The Computer Science (CS) department welcomes prospective students interested in our graduate programs to our annual Prospective Student Visit Day on Friday, Nov. 13. We are looking for strong students with diverse backgrounds to join our MCS and PhD programs. A wide variety of research areas are represented by our world-class faculty including algorithms, computational epidemiology, distributed computing, embedded systems, intelligent hearing technology, human-computer interaction, health and human-centric computing, machine learning, massive data algorithms and technology, mobile computing, cyber-physical systems, speech processing, networks, programming languages, artificial intelligence and more. Write to our Graduate Program Administrator at cs-grad@uiowa.edu if you'd like to visit.
An important part of the prospective Student Visit Day is the 12th Iowa Computer Science Graduate Research Symposium (2026), in which senior PhD students will present talks showcasing their current research. This will be followed by a keynote by a CS faculty member. Talks are intended for a wide audience with interest in CS, including CS juniors and seniors. The talks presented by current CS graduate students at the Symposium are excellent examples of the exciting CS research taking place here on the UI campus!
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Schedule
All times CT [Conversion to your timezone possible here if needed]
Friday, Nov. 13, 2025
Morning Session (By invitation only. To express interest, click here.)
Overview of Graduate Programs by Professor and Director of Graduate Studies Steve Goddard
Short Faculty Research Presentations by University of Iowa CS Professors Tianyu Zhang, Juan Pablo Hourcade, Sriram Pemmaraju, and Garrett Morris.
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Afternoon Sessions (Everyone invited for afternoon sessions! Graduate students encouraged to attend.)
Graduate Student Research Symposium Sessions / PhD Student Presentations
1:30-3:30 p.m., Schaeffer Hall, Room 140
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1:30 p.m. Robert Lorch, PhD Candidate — "Leveraging Deductive Verification for Cryptographic Protocols"
2 p.m. Hieu Vu, PhD Candidate — "Generating and Transforming Temporal Graphs for Healthcare Modeling"
2:30 p.m. Ian Pope, PhD Candidate — "End-to-End EEG-Based Auditory Attention Decoding for Cochlear Implant Users"
3 p.m. Sarmad Chandio, PhD Candidate — "Role of online platforms in shaping public opinion"
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3:30-4 p.m. - Break/Reception
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4-5 p.m. Keynote Speaker: Assistant Professor Weiran Wang
Topic: "Tuning Distributionally Robust Learning via Bilevel Minimax Optimization"
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More on Our Speakers:
Title: Leveraging Deductive Verification for Cryptographic Protocols
Abstract: Cryptographic protocols are hard to prove correct. Defining the notion of protocol correctness is itself nontrivial, even before attempting a proof. The general difficulty of the problem has resulted in flawed “proofs” of correctness, sometimes leading to real-world security impacts. Even worse, security concerns are often eschewed in favor of financial considerations, and so proofs are often not attempted in the first place. In an ideal situation, one would like to define a security protocol, propose a security property, and have some automated tool either prove the protocol correct or return a concrete attack trace, all with minimal human interaction. While great progress has been made, current tools and techniques are still inadequate to achieve this goal. In particular, the approaches with the highest levels of automation are the most difficult to interact with in cases of verification failure, and approaches that offer usable interactivity in the case of verification failure are the least automated. In this talk, we discuss an attempt to achieve the best of both worlds—that is, to provide high automation potential while still offering a promising path forward when (automated) verification fails.
Ph.D. candidate | Advisor: Cesare Tinelli | Areas of research: Automated Reasoning, Computer Security
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Title: Generating and Transforming Temporal Graphs for Healthcare Modeling
Abstract: Modern hospitals generate patient clinical records and operational data about patient locations, healthcare workers, rooms, treatments, and potential interactions among them over time. These data can support patient-outcome prediction, infection tracking, operational planning, and intervention evaluation. Yet high-quality operational data are difficult to collect and share because they are sensitive, costly to obtain, and governed by strict privacy requirements. Even when data are available, their scale and changing structure make machine learning computationally expensive and meaningful patterns difficult to extract. My research addresses these challenges through temporal graphs, which represent entities and their changing relationships. In this talk, I will introduce two complementary approaches: generating realistic synthetic hospital mobility data and transforming large dynamic graphs to make training downstream models more efficient. I will show how incorporating hospital-specific information improves the usefulness of synthetic data for disease-spread simulation, and how graph coarsening can reduce computational cost while maintaining predictive performance. Together, these approaches work toward making complex hospital data more accessible and practical for healthcare research.
