December 2025 - Gradstudents - mailman.cs.vt.edu

A short note of appreciation
by Melendez Maita, Diego 24 Dec '25

24 Dec '25

Dear graduate students, I wanted to personally wish you all a holiday season filled with joy, warmth, and good company, along with a well-earned break that offers calm, perspective, and space to unplug and reflect on how far you have come. Thank you for the care, time, and energy each of you invests in making this community strong and welcoming. Congratulations to our friends and colleagues who have successfully defended their PhD theses; this outstanding achievement reflects years of hard work that are now paying off. We are confident that you will continue to have a lasting impact on the people around you, and we wish you the very best in your future endeavors. Happy Holidays! Diego Meléndez-Maita

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Spring 2026 CS 6724: Special Topics on Adaptive User Interfaces
by Chen, Yan 21 Dec '25

21 Dec '25

Dear Students, Next semester I’m offering CS 6724 (13746): Advanced Topics in HCI (Adaptive User Interfaces), where we’ll read and dissect phenomenal CHI/UIST/IUI papers on interfaces that “think” with us, then redesign them with a sharp eye for failure modes, evaluation, and ethics. We will also be inviting a few researchers from both academia and industry to share their stories and insights on the relevant topics. If that sounds like your kind of fun (or your kind of productive procrastination), check the attached flyer and feel free to reach out. [cid:1ac48cb6-445d-40bd-a166-dba0fb57a850] Yan Chen Assistant Professor, Computer Science Department Virginia Tech, Blacksburg, VA https://chensivan.github.io/

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Participants needed for a Git related project
by Hooshangi, Sara 17 Dec '25

17 Dec '25

Hello, A collaborator of mine (Dr. Thorgeirsson) at ETH Zurich in Switzerland is recruiting participants for a study about Git and version control concepts. If you have some familiarity with Git, for example have used basic commands such as "git push", "git pull", for a project, consider participating and earning some pre holiday cash ! During the session, you will be asked to solve some Git related questions while articulating your thought process with the Think Aloud method. This will help the researchers better understand whether the questions are suitable for a new Git assessment that is being developed. Time & compensation: One 60-minute session; receive a gift card worth of ~$25 (20 CHF, Swiss Franc). Eligibility: 18+ and familiaty with basic Git concepts. To participate: Please fill out this very brief questionnaire and you will be contacted: https://docs.google.com/forms/d/e/1FAIpQLSdQ33BVGEMAMphIYWnGQJTnrkpJTZ3Dbu5… This study is approved by ETH IRB: 25 ETHICS-315. Questions? Contact Dr. Sverrir Thorgeirsson at sverrir.thorgeirsson(a)inf.ethz.ch<mailto:sverrir.thorgeirsson@inf.ethz.ch> Thanks ---------------------------------- Sara Hooshangi, Ph.D. Collegiate Professor Director of Master of Engineering Program Department of Computer Science Virginia Tech

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Arinjoy Basak's PhD Defense
by Shaffer, Cliff 12 Dec '25

12 Dec '25

All are invited to attend the PhD defense for Arinjoy Basak. Title: Developing an Automated Practice Environment and Feedback Engine for Guided Instruction in the Deformable Bodies Course Date: Wednesday, December 17 at 1:00pm EST Zoom link: https://virginiatech.zoom.us/j/81568357317?pwd=TVfVGnABPkUFIWbyRb9eCXxcBnjT… Committee: Cliff Shaffer, Chair Lenwood Heath Chris North Jacob Grohs Nicole Pitterson Kurt Gramoll Abstract: Mathematical problem solving is a fundamental competency demanded by employers and an integral component of engineering education. We seek to improve instruction in mathe- matical concept-based engineering courses. To this end, we created an interactive problem solving environment that provides targeted feedback on a student’s problem solving attempts by having them work mathematical exercises in a dedicated intuitive software interface. We provide syntactic feedback that is independent of the problem statement, as well as seman- tic feedback. Semantic feedback is achieved by systematically comparing the ground-truth solution for the exercises provided by the instructor with the student’s attempt and recon- structing appropriate error messages based on the differences detected between the solutions. Our systematic approach is based on analyzing the dependencies between equations used by the student, and comparing either individual equations or a reduced representation of a se- quence of equations with each other to identify and report differences. We conducted a study to evaluate the quality of the feedback provided by the system. We also conducted surveys on usability of the interface. Based on the studies conducted, we prove the effectiveness of the system and the engine both as a tool for providing rapid and accurate assessment of correctness of exercise attempts. -- Dr. Cliff Shaffer Professor Department of Computer Science Phone: (540) 231-4354 Virginia Tech, Blacksburg, VA 24061 WWW: www.cs.vt.edu/~shaffer<http://www.cs.vt.edu/~shaffer>

