Mentor: Samantha Riesenfeld

Project Title: Learning How to Identify Noisy Features from Persistent Homology

Mentor: Samantha Riesenfeld

Project Title: Topological Features in Drug Tolerant Cells

Project Description: Using a published data set of scRNA-seq data in PC9 cells treated with Erlotinib, I used R and Python to identify characteristics of cells with prolonged treatment and acquired resistance to help learn the manifold of the data.

Mentor: Eamon Duede

Project Title: Evolution of Annoyingness

Project Description:We analyzed the time and the sentiment (as a contributing factor to the emotion of irritation) of the tweets from a data pipeline we had created using the Academic Twitter API. Leveraging Machine Learning algorithms, we found that the day of the month can be almost as predictive as the tweet content for predicting/classifying the sentiment.

Mentor: Ravi Madduri

Project Title: Machine Learning Mobile Applications for Health Promotion

Project Description: This summer, alongside fellow cohort member Daniel Chechelnitsky, she worked with Dr. Ravi Madduri to create a mobile health app implementing machine learning models of disease prediction. Using Flutter, an open-source UI software development kit launched by Google, she designed and implemented the front-end and back-end components of the application, working with SQL databases and TensorFlowLite machine learning models among other things. The code for the final product can be found here.

Mentor: Chenhao Tan

Project Title: AI-Driven Tutorials

Project Description: Leveraging artificial intelligence and medical imaging datasets, this project aims to create an educational tool that will help train future radiology students. I had the pleasure of contributing to the web development work (BE & FE) for this project.

Mentors: Margaret Beale Spencer & Chris Graziul

Project Title: Analysis of Police Broadcast Audio at Scale

Project Description: With policing coming under greater scrutiny in recent years, researchers have begun to more thoroughly study the effects of contact between police and minority communities. Despite data archives of hundreds of thousands of recorded Broadcast Police Communications (BPC) being openly available to the public, a closer look at a large-scale analysis of the language of policing has remained largely unexplored. While this research is critical in understanding a “pre-reflective” notion of policing, the large quantity of data presents numerous challenges in its organization and analysis.

We conducted preliminary work toward enabling Speech Emotion Recognition (SER) in an analysis of the Chicago Police Department’s (CPD) BPC by demonstrating the pipelined creation of a datastore to enable a multimodal analysis of composed raw audio files.

Mentors: Anjali Adukia & Teodora Szasz

Project Title: Measuring Race and Gender Representation in Children’s Books Using Sentiment Analysis

Project Description: We measured race and gender representation in award-winning children’s books using sentiment analysis. Our goal was to find the sentiment towards characters to understand how different racial groups or genders are represented in these books. Sharing our findings with teachers, librarians, and parents will help us move towards a more equitable society.

Mentor: Marshini Chetty

Project Title: Investigating Privacy Implications of Educational Technologies for School Children

Project Description: Using Google sheets and links scraped from school district websites we compiled and analyzed data on student privacy. Using Plotly and Dash we visualized our findings to be displayed on a dashboard.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – Schmidt Ocean Institute, ROV Dive Processing

Project Description: In collaboration with the Schmidt Ocean Institute, our team was tasked with contributing to the foundation of an open source oceanographic video processing pipeline. Our primary goal was to implement an unsupervised video summarization model which will produce highlight reels of underwater ROV dive videos. Our secondary goal was to produce a pipeline which will tag dive video frames with informative labels using a variety of pixel-based algorithms and models.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – PalmWatch

Mentor: Ravi Madduri

Project Title: Exploring Machine Learning Applications in Mobile Health Development

Project Description: Mobile apps have real potential in helping individuals understand their risks for various diseases and help make better choices to lower their risks. Using Flutter, we developed a navigable and scalable mobile app environment, decided on UI/UX design of the app, implemented a static risk calculator (prostate cancer) and a image classification ML model (skin cancer). We also tried building and training our own regression models, but we were not able to deploy them in the Flutter framework.

Mentor: Heather Zheng

Project Title: Exploring POV Effect for Stealthy Adversarial Patch Generation

Project Description: Facial recognition is becoming more popular nowadays, but how can we protect our privacy and prevent cameras from using images to recognize us? Using eyeware that projects light onto the face, flashing the light in a distinct pattern creates an effect called the “pov” effect, essentially making it so when cameras take an image of your face, the image will be distorted and recognition of the individual will fail.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – Schmidt Ocean Institute, ROV Dive Processing

Project Description: The Schmidt Ocean Institute uses a remote operated robot to collect video footage of the ocean but needed ways to efficiently parse through this video. Our team developed deep learning unsupervised models to create highlight reels of the robot dives. We also used various machine learning techniques to create tags for notable aspects of the videos.

Mentor: Sarah Sebo

Project Title: Emotionally Intelligent Robots

Project Description: Developing a machine learning model to predict psychological safety and inclusion for participants of a group conversation from audio-visual data. This could be a jumping off point for social robots, to behave according to group-dynamics, and perhaps even create ways to improve those dynamics.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – PalmWatch

Project Description: We investigate whether top-level corporate commitments to sustainability are reflected down the supply chain, focusing on Indonesian palm oil production, which has nearly quadrupled in the past decade. Combining satellite datasets on deforestation and oil palm vegetation, we modeled the risk profile of individual palm suppliers.

Mentors: Dylan Halpern & Julia Koschinsky

Project Title: Web Geoda Development

Project Description: WebGeoda is an open-source, fully client-side browser geospatial analysis tool that allows researchers with little to no coding experience to quickly develop and share visualizations. Built in ReactJS, it leverages the jsgeoda library to perform analysis in-browser without any server overhead costs.

Mentor: Kyle Chard

Project Title: Generalizing Metadata Extraction Workflows

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – Development Bank Investment Tracker

Mentors: Nick Feamster & Nicole Marwell

Project Title: Mapping and Mitigating the Digital Divide

Project Description: Building an Android app and REST API server to collect and store street-level network infrastructures’ data in AWS S3.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – Human Rights Media Analysis

Project Description: Working with the UN Human Rights Office (UN OHCHR), my team built a feature extraction and NLP classification pipeline that categorised the credibility level of news articles on human rights incidents. In the pipeline, we used sci-kits learn, hugginface, spaCy, and gensim. The resulting pipeline will streamline the process of human rights analysis for UN analysts.

