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The Department of Computer Science
of Colorado State University, in cooperation with ISTeC
(Information Science and Technology Center), offers the CS
Colloquium series as a service to all who are interested
in computer science. When in-person meetings are possible, most seminars are scheduled for
Monday 11:00AM -- 11:50AM in CSB 130. For help finding the locations of our seminar
meetings, consult the on-line CSU campus map. |
Upcoming Events
CS Colloquium Schedule, Spring 2026
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Computer Science Department Colloquium Introduction to the Graduate Program Speaker: Sudipto Ghosh, Professor and Graduate Director, Computer Science Department When: 11:00AM ~ 11:50AM, Monday January 26, 2026  Abstract: Dr. Ghosh introduces the Computer Science graduate program at CSU. Bio: Dr. Sudipto Ghosh is a Professor of Computer Science at Colorado State University with an affiliate appointment in Systems Engineering. He is also the Graduate Program Director of the Computer Science Department. He received the Ph.D. degree in Computer Science from Purdue University in 2000. His research interests are in software engineering (design and testing) and computer science education. He is on the editorial boards of Software and Systems Modeling, Software Quality Journal, and Information and Software Technology. Previously he was on the editorial boards of the IEEE Transactions on Reliability and Journal of Software Testing and Reliability. He was a general co-chair of MODELS 2009 (Denver) and Modularity 2015 (Fort Collins). He was a program co-chair of ICST 2010 (Paris), DSA 2017 (Beijing), ISSRE 2018 (Memphis), ISEC 2024 (Bangalore), and QRS 2024 (Cambridge). He has served on program committees of multiple conferences. He is a member of the ACM and a Senior Member of the IEEE. |
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Computer Science Department Colloquium How to Succeed in the CS Graduate Program Speaker: Sudipto Ghosh, Professor and Graduate Director, Computer Science Department When: 11:00AM ~ 11:50AM, Monday February 2, 2026 Abstract: Continuation of the lecture from Jan 26. Bio: Dr. Sudipto Ghosh is a Professor of Computer Science at Colorado State University with an affiliate appointment in Systems Engineering. He is also the Graduate Program Director of the Computer Science Department. He received the Ph.D. degree in Computer Science from Purdue University in 2000. His research interests are in software engineering (design and testing) and computer science education. He is on the editorial boards of Software and Systems Modeling, Software Quality Journal, and Information and Software Technology. Previously he was on the editorial boards of the IEEE Transactions on Reliability and Journal of Software Testing and Reliability. He was a general co-chair of MODELS 2009 (Denver) and Modularity 2015 (Fort Collins). He was a program co-chair of ICST 2010 (Paris), DSA 2017 (Beijing), ISSRE 2018 (Memphis), ISEC 2024 (Bangalore), and QRS 2024 (Cambridge). He has served on program committees of multiple conferences. He is a member of the ACM and a Senior Member of the IEEE. |
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Computer Science Department Colloquium The art of the 21st Century: Multimodal Interaction for Virtual Reality Sketching and Sculpting Speaker: Francisco R. Ortega, Associate Professor of Computer Science, Colorado State University When: 11:00AM ~ 11:50AM, Monday February 9, 2026 Abstract: In recent years, Virtual Reality (VR) systems have emerged as a novel medium for artists to express their ideas within an immersive 3D Virtual Environment (VE). For instance, artists benefit from the ample virtual space. At the same time, they can magnify a specific area within the VE to make precise adjustments, giving artists the flexibility to build an artwork. Artists also benefit from using novel input devices that provide feedback, enabling them to create enhanced works. These input devices can mimic or improve the tools artists use. A third advantage is that artists can correct errors, which can be challenging or impossible when creating objects with physical materials. Moreover, in the immersive virtual space, artists can focus on their craft by reducing external distractions. Due to these advantages, artists have chosen VR as a medium to express themselves. For example, Gio Nakpil creates intricate 3D sculptures in VR, demonstrating the transformative potential of VR in art. Another example is Collin Leix, who seamlessly integrates classical oil-painting techniques with contemporary VR technology. These artistic VR tools are not here to replace existing mediums but to create opportunities for new ways to communicate art. However, there are still persistent usability challenges, in particular for emerging VR sculpting. For example, existing interaction techniques often fail to capture the materiality of sculpting by requiring unnatural hand use. This talk will present current challenges for multimodal interaction in VR art, the authors' and colleagues' qualitative findings, and a way forward to motivate researchers to help us improve interaction quality, allowing artists to concentrate on their craft rather than the technology. We will also describe our VR Sketch and Sculpt application design for research experiments. Bio: Francisco R. Ortega is an Associate Professor at Colorado State University (CSU) and has been Director of the Natural User Interaction Lab (NUILAB) since Fall 2018. Dr. Ortega earned his Ph.D. in Computer Science (CS) with a focus on Human-Computer Interaction (HCI) and 3D User Interfaces (3DUI) from Florida International University (FIU). He also held the Postdoctoral and visiting assistant professor positions at FIU between February 2015 and July 2018. His research has focused on (1) multimodal and unimodal interaction (gesture-centric), which includes gesture elicitation (e.g., a form of participatory design), (2) information