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PhD Students
PhD Students: A - K

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Rawan Mansour S Alharbi

Student Track: Interfaces
Research Area: Mobile Heath
Advisor(s): Alshurafa, Nabil
Cohort: June 2017
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Mowafak Allaham

Student Track: TSB
Advisor(s): Diakopoulos, Nick
Cohort: September 2021
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Khalil Anderson

Student Track: Artificial Intelligence
Research Area: HCI
Advisor(s): Worsley, Marcelo
Cohort: September 2018
Expected Graduation Date: 2023
Research Statement: Interests in how Machine Learning can help augment, not necessarily replace, humans in tasks and jobs such as driving, learning, manufacturing, and many other areas. My current research focuses on HCI and Multi-Modal Analytics. In these areas, I hope to analyze and change the different ways Multimodal Analytics is currently approached to better integrate the wants and needs of those who are participating in collection. In doing this I hope to develop new algorithms and interactions that combine different modalities that aid in serving those needs we see from the participants.
Khalil's Website
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Zafir Asnari

Student Track: Systems
Advisor(s): Bustamante, Fabian
Cohort: September 2022
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Kerem Aydin

Student Track: Graphics
Advisor(s): Alexander, Emma
Cohort: September 2022
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Abu Bakar

Student Track: Systems
Research Area: Embedded Systems
Advisor(s): Hester, Josiah
Cohort: June 2018
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Jack Bandy

Student Track: TSB
Research Area: HCI
Advisor(s): Diakopoulos, Nick
Cohort: September 2018
Expected Graduation Date: May 2023
Research Statement: My research focuses on ethics and accountability for algorithmic communication platforms like Facebook, Twitter, and TikTok.
Jack's Website
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Julia Barnett

Student Track: TSB
Research Area: Algorithmic Ethics and Transparency
Advisor(s): Diakopoulos, Nick
Cohort: September 2021
Expected Graduation Date: June 2026
Research Statement: My research interests lie in algorithmic ethics and transparency, ethical AI, NLP applications in social contexts, and the intersection of machine learning and music.
Julia's Website
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Cameron Barrie

Student Track: Artificial Intelligence
Research Area: NLP
Advisor(s): Hammond, Kristian
Cohort: September 2019
Expected Graduation Date: June 2024
Research Statement: My primary research interests center around converting information represented in an unstructured format, such as raw text, to a more structured machine-ingestible format. Specifically, I'm currently focusing on extracting entity-to-entity relationships from document corpora and representing them in knowledge graphs, lending such documents to availability as a source for a variety of search applications (e.g., question answering systems)
Cameron's Website
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Jamie Benario

Student Track: CSLS
Advisor(s): O'Rourke, Eleanor
Cohort: September 2016
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Simon Benigeri

Student Track: Artificial Intelligence
Advisor(s): Birnbaum, Lawrence
Cohort: September 2022
Expected Graduation Date: June 2027
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Joseph Blass

Student Track: Artificial Intelligence
Research Area: Cognitive Systems
Advisor(s): Forbus, Kenneth; Hinrichs, Thomas
Cohort: March 2014
Expected Graduation Date: January 2023
Research Statement: I am a student in Northwestern's JD-PhD studying at the intersection of AI and Law. My dissertation research involves building an AI model of precedential reasoning, grounded in analogy and rule-based reasoning. My thesis system takes in a body of common law decisions (specifically Torts: think trespass, assault, battery), compares those cases to each other to build schemas distilling the legal principles involved, converts those legal principles into rules, and reasons about unseen cases using those rules. I also do legal research on how AI will challenge and change existing legal doctrine. For example, I've written on new avenues for algorithmic discrimination, and on the challenges presented by trying to automate components of the legal system.
Joseph's Website
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Herminio Bodon

Student Track: TSB
Advisor(s): Worsley, Marcelo
Cohort: September 2020
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Katya Borgos-Rodriguez

Student Track: TSB
Advisor(s): Piper, Anne Marie
Cohort: September 2017
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Matthew Brucker

