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Data and Compute-Efficient Optimization of Parsimonious Models and Its Applications in Machine Learning and Payment Channel Networks

Dr. Christian Kümmerle University of North Carolina Monday, January 13, 2025 11:00AM – 12:00PM MSB 318 | Zoom Abstract For a machine learning model’s ability to generalize well to unseen data, it has been understood that it needs to be able to capture a hidden, low-dimensional data distribution in the high-dimensional feature space. In this…...
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Causal Machine Learning: Continuous structure learning and identifiability of causal invariances

Dr. Kevin Bello Soroco Thursday, November 21, 2024 11:00AM – 12:00PM HEC 101A | Zoom Abstract Interpretability and causality are key desiderata in modern machine learning systems. Graphical models, and more specifically directed acyclic graphs (DAGs, a.k.a. Bayesian networks), serve as a well-established tool for expressing interpretable causal relationships. However, the task of estimating DAG…...
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Towards Trustworthy AI: From Computer Vision to Medical Imaging

Dr. Yu Tian University of Pennsylvania Thursday, November 14, 2024 11:00AM – 12:00PM HEC 101A | Zoom Abstract In today’s world, deep neural networks drive machine learning systems that pervade every aspect of our daily lives. Yet, their deployment often raises concerns about trustworthiness, including safety, fairness, and other issues. This is particularly important and…...
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Artificial Intelligence for Everything: Are We There Yet?

Dr. Amrit Singh Bedi UCF Department of Computer Science Tuesday, September 3, 2024 12:00PM – 1:00PM HEC 101 Abstract The emergence of foundational models has pushed artificial intelligence (AI) to the forefront across various domains, from everyday tasks like writing to complex processes such as data analysis. Despite these advancements, are we fully prepared to…...
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Towards Exponentially Cheaper AI

Dr. Aditya Desai UC Berkeley Thursday, August 29, 2024 10:00AM – 11:00AM HEC 101A | Zoom Abstract The recent advancements in the capabilities of AI models have been extraordinary. However, training and deploying these models is prohibitively costly. The primary reason for increasing costs is the exponential increase in model sizes, which requires commensurate computing and…...
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Structuring Cooperative Teams for Multi-Agent Reinforcement Learning

Dr. Qi Zhang University of South Carolina Thursday, August 22, 2024 10:00AM – 11:00AM HEC 101A | Zoom Abstract Cooperative artificial intelligence equips a team of autonomous agents with the capability of planning and learning to maximize their joint utility, which finds a wide range of applications. Current solutions to cooperative AI, instantiated as cooperative…...
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On learning distributions: from dynamical system to generative modeling

Dr. Nicole Yang Emory University Tuesday, May 21, 2024 11:00AM – 12:00PM R1 101A | Zoom Abstract Dynamical systems are widely used to model complex real-world phenomena. They are models based on physical principles, which are highly interpretable but have limits in expressive capabilities. Furthermore, the already high dimensional, and complicated behavior make them difficult…...
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From Pixels to Measurements: Understanding the Dynamic World

Dr. Adam Harley Stanford University Monday May 6, 2024 11:00AM – 12:00PM R1 101A | Zoom Abstract In computer vision, “video understanding” typically concerns summarization: tracking the main objects, or describing the main actions. While progress here has been impressive, many practical applications require extracting information which is much more fine-grained. For example, biologists are…...
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Towards Explainable and Reliable AI Models for Optimization

Dr. Jialin Liu DAMO Academy, Alibaba Group US Thursday, April 25, 2024 10:00AM – 11:00AM TCII 222 | Zoom Abstract AI and data science have demonstrated remarkable potential in enhancing optimization algorithms. Compared with traditional methods, utilizing AI/ML techniques can potentially offer improvements in aspects like computational speed and solution quality. Despite these advancements, a…...
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An Efficient One-Class SVM for Novelty Detection in IoT

Dr. Kun Yang Princeton University Monday, April 22, 2024 10:00AM – 11:00AM TCII 222 | Zoom Abstract Novelty detection is important in the Internet of Things (“IoT”) due to the potential threats that IoT devices can present. One-Class Support Vector Machines (OCSVMs) are one of the common approaches for novelty detection due to their ability…...
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Backdoors and bias in text-to-image generative models

