Publications

CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations

Hager Radi Abdelwahed

Mélisande Teng

Robin Zbinden

Laura Pollock

Devis Tuia

Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological re… (see more)search and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.

2025-08-07

ArXiv (preprint)

doi.org

arxiv.org

Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps

Matt Schmittle

Rohan Baijal

Nathan Hatch

Rosario Scalise

Mateo Guaman Castro

Sidharth Talia

Khimya Khetarpal

Byron Boots

Siddhartha Srinivasa

2025-08-07

robot-learning.org/CoRL/2025/Conference (poster)

doi.org

proceedings.mlr.press

Bias-inducing geometries: an exactly solvable data model with fairness implications

Stefano Sarao Mannelli

Federica Gerace

Negar Rostamzadeh

Luca Saglietti

2025-08-06

Physical Review E (published)

doi.org

arxiv.org

Revealing dynamic temporal trajectories and underlying regulatory networks with
<i>Cflows</i>

Alexander Tong

Manik Kuchroo

Shabarni Gupta

Aarthi Venkat

Chen Liu

Beatriz P. San Juan

Laura Rangel

Brandon Zhu

John G. Lock

Christine L. Chaffer

Smita Krishnaswamy

While single-cell technologies provide snapshots of tumor states, building continuous trajectories and uncovering causative gene regulatory … (see more)networks remains a significant challenge. We present Cflows , an AI framework that combines neural ODE networks with Granger causality to infer continuous cell state transitions and gene regulatory interactions from static scRNA-seq data. In a new 5-time point dataset capturing tumorsphere development over 30 days, Cflows reconstructs two types of trajectories leading to tumorsphere formation or apoptosis. Trajectory-based cell-of-origin analysis delineated a novel cancer stem cell profile characterized by CD44 hi EPCAM + CAV1 + , and uncovered a cell cycle–dependent enrichment of tumorsphere-initiating potential in G2/M or S-phase cells. Cflows uncovers ESRRA as a crucial causal driver of the tumor-forming gene regulatory network. Indeed, ESRRA inhibition significantly reduces tumor growth and metastasis in vivo. Cflows offers a powerful framework for uncovering cellular transitions and dynamic regulatory networks from static single-cell data.

2025-08-06

bioRxiv (preprint)

doi.org

Whither symbols in the era of advanced neural networks?

Thomas L. Griffiths

Brenden M. Lake

R. Thomas McCoy

Ellie Pavlick

Taylor Webb

Some of the strongest evidence that human minds should be thought about in terms of symbolic systems has been the way they combine ideas, pr… (see more)oduce novelty, and learn quickly. We argue that modern neural networks -- and the artificial intelligence systems built upon them -- exhibit similar abilities. This undermines the argument that the cognitive processes and representations used by human minds are symbolic, although the fact that these neural networks are typically trained on data generated by symbolic systems illustrates that such systems play an important role in characterizing the abstract problems that human minds have to solve. This argument leads us to offer a new agenda for research on the symbolic basis of human thought.

2025-08-06

ArXiv (preprint)

doi.org

arxiv.org

Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History

Tommaso Tosato

Saskia Helbling

Yorguin-Jose Mantilla-Ramos

Mahmood Hegazy

Alberto Tosato

D. Lemay

Irina Rish

Guillaume Dumas

2025-08-05

ArXiv (preprint)

doi.org

arxiv.org

Manifold Filter-Combine Networks

Joyce Chew

Edward De Brouwer

Smita Krishnaswamy

Deanna Needell

Michael Perlmutter

In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). Our filter-combine fra… (see more)mework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and naturally suggests many interesting families of MNNs which can be interpreted as manifold analogues of various popular GNNs. We propose a method for implementing MFCNs on high-dimensional point clouds that relies on approximating an underlying manifold by a sparse graph. We then prove that our method is consistent in the sense that it converges to a continuum limit as the number of data points tends to infinity, and we numerically demonstrate its effectiveness on real-world and synthetic data sets.

2025-08-04

Sampling Theory, Signal Processing, and Data Analysis (published)

doi.org

arxiv.org

Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens

Usman Anwar

Johannes Von Oswald

Louis Kirsch

David M. Krueger

Spencer Frei

In this work, we make two contributions towards understanding of in-context learning of linear models by transformers. First, we investigate… (see more) the adversarial robustness of in-context learning in transformers to hijacking attacks — a type of adversarial attacks in which the adversary’s goal is to manipulate the prompt to force the transformer to generate a specific output. We show that both linear transformers and transformers with GPT-2 architectures are vulnerable to such hijacking attacks. However, adversarial robustness to such attacks can be significantly improved through adversarial training --- done either at the pretraining or finetuning stage --- and can generalize to stronger attack models. Our second main contribution is a comparative analysis of adversarial vulnerabilities across transformer models and other algorithms for learning linear models. This reveals two novel findings. First, adversarial attacks transfer poorly between larger transformer models trained from different seeds despite achieving similar in-distribution performance. This suggests that transformers of the same architecture trained according to the same recipe may implement different in-context learning algorithms for the same task. Second, we observe that attacks do not transfer well between classical learning algorithms for linear models (single-step gradient descent and ordinary least squares) and transformers. This suggests that there could be qualitative differences between the in-context learning algorithms that transformers implement and these traditional algorithms.

