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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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