Computing Research Week - Open House 2025

Program Details

Monday, 24/2/2025, 10:30 – 11:15 Digital Watermarking in the AI era - Ee-Chien Chang

Recent disruptive advancements in Machine Learning have found diverse applications and prompted new approaches in well-established research problems. One such area is digital watermarking, an active field that have been extensively studied over past two decades, starting from the pioneering work on spread spectrum watermarking in 1996. More recently, there has been significant research on watermarking with AI, which can be broadly categorized into two main categories: "Using AI for watermarking" and "watermarking of AI". Many designs of watermarking encoders and decoders rely on intriguing mathematical coding structure in high dimensional space. Interestingly, Machine Learning can now be leveraged to enhance watermarking by “training” more effective encoders/decoders. On the other hand, watermarking of AI -- both AI-Generated Content (AIGC) and the AI models -- has become increasingly important, particularly for attribution, authentication, and intellectual property protection. While conventional watermarking techniques could be directly, the controllable nature of AI generation and the functionality of AI models offers new opportunities and challenges. This talk will provide an overview on both categories, highlighting some of our contributions.

Bio: Ee-Chien Chang is an Associate Professor in the School of Computing at National University of Singapore. He received his PhD in Computer Science from New York University, and was a postdoctoral fellow with DIMACS in Rutgers University and NEC Labs America. His research areas cover information security, multimedia, and their intersection. His earlier works include image forensic, image watermarking and secure cryptographic techniques for noisy data. More recently, he has been investigating issues in data privacy and cloud security. He has published in reputable conferences and journals, including CCS, EUROCRYPT, USENIX Security, ACM Multimedia, INFOCOM, Journal of Applied and Computational Harmonic Analysis, etc. He is a lead-PI of National Cybersecurity R&D Laboratory.

Monday, 24/2/2025, 11:15 - 12:00 Artistic Vision: Interactive Computational Guidance for Developing Expertise - Jane Little E

Computer scientists have long worked towards the vision of human-AI collaboration for augmenting human capabilities and intellect. My work contributes to this vision by asking: How can computational tools not only help a user complete a task, but also help them develop their own domain expertise while doing so?

I will talk about some of my past work that investigates this question by designing new interactive tools for domains of artistic creativity. This work is inspired by the fact that expert artists have trained their eyes to “see” in ways that embed their expert domain knowledge—in this case, core artistic concepts. As instructors, experts have also designed approaches to intentionally communicate their vision to their students. My work designs creativity tools that leverage these expert structures to help novices develop this expert-like "artistic vision"—specifically through providing guidance to scaffold their design processes. In particular, we design these tools to be able to scaffold novices’ to be more aware of these artistic concepts during their creative process.

Bio: Jane E is an incoming Assistant Professor at National University of Singapore in Fall 2025. She is currently a Postdoctoral Fellow at Stanford HAI under the guidance of her PhD advisor, James Landay. Previously, she was a postdoc at The Design Lab at UCSD working with mentors Steven Dow and Haijun Xia, and earned her PhD in Computer Science from Stanford, co-advised by James Landay and Pat Hanrahan. Jane’s research lies at the intersection of human-computer interaction, computer graphics, and AI with a focus on designing computational guidance to support novices in developing their own expertise. Her work takes inspiration from cognitive science and education theory to design computational tools that scaffold novices’ creative and learning processes. She is excited to start building a lab and working with students excited about this vision.

Monday, 24/2/2025, 13:30 – 14:15 TBA - Nakyung Kyung

Monday, 24/2/2025, 14:15 – 15:00 Agentic AI - Wee Sun Lee

AI agents are supposed to work autonomously on our behalf. To do that, they need to be able to reason and plan, have high reliability, and be able to understand human preferences. I will describe some of our works on learning to reason and plan with large language models. I will then discuss other requirements including reliability and understanding human preferences.

Bio: LEE Wee Sun is a professor in the Department of Computer Science, National University of Singapore. He obtained his B.Eng from the University of Queensland and his Ph.D. from the Australian National University. He has been a research fellow at the Australian Defence Force Academy, a fellow of the Singapore-MIT Alliance, and a visiting scientist at MIT. His research interests include machine learning, planning under uncertainty, and approximate inference. His works have won the IJCAI-JAIR Best Paper Prize 2022, the Test of Time Award at Robotics: Science and Systems (RSS) 2021, the RoboCup Best Paper Award at International Conference on Intelligent Robots and Systems (IROS) 2015, the Google Best Student Paper Award at Uncertainty in AI (UAI) 2014 (as faculty co-author), as well as several competitions and challenges

Monday, 24/2/2025, 15:30 – 16:15 Formal Verification and Software Testing through the Unifying Lens of Logic - Umang Mathur

In this talk, I will consider key problems arising in formal verification and software testing, and discuss how to think about them from a computational, algorithmic and logical perspective. I will start by relating testing techniques such as symbolic testing and the classic decision problem in formal logic, and discuss key computational results that have enabled the proliferation of this technique in practice. I will then talk about how this technique, in the limit, gives a sound and complete procedure to automatically prove properties about programs. I will then discuss a generalization of the classical decision problem that captures the computational aspects of formal verification and recent developments related to it.

