Khoury faculty member David Bau stands and points at a projection screen showing two images of a dog while three students look at the screen

Khoury Research Apprenticeship

Work alongside leading researchers in your field, explore your research interests, and pave a pathway to your future goals through the prestigious and unique Khoury Research Apprenticeship. 

About the Khoury Research Apprenticeship opportunity

Started in 2019, the prestigious Khoury Research Apprenticeship is designed to provide current master’s students the opportunity to participate in relevant research opportunities while being mentored and supported by faculty advisors. The program, held in the fall and spring semesters, is an excellent way to explore your current research interests or help to pave a pathway to a future PhD when you complete your master’s.

Nomination process

Current faculty are encouraged to nominate students who they feel demonstrate both strong academic skills and have presented an outstanding affinity and talent for research. During this nomination process, faculty are asked to present proposals for research projects that apprenticeship recipients can participate in. Academic advisors are also welcomed to nominate students they feel could be good candidates for the apprenticeship, or who have expressed an interest in becoming involved in research or continuing to Khoury College’s PhD program.

Once nominated, students can apply to the projects that they’re interested in and will be interviewed by the corresponding faculty sponsors. A committee will review the faculty selections and the students’ chosen interest to determine final assignments.

The apprenticeship program played a big role in my decision to pursue a PhD. When I was nominated for the apprenticeship program, I neither had any prior research experience, nor did I intend on pursuing a PhD. The program gave me an opportunity to be involved in research in academia and interact with fellow grad students and faculty members

Satyajit Gokhale, PhD in Computer Science student

If you’re awarded an apprenticeship

Students who are awarded apprenticeships will register for the four-credit master’s course, funded by Khoury College, to begin their research work. At the end of the apprenticeship, students will have the opportunity to present their work to an audience of peers and faculty and highlight their findings.

Fall apprenticeship timeline

Spring apprenticeship timeline

2024 Research Apprenticeship participants

Fall 2024 participants

Students who participated in the Fall 2024 Khoury Research Apprenticeship program, their projects, and their faculty advisors:

Spring 2024 participants

Students who participated in the Spring 2024 Khoury Research Apprenticeship program, their projects, and their faculty advisors:

Past Research Apprenticeship participants

Fall 2023 participants

  • Leigh-Riane Amsterdam: VR Training For Interview Preparation: Understanding Challenges Faced By Underrepresented Groups (Caglar Yildirim)
  • Debankita Basu: Unveiling Digital Truths in Deepfake Detection ​with CNN and Transformer Models (Hongyang Zhang)
  • Siddharth Chakravorty: Investigation of Matter Standard Compliance in Vendor IoT Devices. (David Choffnes)
  • Bereket Faltamo: MRI Image Segmentation For Musculoskeletal Model (Jeongkyu Lee)
  • Lauryn Fluellen: Improved Speech Recognition for Impaired Speakers (Aachan Mohan)
  • Sumukhi Ganesan: Decoding Dropouts: ​Mapping Curricular Metrics to Student Attrition in CS Education (Albert Lionelle)
  • Juan Diego Dumez Garcia: A Comprehensive Approach to 3D Scene Analysis and Visualization (Yvonne Coady)
  • Ameya Santosh Gidh: Advancing Design Tools for Engineering Knitted Materials (Megan Hoffman)
  • Tom Henehan: Is Globalization a One-Way Street? An inquiry using MNA (multiplexed network analysis) and M&A (Ravi Sundaram)
  • Diptendu Kar: Evaluation of Large Language Models in Solving Cybersecurity Capture the Flag (CTF) Challenges (Jose Sierra)
  • Shuyi Lin: NN4SYSBENCH: Characterizing Neural Network Verification for Computer Systems (Cheng Tan)
  • Hantong Liu: A Unified Storage Platform for Heterogeneous Storage Devices (Ji-Yong Shin)
  • Ruochen Liu: Privacy and Censorship in Chinese Fandom (Ada Lerner)
  • Cecilia Lopez: Investigating Moderation Challenges to Combating Hate and Harassment: The Case of Mod-Admin Power Dynamics and Feature Misuse on Reddit and Other Platforms (Ada Lerner)
  • Jiayi Lu: Redefining Roles: Adapting Doctor-AI Collaboration in Sepsis Diagnosis (Dakuo Wang)
  • Mahvash Maghrab: Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing (Mohammad Toutiaee)
  • Shagun Saboo: From Paper to Pixel: Enhancing Handwritten Form Validation and Online Filling with Generative AI (Divya Chaudhary)
  • Subhankar Shah: A Metric Pseudo-Grid for ​High-Dimensional Similarity Search (Mario Nascimento)
  • Abdulaziz Arif Suria: LLM driven human-like Non-Playable Characters (NPCs) in Virtual Reality (Mirjana Prpa)
  • Keerthana Velilani: Enriching Cross-Cultural Image Captioning with Generative AI and Human-in-the-Loop (Saiph Savage)
  • Xiaoman Yang: The Use of Feedback and Its impact on Students’ Performance in Project-Based Courses: Instructors’ and Students’ perceptions (Oscar Veliz)
  • Zhiyuan Yang: Solar Panel Farms Mapping ​through Multispectral Satellite Imagery and Enhanced Swin Transformer Model (Ryan M. Rad)
  • Yanting Zheng: Evaluate Render Quality of ​(Mike Shah)
  • Jaiyi Zhou: Exploring Rewrite Rule Behavior Trees for Tile-based Games (Seth Cooper)