Mentor: Kyle Chard

Project Title: Foundry

Mentors: Kate Keahey & Zhuo Zhen, Argonne National Laboratory

Project Title: Bidirectional Edge Computing Research

Project Description: Using Chameleon Cloud resources, I collected and interpreted a variety of network measurements to test possible network configurations between the edge and the cloud. Additionally, I wrote a pipeline over HTTP that allows edge devices to query machine learning models hosted in the cloud.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – Schmidt Ocean Institute, ROV Dive Processing

Mentor: Blase Ur

Project Title: Debugging Trigger Action Programming (TAP) in Smart Home Devices

Project Description: Debugging Trigger Action Programming in Smart Home Devices. Developing software to be used by non-technical participants to help them fix any issues in existing programming rules for smart home devices.

Mentor: Pedro Lopes

Project Title: Batteryless Haptics

Mentor: Sarah Sebo

Project Title: Meaningful Conversations

Mentor: Nick Feamster

Project Title: IoT Activity Recognition Using Audio Data

Mentor: Blase Ur

Project Title: Improving Data Downloads

Mentor: Bryon Aragam

Project Title: Integrating Generative Models and Causal Inference with Applications in Fair Machine Learning

Mentors: Giuseppe B. Cerati & Jeremy Hewes & Daniel Grzenda

Project Title: Exa.TrkX

Project Description: I worked on the Exa.TrkX project which presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). Graphs describing particle interactions are formed by treating each detector hit as a node, with vertices describing the relationships between hits. The model itself is a multihead attention message passing network which performs graph convolutions in order to label each node with a particle type.

Mentor: Blase Ur

Project Title: Debugging Trigger Action Programming (TAP) in Smart Home Devices

Mentor: Pedro Lopes

Project Title: InterventionEMS

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – Development Bank Investment Tracker

Project Description: Analyzed relationship between development bank investments and local complaints, facilitating financing processes and protecting human and environmental rights using data engineering and machine learning; Built automatic and continuous investment data collection mechanism with Google Cloud, created SQL database and APIs for data flow, scaffolded front-end webpages for public access, and generated auto-update graphs to provide insights on data trends.

Mentor: Brian Nord

Project Title: Deep Diagnostics of Convolutional Neural Networks

Project Description: My project focused on how to efficiently access fundamental diagnostics to train and optimize CNN’s. I investigated multiple diagnostics programs to determine how they function to help evaluate model performance. However, Testing these diagnostic tools supported my initial hypothesis that these programs didn’t offer easy access to the fundamental diagnostics of a model I was looking for. So I built a diagnostic package that cuts out extraneous features, and with those, the need for external resources or a deep knowledge of coding to provide new and inexperienced users with the fundamental diagnostics they need.

Mentor: Jai Yu

Project Title: Behavior Modeling in Rats

Mentors: Kyle Chard, Matt Baughman

Project Title: AWS Spot Market Trends from 2018 and 2021

Project Description: In late 2017, Amazon changed the spot market algorithm with the aim of decreasing price variability, increasing spot instance durability, and regularizing the market (Baughman et al, 2019). These changes have made it impossible to rely on the previous strategy of using supply and demand to make decisions. Our research looks at 2021 spot market prices and comparing them to 2018 and compare findings to results found in Deconstructing the 2017 Changes to AWS Spot Market.

Mentor: Junchen Jiang

Project Title: Quality of Experience Personalization Project

Mentors: Jean-Baptiste Reynier & Anna Woodard, Olopade Lab

Project Title: Self-Supervised Deep Learning for Breast Cancer Risk Prediction

Mentor: Nicholas Marchio

Project Title: Interactively Mapping Urban Human Development

Project Description: We studied the deployment of encrypted DNS outside of the mainstream resolvers by measuring DNS query response times and ping times for resolvers located across the world. We compared non-mainstream resolvers to mainstream resolvers, such as Google and Cloudflare, to better understand the reliability of the lesser known resolvers and the DNS encrypted ecosystem as a whole.

Mentor: Ben Zhao

Project Title: Finding Physical Backdoors in Existing Datasets

Project Description: Roma Bhattacharjee is a freshman at Princeton University. This summer, she worked with Professor Ben Zhao and Emily Wenger on a project regarding physical backdoor attacks in computer vision models. She developed an automated process using graph analysis techniques to uncover viable physical triggers in pre-existing object datasets for training backdoored models.

Mentors: Kate Keahey & Zhuo Zhen, Argonne National Laboratory

Project Title: Driving Autonomous Cars From Edge to Cloud with CHI@Edge

Project Description: We created a cloud-based pipeline for driving autonomous cars via Chameleon’s CHI@edge testbed. Specifically, we developed base containers with libraries for access to a car’s interfaces and launched them onboard small, remote-control cars in addition to exploring the effect of different machine learning models on the performance of the car.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – PalmWatch

Project Description: Built a model in Jupyter Lab that compares correlations between columns of risk scores,
created an overlaid histogram of risk scores per mill type, found the risk scores for mills from 2001-2019 using a function, found the year each mill was certified and used this certification column and the risk score columns to build a random forest. The Random Forest predicts for every single year whether or not mills are certified. I also built a logistic regression to predicts what type of certification they have, if they are not certified or certified.

Mentor: Shan Lu

Project Title: An IDE Plugin for Machine Learning Software Testing

Project Description: This project is about the creation of a tool that helps developers use Machine Learning Cloud APIs correctly and more efficiently. The tool automatically generates test cases to thoroughly test an application’s use of Machine Learning Cloud APIs and identify many previously unknown inefficiencies or bugs.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – Human Rights Media Analysis

Project Description: The ongoing pandemic disrupted the UN’s Office of Human Rights’ ability to conduct field monitoring, leading them to identify human rights incidents from news media. We implemented a Human Rights Media Analysis software tool which automates much of the early stages of data processing for the UNOHCHR. Our tool extracts features from a human rights report/news article and assigns a credibility score (low, medium or high) to the article.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – PalmWatch

Mentors: Brian Nord & Yuxin Chen

Project Title: SPOKES: an End-to-End Simulation Facility for Spectroscopic Cosmological Surveys

Project Description: I worked under Brian D. Nord, an astrophysicist and machine learning researcher at Fermi National Accelerator Laboratory, on an open-source Python package providing an end-to-end simulation facility for spectroscopic cosmological surveys called SPOKES. SPOKES is built upon an integrated infrastructure, modular functioning organization, coherent data handling, and fast data access. SPOKES is published on PyPI at https://pypi.org/project/spokes/.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – Development Bank Investment Tracker

Project Description: Xi is a Research Assistant working on the Development Bank Investment Tracker (DeBIT) project to leverage data science for advancing development bank project financing and complaints tracking. Partnered with Accountability Counsel and Inclusive Development International’s Follow the Money initiative, the DeBIT project hopes to hold government, financial institutions, and corporate actors in investment projects around the world accountable for human rights violations and environmental damage.