Student Track: CSLS
Advisor(s): Horn, Michael
Cohort: September 2020
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Victor Soares Bursztyn

Student Track: Artificial Intelligence
Research Area: Natural Language Processing
Advisor(s): Birnbaum, Lawrence
Cohort: September 2017
Expected Graduation Date: November 2022
Research Statement: My research is broadly focused on the computational modeling of human conversations, lying mainly in the subfield of Natural Language Processing but also interfacing with Human-Computer Interaction, Recommender Systems, Information Retrieval, and Machine Learning.
Victor Soares' Website
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Shuwen Chai

Student Track: Security and Privacy
Advisor(s): Xing, Xinyu
Cohort: September 2022
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Ruixiang Chai

Student Track: Artificial Intelligence
Advisor(s): Liu, Han
Cohort: September 2022
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Sayak Chakrabarty

Student Track: Theory
Research Area: Optimization
Advisor(s): VS Subrahmanian
Cohort: September 2021
Expected Graduation Date: June 2025
Research Statement: I am a Ph.D. student in the Department of Computer Science at Northwestern University. I have worked previously on the detection of fake accounts on e-commerce websites and my current research is about creating a model that learns how to judge a court case based on the statements given by witnesses and lawyers. I have a background in pure mathematics. My office is at Northwestern Roberta Buffett Institute for Global Affairs.
Sayak's Website
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Peter Chan

Student Track: Systems
Research Area: Security and Privacy
Advisor(s): Wang, Xiao
Cohort: September 2019
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Connie Chau

Student Track: TSB
Research Area: HCI
Advisor(s): Jacobs, Maia
Cohort: September 2021
Expected Graduation Date: June 2026
Research Statement: I am an interdisciplinary HCI researcher whose work applies critical theory and participatory research methods to develop technologies that support care work and provide equitable health outcomes for marginalized communities. My interests include the design of mental & physical health technologies, sociotechnical opportunities to facilitate healing & resilience for trauma survivors, and community-based research.
Connie's Website
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Victoria Chavez

Student Track: CSLS
Research Area: Computer Science Education
Cohort: September 2021
Expected Graduation Date: June 2026
Research Statement: Victoria is broadly interested in computer science education, accessibility, civic technology, and social justice. Most of their research interests stem from asking "How can we make Computer Science a safe and joyous experience for Black, Disabled, Indigenous, and Latinx college students?"
Victoria's Website
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John Chen

Student Track: CSLS
Advisor(s): Wilensky, Uri
Cohort: September 2019
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Kezhen Chen

Student Track: Artificial Intelligence
Advisor(s): Forbus, Kenneth
Cohort: September 2016
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Yuehan Chen

Student Track: CSLS
Cohort: September 2019
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Yining Chen

Advisor(s): Liu, Han
Cohort: September 2021
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Aidao Chen

Student Track: Theory
Research Area: Algorithms
Advisor(s): Vijayaraghavan, Aravindan
Cohort: June 2017
Expected Graduation Date: August 2023
Research Statement: Aidao is broadly interested in Theoretical Computer Science. Currently, his work focuses on extracting hidden information from (potentially noisy) data in a provable manner.
Aidao's Website
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Melissa Chen

Research Area: HCI
Advisor(s): O'Rourke, Eleanor
Cohort: September 2022
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Angel Espinosa Coarasa

Student Track: Artificial Intelligence
Research Area: Computational Biology
Advisor(s): MacIver, Malcolm; Argall, Brenna
Cohort: January 2016
Expected Graduation Date: March 2023
Research Statement: I create biological planning models using AI techniques.
Angel Espinosa's Website
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Jason Cohn

Student Track: Artificial Intelligence
Advisor(s): Birnbaum, Lawrence
Cohort: September 2013
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Chris Coleman

