Symbolic Execution Engine For Dynamic Analysis Of System Software
This dissertation tackles key problems associated with enabling the dynamic analysis of system software. Because system software interacts directly with hardware, its execution is tightly coupled with the underlying platform and dependent on specific hardware features. Therefore, there is a need to decouple such software from these hardware dependencies and provision the necessary system states for its execution, thereby enabling dynamic analysis. Analyzing such software, which is typically privileged, requires specialized tools. Moreover, we need a foothold to host the analyzer on the system, provide mechanisms to introspect the target, and manage system states. Furthermore, unlike generic tools, there is a lack of user-programmable analysis frameworks that can be customized to combine multiple analysis techniques.
Addressing these challenges, we propose techniques that advance system software analysis. We present a symbolic execution engine as a framework for dynamic analysis of system software. It operate…
Subtitle: PhD Dissertation Defense by Pansilu Madhura Bhashana Pitigalaarachchi PITIGALA ARACHCHILLAGE. Contact: scisseminars@smu.edu.sg. Speaker Details: , Pansilu Madhura Bhashana Pitigalaarachchi PITIGALA ARACHCHILLAGE
PhD Candidate
School of Computing and Information Systems
Singapore Management University, Pansilu Pitigala Arachchillage is a PhD candidate in Computer Science at Singapore Management University, supervised by Prof. Xuhua Ding. His research focuses on software and systems security. His PhD research addresses the challenges in analyzing system programs such as operating system kernels and Trusted Execution Environment firmware by… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UQzlGMThVSlY5QVBPUk0wSTFBMDBUWVRMUy4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Current Student. Academic Community.
Friday, November 14, 2025, 9:00 AM – 10:00 AM.
Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902.
For more info visit computing.smu.edu.sg. Scaling up Cooperative Multi-agent Reinforcement Learning
Over the past decade, multi-agent reinforcement learning (MARL) has emerged as a powerful paradigm for enabling collaborative behaviors among autonomous agents within MAS to solve complex tasks. This dissertation discusses a critical scalability gap and addresses the question: How can we design multi-agent learning systems that simultaneously scale to large agent teams and extended temporal horizons while maintaining the generalizability for practical deployment? As a response, the dissertation makes four interconnected contributions that collectively advance the field from recognizing scalability limitations to implementing practical multi-agent frameworks: a comprehensive survey of scalable multi-agent reinforcement learning, a long-horizon multi-objective multi-agent reinforcement learning benchmark (MOSMAC), a hierarchical multi-agent learning framework with self-organizing neural networks (HiSOMA), and a hierarchical multi-agent learning framework integrating large language models (L2M2). Through these c…
Subtitle: PhD Dissertation Defense by GENG Minghong. Contact: scisseminars@smu.edu.sg. Speaker Details: , GENG Minghong
PhD Candidate
School of Computing and Information Systems
Singapore Management University, GENG Minghong is a Ph.D. Candidate in Computer Science, under the supervision of Professor TAN Ah Hwee. His primary research explores methods for scaling multi-agent reinforcement learning (MARL) systems, specifically addressing coordination and learning challenges within large-scale, complex environments. His work on hierarchical multi-agent systems has been published in leading academic venues,… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UM0RLSTNBMFFITFlFNUhNM0hKMUVOUkY5Ui4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Current Student. Academic Community.
Monday, November 17, 2025, 10:00 AM – 11:00 AM.
Meeting room 4.4, Level 4. SMU SCIS 1, Singapore 178902.
For more info visit computing.smu.edu.sg. Understanding the Role of Large Language Models in Strategic Information Systems and Organizational Decision Making
arge language models (LLMs) are transforming how organizations process information and make decisions. This dissertation empirically examines how LLMs facilitate and reshape decision-making across key organizational actors, investors, managers, and financial analysts, within the context of information systems (IS) research.
The first study evaluates ChatGPT’s performance in management sentiment analysis, showing that general-purpose LLMs can match or exceed domain-specific models like FinBERT in extracting managerial tone from textual data.
The second study develops an LLM-based framework to infer employees’ perceived job security from Glassdoor reviews, offering a novel digital-trace measure of labor market stability.
The third study explores how the emergence of LLMs affects financial analysts’ forecasting performance, finding that LLM adoption improves forecast accuracy and narrows the skill gap between analysts.