2025-08-04

TMLR (accepted)

openreview.net

CellForge: Agentic Design of Virtual Cell Models

Xiangru Tang

Zhuoyun Yu

Jiapeng Chen

Yan Cui

Yanjun Shao

Weixu Wang

Fang Wu

Yuchen Zhuang

Wenqi Shi

Zhi Huang

Arman Cohan

Xihong Lin

Fabian Theis

Smita Krishnaswamy

Mark B. Gerstein

Virtual cell modeling represents an emerging frontier at the intersection of artificial intelligence and biology, aiming to predict quantiti… (see more)es such as responses to diverse perturbations quantitatively. However, autonomously building computational models for virtual cells is challenging due to the complexity of biological systems, the heterogeneity of data modalities, and the need for domain-specific expertise across multiple disciplines. Here, we introduce CellForge, an agentic system that leverages a multi-agent framework that transforms presented biological datasets and research objectives directly into optimized computational models for virtual cells. More specifically, given only raw single-cell multi-omics data and task descriptions as input, CellForge outputs both an optimized model architecture and executable code for training virtual cell models and inference. The framework integrates three core modules: Task Analysis for presented dataset characterization and relevant literature retrieval, Method Design, where specialized agents collaboratively develop optimized modeling strategies, and Experiment Execution for automated generation of code. The agents in the Design module are separated into experts with differing perspectives and a central moderator, and have to collaboratively exchange solutions until they achieve a reasonable consensus. We demonstrate CellForge's capabilities in single-cell perturbation prediction, using six diverse datasets that encompass gene knockouts, drug treatments, and cytokine stimulations across multiple modalities. CellForge consistently outperforms task-specific state-of-the-art methods. Overall, CellForge demonstrates how iterative interaction between LLM agents with differing perspectives provides better solutions than directly addressing a modeling challenge. Our code is publicly available at https://github.com/gersteinlab/CellForge.

2025-08-03

ArXiv (preprint)

doi.org

arxiv.org

Infrared Object Detection with Ultra Small ConvNets: Is ImageNet Pretraining Still Useful?

Srikanth Muralidharan

Heitor Rapela Medeiros

Masih Aminbeidokhti

Eric Granger

Marco Pedersoli

Many real-world applications require recognition models that are robust to different operational conditions and modalities, but at the same … (see more)time run on small embedded devices, with limited hardware. While for normal size models, pre-training is known to be very beneficial in accuracy and robustness, for small models, that can be employed for embedded and edge devices, its effect is not clear. In this work, we investigate the effect of ImageNet pretraining on increasingly small backbone architectures (ultra-small models, with

2025-08-03

ArXiv (preprint)

doi.org

arxiv.org

Responsible AI Day

Ebrahim Bagheri

Faezeh Ensan

Calvin Hillis

Reihaneh Rabbany

Robin Cohen

Benjamin C. M. Fung

Sébastien Gambs

This special day event on Responsible Artificial Intelligence (AI) brings together researchers, practitioners, and policymakers to explore h… (see more)ow data mining and machine learning systems can be designed to align with ethical principles, societal values, and human well-being. As AI technologies increasingly influence decisions in healthcare, finance, governance, and social systems, there is a critical need to develop frameworks that embed fairness, accountability, and privacy directly into the foundations of knowledge discovery. This full-day event will feature a mix of invited talks, interactive debates, expert panels, and peer-reviewed research presentations, all focused on the practical integration of ethical design into data-driven systems. The Responsible AI Day builds on the success of Canada's NSERC CREATE Program on Responsible AI, an interdisciplinary initiative training the next generation of AI researchers across computer science, law, bioethics, public health, and media studies. Topics will span scalable AI governance, privacy-preserving computation, algorithmic bias mitigation, and the socio-legal tensions emerging in generative AI. By positioning responsible AI as a sociotechnical challenge, this special day aligns with KDD's mission of advancing data science that is not only technically robust but also socially conscious.

2025-08-02

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (published)

doi.org

Temporal Graph Learning Workshop

Shenyang Huang

Daniele Zambon

Andrea Cini

Michael M. Bronstein