Student Track: Artificial Intelligence
Research Area: Statistical Language Modeling
Advisor(s): Hammond, Kristian
Cohort: September 2021
Expected Graduation Date: June 2026
Research Statement: My research attempts to understand the strengths, curiosities, and weaknesses of large-scale pre-trained language models. Specifically, I'm fascinated by the limitations and learning mechanisms of transformer-based architectures in the context of commonsense reasoning, constraint satisfaction, and knowledge acquisition. Currently, some approaches that I'm interested in are: multitask learning and knowledge transfer techniques; analysis of learned embedding spaces and hidden state representations; statistical tools for interpreting the importance of specific parameters and training examples.
Chris' Website
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Carl Colglazier

Student Track: TSB
Advisor(s): Shaw, Aaron
Cohort: September 2020
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Yuan Cui

Student Track: Theory
Research Area: Algoritms, Economics
Advisor(s): Hartline, Jason
Cohort: September 2020
Expected Graduation Date: 2025
Research Statement: My research interests lie at the intersection of computer science and behavioral sciences. I'm currently exploring the broad world of algorithms, data visualization, and machine learning. I hope to better understand and guide system design with tools from these disciplines to bring positive social impact to the world we live in.
Yuan's Website
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Michael D'arcy

Student Track: Artificial Intelligence
Research Area: Natural Language Processing
Advisor(s): Downey, Douglas
Cohort: September 2018
Expected Graduation Date: June 2023
Research Statement: My research focus is primarily in natural language processing and machine learning This includes commonsense reasoning and active learning, and more recently I am exploring large-scale mining and analysis of entities from scientific documents to both generate new research ideas and detect methodological pitfalls in papers.
Michael's Website
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Henry Kudzanai Dambanemuva

Student Track: TSB
Advisor(s): Horvat, Emoke-Agnes
Cohort: September 2018
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Maitraye Das

Student Track: TSB
Research Area: HCI
Advisor(s): Gergle, Darren; Piper, Anne Marie
Cohort: June 2021
Expected Graduation Date: August 2022
Research Statement: My doctoral research focuses on understanding and designing for accessible collaborative content production in ability-diverse teams, i.e., teams involving people with and without disabilities. In this space, I have investigated how accessibility is created and negotiated in the contexts of collaborative writing, creative making, and remote work; in teams of blind and sighted individuals as well as among neurodivergent professionals. I take a human-centered, community-based research approach combining in-depth qualitative studies (e.g., contextual interviews, ethnographic field observations) with iterative system building and evaluation. Some examples of my work include developing new auditory techniques for enhancing accessibility in asynchronous and synchronous collaborative writing, an audio-enhanced loom for accessible weaving, and an audio-tactile tool for accessible drafting of fabric patterns. I also draw on Disability Studies literature to critically interrogate what roles technologies play in reshaping group dynamics and redistributing the labor of creating access in ability-diverse teams.
Maitraye's Website
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Govinda Dasu

Student Track: Interfaces
Research Area: HCI
Advisor(s): Zhang, Haoqi; O'Rourke, Eleanor
Cohort: September 2018
Expected Graduation Date: June 2023
Research Statement: My research investigates how we can take readily available open source codebases (i.e. GitHub, web source code) and convert them into opportunities for novices and intermediate developers to learn about how design patterns and architectures are applied in practice. By comparing novice use of both debugging tools (i.e. Chrome DevTools) and graphical tools (i.e. call graph visualization tools, I have found that tools can significantly shape the ways novices explore professional code, and their focus and learnings. We have introduced Scaffolded Exercises, a tool that scaffolds novices as they understand Javascript variable transformation that leads to DOM changes, and Knowledge Maps, a tool that helps novices compare and contrast examples to understand conceptual differences between techniques and build out a map of their CSS knowledge. Finally, we are actively developing RALE Modules (Readily Available Learning Experiences), a process management system which highlights and annotates design patterns and architectures (i.e. MVC, Mixins) in professional python codebases, and generates a series of diagrams and activities that help intermediate developers understand how and why particular patterns are applied in particular professional use cases.
Govinda's Website
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Alexandra Day

