University of California, Berkeley – Applied Mathematics Seminar

Date:
Wednesday, March 5, 2025

Time:
11:10am- 12:00pm

Location:
939 Evans Hall – UC Berkeley Campus
Zoom: https://berkeley.zoom.us/j/98667278310

Speakers(s) and Affiliations(s):
Zane Rossi
University of Tokyo

Title:
A Solovay-Kitaev theorem for quantum signal processing

Abstract:
Quantum signal processing (QSP) studies quantum circuits interleaving known unitaries (the phases) and unknown unitary oracles encoding a hidden scalar (the signal). For a wide class of functions one can quickly compute the phases for a QSP protocol with a matrix element approximately equal to the application of this function to the signal; surprisingly, this simple ability unifies the presentation and analysis of most quantum algorithms. A separate, basic problem in quantum computing is gate approximation: realistic devices support only a subset of possible gates, and extensive results establish methods to (approximately) compile one’s available gates to a desired one. Among these results, the Solovay-Kitaev theorem (SKT) establishes an equivalence between the universality of a gate set (its density in a compact Lie group) and the existence of short approximating sequences. QSP, alternatively, considers a single program (the phases) such that for all among a continuum of possible input signals a desired output unitary is achieved, and so resembles a `lifted’ variant of gate approximation. We make this notion of lifting formal, constructing an `SKT for QSP,’ establishing an equivalence between the density of a circuit ansatz in a specific functional class and the existence of short protocols. These methods provide alternative proofs for basic results in QSP, and establish an intersection between QSP and the theory of gate-approximation. Unlike prior work, our proofs are not required to be constructive and are surprisingly insensitive to the chosen ansatz—they comprise loosely coupled, swappable components amenable to ansatz extensions for which standard QSP proof methods fail completely. These strengths align with an insight of the standard Solovay-Kitaev theorem: that it is often easier to work with Lie algebras than Lie groups. By carefully linearizing certain aspects of QSP, we intermesh algebraic-geometric techniques from gate approximation with well-understood functional-analytic properties of QSP, bootstrapping tools strong enough to analyze diverse unitary ansätze.

Hosts of Seminar:
Zhiyan Ding, Lin Lin, Michael Lindsey, Krutika Tawri and Franziska Weber,  Mathematics Group
____________________________________________________________________
Welcome to the Applied Mathematics seminar for the Spring 2025 semester. This year, the seminar series is being organized by Zhiyan Ding (zding.m@berkeley.edu), Lin Lin (linlin@berkeley.edu), Michael Lindsey (lindsey@math.berkeley.edu), Krutika Tawri (ktawri@berkeley.edu), and Franziska Weber (fweber@berkeley.edu). If you have any inquiries, please contact one of them.

To join the applied math seminar mailing list, click
https://groups.google.com/a/lists.berkeley.edu/forum/#!forum/appliedmathseminar/join
—
LBNL-UCB Applied Math Seminar website https://berkeleyams.lbl.gov/
—

Seminar Type: CS/ESnet Seminar Series

 ~~~~~~~~Calendar event name~~~~~~~~~~~~~ 

CS Seminar: Quantum Seminar with PsiQuantum on “Fault Tolerant Quantum Computing for Scalable Impact”

 ~~~~~~~~Event Details Below~~~~~~~~~~~~~~ 

Berkeley Lab – CS SeminarWafer Scale Computing: Fine Grain Parallelism and Rethinking Parallel Computing 

 Date:03/11/25

Time:1:30pm – 2:30pm 

Location: https://ESnet.zoom.us/j/96604073326

Title:

Quantum Seminar with PsiQuantum on “Fault Tolerant Quantum Computing for Scalable Impact”

Abstract:

A presentation by Dr. Pete Shadbolt, Chief Scientific Officer and Co-Founder at PsiQuantum.