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Blessy Antony's doctoral defence at 4pm on December 17, 2025
by T. M. Murali 10 Dec '25

10 Dec '25

Dear colleagues, Anuj and I invite you to Blessy Antony's doctoral defence at 4pm on December 17, 2025. The details are below. Please join us! Murali *Discovering Viral Hosts, Mutations, and Diseases* *using Machine Learning* Blessy Antony 4pm-6pm, December 17, 2025 D&DS 450 https://virginiatech.zoom.us/my/tmmurali *Thesis committee* Anuj Karpatne and T. M. Murali, Co-chairs Debswapna Bhattacharya Lenwood S. Heath Predrag Radivojac (Northeastern University) The discovery of a novel virus raises three important questions, namely, which host(s) can the virus infect, what mutations in the virus could affect its interaction with its hosts and enable a host shift, and which diseases can the virus cause in humans. We propose novel machine learning (ML)-based solutions to these three different problems in computational virology. (i) We develop a viral protein language model for predicting the host infected by a virus, given only the sequence of one of its proteins. Our approach, ‘Hierarchical Attention for Viral protEin-based host iNference (HAVEN)’, includes a novel architecture comprising segmentation and hierarchical self-attention to tackle the challenges posed by long sequences. Pretrained on 1.2 million viral protein sequences, the model accepts any protein sequence of any virus and predicts its host. We integrate HAVEN with a prototype-based few-shot learning classifier to generalize it to predict rare and unseen hosts, and hosts of unseen viruses. (ii) Structured datasets of known viral mutations and their effects are required to develop computational models that can predict potential detrimental changes in novel animal viruses. We leverage large language models (LLMs) to create these datasets from the results of experimental studies available as unstructured text in scientific literature. We design an open-ended task for ‘scientific information extraction (SIE)’ from publications and propose a unique two-step retrieval augmented generation (RAG) framework for the same. We curate a novel dataset of mutations in influenza A viral proteins. We use this dataset to benchmark our proposed approach, a wide range of LLMs, and RAG-, and agent-based tools for SIE. (iii) Finally, we look at the effects of viral infections in humans. Specifically, we focus on the long-term effects of SARS-CoV-2 (or long COVID) wherein patients experience the persistence of COVID-19 symptoms for a long period of time after their initial SARS-CoV-2 infection. We propose an ML-based classification pipeline to predict the diagnosis of long COVID in COVID-19 patients using their electronic health records in the National COVID Cohort Collaborative, which is the largest collection of clinical data across the US. Using techniques to explain our models’ prediction for each patient, we uncover many features that were correlated with long COVID. We also evaluate the impact of different data sources on our long COVID prediction models using a novel cross-site analysis. For all the three problems, we demonstrate the accuracy and robustness of our proposed solutions through a comprehensive series of experiments. These contributions are aimed towards developing a coherent system for pandemic preparedness and prevention. -- Director, NSF COMPASS Center <https://compass-pipp.org/> Director, Pandemic Prediction and Prevention Destination Area <https://wordpress.cs.vt.edu/pandemicpredictionandprevention/> Associate Department Head for Research Department of Computer Science Virginia Tech http://bioinformatics.cs.vt.edu/~murali/

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Sheikh Shadab Towqir's PhD dissertation defense exam scheduled at 8am on Thursday 12/11/2025
by Meng, Na 10 Dec '25

