Meet Our New Faculty | CCDS NTU Singapore | College of Computing and Data Science

The College of Computing and Data Science (CCDS) at NTU Singapore has welcomed a growing cohort of faculty since 2024, whose work spans artificial intelligence, systems, data science, and the digital economy. Together, they strengthen the College's research depth and expand how computing is taught, studied, and applied.

2026 → 2025 → 2024 →

2026

Cheng Yu

Artificial Intelligence

Nanyang Associate Professor

Research focus

Efficient and sparse architectures – model compression techniques that shrink large models without sacrificing performance, key to scaling generative AI systems
Multimodal learning – models that reason jointly across text, image, and video, reflected in delivered systems like Hailuo Video and Skyreels
Large language model productization – led teams at Microsoft Research Redmond that turned core research into shipped products powering Copilot, DALL-E 2, ChatGPT, and GPT-4

Research interests

Large Language Models Efficient/Sparse Architectures Multimodal Learning

Education

PhD (Northwestern University, 2016, Computer Science and Engineering)
BSc (Tsinghua University, 2006, Bachelor in Automation)

Notable awards

IEEE 2024 SPS Young Author Best Paper Award
Outstanding Paper Award in NeurIPS 2023

Li Penghui

Computing

Assistant Professor

Research Focus

Vulnerability discovery – static and dynamic analysis techniques uncovering critical vulnerabilities in web/cloud applications, OS kernels, device drivers, and distributed systems
Automated vulnerability detection – combining symbolic program analysis with neural reasoning for patch/regression analysis and root-cause diagnosis
Securing AI systems – unsafe ML model deserialization and the attack surface opened by AI coding tools and LLM agents

Research Interests

Software and system security Program analysis, fuzzing, and vulnerability discovery Web, cloud, and OS kernel security Security of AI systems, LLM agents, and AI-assisted development AI for security: neural and symbolic reasoning for vulnerability detection

Education

PhD – Chinese University of Hong Kong, 2023, Computer Science and Engineering
BSc – University of Chinese Academy of Sciences, China, 2019, Computer Science and Technology

Postdoctoral Experience

Post-doctoral Fellowship – Columbia University, USA, with Prof. Junfeng Yang, 2026

Notable Awards

ACM CCS Distinguished Artifact Award, 2025
ACM CCS Distinguished Paper Award, 2024

Keen to connect with CCDS colleagues in program analysis, formal methods, systems, software engineering, and machine learning — exploring how these methods strengthen security analysis, and how security thinking can make AI systems and AI-generated code more trustworthy. Drop me an email or stop by my office!

Jordan Boyd-Graber

Artificial Intelligence

Professor

Research Focus

Human-AI collaborative NLP – building systems that help people explore documents, annotate data, and complete real tasks better, not just optimise benchmark scores
LLM evaluation & adversarial testing – detecting flawed leaderboard questions, human-in-the-loop adversarial datasets, and evaluation methods reflecting real user needs
Multimodal & multilingual applications – question answering, education, and AI-assisted decision making across languages and modalities

Research Interests

Probabilistic models of language LLM evaluation guided by psychometrics Agent negotiation and deception Human-in-the-loop adversarial examples Latent variable models of topics, ideal points, item response

Education

PhD – Princeton University, 2009, Computer Science, with David Blei
BSc – Caltech, 2004, History and Computer Science

Postdoctoral Experience

Postdoc – University of Maryland, with Philip Resnik

Notable Awards

Karen Spärk Jones Award, 2015
Outstanding Paper Award, NAACL, 2025
Best Theme Paper Award, EMNLP, 2023
Best Paper Award, NAACL, 2016
Best Demonstration Award, NeurIPS, 2015
Best Paper Award, CoNLL, 2015
US NSF CAREER Award, 2017

Keen to work on NLP for rarer languages and human-computer collaboration in legal or scientific domains, plus multimodal settings such as image generation and audio QA. Still drawn to interpretable Bayesian models like item response theory and topic models, especially where they can draw on modern world knowledge or improve human-AI coordination.