Pete Shadbolt is Co-Founder and Chief Scientific Officer of PsiQuantum. After earning his PhD in experimental photonic quantum computing from the University of Bristol in 2014, Pete was a postdoc at Imperial College researching the theory of photonic QC. During his time at Bristol, he demonstrated the first-ever Variational Quantum Eigensolver and the first-ever public API to a quantum processor. He has been awarded: the 2014 EPSRC “Rising Star” by the British Research Council; the EPSRC Recognizing Inspirational Scientists and Engineers Award; and the European Physics Society Thesis Prize.

Pete will be presenting an introduction to PsiQuantum and their approach to building and deploying a fault tolerant quantum computer utilizing light as a qubit.

──────────

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Seminar Type: Computing Sciences Area Seminar

~~~~~~~~Calendar event name~~~~~~~~~~~~~
CS Seminar: Wafer Scale Computing: Fine Grain Parallelism and Rethinking Parallel Computing
~~~~~~~~Event Details Below~~~~~~~~~~~~~~
Berkeley Lab – CS Seminar
Wafer Scale Computing: Fine Grain Parallelism and Rethinking Parallel Computing

Date:
03/12/25

Time:
10:30am – 11:30am

Location:
https://lbnl.zoom.us/j/99791568777

Speakers(s) and Affiliations(s):
Rob Schreiber

Title:
Wafer Scale Computing: Fine Grain Parallelism and Rethinking Parallel Computing

Abstract:
Chips are made by photolithographic printing of circuits on thin silicon wafers that today are 12-inch diameter circles. A matrix of identical chips is printed on the surface, and a saw then cuts the wafer into individual chips. But in wafer-scale computing, there is no saw. The whole wafer remains intact, serving as a single “chip”, but with two orders of magnitude more transistors than a conventional chip. As of October 2024, Cerebras Systems is the only manufacturer of (and its CS-3 is the only instance of) commercially available wafer-scale computers.

The CS-3 incorporates all memory and processing on one wafer, a wafer that contains about 840,000 processing elements. With 48KB of local memory, a PE cannot hold very much data. On the other hand, access to that data is at the same rate as peak speed computation. Most interesting, the mesh interconnect has single-clock latency for sending a message (of 4 bytes) to a mesh neighboring PE, and the network can sustain a 4 byte message to and from each neighbor on every clock.

The wafer is therefore a working instance of processing co-located with memory. While it is distributed memory from the addressing perspective, the interconnect’s performance allows programmers to treat distributed data structures — graphs, matrices, data arrays — as if they were shared; they are shared objects housed in a distributed memory substrate.

Wafer-scale computing is therefore a new thing at the hardware level (no saw); at the architecture level (because communication is intrinsic to the architecture and the instruction set); at the algorithmic level (because memory and communication walls have been toppled, allowing strong scaling and effective fine-grain parallelism); and at the programming level, where application code tightly integrates all the wafer resources, explicitly controlling communication as well as computation.

So there are no memory walls and no high-overhead, high-latency, low-bandwidth interconnects on the wafer; the upshot is that very fine-grained parallel applications achieve excellent performance. This in turn allows parallel implementations in which each PE holds only a few words of the problem data, taking full advantage of the easy accessibility of data on near neighbor PEs. Thus, strong scaling is quite successful, which reduces runtimes for problems of the scale that fit the wafer by two orders of magnitude, allowing applications that are impossible with conventional systems. There are now demonstrations of a dramatic speedup for fluid flow, molecular dynamics, and radiation transport applications.

Thus, wafer-scale computing has created the possibility of a dramatic shift in how we build, think about, and use computation for science.