Bio: Umang Mathur is a Presidential Young Professor at the National University of Singapore, where he leads the FOCS lab (https://focs-lab.comp.nus.edu.sg/). He received his PhD from the University of Illinois at Urbana Champaign and was an NTT Research Fellow at the Simons Institute for the Theory of Computing at Berkeley. His research broadly centers on developing techniques inspired from formal methods and logic for answering design, analysis and implementation questions in programming languages, software engineering and systems.

Monday, 24/2/2025, 16:15 – 17:00 Expander decompositions in distributed computing - Yi-Jun Chang

It is well-known that every graph can be decomposed into well-connected components after removing a small fraction of edges. Specifically, an (ϵ,φ)-expander decomposition of a graph removes at most ϵ fraction of the edges so that the conductance of each remaining connected component is at least φ. In recent research, expander decompositions have proved extremely useful in many areas of theoretical computer science, including approximation, sketching, distributed, and dynamic algorithms. In this talk, I will discuss a recent series of research that applies this tool to design efficient distributed graph algorithms.

Bio: Yi-Jun Chang is an NUS Presidential Young Professor in the Department of Computer Science at the National University of Singapore. Previously, he was a junior fellow in the Institute for Theoretical Studies (ETH-ITS) at ETH Zurich. He received his Ph.D. in Computer Science and Engineering from the University of Michigan in 2019. He is broadly interested in theoretical computer science, with a focus on the design and analysis of distributed, parallel, and sublinear graph algorithms. He received the best paper award and the best student paper award from PODC 2019. His doctoral dissertation received the 2020 PODC Doctoral Dissertation Award.

Tuesday, 25/2/2025, 09:00 – 09:45 Software-Defined Inter-Networking - Richard Ma

Although today's Internet enables countless network applications, due to its fundamental design principles of the TCP/IP protocol stack, it still cannot guarantee the end-to-end Quality of Service (QoS), which is a crucial requirement for time-sensitive applications such as tele-surgery. In this talk, I will discuss the limitations of the traditional protocol-based Internet and propose a new approach for various networks to interconnect and collaborate, through the new paradigm of Software-Defined Networking (SDN). We will also discuss the role that Internet exchange points (IXPs) could play by leveraging the programmability provided by the new generations of network switches and the on-going research efforts on building an inter-networking research platform at School of Computing.

Bio: Richard T. B. Ma received the B.Sc. (Hons.) degree in computer science and M.Phil. degree in computer science and engineering from The Chinese University of Hong Kong in 2002 and 2004, respectively, and the Ph.D. degree in electrical engineering from Columbia University in 2010. During his Ph.D. study, he worked as a Research Intern at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA, and Telefonica Research, Barcelona, Spain. From 2010–2014, he worked as a Research Scientist at the Advanced Digital Science Center (ADSC), University of Illinois at Urbana–Champaign, Champaign, IL, USA. He is currently an Associate Professor with the School of Computing, National University of Singapore. His current research interests include distributed systems and network economics. He was a recipient of the Best Paper Award Runners-up from the ACM Mobihoc 2020 and a co-recipient of the Best Paper Award from the IEEE IC2E 2013, the IEEE ICNP 2014, and the IEEE Workshop on Smart Data Pricing 2015. He is a Senior Member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE).

Tuesday, 25/2/2025, 09:45 – 10:30 FAutomated 3D Shape Design and Generation - Bohan Wang

Automated 3D shape generation is transforming diverse aspects of our daily lives --- from creating immersive virtual environments for entertainment to driving innovation in product design, advanced manufacturing, healthcare, and robotics. Yet, realizing its full potential comes with formidable challenges. One key obstacle is developing a unified, compact, intuitive, and expressive representation for generative modeling. Equally important is bridging the gap between simulated environments and the complexities of real-world applications, ensuring that digital designs can be effectively translated into tangible products. Moreover, there is a pressing need for a comprehensive framework that can automatically convert varied user inputs -- whether natural language descriptions or technical specifications -- into versatile and meaningful 3D shapes suitable for both virtual and physical contexts.

In this talk, I will explore current research, highlight the open problems, and discuss promising directions in the evolving field of 3D shape design and generation.