10 Dec '25

Hi all, You are invited to virtually attend Sheikh’s PhD dissertation defense exam. Title: Automating The Detection and Resolution of Build Conflicts in Software Merge for Java Programs Date: Thursday, December 11th, 2025 Time: 8:00am-9:30am EDT Zoom link: https://virginiatech.zoom.us/j/82141326618 Abstract: Version control systems (VCS) like Git provide an efficient environment for collaborative software development. However, a major challenge of using such systems is the conflicts that occur when developers try to merge branches. This research focuses on build conflicts–a category of higher-order software merge conflicts. The goal of our research is to develop and implement methodologies that automate the detection and resolution process of build conflicts. Our research consists of three pieces. First, we designed and implemented a graph-based pattern matching approach, BUCOND (Build Conflict Detector), to detect conflicts via static analysis. Our evaluation shows that BUCOND accurately identified build conflicts on both synthetic and real-world datasets; it demonstrated great applicability in scenarios where compiler-based tools are inapplicable. Second, we created a hybrid program transformation approach, BuCoR (Build Conflict Resolver), to opportunistically resolve conflicts. We evaluated BuCoR in real-world merging scenarios, observing its great capabilities of generating syntactically correct resolutions and mimicking human developers’ resolution practices. Third, we explored a new approach of resolving conflicts using LLMs, and empirically compared the usage of different LLMs as well as diverse prompt designs. We demonstrate that, when properly guided, LLMs can provide highly accurate conflict resolutions. Our research will help developers detect conflicts more efficiently and resolve conflicts with higher effectiveness as well as rigor. Through addressing issues caused by merge conflicts, it will help improve programmer productivity and software reliability. Cheers, Na

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Taufiq Daryanto's Master's Thesis Defense: Towards Human-AI Teaming for Skill Development
by Rho, Eugenia 08 Dec '25

08 Dec '25

Dear CS Faculty and Graduate Students, You are invited to attend Taufiq Daryanto's final Master's defense. Title: Towards Human-AI Teaming for Skill Development: From Dyadic Interview Practice to Triadic Programming Collaboration Date: Wednesday, December 10, 2025 Time: 11:00 AM EST Room: Gilbert Place 3218 Zoom Link: https://virginiatech.zoom.us/j/87582165317?pwd=7UCR545kENIH8wy8PFb5PW4RdZ66… <https://virginiatech.zoom.us/j/87582165317?pwd=7UCR545kENIH8wy8PFb5PW4RdZ66…> Meeting ID: 875 8216 5317 Passcode: 233834 Committee members: * Dr. Eugenia Rho (Chair) * Dr. Yan Chen * Dr. Chris Brown Abstract: As the job market for CS graduates grows increasingly competitive, effective preparation for technical interviews through mock interviews and programming practice has become critical. This thesis explores how AI can support skill development in these domains, progressing from AI as a sole practice partner to AI as a collaborative teammate. Our first two studies investigate LLM-based conversational AI for interview preparation. Study 1 developed an interview system grounded in reflective learning and dialogic feedback, enabling learners to engage in low-stakes practice with personalized, interactive feedback. Study 2 extended this work to technical interviews, exploring how conversational AI can support the think-aloud practice in technical interviews through simulation, feedback, and example. Together, these studies demonstrate the value of AI as a dyadic practice partner. However, participants reported that while AI practice was useful, peer-based engagement may offer stronger social connection and motivation, suggesting that AI should augment rather than replace human collaboration. This insight motivated our third study, which investigates human–AI teaming in a triadic configuration. We introduce human–human–AI triadic programming, where two humans collaborate with a proactive AI agent. Results from 20 participants show that this triadic collaboration improves collaborative learning and social presence while encouraging more responsible AI use. Together, these studies advance understanding of how AI can support skill development, illustrating a trajectory from dyadic human–AI interaction toward richer forms of human–AI teaming that preserve the pedagogical and social benefits of human collaboration. Eugenia Rho<https://eugeniarho.com/> Assistant Professor<https://eugeniarho.com/> Computer Science | Virginia Tech<https://cs.vt.edu/> [cid:6a7d6327-cc85-4e22-b52d-2e50c5effbeb]<https://eugeniarho.com/sail/>

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CSGC: Seeking Volunteer Speakers for CS Quals Q&A Session
by Madabushi, Sindhuja 05 Dec '25