Mentors: Daniel Grzenda & David Uminsky

Project Title: Social Impact Track – Human Rights Media Analysis

Project Description: I built a data pipeline of web scraping, data cleaning and NLP analysis to develop machine learning classification models predicting the credibility of news articles for the United Nations.

Mentor: Lorenzo Orecchia

Project Title: Local Spectral Method for Graph Clustering

Project Description: Using PLINK to clean and analyze a European gene dataset with 2000 samples. Finding the associations between gene and geographical locations based on spectral graph theory.

Mentor: Jai Yu

Project Title: Analyzing Rat Behavior

Project Description: I used exploratory analysis to examine rats’ behavior and choices in different mazes. Further I looked into pose analysis to pick out smaller behavioral patterns within the rats’ movements.

Project: Extracting Scientific Information from Free Text Articles

Mentor: Kyle Chard, Globus Labs

Research Area Keywords: Machine Learning & AI // Systems // Medicine & Health

Project Description: Aarthi Koripelly is an incoming freshman at the University of Chicago studying computer science and statistics, a 2020 Coca Cola Scholar, and a previous intern in the 2019 program. This summer, Aarthi worked with the Globus Labs research group with Dr. Kyle Chard and Zhi Hong, on a project that explored scalable approaches for automatically extracting relations from scientific papers (e.g., melting point of a polymer). The project implemented a dependency parser-based relation extraction model to understand relationships without the need for a Named Entity tagger and integrated several word embeddings models and custom tokenization to boost learning performance for scientific text.

Watch Aarthi’s Final Video

Project: Chameleon-Sage Image

Mentors: Rajesh Sankaran & Kate Keahey, Argonne National Laboratory

Research Area Keywords: Systems // Cloud Computing

Project Description: Akhil Kodumuri is a sophomore at the University of Illinois at Urbana-Champaign majoring in computer engineering. This summer, he worked with Drs. Rajesh Sankaran and Kate Keahey on creating an image containing all of the Sage software stacks and edge plugins that users can interact with Sage’s platform. This image is intended to be compatible with hardware on the Chameleon platform.

Watch Akhil’s Final Video

Project: Combating Misinformation On Twitter Using NLP and Graph Structures

Mentors: Nick Feamster, Department of Computer Science/Center for Data & Computing

Research Area Keywords: Machine Learning & AI // Internet of Things

Project Description: Alex Levi is a student at the University of Chicago pursuing a joint BA/MS program, majoring in the College in mathematics and beginning his first year in the Masters in Computational Social Science (MACSS) program. This summer, he worked with Prof. Nick Feamster on a project focused on building software that detects linkage structures of misinformation on Twitter. Using NLP semantics and network analysis, he built the groundwork for an algorithm that will be able to detect information divergence, both semantically and structurally. He hopes to continue developing this algorithm in the future.

Watch Alex’s Final Video

Project: Max-Flow Min-Cut Theorem with Dynamic Trees

Mentors: Lorenzo Orecchia, Department of Computer Science

Research Area Keywords: Machine Learning // Algorithms & Optimization // Computer Science Theory

Project Description: Andrew Razborov is a junior at the University of Chicago Laboratory Schools. This summer, he worked with Prof. Lorenzo Orecchia and Konstantinos Ameranis on a project working to solve the max flow, min cut problem using dynamic trees, which maintain paths from the root to the source as a forest of vortex disjoint trees.

Watch Andrew’s Final Video

Project: Developing RNAseq Pipelines

Mentors: Jean-Baptiste Reynier & Anna Woodard, Olopade Lab

Research Area Keywords: Medicine & Health // Scientific Computing // Systems

Project Description: Arvind Krishnan is a senior at the University of Chicago studying molecular engineering and biological sciences. This summer, they worked with Jean-Baptiste Reynier and Anna Woodard in the Olopade Lab on a project that consisted of creating a pipeline to analyze RNAseq data composed of sequenced mRNA from breast cancer patients. The pipeline generates an expression profile, uses this to classify tumors into their subtypes, as well as quantify the immune cell types in the microenvironment of the tumor.

Watch Arvind’s Final Video

Project: funcX Chameleon Burstability

Mentor: Kyle Chard, Globus Labs

Research Area Keywords: Systems // Cloud Computing

Project Description: Avery Schwartz is an incoming freshman at Northwestern University studying computer science, and was previously a student at the University of Chicago Lab Schools as well as a 2019 CDAC intern. This summer, Avery worked with the Globus Labs research group with Dr. Kyle Chard and Matt Baughman, on designing resources and a script to allow funcX to burst out to new nodes using Chameleon Cloud.

Watch Avery’s Final Video

Project: Radiomic Texture Analysis of Immunofluorescence Images of Lupus Nephritis Biopsies to Predict Patient Progression to End Stage Renal Disease

Mentor: Maryellen Giger, Department of Radiology

Research Area Keywords: Machine Learning & AI // Image Analysis // Medicine & Health

Project Description: Bradie Ferguson is a pre-med senior at the University of Washington studying bioengineering and chemistry. This summer, she continued work with Drs. Maryellen Giger and Madeleine Durkee on an image analysis project on microscopic images of lupus nephritis biopsies. The goal was to create a multi-feature classifier that can distinguish between patients that progressed to end stage renal disease (ESRD+) and those that did not progress (ESRD-). To accomplish this, radiomic texture analysis was utilized with future plans of using machine learning.

Watch Bradie’s Video Here

Project: Measuring Race and Gender in Children’s Books

Mentor: Anjali Adukia & H. Birali Runesha, Harris School of Public Policy & Research Computing Center

Research Area Keywords: Machine Learning & AI // Image Analysis // Society & Policy

Home Institution: The University of Chicago

Project Description: Callista Christ is a recent graduate of the College at the University of Chicago, where she majored in Physics and Astrophysics. This summer, she continued work with Drs. Anjali Adukia and Teodora Szasz on a CDAC Discovery Grant project seeking to measure messages about race and gender in children’s books. She wokred on classifying race, gender, and age in cartoons in children’s books, and analyzing how those classifications change throughout time. She also worked on analyzing how the sentiment around homeschooling, Trump, and COVID-19 in general has changed since January.

