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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 or Morgan Library
Event Hall. For help finding the locations of our seminar
meetings, consult the on-line CSU campus map. |
Upcoming Events
CS Colloquium Schedule, Spring 2025
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Computer Science Department Colloquium Introduction to the Graduate Program Speaker: Sanjay Rajopadhye, Professor and Graduate Director, Computer Science Department When: 11:00AM ~ 11:50AM, Monday January 27, 2025  Abstract: Dr. Rajopadhye introduces the Computer Science graduate program at CSU. |
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Computer Science Department Colloquium The PIT-Cerberus Framework: Preventing Device Tampering During Transit Speaker: Rakesh Podder, PhD student, Computer Science, Colorado State University When: 11:00AM ~ 11:50AM, Monday February 3, 2025 Abstract: When a computing device, such as a server, workstation, laptop, tablet, etc. is shipped from one site to another (for example, from a vendor to a customer or from one branch location of an organization to another) it can potentially be subjected to unauthorized firmware modifications. The industry has sought to partially address this issue by focusing on securing the boot process. Secure boot provides attestation methods by a hardware root-of-trust to confirm the integrity of the device's BIOS ⁄ UEFI firmware. However, once a device boots up, it is relatively easy for a malicious adversary to tamper with the firmware. In this paper, we address this problem by preventing a secure boot unless done by an authorized user. We extend a hardware root of trust (HRoT) processor's ability to perform secure attestation by implementing a new functionality to securely lock and unlock the BIOS ⁄ UEFI or the BMC (Baseboard Management Controller) and implementing an authentication mechanism in the HRoT for determining authorized users. This ensures that the secure boot process won't commence unless authorized appropriately and provides a robust mechanism for securing the device's firmware during transit. The proposed PIT-Cerberus framework (PIT = Protection In Transit) leverages strong cryptographic techniques and has been implemented within a trusted microcontroller. We have contributed the PIT-Cerberus framework's libraries to Project Cerberus, an open-source project that offers a security platform for server hardware. This paper won a best paper award at 24th International Conference on Software Quality, Reliability, and Security. Bio: Rakesh Podder received his B.Tech. (Undergraduate) degree in Electrical Engineering from the Indian Institute of Technology (IIT), Patna, India, in 2017, and the M.E. (Masters) degree in Control System and Robotics from Jadavpur University (JU), Kolkata, India, in 2020. He is currently pursuing the Ph.D. degree in Computer Science at Colorado State University (CSU), Fort Collins, CO, USA. His research interests include cybersecurity, firmware security, AI planning, ML, and Security Analysis. He contributed to the development of secure frameworks like (PIT, S-RFUF) for Hardware Root of Trust (HRoT) and in projects such as Microsoft Project Cerberus CoRIM, googleDICE. He published papers in various international conferences and journals, and received the Best Paper Awards at the IEEE QRS 2024 and IEEE TPS 2024 Conference. He is also a student member of IEEE. |
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Computer Science Department Colloquium Optimizing random functions: quantum algorithms or Hessian ascent? Speaker: Juspreet Singh Sandhu, Post-doctoral Scholar at UC Santa Cruz When: 11:00AM ~ 11:50AM, Monday February 10, 2025 Abstract: Maximizing the number of satisfied constraints in a constraint satisfaction problem and finding ground-state configurations of certain magnetic alloys are central problems in computer science and statistical physics, respectively. We show that when problem instances are random, these seemingly distinct tasks become mathematically intertwined. Leveraging this connection, we establish fundamental limitations on a broad class of algorithms, including those that encompass near-term quantum algorithms. We then present the first spectral algorithm for optimizing spin glasses—disordered magnetic systems—on the hypercube, and introduce a new class of semidefinite programs that "detect" algorithmic phase transitions. These results resolve multiple conjectures at the intersection of quantum optimization, probability theory, and semidefinite programming. Bio: Juspreet Singh Sandhu is a postdoctoral scholar at UC Santa Cruz hosted by Prof. Alexandra Kolla. He completed his doctoral studies from Harvard University under the guidance of Prof. Peter Love and Prof. Boaz Barak. His work investigates the capabilities of various algorithmic models to optimize random functions, and the consequences of this endeavor on quantum information, the sum-of-squares hierarchy, and quantum advantage in optimization. More generally, his work lies at the confluence of theoretical computer science and mathematical physics, and frequently combines tools from random matrix theory, free probability theory, stochastic analysis and harmonic analysis. |
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Computer Science Department Colloquium Formal Methods or Usable Security: Why Not Both? Speaker: McKenna McCall, Post-doctoral Researcher, Software and Societal Systems Department, Carnegie Mellon University When: 9:00AM ~ 9:50AM, Tuesday February 11, 2025 Abstract: Formal methods research involves using mathematical techniques to specify and verify properties of software and hardware systems. In security and privacy research, formal methods can lead to strong, provable security guarantees—and typically leave questions about how humans might interact with these systems unanswered. Indeed, formal methods and usable security are traditionally distinct areas of research. In this talk, I will demonstrate how techniques from both research areas can be applied—or even combined—to create solutions that are simultaneously mathematically rigorous and usable. In one project, we revisit static analysis tools for home IoT users from a usable security lens and investigate the usability and utility of the workflow involved in using the tools. Later in the talk, I will describe a project with a formal methods focus where we propose a new technique for preventing undesirable information flows on the web. We argue that this approach is usable in more realistic scenarios than what is proposed by prior work—without sacrificing security. Bio: McKenna McCall is a postdoctoral researcher in the Software and Societal Systems Department at Carnegie Mellon University supervised by Lorrie Cranor and Lujo Bauer. She received her PhD from Carnegie Mellon University in 2023, advised by Limin Jia. McKenna’s research spans fields from information flow control and programming languages to security and privacy for home IoT and confidential computing. She is particularly interested in research where formal methods and usable security intersect, and combines techniques from both research areas to produce results that incorporate mathematical rigor as well as usability. |
