CKIDS is hosting DataFest Fall 2020 in collaboration with the GRIDS data science student association. These projects were proposed by USC faculty and researchers through an open call for project proposals.
Below is a short overview of all the projects.
Students can learn more about the projects and sign up to participate during the DataFest Fall 2020 kickoff event.
New DataFest Fall 2020 Projects
1. Worldwide Survey Estimates of Maternal Bereavement
Infant and child mortality rates have been steadily declining worldwide over the last fifty years. Without reservation, these trends represent good news for children and for their parents, but the link between child mortality and parents’ experiences, however, remains loosely defined. Documenting global inequality in maternal bereavement offers a window into how health disparities directly affect the lives and well-being of mothers. In this project, we will offer the first, global analysis of the prevalence of bereaved mothers by leveraging data collected between 2010 and 2018 from 168 countries. I request student support to expand current survey coverage. Student(s) will work to identify and access public-use, nationally-representative reproductive history survey data for select European, Asian, and Latin American countries to supplement current data coverage, and to offer direct estimations to compare to indirectly derived ones based on current fertility and mortality levels. Students will work to adapt code used for other surveys to generate descriptive statistics of the prevalence of ever bereaved mothers in each country. Students will also work to improve and supplement the illustration of key study findings.
SKILLS NEEDED: Stata, Very basic stats (purely descriptive).
WHAT STUDENTS WILL LEARN: Students will develop appreciation for the potential to take a standard, heavily analyzed data source and to analyze the data from a new vantage point in order to offer a distinct view of a social phenomenon of scientific interest. Students will become familiar with the process of identifying and collating survey data from multiple countries, and the importance of documenting variation and similarities in data sources across settings. Finally, students will have the chance to learn and contribute to data reduction efforts to effectively present a large amount of cross-country data in a way that best distills the key findings of interest.
ADVISOR:
- Emily Smith-Greenaway, Dornsife College of Letters, Arts, and Sciences
STUDENT PARTICIPANTS:
- Aditi Singh, B.A. student in Data Science & Economics, Viterbi School of Engineering & Dornsife College of Letters, Arts and Sciences
- Madeleine Thompson, M.S. student in Applied Data Science, Viterbi School of Engineering
- Malika Seth, M.S. student in Computer Science, Viterbi School of Engineering
- Asumi Suguro, M.S. student in Applied Data Science, Viterbi School of Engineering
2. Investigations of a Data Science Online Community
The Kaggle.com competition ecosystem is a rich and active community with a designed Progression System that uses performance medals to rank and differentiate users into tiers. However, winning performance medals in Kaggle is more complex than it appears. Users are bound by the available competitions, characteristics of the competition’s problem statement, the quality of their software submissions, and the quality of other competitors (including collaborators). With these factors, one user’s earned “Gold” medal from one competition may have required more effort and a higher quality solution than another user’s earned “Gold” medal in a different competition. This project has great potential to learn about open competitions in data science. Some example questions are: What features help predict whether a user will win a medal in a competition? How can users be clustered and differentiated from one another using their competition patterns and medal-winning solutions? How quickly (in days) will a user win their next competition medal? What is the probability that a user will assemble a team for a competition? What are features that predict high-performing teams? What features help generate teammate recommendations?
SKILLS NEEDED: R or Python, Introductory statistics, preferably machine learning.
WHAT STUDENTS WILL LEARN: The students will learn how to build predictive models of user outcomes (e.g., winning a competition, choosing to collaborate, etc.).
ADVISOR:
- Marlon Twyman, Annenberg School for Communication
STUDENT PARTICIPANTS:
- Sara Melotte, M.S. student in Computer Science, Viterbi School of Engineering
- Devendra Swami
- Jacob Bickman, M.S. student in Applied Data Science, Viterbi School of Engineering
- Jae Young Kim, M.S. student in Applied Data Science, Viterbi School of Engineering
- Kevin Tsang, M.S. student in Applied Data Science, Viterbi School of Engineering
3. The aging individual brain
Deep learning models are now able to predict how an individual’s face will age in a very realistic manner — ensuring key individual features, for example eye color, are maintained, while more age related features, such as the texture of skin, are altered to be representative of a desired age group. However, little work has been done to try and predict how an individual’s brain will age. Such models may be able to help predict early signatures of neurodegenerative disorders. The goal of this project will be to test several models to realistically predict how a given individual’s brain will look at any age in mid to late adulthood. Either or both deep learning based and image processing based methods would be encouraged. Students will work with a dataset of over 20,000 brain scans of individuals aged 45-80, approximately 1000 of whom have a scan again after two years. This is currently an active project in the lab and students will join researchers already working on this problem to further explore and improve methodology.