Student Track: Computer Engineering
Research Area: Machine Learning and High-Performance Computing
Advisor(s): Choudhary, Alok
Cohort: September 2020
Expected Graduation Date: August 2025
Research Statement: I am currently pursuing interdisciplinary research projects at the intersection of machine learning and high-performance computing. My research group, the Center for Ultra-Scale Computing and Information Security (CUCIS), specializes in both of these areas under the supervision of Professor Alok Choudhary. I am currently working with a group in the Materials Science and Engineering Department to characterize nanoparticle images and accelerate their acquisition and analysis turnaround times. I have an undergraduate degree in physics with a math minor from Wellesley College, and am also an NSF Graduate Research Fellow.
Alexandra's Website
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Tonmoay Deb

Student Track: Artificial Intelligence
Research Area: Machine Learning, Computer Vision, Natural Naguage Processing
Advisor(s): V.S. Subrahmanian
Cohort: September 2021
Expected Graduation Date: 2025
Research Statement: I am a first-year Ph.D. student in Computer Science at Northwestern University, advised by Dr. V.S. Subrahmanian. My primary research focuses are Machine Learning, Computer Vision, and Natural Language Processing. Before joining Northwestern, I was a Master's (thesis-based) student in Computer Science at the University of New Hampshire. I spent Summer 2021 at the Center for Coastal and Ocean Mapping/NOAA-UNH Joint Hydrographic Center, working on Unsupervised Semantic Segmentation under Dr. Yuri Rzhanov and Dr. Kim Lowell. Before that, I was a Research Associate (full-time) at the Artificial Intelligence and Cybernetics (AGenCy) Lab, Independent University, Bangladesh. I was supervised by Dr. Amin Ahsan Ali, Dr. A K M Mahbubur Rahman, and Dr. Iftekhar Tanveer. Some notable projects are Diversity in English Video Captioning, Video Captioning in the Bengali Language, and Scalable Bengali Speech-to-Text. My research interest evolved since my Undergrad at North South University. One of my key projects was PATRON. I was blessed with excellent advisors, Dr. Mohammad Rashedur Rahman, Mr. Adnan Firoze, and Dr. Mohammad Ashrafuzzaman Khan.
Tonmoay's Website
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Enrico Armenio Deiana

Student Track: Systems
Research Area: Compilers
Advisor(s): Campanoni, Simone
Cohort: March 2016
Expected Graduation Date: June 2023
Research Statement: I am interested in how compilers can take advantage of programs' properties to generate novel code optimizations and transformations. Specifically, I have worked on STATS; a compiler for nondeterministic programs that takes advantage of their randomization property to automatically parallelize them. I am also interested in how the synergy between compilers and runtimes can lead us to the creation of effective developer tools to better understand the properties and behavior of programs.
Enrico Armenio's Website
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Walker Demel

Student Track: Artificial Intelligence
Research Area: N/A
Advisor(s): Forbus, Kenneth
Cohort: September 2021
Expected Graduation Date: 2026
Research Statement: I’m currently working on methods for scaling symbolic knowledge representations. I also have interest in Natural language understanding.
Walker's Website
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Natalia Denisenko

Student Track: Artificial Intelligence
Advisor(s): Subrahmanian, VS
Cohort: September 2022
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David Dlott

Student Track: Computer Engineering
Advisor(s): Campanoni, Simone
Cohort: September 2022
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Griffin Dube

Student Track: Systems
Research Area: Parallel Systems, High Performance Computing
Advisor(s): Dinda, Peter
Cohort: September 2021
Expected Graduation Date: May 2026
Research Statement: Griffin is a Computer Science Ph.D.Student at Northwestern University. He is part of the Prescience Lab at Northwestern, led by Peter Dinda. Broadly, his interests lie in the realm of high-performance computing, extremely heterogeneous systems, and performance portability. More specifically Griffin is interested in improving the utilization of diverse hardware resources within/across systems by looking at solutions bridging layers of the hardware-software stack.
Griffin's Website
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Alexander Einarsson

