Singapore

Empirical performance models (EPMs) predict algorithm
performance without execution, enabling applications such
as algorithm selection, surrogate-based optimisation, and
benchmarking. Their effectiveness, however, is constrained
by both the quality of feature representations and the
predictive models themselves. My thesis advances EPMs along
both directions. To further enhance usability and foster
broader adoption, I also develop a Python library that
unifies state-of-the-art methods under a single API. These
contributions aim to make EPMs more accurate, versatile,
and accessible.

AAAI 2026 Main Conference

Next Generation of Empirical Performance Prediction

technical paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Global biodiversity is declining at unprecedented rates,
yet traditional monitoring at the necessary scales remains
costly and biased toward what can be seen. Sound offers a
complementary lens: many species are detected more reliably
by their vocalizations, microphones are inexpensive and
unobtrusive, and they can cover greater spatial and
temporal scales. These advantages have made passive
acoustic monitoring a fast-growing paradigm, yet robust,
generalizable sound distinction in complex soundscapes
remain a central obstacle. My thesis addresses this by
combining data-driven human-inspired representation
learning with knowledge-guided unsupervised learning from
auditory scene analysis and ecological reasoning,
prioritizing hierarchical organization and structure
discovery prior to labelling. Human-in-the-loop oversight
is incorporated as targeted verification under uncertainty,
drawing on active learning and weak supervision to direct
effort where it has the highest value.

Creating Generalizable Data-Driven Approaches for Biodiversity Monitoring via Acoustics

Multi-agent reinforcement learning enables sophisticated
collaborative behaviors in autonomous systems, yet
fundamental scalability barriers persist: existing methods
struggle to coordinate large agent populations and struggle
with extended decision-making horizons. This research
develops hierarchical approaches to scale up multi-agent
learning systems through two complementary directions:
structural scaling for coordinating increasing numbers of
agents and temporal scaling for extending decision-making
horizons. This paper presents four integrated
contributions: a taxonomical survey establishing
hierarchical architectures as the theoretical foundation
for scalable multi-agent learning systems, a benchmark for
long-horizon multi-objective multi-agent reinforcement
learning, a framework integrating self-organizing neural
networks with multiple reinforcement learning agents for
hierarchical tri-level control, and a framework leveraging
large language models for zero-shot multi-agent planning.
Through comprehensive validations, this work demonstrates
that hierarchical heterogeneous modular architectures
provide unified, interpretable solutions to multi-agent
scalability --- bridging theoretical multi-agent
reinforcement learning research with real-world deployment
requirements.

Scaling Up Cooperative Multi-Agent Reinforcement Learning Through Hierarchical Heterogeneous Modular Architectures

The lack of large-scale clean data for learning has been a
challenge that significantly hinders robots from developing
superior level of autonomous intelligence. This urges the
necessity to utilize diverse data in a more efficient way.
This work approaches the challenge from four perspectives:
efficient learning from expert demonstration, efficient
dynamics modeling from in-the-wild videos, efficient
learning from heuristics guidance, and adjustment for
efficient deployment. We provide an overview of preliminary
results in each area and outline proposed research focusing
on extracting controllable representation from data, aiming
at efficient cross- embodiment learning.

Efficient Robot Learning from Diverse Data

Rainfall forecasting presents a dual challenge: extreme \emph{zero inflation}, where dry days dominate and obscure meaningful precipitation patterns, and pronounced \emph{nonstationarity}, where climate dynamics evolve across time and regimes. We propose the \textbf{Deep Extreme Transformer (DET)}, a principled architecture that integrates statistical distribution modeling with neural sequence learning to address both issues simultaneously. DET augments the Transformer with a Tweedie distribution output head that unifies discrete zeros and continuous intensities, a fixed shared-weight mechanism that emphasizes rare but critical events in both attention and loss computation, and a Gaussian perturbation strategy that enhances learning stability without violating physical constraints. DET further incorporates nonstationary attention to adapt to evolving rainfall regimes. Extensive experiments on multi-decadal South Australian climate data demonstrate that DET consistently outperforms existing deep learning and statistical models across forecasting horizons. Our method provides an effective and generalizable framework for zero-inflated, shift-prone time series, bridging statistical rigor with deep temporal modeling in a unified and scalable design.

Deep Extreme Transformer: Tackling Zero-Inflated Time Series for Precipitation Prediction

Rapid post‑earthquake damage assessment is crucial for rescue and resource planning. Still, existing remote sensing methods depend on costly aerial images, expert labeling, and produce only binary damage maps for early-stage evaluation. Although ground-level images from social networks provide a valuable source to fill this gap, a large pixel-level annotated dataset for this task is still unavailable. We introduce EIDSeg, the first large-scale semantic segmentation dataset specifically for post-earthquake social media imagery. The dataset comprises 3,266 images from nine major earthquakes (2008–2023), annotated across five classes of infrastructure damage: Undamaged Building, Damaged Building, Destroyed Building, Undamaged Road, and Damaged Road. We propose a practical three-phase cross-disciplinary annotation protocol with labeling guidelines that enables consistent segmentation by non-expert annotators, achieving over 70% inter-annotator agreement. We benchmark several state-of-the-art segmentation models, identifying Encoder-only Mask Transformer (EoMT) as the top-performing method with a Mean Intersection over Union (mIoU) of 80.8%. By unlocking social networks' rich, ground-level perspective, our work paves the way for a faster, finer-grained damage assessment in the post-earthquake scenario. All data and code will be released upon acceptance of the paper.