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Computer Science Department Colloquium Leveraging the Wisdom of Clouds for Internet Security Speaker: Eric Pauley, Ph.D. Candidate, University Of Wisconsin-Madison When: 11:00AM ~ 11:50AM, Friday February 14, 2025 Abstract: Over the past decade, networked systems have consolidated under just a handful of hyperscale cloud providers (e.g., AWS, Azure). While this offers logistical and economic advantages, attackers specifically target providers and their customers, a shift that has left traditional network vantage points blind to the most sophisticated adversaries. In this talk, I’ll explore how we adapt Internet measurement to these new deployment models to regain situational awareness and defend modern service deployments. I’ll introduce DScope, a new Internet telescope that continuously relocates its vantage point across public cloud infrastructure. Unlike prior approaches that use a fixed vantage point, this allows us to observe the most sophisticated attackers that actively avoid existing measurement infrastructure. Our dynamic approach also achieves a statistically representative view of cloud-based attacks, a property that we prove for the first time. Using data from DScope, I’ll also discuss how the shared networking environment of public clouds leads to new vulnerabilities. We’ll examine the problem of latent configuration, which occurs when cloud customers reference network resources that are then reused by other tenants. This new security risk is uniquely enabled by public clouds, but through rigorous analysis and systems design we can make cloud deployments more secure in practice. I’ll conclude by discussing open problems and future work in leveraging Internet vantage points for security, with a focus on intelligent interactivity and rapid response to emergent threats. Bio: Eric Pauley is a Ph.D. candidate at the University of Wisconsin–Madison, advised by Patrick McDaniel. His research interests encompass data-driven approaches to evaluating and improving the security of networked software systems, with a particular focus on cloud computing. His work has led to practical improvements in the security of cloud-based systems through both remediations by major providers and services offered by his company, DScope Security. His research in security measurement has earned best paper runner-up at the ACM Internet Measurement Conference, a finalist spot in the CSAW Applied Research Competition, and the UW–Madison Computer Sciences Outstanding Graduate Researcher Award. Eric is also an avid backpacker, instrument-rated private pilot, and birder. |
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Computer Science Department Colloquium Presentations of two "Best Paper Award" winning papers Speaker: Shadaab Kawnain Bashir, PhD Student, Department of Computer Science, Colorado State University When: 11:00AM ~ 11:50AM, Monday February 17, 2025 Abstract: Title of first talk: Resiliency Graphs: Modelling the Interplay between Cyber Attacks and System Failures through AI Planning. Abstract: Operation efficiency in cyber physical system (CPS) has been significantly improved by digitalization of industrial control systems (ICS). However, digitalization exposes ICS to cyber attacks. Of particular concern are cyber attacks that trigger ICS failure. To determine how cyber attacks can trigger failures and thereby improve the resiliency posture of CPS, this study presents the Resiliency Graph (RG) framework that integrates Attack Graphs (AG) and Fault Trees (FT). RG uses AI planning to establish associations between vulnerabilities and system failures thereby enabling operators to evaluate and manage system resiliency. Our deterministic approach represents both system failures and cyber attacks as a structured set of prerequisites and outcomes using a novel AI planning language. AI planning is then used to chain together the causes and the consequences. Empirical evaluations on various ICS network configurations validate the framework's effectiveness in capturing how cyber attacks trigger failures and the framework's scalability. Authors: Shadaab Kawnain Bashir, Rakesh Podder, Sarath Sreedharan, Indrakshi Ray, and Indrajit Ray Venue: The Sixth IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications. October 28 - 30, 2024, The Darcy Hotel, Washington D.C., USA Award: The Best Paper Award IEEE TPS 2024 Title of 2nd talk: Investigating Influential COVID-19 Perspectives: A Multifaceted Analysis of Twitter Discourse Abstract: Social media influencers, those with verified accounts or with more than 10,000 followers, played a crucial role in the propagation of narratives during the COVID-19 pandemic. We investigate their impact by characterizing and contrasting the differences in content patterns between influential individuals versus public organizations during the pandemic, analyzing emotions, sentiments, and scientific claims expressed in their Tweets. Advanced machine learning approaches, including customized transformer models, few-shot learning, and large language models such as GPT-3.5, were used. The findings reveal a stark contrast in sentiment usage across sub-domains like vaccines and lockdowns, with organizations predominantly employing neutral tones while individuals displaying a significant negative sentiment bias. Individuals often conveyed more negative emotions, whereas organizations exhibited greater optimism. However, many claims from both groups were not verified, highlighting the need to combat misinformation. Authors: Shadaab Kawnain Bashir, Hossein Shirazi, Noushin Salek Faramarzi, Thomas Harris, Ashmita Shishodia, Hajar Homayouni, and Indrakshi Ray Venue: SocialSec 2024: 10th International Symposium on Security and Privacy in Social Networks and Big Data. Zayed University, Abu Dhabi, UAE | November 20-22, 2024 Award: The Best Paper Award SocialSec 2024 Bio: Shadaab Kawnain Bashir earned her Bachelor's degree in Computer Science Engineering from BRAC University, Bangladesh, in 2016. She subsequently completed a Master's degree in Computer Science at Colorado State University, United States, in 2023. Currently, she is pursuing a Ph.D. in Computer Science at Colorado State University. Before joining Colorado State University, she worked as a Lecturer at Daffodil International University, Bangladesh for a couple of years. Her research interests encompass Cybersecurity, AI planning, Machine Learning (ML), and Natural Language Processing (NLP). She has presented her work at several international conferences, where her papers received Best Paper Awards at the IEEE TPS 2024 Conference and SocialSec 2024. Additionally, she is a student member of IEEE. |
