School of Computing and Information Systems

School of Computing and Information Systems http://sis.smu.edu.sg/events en-us 21 Jul 2026 08:06:33 GMT Calendar Feed powered by Trumba https://www3.trumba.com/images/trumba_logo_sm.gif http://sis.smu.edu.sg/events -//Trumba Corporation//Trumba Calendar Services 0.11.25916//EN 2.0 PUBLISH GREGORIAN School of Computing and Information Systems Asia/Singapore From Behaviour to Explanation: Actual Causality in Process Algebra Meeting Room 4-1, Level 4. SMU SCIS 2, Singapore 178903 Tuesday, July 21, 2026, 2 - 3pm From Behaviour to Explanation: Actual Causality in Process Algebra Formal verification can tell us whether a system satisfies a property, but often not why this property holds. In this talk, I will discuss ongoing work on integrating actual causality into process algebra and concurrent-system semantics. We introduce the Causal Transition Calculus (CTC), a framework combining process algebra, modal logic, and intervention-based reasoning inspired by Halpern and Pearl. The framework allows us to reason about causes of behavioural properties in concurrent systems, while connecting variable-level interventions with operational semantics and labelled transition systems. A central insight is that causality depends not only on observable behaviour, but also on the internal structure generating that behaviour: systems that are behaviourally equivalent may still differ from a causal perspective. This work aims to build bridges between concurrency theory, formal verification, causal reasoning, and explainable system analysis, opening new directions for understanding complex reactive and distributed systems. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/research-seminar-dr-georgiana-caltais?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D204776067 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/sspxkn6mhpb1txdr6mh2rf6rj3 2026/07/21 (Tue) 21 Jul 2026 06:00:00 GMT http://uid.trumba.com/event/204776067 From Behaviour to Explanation: Actual Causality in Process Algebra Meeting Room 4-1, Level 4. SMU SCIS 2, Singapore 178903 2026-07-21T06:00:00Z

2026-07-21T14:00:00

Tuesday, July 21, 2026, 2 - 3pm

2026-07-21T07:00:00Z

2026-07-21T15:00:00

false

Formal verification can tell us whether a system satisfies a property, but often not why this property holds. In this talk, I will discuss ongoing work on integrating actual causality into process algebra and concurrent-system semantics. We introduce the Causal Transition Calculus (CTC), a framework combining process algebra, modal logic, and intervention-based reasoning inspired by Halpern and Pearl. The framework allows us to reason about causes of behavioural properties in concurrent systems, while connecting variable-level interventions with operational semantics and labelled transition systems. A central insight is that causality depends not only on observable behaviour, but also on the internal structure generating that behaviour: systems that are behaviourally equivalent may still differ from a causal perspective. This work aims to build bridges between concurrency theory, formal verification, causal reasoning, and explainable system analysis, opening new directions for understanding complex reactive and distributed systems. http://uid.trumba.com/event/204776067

SMU_NextWeb_Common_01

Research Seminar by Dr Georgiana Caltais

https://www.trumba.com/i/DgBbH2uT9IrSOn-X1j7XzIXS.jpg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2026-06/georgiana.png"> Georgiana Caltais Assistant Professor Formal Methods and Tools Group University of Twente Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS)<hr />Georgiana Caltais is an Assistant Professor in the Formal Methods and Tools group at the University of Twente, the Netherlands. She received her PhD from Radboud University and Reykjavík University and subsequently held research positions at ETH Zürich and the University of Konstanz.  Her research focuses on the formal modelling, semantics, and verification of concurrent systems. She works at the intersection of process algebra, modal logic, coalgebra, and operational semantics, with a particular interest in equivalence checking, and automated reasoning. Over the years, she has developed a sustained research line on causal and counterfactual reasoning for computational systems, investigating how formal methods can support explainability, debugging, and system analysis.  She has led and contributed to nationally and internationally funded research projects on causality, software-defined networks, software correctness, and intelligent diagnostics for complex cyber-physical systems. She has co-chaired several international conferences and workshops, including FSEN, EXPRESS/SOS, STAF, and the Dutch ICT.Open Software Engineering. Georgiana serves on the Steering Committee of SPIN, and is actively involved in the Dutch formal methods and software engineering communities through VERSEN and related initiatives. She regularly contributes to program committees of leading conferences in formal methods and software verification.  Her current work explores executable foundations for causal reasoning, combining ideas from process algebra, modal logic, and intervention-based causality.

https://forms.office.com/pages/responsepage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTbOXO7ktpRtBsn62WuekYtpUQTMzSzY3R0FDVzJNNUIwRUxCUUVaQU5HQy4u&route=shorturl

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Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/research-seminar-dr-georgiana-caltais?newsletter

Generative AI and Large Language Models (LLMs) for Cyber-Security and Political Sciences Seminar Room 4-2, Level 4. SMU SCIS 2, Singapore 178903 Tuesday, July 21, 2026, 3 - 4pm Generative AI and Large Language Models (LLMs) for Cyber-Security and Political Sciences This presentation explores the transformative potential of generative AI—particularly large language models (LLMs)—in addressing critical challenges in domains such as cybersecurity, intelligent transportation systems (ITS) and political sciences. In this talk, the speaker will cover the following and other related topics. - Generative AI-Enhanced Threat Modeling in ITS: We develop an LLM-based framework to automate threat modeling for complex intelligent transportation systems by mapping information flows to MITRE ATT&CK techniques and NIST Cybersecurity Framework controls. The approach evaluates multiple AI methods, including zero-shot learning, RAG, multimodal reasoning, in-context learning, and fine-tuning. - Policy Analysis for Secure Transportation Systems: This project enhances transportation cybersecurity policy using AI-driven legal analysis and stakeholder engagement. Building on the TraCR AI system, it integrates U.S. and international regulations and uses agentic AI and graph-based retrieval to identify policy gaps and propose improvements for data security and privacy in autonomous transportation. - Conflict and Political Violence Monitoring: We develop a domain-specific pretrained language model for analyzing conflict and political violence data, which outperforms general-purpose LLMs in classification and question-answering tasks and has over 14,000 downloads on GitHub and Hugging Face. We also proposed ensemble-based active learning methods—Ensemble Union and Ensemble Intersection—that combine multiple heuristics to improve sample selection. Experiments on the United Nations Parallel Corpus show these approaches achieve performance comparable to full-dataset training while requiring far fewer labeled examples. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/research-seminar-dr-latifur-khan?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D204853014 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/cct3k2jns7n9z4x07zg43wbx0m 2026/07/21 (Tue) 21 Jul 2026 07:00:00 GMT http://uid.trumba.com/event/204853014 Generative AI and Large Language Models (LLMs) for Cyber-Security and Political Sciences Seminar Room 4-2, Level 4. SMU SCIS 2, Singapore 178903 2026-07-21T07:00:00Z