Bio:

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Meeting ID: 997 9156 8777

Host of Seminar:
Stefan Wild, Applied Mathematics and Computational Research Division, LBNL

University of California, Berkeley – Applied Mathematics Seminar

Date:
Wednesday, March 5, 2025

Time:
11:10am- 12:00pm

Location:
939 Evans Hall – UC Berkeley Campus
Zoom: https://berkeley.zoom.us/j/98667278310

Speakers(s) and Affiliations(s):
Zane Rossi
University of Tokyo

Title:
A Solovay-Kitaev theorem for quantum signal processing

Abstract:
Quantum signal processing (QSP) studies quantum circuits interleaving known unitaries (the phases) and unknown unitary oracles encoding a hidden scalar (the signal). For a wide class of functions one can quickly compute the phases for a QSP protocol with a matrix element approximately equal to the application of this function to the signal; surprisingly, this simple ability unifies the presentation and analysis of most quantum algorithms. A separate, basic problem in quantum computing is gate approximation: realistic devices support only a subset of possible gates, and extensive results establish methods to (approximately) compile one's available gates to a desired one. Among these results, the Solovay-Kitaev theorem (SKT) establishes an equivalence between the universality of a gate set (its density in a compact Lie group) and the existence of short approximating sequences. QSP, alternatively, considers a single program (the phases) such that for all among a continuum of possible input signals a desired output unitary is achieved, and so resembles a `lifted' variant of gate approximation. We make this notion of lifting formal, constructing an `SKT for QSP,' establishing an equivalence between the density of a circuit ansatz in a specific functional class and the existence of short protocols. These methods provide alternative proofs for basic results in QSP, and establish an intersection between QSP and the theory of gate-approximation. Unlike prior work, our proofs are not required to be constructive and are surprisingly insensitive to the chosen ansatz—they comprise loosely coupled, swappable components amenable to ansatz extensions for which standard QSP proof methods fail completely. These strengths align with an insight of the standard Solovay-Kitaev theorem: that it is often easier to work with Lie algebras than Lie groups. By carefully linearizing certain aspects of QSP, we intermesh algebraic-geometric techniques from gate approximation with well-understood functional-analytic properties of QSP, bootstrapping tools strong enough to analyze diverse unitary ansätze.

Hosts of Seminar:
Zhiyan Ding, Lin Lin, Michael Lindsey, Krutika Tawri and Franziska Weber,  Mathematics Group
____________________________________________________________________
Welcome to the Applied Mathematics seminar for the Spring 2025 semester. This year, the seminar series is being organized by Zhiyan Ding (zding.m@berkeley.edu), Lin Lin (linlin@berkeley.edu), Michael Lindsey (lindsey@math.berkeley.edu), Krutika Tawri (ktawri@berkeley.edu), and Franziska Weber (fweber@berkeley.edu). If you have any inquiries, please contact one of them.

To join the applied math seminar mailing list, click
https://groups.google.com/a/lists.berkeley.edu/forum/#!forum/appliedmathseminar/join
—
LBNL-UCB Applied Math Seminar website https://berkeleyams.lbl.gov/
—

Berkeley Lab – CS Seminar
AI for Data Editing: Advancing Data’s AI Readiness via Reinforcement, GenAI, and Self-supervision Intelligence

Date:
03/12/25

Time:
2pm – 3pm

Location:
59-3-3042-CR (8)

Speakers(s) and Affiliations(s):
Yanjie Fu Arizona State University

Title:
AI for Data Editing: Advancing Data’s AI Readiness via Reinforcement, GenAI, and Self-supervision Intelligence

Abstract:
Abstract: In this talk, I will introduce the four major research projects on data, predictive, decision, and reasoning/tasking intelligence in my lab. Specifically, I will introduce the concept of AI for data editing that learns data patterns and structures, and acquire actionable knowledge to transform, reprogram, and augment data. I will show three unique perspectives: 1) reinforcement, 2) generativeAI, 3) self-supervision, and demonstrate how the techniques can be used to advance scientific data’s AI readiness. Finally, I will conclude by discussing the future research directions and visions toward AI4Data.