Student Track: Artificial Intelligence
Research Area: Applied AI
Advisor(s): Hammond, Kristian
Cohort: September 2019
Expected Graduation Date: 2025
Research Statement: My research interests lie in the area of artificial intelligence for social good, mainly in analytics and education, where I strive to bridge the information gap between data and educational stakeholders by automating data science techniques. I believe that data-driven education will be the next big step in education, and want to be in the forefront of that development as it moves forward. Recently I have been working with CASMI and underwriters laboratories on a project aimed to build a framework for how to make predictive policing systems safe and equitable for society.
Alexander's Website
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Tochukwu Eze

Student Track: Systems
Research Area: Programming Languages
Advisor(s): Dimoulas, Christos
Cohort: January 2021
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Glenn Fernandes

Student Track: Graphics and Interactive Media
Research Area: HCI
Advisor(s): Alshurafa, Nabil
Cohort: September 2020
Expected Graduation Date: 2025
Research Statement: PhD Student in Computer Science, working with HABits Lab at the intersection of Computer Science and Preventive Medicine. My research revolves around user centric design and evaluation of end-to-end mobile health systems, which involves the integration of technology, healthcare and data science.
Glenn's Website
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Nicholas Franzese

Student Track: Systems
Research Area: Security and Privacy
Advisor(s): Wang, Xiao
Cohort: September 2020
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Chongyang Gao

Student Track: Interfaces
Research Area: Computer Vision
Advisor(s): Subrahmanian, VS
Cohort: September 2021
Expected Graduation Date: June 2026
Research Statement: I am a Ph.D. student at Northwestern University and I am interested in computer vision, NLP, and V-L Tasks. I have published several papers related to image captioning and text few-shot learning.
Chongyang's Website
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Hugo Flores Garcia

Student Track: Artificial Intelligence
Research Area: HCI
Advisor(s): Pardo, Bryan
Cohort: September 2020
Expected Graduation Date: 2025
Research Statement: Hugo is a Ph.D. student in Computer Science at Northwestern University, working under Prof. Bryan Pardo in the Interactive Audio Lab. Hugo's research interests lie at the intersection of machine learning, signal processing, and human computer interaction for music and audio. Hugo has previously worked on a deep learning framework for Audacity, an open source audio editor, and will be a research intern at Spotify and Descript during the latter half of 2022. Hugo holds an B.S. in Electrical Engineering from Georgia Southern University (2020). He is a jazz guitarist, and can be seen playing with various groups local to the Chicago area. Hugo enjoys augmenting musical instruments with technology, as well as making interactive music and art in SuperCollider and Max/MSP.
Hugo Flores' Website
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Kapil Garg

Student Track: TSB
Advisor(s): Zhang, Haoqi; Gergle, Darren
Cohort: September 2018
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Radhika Garg

Student Track: Security and Privacy
Advisor(s): Wang, Xiao
Cohort: September 2022
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Lily Ge

Student Track: Interfaces
Research Area: HCI and Information Visualization
Advisor(s): Kay, Matthew
Cohort: September 2021
Expected Graduation Date: June 2026
Research Statement: My research interests are broadly within HCI and information visualization. Specifically, I'm interested in visualization literacy and taking a more comprehensive look at ways we can better assess people's abilities to reason about and critically interpret visualizations. In addition to understanding people's ability to read from visualizations, it is also necessary to better understand the ability to identify erroneous and misleading visualizations, which may be present in instances of visualization misinformation. Furthermore, I'm interested in how visualizations can assist in the decision-making and problem-solving process.
Lily's Website
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Ammar Gilani

Student Track: Artificial Intelligence
Research Area: Machine Learning
Advisor(s): Liu, Han
Cohort: September 2019
Expected Graduation Date: June 2024
Research Statement: My research interests are convex, non-convex optimization. More specifically, I aim to solve sequential decision making problems by formulating them into convex optimization problems. Regarding non-convex optimization, my research is focused on bilevel optimization, an application of which is hyperparameter tuning.
Ammar's Website
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Anthony Goeckner