2026-07-21T15:00:00

Tuesday, July 21, 2026, 3 - 4pm

2026-07-21T08:00:00Z

2026-07-21T16:00:00

false

This presentation explores the transformative potential of generative AI—particularly large language models (LLMs)—in addressing critical challenges in domains such as cybersecurity, intelligent transportation systems (ITS) and political sciences. In this talk, the speaker will cover the following and other related topics. - Generative AI-Enhanced Threat Modeling in ITS: We develop an LLM-based framework to automate threat modeling for complex intelligent transportation systems by mapping information flows to MITRE ATT&CK techniques and NIST Cybersecurity Framework controls. The approach evaluates multiple AI methods, including zero-shot learning, RAG, multimodal reasoning, in-context learning, and fine-tuning. - Policy Analysis for Secure Transportation Systems: This project enhances transportation cybersecurity policy using AI-driven legal analysis and stakeholder engagement. Building on the TraCR AI system, it integrates U.S. and international regulations and uses agentic AI and graph-based retrieval to identify policy gaps and propose improvements for data security and privacy in autonomous transportation. - Conflict and Political Violence Monitoring: We develop a domain-specific pretrained language model for analyzing conflict and political violence data, which outperforms general-purpose LLMs in classification and question-answering tasks and has over 14,000 downloads on GitHub and Hugging Face. We also proposed ensemble-based active learning methods—Ensemble Union and Ensemble Intersection—that combine multiple heuristics to improve sample selection. Experiments on the United Nations Parallel Corpus show these approaches achieve performance comparable to full-dataset training while requiring far fewer labeled examples. http://uid.trumba.com/event/204853014

SMU_NextWeb_Common_01

Research Seminar by Dr Latifur Khan

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scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2026-06/latifur-khan.jpg"> Latifur Khan Professor Department of Computer Science University of Texas at Dallas (UT Dallas), USA Fellow of IEEE, AAAS, IET & BCS<hr />Dr. Latifur Khan is a full Professor in the Computer Science department at the University of Texas at Dallas, USA and Director of the Artificial Intelligence and Cyber Security Center. Dr. Khan is a fellow of IEEE, AAAS, and the British-based IET and BCS, and an ACM Distinguished Scientist. He has received prestigious awards including the IEEE ITSS ISI 2012 Technical Achievement Award, IEEE Big Data Security 2019 Senior Research Award, and 2016 IBM Faculty Award. His research focuses on AI and data science and their applications in cyber security and transportation systems, as well as in complex data management including geospatial and multimedia data. More details can be found at <a href="https://www.utdallas.edu/~Ikhan" dir="ltr">www.utdallas.edu/~Ikhan</a>.

https://forms.office.com/pages/responsepage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTbOXO7ktpRtBsn62WuekYtpUQTFSQlY0UTQ2OTJVQzI4M1Y4MEQxRzA2MC4u&route=shorturl

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/research-seminar-dr-latifur-khan?newsletter

SMU: Bangkok Coffee Session Sofitel Bangkok Sukhumvit Wednesday, July 22, 2026, 6 - 8pm SMU: Bangkok Coffee Session http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205338067 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/wtketawdx98tvf393284pzzjcx 2026/07/22 (Wed) 22 Jul 2026 10:00:00 GMT http://uid.trumba.com/event/205338067 SMU: Bangkok Coffee Session Sofitel Bangkok Sukhumvit 2026-07-22T10:00:00Z

2026-07-22T18:00:00

Wednesday, July 22, 2026, 6 - 8pm

2026-07-22T12:00:00Z

2026-07-22T20:00:00

false

http://uid.trumba.com/event/205338067

SMU_NextWeb_InfoSessions

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6:00pm to 8:00pm

https://calendly.com/smupgppmg/smu-consultation?month=2026-07&date=2026-07-22

Join us for an insightful session to discover how our post-graduate programmes can support your personal and professional aspirations. Our Admissions Advisors will speak with you directly to provide detailed guidance on the application process and to discuss the financial aid and scholarship opportunities that may be available to you. For a pre-application assessment, please bring along a copy of your resume for a personalised review. Kindly note that sessions may be conducted in a group setting. You are welcome to bring a friend with you. Simply forward them this registration link: https://calendly.com/smupgppmg/smu-consultation?month=2026-07&date=2026-07-22

Information Sessions

Analytics for Business, Consumer & Social Insights,Business,Finance & Financial Markets,Innovation & Entrepreneurship

Prospective Student,Public

School of Computing and Information Systems|Master of IT in Business|Public Events

SMU: Ho Chi Minh City Coffee Session Lotte Hotel Saigon 2A-4A Tôn Đức Thắng, Phường, Sài Gòn, Hồ Chí Minh 700000, Vietnam Friday, July 24, 2026, 5 - 8pm SMU: Ho Chi Minh City Coffee Session http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D204932056 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/8n2s3g7ds1j09x32jmt3fra68e 2026/07/24 (Fri) 24 Jul 2026 09:00:00 GMT http://uid.trumba.com/event/204932056 SMU: Ho Chi Minh City Coffee Session Lotte Hotel Saigon 2A-4A Tôn Đức Thắng, Phường, Sài Gòn, Hồ Chí Minh 700000, Vietnam 2026-07-24T09:00:00Z

2026-07-24T17:00:00

Friday, July 24, 2026, 5 - 8pm

2026-07-24T12:00:00Z

2026-07-24T20:00:00

false

http://uid.trumba.com/event/204932056

SMU_NextWeb_InfoSessions

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5:00pm to 8:00pm

https://calendly.com/danielpang-smu/smu-hcmc-jul26

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Business,Finance & Financial Markets

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Designing Context-Aware AI Intervention for Interpersonal Communication Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 Monday, July 27, 2026, 1 - 2pm Designing Context-Aware AI Intervention for Interpersonal Communication Conversational agents are increasingly evolving from tools that assist individual users into systems that actively participate in communication between people. Unlike traditional human-AI interaction, interpersonal communication requires AI systems to reason not only about individual users but also about the social, relational, and epistemic contexts in which communication takes place. However, existing research provides limited understanding of how AI interventions should be designed and adapted across different communication contexts. This dissertation investigates how context-aware AI systems can effectively intervene in and support interpersonal communication. It addresses two complementary research problems: (1) establishing a systematic design space for AI interventions and (2) understanding how intervention strategies should adapt to different communication contexts. To address the first problem, the dissertation begins with a systematic literature review of group conversational agents, synthesizing prior work into a conceptual design space of interaction challenges, agent roles, and intervention strategies. Building on this foundation, it examines AI intervention in two contrasting contexts: socially intimate communication, through a scenario-based design study of intergenerational communication, and professional communication, through an ongoing Wizard-of-Oz prototype study of interdisciplinary collaboration. Collectively, these studies establish a conceptual design space for AI interventions, provide empirical insights into how expectations of AI intervention vary across communication contexts, and generate design implications for developing context-aware conversational agents that effectively support interpersonal communication. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-zhang-tianyi-designing-context-aware-ai-intervention?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205662524 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/m0g7u0x6hzb5gya1bn85n69erw 2026/07/27 (Mon) 27 Jul 2026 05:00:00 GMT http://uid.trumba.com/event/205662524 Designing Context-Aware AI Intervention for Interpersonal Communication Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 2026-07-27T05:00:00Z