Watch Callista’s Video Here

Project: Spot Market Prediction

Mentor: Kyle Chard, Globus Labs

Research Area Keywords: Machine Learning // Scientific Computing

Project Description: Tala Germani is a sophomore at the University of Chicago studying computational and applied mathematics (CAM) and economics. This summer, she worked with Dr. Kyle Chard and Matt Baughman in Globus Labs on a project aiming to help users predict price changes in the Amazon spot market. She developed an interactive notebook that summarizes and visualizes past pricing data for users.

Watch Chantal’s Video Here

Project: Xtract NLP

Mentor: Kyle Chard, Globus Labs

Research Area Keywords: Machine Learning // Scientific Computing

Project Description: Chimaobi Amanchukwu is a senior at George Bush High School. This summer, he worked with Dr. Kyle Chard and Tyler Skluzacek in Globus Labs on Xtract NLP, a software which takes a folder of different scientific papers and clusters them based on topics. Xtract NLP is customizable for the user and showcases different graphs for insights.

Watch Chimaobi’s Final Video

Project: Self-Driving Trigger For Large Hadron Collider (LHC) Data

Mentors: Yuxin Chen & David Miller, Department of Computer Science & Department of Physics

Research Area Keywords: Machine Learning & AI // Physics & Astronomy

Project Description: Chinmaya Mahesh is a junior at the University of Illinois at Urbana-Champaign majoring in computer science. This summer, he worked with Drs. Yuxin Chen and David Miller on a CDAC Discovery Grant research project titled, “A Data-Driven Trigger System for the Large Hadron Collider.” The project aims to build a machine learning powered replacement for the current trigger system. The main goal of this project was to finish the first step, which is to build an explainable AI model which can interpret and explain in a cost effective way the decisions of a machine learning based trigger system.

Watch Chinmaya’s Final Video

Project: ML Approaches to Reduce Voice Bias

Mentors: Ben Zhao, SAND Lab

Research Area Keywords: Machine Learning & AI // Medicine & Health

Project Description: Christina Tuttle is a junior at Yale University studying computer science and global affairs. This summer, she worked with Prof. Ben Zhao in the SAND Lab on a project to determine what causes voice bias, and whether machine learning can be used to reduce bias in samples.

Project: Fairness Jupyter Study

Mentors: Blase Ur & Nick Feamster, SUPERGroup & Department of Computer Science/Center for Data & Computing

Research Area Keywords: Machine Learning & AI // Internet of Things // Security & Privacy

Project Description: Christine Jacinto is a senior at Lane Tech College Prep, and previous intern in the 2019 CDAC Summer Lab program. This summer, she continued worked on two projects: one with Prof. Blase Ur in the SUPERGroup Lab focused on creating an experiment design to test a Jupyter notebook plugin; and a second one with Prof. Nick Feamster centered on capturing network traffic for IoT devices to test firewall rules. In her final video, Christine speaks to the first project on the Jupyter pluging.

Watch Christine’s Final Video

Project: Fairness in Machine Learning

Mentors: Blase Ur, SUPERGroup

Research Area Keywords: Machine Learning & AI //Security & Privacy

Project Description: Daniel Serrano is a sophomore at the University of Chicago majoring in computer science. This summer, he worked with Prof. Blase Ur and Galen Harrison in the SUPERGroup Lab on a project developing a Jupyter plugin called Retrograde that can be used by data scientists to create fairer machine learning (ML) models. Rather than testing the model for fairness after the model is created, Retrograde intervenes during the ML building process helping data scientists to think about and document fairness in relation to the data they are working with. His work this summer consisted of creating a study design to help develop Retrograde with the hopes of collecting and analyzing qualitative data from interviews of different ML stakeholders.

Watch Daniel’s Final Video

Project: Distance Matters

Mentors: Eamon Duede, Knowledge Lab

Research Area Keywords: Data Analysis // Spatial Data // Scientific Computing

Project Description: Dimitriy Leksanov is a junior at the University of Chicago studying computational and applied mathematics (CAM) and economics. This summer, he worked with Eamon Duede in the KnowledgeLab on a project exploring how various dimensions of distance affect the influence that one academic’s work has on another. These include physical distance, cultural distance, temporal distance, and distance by knowledge practice. The project explored the latter by using word embedding models to calculate the similarities between different academic papers.

Watch Dimitriy’s Final Video

Project: Spatial Insights in GeoDa

Mentors: Julia Koschinsky, Center for Spatial Data Science

Research Area Keywords: Data Analysis // Spatial Data // Scientific Computing

Project Description: Felix Farb is a junior at Walter Payton College Prep. This summer, he worked with Dr. Julia Koschinsky in the Center for Spatial Data Science on a project involving spatial data science related research, specifically in the subject of the causes of racial diversity in Chicago. Additionally, he worked on creating a framework to help students do research of their own in a productive way, by guiding them through the process of hypothesis creation.

Watch Felix’s Final Video

Project: DeepScribe

Mentors: Sanjay Krishnan & Miller Prosser & Sandra Schloen & Susanne Paulus // Department of Computer Science, OCHRE Data Service at the Oriental Institute, Department of Near Eastern Languages & Civilizations

Research Area Keywords: Image Analysis // Machine Learning & AI

Project Description: Grace Su is a sophomore at Columbia University majoring in computer science. This summer, she worked with Drs. Sanjay Krishnan, Sandra Schloen, and Miller Prosser on a CDAC Discovery Grant project titled, “Deciphering Cuneiform with Artificial Intelligence.” She worked on researching and developing DeepScribe, a tool that deciphers cuneiform with artificial intelligence, using a training set of 100,000+ images from the Oriental Institute’s OCHRE data service. She developed an image classification model with Keras, built a Python module for the computer vision pipeline, and performed experiments to investigate and improve the computer vision model.

Watch Grace’s Final Video

Project: Website Templates for OCHRE Archaeology Projects

Mentors: Miller Prosser & Sandra Schloen, OCHRE Data Service at the Oriental Institute

Research Area Keywords: Data Analysis // Systems

Project Description: Helena Abney-McPeek is an undergraduate at Harvard University studying computer science, and a previous intern in the 2018 and 2019 Summer Lab programs. This summer, she worked with Drs. Sandra Schloen and Miller Prosser at the OCHRE Data Service on a project creating website templates for OCHRE archaeology research projects.