Collectively, the findings highlight the transformative role of LLMs in advancing the fron…
Subtitle: PhD Dissertation Defense by YANG Xu. Contact: scisseminars@smu.edu.sg. Speaker Details: , YANG Xu
PhD Candidate
School of Computing and Information Systems
Singapore Management University, YANG, Xu is a Ph.D. candidate in Information Systems at Singapore Management University, supervised by Associate Professor HU, Nan. His research examines how emerging large language model technologies transform organizational decision-making and knowledge processes. He has presented his work at leading academic conferences, including the International Conference on Information Systems (ICIS) and the… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UMUpMMEtPVkxGRzZKTklBWjJDUzVJSlpXVC4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Current Student. Academic Community.
Monday, November 17, 2025, 1:00 PM – 2:00 PM.
Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902.
For more info visit computing.smu.edu.sg. Addressing Sparsity For Knowledge Graph Completion: Data And Model Perspectives
This dissertation addresses data sparsity in KGC through complementary data- and model-level solutions. It introduces (1) Diversified and Adaptive Negative Sampling (DANS) to produce more informative negatives for supervised learning, (2) FusionAdapter for modality-preserving, parameter-efficient multimodal fusion to mitigate long-tail relation issues, and (3) RelAdapter, a context-aware adapter that enables relation-specific adaptation under distribution shift. In addition, an ongoing line of work is outlined as future research, which explores bridging knowledge graphs with large language models through interpretable tokenization. Overall, these contributions provide a cohesive path toward more robust and generalizable knowledge graph completion.
Subtitle: PhD Dissertation Defense by LIU Ran. Contact: scisseminars@smu.edu.sg. Speaker Details: , LIU Ran
PhD Candidate
School of Computing and Information Systems
Singapore Management University, LIU Ran is a PhD candidate in Computer Science at Singapore Management University, supported by the ASTAR Graduate Scholarship. His research focuses on knowledge graphs, few-shot learning, and multimodal fusion, and he has published in venues such as EMNLP and GDMA. Previously, he worked as a research attachment at ASTAR I2R and completed a data science internship at Point72, where he applied graph-based… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UNFo0RURFUTUzNlpNOEdVR0U5VUQ3RlMwRC4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Current Student. Academic Community.
Tuesday, November 18, 2025, 10:00 AM – 11:00 AM.
Meeting room 4.4, Level 4. SMU SCIS 1, Singapore 178902.
For more info visit computing.smu.edu.sg. Enhancing Multi-view, Multi-modal Sensing, Perception and Actuation For Edge Intelligence
Artificial Intelligence of Things (AIoT) technologies have revolutionized intelligent sensing within Cyber-Physical Systems (CPS), powering applications such as large-scale surveillance and autonomous transportation. These systems rely heavily on vision-based Deep Neural Networks (DNNs), yet executing such complex models on resource-constrained edge devices remains a challenge. Transmitting high-bandwidth sensor data to the cloud is impractical, while lightweight DNNs deployed locally often compromise accuracy. This dissertation addresses this core tradeoff by introducing the concept of collaborative DNN inference, where multiple IoT nodes with overlapping Fields-of-View (FoVs) share intermediate representations or compact features to enhance perception quality while minimizing bandwidth usage.
The research spans three major contributions. ComAI introduces feature-level collaboration among stationary cameras, enabling small edge DNNs to achieve detection accuracy close to that of larger models while sustaini…
Subtitle: PhD Dissertation Defense by Dhanuja Tharith WANNIARACHCHIGE. Contact: scisseminars@smu.edu.sg. Speaker Details:
Dhanuja Tharith WANNIARACHCHIGE
PhD Candidate
School of Computing and Information Systems
Singapore Management University, Dhanuja Wanniarachchi is a Ph.D. Candidate in Computer Science, under the supervision of Professor Archan MISRA. His research focuses on enhancing Edge Artificial Intelligence in Networked Settings. His PhD research addresses the challenges in multi-view, multimodal sensing, perception, and actuation for enhancing edge intelligence, utilizing lower complexity models to achieve… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UQ1pSNk9PNUxENk5VQk5JSEVKQVhLQ1QyRC4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Current Student. Academic Community.
Tuesday, November 18, 2025, 1:00 PM – 2:00 PM.
Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902.
For more info visit computing.smu.edu.sg. The International Conference on Human-Engaged Computing (ICHEC 2025)
[ Venue ]
ICHEC 2025 will take place in the heart of Singapore at the Singapore Management University (SMU).
Day 1 (November 21, 2025): School of Computing and Information Systems 2 (School of Economics), 90 Stamford Road.
Google Map | Baidu Map, Days 2 & 3 (November 22–23, 2025): SMU Administration Building, 81 Victoria Street.