2026-07-27T13:00:00

Monday, July 27, 2026, 1 - 2pm

2026-07-27T06:00:00Z

2026-07-27T14:00:00

false

Conversational agents are increasingly evolving from tools that assist individual users into systems that actively participate in communication between people. Unlike traditional human-AI interaction, interpersonal communication requires AI systems to reason not only about individual users but also about the social, relational, and epistemic contexts in which communication takes place. However, existing research provides limited understanding of how AI interventions should be designed and adapted across different communication contexts. This dissertation investigates how context-aware AI systems can effectively intervene in and support interpersonal communication. It addresses two complementary research problems: (1) establishing a systematic design space for AI interventions and (2) understanding how intervention strategies should adapt to different communication contexts. To address the first problem, the dissertation begins with a systematic literature review of group conversational agents, synthesizing prior work into a conceptual design space of interaction challenges, agent roles, and intervention strategies. Building on this foundation, it examines AI intervention in two contrasting contexts: socially intimate communication, through a scenario-based design study of intergenerational communication, and professional communication, through an ongoing Wizard-of-Oz prototype study of interdisciplinary collaboration. Collectively, these studies establish a conceptual design space for AI interventions, provide empirical insights into how expectations of AI intervention vary across communication contexts, and generate design implications for developing context-aware conversational agents that effectively support interpersonal communication. http://uid.trumba.com/event/205662524

SMU_NextWeb_Common_01

PhD Dissertation Proposal by ZHANG Tianyi

https://www.trumba.com/i/DgAFANwgRJwf1jWQYBh7fM-Y.jpeg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2023-08/tianyizhang-2023.jpg" width="100" height="120"> ZHANG Tianyi PhD Candidate School of Computing and Information Systems Singapore Management University<hr />ZHANG Tianyi is a PhD candidate in Computer Science at the School of Computing and Information Systems, Singapore Management University, under the supervision of Associate Professor Tony TANG and Assistant Professor Li Jiannan. Her research primarily focuses on Human–AI Interaction, particularly the design of AI systems that support interpersonal and group communication.

https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UMFRDS1E4MURWWUM2MldYWVpDM1NBNlBDTy4u

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-zhang-tianyi-designing-context-aware-ai-intervention?newsletter

Generative AI for Cardiovascular Health Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 Monday, July 27, 2026, 3:30 - 4:30pm Generative AI for Cardiovascular Health Cardiovascular disease remains a major global health challenge, making reliable and accessible monitoring increasingly important. Two of the most widely used signals for assessing cardiovascular function are the electrocardiogram (ECG), which records the heart’s electrical activity, and the photoplethysmogram (PPG), which measures blood-volume changes through optical sensing. Recent advances in generative AI have opened new possibilities for learning from these signals. However, important challenges remain in ensuring reliable performance on real-world signals, generalizing across diverse populations and healthcare settings, and enabling flexible interaction beyond narrowly defined prediction tasks. In this talk, I will present a progression of methods for building more reliable, transferable, and interactive cardiovascular AI. Drawing on research in signal quality enhancement and disentangled learning, large-scale multimodal representation learning, and language models, I will show how physiology-informed design can improve the full pipeline from sensing to reasoning, ultimately supporting more clinically useful AI systems for cardiovascular healthcare. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-pham-hung-manh-generative-ai-cardiovascular-health?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205633331 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/cjvdyc0xnt7n8635yg2e9exjg9 2026/07/27 (Mon) 27 Jul 2026 07:30:00 GMT http://uid.trumba.com/event/205633331 Generative AI for Cardiovascular Health Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 2026-07-27T07:30:00Z

2026-07-27T15:30:00

Monday, July 27, 2026, 3:30 - 4:30pm

2026-07-27T08:30:00Z

2026-07-27T16:30:00

false

Cardiovascular disease remains a major global health challenge, making reliable and accessible monitoring increasingly important. Two of the most widely used signals for assessing cardiovascular function are the electrocardiogram (ECG), which records the heart’s electrical activity, and the photoplethysmogram (PPG), which measures blood-volume changes through optical sensing. Recent advances in generative AI have opened new possibilities for learning from these signals. However, important challenges remain in ensuring reliable performance on real-world signals, generalizing across diverse populations and healthcare settings, and enabling flexible interaction beyond narrowly defined prediction tasks. In this talk, I will present a progression of methods for building more reliable, transferable, and interactive cardiovascular AI. Drawing on research in signal quality enhancement and disentangled learning, large-scale multimodal representation learning, and language models, I will show how physiology-informed design can improve the full pipeline from sensing to reasoning, ultimately supporting more clinically useful AI systems for cardiovascular healthcare. http://uid.trumba.com/event/205633331

SMU_NextWeb_Common_01

PhD Dissertation Proposal by PHAM Hung Manh

https://www.trumba.com/i/DgAFANwgRJwf1jWQYBh7fM-Y.jpeg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2023-08/hm-pham-2023.jpg" width="100" height="120"> PHAM Hung Manh PhD Candidate School of Computing and Information Systems Singapore Management University<hr />Hung Manh PHAM is a third-year PhD candidate in Computer Science, supervised by Prof. Pan Zhou and co-supervised by Prof. Dong Ma and Prof. Bin Zhu. He is also a visiting student at the University of Cambridge. His research focuses on machine learning for healthcare and biomedicine, particularly physiology-informed learning and medical foundation models. His broader interests include causal inference, interpretable learning, and trustworthy medical AI.

https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UQUk0RU5QQlVMTDk5TDZHUFRYS1REU1pYNC4u

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-pham-hung-manh-generative-ai-cardiovascular-health?newsletter

SMU: Manila Coffee Session Fairmont Makati Hotel Monday, July 27, 2026, 7:30 - 9:30pm SMU: Manila Coffee Session http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205400534 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/ctt9xw0zbaust6kkzzrwfbnwa0 2026/07/27 (Mon) 27 Jul 2026 11:30:00 GMT http://uid.trumba.com/event/205400534 SMU: Manila Coffee Session Fairmont Makati Hotel 2026-07-27T11:30:00Z

2026-07-27T19:30:00

Monday, July 27, 2026, 7:30 - 9:30pm

2026-07-27T13:30:00Z

2026-07-27T21:30:00

false

http://uid.trumba.com/event/205400534

SMU_NextWeb_InfoSessions

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https://calendly.com/ivysim-smu/manilacs270726?month=2026-07

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Business,Finance & Financial Markets,Innovation & Entrepreneurship

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School of Computing and Information Systems|Master of IT in Business|Public Events

Conversational Understanding in the Open World: Intention Discovery, Structuring, and Defense Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 Tuesday, July 28, 2026, 9 - 10am Conversational Understanding in the Open World: Intention Discovery, Structuring, and Defense Conversational understanding—inferring the intentions behind user utterances—constitutes the interpretive foundation of conversational AI. However, existing research predominantly operates under a closed-world assumption, where user needs are drawn from a predefined ontology and expressed through rigid semantic representations. This assumption proves increasingly restrictive in real-world conversations, where new user needs continually emerge, complex intentions involve rich contextual and affective semantics, and malicious intentions may be strategically concealed and refined over multiple turns. This dissertation advances conversational understanding in the open world along three interrelated directions: (1) intention discovery, which recognizes and characterizes emerging intents beyond predefined ontologies; (2) intention structuring, which represents fine-grained intentions through expressive yet consistent multi-aspect structures; and (3) intention defense, which safeguards conversational systems against malicious intentions that are concealed and adaptively refined across multi-turn interactions. These studies aim to establish more adaptive, expressive, and reliable conversational understanding for open-world interactions. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-liang-jinggui-conversational-understanding-open-world?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205701195 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/cy1vzd9z5p99yv7y7yjeyh52p8 2026/07/28 (Tue) 28 Jul 2026 01:00:00 GMT http://uid.trumba.com/event/205701195 Conversational Understanding in the Open World: Intention Discovery, Structuring, and Defense Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 2026-07-28T01:00:00Z