Vinay Mysore Sachidananda

Computing

Senior Lecturer

Research Focus

AI-driven security systems – scientifically rigorous yet operationally deployable, at the boundary of foundational research and real-world adversarial environments
Threat detection & monitoring – malware authorship attribution (AISG 100E MAASS), malicious PyPI package detection (USENIX Security 2026)
Cross-institutional trust research – security/privacy/trust in connected healthcare (IN-CYPHER, with Imperial College London), explainable AI for cybersecurity (TAiCEN)

Research Interests

AI-driven cybersecurity systems Security monitoring and threat detection Malware analysis, detection, and attribution Threat intelligence and adversarial analytics IoT and cyber-physical systems security

Education

PhD – Technical University of Darmstadt, Germany, 2014, Computer Science
MSc – University of Trento, Italy, 2008, Computer Science
BSc – Visvesvaraya Technological University, India, 2006, Computer Science & Engineering

Postdoctoral Experience

Post-doctoral Fellowship – iTrust, SUTD, Singapore, with Prof. Yuval Elovici and Prof. Aditya Mathur, 2016–2019

Keen to connect with colleagues in ML, LLMs/agentic AI, data mining, or NLP to advance security monitoring, threat detection, and SOC automation – and with systems, software engineering, and human factors researchers on threat intelligence and vulnerability research. Currently developing work in AI-driven security operations and securing AI against adversarial threats; open to grant collaborations, teaching, and CTF initiatives. Drop me an email or stop by my office!

Djordje Zikelic

Computing

Assistant Professor

Research Focus

Designs formal verification methods for probabilistic programs and AI systems
Develops safe reinforcement learning algorithms with provable correctness guarantees
Builds certified learning frameworks for formal verification of machine learning models

Research Interests

Formal Methods Programming Languages Trustworthy AI Safe Autonomy

Education

PhD in Computer Science, Institute of Science and Technology Austria (ISTA), 2023
MMath in Mathematics, University of Cambridge, 2018
BA in Mathematics, University of Cambridge, 2018

Notable Awards

Distinguished Tool Paper Award, ATVA 2025
Distinguished Paper Award, FM 2024
Outstanding PhD Thesis Award at ISTA, 2024

I am always happy to discuss potential topics that we could collaborate on, or to simply chat about research over a coffee. If any of the above topics sound interesting, please reach out.

Li Yingzhen

Data Science

Nanyang Associate ProfessorNRF Investigator

Research Focus

Renovates Bayesian inference procedures for auto-regressive models including LLMs
Scales causal representation learning to large-scale generative AI models
Develops scalable algorithms connecting statistical theory with practical ML systems

Research Interests

Probabilistic ML – deep generative models, uncertainty quantification, reliable ML Bayesian statistics – approximate inference, sequential decision-making Causal discovery & causal representation learning Sequential data & dynamic modelling

Education

PhD in Engineering, University of Cambridge, UK, 2018
BSc in Mathematics, Sun Yat-Sen University, China, 2013

Happy for chats regarding any topic within machine learning. I’d be happy to explore collaborations within the following topics: - Statistical machine learning - Generative modelling - Structured r...

Bian Jiawang

Artificial Intelligence

Nanyang Assistant ProfessorNRF Fellow

Research Focus

Develops spatial intelligence systems enabling machines to understand the 3D world
Builds neural scene representations using NeRF and 3D Gaussian Splatting
Designs vision-language-action models for embodied AI and robotics applications

Research Interests

3D Vision World Models VLM and VLA Embodied AI

Education

PhD in Computer Vision, University of Adelaide, 2022
BSc in Computer Science, Nankai University, 2016

Postdoctoral Experience

Postdoctoral Researcher, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), UAE, 2024–2025
Postdoctoral Researcher, University of Oxford, UK, 2022–2024

I am keen to connect with colleagues whose work touches on world models, embodied AI, or vision-language-action models. This includes people working on robotics and manipulation, autonomous driving...

