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Fangyin Wei FPO

Date and Time
Monday, April 22, 2024 - 1:30pm to 3:30pm
Location
Computer Science 302
Type
FPO

Fangyin Wei will present her FPO "Learning to Edit 3D Objects and Scenes" on Monday, April 22, 2024 at 1:30 PM in CS 302

Location: CS 302

The members of Fangyin’s committee are as follows:
Examiners: Szymon Rusinkiewicz (Adviser), Thomas Funkhouser (Adviser), Jia Deng
Readers: Felix Heide, Olga Russakovsky

A copy of her thesis is available upon request.  Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis.

Everyone is invited to attend her talk.

Abstract follows below:
3D editing plays a key role in many fields ranging from AR/VR, industrial and art design, to robotics. However, existing 3D editing tools either (i) demand labor-intensive manual efforts and struggle to scale to many examples, or (ii) use optimization and machine learning but produce unsatisfactory results (e.g., losing details, supporting only coarse editing, etc.). These shortcomings often arise from editing in geometric space rather than structure-aware semantic space, where the latter is the key to automatic 3D editing at scale. While learning a structure-aware space will result in significantly improved efficiency and accuracy, labeled datasets to train 3D editing models don’t exist. In this dissertation, we present novel approaches for learning to edit 3D objects and scenes in structure-aware semantic space with noisy or no supervision.

We first address how to extract the underlying structure to edit 3D objects, with a focus on editing two critical properties: semantic shape parts and articulations.

Our semantic editing method enables specific edits to an object’s semantic parameters (e.g., the pose of a person’s arm or the length of an airplane’s wing), leading to better preservation of input details and improved accuracy compared to previous work.

Next, we introduce a 3D annotation-free method that learns to model geometry, articulation, and appearance of articulated objects from color images. The model works on an entire category (as opposed to typical NeRF extensions that only overfit on a single scene) and enables various applications such as few-shot reconstruction and static object animation. It also generalizes to real-world captures.

Then, we tackle how to extract structure for scene editing. We present an automatic system that removes clutter (frequently moving objects such as clothes or chairs) from 3D scenes and inpaints the resulting holes with coherent geometry and texture. We address challenges including the lack of well-defined clutter annotations, entangled semantics and geometry, and multi-view inconsistency.

In summary, this dissertation demonstrates techniques to exploit the underlying structure of 3D data for editing. Our work opens up new research directions such as leveraging structures from other modalities (e.g., text, images) to empower 3D editing models with stronger semantic understanding.

Marcelo Orenes Vera FPO

Date and Time
Friday, May 3, 2024 - 11:00am to 1:00pm
Location
Computer Science Small Auditorium (Room 105)
Type
FPO

details forthcoming

Uma Girish FPO

Date and Time
Thursday, May 2, 2024 - 11:00am to 1:00pm
Location
Computer Science Tea Room
Type
FPO

details forthcoming

Ben Burgess FPO

Date and Time
Tuesday, May 7, 2024 - 2:15pm to 4:15pm
Location
Not yet determined.
Type
FPO

details forthcoming

Mary Hogan FPO

Date and Time
Thursday, May 9, 2024 - 1:00pm to 3:00pm
Location
Friend Center 125
Type
FPO

details forthcoming

Kritkorn Karntikoon FPO

Date and Time
Thursday, May 9, 2024 - 10:30am to 12:30pm
Location
Computer Science 402
Type
FPO

details forthcoming

Kathy Chen FPO

Date and Time
Tuesday, April 9, 2024 - 10:00am to 12:00pm
Location
Carl Icahn Lab 280
Type
FPO

Kathy Chen will present her FPO "Decoding the sequence basis of gene regulation" on Tuesday, April 9, 2024 at 10:00 AM in Icahn 280 and Zoom.

Location: Zoom link: https://princeton.zoom.us/j/95565860844?pwd=LzNkcHZ6bnVackxZRnZJSitXdW9NUT09

The members of Kathy’s committee are as follows:
Examiners: Olga Troyanskaya (Adviser), Mona Singh, Kai Li
Readers: Ryan Adams, Jian Zhou (UT Southwestern)

Everyone is invited to attend her talk.  

