Contact Us

Give us a call or drop by anytime, we endeavor to answer all inquiries within 24 hours.

map

Find us

PO Box 16122 Collins Street West Victoria, Australia

Email us

info@domain.com / example@domain.com

Phone support

Phone: + (066) 0760 0260 / + (057) 0760 0560

CSE

  1. Events
  2. By Venues
  3. CSE
Events from this category of venues
Today

Encore Industry Day

Computer Science & Engineering Building (CSE), Room 1242 3234 Matthews Ln, La Jolla, CA, United States

The UC San Diego EnCORE is excited to host Industry Day on Wednesday, September 4th, 2024, at the UCSD campus. This all-day event will showcase exciting research in foundations of data science, ML systems and AI, with contributions from leading experts in industry and academia. Attendees will have the opportunity to network with entrepreneurs, industry researchers, faculty […]

Event Series Special Seminar Series

Building and Deploying Large Language Model Applications Efficiently and Verifiably | Ying Sheng

Computer Science & Engineering Building (CSE), Room 1242 3234 Matthews Ln, La Jolla, CA, United States

The applications of large language models (LLMs) are increasingly complex and diverse, necessitating efficient and reliable frameworks for building and deploying them. In this talk, I will begin with algorithms and systems for serving LLMs for everyone (FlexGen, S-LoRA, VTC), highlighting the growing trend of personalized LLM services. My work addresses the need to run LLMs locally for isolated individual needs. It also tackles the problem of efficiency and service fairness when resource sharing among many users is required. Once we have efficient deployment, a primary concern is the reliability of generation. The second part of this talk aims to address this issue by exploring verifiable code generation. To achieve this, I adopt tools in formal verification to facilitate LLMs in generating correctness certificates alongside other artifacts (Clover). Finally, I will touch on future research avenues, such as integrating formal methods with LLMs and developing programming systems for generative AI.

Event Series Special Seminar Series

“Contextualized learning for adaptive yet persistent AI in biomedicine” | Ben Lengerich

Computer Science & Engineering Building (CSE), Room 1202

Abstract: "In biomedical data analysis, an emerging trend focuses on contextualizing observations within biological and real-world processes. This approach facilitates high-resolution, context-specific insights by integrating information across datasets, but it is difficult to design systems which both share information and dynamically adapt to context. Toward this aim, this presentation will examine “contextualized learning”, a meta-learning […]

How Do We Get There?: Toward Intelligent Behavior Intervention | Xuhai Xu

Computer Science & Engineering Building (CSE), Room 1242 3234 Matthews Ln, La Jolla, CA, United States

Abstract: As the intelligence of everyday smart devices continues to evolve, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent behavior change intervention pipeline for health -- combining behavior modeling & interaction design -- seems to be within reach. How do we get there? In […]

Learning Inductive Representations for Reasoning over Knowledge Graphs | Zhaocheng Zhu

Computer Science & Engineering Building (CSE), Room 1242 3234 Matthews Ln, La Jolla, CA, United States

Abstract: Reasoning, the ability to logically draw conclusions from existing knowledge, has been long pursued as a goal of artificial intelligence. Although numerous learning algorithms have been developed for reasoning, most of them are limited to the domain they are trained on. By contrast, humans often derive high-level rules or principles from experience and apply them to new domains — an ability referred as inductive generalization. In this talk, we present a series of works that learn inductive representations for reasoning over knowledge graphs. First, we introduce Neural Bellman-Ford Networks (NBFNet) that captures paths between entities and can generalize to graphs of new entities. Then we discuss Graph Neural Network Query Executor (GNN-QE), an extension of NBNet that answers multi-hop logical queries and generalizes well on our inductive benchmark. Finally, by learning inductive representations for both entities and relations, we demonstrate that a model can generalize to any graph with arbitrary entity and relation vocabularies, paving the way for foundation models for knowledge graph reasoning. 

ENCORE: NSF TRIPODS Workshop

Computer Science & Engineering Building (CSE), Room 1242 3234 Matthews Ln, La Jolla, CA, United States

Event Series Special Seminar Series

Inference and Decision-Making amid Social Interactions | Shuangning Li

Computer Science & Engineering Building (CSE), Room 4140 3234 Matthews Ln, La Jolla, CA, United States

From social media trends to family dynamics, social interactions shape our daily lives. In this talk, I will present tools I have developed for statistical inference and decision-making in light of these social interactions.

(1) Inference: I will talk about estimation of causal effects in the presence of interference. In causal inference, the term “interference” refers to a situation where, due to interactions between units, the treatment assigned to one unit affects the observed outcomes of others. I will discuss large-sample asymptotics for treatment effect estimation under network interference where the interference graph is a random draw from a graphon. When targeting the direct effect, we show that popular estimators in our setting are considerably more accurate than existing results suggest. Meanwhile, when targeting the indirect effect, we propose a consistent estimator in a setting where no other consistent estimators are currently available.

(2) Decision-Making: Turning to reinforcement learning amid social interactions, I will focus on a problem inspired by a specific class of mobile health trials involving both target individuals and their care partners. These trials feature two types of interventions: those targeting individuals directly and those aimed at improving the relationship between the individual and their care partner. I will present an online reinforcement learning algorithm designed to personalize the delivery of these interventions. The algorithm's effectiveness is demonstrated through simulation studies conducted on a realistic test bed, which was constructed using data from a prior mobile health study. The proposed algorithm will be implemented in the ADAPTS HCT clinical trial, which seeks to improve medication adherence among adolescents undergoing allogeneic hematopoietic stem cell transplantation.

Event Series TILOS Seminar Series

Canceled TILOS Seminar: Medical image reconstruction via deep learning: new architectures, data reduction and theoretical guarantees

Computer Science & Engineering Building (CSE), Room 2154 3234 Matthews Ln, La Jolla, CA, United States

In this talk I will discuss the challenges and opportunities for using deep learning in medical image reconstruction. Contemporary techniques in this field rely on convolutional architectures that are limited by the spatial invariance of their filters and have difficulty modeling long-range dependencies. To remedy this, I will discuss our work on designing new transformer-based architectures called HUMUS-Net that lead to state of the art performance and do not suffer from these limitations. In the next part of the talk I will report on techniques to significantly reduce the required data for training. Finally, I will briefly discuss our recent attempts to develop rigorous theory for simple end-to-end training methods used in image reconstruction problems which is surprisingly quite challenging even for simple target functions. Notability, our theory will be in the rich (or beyond NTK regime) that conforms with practical choice of hyperparameters. Time permitting I will discuss other exciting directions for the use of deep learning in MR.

Deep Latent Variable Models for Compression and Natural Science | Stephan Mandt

Computer Science & Engineering Building (CSE), Room 1202

Latent variable models have been an integral part of probabilistic machine learning, ranging from simple mixture models to variational autoencoders to powerful diffusion probabilistic models at the center of recent media attention. Perhaps less well-appreciated is the intimate connection between latent variable models and data compression, and the potential of these models for advancing natural science. This talk will explore these topics.