School of Computing and Information Systems

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Collaborative Freight Forwarding: A Game-Theoretic Perspective

Freight forwarders act as intermediaries between shippers and carriers, purchasing capacity in advance and reselling it to shippers. By consolidating shipments, they profit from economies of scale but face the challenge that capacity must be procured months ahead while shipper demand remains uncertain. This often leads to over- or under-procurement, eroding profitability. Collaborative capacity sharing offers a solution to better align capacity with demand, but equitably allocating costs among partners remains complex. This dissertation employs game-theoretic approaches to design fair and stable cost-sharing mechanisms that incentivize cooperation. First, we formulate the Freight Forwarder Collaboration Problem (FFCP), an integer linear program that minimizes total shipping costs under collaboration. A two-step approach combining greedy heuristics and bounded optimization achieves optimal solutions while reducing computation time by up to 81%, enabling practical scalability. Second, in the static setting, wh… Subtitle: PhD Dissertation Defense by TAN Pang Jin. Contact: scisseminars@smu.edu.sg. Speaker Details: , TAN Pang Jin PhD Candidate School of Computing and Information Systems Singapore Management University, TAN Pang Jin is a PhD candidate with SCIS supervised by Assoc. Prof. CHENG Shih-Fen. His research interests are in collaborative logistics, platform economics, cooperative game theory and their applications to the freight forwarding industry. He is currently working as Instructor in SCIS. Previously he has had stints with DHL Group and PSA Corporation. He obtained his Bachelor of Engineering… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UOFpBQ1JQU1FPMTM5TE9HNjBTMjNQVU5KQS4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Current Student. Academic Community. Monday, November 10, 2025, 11:00 AM – 12:00 PM. Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902. For more info visit computing.smu.edu.sg.

No Experts, No Problem: Avoidance Learning from Bad Demonstrations

This paper addresses the problem of learning avoidance behavior within the context of offline imitation learning. In contrast to conventional methodologies that prioritize the replication of expert or near-expert demonstrations, our work investigates a setting where expert (or desirable) data is absent, and the objective is to learn to eschew undesirable actions by leveraging demonstrations of such behavior (i.e. learning from negative examples). To address this challenge, we propose a novel training objective grounded in the maximum entropy principle. We further characterize the fundamental properties of this objective function, reformulating the learning process as a cooperative inverse Q-learning task. Moreover, we introduce an efficient strategy for the integration of unlabeled data (i.e. data of indeterminate quality) to facilitate unbiased and practical offline training. The efficacy of our method is evaluated across standard benchmark environments, where it consistently outperforms state-of-the-art b… Subtitle: Pre-Conference Talk by HOANG Minh Huy. Contact: scisseminar@smu.edu.sg. Speaker Details: , HOANG Minh Huy PhD Student School of Computing and Information Systems Singapore Management University, Hoang Minh Huy is a PhD student in Computer Science at Singapore Management University, under the supervision of Assistant Professor Tien Mai and Dr. Pavitra Krishnaswamy. He is supported by the SINGA A*STAR Merit Award. His research focuses on Deep Reinforcement Learning and Imitation Learning, particularly developing novel algorithms for Offline Imitation Learning from mixed-quality data (including… RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UQjNUQlg0MktORUNIOEI4NE1KWk1HOVI3NC4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Tuesday, November 11, 2025, 11:00 AM – 11:30 AM. Meeting room 4.4, Level 4. School of Computing and Information Systems 1, Singapore Management University, 80 Stamford Road, Singapore 178902. For more info visit computing.smu.edu.sg.

Defects4C: Benchmarking Large Language Model Repair Capability with C/C++ Bugs

Automated Program Repair (APR) has achieved remarkable progress in Java, yet research on C/C++ repair remains limited due to the lack of suitable benchmarks. To bridge this gap, we present Defects4C, a comprehensive benchmark for C/C++ program repair built from real-world repositories. Defects4C provides millions of bug-related commits and hundreds of curated buggy and vulnerable functions with executable test cases, enabling systematic evaluation and model retraining. Leveraging this dataset, we evaluate 24 state-of-the-art foundation language models on C/C++ repair tasks, revealing key strengths, weaknesses, and future directions for LLM-based APR in various programming languages. This is a Pre-Conference talk for 40th IEEE/ACM International Conference on Automated Software Engineering (ASE 2025). Subtitle: Pre-Conference Talk by YU Jiongchi. Contact: scisseminar@smu.edu.sg. Speaker Details: , YU Jiongchi PhD Candidate School of Computing and Information Systems Singapore Management University, Jiongchi YU is a Ph.D. candidate in Computer Science at the SMU School of Computing and Information Systems, supervised by Prof. Xiaofei XIE. His research interests include program analysis, software engineering and security. RSVP: . Reserve a seat: https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UQ0lVR0xDWUNDVE1CQkg3SjBGOElKU0tYMC4u. Type: Seminars & Workshops. Subject: Information Technology & Systems. Audience: Public. Tuesday, November 11, 2025, 3:05 PM – 3:15 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.

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.

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.

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.

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.