SKILLS NEEDED: Python, git; Also beneficial may be: some familiarity with deep learning / some familiarity with 3D image processing/filters, statistics.
WHAT STUDENTS WILL LEARN: Working with biomedical images (brain MRI), 3D DL approaches, image normalization and registration/correspondence, neuroscience applications related to aging and neurodegeneration, working with big complex datasets.
ADVISORS:
- Neda Jahanshad, Keck School of Medicine
STUDENT PARTICIPANTS:
- Aleck Cervantes
- David Lin, M.S. student in Computer Science, Viterbi School of Engineering
- Shunlin Lu, M.S. student in Electrical Engineering, Viterbi School of Engineering
- Tianyu Zhu, M.S. student in Electrical Engineering, Viterbi School of Engineering
- Yichao Zhu, M.S. student in Healthcare Data Science, Viterbi School of Engineering & Keck School of Medicine
4. Mapping the Uncanny Valley
While many stories involve the friendly and familiar, scary stories across cultures, from Hamlet to Yotsuya Kaidan to Siren Head involve beings that are almost—but not quite—human. Can these stories give us insight into the “nearly-human” uncanny valley? And are the most popular stories at the nadir of this valley? In this project we aim to explore the uncanny valley through analysis of several thousand stories posted on Reddit posted over a decade. These data contain stories that cross a range of topics, and include user comments and story scores. We will explore the prevalence of the monsters over time, and explore whether there is some optimal characteristics of these monsters that make them so scary. While some research has explored the uncanny valley for images, the research is limited and virtually unexplored in text format. The students will build on initial work by the advisor to apply NLP methods to these texts and improve upon existing initial results.
POINTERS: Data on stories that we will analyze, including creepy stories, are gathered from Reddit and can be accessible via the Pushshift API (for those who know Python and pip, just type “pip install psaw”).
SKILLS NEEDED: Python and an interest in NLP (deep knowledge of NLP is not needed – the tasks can be learned on the go). Optional skills are knowledge of nltk, keras, and genism libraries.
WHAT STUDENTS WILL LEARN: Students will combine fields of computational social science, NLP, and other subfields of AI to analyze large text datasets. They will explore a range of possible tasks including classification and sentiment analysis (e.g., what separates “creepy” and not-so-creepy stories), text embedding, and clustering elements of a story, i.e., separating information on a cryptid from information on the protagonist. The broader goal will be to better understand a deep psychological problem, the problem of the uncanny valley, that risks inhibiting the goals of human-computer interaction. When students better understand what makes something creepy, they can explore how AI can avoid the uncanny valley and appear familiar, friendly, and safe to the public at large.
ADVISORS:
- Keith Burghardt
STUDENT PARTICIPANTS:
- Yuchen Zhang, M.S. student in Computer Science, Viterbi School of Engineering
- Nai-Cih Liou, M.S. student in Applied Data Science, Viterbi School of Engineering
- Haripriya Dharmala, M.S. student in Computer Science, Viterbi School of Engineering
- Sakshi Goel, M.S. student in Computer Science, Viterbi School of Engineering
- Raveena Kshatriya, M.S. student in Computer Science, Viterbi School of Engineering
5. Mapping the impacts of climate change across LA County
We are increasingly hyper-aware of the effects of climate change. But we are not always aware of how hyper-local those effects can be. Across the 2,500 square miles of Los Angeles County, the impact of climate change is playing out in different ways. For example, some areas are experiencing more frequent spikes in extreme temperatures, while others are not. We propose a project that would fuse together several different datasets in order to map how temperature changes and other variables are hitting some corners of Los Angeles harder than others. Often, these areas are inhabited by people facing numerous other inequities, such as poor healthcare access. By examining several years’ worth of hourly average temperatures from thousands of spots across Los Angeles County, and combining that with other datasets, such as tree cover, cases of asthma, and so forth, it is possible to create an interactive map that illustrates where the impacts of climate change are most acute. This project would be published by Annenberg’s Crosstown publishing outlet and would be distributed widely. The project would have immediate practical applications and could inform policy decisions on issues such as where to place parks and green spaces.