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Computer Science Department Colloquium Application-Level Benchmarking of Quantum Hardware using Nonlocal Games Speaker: Carlos Ortiz, Data Scientist, Pacific Northwest National Laboratory (PNNL); Research Assistant Professor, North Carolina State University; Deputy lead of the software thrust, DOE Co-design Center for Quantum Advantage (C2QA) When: 11:00AM ~ 11:50AM, Thursday February 20, 2025 Abstract: In a nonlocal game, two noncommunicating players cooperate to convince a referee that they possess a strategy that does not violate the rules of the game. Quantum strategies allow players to optimally win some games by performing joint measurements on a shared entangled state, but finding these strategies can be challenging. In this talk, we will provide an overview of some interesting nonlocal games on graphs and discuss a variational algorithm for computing these quantum strategies. When applied to a specific graph, our algorithm was able to generate a novel short-depth circuit that implements a perfect quantum strategy, i.e. a strategy that utilizes entanglement as a resource to win the game with probability one. We will argue how these quantum strategies can act as high-level benchmarks of quantum devices since these strategies require the ability to generate an entangled resource state followed by precise control of a set of measurements for their successful execution. Finally, we will discuss recent results and challenges when running these quantum strategies on superconducting and ion-trap quantum devices, as well as outline some future research directions for scaling and improving the validation capabilities of such nonlocal games. Bio: Dr. Carlos Ortiz Marrero is a data scientist at Pacific Northwest National Laboratory (PNNL), Research Assistant Professor at North Carolina State University, and deputy lead of the software thrust at the DOE Co-design Center for Quantum Advantage (C2QA). His current focus is on utility-scale and scientific quantum algorithms, particularly in quantum machine learning, that have applications to quantum chemistry, benchmarking, and quantum sensing. He received his PhD in Mathematics from the University of Houston for studying the mathematical foundations of graph nonlocal games and quantum channels. Website: https: ⁄ ⁄ cmortiz.github.io ⁄ |
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Computer Science Department Colloquium Dependable Quantum-Classical Systems Engineering - A holistic approach Speaker: Edoardo Giusto, Assistant Professor, Department of Electrical Engineering and Information Technology, University of Naples Federico II, Italy When: 11:00AM ~ 11:50AM, Friday February 21, 2025 Abstract: Quantum computing is poised to revolutionize the landscape of computation. By leveraging the unique properties of quantum mechanics, qubits enable unprecedented levels of computational parallelism and speed. To fully realize the potential of this emerging technology in scientific computing, quantum devices must be seamlessly integrated into classical High-Performance Computing (HPC) infrastructures. This integration opens up exciting opportunities but also introduces significant challenges, particularly in maintaining the dependability of these hybrid HPC-Quantum Computing (HPC-QC) systems. To ensure their practical utility, such systems must be resilient, secure, and capable of delivering reproducible results. Addressing these demands calls for a collaborative effort within the scientific community to develop robust co-design strategies that align quantum and classical resources effectively. This presentation will explore the critical challenges in building dependable HPC-QC systems and identify synergistic approaches to streamline the co-design process. A particular focus will be placed on one of the key pillars of system dependability: fault management. Given the intrinsic sensitivity of quantum devices to various noise sources, effective noise modeling and the development of innovative fault-tolerance techniques are imperative. To address these issues, we introduce QUFI—the Quantum Fault Injector—a novel tool designed to model noise and evaluate fault-hardening techniques in quantum devices. The presentation will showcase QUFI's capabilities, offering real-world insights into its performance and highlighting its potential to enhance the reliability of quantum computing systems. Bio: Dr. Edoardo Giusto is an Assistant Professor in the Department of Electrical Engineering and Information Technology at the University of Naples Federico II, Italy. He holds membership in both IEEE and ACM. Dr. Giusto earned his M.S. in Computer Engineering in 2017 and his Ph.D. in Computer and Systems Engineering in 2021, both from Politecnico di Torino, Italy. He previously served as a visiting postdoctoral researcher at the Superconducting Quantum Materials and Systems Center at Fermilab in Batavia, IL, USA. This role was part of the Next Generation Internet Transatlantic Fellowship Program, funded by the European Commission under Horizon Europe. Dr. Giusto plays an active role in the academic community, serving as a technical committee member for leading conferences such as IEEE Quantum Week (QCE), the Design, Automation, and Test in Europe Conference (DATE), and the Design Automation Conference (DAC). Additionally, he holds the position of Vice-Chair for IEEE Consumer Technology Society's Quantum in Consumer Technology initiative. His research focuses on quantum computing, with interests that include quantum applications, problem mapping, and the reliability and fault tolerance of quantum devices. He is also involved in exploring the integration of quantum computing into high-performance computing infrastructures. |
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Computer Science Department Colloquium Many-body quantum games and computational phases of matter Speaker: Oliver Hart, Postdoctoral Fellow, Center for Theory of Quantum Matter, University of Colorado, Boulder When: 11:00AM ~ 11:50AM, Monday February 24, 2025 Abstract: For which tasks do ideal quantum computers provably outperform their classical counterparts? And how does this advantage hold up when using today’s noisy quantum hardware? These questions are central to understanding the practical power of quantum computing. Nonlocal quantum games – computational tasks that leverage the exotic correlations of quantum mechanics – offer a compelling framework for demonstrating unconditional quantum advantage. Namely, advantage that does not rely on complexity-theoretic assumptions or comparisons to the dynamic landscape of classical algorithms. In this talk, I will draw on insights from theoretical condensed matter physics and error-correcting codes to design computational tasks – formulated as multiplayer nonlocal quantum games – that exhibit quantum advantage over all classical strategies. I will also show how the concept of "topological order" can be used to construct quantum strategies that retain their advantage in the presence of noise. This advantage is demonstrated through experiments on Quantinuum’s trapped-ion quantum processor, H1-1. By extending multiplayer nonlocal games into the realm of many-body physics, this work establishes connections to areas such as cryptography, contextuality, and measurement-based quantum computation. Bio: Oliver Hart is a postdoctoral fellow at the Center for Theory of Quantum Matter at the University of Colorado, Boulder and completed his PhD in theoretical physics at the University of Cambridge. His research combines ideas from computer science, quantum information, and condensed matter physics to tackle key theoretical and practical problems in quantum computing. He is particularly interested in quantum games, error correction, and state preparation, as well as topological phases of matter and nonequilibrium quantum dynamics. |