2026-07-28T09:00:00

Tuesday, July 28, 2026, 9 - 10am

2026-07-28T02:00:00Z

2026-07-28T10:00:00

false

Conversational understanding—inferring the intentions behind user utterances—constitutes the interpretive foundation of conversational AI. However, existing research predominantly operates under a closed-world assumption, where user needs are drawn from a predefined ontology and expressed through rigid semantic representations. This assumption proves increasingly restrictive in real-world conversations, where new user needs continually emerge, complex intentions involve rich contextual and affective semantics, and malicious intentions may be strategically concealed and refined over multiple turns. This dissertation advances conversational understanding in the open world along three interrelated directions: (1) intention discovery, which recognizes and characterizes emerging intents beyond predefined ontologies; (2) intention structuring, which represents fine-grained intentions through expressive yet consistent multi-aspect structures; and (3) intention defense, which safeguards conversational systems against malicious intentions that are concealed and adaptively refined across multi-turn interactions. These studies aim to establish more adaptive, expressive, and reliable conversational understanding for open-world interactions. http://uid.trumba.com/event/205701195

SMU_NextWeb_Common_01

PhD Dissertation Proposal by LIANG Jinggui

https://www.trumba.com/i/DgAFANwgRJwf1jWQYBh7fM-Y.jpeg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2023-08/jg-liang-2023.jpg"> LIANG Jinggui PhD Candidate School of Computing and Information Systems Singapore Management University<hr />LIANG Jinggui is a PhD candidate in Computer Science at the School of Computing and Information Systems, Singapore Management University, supervised by Prof. LIAO Lizi. His research focuses on conversational understanding, LLM safety, and multi-agent systems.

https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UREJITE4ySlRUMU1HTTBDVTRYUlNSWjMwSS4u

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-liang-jinggui-conversational-understanding-open-world?newsletter

Peer Group Analysis using Knowledge Graphs and Large Language Models for Business Optimization Meeting room 4.4, Level 4. SMU SCIS 1, Singapore 178902 Tuesday, July 28, 2026, 11am - 12pm Peer Group Analysis using Knowledge Graphs and Large Language Models for Business Optimization Comparing similar entities, or peers, is central to decision-making over complex and restricted information, yet doing it reliably remains hard. It requires three linked capabilities: finding the right peers, matching up data that is organized differently across sources, and enabling analysis when the underlying data cannot be shared. This dissertation presents a unified framework addressing all three. First, a knowledge-graph-based exploration system finds meaningful peers by connecting specific entities through shared concepts, letting analysts zoom out to broader categories or zoom in to supporting evidence. Second, a matching approach uses large language models to reconcile differently structured data sources, handling complex real-world cases where simple similarity fails. Third, a privacy-safe framework generates realistic synthetic data guided only by summary statistics, so analysis can proceed without ever exposing real records. Together, these components turn peer analysis from isolated tasks into a coherent workflow, making complex information findable, comparable, and shareable. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/phd-dissertation-defense-wang-sha-peer-group-analysis-using-knowledge-graphs-and-large?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205848001 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/853d2yp0cxh6rn64nvew4vra3n 2026/07/28 (Tue) 28 Jul 2026 03:00:00 GMT http://uid.trumba.com/event/205848001 Peer Group Analysis using Knowledge Graphs and Large Language Models for Business Optimization Meeting room 4.4, Level 4. SMU SCIS 1, Singapore 178902 2026-07-28T03:00:00Z

2026-07-28T11:00:00

Tuesday, July 28, 2026, 11am - 12pm

2026-07-28T04:00:00Z

2026-07-28T12:00:00

false

Comparing similar entities, or peers, is central to decision-making over complex and restricted information, yet doing it reliably remains hard. It requires three linked capabilities: finding the right peers, matching up data that is organized differently across sources, and enabling analysis when the underlying data cannot be shared. This dissertation presents a unified framework addressing all three. First, a knowledge-graph-based exploration system finds meaningful peers by connecting specific entities through shared concepts, letting analysts zoom out to broader categories or zoom in to supporting evidence. Second, a matching approach uses large language models to reconcile differently structured data sources, handling complex real-world cases where simple similarity fails. Third, a privacy-safe framework generates realistic synthetic data guided only by summary statistics, so analysis can proceed without ever exposing real records. Together, these components turn peer analysis from isolated tasks into a coherent workflow, making complex information findable, comparable, and shareable. http://uid.trumba.com/event/205848001

SMU_NextWeb_Common_01

PhD Dissertation Defense by WANG Sha

https://www.trumba.com/i/DgCgLWgt2HvZj3rApyNgMUxd.jpeg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2023-07/sha-wang-2021.jpg" width="100" height="120"> WANG Sha PhD Candidate School of Computing and Information Systems Singapore Management University<hr />Sha's dissertation is focused on structured methods for analyzing and comparing complex, restricted datasets — combining data mining, knowledge graphs, and LLM-driven automation to make heterogeneous information easier to search, align, and share safely. Her research interests center on data mining and automation: building systems that reduce manual effort in data discovery, integration, and preparation, particularly where data is messy, siloped, or privacy-sensitive. Leisure activities she enjoys include: reading (mostly non-fictions), sports and history.

https://forms.office.com/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTacjffrphqhDrJfv1DjxSmNUNFE5MTgwRjlESjNZMEEzMVFQS1pIRTlWNS4u

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/phd-dissertation-defense-wang-sha-peer-group-analysis-using-knowledge-graphs-and-large?newsletter

AI-Driven Interactive Knowledge Synthesis and Explorable Learning Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 Tuesday, July 28, 2026, 1 - 2pm AI-Driven Interactive Knowledge Synthesis and Explorable Learning Artificial Intelligence (AI) has transformed how people access information and learn, yet most AI systems remain largely text-centric, providing generated prose with limited support for visual synthesis and interactive exploration. This dissertation argues that AI should move beyond generating text to constructing interactive explanatory artifacts that combine generated content, visual structure, and user interaction to support deeper understanding. Such artifacts enable people to inspect, manipulate, and reason about complex information and concepts, whether synthesizing knowledge from multiple sources or learning abstract subjects such as mathematics. To investigate this vision, the dissertation presents three AI-driven systems spanning knowledge synthesis and mathematics education: Compendia, which transforms fragmented information from online documents into interactive visual data stories; MathVibe, which supports teacher-AI co-creation of mathematics explorable explanations; and EETutor, which dynamically generates and adapts interactive explanations during tutoring conversations. Together, these systems demonstrate how AI can act as a medium for creating interactive explanatory artifacts that organize information, connect representations, and foster learning through exploration rather than text alone. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-vidana-gamage-manusha-imesh-karunathilaka-ai-driven?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205633372 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/g80wwagn8t79a225cb451z57rg 2026/07/28 (Tue) 28 Jul 2026 05:00:00 GMT http://uid.trumba.com/event/205633372 AI-Driven Interactive Knowledge Synthesis and Explorable Learning Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 2026-07-28T05:00:00Z

