AI Innovation Institute

Abstract: The development of embodied AI has largely focused on scaling data and computational power, often at the cost of energy efficiency. In contrast, biological intelligence achieves remarkable adaptability with minimal resources, inspiring a shift toward neuromorphic AI, an approach that mimics the structure and dynamics of biological neural systems. In this talk, I will explore the promises and challenges of neuromorphic computer vision from three key perspectives: algorithms, robot actions, and data. First, I will discuss algorithmic advances, including continuous visual hull reconstruction, continuous-time human motion field estimation, and unsupervised independent motion segmentation. Next, I will illustrate how neuromorphic vision enables agile robotic actions by leveraging event-based perception for real-time decision-making. Finally, I will address challenges in training data-driven models with event data, highlighting strategies to enhance data availability and efficiency. By integrating these elements, neuromorphic AI paves the way for energy-efficient, high-performance embodied intelligence in dynamic real-world environments.

Speaker Bio: Ziyun (Claude) Wang is a fifth-year Ph.D. student in the General Robotics, Automation, Sensing & Perception (GRASP) Lab at the University of Pennsylvania, advised by Professor Kostas Daniilidis. His research focuses on developing algorithms for neuromorphic computer vision and integrating them with real hardware to enable agile perception in embodied AI systems. Prior to his Ph.D., he worked at the Samsung AI Center New York, where he developed 3D reconstruction techniques for robotic applications and earned three patents. He also contributed to the Apple Vision Pro team, enhancing user comfort for AR glasses. His research work has been recognized at major computer vision, robotics, and machine learning venues including the AAAI Conference on Artificial Intelligence (AAAI), European Conference on Computer Vision (ECCV), International Conference on Learning Representations (ICLR), Conference on Computer Vision and Pattern Recognition (CVPR) workshops, and IEEE Robotics and Automation Letters (R-AL), with an oral presentation at ECCV placing in the top 2.7%. His research aims to drive the development of next-generation bio-inspired AI systems, enabling more efficient, adaptive, and intelligent embodied perception.

Kate Armstrong, a Vancouver-based artist, writer, and independent curator, will explore the role of AI in art and creativity through three AI-driven projects: KEKE Terminal, Botto, and Sasha Stiles' AI collaborator Technelegy. She will compare these projects to historical artistic movements and investigate AI's role as an autonomous creative agent, the function of community participation, and the shifting dynamics of authorship.

Location: Humanities Institute Room 1008

KDD 2020 - The premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. https://www.kdd.org/kdd2020/

Abstract: Generative image models trained on massive datasets encode the statistics of our visual world. An off-the-shelf diffusion or flow-matching model can therefore serve as a general-purpose image prior, providing information about images across a wide range of tasks. Harnessing these capabilities, however, requires combining the model at inference time with additional signals or constraints unknown during training. This thesis will develop training-free methods for inference under a generative denoising prior. I first show how sampling from a trained denoiser can be formulated as an optimization problem, which combined with a constraint, can solve tasks ranging from conditional generation and weakly supervised segmentation to combinatorial optimization. I then demonstrate how the inherent properties of denoisers can accelerate inference under such constraints. Replacing gradient descent with an inexact Newton update, based on the symmetry of the denoiser's Jacobian, substantially reduces inference costs without tradeoffs. I also explore a middle ground between model adaptation and fully training-free inference by using the denoiser's robust internal representations to learn constraints from limited labeled data. These methods are applied to gigapixel image domains such as digital histopathology and remote sensing, where generative models can only be trained at a patch scale. To synthesize arbitrarily large images at resolutions unseen during training, I introduce inference-time algorithms that enforce consistency across spatially overlapping patches and image scales. Finally, I propose repurposing inference-time algorithms from sampling tools, to mechanisms for understanding the prior learned by a denoising model. Extending the previously developed techniques, I analyze the Jacobians of generative denoisers, where preliminary results indicate that Jacobian spectra correlates with generative quality. This motivates a Jacobian-spectrum regularization as a way to improve model performance using insights derived from inference-time algorithms.

Speaker: Alexandros Graikos

Location: NCS 220

LIN 627: Pragmatics Seminar
Th 3.30-6.20, Old Computer Science Building - CS2311, Zoom option
Instructor: Owen Rambow

Pragmatics is the study of how context (linguistic and non-linguistic) affects language use: the utterer (speaker or writer) chooses among many linguistic means licensed by the grammar of the language. Core explanatory notions in pragmatics are the common ground between utterer and addressee, and the use of speech acts to change the common ground.

This seminar will investigate the proposal of Grice, which has been called intentionalism. According to Grice, an utterer forms a communicative intention, performs a speech act to achieve the intention, and the speech act succeeds when the addressee recognizes the intention. The result is a change in common ground. With intentionalism, the notion of cognitive state becomes central to pragmatic theory. We will investigate Gricean intentionalism, including formalizations such as Stalnaker's, empirical evidence from cognitive science, and alternate pragmatic theories. In the second part of the course, we will then examine specific issues in pragmatics in light of the foundational theories we have discussed, including information structure, specific linguistic issues such as discourse particles, intonation, and politeness, and neuro-divergent communication. We will pay attention to empirical evidence throughout the course.

You can enroll for 0-3 credits, and further topics of interest may be included.