Abstract follows below:

Deciphering the regulatory code of gene expression is a critical challenge in human genetics, instrumental to unlocking the potential of personalized medicine. Modern experimental technologies have resulted in an abundance of high-dimensional genome-wide data, revealing the complex system of epigenetic interactions encoded in the genome. The development of computational approaches which can leverage this vast data to model chromatin interactions globally offer a new understanding of how genomic sequences specify regulatory functions. Specifically, sequence-based deep learning models have become the de facto standard for learning the functional properties encoded in DNA sequences based on large sequencing datasets. These models are powerful tools for interpreting molecular and phenotypic effects, capable of predicting the impact of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterizing their consequences beyond what is tractable from experiments and quantitative genetics alone.

In this thesis, we present two deep learning-based sequence models, which predict different epigenetic properties of the genome that contribute to transcriptional regulation. First, Sei is a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues. Sequence classes provide a global classification and quantification of sequences and variants based on diverse regulatory activities, such as cell type-specific enhancers.

Next, we developed a model Hedgehog, which enables the quantification of variation on methylation sites. Hedgehog predicts 296 continuous-valued methylation profiles across a range of cell types and tissues. Hedgehog is complementary to Sei and reveals new insights into the relationship between DNA methylation and other epigenetic modifications.

Finally, we show how deep learning-based methods can be applied to elucidate the regulatory basis of human health and disease. Specifically, we use Sei to study the contribution of noncoding mutations in cancer. Collectively, we demonstrate novel frameworks for modeling the sequence dependencies of the epigenome and the capability of such approaches to delineate the regulatory mechanisms underlying complex diseases.

Angelina Wang FPO

Date and Time
Monday, May 6, 2024 - 2:30pm to 4:30pm
Location
Computer Science 402
Type
FPO

Angelina Wang will present her FPO "Operationalizing Responsible Machine Learning: From Equality Towards Equity" on Monday, May 6, 2024 at 2:30 PM in CS 402.

Location: CS 402

The members of Angelina’s committee are as follows:
Examiners: Olga Russakovsky (Adviser), Arvind Narayanan, Solon Barocas (Cornell)
Readers: Aleksandra Korolova, Janet Vertesi

A copy of her thesis is available upon request.  Please email gradinfo@cs.princeton.edu if you would like a copy of the thesis.

Everyone is invited to attend her talk.

Abstract follows below:

With the widespread proliferation of machine learning, there arises both the opportunity for societal benefit as well as the risk of harm. Approaching responsible machine learning is challenging because technical approaches may prioritize a mathematical definition of fairness that correlates poorly to real-world constructs of fairness due to too many layers of abstraction. Conversely, social approaches that engage with prescriptive theories may produce findings that are too abstract to effectively translate into practice. In my research, I bridge these approaches and utilize social implications to guide technical work. I will discuss three research directions that show how, despite the technically convenient approach of considering equality acontextually, a stronger engagement with societal context allows us to operationalize a more equitable formulation. First, I will introduce a dataset tool that we developed to analyze complex, socially-grounded forms of visual bias. Then, I will provide empirical evidence to support how we should incorporate societal context in bringing intersectionality into machine learning. Finally, I will discuss how in the excitement of using LLMs for tasks like human participant replacement, we have neglected to consider the importance of human positionality. Overall, I will explore how we can expand a narrow focus on equality in responsible machine learning to encompass a broader understanding of equity that substantively engages with societal context

Ksenia Sokolova FPO

Date and Time
Monday, March 18, 2024 - 10:30am to 12:30pm
Location
252 Nassau Street Conference room
Type
FPO

Ksenia Sokolova will present her FPO "Deep Learning for Sequence-Based Gene Expression Prediction" on Monday, March 18, 2024 at 10:30 AM in the 252 Nassau Street Conference room.

Location: 252 Nassau Street Conference room

The members of Ksenia’s committee are as follows:
Examiners: Olga Troyanskaya (Adviser), Kai Li, Ellen Zhong
Readers: Mona Singh, Yuri Pritykin

Everyone is invited to attend her talk.