2026-07-28T13:00:00

Tuesday, July 28, 2026, 1 - 2pm

2026-07-28T06:00:00Z

2026-07-28T14:00:00

false

Artificial Intelligence (AI) has transformed how people access information and learn, yet most AI systems remain largely text-centric, providing generated prose with limited support for visual synthesis and interactive exploration. This dissertation argues that AI should move beyond generating text to constructing interactive explanatory artifacts that combine generated content, visual structure, and user interaction to support deeper understanding. Such artifacts enable people to inspect, manipulate, and reason about complex information and concepts, whether synthesizing knowledge from multiple sources or learning abstract subjects such as mathematics. To investigate this vision, the dissertation presents three AI-driven systems spanning knowledge synthesis and mathematics education: Compendia, which transforms fragmented information from online documents into interactive visual data stories; MathVibe, which supports teacher-AI co-creation of mathematics explorable explanations; and EETutor, which dynamically generates and adapts interactive explanations during tutoring conversations. Together, these systems demonstrate how AI can act as a medium for creating interactive explanatory artifacts that organize information, connect representations, and foster learning through exploration rather than text alone. http://uid.trumba.com/event/205633372

SMU_NextWeb_Common_01

PhD Dissertation Proposal by VIDANA GAMAGE Manusha Imesh Karunathilaka

https://www.trumba.com/i/DgAFANwgRJwf1jWQYBh7fM-Y.jpeg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2023-08/gmik-vidana-2023.jpg"> VIDANA GAMAGE Manusha Imesh Karunathilaka PhD Candidate School of Computing and Information Systems Singapore Management University<hr />Manusha Karunathilaka is a Ph.D. candidate in the School of Computing and Information Systems at Singapore Management University. His research lies at the intersection of human-computer interaction, artificial intelligence, data visualization, and educational technologies. He is particularly interested in developing AI-driven interactive artifacts that support knowledge synthesis, explanation, and learning. His work has been published in leading venues, including the IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG) and the ACM Conference on Human Factors in Computing Systems (CHI).

https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UOTcxQURGSkRORjdNUktMTElXU1RGNkVQWC4u

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-vidana-gamage-manusha-imesh-karunathilaka-ai-driven?newsletter

Principled Configuration Optimization for Parameter-Efficient Fine-Tuning of Foundation Models Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 Tuesday, July 28, 2026, 3:30 - 4:30pm Principled Configuration Optimization for Parameter-Efficient Fine-Tuning of Foundation Models Full fine-tuning of large pre-trained models incurs prohibitive computational cost and risks catastrophic forgetting. Parameter-efficient fine-tuning (PEFT) addresses this by updating only a small set of additional parameters while keeping the pre-trained model frozen. Low-Rank Adaptation (LoRA) has emerged as the de facto PEFT method for both large language models and vision transformers. However, in current practice, LoRA is configured uniformly: the same adaptation at every network position, and the same treatment of every task and every class. This thesis formulates LoRA configuration as a constrained resource allocation problem: a fixed parameter budget distributed over the structure of the model and the distribution of the data. We establish that the optimal allocation is non-uniform, and that it can be derived from properties of the model and the data rather than found by search. Configuration alone moves accuracy between 8.1% and 91.1% at a fixed parameter budget, and principled non-uniform allocation achieves state-of-the-art performance with 47% fewer parameters. Specifically, we address structural configuration in single-task adaptation (CVPR 2025), the training objective under data imbalance (NeurIPS 2024), and multi-task parameter sharing at scale (ICLR 2026). Building on these results, we propose a unified framework that jointly determines the complete allocation in multi-task settings, replacing configuration search with measurement followed by allocation. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-tian-zichen-principled-configuration-optimization-parameter?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205714710 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/cvumnn5w8j86badv25urfkrnhs 2026/07/28 (Tue) 28 Jul 2026 07:30:00 GMT http://uid.trumba.com/event/205714710 Principled Configuration Optimization for Parameter-Efficient Fine-Tuning of Foundation Models Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 2026-07-28T07:30:00Z

2026-07-28T15:30:00

Tuesday, July 28, 2026, 3:30 - 4:30pm

2026-07-28T08:30:00Z

2026-07-28T16:30:00

false

Full fine-tuning of large pre-trained models incurs prohibitive computational cost and risks catastrophic forgetting. Parameter-efficient fine-tuning (PEFT) addresses this by updating only a small set of additional parameters while keeping the pre-trained model frozen. Low-Rank Adaptation (LoRA) has emerged as the de facto PEFT method for both large language models and vision transformers. However, in current practice, LoRA is configured uniformly: the same adaptation at every network position, and the same treatment of every task and every class. This thesis formulates LoRA configuration as a constrained resource allocation problem: a fixed parameter budget distributed over the structure of the model and the distribution of the data. We establish that the optimal allocation is non-uniform, and that it can be derived from properties of the model and the data rather than found by search. Configuration alone moves accuracy between 8.1% and 91.1% at a fixed parameter budget, and principled non-uniform allocation achieves state-of-the-art performance with 47% fewer parameters. Specifically, we address structural configuration in single-task adaptation (CVPR 2025), the training objective under data imbalance (NeurIPS 2024), and multi-task parameter sharing at scale (ICLR 2026). Building on these results, we propose a unified framework that jointly determines the complete allocation in multi-task settings, replacing configuration search with measurement followed by allocation. http://uid.trumba.com/event/205714710

SMU_NextWeb_Common_01

PhD Dissertation Proposal by TIAN Zichen

https://www.trumba.com/i/DgAFANwgRJwf1jWQYBh7fM-Y.jpeg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2023-08/zichen-tian-2023.jpg"> TIAN Zichen PhD Candidate School of Computing and Information Systems Singapore Management University<hr />Zichen TIAN is a PhD candidate in the School of Computing and Information Systems, Singapore Management University, advised by Prof. Qianru Sun. He holds an M.Sc. from Nanyang Technological University and a B.Eng. from Beijing University of Posts and Telecommunications. His research focuses on parameter-efficient adaptation of foundation models, systematically addressing robustness and scalability challenges. He has published three first-author papers at NeurIPS 2024, CVPR 2025 (Highlight), and ICLR 2026, and co-authored several papers at CVPR 2022, CVPR 2023, and IEEE TMM. He is a recipient of the Presidential Doctoral Fellowship and the Doctoral Dean's List (AY24-25, AY25-26), and has been serving as a reviewer for 10+ top-tier AI conferences including NeurIPS, CVPR, ICML, ICLR, IJCV and TNNLS.

https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UMFVJUkdRN0tVSkdBTVROVEFMT05WTU5SQi4u

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-tian-zichen-principled-configuration-optimization-parameter?newsletter

Towards Embodied Human Motion Generation Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 Wednesday, July 29, 2026, 9 - 10am Towards Embodied Human Motion Generation Generative models now produce human motion that faithfully follows text, music, or user intent, yetsuch motion is optimized for how it looks against captured data, not for whether a body could performit in an environment. This dissertation calls this distance the embodiment gap and pursues embodiedhuman motion generation by decomposing the gap into four requirements: coherence over long horizonsat tractable cost (R1), a body free of self-collision (R2), closed-loop response to streaming user input (R3), and physical executability on a humanoid robot (R4). Three completed works address the first three. Lagrangian Motion Fields addresses R1 with a compact, training-free motion abstraction that improves long-term generation quality while reducing inference cost. FreeMo addresses R2 witha differentiable, trajectory-level collision energy that removes self-collision from pretrained generators without retraining. TINMO addresses R3 by discovering unsupervised latent actions over motion primitives, recasting interactive generation as closed-loop control on a learned world model. The proposed work addresses R4. Generated motion specifies where joints should be, not the forces a robot can supply, and routinely demands effort that fights the robot’s own inertia without serving the motion. The proposedparasitic-effort projection detects and removes this effort, repairing physically impossible segments andlowering the cost of feasible ones while preserving the motion’s task and meaning. Preliminary results on a simulated humanoid support the approach, and the remaining work will contribute an executability benchmark for humanoid reference motion and a full evaluation toward executable, semantically faithful motion generation. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-yang-yifei-towards-embodied-human-motion-generation?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205663385 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/8yknpp3gp4d9a0v6ych15gb3r2 2026/07/29 (Wed) 29 Jul 2026 01:00:00 GMT http://uid.trumba.com/event/205663385 Towards Embodied Human Motion Generation Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 2026-07-29T01:00:00Z