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Computer Science Department Colloquium Bridging Software Engineering and Cybersecurity to Enhance Early-Stage Software Security Speaker: Viktoria Koscinski, PhD Candidate, Computing and Sciences, Rochester Institute of Technology When: 9:00AM ~ 9:50AM, Tuesday February 25, 2025 Abstract: Software security is commonly overlooked during the early stages of the software engineering lifecycle, resulting in security vulnerabilities that are later difficult and costly to fix. Insecure software design is known as a critical risk that can arise from the early stages of requirements engineering, often due to missing or underspecified security requirements. This talk introduces my research, which uses automated machine learning (ML)-based requirements synthesis to assist software engineers in creating security requirements for systems they are designing, and formal modeling and analysis techniques in order to evaluate software security in the early requirements stage. By combining ML with formal modeling and analysis techniques, my research aims to bridge the gap between software engineering and cybersecurity by automatically suggesting domain-specific security requirements and detecting underspecified requirements. This talk will reflect upon the potentials of this research to improve the security of systems during the early design stage, introduce my preliminary research on vulnerability management for cases in which vulnerabilities are already present, as well highlight future directions and additional applications of using these techniques within software engineering. Bio: Viktoria Koscinski is a Computing and Sciences PhD candidate at the Rochester Institute of Technology in Rochester, NY. She earned her bachelor’s and master’s degrees in Computer and Information Science from the State University of New York Polytechnic Institute in Utica, NY. Her research focuses on applying formal analysis and artificial intelligence ⁄ machine learning techniques to software engineering processes in order to improve software security at the early design stages. Her work has been published in top-tier software engineering (SE) conferences, such as ICSE and RE. Viktoria has been involved with various collaboration and outreach efforts, such as completing two research internships with the Griffiss Institute, a nonprofit talent and technology accelerator for the US DoD, as well being invited as a guest speaker for the Society of Hispanic Professional Engineers 2024 Cybersecurity Awareness Month edition of #SHPEReads: GRADS Edition research seminar. Her mentorship and teaching experiences include being a team lead for eight undergraduate researchers involved in a vulnerability management R&D project as well as teaching graduate-level SE courses since 2023. |
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Computer Science Department Colloquium Designing for Social Cybersecurity and Safety Speaker: Matthias Fassl When: 12:00PM ~ 12:50PM, Wednesday February 26, 2025 Abstract: Since cryptography became more accessible in the 1960s, governments have tried to limit access to it in several crypto wars. These discussions pitch privacy and safety, both very valuable, against each other, saying that one undermines the other. However, safety comes not only from police investigations and increased surveillance but also from deploying hard-to-abuse tools and providing support when needed. Careful design can help achieve these goals. Making technology harder to abuse and harm others and offering a technology-mediated version of the social support we know from our everyday lives. Based on my research on the role of social norms in security and privacy behavior and designing safety mechanisms against intimate-image-based abuse, I will present my vision of policy-oriented HCI research in cybersecurity that supports safety without surveillance. Bio: Matthias Fassl, a postdoctoral researcher at the CISPA Helmholtz Center for Information Security, is an HCI researcher who focuses on security, privacy, and safety. He is interested in the social aspects of security behavior and safety from technology-mediated interpersonal harms. Since his traineeship in the European Commission's cybercrime policy unit, he wants his research to contribute to ongoing policy discussions. He has a PhD from Saarland University and a Master's in Computer Engineering from TU Wien. |
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Computer Science Department Colloquium CS501 topics Speaker: Sanjay Rajopadhye, Professor of Computer Science, Colorado State University When: 11:00AM ~ 11:50AM, Monday March 3, 2025 Abstract: Bio: |
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Computer Science Department Colloquium Sublinear Algorithms and Quantum Computing Speaker: John Kallaugher, Senior Member of Technical Staff, Sandia National Laboratories When: 11:00AM ~ 11:50AM, Friday March 7, 2025 Abstract: Quantum computers have been the subject of extensive research and investment due to their potential to dramatically outperform classical computers for certain tasks. However, despite exciting technical progress in recent years, the resources needed for quantum computing will be very expensive for the foreseeable future: qubits are far more expensive than their classical counterparts, while quantum processes and devices are much more challenging to characterize and test. I will discuss my work on addressing these challenges with tools from the domain of sublinear algorithms, algorithms that use very little of some resource relative to the size of the input they process. This includes the first known examples of quantum advantage for natural problems in the streaming model, a core model for big data analytics. Bio: John is a Senior Member of Technical Staff at Sandia National Laboratories in Albuquerque, New Mexico, where he works on problems in quantum algorithms. He earned his PhD in Computer Science from the University of Texas at Austin, advised by Eric Price. |
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Computer Science Department Colloquium Monitoring Problem for Signal Temporal Logic with Value Freezing Operators (STL*) Best Paper Award winner at MEMOCODE 2024 Speaker: Bassem Ghorbel, PhD Student, Department of Computer Science, Colorado State University When: 11:00AM ~ 11:50AM, Monday March 10, 2025 Abstract: Temporal logic has been widely used in formal verification and runtime monitoring of complex systems. Signal Temporal Logic (STL), in particular, has been extended with the value freezing operator to express properties that depend on data values observed over time, leading to the logic known as STL*. While previous approaches to STL* monitoring faced scalability challenges, recent advances have enabled more efficient techniques. This talk will provide an overview of STL* and its role in monitoring temporal properties, along with background on various temporal logics, including Linear Temporal Logic (LTL), Metric Temporal Logic (MTL), and STL. The impact of these logics in both research and industry will be discussed, highlighting their applications in cyber-physical systems and formal verification. Bio: Bassem Ghorbel is a Ph.D. candidate in Computer Science at Colorado State University, Fort Collins, CO, USA. His research focuses on formal methods, model checking, and temporal logics, particularly in the area of monitoring for signal temporal logic and its extensions. He has published research in the International Conference on Hybrid Systems: Computation and Control (HSCC), IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, and ACM ⁄ IEEE international symposium on formal methods and models for system design (MEMOCODE), contributing to the development of scalable and efficient algorithms for runtime verification and monitoring of complex temporal properties in cyber-physical systems. |