Student Track: Computer Engineering
Research Area: Multi-Agent Systems, Robotics
Advisor(s): Zhu, Qi
Cohort: September 2021
Expected Graduation Date: June 2026
Research Statement: I am now studying adaptation in multi-agent systems. Prior to joining Northwestern, my work focused on cyber-physical systems and swarm robotics.
Anthony's Website
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Nathan Greiner

Student Track: Computer Engineering
Advisor(s): Campanoni, Simone
Cohort: September 2022
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Ziyang Guo

Research Area: HCI
Advisor(s): Hullman, Jessica
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Can Gurkan

Student Track: Artificial Intelligence
Research Area: Agent-Based Modeling
Advisor(s): Wilensky, Uri
Cohort: September 2017
Expected Graduation Date: December 2023
Research Statement: My research is about combining multi-agent systems with open-ended neuro-evolutionary processes, that is building open-ended multi-agent systems where each interacting agent has evolving neural nets that can increase arbitrarily in complexity, in order to understand the nature and evolution of intelligence better as well as finding novel machine learning technologies using this approach.
Can's Website
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William Hancock

Student Track: Artificial Intelligence
Advisor(s): Forbus, Kenneth
Cohort: September 2017
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Haiyang Hancock

Student Track: Computer Engineering
Research Area: Architecture
Advisor(s): Hardavellas, Nikos
Cohort: September 2015
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Bryan Head

Student Track: Artificial Intelligence
Research Area: Agent-Based Modelling
Advisor(s): Wilensky, Uri
Expected Graduation Date: June 2023
Research Statement: I research and develop tools and techniques for simulating and analyzing complex systems. In particular, my work focuses on extending the capabilities of traditional agent-based modeling, analyzing agent-based models with machine learning, and incorporating machine learning techniques into the behaviors of agents.
Bryan's Website
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Maryam Hedayati

Student Track: CSLS
Research Area: HCI
Advisor(s): Kay, Matthew
Cohort: September 2019
Expected Graduation Date: June 2024
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Garrett Hedman

Student Track: CSLS
Advisor(s): O'Rourke, Eleanor
Cohort: September 2017
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Samuel Hill

Student Track: Artificial Intelligence
Cohort: September 2017
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Brian Homerding

Student Track: Systems
Research Area: Compilers
Advisor(s): Campanoni, Simone
Cohort: September 2020
Expected Graduation Date: May 2025
Research Statement: My research is focused on addressing the challenges involved with generating effective parallelism. I'm developing compiler abstractions to leverage automatic parallelization while capturing the semantics of explicitly parallel applications. These abstractions enable decisions about the parallelism of an application to be determined within the compiler enabling more performance, scalable parallelism.
Brian's Website
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Donna Hooshmand

Student Track: Artificial Intelligence
Research Area: AI/ML
Advisor(s): Hammond, Kristian
Cohort: September 2021
Expected Graduation Date: 2026
Research Statement: I work in the Cognition, Creativity, and Communication (C3) Lab, lead by Professor Kristian Hammond. My research interest is in developing human-centered AI applications.
Donna's Website
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Kai-Yuan Hou

Student Track: Computer Engineering
Research Area: High-Performance Computing
Advisor(s): Choudhary, Alok
Cohort: September 2016
Expected Graduation Date: December 2022
Research Statement: My research focus on parallel I/O performance on large scale super computers. Most of my projects are about improving the I/O performance and efficiency in high-level parallel I/O libraries.
Kai-Yuan's Website
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Jerry Yao-Chieh Hu

Student Track: Artificial Intelligence
Research Area: Machine Learning
Advisor(s): Liu, Han
Cohort: September 2021
Expected Graduation Date: 2026
Research Statement: My primary research interests lie at the intersection of modern artificial intelligence, physics, and mathematics.
Jerry Yao-Chieh's Website
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Evey Huang