Spring 2023 participants

  • Swati Agarwal: GigSense (Saiph Savage)
  • Pavan sai kumar Alladi: In-memory data store for Serverless functions (Ji-Yong Shin)
  • Nidutt Bhuptani: Human-AI-NLP (Christoph Riedl)
  • Zeyu Cui: Container storage game (Seth Cooper)
  • Xueyan Feng: Wide FOV HMD on Augment Memory, Perception, and Cognition (Clifton Forlines)
  • Tushita Gupta: Benchmarking of statistical methods for mass-spectrometry based proteomics (Olga Vitek)
  • Jose Notsky Lou: The effect of feedback on a team’s performance, motivation, and results (Oscar Veliz)
  • Divyadharshini Muruganandham: Analyzing conversational alignment and second language development with NLP (Tony Mullen)
  • Weder Ribas: Representations of Reality Aerial NeRFs in Canadian Clouds (Yvonne Coady)
  • William Rhodes (Cheng Tan)
  • Sumeet Sachdev: Multimodal User-level Models for Mental Health Applications on Social Media (Silvio Amir)
  • Kartik Sharma: What goes on the Internet stays on the Internet (Engin Kirda)
  • Hritesh Sharad Sonawane: Attacks against congestion control algorithms (Cristina Nida-Rotaru)
  • Shubham Sonawane: Improving the Reproducibility of Open Source Software Artifact Datasets’ (Jonathan Bell)
  • Minyi Xu: Representations of Reality Aerial NeRFs in Canadian Clouds (Yvonne Coady)
  • Ruohe Zhou: Segmentation and auto-labeling of insects’ legs using machine learning for DeepLabCut (Jeongkyu Lee)

Fall 2022 participants

  • SzeYi “Reina” Chan: Brukel: Effectiveness in Storytelling (Bob DeSchutter)
  • Jianhua “Chandler” Che: GigSense: Intelligent Collective Action Interfaces for Gig Workers (Saiph Savage)
  • Caroline Craig: Learning Alignment Models from Multiple Translations of Ancient Greek Texts (David Smith)
  • Shriya Dhaundiyal: VOICE ASSISTANT ​using profiling (David Choffnes)
  • Mingxi Jia: SEIL: Simulation-augmented Equivariant Imitation Learning (Robert Platt)
  • Charles “Chip” Kirchner: Macro-Action Value Decomposition for Multi-Agent Deep Reinforcement Learning (Chris Amato)
  • Aniruth Ramesh and Parthasarathy Murugesan​: Reddit as an evaluation for Dialog responses (David Smith)
  • Omar Rashwan: Reconfiguration as a Common Operation in Distributed Systems (Ji-Yong Shin)
  • Keith Rebello: Improving User Affect Through Empathic Conversational Agents​ (Tomothy Bickmore)
  • Sumukh Vasisht Shankar: Equivariant Neural Networks​ for Spatial Light Modulator (Robin Walters)
  • Kinshuk Sharma: Benchmarking Distribution Shifts for Domain Generalization (Hongyang Zhang)
  • Dmitrii Troitskii: dewimplify: Validating Intermediate ​Representation of WebAssembly (Frank Tip)