Bio:
Bio: Dr. Yanjie Fu is an associate professor in the School of Computing and AI at Arizona State University. He received his Ph.D. degree from Rutgers, the State University of New Jersey in 2016, the B.E. degree from the University of Science and Technology of China in 2008, and the M.E. degree from the Chinese Academy of Sciences in 2011. He has research experience in industry research labs, such as Microsoft Research Asia and IBM Thomas J. Watson Research Center. He has published prolifically in refereed journals and conference proceedings, such as IEEE TKDE, IEEE TMC, ACM TKDD, ACM SIGKDD, AAAI, IJCAI, VLDB, WWW, ACM SIGIR. His research has been recognized by: 1) three junior faculty awards: US NAE Grainger Foundation Frontiers of Engineering early career engineer (2023), US NSF CAREER (2021), and NSF CRII (2018) awards; 2) several best paper (runner-up, finalist) awards (e.g., KDD18 best finalist, SIGSpatial20 best runner-up); 3) several community and industrial recognitions: 2024 Stanford Elsevier World’s Top 2% Scientists, 2022 Baidu Scholar global top Chinese young scholars in AI, 2021 Aminer.org AI 2000 Most Influential Scholar Award Honorable Mention in Data Mining, 2016 Microsoft Azure Research Award; 4) several university-level awards: Reach the Stars Award, University System Research Board Award and University Interdisciplinary Research Award. He is committed to data science education. His graduated Ph.D. students have joined academia as tenure-track faculty members. He is broadly interested in data mining, machine learning, and their interdisciplinary applications. His research aims to develop robust machine intelligence with imperfect and complex data by building tools to address framework, algorithmic, data, and computing challenges. His recent focuses are ML with geospatial, time series data, data-centric AI (AI4data), AI for simulation and decisions, multimodal AI and foundation models, LLM. He currently serves as an Associate Editor of ACM Transactions on Knowledge Discovery from Data (TKDD). He is a senior member of ACM and IEEE.

Virtual Information:
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Meeting ID: 962 9018 1577
Passcode: 281176

Host of Seminar:
Bin Dong

Berkeley Lab – CS Seminar
Advancing Personal Health and Wellness Leveraging Agentic AI

Date:
03/1​3/25

Time:
​12pm – ​1pm

Location:
​Zoom

Speakers(s) and Affiliations(s):
A. Ali Heydari​, Google Research

Title:
Advancing Personal Health and Wellness Leveraging Agentic AI

Abstract:
Recent breakthroughs in AI and wearable technology have the potential to revolutionize healthcare, shifting the paradigm from reactive treatment to proactive, personalized wellness. Wearables are evolving from passive data collectors to sources of real-time, high-fidelity physiological information. Simultaneously, AI, particularly leveraging the power of Large Language Models (LLMs), is gaining the ability to analyze and reason over complex, high-dimensional data streams to generate novel and personalized insights. This talk explores the convergence of these technologies: by integrating personal health records with the reasoning capabilities of LLMs, we can design cognitive agents capable of providing personalized health coaching, recommending lifestyle changes, and even offering differential diagnosis with unprecedented accuracy. This moves us closer to the vision of readily-available personalized health assistants. The synergy between personal health data and LLM-powered agents promises a fundamental shift towards continuous, personalized health optimization – effectively creating virtual health companions that guide individuals toward their optimal wellness. This talk will cover the ongoing development and technical challenges that need to be solved to achieve this future.

Bio:
Dr. A. Ali Heydari received a PhD in Applied Mathematics from University of California, Merced, mentored by Professor Suzanne Sindi (UC Merced) and Professor Elana Fertig (Johns Hopkins University). The goal of his research is to develop deep learning and numerical frameworks that enable a more accurate and robust analysis of complex biological systems, particularly systems relating to human diseases.

Virtual Information:
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Meeting ID: 914 8598 8619

Passcode: 060729

Host of Seminar:
Yumary Vasquez

Berkeley Lab – CS Seminar

Date:
Friday, March 14, 2025

Time:
3:00pm – 4:00pm

Location:
Zoom: https://lbnl.zoom.us/j/92855447451?pwd=Uzdob2lsR1YrTTVuZUd2ZUMyR1BvUT09

Speakers(s) and Affiliations(s):
Various speakers
Lawrence Berkeley National Laboratory

Title:
Software Lightning Talks (Session 18)

Abstract:
Lightning Talks are periodic sessions where coders from across the Lab can get together for a number of short, informal talks about coding-related topics.

