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Events for the 1st week of April
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The Bekey Distinguished Lecture & Munushian Distinguished Lecture Present: Gordon Bell, Microsoft Researcher Emeritus
Mon, Apr 01, 2024 @ 03:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Gordon Bell, Microsoft Researcher Emeritus
Talk Title: Bell's Law of Computer Classes. Why We Have All Kinds of Computers
Abstract: In 1951, a person could walk inside a computer and by 2010 a single computer (or “cluster’) with millions of processors has expanded to building size. Alternatively, computers are “walking” inside of us. These ends illustrate the vast dynamic range in computing power, size, cost, etc. for early 21st century computer classes. A computer class is a set of computers in a particular price range with unique or similar programming environments (e.g. Linux, OS/360, Palm, Symbian, Windows) that support a variety of applications that communicate with people and/or other systems. A new computer class forms roughly each decade establishing a new industry. A class may be the consequence and combination of a new platform with a new programming environment, a new network, and new interface with people and/or other information processing systems. Bell’s Law accounts for the formation, evolution, and death of computer classes based on logic technology evolution beginning with the invention of the computer and the computer industry in the first generation, vacuum tube computers (1950-1960), second generation, transistor computers (1958-1970), through the invention and evolutions of the third generation TTL and ECL bipolar Integrated Circuits (1965-1985), and the fourth generation bipolar, MOS and CMOS ICs enabling the microprocessor, (1971) represents a “break point” in the theory because it eliminated the other early, more slowly evolving technologies. Moore’s Law (Moore 1965, revised in 1975) is an observation about integrated circuit evolution. In summary, Moore’s Law and Bell’s effectively predict the ensuing fifty years of the computer. This lecture satisfies requirements for CSCI 591: Research Colloquium. To register visit: https://docs.google.com/forms/d/e/1FAIpQLSe6If3BkOATE8onTmrYZNSr0pzWF47TedNKMrwnukr0Ue_k8w/viewform
Biography: Gordon Bell is a Microsoft Researcher Emeritus He spent 23 years at Digital Equipment Corporation as Vice President of R&D, responsible for the first mini- and time-sharing computers and DEC's VAX, with a 6 year sabbatical at Carnegie Mellon. In 1987, as NSF's first, Ass't Director for Computing (CISE), he led the National Research and Education Network panel that became the Internet. In 1987 he established the Gordon Bell Prize to recognize the extraordinary efforts to exploit modern highly parallel computers. Bell maintains three interests: computers: their evolution and use, technology-based startup companies, and lifelogging. He is a member or Fellow of the American Academy of Arts and Sciences, Association of Computing Machinery, Institute of Electrical and Electronic Engineers, the National Academy of Engineering, National Academy of Science, the Australia Academy of Technological Sciences and Engineering and received The 1991 National Medal of Technology. He is a founding trustee of the Computer History Museum, Mountain View, CA. and lives in San Francisco. http://gordonbell.azurewebsites.net
Host: Cyrus Shahabi
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: CS Events
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CS Colloquium: Jane E. - Artistic Vision: Interactive Computational Guidance for Developing Expertise
Tue, Apr 02, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jane E., UC San Diego
Talk Title: Artistic Vision: Interactive Computational Guidance for Developing Expertise
Series: Computer Science Colloquium
Abstract: Computer scientists have long worked towards the vision of human-AI collaboration for augmenting human capabilities and intellect. My work contributes to this vision by asking: How can computational tools not only help a user complete a task, but also help them develop their own domain expertise while doing so?
I investigate this question by designing new interactive tools for domains of artistic creativity. My work is inspired by the fact that expert artists have trained their eyes to “see” in ways that embed their expert domain knowledge—in this case, core artistic concepts. As instructors, experts have also designed approaches to intentionally communicate their vision to their students. My work designs creativity tools that leverage these expert structures to help novices develop this expert-like "artistic vision"—specifically through providing guidance to scaffold their design processes. In this talk, I will demonstrate my approach for designing tools that embed such guidance for photography and visual design that embed the underlying design principles. I will show that these tools are able to scaffold novices’ to be more aware of these artistic concepts during their creative process.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Jane E is Postdoctoral Fellow at The Design Lab at UCSD under the guidance of mentors Steven Dow and Haijun Xia. She earned her PhD in Computer Science from Stanford University, where she was co-advised by James Landay and Pat Hanrahan. Her research lies at the intersection of human-computer interaction and computer graphics with a focus on designing computational guidance to support novices in developing their own creative expertise. Her work takes inspiration from cognitive science and education theory to design computational tools that scaffold novices’ creative processes. Jane is grateful to have been selected as a Rising Star in EECS and to have been supported by a Microsoft Research Dissertation Grant, Hasso Plattner Institute’s Design Thinking Research Program, Brown Institute for Media Innovation, and UCSD CSE’s Postdoctoral Fellowship Program. She previously worked on the Microsoft Photos app as a software engineer after receiving her BSE from Princeton University. For more information, see her website: ejane.me
Host: Souti Chattopadhyay
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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CS Colloquium: Sai Praneeth Karimireddy - Building Planetary-Scale Collaborative Intelligence
Wed, Apr 03, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Sai Praneeth Karimireddy, University of California, Berkeley