Watch Helena’s Final Video

Project: Chameleon Reproducibility Project

Mentors: Kate Keahey & Zhuo Zhen, Argonne National Laboratory

Research Area Keywords: Systems // Cloud Computing

Project Description: Isabel Brunkan is a junior at the Minerva Schools at KGI studying computer science. This summer, she worked with Drs. Kate Keahey and Zhuo Zhen in the Chameleon Cloud group on a project that created a digital artifact repository with experiments replicated and reproduced on Chameleon using Jupyter Notebook. She worked on replicating machine learning experiments, specifically image processing models, and created an experiment structure template to encourage reproducibility. These experiments are stored on Chameleon’s sharing portal for community use.

Watch Isabel’s Final Video

Project: Learning Manifolds From Point Clouds

Mentors: Lorenzo Orecchia, Department of Computer Science

Research Area Keywords: Machine Learning // Algorithms & Optimization // Medicine & Health

Project Description: Isabella DeClue is a sophomore at the University of Chicago majoring in statistics and minoring in computer science. This summer, she worked with Professor Lorenzo Orecchia and Ryan Robinett on a project investigating a version of the Moving Least Squares algorithm to estimate at what radii local hyperplane approximations for complex, higher dimensional manifolds are valid.

Watch Isabella’s Final Video

Project: Ishan Malhotra

Mentors: Brian Nord, Fermilab & Department of Astronomy and Astrophysics

Research Area Keywords: Machine Learning & AI // Physics & Astronomy

Project Description: Ishan Malhotra is a sophomore at the University of Chicago studying computer science and economics. This summer, he worked with Dr. Brian Nord and the DeepSkies Lab on the early stages of a project aiming to devise a self-driving telescope. His contributions to the project centered around creating a reinforcement learning model to train the self-driving telescope.

Watch Ishan’s Final Video

Project: Latent Attention & Training ML Algorithms

Mentors: Blase Ur, SUPERGroup

Research Area Keywords: Machine Learning // Security & Privacy

Project Description: Jamar Sullivan is an incoming freshman at the University of Chicago studying computer science and astrophysics, and recent high school graduate from Gwendolyn Brooks College Prep. This summer, he continued work with Prof. Blase Ur in the SUPERGroup Lab to explore the difference in machine learning models’ performance when using human-collected vs. machine-learned attention. The project created a user interface that requires users to select words that they believe indicate the sentiment of a movie review, and then created a model that would learn the indicative words in a movie review dataset. It’s understood that attention can lead to greater performance in machine learning models, but collecting human information means that it is possible to collect more data from the same sized dataset, and get high accuracy with a smaller model.

Watch Jamar’s Final Video

Project: Misinformation WhatsApp

Mentors: Marshini Chetty, SUPERGroup

Research Area Keywords: Human-Computer Interaction // Security & Privacy

Project Description: Jason Chee is a sophomore at the University of Chicago majoring in computer science. This summer, he continued work with Professor Marshini Chetty in the SUPERGroup Lab on a project that looked at misinformation on coronavirus news on end-to-end encrypted platforms like WhatsApp. On the qualitative side, he helped write an interview script and researched different fact-checker APIs. On the quantitative side, he designed and developed a cross-platform WhatsApp URL and metadata extraction app using JavaScript and React Native.

Watch Jason’s Final Video

Project: Fawkes

Mentors: Heather Zheng, SAND Lab

Research Area Keywords: Machine Learning // Image Analysis // Human-Computer Interaction

Project Description: Jiawen Shen is a senior at Bellevue High School. This summer, she worked with Prof. Heather Zheng in the SAND Lab on developing Fawkes, a software tool that help protect users privacy against unregulated third party. She tested many different images to help the team make improvement on Fawkes.

Watch Leica’s Final Video

Project: Security & Functionality in IoT Devices Through SmartWall

Mentors: Blase Ur & Nick Feamster, SUPERGroup & Department of Computer Science/Center for Data & Computing

Research Area Keywords: Machine Learning & AI // Internet of Things // Security & Privacy

Project Description: Julio Ramirez is a senior at Northside College Preparatory High School, and previous intern in the 2019 CDAC Summer Lab program. This summer, he continued worked on two projects: one with Prof. Blase Ur in the SUPERGroup Lab where he helped design an evaluation study for a Jupyter Notebook plugin created to help people who develop machine learning models better understand the data they use to train their model; and a second one with Prof. Nick Feamster where he installed smart devices at home and collected packet captures while implementing firewall rules generated for those devices, examining how the rules impact a device’s functionality and security. In his final video, Julio speaks to the second project on functionality and security in IoT devices.

Watch Julio’s Final Video

Project: Identifying Malicious Network Activity

Mentor: Nick Feamster, Department of Computer Science/Center for Data & Computing

Research Area Keywords: Machine Learning & AI // Internet & Communications

Project Description: Lia Troy is a recent graduate of the College at the University of Chicago. This summer, she worked with Prof. Nick Feamster on a project working to identify malicious network activity.

Watch Lia’s Final Video

Project: Safety Guidelines for BLM Activists

Mentor: Blase Ur, SUPERGroup

Research Area Keywords: Security & Privacy // Society & Policy

Project Description: Maia Boyd is a sophomore at the University of Chicago majoring in computer science and minoring in math. This summer, she worked with Prof. Blase Ur in the SUPERGroup Lab on a project that seeks to understand the computer security and technology safety concerns that Black Lives Matter (BLM) supporters have surrounding protests, and how they address those concerns. In order to achieve this goal, she helped to collect safety guides used by BLM protesters to see what advice is given to protesters. Next, the project team launched an online survey, taken by 167 BLM protesters, that asked about their concerns and if they had heard of or follow the pieces of advice that we collected from the safety guides.

Watch Maia’s Final Video

Project: Scratch Encore – Exploring Student Behavior

Mentor: Diana Franklin, CANON Lab

Research Area Keywords: STEM Education // Data Analysis

Project Description: Melissa Tovar is a senior at the University of Chicago studying computer science. This summer, she worked with Prof. Diana Franklin in the CANON Research Lab on the Scratch Encore team. The project sought to adapt the Scratch Encore curriculum so that it became combatible to remote learning. This includes worksheets now available in google forms or google slides. Another part of the summer was spent analyzing student responses on said worksheets from the previous school year.