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Computer Science Department Colloquium Speaker: SPRING BREAK When: 11:00AM ~ 11:50AM, Monday March 17, 2025 Abstract: Bio: |
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Computer Science Department Colloquium Probabilistic Safety Analysis of Learning-Enabled Autonomous Systems Speaker: Ravi Mangal, Assistant Professor, Computer Science, Colorado State University When: 11:00AM ~ 11:50AM, Monday March 24, 2025 Abstract: Deep Neural Networks (DNNs) are able to solve increasingly complex computational tasks that involve visual reasoning, language processing, and decision making. This has led to an emergence of autonomous systems such as self-driving cars and LLM agents that use these DNNs to interact with the physical and digital worlds for achieving goals under minimal human supervision. Often, such systems are deployed in safety-critical settings, so it is essential to provide formal guarantees of safe system behavior. In this talk, through the means of a case study involving an autonomous system for guiding airplanes on taxiways with DNN-based visual perception, I will discuss how we can automatically compute the probability that the system behaves safely. I will also present methods for improving system safety via run-time guards. Bio: Ravi Mangal is an assistant professor at Colorado State University. He is interested in all aspects of designing and applying formal methods for assuring the correctness and safety of software systems. His current research focuses on developing methods for formally analyzing the safety and trustworthiness of learning-enabled systems. Previously, he was a postdoctoral researcher at Carnegie Mellon University in the Security and Privacy Institute (CyLab) and recevied his PhD in Computer Science from Georgia Institute of Technology. |
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Computer Science Department Colloquium Development of AI Automated Diagnosis Coding Tools at Colorado State University Speaker: Adam Kiehl, Health Data Scientist, College of Veterinary Medicine and Biomedical Sciences, Colorado State University When: 11:00AM ~ 11:50AM, Monday March 31, 2025 Abstract: Veterinary medical records represent a large data resource for application to veterinary and One Health clinical research efforts. Use of the data is limited by interoperability challenges including inconsistent data formats and data siloing. Clinical coding using standardized medical terminologies enhances the quality of medical records and facilitates their interoperability with veterinary and human health records from other sites. Previous studies, such as DeepTag and VetTag, evaluated the application of Natural Language Processing (NLP) to automate veterinary diagnosis coding, employing long short-term memory (LSTM) and transformer models to infer a subset of Systemized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) diagnosis codes from free-text clinical notes. This study expands on these efforts by incorporating all 7,739 distinct SNOMED-CT diagnosis codes recognized by the Colorado State University (CSU) Veterinary Teaching Hospital (VTH) and by leveraging the increasing availability of pre-trained large language models (LLMs). Ten freely-available pre-trained LLMs were fine-tuned on the free-text notes from 246,473 manually-coded veterinary patient visits included in the CSU VTH's electronic health records (EHRs), which resulted in superior performance relative to previous efforts. The most accurate results were obtained when expansive labeled data were used to fine-tune relatively large clinical LLMs, but the study also showed that comparable results can be obtained using more limited resources and non-clinical LLMs. The results of this study contribute to the improvement of the quality of veterinary EHRs by investigating accessible methods for automated coding and support both animal and human health research by paving the way for more integrated and comprehensive health databases that span species and institutions. Preprint: https: ⁄ ⁄ arxiv.org ⁄ abs ⁄ 2410.15186 Bio: Adam Kiehl is a health data scientist at Colorado State University’s Veterinary Teaching Hospital. As a student at CSU, he studied data science and applied statistics and interned with the hospital in a data engineering role. His work focused on the conversion of veterinary medical records data into the OHDSI OMOP common data model format to increase its usability, accessibility, and interoperability. His work has since expanded to include the development of AI solutions to improve data utility and support ongoing clinical research projects at the university. He additionally serves on the research committee of CSU’s AI task force and is an affiliate of the CSU Data Science Research Institute. |
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ISTeC Distinguished Lecture and Computer Science Department and Electrical and Computer Engineering Department Democratizing Edge Computing – Enabling Open and Scalable AI at the Edge Speaker: Hossam Hassanein, Professor and Director, School of Computing, Queen’s University, Canada When: 11:00AM ~ 11:50AM, Monday April 7, 2025 Abstract: In a world driven by data, access to computing power should not be limited to a select few. This talk explores how democratized Edge Computing (EC) and Edge Intelligence (EI) can unlock affordable, scalable, and inclusive AI-driven solutions for diverse industries. Rather than relying on expensive, centralized infrastructure, our approach leverages underutilized, heterogeneous edge devices, from smartphones to IoT sensors, to create an intelligent, resource-aware computing ecosystem. By optimizing resource allocation, benchmarking edge devices, and designing adaptive, self-organizing networks, we can expand access to AI at the edge without proprietary constraints. This talk will highlight cutting-edge techniques for task scheduling, federated learning, service continuity, and autonomous decision-making enabling resilient applications, from smart cities to industrial automation. We explore how democratized edge computing can transform AI accessibility, innovation, and efficiency on a global scale. Bio: Hossam Hassanein is a leading researcher in the areas of broadband, wireless and mobile networks architecture, protocols, control and performance evaluation. His record spans more than 700 publications in journals, conferences and book chapters, in addition to numerous keynotes and plenary talks in flagship venues. Dr. Hassanein has received several recognition and best paper awards at top international conferences. He is the founder and director of the Telecommunications Research Lab (TRL) at Queen's University School of Computing, with extensive international academic and industrial collaborations. He is the recipient of the 2016 IEEE Communications Society Communications Software Technical Achievement Award for outstanding contributions to routing and deployment planning algorithms in wireless sensor networks, and the 2020 IEEE IoT, Ad Hoc and Sensor Networks Technical Achievement and Recognition Award for significant contributions to technological advancement of the Internet of Things, ad hoc networks and sensing systems. Dr. Hassanein is a fellow of the IEEE, and is a former chair of the IEEE Communication Society Technical Committee on Ad hoc and Sensor Networks (TC AHSN). He is an IEEE Communications Society Distinguished Speaker (Distinguished Lecturer 2008-2010). |