SKILLS NEEDED: SQL, R, GIS.
WHAT STUDENTS WILL LEARN: The students would learn how new meanings emerge from combining different datasets. They would also gain experience in working in teams, mapping, and creating dynamic visualizations. They would be able to work with faculty and PhD students who are experts in this. However, most of all, they would learn how their work and their skills can have practical impact for millions of people living in Los Angeles.
ADVISORS:
- Gabriel Kahn, Annenberg School for Communication and Journalism
STUDENT PARTICIPANTS:
- Simon Khan, M.S. student in Computer Science, Viterbi School of Engineering
- Vinith Angadi, M.S. student in Analytics, Viterbi School of Engineering
- Yingxi Lin, M.S. student in Applied Data Science, Viterbi School of Engineering
- Shalini Mustala, M.S. student in Public Policy Data Science, Viterbi School of Engineering & Sol Price School of Public Policy
6. A framework for enabling software comparison and classification
The number of scientific products, including scientific software, has been steadily growing in the last years. This growth makes it difficult for researchers to understand all the latest code and publications available. A great body of research has attempted at classifying similar papers and literature. However, there aren’t to date good approaches for finding similar or related code. In this project, the students will analyze different unsupervised methods to find scientific software similarities based on a) An automated analysis of their dependencies; b) By classifying the main functionality of software components.
SKILLS NEEDED: Python, machine learning/sklearn, data manipulation.
WHAT STUDENTS WILL LEARN: Build and extend a corpus to solve a data science problem (in this case, using unsupervised and supervised methods for clustering and classification respectively); Think about different ways to organize the data to solve the problem; Train classifiers to produce alternative results; Build a testbed for comparing results in a fair manner and without overfitting. The students will also have to learn to post-process their results in order to incorporate them to use them in an application to create software comparisons.
ADVISOR:
- Daniel Garijo, Viterbi School of Engineering
STUDENT PARTICIPANTS:
- Yi Xie, M.S. student in Applied Data Science, Viterbi School of Engineering
- Bin Zhang, M.S. student in Applied Data Science, Viterbi School of Engineering
- Mohan Krishna Thota
- Param Bole, M.S. student in Computer Science, Viterbi School of Engineering
7. Integration of Frame Semantics to Cyber Ontologies
Cyber ontologies such as STIX and ATT&CK can represent complex relationships between cyber threat actors, attacks and infrastructure. While such representations are conducive to interoperability between systems, they are often unwieldy for human cyber analysts to deal with directly. Conversely, Natural language generation (NLG) frameworks like FrameNet represent language in a structured manner, but frame specifications are often not specific enough for specialized domains (such as cyber security). Leveraging and combining the semantic structure of both forms can create a tool that can translate cyber threat data in standard interoperable formats (such as STIX) to human-readable reports, via existing NLG frameworks. Working on a project such as this provides an opportunity for significant impact, as the fusion of these two structures could greatly increase both the adoption and the utility of cyber threat ontologies.
POINTERS: STIX: https://oasis-open.github.io/cti-documentation/stix/intro FrameNet: https://framenet.icsi.berkeley.edu/fndrupal/
SKILLS NEEDED: Data science (Python), OSINT/cybersecurity, or computational linguist (NLG/NLP) skills, and a great attitude!
WHAT STUDENTS WILL LEARN: Students will learn about the different forms of cyber threat ontologies, such as STIX or ATT&CK, and will learn aspects of Natural Language Generation tools such as FrameNet.
ADVISOR:
- Jeremy Abrahamson, Viterbi School of Engineering
STUDENT PARTICIPANTS:
- Folk Narongrit, M.S. student in Electrical Engineering, Viterbi School of Engineering
- Carol Varkey, M.S. student in Cybersecurity Engineering, Viterbi School of Engineering
- Francis Sun
8. Brain morphometry from contrast-enhanced T1-weighted brain MRIs
Cancer remains the second leading cause of death in the US. However, recent advancements have increased cancer survivorship, now numbering tens of millions. Given this, there is tremendous interest in studying cancer-related cognitive impairment (CRCI) and CRCI due to chemotherapy or “chemobrain”, can afflict up to 78% of survivors. The neural substrates of CRCI are unknown and understanding this may improve survivors’ quality of life. The CRCI neuroimaging literature is still in its infancy and these studies have used small sample sizes from traditional research-dedicated nCE scans. Because conducting well-powered neuroimaging studies is very expensive, adapting clinical CE T1w scans could prove useful for CRCI and many other diseases like dementia. The promary objective of this project is to develop a novel deep learning method to generate nCE images from acquired CE T1w scans to allow accurate brain morphometry and be a plentiful source of inexpensive neuroimaging data.