Talk Title: Building Planetary-Scale Collaborative Intelligence
Abstract: Today, access to high-quality data has become the key bottleneck to deploying machine learning. Often, the data that is most valuable is locked away in inaccessible silos due to unfavorable incentives and ethical or legal restrictions. This is starkly evident in health care, where such barriers have led to highly biased and underperforming tools. Using my collaborations with Doctors Without Borders and the Cancer Registry of Norway as case studies, I will describe how collaborative learning systems, such as federated learning, provide a natural solution; they can remove barriers to data sharing by respecting the privacy and interests of the data providers. Yet for these systems to truly succeed, three fundamental challenges must be confronted: These systems need to 1) be efficient and scale to massive networks, 2) manage the divergent goals of the participants, and 3) provide resilient training and trustworthy predictions. I will discuss how tools from optimization, statistics, and economics can be leveraged to address these challenges. This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Sai Praneeth Karimireddy is a postdoctoral researcher at the University of California, Berkeley with Mike I. Jordan. Karimireddy obtained his undergraduate degree from the Indian Institute of Technology Delhi and his PhD at the Swiss Federal Institute of Technology Lausanne (EPFL) with Martin Jaggi. His research builds large-scale machine learning systems for equitable and collaborative intelligence and designs novel algorithms that can robustly and privately learn over distributed data (i.e., edge, federated, and decentralized learning). His work has seen widespread real-world adoption through close collaborations with public health organizations (e.g., Doctors Without Borders, the Red Cross, the Cancer Registry of Norway) and with industries such as Meta, Google, OpenAI, and Owkin. Karimireddy's research has been recognized by the EPFL Patrick Denantes Memorial Prize for the best computer science thesis, the Dimitris N. Chorafas Foundation Award for exceptional applied research, an EPFL thesis distinction award, a Swiss National Science Foundation fellowship, and best paper awards at the International Workshop on Federated Learning for User Privacy and Data Confidentiality at ICML 2021 and the International Workshop on Federated Learning: Recent Advances and New Challenges at NeurIPS 2022.
Host: Jiapeng Zhang / Mahdi Soltanolkotabi
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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CS Colloquium: Jason Wu - Computational Understanding of User Interfaces
Thu, Apr 04, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jason Wu, CMU
Talk Title: Computational Understanding of User Interfaces
Series: Computer Science Colloquium
Abstract: A grand challenge in human-computer interaction (HCI) is constructing user interfaces (UIs) that make computers useful for all users across all contexts. Today, most UIs are manually designed for a rigid set of assumptions and are unable to dynamically accommodate the diversity of user abilities, usage contexts, or computing technologies. The goal of my research is to build a machine that can understand and operate any UI then dynamically convert it into a new personalized, context-dependent representation. In this talk, I focus on three areas that define this approach for enhancing human-computer interaction. First, I describe approaches for understanding user ability and context embodied by a recommendation system that recommends device settings (e.g., accessibility features) based on sensed usage behaviors and user interaction logs. Next, I introduce several machine learning models that reliably understand the semantics (content and functionality) of any graphical UI from its visual appearance, unlocking new possibilities for many existing systems such as assistive technology, software testing, and UI automation. Finally, I present systems that incorporate both user and UI understanding to synthesize improved interfaces using a novel fine-tuned large language model (LLM) for UI generation. Improved machine understanding of UIs has the potential to redefine how we use computers in the future and drive advances in many fields such as HCI, machine learning and software engineering.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Jason Wu is a PhD candidate in the HCI Institute at Carnegie Mellon University advised by Jeffrey Bigham. In his research, Jason builds data-driven and computational systems that understand, manipulate, and synthesize user interfaces to maximize the usability and accessibility of computers . His research has been published in top venues for human-computer interaction, user interface technology, accessibility, and machine learning, where he has received several best paper awards (CHI 2021, W4A 2021) and honorable mention awards (CHI 2020, CHI 2023). His work has also been recognized outside of academic conferences by a Fast Company Innovation by Design Student Finalist Award, press coverage in major outlets such as TechCrunch and AppleInsider, and by the FCC Chair Awards for Advancements in Accessibility. Jason is a recipient of the NSF Graduate Research Fellowship and selected as a Heidelberg Laureate Forum Young Researcher.
Host: Souti Chattopadhyay
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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PhD Defense - Jared Coleman
Thu, Apr 04, 2024 @ 10:00 AM - 11:30 AM
Thomas Lord Department of Computer Science
University Calendar
PhD Defense: Jared Coleman
Title: Dispersed Computing for Dynamic Environments Committee: Bhaskar Krishnamachari (Chair), Konstantinos Psounis, Jyotirmoy Deshmukh
Abstract: Scheduling a distributed application modeled as a directed acyclic task graph over a set of networked compute nodes is a fundamental problem in distributed computing and thus has received substantial scholarly attention. Most existing solutions, however, fall short of accommodating the dynamic and stochastic nature of modern dispersed computing systems (e.g., IoT, edge, and robotic systems) where applications and compute networks have stricter and less stable resource constraints. In this dissertation, we identify problems and propose solutions that address this gap and advance the current state-of-the-art in task scheduling.Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Everyone Is Invited
Contact: Asiroh Cham