Watch Melissa’s Final Video

Project: Analyzing Human Behavior with Smart Home Devices

Mentor: Nick Feamster, Department of Computer Science/Center for Data and Computing

Research Area Keywords: Machine Learning & AI // Internet & Communications // Security & Privacy

Project Description: Nikki Chakravarthy is a sophomore at the University of Chicago studying computer science and economics, and a previous intern in the 2019 Summer Lab program. This summer, she worked with Prof. Nick Feamster on a project aiming to understand human behavior related to smart home devices. She used Wireshark to analyze packet captures collected from a Jetson Nano and other IoT devices in her home.

Watch Nikki’s Final Video

Project: Spatial Analysis of Access to MOUD (Medications for Opioid Use Disorder) Resources

Mentor: Qinyun Lin & Marynia Kolak, Center for Spatial Data Science

Research Area Keywords: Human-Computer Interaction // Wearables & Devices

Project Description: Olina Liang is a junior at the University of Chicago majoring in astrophysics. This summer, she worked with Drs. Marynia Kolak and Qinyun Lin in the Center for Spatial Data Science on a project focused on scraping opioid-related policy data from over 10 PDFs of around 1,000 pages each, geocoded locations of health facilities, and calculated zipcode level access scores.

Watch Olina’s Final Video

Project: Making Machine Learning More Human – Quantifying Parent-Child Language Alignment Using Neural Language Models

Mentor: Allyson Ettinger, Department of Linguistics & Susan Goldin-Meadow, Department of Psychology

Research Area Keywords: Computational Linguistics // Natural Language Processing // Data Analysis

Project Description: Ray Fregly is a junior at the University of Chicago double majoring in linguistics and computer science. This summer, she worked with Drs. Allyson Ettinger and Susan Goldin-Meadow on a project that altered and implemented neural network language models to Pytorch to quantify parent-child language alignment based on previously collected data. The long-term goal of the project is to use the results of this study to improve our understanding of child language acquisition.

Watch Rachel’s Final Video

Project: Data Mining and NLP For Financial Markets

Mentor: Dacheng Xiu, Booth School of Business

Research Area Keywords: Economics & Business // Natural Language Processing // Data Analysis

Project Description: Rachit Surana is a sophomore at the University of Chicago. This summer, he worked with Prof. Dacheng Xiu on a project using data mining of textual data related to financial markets using dynamic scraper and network traffic analysis. In the future, NLP modelling will be used to perform correlational analysis with market prices and other metrics.

Project: Promoting Explanatory Insights in GeoDa

Mentor: Julia Koschinsky, Center for Spatial Data Science

Research Area Keywords: Spatial Data // Data Analysis

Project Description: R.E. Stern is a sophomore at the University of Chicago. This summer, he worked as part of a team led by Dr. Julia Koschinsky developing best practices for spatial data research in the Center for Spatial Data Science‘s exploratory data analysis program GeoDa. Focused on user interaction with theri research’s underlying hypotheses, and on using quasi-experimental design to allow users to consistently develop explanatory insights rather than descriptive ones.

Watch R.E.’s Final Video

Project: d-gen: Database Generation & Relational Databases

Mentor: Raul Castro Fernandez, ChiData/Department of Computer Science

Research Area Keywords: Data Analysis // Systems

Project Description: Ryan Wong is a senior at Whitney Young High School, and a previous CDAC intern in the 2019 program. This year, he worked with Professor Raul Castro Ferandez on d-gen, a synthetic relational database generator. d-gen aims to help database users benchmark queries by generating data that adheres to relational database schemas.

Watch Ryan’s Final Video

Project: Optimizing Thermal Dissipation of 3D-Printed Objects

Mentor: Pedro Lopes, Human-Computer Integration Lab

Research Area Keywords: Human-Computer Interaction // Wearables & Devices

Project Description: Svitlana Midianko is a junior at the Minerva Schools at KGI studying human behavior. This summer, she worked with Prof. Pedro Lopes in the Human Computer Integration Lab on a project centered around optimizing thermal dissipation of 3-D printed objects. The project included the development of the Fusion360 plugin, written in Python. With the help of such plugin, makers can optimize the heat dissipation of their hardware without having much knowledge in heat dynamics. The plugin’s execution results in modification of the device’s design, explicitly adding extra holes in the upper case. Such change is optimized for minimum temperature of the device.

Project: Automated Experimental Design for Cosmic Discovery

Mentor: Brian Nord & Yuxin Chen // Fermilab, Department of Astronomy and Astrophysics, Department of Computer Science

Research Area Keywords: Machine Learning & AI // Physics & Astronomy

Project Description: Yair Atlas is a junior at the University of Chicago studying physics and philosophy, and was a previous CDAC intern in the 2019 program. This summer he worked with Drs. Yuxin Chen and Brian Nord on a CDAC Discovery Project titled, “Automated Experimental Design for Cosmic Discovery.” This project focused on using machine learning to improve astrophysical surveys. Specifically, the project used a simulation facility to better understand how design features affect experimental results.

Watch Yair’s Final Video

Project: Using Machine Learning to Identify Blurry SEM Images

Mentor/Lab: Ryan Chard/Globus Labs

Research Area Keywords: Machine Learning // Image Analysis // Systems & Architecture // Databases

I worked on a project using machine learning and computer vision to detect Scanning Electronic Microscope images of mice brains that were either blurry or focused. I was able to create three different models using first a Laplacian of Gaussian blob detection; then, a neural network; and finally a convolutional neural network. Then, I helped push the models to DLHub for others to use.

Project: Password Reuse & Vulnerability Detection

Mentor/Lab: Blase Ur/SUPERGroup

Research Area Keywords: Security & Privacy // Human-Computer Interaction

In this project, in collaboration with UChicago IT Services, we leverage publicly available data breaches and password modification strategies to generate password guesses for current and previous UChicago accounts, and run a simulated attack to assess the vulnerability of UChicago accounts to password guessing attacks. Information is obtained from users who are identified as vulnerable in this simulated attack using anonymous online surveys. Attitudes, intentions, and planned behavior change are surveyed in an effort to design interventions that promote better password security habits.

Project: Generating Deepfakes with Autoencoders

Mentor/Lab: Matt Baughman/Globus Labs

Research Area Keywords: Machine Learning // Security & Privacy

In recent news, Deepfakes have been written to seem alarmingly easy to make. The aim of my summer project was to generate Deepfakes myself, to gain greater exposure to Machine Learning and to test the learning curve. Using an encoder-decoder architecture, we roughly swapped the faces of two program coordinators (as well as that of Nicholas Cage).