SKILLS NEEDED: AI methods, some familiarity with MRI.
WHAT STUDENTS WILL LEARN: Advanced MRI processing techniques, clinical research methods.
ADVISORS:
- Mark Shiroish, Keck School of Medicine
- Neda Jahanshad, Keck School of Medicine
- Paul Thompson, Keck School of Medicine
STUDENT PARTICIPANTS:
- Ming Lyu, M.S. student in Computer Science, Viterbi School of Engineering
- Danielle Sim, M.S. student in Applied Data Science, Viterbi School of Engineering
- Devansh Shah, M.S. student in Computer Science, Viterbi School of Engineering
- Ting Fung Lam, Ph.D. student in Chemistry, Dornsife College of Letters, Arts and Sciences
9. Social Graph Analysis and Attribution of Software Exploit Contributors Using GitHub
Attribution of cyber threat actors is an increasingly important and difficult problem. One potential mitigation is the early detection of potential threat actors via analysis of open-source intelligence (OSINT). This project will analyze the social graph of users who contribute to, follow, star, and otherwise interact with proof-of-concept CVE implementations and other relevant potentially malicious (e.g. software vulnerability) repositories on GitHub.
POINTERS: https://cve.mitre.org
SKILLS NEEDED: Data science (Python), graph analysis, interest in cybersecurity.
WHAT STUDENTS WILL LEARN: Data acquisition skills, social graph analysis, cybersecurity domain knowledge.
ADVISOR:
- Jeremy Abrahamson, Viterbi School of Engineering
STUDENT PARTICIPANTS:
- Atharva Rishi, M.S. student in Computer Science, Viterbi School of Engineering
- Erica Xia, M.S. student in Applied Data Science, Viterbi School of Engineering
- Erin Szeto, M.S. student in Applied Data Science, Viterbi School of Engineering
- Jiaxin Liang, M.S. student in Applied Data Science, Viterbi School of Engineering
- Kshitij Gupta, M.S. student in Computer Science, Viterbi School of Engineering
- Nghi Le, M.S. student in Computer Science, Viterbi School of Engineering
10. Machine Learning to Analyze Rock Microstructures
Students will analyze images from optical microscopes that reveal features of materials and microstructures using machine learning techniques. These images have been collected by geologists, who use them to study the rock samples that they collect in the field and determine their properties and origins. We have a baseline system already implemented, and the goal is to improve it with new machine learning techniques, guided by the insights of our collaborating geologists.
SKILLS NEEDED: Machine learning for image analysis in Python.
WHAT STUDENTS WILL LEARN: Deep learning skills in a challenging practical application for image analysis.
ADVISOR:
- Yolanda Gil, Viterbi School of Engineering
STUDENT PARTICIPANTS:
- Abhivineet Veeraghanta, M.S. student in Computer Science, Viterbi School of Engineering
- Bolong Pan, M.S. student in Applied Data Science, Viterbi School of Engineering
- Bryan Beh, M.S. student in Computer Science, Viterbi School of Engineering
- Hanzhi Zhang, M.S. student in Applied Data Science, Viterbi School of Engineering
- Feilong Wu
11. Generation of a Sports-based Introductory Data Science Curriculum to Increase Participation of Underrepresented Groups in STEM
As the requirements for success in the workforce become increasingly technical, there is a commensurate need for curricula that can engage and capture the imagination of students, especially those from traditionally underrepresented groups in STEM. One way to reach these groups is via curricula that appeals to contexts in which they’re familiar and engaged, such as sports. To that end, this project will explore the development of a sports-based introductory data science curriculum with the goal of engaging students who might otherwise not be interested in pursuing data science as a career. Students will work on generation of illustrative code examples/problem sets in Python using sports examples.
SKILLS NEEDED: Python, education, pedagogy, social science.
