was a very new way of organizing a hackathon and required a lot of coordination and arrangements to spread the word and engage the participants. Even though in some sense the online format limited our power to collaborate in person, it definitely helped us get participation from around the world. We had several teams with members from different time-zones working around the clock. We were also able to bring in experts in the field of data science from different states to offer introductory workshops on the first day of the hackathon.

What platforms did you use to host the hackathon? Can you describe how participants and teams were able to participate virtually?

We used Slack extensively for offline communications with the participants before and during the hackathon. During the two days of the hackathon, all the workshops, events and check-ins were done via Zoom. For collaboration on the projects, we asked teams to use GitHub, which was also how we asked teams to make their submissions.

Who were the judges? 

• Tim Randolph, Associate Member at Fred Hutchinson Cancer Research Center 
• Anna Talman Rapp, Program Officer at the Bill & Melinda Gates Foundation
• Duncan Wadsworth, Data Scientist at Microsoft
• Ying Li, Chief Scientist at Giving Tech Labs

 

What workshops did you conduct? 

We conducted 2 introductory workshops on Day 1:

• Intro to NLP (natural language processing) by Grishma Jena, Data Scientist at IBM
• Intro to Time Series by Stanislav Panev, Project Scientist at Carnegie Mellon University

 

What kinds of datasets did teams use?

We provided two datasets (OxCGRT: COVID Policy Tracker, NYTimes: COVID-19 Data) for teams to explore. However in the spirit of open ended research and creativity, participants also had the option of using any other public dataset they liked and we did see several teams take advantage of it.

 

Describe the awards categories.

Track I: Best Storytelling/Data-Science Process

• Clear hypotheses and assumptions
• Exploratory data analysis
• Problem solving
• Comprehensive take-aways
• Reproducibility

Track II: Best Prediction Model

• Problem setup and metric definition
• Quality of features 
• Explanation of choice of model
• Model evaluation
• Explainability and model interpretation

Track III: Best Interactive Visualization/Dashboard

• Simplicity and ease of navigation
• Choice of encodings and colors
• Ease of understanding
• Impact and take-aways
• Documentation

Describe the winning teams’ projects below.

The Unpredictables won the prediction model category. This group investigated the impact of governmental policies on rates of COVID-19 infections in three states with the highest number of cases at that time (California, New York, and Pennsylvania).

Curious Duo won the storytelling category. This group focused on two states, Washington and Florida, for their analysis. The objective was to identify and collect tweets from the states, and identify the sentiment trends for the state-specific user and how this impacted the spread of COVID-19.

Data visualization had two winners: 

Java’s Just Coffee visualization allows the user to interact and explore COVID-related data on the number of cases/deaths and policies on which governments have focused to counteract this pandemic. This visualization also allows the user to interact with how people have responded to COVID in the United States.

JiaLiDun did a visualization to show the effectiveness of governments’ policy responses towards the COVID-19 pandemic in different countries. This group looked at three different major categories of policies: containment and closure policies, economic policies, and health system policies. Within each category, there are different levels of stringency that were also taken into consideration.

Hi Florencia! Tell us a bit about yourself.

I’m originally from Argentina. Before coming to the U.S., I worked as a Big Data Analyst for Telefonica, a telecommunications company. In this position, I developed Machine Learning models and KPIs to improve the quality of their services.

I was awarded a Fulbright Scholarship to pursue an M.S. in Data Science in the U.S., and I chose to come to the UW. In addition to being a graduate student, I’m currently a Data Science Intern with the Academic Experience Design and Delivery team at the UW-IT Department. I have the opportunity to work with an incredible team on projects that impact student persistence and pathways, building Data Science solutions to improve their academic experience.

Why did you choose the M.S. in Data Science program at the UW?

I chose the MSDS program at the UW because the curriculum is interdisciplinary and covers the breadth of data science. The program offers classes in all the areas that I was interested in learning and improving. Additionally, I really liked how the program is industry-focused, which is enhanced by its location in Seattle.

You finished your first year in the program. What has it been like so far?

I have learned a lot in my classes, from the fundamentals to more advanced topics. Even for topics that I had been exposed to previously, I’m learning the more in-depth “why” that I was missing before the program.

Another thing that has impressed me are the many career events organized by the program, where we are given tools to better prepare for the next step in our professional lives. In particular, the Technical Interview Workshop was an extremely helpful resource when preparing for summer internship interviews, and now for full-time positions.

My first year in the program was impacted by the COVID-19 pandemic. Fortunately, both the professors and the program staff were able to quickly adapt to the situation and deliver a strong remote learning experience.

You were awarded a Merit & Opportunity Scholarship. How has this scholarship impacted you?

The scholarship has positively impacted my experience in the program. It has enabled me to dedicate myself full-time to my studies, as well as take advantages of the many social activities and career events organized by the program and the university.

What tips do you have for incoming students?

I recommend building relationships with your classmates and senior cohorts and taking advantage of the career development events organized by the MSDS program and the UW.

Hi Alison! Tell us about yourself.

I grew up in Virginia in a suburb of DC. I majored in Computer Science at Brown University, but outside of my major, I was very interested in Math and Economics. During my final semester, I took an Intro to Data Science class which helped spark an interest in the field. Outside of school and work, I really enjoy being outdoors so I can often be found hiking, running, or playing soccer.

Tell us about your professional background to date.