access effort in augmented reality (e.g., visual cues and automation bias), (3) AR notifications, and (4) stress reduction using virtual reality forest bathing. For multimodal interaction research, Dr. Ortega focuses on enhancing user interaction through (a) multimodal elicitation, (b) developing interactive techniques, and (c) refining augmented reality visualization techniques. The primary domains for interaction include general environments, immersive analytics, and VR sketching. His research has resulted in over 90 peer-reviewed publications, including books, journals, conferences, workshops, and magazine articles, in venues such as ACM CHI, ACM VRST, IEEE VR, IEEE TVCG, IEEE ISMAR, ACM PACMHCI, ACM ISS, ACM SUI, IEEE 3DUI, HFES, and Human Factor Journals, among others. Dr. Ortega has experience with multiple government-funded projects. For example, Dr. Ortega served as a co-Principal Investigator for the DARPA Communicating with Computers project. He is a principal investigator (PI) for a 3-year effort funded by ONR, titled "Perceptual ⁄ Cognitive Aspects of Augmented Reality: Experimental Research and a Computational Model." He was recently awarded a new ONR grant titled “Assessing Cognitive Load and Managing Extraneous Load to Optimize Training.” The National Science Foundation and other agencies and companies have also funded him. This includes the NSF CAREER 2023 for microgestures and multimodal interaction. Since his tenure-track appointment at CSU in August 2018, Dr. Ortega has brought over 5 million dollars in external funding (with 4.2 million as principal investigator). His lab website is https: ⁄ ⁄ nuilab.org |
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Computer Science Department Colloquium Validation and Uncertainty Quantification for Spatial Data Speaker: David Burt, Post-doctoral Associate, MIT When: 11:00AM ~ 11:50AM, Monday February 16, 2026 Abstract: Spatial prediction is central to weather forecasting, quantifying air pollution impacts, and many other scientific problems. But to draw credible conclusions, we need validation methods that tell us when predictions from statistical or physical models can be trusted. Classical validation often breaks down when the locations used for validation systematically differ from the fixed target locations where we ultimately want to predict. This mismatch is often not an instance of covariate shift (as commonly formalized) because the validation and target locations are fixed (e.g., on a grid or at select points) rather than independent and identically distributed draws from two distributions. We formalize a check on validation methods: that they become arbitrarily accurate as validation data become arbitrarily dense. We show that widely used classical, covariate-shift, and spatial approaches can fail this check. We then introduce a reweighting method that builds on existing ideas in the covariate-shift literature, but adapts them to the validation data at hand. We prove that our proposed method passes our check, and we demonstrate its advantages empirically on simulated and real data. I conclude with a discussion of my broader research agenda: developing methods for validation and uncertainty quantification with spatiotemporal data, aimed at making machine learning reliable in environmental health applications. Bio: David Burt is a postdoctoral associate in the Laboratory for Information and Decision Systems at MIT. Previously, he received a PhD from the Machine Learning Group within the Department of Engineering at the University of Cambridge. He develops tools to evaluate prediction methods and to quantify uncertainty when data has spatial or spatiotemporal structure. |
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Computer Science Department Colloquium Understanding the Online Information Ecosystem Speaker: Pranav Goel, Postdoctoral Research Associate, Northeastern University’s Network Science Institute When: 11:00AM ~ 11:50AM, Monday February 23, 2026 Abstract: Individuals increasingly spend a significant portion of their lives online, and the Internet serves as a primary medium for obtaining and disseminating information. A complex network of people, content, information sources, technologies, and platforms (online structures) shapes how information is created, shared, and consumed on the Internet. Yet, due to a variety of reasons such as lack of feasible operationalization or appropriate data, studies of online information often neglect the social nature of information as well as the cross-platform experiences that define the modern Internet, resulting in a limited, platform- and content-specific understanding of online information, removed from its social context. In addition, any understanding of the online information experience today needs to reckon with the impact of generative AI, and this impact also needs to be understood in the context of different information-seeking behaviours facilitated by different kinds of platforms. In this talk, I will address these limitations by first discussing how mainstream news articles get used to support misleading narratives on social media, using network science and natural language processing methods. Next, I will discuss ongoing research work that increases our understanding of the impact of generative AI on information-seeking behavior, especially in terms of web referrals. I will then briefly discuss ongoing and future research work tracking differences and consistencies in information consumption experiences for the same end-users across different online platforms. Through new research contributions that incorporate the social nature of information, study the impact of generative AI, and analyze the cross-platform experiences of the same set of Internet users, this talk aims to broaden the scope of our understanding of the online information ecosystem. Bio: Dr. Pranav Goel is a Postdoctoral Research Associate at Northeastern University’s Network Science Institute. He earned his PhD in Computer Science at the University of Maryland in 2023. His research interests broadly span natural language processing and computational social science, and he specializes in using web and text data as a potent digital trace of societal dynamics to empirically investigate interdisciplinary research questions. He is currently interested in investigating the impact of generative AI on online information-seeking behavior, building a cross-platform understanding of online information consumption including generative AI as a component of the broader information ecosystem, and the sociopolitical phenomena of framing and narratives in news and social media. His work has been published in major computer science conferences such as NeurIPS, EMNLP, and ICWSM, as well as interdisciplinary journals with a broad audience, such as Nature Human Behaviour and Nature Scientific Data. |