WHAT STUDENTS WILL LEARN: How to build example data and problem sets, record linkage and normalization.
ADVISOR:
- Jeremy Abrahamson, Viterbi School of Engineering
STUDENT PARTICIPANTS:
- Everest Law, Ph.D. student in Physics, Dornsife College of Letters, Arts and Sciences
- Sushmitha Ravikumar, M.S. student in Computer Science, Viterbi School of Engineering
- Zhongying Wang, M.S. student in Spatial Data Science, Viterbi School of Engineering and Dornsife College of Letters, Arts and Sciences
12. User-centered building design preference assessment to develop data-driven interactive architectural design guideline models
In many architectural designing scenarios, architects and clients inevitably spend a lot of time determining design agreements due to a lack of understanding about the client’s design needs and preferences. An architectural design process could be significantly expedited and simplified if modeling software can accurately extract the user’s preferred design features and integrate them into the design process. In this project, we addressed the challenges of demonstrating a stochastic model with the consideration of the user’s physiological responses and subjective design perceptions by using data analytic methods. This technical principle exploited personal design preferences that would adopt them to the design process to effectively complete an architecture project.
SKILLS NEEDED: Statistics, Machine Learning, Python, R (or WEKA or sklearn).
WHAT STUDENTS WILL LEARN: Students will learn technical principles of human physiological signals per psychological perception and the signal interpretation skills to estimate and apply human psychological perception as a function of the physiological signals to the architectural/construction design process.
ADVISORS:
- Joon-Ho Choi, School of Architecture
STUDENT PARTICIPANTS:
- Muhammad Oneeb Ul Haq Khan, M.S. student in Computer Science, Viterbi School of Engineering
- Adwaita Jadhav, M.S. student in Computer Science, Viterbi School of Engineering
- Rosy Zhou, M.S. student in Applied Data Science, Viterbi School of Engineering
- Yuka Kaku, M.S. student in Applied Data Science, Viterbi School of Engineering
13. Detecting Biases in College Football Recruiting
College football recruiting is big business. This project aims to determine if there are biases in who and how college football coaches recruit players. By creating a comprehensive data set of college recruits and integrating relevant data with current socioeconomic markers (i.e. census data) we hope to determine if there are patterns in who and where football coaches recruit their players, regardless of talent.
POINTERS: Rivals.com football recruiting https://n.rivals.com/prospect_rankings/rivals250/2021 SafeGraph census data: https://www.safegraph.com/open-census-data
SKILLS NEEDED: Data science (Python), [computational] social science, economics, mapmaking.
WHAT STUDENTS WILL LEARN: To process, clean and link data sets, and to integrate and analyze socioeconomic markers (i.e. Census data) with sports data.
ADVISORS:
- Jeremy Abrahamson, Viterbi School of Engineering
STUDENT PARTICIPANTS:
- Saurabh Jain, M.S. student in Applied Data Science, Viterbi School of Engineering
- Jackie Fan, B.S. student in Computer Science & Business Administration, Viterbi School of Engineering & Marshall School of Business
- Akansha Das, M.S. student in Computer Science, Viterbi School of Engineering
Continuing Projects from DataFest Spring 2020
1. Tracking health and nutrition signals from social media data
This project will explore the ability to track real-life health and nutrition signals from social media data, focusing on data from Instagram and Foursquare. We will investigate the quality of Instagram posts as a source of data for measurements of dietary patterns and nutrition quality, focusing on spatial, textual, and (*new in this semester*) image content of posts linked to food outlets in Los Angeles, as well as nutritional content analysis of menus available online. Multiple aims will be investigated in this project, including: scraping data from social media; NLP of tag, comments, and menu data; image analysis; predictive models and social network analysis; and more. Also new in this semester: “ground truth” data on dietary patterns of LA residents will be available, enabling validation of dietary measures and predictive models built from Instagram posts.
The project will build on the DataFest 2019 project, and will expand the scope to actually access up-to-date data from Instagram, in particular: data with images, the underlying social connections / social network, and of course more timely (which requires data scraping).
SKILLS NEEDED: Programming in Python or R; machine learning; statistical analysis Optional: Social network analysis, image analysis, NLP, sentiment analysis
WHAT STUDENTS WILL LEARN: Tracking real-life health signals from social media data; evaluating its quality and representativeness from a health perspective; Spatial statistical analysis using big data, combined from various sources (social media data, official public health statistics); Building predictive models for public health; Possible experience to participate in writing conference abstracts and journal papers.