Abstract follows below:

Human biology is rooted in highly specialized cell types programmed by a common genome, 98% of which is outside of genes. While genetic variation in the enormous noncoding space is linked to the majority of disease risk, the impact of this variation is poorly understood. The recent advances in sequencing technology made it possible to perform whole genome sequencing of the large cohorts, uncovering many variants per individual. A crucial challenge is to understand the collective impact of these variants on gene expression across varied human cell types and their subsequent roles in disease progression.

This dissertation begins by tackling the challenge of associating noncoding genetic variants with changes in gene expression in primary human cell types. We introduce ExPectoSC, an atlas of modular deep-learning-based models for predicting cell-type-specific gene expression directly from sequence. With models spanning 105 primary human cell types across seven organ systems, it offers a detailed insight into the effect of variation. The resulting atlas of sequence-based gene expression and variant effects is publicly available in a user-friendly interface and readily extensible to any primary cell types. We follow this work with an example application of the ExpectoSC to the study of glomerular diseases, a major cause of end stage renal disease in the US. Despite having similar clinical presentations, these diseases are known for their heterogeneity and variable patient outcomes. By integrating whole-genome sequencing data with ExPectoSC's predictions, we construct comprehensive gene expression disruption profiles for patients.  4 Finally, we developed a new method for genomic-centered contrastive pre-training, called cGen, to improve training of the models from sequence alone in limited-data contexts. Utilizing sequence augmentations, after pre-training cGen generates unsupervised embeddings that highlight functional clusters and are informative of gene expression in the absence of any labeled information.

Together, these contributions highlight the power of computational approaches to decode the noncoding genome, offering new avenues for the diagnosis, prognosis, and treatment of human diseases.

Uthsav Chitra FPO

Date and Time
Friday, March 1, 2024 - 10:00am to 12:00pm
Location
Computer Science 302
Type
FPO

Uthsav Chitra will present his FPO "Algorithms for understanding the spatial and network organization of biological systems" on Friday, March 1, 2024 at 10:00 AM in COS 302 and Zoom.

Location: Zoom link: https://princeton.zoom.us/j/99220301104

The members of Uthsav’s committee are as follows:
Examiners: Ben Raphael (Adviser), Bernard Chazelle,Yuri Pritykin
Readers: Ellen Zhong, Fei Chen (Harvard)

Everyone is invited to attend his talk.

Abstract follows below:

Biological systems are characterized by their spatial organization and network interactions at a hierarchy of scales. For example, the spatial arrangement of different cells in a tissue underlies fundamental multicellular processes such as tissue differentiation and disease response, while interactions between genes/proteins comprise the biological pathways that regulate cellular state and function. Recent developments in high-throughput sequencing have enabled the systematic analysis of spatial and network processes in many complex biological systems including the brain and tumor microenvironment. However, such analyses are challenged by high levels of sparsity and/or noise in high-throughput sequencing datasets—underscoring the need for principled and rigorous computational methods for biological data analysis.

In this dissertation, we present a collection of mathematical frameworks and machine learning algorithms for modeling the spatial and network organization of biological systems. First, we derive a model of discrete and continuous spatial variation in gene expression. We present two algorithms, Belayer and GASTON, which learn the parameters of this model using complex analysis and interpretable deep learning, respectively.

Second, we present a mathematical framework for the identification of altered subnetworks, or subnetworks of a biological interaction network containing genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared to other genes/proteins. We prove that many existing algorithms are statistically biased, resolving the open question of why these algorithms often identify very large subnetworks that are difficult to interpret. We derive two altered subnetwork identification algorithms, NetMix and NetMix2, which we show are asymptotically unbiased and outperform existing approaches in practice.

Finally, we present two frameworks for learning and modeling higher-order interactions. We first derive a statistical framework for learning higher-order genetic interactions from experimental fitness data, unifying decades of existing work in the genetics literature. Then, we derive a theoretical framework for modeling random walks on hypergraphs that provably utilizes higher-order interactions in data, in contrast to many existing hypergraph methods which only utilize pairwise interactions.

Taken together, the approaches in this dissertation provide a theoretical and practical foundation for overcoming the computational challenges of modeling complex biological systems.

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