05 Dec '25

Dear Computer Science Graduate Students, The Qualifying Exams are coming up soon for many of our colleagues in the CS graduate program. To support them, the CS Grad Council is organizing a Q&A session focused on navigating the qualifier exam process. We are seeking volunteers who have already completed their qualifiers and are willing to share practical guidance on preparation, expectations, and lessons learned. This is a great opportunity to help fellow students feel confident and informed as they approach an important milestone. If you are interested in speaking at the event, please fill in the form below: https://docs.google.com/forms/d/e/1FAIpQLSd8pEw1nNVZ3H0GhvSKkFmnxhxHdmzRNq7… Thank you for considering this and for contributing to a stronger, more supportive graduate community. Sincerely, — Sindhuja Madabushi, Secretary, Computer Science Graduate Council PhD Student in CS@VT (505) 457-7721 msindhuja(a)vt.edu<mailto:msindhuja@vt.edu> | Website<https://sindhujamadabushi.github.io/>

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Quick Reminder: CS Graduate Climate Survey
by Madabushi, Sindhuja 04 Dec '25

04 Dec '25

Hello CS Grad Students, This is a friendly reminder to complete the CS Graduate Climate Survey. Your anonymous feedback plays a critical role in helping the department understand student experiences and improve our graduate program. If you have already submitted the survey this week, Thank you! No further action is needed. If not, please take a few minutes to fill it out here: https://forms.gle/BiieFZCSPWEcCTWx5 Your input genuinely helps drive improvements that benefit all of us. PS: This data will be collected anonymously and it will only be used for the purpose of strategic improvement. A report of the aggregated data will be shared with department administrators to be used when determining strategic priorities for the program and to facilitate transparency. If you have any questions about the survey, please contact the CS Grad Council: bernardm(a)vt.edu<mailto:bernardm@vt.edu> Sincerely, — Sindhuja Madabushi, Secretary, Computer Science Graduate Council PhD Student in CS@VT (505) 457-7721 msindhuja(a)vt.edu<mailto:msindhuja@vt.edu> | Website<https://sindhujamadabushi.github.io/>

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Invitation: PhD Dissertation Defense — Han Jun Yoon (Dec 5, 10:00 AM–12:00 PM)
by Cho, Jin-Hee 03 Dec '25

03 Dec '25

Dear CS Faculty and Graduate Students, You are cordially invited to attend the final PhD dissertation defense of Han Jun Yoon, to be held on: Date: Friday, December 5, 2025 Time: 10:00 AM – 12:00 PM Location: https://virginiatech.zoom.us/my/jicho Title: Cyber-Physical-Social Systems for Autonomous Defense: Enabling Mission-Centric, Adaptive, and Anytime Intelligence Abstract: Autonomous Cyber-Physical-Social Systems (CPSSs) are increasingly deployed in mission-critical domains such as transportation, defense, emergency response, and smart infrastructure. Their growing scale and interconnectivity introduce complex security challenges, especially under uncertain, adversarial environments. This dissertation presents a unified, trustworthy decision-making framework that enhances CPSS resilience through three integrated research tasks: mission impact assessment, uncertainty-aware intrusion response, and anytime probabilistic inference. First, I develop iMIA, an interdependent Mission Impact Assessment framework that models mission assets, services, and tasks using Subjective Bayesian Networks and captures attacker–defender perception gaps through Subjective Logic-based Hypergame Theory. iMIA identifies critical mission components and supports adaptive reinforcement to improve mission agility and resilience. Second, I design a Deep Reinforcement Learning (DRL)-based Intrusion Response System (IRS) for in-vehicle networks, capable of selecting optimal defense actions against diverse cyber threats. The system integrates sub-action spaces, entropy regularization, and human-in-the-loop feedback to support adaptive, interpretable, and robust cyber defense decision-making. Third, I propose an anytime simulation-based inference algorithm for Subjective Bayesian Networks, enabling efficient, real-time reasoning under computational constraints. By incorporating entropy- and Bayes factor-guided resource allocation, the method supports incremental, interruptible inference suitable for dynamic and resource-limited CPSS environments. Collectively, these contributions establish a holistic and scalable approach for mission-centric, adaptive, and anytime intelligence in autonomous CPSSs, advancing the state of resilient and trustworthy cyber defense. We hope you will join us in supporting Han Jun Yoon and engaging in the discussion. Best regards, Jin-Hee Cho

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