Google Map | Baidu Map
Welcome to the International Conference on Human-Engaged Computing (ICHEC 2025), hosted in vibrant, multicultural Singapore. Rooted in the spirit of human-computer interaction (HCI), ICHEC is a global platform for exploring how computing—especially Artificial Intelligence (AI) and emerging technologies—is intertwined with human values, cultures, and lived experiences. Formerly known as Chinese CHI, the conference has been reimagined with a broader and more inclusive vision. We welcome HCI researchers, AI practitioners, designers, artists, and technologists from around the world to share their work, engage in dialogue, and build new collaborations. In the…
RSVP: . Reserve a seat: https://eventregistration.smu.edu.sg/event/1a2bb38d-dcce-444e-a34a-6f44d27219eb/regPage:3071649c-ce3d-48a8-b058-ed93fbe115ea?tm=UU_oYFuNFTMlyI_pC4GDo18ow71wDt-SJtcwKijOag4. Type: Conferences & Symposiums. Subject: Information Technology & Systems. Audience: Public. Academic Community. Current Student. SMU Faculty & Staff.
Friday, November 21, 2025 – Sunday, November 23, 2025.
For more info visit ichec.icachi.org. Graph Perturbations for Robust Knowledge Discovery and Retrieval
Graph perturbation studies how small structural edits, e.g. adding or deleting edges, affect graph properties and downstream tasks. While prior work largely targets global statistics or model outputs, robustness for knowledge discovery and information retrieval on attributed graphs is less explored. This dissertation introduces formulations and algorithms that generate and leverage perturbations to make these tasks more reliable.
First, we assess the robustness of outstanding facts on knowledge graphs. We formalize two perturbation types: (i) context‑entity swaps and (ii) plausible data insertions and estimate robustness via a perturbation‑relevance distribution, with powerful relevance‑based pruning and importance‑sampling estimators. 
Second, we study subgraph counting under adversarial edge additions. We prove the kSub problem is NP‑hard and inapproximable, derive the topkSub relaxation, and design connected/disconnected processing with pruning and unbiased samplers, enabling practical stress tests th…
Subtitle: PhD Dissertation Defense by XIAO Hanhua. Contact: scisseminars@smu.edu.sg. Speaker Details: , XIAO Hanhua
PhD Candidate
School of Computing and Information Systems
Singapore Management University, XIAO Hanhua is a Ph.D. candidate in Computer Science at Singapore Management University, under the supervision of Associate Professor Li Yuchen. His research focuses on graph mining, with particular interests in knowledge graph mining, graph perturbation, and large language model (LLM)-based graph analytics. Before joining SMU, Hanhua received his B.Sc. and M.Sc. degrees in Electronic Engineering from… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UNzNNN1pRVzgxVEw2N1JHS1pHNE1XTVZVWC4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Current Student. Academic Community.
Friday, November 21, 2025, 3:30 PM – 4:30 PM.
Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902.
For more info visit computing.smu.edu.sg. OSTOM: Offline Imitation Learning from Observations via State Transition Occupancy Matching
Offline Learning from Observation (LfO) focuses on enabling agents to imitate expert behavior using datasets that contain only expert state trajectories and separate transition data with suboptimal actions. This setting is both practical and critical in real-world scenarios where direct environment interaction or access to expert action labels is costly, risky, or infeasible. Most existing LfO methods attempt to solve this problem through state or state-action occupancy matching. They typically rely on pretraining a discriminator to differentiate between expert and non-expert states, which could introduce errors and instability—especially when the discriminator is poorly trained. While recent discriminator-free methods have emerged, they generally require substantially more data, limiting their practicality in low-data regimes. In this paper, we propose IOSTOM (Imitation from Observation via State Transition Occupancy Matching), a novel offline LfO algorithm designed to overcome these limitations. Our approac…
Subtitle: Pre-Conference Talk by PHAM Quang Anh. Contact: scisseminar@smu.edu.sg. Speaker Details: , PHAM Quang Anh
PhD Student
School of Computing and Information Systems
Singapore Management University, Quang Anh PHAM is a first-year PhD student in Computer Science at SMU School of Computing and Information Systems, supervised by Associate Prof Akshat Kumar and Assistant Prof Mai Anh Tien. His research interests are Artificial Intelligence (Imitation Learning, Reinforcement Learning and Heuristic Search), Operations Research (Routing and Scheduling problems), and Combinatorial Optimization… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UNTNVVE9KMVJERFRLSlk0OTFEWVdIREQwOC4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public.
Tuesday, November 25, 2025, 11:30 AM – 12:00 PM.
Meeting room 5.1, Level 5. School of Computing and Information Systems 1,
Singapore Management University, 80 Stamford Road, Singapore 178902.
For more info visit computing.smu.edu.sg.