List of papers: https://protect.checkpoint.com/v2/r01/___https://docs.google.com/spreadsheets/d/1b7UC6leIHLxIa5yMWMAkUBZzT9Vg2SpS4XEfgH50ugY/edit?usp=sharing___.YzJ1OnN0b255YnJvb2s6YzpnOmE1Y2UxOTNiNTUyZWU4N2E0MzdkYzZmYWZlMTFmZWUxOjc6NGI2MjphZWY1NTUzNTdkMGJiN2U4MTI2MWMxYWQxZTU4OWNiY2NjYTlhNzk0NzlkZjU2ZDNmMzc5NDBkYjFhMjhhNTEwOnA6VDpG

Join the Department of Computer Science as we welcome Lyle Ungar, University of Pennsylvania, who will be delivering a lecture on 'Measuring Cultural Variation using Natural Language Processing.' When: 11/08/24 @ 2:30 PM Where: New Computer Science Building, Room 120. Reception to follow. Abstract: Cultures vary widely in how they view the world, for example being more individualist or collectivist. Such cultural differences are, of course, reflected in the words that people use. We first show a variety of ways in which multilingual language models are not multicultural; they speak Hindi or Mandarin, but still think like Americans. In contrast, we then present a scalable method that uses embedding-derived lexica to successfully measure regional variation in culture. Bio: Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he also holds secondary appointments in Psychology, Bioengineering, Genomics and Computational Biology, and Operations, Information and Decisions. His group uses natural language processing and explainable AI for psychological research, including analyzing social media and cell phone sensor data to better understand the drivers of physical and mental well-being. They are currently building socio-emotionally sensitive GPT-based tutors and coaches.

Abstract: Large Language Models (LLMs) have revolutionized how people interact with knowledge, offering unprecedented opportunities to accelerate the pace of scientific discovery. In this talk, I will discuss my research on the synergy between LLMs and scientific knowledge--specifically how these models extract, induce, and verify knowledge to automate the research lifecycle. First, I will cover our work on improving knowledge extraction from vast scientific literature, focusing on enabling models to comprehend long documents in a cost-efficient and comprehensive manner. I will describe a novel paradigm for representing document-level structured information as question-answer pairs and how we address the challenges of long-context understanding by leveraging global context through retrieval-augmented modeling. Next, I present our pioneering work on using LLMs for new scientific hypothesis generation. We introduce a framework employing reinforcement learning with fine-grained reward modeling and adaptive controllers.
This approach balances novelty, feasibility, and effectiveness to generate inspiring and actionable research hypotheses. Finally, I will discuss work on the first LLM Scientist for machine learning research. I will demonstrate how LLMs can move beyond hypothesis generation to participate in the execution and validation of scientific hypotheses, ensuring that the discovered knowledge is not only innovative but also grounded and verified.

Bio: Xinya Du is a tenure-track assistant professor at UT Dallas Computer Science Department. He earned a Ph.D. degree from Cornell University and was a Postdoctoral Research Associate at the University of Illinois (UIUC). He has also worked at Microsoft Research, Google Research, and Allen Institute AI. His research is on large language models, deep learning, and their applications in science.His work has been published in leading NLP and ML conferences (ACL, ICLR, NeurIPS). His research has received multiple recognitions, including a Best Paper Award at AAAI AI for Research and a Best Poster Award at ICML AI for Science workshop. His work was included in the list of Most Influential ACL Papers and has been covered by major media like New Scientist. He was named a Spotlight Rising Star in Data Science by the University of Chicago and is the recipient of several prestigious awards, including the Amazon Research Award, Cisco Research Award, Open Philanthropy Award, and the NSF CAREER Award.

Location: NCS 120

What comes after today's large language models and deep neural networks? Join the Computing Community Consortium (CCC) for a virtual 30-min community chat led by David Jensen, CCC Council Member and lead author of the new CCC whitepaper, Envisioning Possible Futures for AI Research. Jensen will explore paradigm-shifting AI Research Futures like Neuro-Symbolic, Embodied, Multi-Agent, and Quantum AI, and then open the floor to the audience for an engaging Q&A discussion.

Register here.

Communication-Efficient Heterogeneity-Aware Machine Learning System and Architecture by Xuehai Qian

ABSTRACT: The key success of deep learning is the increasing size of models that can achieve high accuracy. At the same time, it is difficult to train the complex models with large data sets. Therefore, it is crucial to accelerate training with distributed systems and architectures, where communication and heterogeneity are two key challenges. In this talk, I will present two heterogeneity-aware decentralized training protocols without communication bottleneck. Specifically, Hop supports arbitrary iteration gap between workers by novel queue-based synchronization which can tolerate heterogeneity with system techniques. Prague uses randomized communication to tolerate heterogeneity with a new training algorithm based on partial reduce -- an efficient communication primitive. If time permits, I will present the systematic tensor partitioning for training on heterogeneous accelerator arrays (e.g., GPU/TPU). We believe that our principled approaches are crucial for achieving high-performance and efficient distributed training.

BIO: Xuehai Qian is an assistant professor at University of Southern California. His research interests include domain-specific systems and architectures, performance tuning and resource management of cloud systems and parallel computer architectures. He received his PhD from the University of Illinois Urbana Champaign and was a postdoc at UC Berkeley. He is the recipient of W.J Poppelbaum Memorial Award at UIUC, NSF CRII and CAREER Award, and the inaugural ACSIC (American Chinese Scholar In Computing) Rising Star Award.