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Computer Science Department Colloquium Speaker: Kristina Quynn, Founding Director of CSU Writes When: 11:00AM ~ 11:50AM, Monday March 2, 2026 Abstract: |
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Computer Science Department Colloquium Student Presentations of Award-winning Papers at Conferences Speaker: Abdul Matin, Tanjim Bin Faruk, Brian Tan, PhD students in Computer Science When: 11:00AM ~ 11:50AM, Monday March 9, 2026 Abstract: Paper 1: A. Matin, T. Bin Faruk, S. Pallickara and S. L. Pallickara, "HyperKD: Distilling Cross-Spectral Knowledge in Masked Autoencoders via Inverse Domain Shift with Spatial-Aware Masking and Specialized Loss," 2025 IEEE 12th International Conference on Data Science and Advanced Analytics (DSAA), Birmingham, United Kingdom, 2025, pp. 1-11. https: ⁄ ⁄ ieeexplore.ieee.org ⁄ abstract ⁄ document ⁄ 11248016. Best Paper Award at DSAA 2025. The proliferation of foundation models, pretrained on large-scale unlabeled datasets, has emerged as an effective approach in creating adaptable and reusable architectures that can be leveraged for various downstream tasks using satellite observations. However, their direct application to hyperspectral remote sensing remains challenging due to inherent spectral disparities and the scarcity of available observations. In this work, we present HyperKD, a novel knowledge distillation framework that enables transferring learned representations from a teacher model into a student model for effective development of a foundation model on hyperspectral images. Unlike typical knowledge distillation frameworks, which use a complex teacher to guide a simpler student, HyperKD enables an inverse form of knowledge transfer across different types of spectral data, guided by a simpler teacher model. Building upon a Masked Autoencoder (MAE) with a Vision Transformer (ViT) backbone, HyperKD distills knowledge from Prithvi (a ViT-based MAE geospatial foundation model trained on lower-dimensional multispectral data) into a student tailored for EnMAP hyperspectral imagery. HyperKD addresses the inverse domain adaptation problem with spectral gaps by introducing a feature-based strategy that includes spectral range-based channel alignment, spatial featureguided masking, and an enhanced loss function tailored for hyperspectral images. HyperKD bridges the substantial spectral domain gap, enabling the effective use of pretrained foundation models for geospatial applications. Extensive experiments show that HyperKD significantly improves representation learning in MAEs, leading to enhanced reconstruction fidelity and more robust performance on downstream tasks such as land cover classification, crop type identification, and soil organic carbon prediction, underpinning the potential of knowledge distillation frameworks in remote sensing analytics with hyperspectral imagery. Paper 2: Brian Tan, Ewan S. D. Davies, Indrakshi Ray, and Mahmoud A. Abdelgawad. 2025. Safety Analysis in the NGAC Model. In Proceedings of the 30th ACM Symposium on Access Control Models and Technologies (SACMAT '25). Association for Computing Machinery, New York, NY, USA, 91–98. https: ⁄ ⁄ doi.org ⁄ 10.1145 ⁄ 3734436.3734444. Best Paper Runner-Up Award at SACMAT 2025. We study the safety problem for the next-generation access control (NGAC) model. We show that under mild assumptions it is coNP-complete, and under further realistic assumptions we give an algorithm for the safety problem that significantly outperforms naive brute force search. We also show that real-world examples of mutually exclusive attributes lead to nearly worst-case behavior of our algorithm. |
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Computer Science Department Colloquium Speaker: Spring Break When: 11:00AM ~ 11:50AM, Monday March 16, 2026 Abstract: |
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Computer Science Department Colloquium Speaker: Gowtham Kaki, Assistant Professor, Univesity of Colorado, Boulder When: 11:00AM ~ 11:50AM, Monday March 23, 2026 Abstract: |
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Computer Science Department Colloquium Speaker: When: 11:00AM ~ 11:50AM, Monday March 30, 2026 Abstract: |
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Computer Science Department Colloquium Speaker: David Newton When: 11:00AM ~ 11:50AM, Monday April 6, 2026 Abstract: |
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ISTeC Distinguished Lecture; Electrical and Computer Engineering, and Computer Science Department Colloquium TBD Speaker: ISTeC DL (C Lindsay Anderson) When: 11:00AM ~ 11:50AM, Monday April 13, 2026 Abstract: |
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Computer Science Department Colloquium Speaker: When: 11:00AM ~ 11:50AM, Monday April 20, 2026 Abstract: |
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Computer Science Department Colloquium Speaker: Jason Corso, University of Michigan When: 11:00AM ~ 11:50AM, Monday April 27, 2026 Abstract: |
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Computer Science Department Colloquium Speaker: When: 11:00AM ~ 11:50AM, Monday May 4, 2026 Abstract: |
