Lessons Learned From Fatal Car Crash Data

Motor vehicle travel is a major means of transportation in the United States, yet for all its advantages, each year fatal motor vehicle crashes in the U.S. lead to an estimated societal burden of more than $230 billion from medical and other costs [1]. Motor vehicle crashes are also the leading cause of death for persons every age from 5 to 32 years old [2]. In this project, we deep dived into the fatal car crash records in the U.S. in year 2016, collected by National Highway Traffic Safety Administration (NHSTA) [3] and encoded using the government’s Fatality Analysis Reporting System. By wielding this dataset and related research, we developed an interactive essay trying to thoroughly explore the top risk factors that are highly correlated to fatal motor vehicle crashes.

To read the interactive essay in your web browser, visit here.

The report detailing our methodologies and poster summary of this project are also available.

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Snippets from the report:

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The Fall of The Simpsons

PDF version.

This visualization is designed to show how the once most beloved TV show declined in its reputation and popularity over time and why this happened.

Tools used: Python matplotlib library for generating time series charts, Google Sheet for the heatmap, and Sketch to put together the visual design.

Four HCI Design Studio Projects

CS 247: HCI Design Studio at Stanford aimed at deepening students’ fluency with design for interactive technology. It provided an engaging learning environment in a design studio setting, connecting the concepts about iterative design practice with four hands-on projects.

The goal of this course is to enable students:

  • Become comfortable critiquing, receiving critiques, and iterating on their designs in an advanced studio environment.
  • Perform needfinding with community members and stakeholders that goes beyond surface-level observations and produces deeper insights and needs.
  • Communicate design ideas: visually through sketching and mocks, through show-and-tell reports to studio, and through presentation to large groups.
  • Identify the most appropriate question to answer for their design, and to rapidly create a prototype to do so.
  • Utilize current tools and technologies to produce high-quality designs.

Here I’m publishing the details about all of the four course projects and my design deliverables:

Project #1 – The Problem With Lunch – Final Deliverable

The Prompt:

Every day for five days, go to a different eatery on and off campus during the height of the lunch rush. You do not have to eat there, but you do need to note (in your sketchbook) the flow of people and any problem areas or areas of opportunity.

Skills involved:

  • Observation: Observations are diverse, take a point of view, and are captured effectively in sketches.
  • Synthesis: Problem statement synthesizes the observations into a novel point of view.
  • Ideation: Micro ideas, index card ideas, and final detail idea are clearly communicated and effectively address the problem statement.

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Project #2 – Change Is Hard – Deliverable

The Prompt:

Design a system that helps people achieve the behavior change goal. How do you intercede with the user’s motivation, ability, and trigger (http://www.behaviormodel.org/)?

Part 1: Research the Opportunity Space: form a team, pick a behavior design goal, and launch a diary study.

Part 2: Model the System: synthesize the results of the study into a journey map and plan out the flow of a design informed by the results.

Part 3: Specify the Interaction: refine this flow into a prototype that you can test

Part 4: Field Study: perform a field study of the prototype

Skills involved:

  • User ResearchDiary study uncovers nontrivial insights about the habit.

  • Flow and Interaction DesignDesign represents creative, effective intervention on the habit.

  • Field Study: Field study uncovers nontrivial insights about the design the habit.

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Project #3 – Redesigning Alma – Deliverable

The Prompt:

It is a rare and lucky designer who gets to start a design from scratch. In this project, we’ll take a current application and use visual design and insights from testing to make it better. “Le mieux est l’ennemi du bien” – Voltaire.

Part 1: Evaluate:  Do usability testing on our project and two competitors. What is wrong with the current system? Is there a better way to do the job they are setting out to do? Bonus: interview the creators.

Part 2: Ideate: Come up with multiple “hypotheses” about better designs, including a Dark Horse: an idea so crazy no one thinks it will work.

Part 3: Iterate:  Use heuristics and critique to bring this final design to an exquisite perfect. Or at least make it pretty darn good. First, you’ll make a medium fidelity prototype, using software such as InVision or Marvel. Add your new brand look and feel to this prototype! Then run another usability test with 5-8 people to make sure you’ve improved things. See RITE method to quickly evolve our leading contenders.

Part 4: Elevate: Take a polish pass to make sure the new version is as beautiful as it is usable.

Skills involved:

  • User ResearchUsability Study & RITE uncovers nontrivial insights and leads to significant improvement.

  • Create Solutions: Design represents a creative, effective resolution of problems.

  • Execution: Design has high polish: easy to use and easy on the eyes.