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Computer Science Department and Electrical and Computer Engineering Department Vehicular Edge Services Speaker: Hossam Hassanein, Professor and Director, School of Computing, Queen’s University, Canada When: 9:30AM ~ 10:30AM, Tuesday April 8, 2025 Abstract: The rise of Autonomous and Connected Vehicles (AVs and CVs) has created a need for innovative edge computing solutions to meet their growing demands and leverage their computational power. By utilizing the unused resources of these vehicles, we can offload intensive tasks for parallel processing at the extreme edge, drastically reducing latency. However, since AVs and CVs are user-owned and highly dynamic, their availability can be intermittent, leading to uncertainty and impacting Quality of Service (QoS). To tackle this, we predict vehicle availability to adapt to the dynamic nature of vehicular edge computing and integrate these predictions into resource allocation. We also enhance reliability by developing a reputation scoring system that assesses vehicle reliability based on past performance, allowing for proactive task replication. Additionally, we address the growing demands of AVs and CVs by enabling quality-aware offloading of tasks related to cooperative perception, improving traffic situational awareness. By minimizing perception redundancy and maximizing the Value of Information (VOI), our strategies improve road safety, traffic management, and the overall driving experience in intelligent transportation systems. Bio: Hossam Hassanein is a leading researcher in the areas of broadband, wireless and mobile networks architecture, protocols, control and performance evaluation. His record spans more than 700 publications in journals, conferences and book chapters, in addition to numerous keynotes and plenary talks in flagship venues. Dr. Hassanein has received several recognition and best paper awards at top international conferences. He is the founder and director of the Telecommunications Research Lab (TRL) at Queen's University School of Computing, with extensive international academic and industrial collaborations. He is the recipient of the 2016 IEEE Communications Society Communications Software Technical Achievement Award for outstanding contributions to routing and deployment planning algorithms in wireless sensor networks, and the 2020 IEEE IoT, Ad Hoc and Sensor Networks Technical Achievement and Recognition Award for significant contributions to technological advancement of the Internet of Things, ad hoc networks and sensing systems. Dr. Hassanein is a fellow of the IEEE, and is a former chair of the IEEE Communication Society Technical Committee on Ad hoc and Sensor Networks (TC AHSN). He is an IEEE Communications Society Distinguished Speaker (Distinguished Lecturer 2008-2010). |
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Computer Science Department Colloquium Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking Systems Speaker: Fabio Abreu de Santos, Computer Science Scholar, Department of Computer Science, Colorado State University When: 11:00AM ~ 11:50AM, Monday April 14, 2025 Abstract: Selecting an appropriate task is challenging for contributors to Open Source Software (OSS), mainly for those contributing for the first time. Therefore, researchers and OSS projects have proposed various strategies to aid newcomers, including labeling tasks. In this research, we investigate the automatic labeling of open issues strategy to help the contributors pick a task to contribute. We label the issues with skills based on libraries and functions from the source code used to solve the issues. Additionally, inspired by previous research, label prediction might benefit from leveraging metrics derived from communication data and social network analysis (SNA) for issues in which social interaction occurs. Thus, we study how these "social metrics'' can improve the automatic labeling of open issues with API domains. Future work includes using a skill ontology to assist the matching process between contributors and tasks encompassing multi-level skills and expertise, as well as automatic recruitment of contributors given a profile or a job post. By investigating this research topic, we expect to assist OSS communities in attracting and onboarding new contributors. Bio: Fabio Santos earned his Bachelor's and Master's degrees in Informatics, specializing in databases, from the Pontifícia Universidade Católica do Rio de Janeiro, Brazil. He obtained his Ph.D. in Informatics, focusing on Information Systems, Knowledge Modeling, and Reasoning, from the Universidade Federal do Estado do Rio de Janeiro, Brazil, in 2022. Additionally, he completed a Ph.D. in Computer Science at Northern Arizona University, USA, in 2023. With a career spanning over two decades in the IT sector, Santos started as a Software Engineer and eventually became the IT Superintendent for the Brazilian Navy. His research interests lie in knowledge modeling for system and ontology network integration, applying artificial intelligence in software engineering, open-source software, recommendation systems, mining software repositories, and analyzing social networks. |
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Computer Science Department Colloquium AIs Playing Games with Language: Balancing Uncertainty, Truth, and Usefulness in Large Language Models Speaker: Jordan Boyd-Graber, Professor of University of Maryland Computer Science Department, Institute of Advanced Computer Studies, INFO, and Language Science Center When: 11:00AM ~ 11:50AM, Monday April 21, 2025 Abstract: In this talk, I'll discuss three silly games that show the same pattern: computers are very good, there are still things humans are better at, and that we can create much stronger human--computer teams. We first begin with games that test memory: testing the recall of obscure facts. While AI has been viewed as superhuman at the task of question answering, it isn't universally so. After building a new human-in-the-loop authoring system for drafting challenging examples, we show that a new measure of adversarial datasets based on item response theory (which can capture the gap between humans and computer skill) is decreasing but not yet closed, with computers still struggling on abstract reasoning and knowing when they know the correct answer. Given these disparate skill sets, we then analyze how we can best build human and computer teams to learn new facts and detect false statements: computers can help humans identify false statements---but only when the computer is not confidently incorrect. Finally, I close with a similar line of results for another silly language game, Diplomacy, where computers have still not reached dominance but can be used to assist human players think strategically and detect lies, which we capture using an analysis of grounded statements with abstract meaning representation and value functions. I'll then close with how these results help inform how we can construct better human--computer interactions in "the real world". Bio: Jordan Boyd-Graber is a full professor at the University of Maryland. He has worked on model evaluations for human-centered topic models, psychologically inspired leaderboards, human–computer machine translation, and question answering. He also contributed new models for improving generative models with RL, interactive approaches for question answering, topic models, and negotiations. Of his twenty former PhD students, seven have gone on to tenure track positions. He and his students have been recognized with paper awards at EMNLP (2023), IUI (2018), NAACL (2016), and NeurIPS (2009, 2015), and he won the 2015 Karen Spärk Jones Award and a 2017 NSF CAREER Award. He served as PC for ACL 2023, SAC for EMNLP and NAACL, AC for ACL, NAACL, EMNLP, and NeurIPS, Poster Chair for EMNLP 2022, Tutorial Chair for ACL 2017, and Advisor for the ACL 2014 SRW. He previously was an assistant professor at the University of Colorado, Visiting Research Scientist at Google Zürich, and Praktikant at the Berlin-Brandenburg Akademie der Wissenschaften. His undergraduate degrees are in Computer Science and History at the California Institute of Technology, and he received his PhD from Princeton University. His Erdös number is 2 (via Maria Klawe), and his Bacon number is 3 (by embarrassing himself on Jeopardy!). He lives in Silver Spring, Maryland with his wife, two roombas, three fish, two daughters, and their 外婆. |