2026-07-29T09:00:00

Wednesday, July 29, 2026, 9 - 10am

2026-07-29T02:00:00Z

2026-07-29T10:00:00

false

Generative models now produce human motion that faithfully follows text, music, or user intent, yetsuch motion is optimized for how it looks against captured data, not for whether a body could performit in an environment. This dissertation calls this distance the embodiment gap and pursues embodiedhuman motion generation by decomposing the gap into four requirements: coherence over long horizonsat tractable cost (R1), a body free of self-collision (R2), closed-loop response to streaming user input (R3), and physical executability on a humanoid robot (R4). Three completed works address the first three. Lagrangian Motion Fields addresses R1 with a compact, training-free motion abstraction that improves long-term generation quality while reducing inference cost. FreeMo addresses R2 witha differentiable, trajectory-level collision energy that removes self-collision from pretrained generators without retraining. TINMO addresses R3 by discovering unsupervised latent actions over motion primitives, recasting interactive generation as closed-loop control on a learned world model. The proposed work addresses R4. Generated motion specifies where joints should be, not the forces a robot can supply, and routinely demands effort that fights the robot’s own inertia without serving the motion. The proposedparasitic-effort projection detects and removes this effort, repairing physically impossible segments andlowering the cost of feasible ones while preserving the motion’s task and meaning. Preliminary results on a simulated humanoid support the approach, and the remaining work will contribute an executability benchmark for humanoid reference motion and a full evaluation toward executable, semantically faithful motion generation. http://uid.trumba.com/event/205663385

SMU_NextWeb_Common_01

PhD Dissertation Proposal by YANG Yifei

https://www.trumba.com/i/DgAFANwgRJwf1jWQYBh7fM-Y.jpeg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2023-08/yifei-yang-2023.jpg" width="100" height="120"> YANG Yifei PhD Candidate School of Computing and Information Systems Singapore Management University<hr />Yifei YANG is a PhD candidate in Computer Science at the School of Computing and Information Systems,Singapore Management University, supervised by Prof. Shengfeng He. His research focuses onhuman motion generation, with the goal of closing the gap between motion that looks right and motionthat a physical body can execute. His work on Lagrangian Motion Fields is published in IEEE Transactionson Pattern Analysis and Machine Intelligence (TPAMI), and his subsequent works on collision-freeand interactive motion generation are under review at IEEE Transactions on Multimedia and NeurIPS, respectively.

https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UN0ZUUzFMWkpGUUlVQ0IyN1NaNkZBUFI4Qi4u

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-yang-yifei-towards-embodied-human-motion-generation?newsletter

"I like to use my brain": Neurodiversity in Software Engineering Meeting Room 4-1, Level 4. SMU SCIS 2, Singapore 178903 Thursday, July 30, 2026, 10 - 11am "I like to use my brain": Neurodiversity in Software Engineering Neurodiversity is an umbrella term that describes variation in brain function among individuals, including conditions such as Autism spectrum disorder (ASD), Attention deficit hyperactivity disorder (ADHD), or dyslexia. Neurodivergent individuals can face substantial barriers in society due to their conditions, e.g., due to difficulties or differences in communication, reading or writing difficulties, or reduced attention span. While conditions included in the neurodiversity term have traditionally been considered as disabilities in medical terms, these individuals often exhibit strengths in comparison to neurotypical individuals, for instance high attention to detail or higher creativity. In ongoing research, we try to understand how neurodivergent individuals can be involved better in software engineering activities, what relevant strengths they exhibit, and how we can tailor software engineering methods to better support their needs and strengths. In this talk, I will give an overview of current research on neurodiversity in software engineering and future directions open for exploration. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/research-seminar-dr-grischa-liebel?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205869349 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/8vuwjap9sc4dhgpekr6az9gm3e 2026/07/30 (Thu) 30 Jul 2026 02:00:00 GMT http://uid.trumba.com/event/205869349 "I like to use my brain": Neurodiversity in Software Engineering Meeting Room 4-1, Level 4. SMU SCIS 2, Singapore 178903 2026-07-30T02:00:00Z

2026-07-30T10:00:00

Thursday, July 30, 2026, 10 - 11am

2026-07-30T03:00:00Z

2026-07-30T11:00:00

false

Neurodiversity is an umbrella term that describes variation in brain function among individuals, including conditions such as Autism spectrum disorder (ASD), Attention deficit hyperactivity disorder (ADHD), or dyslexia. Neurodivergent individuals can face substantial barriers in society due to their conditions, e.g., due to difficulties or differences in communication, reading or writing difficulties, or reduced attention span. While conditions included in the neurodiversity term have traditionally been considered as disabilities in medical terms, these individuals often exhibit strengths in comparison to neurotypical individuals, for instance high attention to detail or higher creativity. In ongoing research, we try to understand how neurodivergent individuals can be involved better in software engineering activities, what relevant strengths they exhibit, and how we can tailor software engineering methods to better support their needs and strengths. In this talk, I will give an overview of current research on neurodiversity in software engineering and future directions open for exploration. http://uid.trumba.com/event/205869349

SMU_NextWeb_Common_01

Research Seminar by Dr Grischa Liebel

https://www.trumba.com/i/DgBbH2uT9IrSOn-X1j7XzIXS.jpg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2026-07/grischa.png"> Grischa Liebel Associate Professor Department of Computer Science Reykjavik University<hr />Grischa Liebel is an Associate Professor in Software Engineering and the director of the CRESS research centre at Reykjavik University, Iceland. He holds a Ph.D. degree from Chalmers University of Technology, Sweden. His research focuses on human factors in software engineering, typically in areas such modelling and model-based engineering, requirements engineering, processes, and education. Much of Grischa’s research is done in collaboration with industry - and with humans.

https://forms.office.com/pages/responsepage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTbOXO7ktpRtBsn62WuekYtpUODI4SlBBNTBUMEtSMlRXQlNGODdNWUFJNC4u&route=shorturl

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/research-seminar-dr-grischa-liebel?newsletter

Conversational Search for Video Retrieval Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 Thursday, July 30, 2026, 1 - 2pm Conversational Search for Video Retrieval As digital video libraries expand exponentially, our search engines remain trapped in a passive, "one-shot" paradigm that forces users through exhausting loops of visual browsing fatigue. This talk introduces a transition in search paradigm: transforming video retrieval from a rigid query-ranking mechanism into an active, cooperative human-machine dialogue grounded in information theory. Moving beyond traditional system limitations, we will explore a conversational framework designed to dynamically narrow down the search space in real time. We will examine how training-free active concept pruning optimizes textual search by breaking away from the pitfalls of classical feedback loops. From there, we will discuss about how entropy can be use as a "central controller" to dynamically orchestrate cross-modal interaction modalities—like visual exemplars and fluid open questions—without the constraints of a static vocabulary. Finally, we will preview how this framework extends active reasoning directly into fine-grained scene layouts using intelligent spatial layout questioning. Ultimately demonstrating how bridging multimodal representation with rigorous information theory can fundamentally redefine how we navigate the massive visual landscapes of tomorrow. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-cheng-yu-tong-conversational-search-video-retrieval?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205701193 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/myzvc30bnyvz6eaxha01ec0gns 2026/07/30 (Thu) 30 Jul 2026 05:00:00 GMT http://uid.trumba.com/event/205701193 Conversational Search for Video Retrieval Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 2026-07-30T05:00:00Z