Bio: Dr. Wang Bohan is an assistant professor at the Department of Computer Science, NUS. He was a postdoctoral associate in the Computational Design & Fabrication Group (CDFG) under the supervision of Prof. Wojciech Matusik at CSAIL, MIT. Before that, he earned his Ph.D. in Computer Science from the University of Southern California (USC), where he was advised by Prof. Jernej Barbič. He earned a bachelor’s degree in computer science and technology from Huazhong University of Science and Technology (HUST), China.

His research interests are in computer graphics, specifically focusing on 3D shape design and generation, as well as computer animation and simulation. Dr. Wang has published papers at top computer graphics venues such as SIGGRAPH (Asia) and ACM Transactions on Graphics (ToG), and he won Best Paper Awards at Pacific Graphics (PG). He regularly serves as a reviewer for many graphics and vision venues, and as an area chair and committee member for ICLR and PG, respectively. He was a research intern at Meta Reality Labs and served as a consultant for the Video Generation Foundation Model team at ByteDance.

Tuesday, 25/2/2025, 11:00 – 11:45 Searching for Product-Market Fit with Low Code/No Code Tools: Effects on Time to Product-Market Fit of Digital Start-up - Yichen Sun

Increasingly, digital start-ups (hereafter, "start-ups") are using low-code/no-code (LCNC) tools to search for and develop the features needed in their digital technology. However, there is limited knowledge about how start-ups use portfolios of LCNC tools to search for the configuration of features customers desire for product-market fit (PMF). Drawing on organizational search theory, we examine the relationship between LCNC-enabled search and start-ups’ time to PMF (proxied by the receipt of Series A funding) across three dimensions: (1) search breadth (i.e., to explore different feature categories), (2) search depth (i.e., to iterate within a single feature category), and (3) search distance (i.e., to explore feature categories distant from those commonly used by peers). We further examine how interdependence among feature categories, i.e., the degree to which the value of one feature category depends on the performance of others, moderates the effect of search breadth on time to PMF. Using data from Crunchbase, we apply an accelerated failure time duration model to conduct our analyses. We find that start-ups with a one-unit increase in search breadth and search distance take a shorter time to attain PMF by about 1.27 and 12.59 months, respectively. Conversely, start-ups with a one-unit increase in search depth take longer to do so by about 1.44 months. Additionally, the effect of increased search breadth on time to PMF is strengthened when the interdependence among feature categories is higher. This study, therefore, provides theoretical contributions to the literature on LCNC tools, organizational search, and digital entrepreneurship. We integrate the organizational search lens and distinguish between broad and distant search to examine the effects of different LCNC-enabled search strategies on start-ups’ time to attain PMF. We also offer suggestions for how nascent digital start-ups should use LCNC tools to search for digital technology features needed for PMF.

Bio: SUN Yichen is a fourth-year PhD candidate in Information Systems. Her research focuses on digital entrepreneurship, particularly examining how entrepreneurs and investors navigate emerging technologies and trends in the venture capital space. Yichen’s work has been accepted for presentation at major conferences, including the International Conference of Information Systems (ICIS) in 2023 and 2024, as well as the Academy of Management (AOM) in 2023.

Tuesday, 25/2/2025, 11:45 – 12:30 From Signals to Solutions: AI's Impact on Healthcare and Cultural Heritage - Ganesh Neelakanta Iyer

This talk examines how artificial intelligence is transforming computational humanities and healthcare. We'll begin by exploring the use of TinyML in healthcare, specifically its ability to analyze medical signals in real-time at the network edge for local diagnosis and monitoring. Next, we'll shift our focus to the humanities, showcasing projects that leverage AI for cultural heritage preservation. Using techniques like deep learning and image processing, we'll demonstrate how AI can analyze diverse forms of cultural expression, including traditional dances, puppet arts, and cultural texts.

Bio: Dr. Ganesh Neelakanta Iyer is a Computer Science faculty at NUS. He has a decade of industry experience at companies like Salesforce and NXP, holding roles from QA Architect to Lead DevOps Engineer. A strong proponent of Agile methodologies, he brings practical expertise to his teaching, having managed Agile teams as Scrum Master and Product Owner. His expertise and research focus spans computational humanities, AI for edge healthcare and software engineering education. In numerous academic and business conferences held in a number of nations, including the USA, Europe, Australia,Latin America and Asia, Dr. Iyer has presented a number of hands-on workshops and talks on a variety of cutting-edge technology topics.

Computing Research Week - Open House 2025

February 24 and 25

Registration

The event is free of charge. However, please register to help with the organization (to cater for coffee and lunch).

Program

Program Details

Research Group / PI Meeting

Additional Information for Visiting International Students

Travel to Singapore

Accommodation in Singapore

Commute in Singapore

Data roaming / WiFi

Contact

Organizers

Venue

All talks will be held in Multipurpose Hall 1 (COM3-01-26), in the NUS School of Computing, COM3 building. Poster presentation will be held in the atrium outside multipurpose hall 1.