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Computer Science Department Colloquium TDK Group and Digital Transformation Speaker: Roshan Thapliya, Corporate Officer, Chief Digital Transformation Officer (CDXO) and General Manager of Management Systems HQ, at TDK Corporation headquartered in Tokyo, Japan When: 11:00AM ~ 11:50AM, Tuesday April 22, 2025 Abstract: Recent technological trends in cybersecurity, data platform, AI and IoT have rapidly accelerated the transformation of society. From TDK’s perspective this provides opportunities to serve society better by bringing value to our extended business portfolio developed through our history of continuous innovation and venture spirit. In this talk, I will introduce TDK as a Company, how Digital Transformation (DX) is accelerating the realization of our long-term vision and provide with you some examples of our latest developments in technology and business innovation. Bio: Dr. Roshan Thapliya is Corporate Officer, Chief Digital Transformation Officer (CDXO) and General Manager of Management Systems HQ, at TDK Corporation headquartered in Tokyo, Japan. He is responsible for global strategy, corporate policies, and implementation of Digital and IT technologies throughout the TDK Group that covers Europe, Americas and Asia. His responsibilities include developing and promoting IT ⁄ Digital technologies for TDK Groups core businesses in the field of Automotive, ICT and Industrial & Energy sectors which drive Sustainability, Digital Transformation (DX) and Energy Transformation (EX). As CDXO, he is also responsible for the operational and process transformation through DX within TDK Group to support new value creation and enhance operational efficiency through global collaboration. Dr. Thapliya has experience in the field of developing and implementing digital technologies in a variety of industries throughout his career. He was the Chief Digital Director and Division Head at Bridgestone Corporation headquartered at Tokyo, were his roles and responsibilities included global strategy formulation, execution, and business transfer in areas of tire-centric and mobility solutions with special focus in promoting DX through organizational transformation and customer facing value creation. There he helped to develop SaaS and tire sensor ⁄ AI technologies to remotely monitor tire health in-situ for new business models. He was also Group Manager at former Fuji Xerox Co., Ltd. (currently, Fuji Film Business Innovation Corp.) where he led incubation teams in the field of IoT, edge computing, robotics and bandwidth allocation algorithms in cellular telecommunications. There, he was responsible for establishing research in Social Robotics for office applications and served as Advisory Board Member at the National Facility for Human-Robot Interaction Research at the University of New South Wales, Sydney. |
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Computer Science Department Colloquium Presentations of "Best Paper Award" winning papers: (1) Combating Spatial Disorientation in a Dynamic Self-Stabilization Task Using AI Assistants, and (2) Using Eye Gaze to Differentiate Internal Feelings of Familiarity in Virtual Reality Environments: Challenges and Opportunities Speaker: Sheikh Mannan, PhD Student, and Trevor Chartier, Undergrad Student, Department of Computer Science When: 11:00AM ~ 11:50AM, Monday April 28, 2025 Abstract: First Abstract: Best paper nomination at ACM's Human-Agent Interaction 2024. Sheikh Abdul Mannan, Paige Hansen, Vivekanand Pandey Vimal, Hannah N. Davies, Paul DiZio, and Nikhil Krishnaswamy. 2024. Combating Spatial Disorientation in a Dynamic Self-Stabilization Task Using AI Assistants. In Proceedings of the 12th International Conference on Human-Agent Interaction (HAI '24). Association for Computing Machinery, New York, NY, USA, 113–122. https: ⁄ ⁄ doi.org ⁄ 10.1145 ⁄ 3687272.3688329 Spatial disorientation is a leading cause of fatal aircraft accidents. This paper explores the potential of AI agents to aid pilots in maintaining balance and preventing unrecoverable losses of control by offering cues and corrective measures that ameliorate spatial disorientation. A multi-axis rotation system (MARS) was used to gather data from human subjects self-balancing in a spaceflight analog condition. We trained models over this data to create “digital twins” that exemplified performance characteristics of humans with different proficiency levels. We then trained various reinforcement learning and deep learning models to offer corrective cues if loss of control is predicted. Digital twins and assistant models then co-performed a virtual inverted pendulum (VIP) programmed with identical physics. From these simulations, we picked the 5 best-performing assistants based on task metrics such as crash frequency and mean distance from the direction of balance. These were used in a co-performance study with 20 new human subjects performing a version of the VIP task with degraded spatial information. We show that certain AI assistants were able to improve human performance and that reinforcement-learning based assistants were objectively more effective but rated as less trusted and preferable by humans. Second Abstract: CYBER Student Award at the 27th Annual CyberPsychology, CyberTherapy & Social Networking Conference (CYPSY27). Our group previously reported a feasible approach to detect the internal state of familiarity with eye-gaze features \cite{castillon2024automatically}. Utilizing an existing paradigm \cite{okada2023virtual}, we examined participants' feelings of familiarity during immersion within virtual reality (VR) scenes, some of which had had their spatial layout familiarized through prior presentation of a different scene with the same configuration. While immersed in a test scene, participants indicated the onset of familiarity via a button press on a handheld controller, then verbally indicated whether they could state the source of the familiarity or not. A potential issue is that machine learning models may have detected eye-gaze features reflecting the act of pressing the button rather than features associated with the internal state of familiarity. Although in \cite{castillon2024automatically} we addressed this challenge by including a buffer period between the button press and the window of data