EIDSeg: A Pixel-Level Semantic Segmentation Dataset for Post-Earthquake Damage Assessment from Social Media Images

Self Supervised Learning (SSL) has emerged as a prominent paradigm for label-efficient learning, and has been widely utilized by remote sensing foundation models (RSFMs). Recent RSFMs including SatMAE and DoFA primarily rely on masked autoencoding (MAE), contrastive learning or some combination of them. However, these pretext tasks often overlook the unique temporal characteristics of agricultural landscape, namely nature's cycle of sowing, growth, and harvest. Motivated by this gap, we propose three novel agriculture-specific pretext tasks, namely Time-Difference Prediction (TD), Temporal Frequency Prediction (FP), and Future-Frame Prediction (FF). Comprehensive evaluation on SICKLE dataset shows FF achieves 69.6% IoU on crop mapping and FP reduces yield prediction error to 30.7% MAPE, outperforming all baselines, and TD remains competitive on most tasks. Further, we also scale FF to the national scale of India, achieving 54.2% IoU outperforming all baselines on field boundary delineation on FTW India dataset.

Time2Agri: Temporal Pretext Tasks for Agricultural Monitoring

Scientific image manipulation in biomedical publications poses a growing threat to research integrity and reproducibility. Unlike natural image forensics, biomedical forgery detection is uniquely challenging due to domain-specific artifacts, complex textures, and unstructured figure layouts. We present the first vision-language guided framework for both generating and detecting biomedical image forgeries. By combining diffusion-based synthesis with vision-language prompting, our method enables realistic and semantically controlled manipulations—including duplication, splicing, and region removal—across diverse biomedical modalities. We introduce Rescind, a large-scale benchmark featuring fine-grained annotations and modality-specific splits, and propose Integscan, a structured state-space modeling framework that integrates attention-enhanced visual encoding with prompt-conditioned semantic alignment for precise forgery localization. To ensure semantic fidelity, we incorporate a VLM-based verification loop that filters generated forgeries based on consistency with intended prompts. Extensive experiments on Rescind and existing benchmarks demonstrate that Integscan achieves state-of-the-art performance in both detection and localization, establishing a strong foundation for automated scientific integrity analysis.

Rescind: Countering Image Misconduct in Biomedical Publications with Vision-Language and State-Space Modeling

The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effect size and unreliable conclusions. A Bayesian approach may allow analysis to determine if the claim of a positive effect is still warranted, and we build a Bayesian approach to this common medical scenario. To demonstrate its utility, we assist professional OBGYNs in evaluating Trial of Labor After a Cesarean-section (TOLAC) situations where few interventions are available for patients and find the support needed for physicians to advance patient care.

Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications

Studies of LLMs’ political opinions mainly evaluate their open-ended responses. Recent work indicates misalignment between LLMs responses and their internal intentions. This motivates us to probe LLMs' internal mechanisms and uncover their internal political states. Additionally, analysis of LLMs' political opinions often relies on single-axis concepts, which can lead to concept confounds.

Our work extends this to multi-dimensions and applies interpretable techniques for more transparent LLM political concept learning. Specifically, we designed a four-dimensional political learning framework and constructed a corresponding dataset for fine-grained political concept vector learning. These vectors can detect and intervene in LLM internals. 

Experiments are conducted on eight open-source LLMs with three representation engineering techniques. Results show these vectors can disentangle political concept confounds. Detection tasks validate the semantic meaning of the vectors and show good generalization and robustness in OOD settings. Intervention experiments show that these vectors can implicitly intervene in LLMs, generating responses with targeted political leanings. These insights reveal the need for more transparent auditing for future AI governance.

Fine-Grained Interpretation of Political Opinions in Large Language Models

AI models are often evaluated based on their ability to predict the outcome of interest. However, in many AI for social impact applications, the presence of an intervention that affects the outcome can bias the evaluation. Randomized controlled trials (RCTs) randomly assign interventions, so data from the control group can be used to generate unbiased model performance estimates. However, this approach is inefficient because it ignores data from the treatment group. Given the complexity and cost often associated with RCTs, making the most use of the data is essential. Thus, we investigate model evaluation strategies that leverage all data from an RCT. First, we theoretically quantify estimation bias from naïvely aggregating performance estimates with data from treatment and control groups, and derive the condition under which the bias leads to incorrect model selection. Leveraging these theoretical insights, we propose an unbiased evaluation strategy that reweights data from the treatment group to mimic the distributions of samples that would or would not experience the outcome under no intervention. Using synthetic and real-world datasets, we show that our proposed evaluation approach consistently leads to better model selection than the standard approach that ignores data from the treatment group across various intervention effect and sample size settings. Our contribution represents a meaningful step towards more efficient model evaluation in real-world contexts.

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