Spring 2022 participants

  • Gerard Otalora Canovas: Designing an A.I. Interface to Empower Gig Workers (Saiph Savage)
  • Brenden Collins: Running computational benchmarks for graph layout algorithms (Cody Dunne)
  • Shireen Firdoz: Design and implementation of a new reconfiguration protocol in etcd (Ji-Yong Shin)
  • Riley Grant: Reimagining Programming with AlphaZero (Karl Lieberherr)
  • Haoyu He: A benchmark suite for neural network verification for systems (Cheng Tan)
  • Thai Huynh: Gender box 2 (Ari Waldman)
  • Maanasa Kaza: MSstatsShiny: An Interactive Cloud-Based UI for High Quality Analysis of Proteomic Experiments (Olga Vitek)
  • Jonathan Merrin: Improving human task performance with Quality Diversity inspired tools (Mike Shah)
  • Nicolas Osborn: Quality Diversity in Human Computation (Seth Cooper)
  • Mino Reyes: Computer Vision and Rough 2D Maps for Navigation in Novel Environments (Lawson Wong)
  • Shreya Singh: A Calibrated approach to Semi-Supervised Learning For Medical Image Classification (Hongyang Zheng)
  • Tim Swierzewski: Studying Dark Patterns in Voice Assistants (David Choffnes)
  • Calvin Yu: Finding Structural Similarities in Research Papers Through Visual and Textual Analysis (Cody Dunne)
  • Kicho Yu: Data Localization Compliance with Internet Measurement about Companies in European Union (David Choffnes)

Fall 2021 participants

  • Ian Dardik: Formal Verification of a Distributed Dynamic Reconfiguration Protocol (Stavros Tripakis)
  • Tarek Elaydi: Reducing irreducible error in image datasets (Bruce Maxwell)
  • Samuel Engida: Quantifying the Nauseogenicity of Virtual Reality experiences (Caglar Yildirim)
  • Ari Fleischer: Misinformation and Discrimination (Ari Waldman)
  • Akanksha Gupta: FIND-M: Identify Counterfeit Medical Products through Crowdsourcing (Ravi Sundaram)
  • Huiyu He: Exploring Consistency Semantics for Distributed Systems in Serverless Environments (Ji-Yong Shin)
  • Haoyu He: Persuasive Robots for Health Behavior Change (Timothy Bickmore)
  • Devina Raithatha: Centering Joy in BIPOC PWI Experiences (Alexandra To)
  • Sunny Sandeepbhai Shukla: Attention and Transformer models for Speaker – Follower VLN tasks (Lawson Wong)
  • Virender Singh: BERT Language Model analysis and application to fine tuning tasks (Hongyang Zhang)

Spring 2021 participants

  • Joseph Burns: VizioT: Visualizing Network Traffic for IoT Devices (David Choffnes)
  • Ian Dardik: Proving Correctness of a Novel Consensus Protocol (Stavros Tripakis)
  • Colin Dsouza: Design explorations for efficient and verified distributed system compositions (Ji-Yong Shin)
  • Satyajit Gokhale: Desynchronizer: Introducing Asynchrony in JavaScript Applications (Frank Tip)
  • Xingyu Lu: Deep Multi-Agent Reinforcement Learning (Christopher Amato)
  • Rebecca Mashaido: Detecting Neural Network Integrity Violations via Sensitive Samples (Thomas Wahl)
  • Vishal Maurya: QGen: a workload generator for benchmarking analytic frameworks (Peter Desnoyers)
  • Urvaksh Padamsi: The Truth Pill: Empowering Consumers to Identify Counterfeit Medical Products through Crowdsourcing and Deep Networks (Ravi Sundaram)
  • Waleed Saeed: Gender Binary in Law (Ari Waldman)
  • Hearan Won, Rachelle Angeli Maranon: Data-driven Modeling of Disaster Response (Stacy Marsella)