Ph.D. candidate | Advisor: Bijaya Adhikari | Areas of research: Data-centric machine learning on temporal graphs for healthcare,
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Title: End-to-End EEG-Based Auditory Attention Decoding for Cochlear Implant Users
Abstract: Cochlear implant (CI) users continue to experience difficulty understanding speech in complex listening environments. Auditory attention decoding (AAD) from EEG offers a promising pathway toward neurofeedback-based training, but remains challenging in CI users due to limited data, implant-related artifacts, and high inter-subject variability.
In this work, we investigate end-to-end convolutional neural network (CNN) models for decoding auditory attention directly from single-trial EEG, without access to the auditory stimulus. We introduce a sequence of task-aligned, EEGNet-based architectural refinements, culminating in a multi-scale, one-dimensional temporal–spatial model tailored to auditory attention dynamics. Across a cohort of 105 CI users, the proposed architectures substantially outperform traditional temporal response function (TRF) baselines, achieving average improvements of 17.30% in accuracy and 28.10% in AUC.
Strategies targeting label noise, data scarcity, and inter-subject and inter-trial variability provide complementary gains, but architectural design is the primary driver of performance. These results demonstrate that carefully designed EEG-only CNN architectures can reliably decode auditory attention in CI users, supporting practical neurofeedback systems for real-world listening scenarios.
Ph.D. candidate | Advisor: Octav Chipara | Areas of research: Intelligent Hearing Technology
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Title: Role of online platforms in shaping public opinion
Abstract: Social media platforms have become an integral part of our lives, influencing how individuals consume news, discover products, and interact with peers. Since the process is so seamless, we have essentially offloaded parts of our cognitive and decision-making processes to platform algorithms. This increased reliance on systems has enabled them to shape our opinions and ideologies.
However, these “powerful systems” largely operate as opaque black boxes, leaving users and researchers with little visibility into how recommendations are generated and how they drive shifts in human behavior. This talk investigates the black-box algorithms powering major online platforms and examines their impact on user opinions and ideologies. I leverage language as a behavioral proxy, analyzing what users watch, produce, and consume to understand ideological shifts.
Ph.D. Candidate | Advisor: Rishab Nithyanand | Areas of research: Computational Social Science - Platforms, Ideologies, Audits
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Keynote Speaker: Weiran Wang
Title: Tuning Distributionally Robust Optimization via Bilevel Minimax Optimization
Abstract: Deep learning models increasingly rely on Distributionally Robust Optimization (DRO) to combat subpopulation shifts and spurious correlations. However, advanced DRO methods depend on sensitive hyperparameters that traditionally demand computationally costly grid searches. We propose a framework that treats the robustness mechanism itself as a learnable component, continuously tuning these parameters on held-out validation data directly within the training loop.
Because robust training is inherently a minimax game, optimizing its hyperparameters creates a complex nested bilevel minimax structure. To solve this, we introduce a novel penalty-based first-order method that operates efficiently without relying on restrictive lower-level strong convexity assumptions. Furthermore, we provide a generalization guarantee for our method, which dynamically discovers the bias-variance trade-off on the fly, bypassing the discretization errors and massive computational overhead of grid searches.
This talk covers joint work with Yiyang Shen (PhD@Informatics), Qihang Lin (Tippie College of Business), and AI.
Bio: Weiran Wang is an Assistant Professor in the Department of Computer Science (with joint affiliations in AMCS and Informatics) at the University of Iowa. Prior to joining academia, he spent seven years in industrial research positions. He received his PhD from the University of California, Merced and was a postdoctoral researcher at the Toyota Technological Institute at Chicago (TTIC). His research takes an algorithmic approach to machine learning, focusing on multimodal representation learning, generative models, optimization for machine learning, and their applications to speech processing and large-scale language models.