Project: Distance Matters in Science & Scholarship — Analyzing the Impact of Scientific Citations

Mentor/Lab: Eamon Duede/Knowledge Lab

Research Area Keywords: Data Analysis // Spatial Data // Scientific Computing

After extracting metadata from over 26,000 surveyed authors, we ran relational analysis on our data and found a relationship between the influence of an author’s references and the geospatial distance between the author and referenced author’s institution. This relationship is intriguing and offers insight into understanding what types of publications are more influential for authors.

Project: funcX Website Design

Mentor/Lab: Ryan Chard/Globus Labs

Research Area Keywords: Programming Languages // Databases // Web Design

My project was to build a website for the Globus service, funcX (a FaaS platform for science), which is designed to be applied to existing cyberinfrastructures to provide scalable, secure, and on-demand execution of short duration scientific functions. I learned how to use Bootstrap, Flask, and PSQL in order to construct a fully-functional website that scientists and researchers can use to remotely run functions with specified input data.

Project: Fairness in Machine Learning

Mentors/Lab: Prof. Blase Ur, Julia Hanson (BS 2018), Galen Harrison (PhD, CS)/SUPERGroup

Research Area Keywords: Machine Learning // Human-Computer Interaction

I worked on qualitative coding for interviews concerning cloud usage, as well as for data for a fairness in Machine Learning research paper submitted to FAT. I also started a visualization using D3 for sample data from COMPAS.

Project News: The paper that grew out of this research, titled “An Empirical Study on the Perceived Fairness of Realistic, Imperfect Machine Learning Models”, was accepted at the ACM FAT* 2020 Conference.

Project: Data and Learning Hub for Science (DLHub)

Mentors/Lab: Logan Ward & Ryan Chard/Globus Labs

Research Area Keywords: Machine Learning // Systems & Architecture

I began by working on DLHub by creating a module to extract data from various machine learning model architectures and translating them to PyTorch models. I then transitioned to learning about image segmentation, and attempting to determine whether transfer learning with image segmentation could be done efficiently.

Project: Mobile Decision-Making for Doctors Without Internet

Mentor/Lab: Pedro Lopes/HCI Lab

Research Area Keywords: Medicine & Health // Human-Computer Interaction // Mobile Computing

I worked on the Mobile Medicine project, which is aimed at providing Nigerian Doctors with an internet-independent phone interface tool to run computational diagnostics, answer medical queries, and provide realtime knowledge on the spread of infectious disease.

Project: Physical Backdoors in Neural Networks

Mentor/Lab: Ben Zhao/SAND Lab

Research Area Keywords: Artificial Intelligence // Machine Learning // Security & Privacy

My project was about detecting physical backdoor attacks in image-detecting neural networks. I added physical triggers to traffic signs (blue tape or sticky notes along the bottom of the sign) and added these images to a training dataset for a neural network that classifies traffic signs. The goal of the project was to find a way to detect that the network had been attacked and mitigate the attack.

Project: EMS Gesture Classification

Mentor/Lab: Heather Zheng/SAND Lab

Research Area Keywords: Artificial Intelligence // Human-Computer Interaction // Machine Learning // Security & Privacy

I helped build a gesture classification model that classifies 32 different hand gestures generated by electrical muscle stimulation. In doing so, our lab hopes to develop novel and secure human-computer interaction methods and secure communication techniques.

Project: Fairness in Machine Learning

Mentors/Lab: Prof. Blase Ur, Julia Hanson (BS 2018), Galen Harrison (PhD, CS)/SUPERGroup

Research Area Keywords: Machine Learning // Human-Computer Interaction

I worked on the Fairness in Machine Learning project with SUPERgroup. We focused on analyzing empirical data to understand what people perceived to determine a fair machine learning model, among other things. I programmed in JavaScript to help work on a process visualization as part of the project. I also used JavaScript to try and develop summary statistics of the data that was collected as part of our project. I also did qualitative coding for the survey responses used in the project.

Project News: The paper that grew out of this research, titled “An Empirical Study on the Perceived Fairness of Realistic, Imperfect Machine Learning Models”, was accepted at the ACM FAT* 2020 Conference.

Project: Is Climate Change Changing Clouds?

Mentors/Lab: Ian Foster, Michael Maire, Elisabeth Moyer, Rebecca Willett/RDCEP

Research Area Keywords: Biology & Environment // Image Analysis // Machine Learning

My work during the program centered on climate science: because clouds introduce uncertainty into climate projections, our project uses unsupervised machine learning methods to evaluate changing cloud characteristics. In my contribution to the project, I tackled the data processing issues brought on by immense but previously under-utilized NASA satellite data: through employing web scraping and API calls as well as mapping and other data visualization techniques, I simplified and expedited the data downloading and wrangling process.

Project News: Two papers arose from this research, and both were accepted to the American Geophysical Union Fall 2019 meeting: “Cloud Characterization With Deep Learning II” and “Developing Unsupervised Learning Models for Cloud Classification.”

Project: Internet Tracking Transparency

Mentor/Lab: Blase Ur/SUPERGroup

Research Area Keywords: Human-Computer Interaction // Security & Privacy

I worked on a browser extension that highlights the prevalence of third-party tracking on the internet, specifically focusing on drawing attention to the more sinister aspects of tracking. I also worked on a project about advertising and ad transparency on Twitter.

Project: Research Sharing in JupyterHub with Chameleon Cloud

Mentors/Lab: Kate Keahey & Jason Anderson/Chameleon Project Nimbus Team

Research Area Keywords: Scientific Computing // Systems & Architecture

Working with the Nimbus team, we integrated Chameleon’s JupyterHub with Zenodo to make it easier to share, publish, and import research notebooks. I worked on making it easier to share notebook-based research in and out of the JupyterHub environment on Argonne’s Chameleon open testbed. I wrote a full-scale, front-end, and back-end publishing extension for JupyterLab, adjusted the JupyterHub configuration to make it easy to import notebooks, and created a Django website to allow users to share and browse research.

Project News: The poster for this project, titled “Sharing and Replicability of Notebook-Based Research on Open Testbeds”, was accepted at the SC19 International Conference for High Performance Computing, Networking, Storage and Analysis.

Project: Enhancing Breathability of Wearable Devices

Mentor/Lab: Pedro Lopes/HCI Lab

Research Area Keywords: Human-Computer Interaction // Wearable Devices

My project was to improve the breathability of wearable devices that capture biometric data, such as Apple watches and Fitbits, by creating silicone interfaces that can be slotted between the skin and a wearable. The aim for the silicone interface is to pipe out sweat and allow trapped heat to escape, improving user comfort. I experimented with manufacturing techniques and principles of microfluidics to design a process that can be used by hobbyists who only have access to machinery typically found in a makerspace.