used for model training, it remains uncertain within what time frame features associated with the button press may persist. Here, we introduce an approach for potentially overcoming the confounding effects of the button-press by holding it constant. We examine machine learning models' ability to detect whether a scene's layout had been experimentally familiarized among only instances where subjective familiarity was reported. We then repeat this method for instances where no familiarity was reported. Finally, we examine experimentally familiarized scenes where familiarity was reported to detect recall-success vs. recall-failure for the familiarity's source. Bio: Mannan: Sheikh Mannan is a Ph.D. student in the Computer Science department at Colorado State University. He received his Bachelor's degree in Computer Science from Lahore University of Management Sciences (LUMS), Pakistan, in 2020, with a concentration in Software Engineering, Distributed Systems, and Networks Security. In 2021, he began his Ph.D., focusing on Machine Learning and AI, specifically in developing assistive AI agents for task guidance and human upskilling in spaceflight and healthcare. His innovative work has been published at several international conferences, such as ACL, HCII, and AAAI, and received a best paper nomination at ACM's Human-Agent Interaction 2024. Trevor: Trevor Chartier is an Undergraduate Research Assistant working in Dr. Nathaniel Blanchard’s IMPACT lab and studying Computer Science. |
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ISTeC Distinguished Lecture and Computer Science Department and Electrical and Computer Engineering Department Connecting Visual Framing with Computational Communication Science Speaker: Daniela Dimitrova, University Professor, Iowa State University, LAS Dean’s Professor, Greenlee School of Journalism and Communication When: 11:00AM ~ 11:50AM, Thursday May 1, 2025 Abstract: While framing is a well-established theoretical perspective in the social sciences, visual framing remains underexplored. Understanding framing through a journalistic lens, the talk will provide an overview of the main aspects of visual framing analysis, discussing both quantitative and qualitative approaches to studying visuals. A four-tiered model of visual framing will be summarized and new ways of connecting each level of visual framing with Computational Communication Science (CCS) approaches will be proposed. Bio: Daniela V. Dimitrova (Ph.D., University of Florida) is a University Professor at Iowa State University and LAS Dean’s Professor in the Greenlee School of Journalism and Communication. She serves as the Editor-in-Chief of Journalism & Mass Communication Quarterly, the flagship journal of AEJMC. Her research interests include political communication, cross-cultural journalism studies, and refugee communications. Dimitrova’s research has been published widely in leading journals such as Communication Research, Journalism & Mass Communication Quarterly, New Media & Society, Press ⁄ Politics, International Communication Gazette, Journalism Studies, and the Journal of Computer-Mediated Communication and cited more than 7,000 times. Her edited volume titled Global Journalism: Understanding World Media Systems is now in its second edition. Dimitrova is the recipient of multiple grants and awards, including Fulbright-University of Vienna Professor of Social Sciences, AEJMC Senior Scholar, Arthur W. Page Center for Integrity in Public Communication Legacy Scholar, and LAS International Service and Excellence in Graduate Mentoring awards. |
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ISTeC What Is Visual Framing? Speaker: Daniela Dimitrova, University Professor, Iowa State University, LAS Dean’s Professor, Greenlee School of Journalism and Communication When: 11:00AM ~ 12:00PM, Friday May 2, 2025 Abstract: Media framing is a well-established theoretical perspective, but visual framing remains underexplored. What are the main aspects of visual framing? How can images be studied in the field of journalism and mass communication? Dr. Dimitrova will provide an overview of the main aspects of visual framing analysis, discussing both quantitative and qualitative approaches to studying visuals. The speaker will also present a four-tiered model of visual framing and suggest different ways to capturing content and meaning in visual communication. Bio: Daniela V. Dimitrova (Ph.D., University of Florida) is a University Professor at Iowa State University and LAS Dean’s Professor in the Greenlee School of Journalism and Communication. She serves as the Editor-in-Chief of Journalism & Mass Communication Quarterly, the flagship journal of AEJMC. Her research interests include political communication, cross-cultural journalism studies, and refugee communications. Dimitrova’s research has been published widely in leading journals such as Communication Research, Journalism & Mass Communication Quarterly, New Media & Society, Press ⁄ Politics, International Communication Gazette, Journalism Studies, and the Journal of Computer-Mediated Communication and cited more than 7,000 times. Her edited volume titled Global Journalism: Understanding World Media Systems is now in its second edition. Dimitrova is the recipient of multiple grants and awards, including Fulbright-University of Vienna Professor of Social Sciences, AEJMC Senior Scholar, Arthur W. Page Center for Integrity in Public Communication Legacy Scholar, and LAS International Service and Excellence in Graduate Mentoring awards. |
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Computer Science Department Colloquium Predicting Attrition among Software Professionals: Antecedents and Consequences of Burnout and Engagement Speaker: Bianca Trinkenreich, Assistant Professor, Department of Computer Science, Colorado State University When: 11:00AM ~ 11:50AM, Monday May 5, 2025 Abstract: In this talk, Bianca Trinkenreich presents findings from a large-scale study of over 13,000 software professionals examining the relationship between burnout, engagement, and retention. Grounded in the Job Demands-Resources (JD-R) model, the study identifies key organizational factors—such as learning opportunities—that both reduce burnout and increase engagement. Extending the JD-R framework, Bianca links these affective states to developers’ intention to stay and actual retention behavior, using employment data tracked 90 days post-survey. Through structural equation modeling and machine learning, the results reveal that engagement and opportunities to learn are the strongest predictors of whether developers remain in an organization. The talk also highlights practical implications, emphasizing what should be a priority for organizations aiming to retain technical talent. Bio: Bianca Trinkenreich is an Assistant Professor of Computer Science at Colorado State University, specializing in human factors in software engineering. With 20 years of industry experience, she began her academic career in Brazil, earned her Master’s at UNIRIO, Ph.D. at Northern Arizona University, and completed a postdoc at Oregon State University before joining Colorado State University on 2024. Her interdisciplinary research focuses on how software engineering teams can collaborate and thrive. Her work has been recognized with several honors, including the ACM SIGSOFT 2024 Outstanding Doctoral Dissertation Award and multiple best paper awards. |