Student Track: TSB
Research Area: HCI
Advisor(s): Gerber, Ellizabeth; Easterday, Matthew
Cohort: September 2019
Expected Graduation Date: December 2024
Research Statement: I am interested in designing and building intelligent technology that is capable of collaborating with human users to support learning, innovation, and work. Currently I am designing Human-AI systems to scale and support learning in ill-defined domains like design, teaching, and innovation. Specifically, I am collaborating with an innovation incubator and building a mixed-initiative intelligent system to collaborate with coaches to support novice entrepreneurs in creating successful and innovative products.
Evey's Website
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Zanhua Huang

Student Track: Computer Engineering
Research Area: HPC
Advisor(s): Choudhary, Alok
Cohort: September 2021
Expected Graduation Date: June 2026
Research Statement: My research interests lie in large-scale applications in parallel and distributed environments with a focus on parallel file I/O, distributed storage system design, and communications.
Zanhua's Website
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Ayse Hunt

Student Track: CSLS
Advisor(s): Horn, Michael
Cohort: September 2020
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Monisola Mercy Jayeoba

Student Track: TSB
Cohort: June 2022
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Ruochen Jiao

Student Track: Computer Engineering
Research Area: Machine Learning
Advisor(s): Zhu, Qi
Cohort: September 2019
Expected Graduation Date: June 2024
Research Statement: I mainly focus on machine learning and its application in autonomous driving, including perception, prediction and planning modules. My research contains both system-level design with an emphasis on safety and connectivity and ML algorithm level design with emphasis on generative models and adversarial robustness.
Ruochen's Website
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Stephanie Jones

Student Track: CSLS
Advisor(s): Worsley, Marcelo
Cohort: September 2018
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Nirmit Joshi

Student Track: Theory
Research Area: Theoretical Machine Learning and Optimization
Advisor(s): Vijayaraghavan, Aravindan
Cohort: September 2021
Expected Graduation Date: June 2026
Research Statement: I have broad interests in various aspects of theoretical computer science and mathematics. My research focuses on the mathematical foundations of machine learning and optimization, especially in random graphs theory, deep learning theory, and distributed optimization. My research aims to design efficient algorithms with provable performance guarantees for various learning problems.
Nirmit's Website
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Byungjin Jun

Student Track: Systems
Research Area: Networking
Advisor(s): Bustamante, Fabian
Cohort: September 2017
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Sanchit Kalhan

Student Track: Theory
Research Area: Machine Learning
Advisor(s): Makarychev, Konstantin; Vijayaraghavan, Aravindan
Cohort: September 2018
Expected Graduation Date: December 2023
Research Statement: Working on It
Sanchit's Website
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Negar Kamali Zonouzi

Research Area: HCI
Advisor(s): Jacobs, Maia
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Vijay Kandiah

Student Track: Computer Engineering
Research Area: Architecture
Advisor(s): Hardavellas, Nikos
Cohort: January 2018
Expected Graduation Date: December 2023
Research Statement: My current research interests are at the intersection of computer architecture and compilers for energy-efficient high-performance computing. My earlier PhD work include AccelWattch, a power modeling framework for modern GPUs and ST2GPU, an energy-efficient GPU Architecture Design.
Vijay's Website
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Peng Kang

Student Track: Interfaces
Research Area: Robotics, Graphics
Advisor(s): Cossairt, Oliver Strides
Cohort: September 2018
Expected Graduation Date: 2023-2024
Research Statement: I have a broad interest in artificial intelligence and its applications. Previously, my research focused on 2nd generation neural networks – ANNs, including numerical analysis and their applications in recommender systems and computer vision. And my current research focuses on 3rd generation neural networks – SNNs and their event-driven neuromorphic applications in vision, audio, and robotic learning. I believe building SNNs with the lessons from ANNs can lead us to energy-efficient human-like artificial intelligence.
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Mohammad Kavousi

Student Track: Systems
Research Area: Security and Privacy
Advisor(s): Chen, Yan
Cohort: September 2017
Expected Graduation Date: December 2022
Research Statement: My area covers many aspects of security practices. From configuration, to data collection, detection, forensics and remediation.
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Paula Patricia Ogol Kayongo

