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Publications
DTPSP: A Deep Learning Framework for Optimized Time Point Selection in Time-Series Single-Cell Studies
Time-series studies are critical for uncovering dynamic biological processes, but achieving comprehensive profiling and resolution across mu… (see more)ltiple time points and modalities (multi-omics) remains challenging due to cost and scalability constraints. Current methods for studying temporal dynamics, whether at the bulk or single-cell level, often require extensive sampling, making it impractical to deeply profile all time points and modalities. To overcome these limitations, we present DTPSP, a deep learning framework designed to identify the most informative time points in any time-series study, enabling resource-efficient and targeted analyses. DTPSP models temporal gene expression patterns using readily obtainable data, such as bulk RNA-seq, to select time points that capture key system dynamics. It also integrates a deep generative module to infer data for non-sampled time points based on the selected time points, reconstructing the full temporal trajectory. This dual capability enables DTPSP to prioritize key time points for in-depth profiling, such as single-cell sequencing or multi-omics analyses, while filling gaps in the temporal landscape with high fidelity. We apply DTPSP to developmental and disease-associated time courses, demonstrating its ability to optimize experimental designs across bulk and single-cell studies. By reducing costs, enabling strategic multi-omics profiling, and enhancing biological insights, DTPSP provides a scalable and generalized solution for investigating dynamic systems.
In single-cell sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been d… (see more)one to map or create embeddings of the gene space. Here, we formulate the gene embedding problem, design tasks with simulated single-cell data to evaluate representations, and establish ten relevant baselines. We then present a graph signal processing approach we call gene signal pattern analysis (GSPA) that learns rich gene representations from single-cell data using a dictionary of diffusion wavelets on the cell-cell graph. GSPA enables characterization of genes based on their patterning on the cellular manifold. It also captures how localized or diffuse the expression of a gene is, for which we present a score called the gene localization score. We motivate and demonstrate the efficacy of GSPA as a framework for a range of biological tasks, such as capturing gene coexpression modules, condition-specific enrichment, and perturbation-specific gene-gene interactions. Then, we showcase the broad utility of gene rep-resentations derived from GSPA, including for cell-cell communication (GSPA-LR), spatial transcriptomics (GSPA-multimodal), and patient response (GSPA-Pt) analysis.
Combining multiple machine learning models has long been a technique for enhancing performance, particularly in distributed settings. Tradit… (see more)ional approaches, such as model ensembles, work well, but are expensive in terms of memory and compute. Recently, methods based on averaging model parameters have achieved good results in some settings and have gained popularity. However, merging models initialized differently that do not share a part of their training trajectories can yield worse results than simply using the base models, even after aligning their neurons. In this paper, we introduce a novel approach, Non-uniform Parameter-wise Model Merging, or NP Merge, which merges models by learning the contribution of each parameter to the final model using gradient-based optimization. We empirically demonstrate the effectiveness of our method for merging models of various architectures in multiple settings, outperforming past methods. We also extend NP Merge to handle the merging of multiple models, showcasing its scalability and robustness.
In this work, we address the evolving landscape of roboethics, expanding beyond physical safety to encompass broader societal implications. … (see more)Recognizing the siloed nature of existing initiatives to teach and inform ethical implications of artificial intelligence (AI) and robotic systems, we present a roboethics teaching module designed for K-12 students and general audiences. The module focuses on the high-level analysis of the interplay between robot behaviour design choices and ethics, using everyday social dilemmas. We delivered the module in a workshop to high school students in Montreal, Canada. From this experience, we observed that the module successfully fostered critical thinking and ethical considerations in students, without requiring advanced technical knowledge. This teaching module holds promise to reach a wider range of populations. We urge the education community to explore similar approaches and engage in interdisciplinary training opportunities regarding the ethical implications of AI and robotics.
2024-12-20
Proceedings of the Canadian Engineering Education Association (CEEA) (published)
In this work, we address the evolving landscape of roboethics, expanding beyond physical safety to encompass broader societal implications. … (see more)Recognizing the siloed nature of existing initiatives to teach and inform ethical implications of artificial intelligence (AI) and robotic systems, we present a roboethics teaching module designed for K-12 students and general audiences. The module focuses on the high-level analysis of the interplay between robot behaviour design choices and ethics, using everyday social dilemmas. We delivered the module in a workshop to high school students in Montreal, Canada. From this experience, we observed that the module successfully fostered critical thinking and ethical considerations in students, without requiring advanced technical knowledge. This teaching module holds promise to reach a wider range of populations. We urge the education community to explore similar approaches and engage in interdisciplinary training opportunities regarding the ethical implications of AI and robotics.
2024-12-20
Proceedings of the Canadian Engineering Education Association (CEEA) (published)
Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two … (see more)main approaches have emerged: the forward approach, which learns a mapping from input to its value, thereby acting as a proxy to guide optimization, and the inverse approach, which learns a mapping from value to input for conditional generation. (a) Although proxy-free~(classifier-free) diffusion shows promise in robustly modeling the inverse mapping, it lacks explicit guidance from proxies, essential for generating high-performance samples beyond the training distribution. Therefore, we propose \textit{proxy-enhanced sampling} which utilizes the explicit guidance from a trained proxy to bolster proxy-free diffusion with enhanced sampling control. (b) Yet, the trained proxy is susceptible to out-of-distribution issues. To address this, we devise the module \textit{diffusion-based proxy refinement}, which seamlessly integrates insights from proxy-free diffusion back into the proxy for refinement. To sum up, we propose \textit{\textbf{R}obust \textbf{G}uided \textbf{D}iffusion for Offline Black-box Optimization}~(\textbf{RGD}), combining the advantages of proxy~(explicit guidance) and proxy-free diffusion~(robustness) for effective conditional generation. RGD achieves state-of-the-art results on various design-bench tasks, underscoring its efficacy. Our code is at https://anonymous.4open.science/r/RGD-27A5/README.md.
This editorial summarizes the content of the Special Issue on Software Engineering and AI for Data Quality of the Journal of Data and Inform… (see more)ation Quality (JDIQ).
2024-12-19
Journal of Data and Information Quality (published)
This editorial summarizes the content of the Special Issue on Software Engineering and AI for Data Quality of the Journal of Data and Inform… (see more)ation Quality (JDIQ).
2024-12-19
Journal of Data and Information Quality (published)
This editorial summarizes the content of the Special Issue on Software Engineering and AI for Data Quality of the Journal of Data and Inform… (see more)ation Quality (JDIQ).
2024-12-19
Journal of Data and Information Quality (published)
Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video. However, prior work has focused evaluations… (see more) on semantic-related tasks