2026-07-30T13:00:00

Thursday, July 30, 2026, 1 - 2pm

2026-07-30T06:00:00Z

2026-07-30T14:00:00

false

As digital video libraries expand exponentially, our search engines remain trapped in a passive, "one-shot" paradigm that forces users through exhausting loops of visual browsing fatigue. This talk introduces a transition in search paradigm: transforming video retrieval from a rigid query-ranking mechanism into an active, cooperative human-machine dialogue grounded in information theory. Moving beyond traditional system limitations, we will explore a conversational framework designed to dynamically narrow down the search space in real time. We will examine how training-free active concept pruning optimizes textual search by breaking away from the pitfalls of classical feedback loops. From there, we will discuss about how entropy can be use as a "central controller" to dynamically orchestrate cross-modal interaction modalities—like visual exemplars and fluid open questions—without the constraints of a static vocabulary. Finally, we will preview how this framework extends active reasoning directly into fine-grained scene layouts using intelligent spatial layout questioning. Ultimately demonstrating how bridging multimodal representation with rigorous information theory can fundamentally redefine how we navigate the massive visual landscapes of tomorrow. http://uid.trumba.com/event/205701193

SMU_NextWeb_Common_01

PhD Dissertation Proposal by CHENG Yu Tong

https://www.trumba.com/i/DgAFANwgRJwf1jWQYBh7fM-Y.jpeg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2023-08/yt-cheng-2023.jpg" width="100" height="120"> CHENG Yu Tong PhD Candidate School of Computing and Information Systems Singapore Management University<hr />CHENG Yu Tong is a Ph.D. candidate supervised by Prof. NGO Chong Wah at Singapore Management University. His research primarily focuses on the field of multimedia retrieval, with a specialization in the known-item search task. He also has an extensive background in system design and competitive benchmarking, having participated in numerous iterations of the Video Browser Showdown (VBS). Notably, in the most recent VBS competition, his team's retrieval system, VIREO, achieved first place in the visual known-item search category. His current research investigates the development of smart conversational search systems, aiming to leverage interactive querying to streamline user interaction and improve retrieval accuracy in large-scale video datasets like TRECVid.

https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UMkFLRjRDRDVTVEhGODlSTlQ2WTFXMlE2US4u

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-cheng-yu-tong-conversational-search-video-retrieval?newsletter

SMU: Tokyo Coffee Session SALONE VENDREDI 103-0022 Tokyo, Chuo-City, Nihonbashimuromachi, 3-4-4 Floor 1 Friday, July 31, 2026, 11am - 7pm SMU: Tokyo Coffee Session http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D204445594 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/w1f7rs6f9ggb81rg47cra031j4 2026/07/31 (Fri) 31 Jul 2026 03:00:00 GMT http://uid.trumba.com/event/204445594 SMU: Tokyo Coffee Session SALONE VENDREDI 103-0022 Tokyo, Chuo-City, Nihonbashimuromachi, 3-4-4 Floor 1 2026-07-31T03:00:00Z

2026-07-31T11:00:00

Friday, July 31, 2026, 11am - 7pm

2026-07-31T11:00:00Z

2026-07-31T19:00:00

false

http://uid.trumba.com/event/204445594

SMU_NextWeb_InfoSessions

https://www.trumba.com/i/DgA2CG3vgqMQvnY%2Age5Pdwrx.jpg

12:00pm - 3:00pm (JST) (hourly sessions) 6:00pm - 8:00pm (JST) (hourly sessions)

https://calendly.com/smuai/tokyo-coffee-session

Information Sessions

Business,Finance & Financial Markets

Prospective Student,Public

School of Computing and Information Systems|Master of IT in Business|Public Events

Building Trustworthy AI for Code: From Model Training to Agent Evaluation and Benchmark Reliability Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 Friday, July 31, 2026, 1 - 3pm Building Trustworthy AI for Code: From Model Training to Agent Evaluation and Benchmark Reliability AI for Code is evolving from code-completion models toward software development agents that inspect repositories, edit files, execute tests, and submit patches. As these systems participate in increasingly complex development workflows, final-output metrics alone cannot explain how results are produced, what risks they may hide, or whether the benchmarks used to measure them are reliable. This dissertation studies trustworthy AI for Code through three connected perspectives: model training, agent evaluation, and benchmark reliability. It examines the effectiveness and memorization risks of collaborative code-model training; evaluates software agents through their generated patches, execution trajectories, testing behavior, and agent-written tests; and investigates the stability and validity of repository-level performance-optimization benchmarks. Across five studies, the dissertation shows that reported success should be interpreted beyond headline metrics. Effective training may still introduce memorization and leakage risks. Agents may pass tests while over-modifying code, following different solution paths from human developers, or failing to recover from execution errors. Agent-written tests may consume substantial resources without improving resolution outcomes, while optimization benchmark scores may be affected by runtime instability, reference patches, and scoring rules. Together, these findings show that trustworthy AI for Code requires evaluating not only final correctness or performance, but also training risks, agent behavior, and the reliability of the underlying measurements. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-chen-zhi-building-trustworthy-ai-code-model-training-agent?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205701259 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/0p44jc13je4b15jng8kkv4nv3e 2026/07/31 (Fri) 31 Jul 2026 05:00:00 GMT http://uid.trumba.com/event/205701259 Building Trustworthy AI for Code: From Model Training to Agent Evaluation and Benchmark Reliability Meeting room 5.1, Level 5. SMU SCIS 1, Singapore 178902 2026-07-31T05:00:00Z

2026-07-31T13:00:00

Friday, July 31, 2026, 1 - 3pm

2026-07-31T07:00:00Z

2026-07-31T15:00:00

false

AI for Code is evolving from code-completion models toward software development agents that inspect repositories, edit files, execute tests, and submit patches. As these systems participate in increasingly complex development workflows, final-output metrics alone cannot explain how results are produced, what risks they may hide, or whether the benchmarks used to measure them are reliable. This dissertation studies trustworthy AI for Code through three connected perspectives: model training, agent evaluation, and benchmark reliability. It examines the effectiveness and memorization risks of collaborative code-model training; evaluates software agents through their generated patches, execution trajectories, testing behavior, and agent-written tests; and investigates the stability and validity of repository-level performance-optimization benchmarks. Across five studies, the dissertation shows that reported success should be interpreted beyond headline metrics. Effective training may still introduce memorization and leakage risks. Agents may pass tests while over-modifying code, following different solution paths from human developers, or failing to recover from execution errors. Agent-written tests may consume substantial resources without improving resolution outcomes, while optimization benchmark scores may be affected by runtime instability, reference patches, and scoring rules. Together, these findings show that trustworthy AI for Code requires evaluating not only final correctness or performance, but also training risks, agent behavior, and the reliability of the underlying measurements. http://uid.trumba.com/event/205701259