In this 18th session, speakers will discuss topics like: data serialization with Protocol Buffers, and implementing 3D graphics in Python with OpenGL.

There’s room for more speakers!

Sign up to give a talk: https://docs.google.com/spreadsheets/d/1FC9S9uFtWiktrXoXVPBQtvbYUI4mT5aT1nKZOV_KjhI
Website: https://sites.google.com/lbl.gov/lightningtalkslunch

Virtual Information:
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Meeting ID: 928 5544 7451
Passcode: 152472

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Meeting ID: 928 5544 7451
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Host of Seminar:
Keith Beatie and Matthew Li
Lawrence Berkeley National Laboratory

CS/AMCR Seminar Series
Linear Solvers for GPU-accelerated High-order Finite Element Methods for Scalar Elliptic Problems

Date:
03/1​7/25

Time:
9:00 AM – 10:00 AM

Location:
​Zoom

Speakers(s) and Affiliations(s):
Yichen Guo, Virginia Polytechnic Institute and State University

Title:
Linear Solvers for GPU-accelerated High-order Finite Element Methods for Scalar Elliptic Problems

Abstract:
Solving large-scale linear systems from high-order finiteelement discretizations is time-consuming—on Summit supercomputer, nearly 80%of the computation time is spent on a series of pressure Poisson solves inreactor simulations [1]. In this talk, I will share several strategies toenhance the convergence and performance of linear solvers for high-order finiteelement Poisson problems. I will discuss techniques such as selecting optimalinitial guesses for sequential solves, developing efficient preconditionerstailored to high-order discretizations on deformed meshes, and designingefficient stopping criteria [2] that balance discretization and algebraicerrors to prevent both premature termination and over-solving, as well asongoing work to further boost the performance of solvers.


Virtual Information:

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Meeting ID: 955 0035 6389

Host of Seminar: Sherry Li, AMCR, Scalable Solvers Group (SSG)

University of California, Berkeley – Applied Mathematics Seminar

Date:
Wednesday, March 19, 2025

Time:
11:10am- 12:00pm

Location:
939 Evans Hall – UC Berkeley Campus
Zoom: https://berkeley.zoom.us/j/98667278310

Speakers(s) and Affiliations(s):
Noel Walkington
Carnegie Mellon University

Title:
Modeling Multiphase Porous Flows

Abstract:
In order to model many geological flows of contemporary interest it isnecessary to include the thermodynamics of the underlying processes. Examples include CO_2 sequestration and the release of greenhouse gasses dissolved in melting permafrost. Tractable models of such problems can only involve gross (macroscopic) properties, since a precise description of the physical system is neither available nor computationally tractable. This talk will first briefly review the role thermodynamics plays in classical continuum mechanics; in particular, how dissipation principles give rise to bounds above and beyond the natural conservation properties. The development of macroscopic models of geological flows involving multiple components undergoing changes of phase will then be considered. These models involve an amalgamation of classical and continuum thermodynamics, which gives rise to systems of conservation laws which inherit natural dissipation principles. The convexity (concavity) of the free energy (entropy) functions is essential for the development of stability estimates of solutions to these equations, and the development of stable numerical schemes.

Hosts of Seminar:
Zhiyan Ding, Lin Lin, Michael Lindsey, Krutika Tawri and Franziska Weber,  Mathematics Group
____________________________________________________________________
Welcome to the Applied Mathematics seminar for the Spring 2025 semester. This year, the seminar series is being organized by Zhiyan Ding (zding.m@berkeley.edu), Lin Lin (linlin@berkeley.edu), Michael Lindsey (lindsey@math.berkeley.edu), Krutika Tawri (ktawri@berkeley.edu), and Franziska Weber (fweber@berkeley.edu). If you have any inquiries, please contact one of them.

To join the applied math seminar mailing list, click
https://groups.google.com/a/lists.berkeley.edu/forum/#!forum/appliedmathseminar/join
—
LBNL-UCB Applied Math Seminar website https://berkeleyams.lbl.gov/
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