Dau Hai-Dang

Data Science

Nanyang Assistant Professor

Research Focus

Develops sampling methods for probability distributions in Bayesian inference
Improves classical Monte Carlo techniques with modern generative modelling methods
Studies particle filters and sequential Monte Carlo for state-space model inference

Research Interests

Monte Carlo methods, such as MCMC Sequential Monte Carlo, particle filters Generative modelling, diffusion models Bayesian computation

Education

PhD in Applied Mathematics, ENSAE Paris, 2022
MSc in Applied Mathematics, Ecole Polytechnique, 2019

Postdoctoral Experience

Postdoctoral Research Fellow, Department of Statistics and Data Science, National University of Singapore (NUS), 2024–2025
Postdoctoral Researcher, Department of Statistics, University of Oxford, UK, 2022–2024

I am actively looking for collaborators in MCMC, diffusion models, Bayesian computation, and inverse problems. Furthermore, I am interested in challenging high-dimensional sampling problems appeari...

Yeshwanth Cherapanamjeri

Computing

Nanyang Assistant Professor

Research Focus

Designs efficient algorithms for statistical estimation with noisy or biased data
Builds training frameworks for diffusion models using mixed-quality datasets
Explores theoretical foundations for personalisation of large language models

Research Interests

Algorithmic Statistics Learning Theory Machine Learning Optimization

Education

PhD in Computer Science, University of California, Berkeley, 2021
BTech & MTech in Engineering Physics
Indian Institute of Technology (IIT) Bombay, 2015

Postdoctoral Experience

Postdoctoral Associate, Massachusetts Institute of Technology (MIT)

I am excited to build collaborations with colleagues working across the theory and practice of machine learning and statistics. I am particularly interested in connections between statistical theor...

Joel Quek

Data Science

Lecturer

Research Focus

Applies large language models to improve industry classification from text data
Researches learning analytics and educational data mining using student artefacts
Develops AI-enhanced feedback systems for computing and data science education

Research Interests

AI in Education AI for Finance and Business Applications Learning Analytics / Educational Data Mining

Education

PhD in Information Systems & Analytics, National University of Singapore (expected 2026)
MComp in Computer Science, National University of Singapore (NUS), 2019
BEng (Computer Science) & Bus (Business), Nanyang Technological University (NTU), 2015

I’m keen to connect with and learn from colleagues interested in pedagogical research at the intersection of GenAI and CS/AI education. In particular, I hope to work on (1) learning analytics and e...

Stefano Albrecht

Artificial Intelligence

Associate Professor

Research Focus

Develops reinforcement learning algorithms for autonomous systems in multi-agent environments
Builds multi-robot warehouse systems powered by multi-agent reinforcement learning
Designs LLM-based multi-agent systems for human-AI workflow orchestration

Research Interests

Reinforcement learning Multi-agent interaction Game theory LLM-based agents Real-world applications

Education

PhD in Artificial Intelligence, University of Edinburgh, UK, 2015
MSc in Artificial Intelligence, University of Edinburgh, UK, 2011
BSc in Computer Science, Technical University of Darmstadt, Germany, 2010

Postdoctoral Experience

Postdoctoral Fellow, University of Texas at Austin

I am excited to meet all my new colleagues and to explore shared interests! Please do get in touch and I would love to meet.

Arne Leitert

Computing

Senior Lecturer

Research Focus

Investigates algorithmic graph and hypergraph theory across all publications
Studies subclasses of acyclic hypergraphs and union join graph computability
Explores generative AI impacts on computer science education and pedagogy

Research Interests

Algorithmic graph and hypergraph theory Discrete algorithms and data structures Algorithms for huge-scale networks

Education

PhD in Computer Science (Kent State University, USA, 2017
MSc in Computer Science (University of Rostock, Germany, 2012

I left my previous teaching position in summer of 2022, before the AI boom. I therefore do not have first-hand experience of how AI affected teaching.

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