SMU_NextWeb_Common_01

PhD Dissertation Proposal by CHEN Zhi

https://www.trumba.com/i/DgAFANwgRJwf1jWQYBh7fM-Y.jpeg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2023-08/zhi-chen-2023.jpg" width="100" height="120"> CHEN Zhi PhD Student School of Computing and Information Systems Singapore Management University<hr />Zhi CHEN is a Ph.D. student in Computer Science at Singapore Management University, supervised by Prof. Lingxiao Jiang. His research focuses on AI for Code and software development agents. He has published papers at leading software engineering conferences, including ICSE, ASE, and SANER. He also has industry R&D experience at technology companies such as TikTok AI Innovation Center and Sea Labs. More information is available at <a href="https://chenzhi-cz.github.io/" dir="ltr">https://chenzhi-cz.github.io/</a>.

https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTejbEKPlArBJhZomj91naG9UMFU1NVlJRjIzUEs5WUtJS1haSFdTRk5TNS4u

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/phd-dissertation-proposal-chen-zhi-building-trustworthy-ai-code-model-training-agent?newsletter

Privacy Preservation in Large Language Models (LLMs) Meeting Room 4-4, Level 4. SMU SCIS 1, Singapore 178902 Monday, August 3, 2026, 10 - 11am Privacy Preservation in Large Language Models (LLMs) Large Language Models (LLMs) have revolutionized artificial intelligence but introduced profound privacy vulnerabilities, including data leakage, model inversion, and the inadvertent exposure of sensitive information. As LLMs integrate into high-stakes applications, mitigating these risks is paramount. This talk explores the fundamental privacy challenges inherent in LLM operations and introduces NOIR, the first privacy-preserving LLM model. To prevent prompting contents exposure to honest-but-curious cloud service providers, NOIR utilizes a secure, distributed architecture that transmits only encoded embeddings. By employing local differential privacy at the token embedding level alongside a data-independent, randomized tokenizer, NOIR effectively shields both proprietary prompts and resultant answers. The talk will demonstrate how NOIR achieves an optimal balance, providing rigorous privacy guarantees without compromising computational efficiency or downstream model performance. More info: computing.smu.edu.sg…: https://computing.smu.edu.sg/newsletter/research-seminar-dr-my-t-thai?newsletter http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205365878 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/w5mumcedr0z71pptu1pu2jkxf7 2026/08/03 (Mon) 03 Aug 2026 02:00:00 GMT http://uid.trumba.com/event/205365878 Privacy Preservation in Large Language Models (LLMs) Meeting Room 4-4, Level 4. SMU SCIS 1, Singapore 178902 2026-08-03T02:00:00Z

2026-08-03T10:00:00

Monday, August 3, 2026, 10 - 11am

2026-08-03T03:00:00Z

2026-08-03T11:00:00

false

Large Language Models (LLMs) have revolutionized artificial intelligence but introduced profound privacy vulnerabilities, including data leakage, model inversion, and the inadvertent exposure of sensitive information. As LLMs integrate into high-stakes applications, mitigating these risks is paramount. This talk explores the fundamental privacy challenges inherent in LLM operations and introduces NOIR, the first privacy-preserving LLM model. To prevent prompting contents exposure to honest-but-curious cloud service providers, NOIR utilizes a secure, distributed architecture that transmits only encoded embeddings. By employing local differential privacy at the token embedding level alongside a data-independent, randomized tokenizer, NOIR effectively shields both proprietary prompts and resultant answers. The talk will demonstrate how NOIR achieves an optimal balance, providing rigorous privacy guarantees without compromising computational efficiency or downstream model performance. http://uid.trumba.com/event/205365878

SMU_NextWeb_Common_01

Research Seminar by Dr My T. Thai

https://www.trumba.com/i/DgBbH2uT9IrSOn-X1j7XzIXS.jpg

scisseminars@smu.edu.sg

<img src="https://computing.smu.edu.sg/sites/scis.smu.edu.sg/files/2026-07/t-thai_0.jpg"> My T. Thai Professor Department of Computer & Information Science & Engineering University of Florida<hr />My T. Thai is a Research Foundation Professor, Associate Director of UF Nelms Institute for the Connected World, and a Fellow of IEEE and AAIA. Dr. Thai is a leading authority who has done transformative research in Trustworthy AI and Optimization, especially for complex systems with applications to healthcare, social media, critical networking infrastructure, and cybersecurity. The results of her work have led to 9 books and 350+ publications in highly ranked international journals and conferences, including several best paper awards from the IEEE, ACM, and AAAI.    In responding to a world-wide call of responsible and safety AI, Dr. Thai is a pioneer in designing deep explanations for black-box ML models, while defending against explanation-guided attacks, evident by her Distinguished Papers Award at the Association for the Advancement of Artificial Intelligence (AAAI) conference on AI, 2023. At the same year, she was also awarded an ACM Web Science Trust Test-of-Time award, for her landmark work on combating misinformation in social media. In 2022, she received an IEEE Big Data Security Women of Achievement Award.  In 2009, she was awarded the Young Investigator (YIP) from the Defense Threat Reduction Agency (DTRA) and in 2010, she won the NSF CAREER Award.     She is presently the Editor-in-Chief of ACM Computing Surveys, Springer Journal of Combinatorial Optimization, IET Blockchain Journal, and book series editor of Springer Optimization and Its Applications.

https://forms.office.com/pages/responsepage.aspx?id=ynmKyZpakUeiQ_Bq_WdGTbOXO7ktpRtBsn62WuekYtpUMDdEMzI3V0xUMVQyM0RUV0JUQzlLUlQyOS4u&route=shorturl

Seminars & Workshops

Information Technology & Systems

Academic Community,Current Student,Public

School of Computing and Information Systems

https://computing.smu.edu.sg/newsletter/research-seminar-dr-my-t-thai?newsletter

SMU Doctor of Engineering (EngD) Virtual Information Session Virtual via Zoom Details will be provided via email upon registration. Thursday, August 20, 2026, 7 - 8pm SMU Doctor of Engineering (EngD) Virtual Information Session : https://scispg.smu.edu.sg/acton/media/44865/engdis0826 http://sis.smu.edu.sg/events?trumbaEmbed=view%3Devent%26eventid%3D205376184 https://www.trumba.com/eventactions/SMU_SchoolInfoSys#/actions/40jmcf4r9grjjp79a8hzb96n56 2026/08/20 (Thu) 20 Aug 2026 11:00:00 GMT http://uid.trumba.com/event/205376184 SMU Doctor of Engineering (EngD) Virtual Information Session Virtual via Zoom Details will be provided via email upon registration. 2026-08-20T11:00:00Z

2026-08-20T19:00:00

Thursday, August 20, 2026, 7 - 8pm

2026-08-20T12:00:00Z

2026-08-20T20:00:00

false

: https://scispg.smu.edu.sg/acton/media/44865/engdis0826 http://uid.trumba.com/event/205376184

SMU_NextWeb_InfoSessions

https://www.trumba.com/i/DgAlMnEREERPBjeySuQe1ifz.jpg

https://scispg.smu.edu.sg/acton/media/44865/engdis0826

Information Sessions,Webinar & Online Learning

Analytics for Business, Consumer & Social Insights,Business,Information Technology & Systems,Leadership,Operations Management,Organisational Behaviour,Strategic Management

Professionals,Prospective Student

School of Computing and Information Systems|Doctor of Engineering|Infosessions