After graduating in 2014, I moved to Seattle to work as a software engineer at Google. On my first team, I worked on a product that was then called DoubleClick Search, but has since been rebranded to Google Marketing Platform. I focused on developing a suite of chart building tools that enabled customers to create reports detailing key metrics of their ad campaigns. This was my first professional exposure to the power of analyzing and visualizing large sets of data, and I really enjoyed learning about what features our customers needed to better understand their datasets.

Currently I’m working on Google Cloud, focusing on frontend development. Initially I worked on the user interface, but more recently I’ve been focusing on front end infrastructure. This includes things like how our app initializes, handling navigation between pages, and supporting migrations to the latest technologies. The main goal of my work is to improve the reliability and performance of the application.

What made you decide to become a data scientist?

Throughout my professional career, I kept finding myself drawn towards projects that involve analyzing and visualizing data. On my current team, I’ve driven many efforts to analyze the performance of our application. Identifying the slow parts of the application will allow us to focus our efforts to improve performance. Becoming a data scientist will provide me with more tools to analyze and improve performance for our end users.

Outside of work, I’m a big fan of women’s soccer. While there is extensive analysis of the men’s game, there is a lack of coverage and analysis of women’s soccer data. It would be awesome to apply data science techniques to analyze and visualize women’s soccer data.

What attracted you to the Master of Science in Data Science program at the University of Washington?

I was attracted to the fact that the program has a part-time option while still being an in-person program. I’m really looking forward to taking what I learn in classes and applying it to problems I’m facing at work. Additionally, the program puts a lot of work into keeping the content and curriculum relevant for work in industry. The special topics classes and capstone project sound like great ways to engage with industry professionals and learn practical skills.

What aspects of the program are you most looking forward to this fall?

It has been a while since I’ve been in school so I’m looking forward to being in an academic environment again. I loved math in college but I haven’t worked with it much in the last six years so I’m looking forward to diving back into topics like probability and statistics.

 

 

 

Name: Matthew Rhodes

Undergraduate Institution: Michigan State University

Undergraduate Major: Computer Science

Tell us a little bit about yourself.

Born and raised in Detroit, I come from humble beginnings. I discovered my love for computers early in life by playing video games on my mom’s PC and my Nintendo 64. After graduating from high school, I went on to pursue a bachelor’s degree in computer science from Michigan State University. GO GREEN! I interned at Clinc during my junior year of college and fell in love with machine learning. I decided that I wanted to learn more about data science so I made the choice to attend the MSDS program at the University of Washington. I make music in my free time and I like to meditate every chance I get.

Why did you choose the M.S. in Data Science program at the UW?

I knew I wanted to pursue a career using machine learning to solve business problems. The career switch to data science was a no brainer. I also knew that the University of Washington had a very rigorous program that would prepare me for an industry career. All of the companies in the area that partner with UW were the icing on the cake.

 You are halfway through your first year in the program. What has it been like so far?

The coursework is simultaneously engaging and challenging. In addition to my classes, I’m doing research with Professor Bill Howe which takes up a lot of my time. The first two statistics courses are the toughest classes so far. My favorite courses so far are Data Visualization, Data Reproducibility and Data Management for Data Scientists.

You interned at Amazon last summer. Can you tell us a little bit about that experience?

My experience at Amazon was amazing. I interned with the AWS Ground Truth team. I learned so much about how research is conducted in industry. I also had the opportunity to apply different concepts, such as Bayesian statistics and convolutional neural networks, to industry problems. My team members always made me feel like I had a strong group of individuals I could reach out to for help. I was also lucky to have a great manager.

What tips do you have for incoming students?

I would give three tips to incoming students:

• Save every assignment. A lot of the concepts we cover in class come up during interviews and on the job.
• Find out what helps you with deal with stress. No matter your background there will be concepts introduced to you that you have never encountered before. Try your best. When things get tough, you should do something that helps relieve stress.
• Find a friend in the program. It always helps to have someone you can talk to about lectures, concepts, interviews or just to hang out with.

Name: Prakhar Agarwal

Position: Machine Learning Intern

Company: Apple Inc.

Location: Cupertino, CA.

Congratulations on your summer internship! Tell us about the position.

I worked on Knowledge Extraction for the Maps team at Apple.

My internship involved natural language processing, feature extraction and building a context-aware human intent inference. I was involved in the entire pipeline, starting with data (purification, enrichment, and matching), defining the model evaluation metric, training the Deep Learning model and serving the model as an API for downstream consumption.

I also worked with other senior engineers and scientists on a wide spectrum of machine learning and data science approaches to improving the user’s search experience.

What resources did you utilize in your summer internship search? 

In terms of resources, I used the following:

• Data Science Career Fair – Submitting your resume to the recruiters at career fairs goes a long way
• Employee referral – I reached out to my friends, cohort members, and previous colleagues with my resume to refer me for positions
• Handshake online job and internship database
• Networking at open houses

What skills from the MSDS program are you applying to the internship? 

The Data 511: Data Visualization for Data Scientists course provided me with a set of tools to effectively communicate results.

My understanding of Kernel Methods from Data 558: Statistical Machine Learning for Data Science enabled me to add non-linearity to any linear model.

What tips do you have for incoming students who are thinking ahead about summer internships? 

First and foremost, start early! Have a concise, well-written resume ready. Customize your resume for each role. You should have an updated LinkedIn. This will help you get noticed by companies.

Identify the gaps in your preparation and try to address them. If you think you lack strong coding skills, work on this problem, and fix it to the best of your ability. It is important to realize that there are no shortcuts to being prepared.