Student Track: Interfaces
Advisor(s): Hullman, Jessica; Hartline, Jason
Cohort: September 2018
Expected Graduation Date: December 2022
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Yiduo Ke

Student Track: Interfaces
Research Area: Theory
Advisor(s): Khuller, Samir
Cohort: September 2021
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Jacob Kelter

Student Track: CSLS
Advisor(s): Wilensky, Uri
Cohort: September 2018
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Aman Khalid

Cohort: September 2021
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 Kilic

Muhammed Nur Talha Kilic

Student Track: Artificial Intelligence
Advisor(s): Choudhary, Alok
Cohort: September 2022
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Hyeok Kim

Student Track: Interfaces
Research Area: Data Visualization
Advisor(s): Hullman, Jessica
Cohort: September 2019
Expected Graduation Date: June 2024
Research Statement: My research interests include responsive visualization, visualization retargeting, and design research. Responsive visualization refers to adapting visualizations for different devices, such as desktop/laptop, smartphone, etc., to address recent increases in mobile readers to web-based data visualizations. My current work focuses on mixed-initiative systems to support an author choosing among responsive transformations given a source visualization. In a broader sense, I am interested in tools for retargeting data visualizations for different audience, context, and goals. Lastly, I collaborate on various design research projects for sexual violence survivors, caregivers for people with dementia, and novice researchers.
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Taewook Kim

Student Track: TSB
Research Area: HCI
Advisor(s): Kay, Matthew
Cohort: September 2021
Expected Graduation Date: 2026
Research Statement: I am interested in leveraging NLP/ML models to resolve problems in human-human communication. I design, build, and evaluate systems to encourage people to share affirmative emotions. Recently, I am working on designing systems for supporting the mental health of caregivers of a person with dementia.
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Chris Kraemer

Student Track: Computer Engineering
Research Area: Embedded Systems
Advisor(s): Dinda, Peter; Hester, Josiah
Cohort: September 2019
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Rashna Kumar

Student Track: Systems
Research Area: Networking
Advisor(s): Bustamante, Fabian E,Kuzmanovic, Aleksandar
Cohort: September 2019
Expected Graduation Date: June 2024
Research Statement: My research focuses on the trends towards and the implications of third-party and centralization on the Internet, developing approaches to characterize these trends and potentially addressing their impact. I am currently looking at these trends globally, across services like DNS, content and hosting infrastructure, and developing client-based solutions for DNS centralization.
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Mukundan Kuthalam

Student Track: Artificial Intelligence
Research Area: Natural Language
Advisor(s): Forbus, Kenneth
Cohort: September 2020
Expected Graduation Date: June 2025
Research Statement: My research interests currently revolve around NLU and commonsense reasoning. The project I am currently working on involves generating qualitative representations of the entries in the ATOMIC dataset, which focuses on capturing social commonsense knowledge. These representations can then be used as a library of knowledge for use in analogical chaining, which entails starting with a representation of a person’s current state of knowledge and using analogy to integrate this knowledge with past experiences. The result of the repeated chaining can be both explanations for an observation and inferences that follow from an observation. I believe this project can lead to better QA systems and provide more insight into the distinctions between quantitative and qualitative approaches to commonsense reasoning.
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Harrison Kwik

Student Track: TSB
Research Area: HCI
Advisor(s): O'Rourke, Eleanor
Cohort: September 2019
Expected Graduation Date: June 2024
Research Statement: Learning how to program is hard, and many students in introductory computer science courses struggle to overcome programming challenges on their own. As computer science courses continue to grow in size, however, it becomes increasingly important for students to be able to do so. In order for students to tackle problems in these self-directed ways, they need to "learn how to learn" by developing important metacognitive learning skills such as planning, reflecting, and seeking help, which allow students to regulate their learning process. In my research, I explore the learning struggles that students face in computer science, and build tools to help support their process of monitoring, diagnosing, and addressing the underlying issues behind them.
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