Dr. Ajmal Mian The University of Western Australia Thursday, April 18, 2024 12:00PM – 1:00PM HEC 101 | Zoom Abstract In this presentation, I will explore the manipulation of text-to-image (T2I) generative models through backdoors. I will present our Backdoor Attack on Generative Models (BAGM), which infuses the generated images with subtle manipulative details by…...
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Unleashing the Power of Discrete Optimization in the New Era of AI

Dr. Arman Zharmagambetov Fundamental AI Research (FAIR) group at Meta Thursday, April 11, 2024 10:00AM – 11:00AM HEC 101A | Zoom Abstract Modern machine learning (ML) models, trained on real-world data, now underpin a broad spectrum of applications. Behind the success of these models, discrete optimization lays the foundation of the modeling, decision making and…...
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Bridging the Gap: Translational AI in Biomedicine and Healthcare

Dr. Laura Brattain UCF College of Medicine, AI for Healthcare Monday, April 1, 2024 12:00PM – 1:00PM HEC 101 Abstract Recent research in biomedical AI has demonstrated the ability to not only aid in disease diagnosis and prognosis, but also facilitate procedure guidance and empower precision medicine approaches tailored to individual patients. To translate research…...
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Toward Next-Generation Medical Care: Computer Vision for Healthcare AI

Mr. Haomiao Ni Pennsylvania State University Thursday, March 14, 2024 10:30AM – 11:30AM HEC 101A | Zoom Abstract In the past two years, transformative AI products such as ChatGPT have underscored the profound impact of AI in our daily lives. Beyond natural language and image understanding, AI is revolutionizing healthcare, offering advantages such as assisting…...
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Quantifying Uncertainties of Deep Neural Networks and Its Applications

Dr. Jae Oh Woo Samsung SDS Research America Monday, March 11, 2024 11:00AM – 12:00PM MSB 318 | Zoom Abstract In this presentation titled “Quantifying Uncertainties of Deep Neural Networks and Its Applications,” we delve into the critical task of measuring uncertainties in Deep Neural Networks. The talk will commence with a concise overview of…...
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Adversarial Graph Machine Learning on Blockchains

Dr. Cuneyt Akcora Barry S. Miller College of Business, AI for Finance Monday, March 4, 2024 12:00PM – 1:00PM HEC 101 Abstract Blockchains allow pseudo-anonymous transactions, which has made it easier to create a payment ecosystem used worldwide. However, the ease of blockchain use has also attracted e-crime actors with malicious activities ranging from sextortion…...
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Optimization for Fairness-aware Machine Learning

Ms. Yao Yao University of Iowa Thursday, February 8, 2024 11:00AM – 12:00PM TC 222 | Zoom Abstract Artificial intelligence (AI) and machine learning technologies have been used in high-stakes decision making systems such as lending decision, criminal justice sentencing and resource allocation. A new challenge arising with these AI systems is how to avoid…...
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Graph Based Machine Learning: Enhancing Artificial Intelligence to Better Understand Relations of Concepts

Dr. Yiwei Wang University of California, Los Angeles Tuesday, November 28, 2023 11:00AM – 12:00PM HEC 101A | Zoom Abstract When students learn knowledge in a curriculum, understanding the relations of different concepts helps students to remember and utilize the knowledge. The structured data, that describe the relations, help to build humans’ intelligence. This raises…...
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Democratizing AI through Controlled Narrative Generation and Knowledge Grounding

Dr. Shubhra Kanti Karmaker Auburn University Monday, November 20, 2023 11:00AM – 12:00PM HEC 101 | Zoom Abstract Even though Artificial Intelligence (AI) has existed for a long time, its broad accessibility is a recent development, thanks to Generative AI models like ChatGPT for its human-like interactions. While such broad accessibility provides a great opportunity…...
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Towards Secure, Privacy-preserving and Fair Artificial Intelligence

Dr. Murat Kantarcioglu The University of Texas at Dallas Wednesday, September 6, 2023 10:00AM – 11:00AM HEC 101A | Zoom Abstract In the age of big data and Artificial Intelligence (AI), protecting the security and privacy of stored data is paramount for maintaining public trust, accountability and getting the full value from the collected data.…...
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