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Project #4 – Planning A Vacation (Final Design Challenge) – Deliverable

The Prompt:

We are expecting that the design challenge should take you 4-6 hours and you will go through at least one full cycle of the design thinking process. The main skill being tested in this final is your ability to reason through the large set of methods and skills you have acquired in the course thus far, and apply them in the correct circumstances and for the correct goals. Not every method is appropriate for the type of problem you are solving or the data you have gathered. This is a test of how you make decisions about which approaches to apply and your ability to motivate them.

Skills involved:

  • PlanningPlan is a strong, comprehensive approach to solving the selected design challenge.

  • Decision-making: Decisions and selected processes are effective, appropriate and thoughtfully selected with strong understanding of what is being done at each step and why.

  • Design process execution & Documentation: Design deliverables are exemplary. Every artifact shared shows a deep understanding of the goal. Report is thorough and does very effective job in clearly communicating what was done and why.

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Software Environment Incident Analytics

Incident Analytics was an intelligent incident management tool we developed for AppDynamics DevOps customers during a Hackathon.  AppDynamics customers were able to configure health rules based on a few key metrics of their interest and get alerted when these metrics saw unexpected patterns. However, without knowing about historical data, DevOps may spend hours figuring out a resolution when someone had solved a similar issue before. In this project, we built a tool based on machine learning algorithms to automatically identify root cause analyses (RCAs) for incidents — this task previously would take hours if not days of manual work. The solution we built helped customers understand the context around incoming incidents and get to resolution much faster. We applied machine learning to grouping incidents together, correlating incidents with RCAs, and analyzing if incidents were triggered by a global issue. This constitutes a big improvement over current AppDynamics solution which provides zero out-of-box analytics. 
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Incident Analytics – User Interface
Incident Analytics – UX Prototype


Interactive big data visualization for app performance monitoring

For the years out of college, I’ve been working as a software engineer (focusing on UI) on the core APM team at AppDynamics (now part of Cisco) based in downtown San Francisco, California. Application Performance Management (APM) is a technology that provides end-to-end business transaction-centric management of complex and distributed software applications. Auto-discovered transactions, dynamic baselining, code-level diagnostics, and Virtual War Room collaboration ensure rapid issue identification and resolution to maintain an ideal user experience. At AppDynamics, I developed complex yet performant AngularJS-based web application UI providing rich user interaction with a wealth of APM data in large scale. I’ve been made seasoned in all phases of the software product lifecycle: designing, prototyping, developing, maintaining, test automation, and shipping out useful features to our customers.

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Automatic detection of epileptiform events in EEG recordings

An electroencephalogram (EEG) is the most important tool in the diagnosis of seizure disorders. Between seizures, epileptiform neural activities in EEG recordings occur in the forms of spikes or spike-and-slow wave complexes. Seeking for an automated EEG interpretation algorithm that is well-accepted by clinicians has been a research goal stretched for decades. As a participant in an NSF-funded Research Experience for Undergraduates (REU) program hosted at Clemson University School of Computing, I continued on this endeavor to develop an automated system that detected epilepsy-related events, in real-time, from scalp EEG recordings.

In finding the optimal algorithm for this purpose, I constructed a multi-stage processing pipeline. In the first stage, I cleaned up the clinic data gathered from 100 epileptic patients and treated them with cross-validation. Next, I used wavelet transformations to generate the features for study from EEG signal in a “sliding window” approach. I then applied machine learning algorithms and analyzed their performances in classifying data patterns into epileptiform activities versus other activities. For this stage I also explored the use of hidden Markov model to fit the time sequence in which epileptiform events occurred. In the final step, I further separated target eplieptiform events from noise signals, by applying a statistical model locally, and stitched outputs from different signal windows together. – source code

The automation results were highlighted these findings in realtime on the eegNet (standardized EEG database developed by Clemson) web interface.

Automatic detection of epileptiform events in EEG recordings – poster

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The Open Science Investigation

Barriers for scientists to practice open science prevail due to a range of cultural and technological reasons. This undergraduate thesis, developed under the guidance of the Center for Open Science, seeks to understand the incentive structure for open science from a sociotechnical perspective, and attempts at a software solution to facilitate its implementation. The research paper, Incentive structure for Open Science in Web 2.0, elucidates how current reward system needs to be changed to encourage more practices of open science: to create incentives for researchers to open up their research materials for the broader community, organizations need to provide researchers with intrinsic rewards, proper credit allocation, and tangible career benefits. In the technical portion of the project, Designing Data Visualizations for Open Science, I prototyped an interactive research exploration and organizing tool for the Open Science Framework. The thesis contributes to this collective effort towards open science by making the creation of incentives as an explicit design goal for open science web applications. – thesis cover   |  STS paper