ADVISORS:
- Andrés Abeliuk, Viterbi School of Engineering
- Abigail Horn, Keck School of Medicine
- Kayla de la Haye, Keck School of Medicine
- Yelena Mejova, ISI Foundation in Turin, Italy
STUDENTS:
- Abhilash Karpurapu, M.S. student in Applied Data Science, Viterbi School of Engineering
- Erica Xia, M.S. student in Applied Data Science, Viterbi School of Engineering
- Iris Liu, M.S. student in Computer Science, Viterbi School of Engineering
- Spoorti Nidagundi, M.S. student in Computer Science, Viterbi School of Engineering
2. Digital Democracy: Using Social Media to Improve Political Discourse
Politicians in modern democracies across the world have eagerly adopted social media for engaging their constituents, entering into direct dialogs with citizens. From the perspective of political actors, there is a need to continuously gather, monitor, analyze, and visualize politically relevant information from online social media with the goal to improve communication with citizens and voters. The goal of this proposal is to create a tool that enhances interaction and dialogue between political actors and their followers. This will be achieved by creating compact and comprehensive summaries that aggregate and visualize common narratives, thus, reducing the cognitive load required to read all the messages and streamlining the dialogue experience.
POINTERS: We will be using Twitter data from the current US presidential campaign.
SKILLS NEEDED: programing, NLP experience.
WHAT STUDENTS WILL LEARN: to implement state-of-the-art NLP techniques, research skills, and teamwork skills.
ADVISORS:
- Andrés Abeliuk, Viterbi School of Engineering
STUDENTS:
- Alex Spangher, Ph.D. student in Computer Science, Viterbi School of Engineering
- Yash Shah, M.S. student in Computer Science, Viterbi School of Engineering
- Swetha Thomas, M.S. student in Electrical Engineering, Viterbi School of Engineering
- Hongyu Li, M.S. student in Analytics, Viterbi School of Engineering
- Raveena Kshatriya, M.S. student in Computer Science, Viterbi School of Engineering
- Abhi Thadeshwar, M.S. student in Computer Science, Viterbi School of Engineering
3. Characterizing the counter-narratives of climate change
Top climate scientists post their findings and views regularly on social media. These very scientists are met with tweets from those with opposing views, often containing vitriolic and false information. It is important that we can identify and characterize these tweets to understand the counter-narratives of climate change. We will address topics including false information, bot campaigns, and harassment.
POINTERS: Students will collect tweets from Twitter.
SKILLS NEEDED: Data collection, basic classification.
WHAT STUDENTS WILL LEARN: Data scraping, Machine learning, Text classification, Computational social science
ADVISORS:
- Fred Morstatter, Viterbi School of Engineering
- Deborah Khider, Viterbi School of Engineering
STUDENTS:
- Abhilash Pandurangan, M.S. student in Computer Science, Viterbi School of Engineering
- Aditya Jajodia, M.S. student in Computer Science, Viterbi School of Engineering
- Sushmitha Ravikumar, M.S. student in Computer Science, Viterbi School of Engineering
- Vanshika Sridharan, M.S. student in Computer Science, Viterbi School of Engineering
3. Team Dynamics in Online Multiplayer Games
Competitive online multiplayer team games such as CounterStrike, PUBG, or League of Legends are extremely popular. Multiple teams of professional players compete in hundreds of tournaments yearly. Player transfer between teams is common. The goal of the project is to measure the effects of player transfers and to answer some of the questions such as: How does a new player affect the team’s performance?; How does the change of a team affect a player’s performance? The world of online games can be used as a fruitful area for tackling more fundamental questions on human society and collaboration dynamics in different settings.
POINTERS: Datasets are at https://liquipedia.net/
SKILLS NEEDED: Python
WHAT STUDENTS WILL LEARN: Statistical analysis, building a research pipeline, REST APIs, data visualization and representation
ADVISOR:
- Goran Muric, Viterbi School of Engineering
STUDENTS:
- Kevin Tsang, M.S. student in Applied Data Science, Viterbi School of Engineering
- Jiaqi Liu, M.S. student in Electrical Engineering, Viterbi School of Engineering