Project: Kubernetes Backend for VC3 + SLATE

Mentors/Lab: Dr. Rob Gardner, Lincoln Bryant, & Chris Weaver/MANIAC Lab

Research Area Keywords: Databases // Systems & Architecture

I iterated on scientific software projects VC3 and SLATE by replacing OpenStack backend to create ‘login pods’ that can be dynamically provisioned on a lightweight Kubernetes backend, which could improve hardware usage by 2-3 times. I used tools such as Docker, Kubernetes, and Jenkins, and expanded my knowledge of Python, UNIX Shell programming languages, and Linux authorization and authentication mechanisms.

Project: Queue Prediction for Supercomputers

Mentor/Lab: Ryan Chard/Globus Labs

Research Area Keywords: Machine Learning // Systems & Architecture

Research in many scientific domains require significant computational power resulting in limitations such as the expense to grab new resources and jobs being initiated only after a certain number of nodes become available. Therefore it is essential to incorporate intelligence in computing resource management. One of the key components of this intelligence is being able to predict queue wait times for jobs running through supercomputers using AWS and Parsl. In my project, I leveraged Amazon SageMaker, a cloud Machine Learning (ML) platform to create, train, and deploy a machine learning model to predict queue wait times.

Project: “If This Then That” — Generating Predictive Rules for Smart Devices Based on Human Behavior

Mentor/Lab: Dr. Blase Ur & Weijia He (PhD Candidate, CS)/SUPERGroup

Research Area Keywords: Human-Computer Interaction // Internet of Things

I programmed features into the backend of an IoT application using a Django web framework. I helped a team of IoT researchers collect data using this app on participants’ interactions with smart devices. Our team is working on synthesizing data to automatically generate predictive IFTTT-style rules tailored to an individual user’s behavior. I also designed a visualization feature so that individuals can better understand their usage patterns and determine whether the generated rules align with their daily behaviors.

Project: Quantifying Social Determinants of Cardiovascular Disease

Mentor: Corey Tabit

Research Area Keywords: Machine Learning // Medicine & Health // Spatial Data

Our project focused on determining spatiotemporal individual and composite social risk scores as they relate to blood pressure. This summer, we aimed to identify the optimal spatiotemporal buffer of crime that affects people’s blood pressure most.

Project News: The paper that grew out of this research, titled, “Acute Effects of Violent Crime on Blood Pressure in Chicago,” was published in the Journal of the American College of Cardiology in March 2020.

Project: Is Climate Change Changing Clouds?

Mentors/Lab: Ian Foster, Michael Maire, Elisabeth Moyer, Rebecca Willett/RDCEP

Research Area Keywords: Biology & Environment // Image Analysis // Machine Learning

We explored how deep learning can be applicable to discover new cloud features from big satellite data. To find the new cloud features, we built a data analysis pipeline where we trained a deep neural network, extracted dimension-reduced clouds information, and then passed the compressed representation to a clustering algorithm.

Project News: Two papers arose from this research, and both were accepted to the American Geophysical Union Fall 2019 meeting: “Cloud Characterization With Deep Learning II” and “Developing Unsupervised Learning Models for Cloud Classification.”

Project: Extracting Metadata with XtractHub

Mentor/Lab: Tyler Skluzacek/Globus Labs

Research Area Keywords: Databases

XtractHub is a dynamic metadata extraction workflow. This summer I improved the efficiency of multiple metadata extractors, implemented Docker into the XtractHub workflow, and developed a web server for processing individual file metadata.

Project News: The paper that grew out of this research, titled “Serverless Workflows for Indexing Large Scientific Data”, was accepted at the Fifth International Workshop on Serverless Computing (WoSC) 2019.

Project: Is Climate Change Changing Clouds?

Mentors/Lab: Ian Foster, Michael Maire, Elisabeth Moyer, Rebecca Willett/RDCEP

Research Area Keywords: Biology & Environment // Image Analysis // Machine Learning

I helped develop a machine learning algorithm for identifying different cloud classes based on satellite images. We hope to use our model to better understand trends in cloud cover, which plays a critical role in climate change.

Project News: Two papers arose from this research, and both were accepted to the American Geophysical Union Fall 2019 meeting: “Cloud Characterization With Deep Learning II” and “Developing Unsupervised Learning Models for Cloud Classification.”

Project: Mobile Decision-Making for Doctors Without Internet

Mentor/Lab: Pedro Lopes/HCI Lab

Research Area Keywords: Medicine & Health // Human-Computer Interaction // Mobile Computing

Mobile Medicine is a new approach to patient diagnostic and record-keeping approach. Due to restrictions in wifi growth, areas in under-developed nations lack modern diagnostic-aids and electronic medical records. Mobile Medicine aims to fix that by creating an SMS-protocol based app that both collects patient information, and provide an accurate adaptive diagnosis.

Project: funcX Website Design

Mentor/Lab: Ryan Chard/Globus Labs

Research Area Keywords: Databases // Programming Languages // Website Design

This summer, I worked on funcX web service, which is a serverless supercomputing project. It allows users to write, edit, delete, and execute functions over mobile devices with registered endpoints. I mainly worked on the user interface – the website – by writing codes to determine the design of webpages and ways of extracting data from the database with Python, HTML, CSS, and SQL codes.

Project: Globus Automate Cloud Service

Mentor/Lab: Ryan Chard/Globus Labs

Research Area Keywords: Databases // Human-Computer Interaction // Systems & Architecture

Project: Bioinformatics Workflow Migration Between Fred Hutchinson Cancer Research Center & Globus Genomics

Mentors/Lab: Paul Davé & Alex Rodriguez/Globus Genomics

Research Area Keywords: Medicine & Health // Systems & Architecture

Project: Making Spatial Access Measures Accessible: A New Python Package and AWS Tool

Mentors/Lab: Dr. James Saxon & Dr. Julia Koschinsky/Center for Spatial Data Science

Research Area Keywords: Medicine & Health // Spatial Data

I worked on a project aiming to help researchers calculate spatial access with a very simple interface. I made a website which allows users to easily calculate how easily people living in a certain location have access to a certain resource. This can be used to determine where there isn’t enough healthcare and other important goods.

Project News: The PySAL (Python Spatial Analysis Library) package in which this research project culminated is now available online.