All graduate seminars are held on Mondays and Wednesdays from 4:10 p.m. to 5:25 p.m. in Peterson 118. Join us for this open seminar!
Spring 2023
Scalable Computing for Computing: Simulating the Future of Extreme Scale Systems
Wednesday, April 19
Dr. John Leidel
Chief Scientist/Founder, Tactical Computing Laboratories
Abstract
Historic computer architecture and design relied upon micro-scale simulation and emulation techniques to develop incremental improvements to architectural efficiency and performance. Future extreme scale data-intensive applications and workloads require computing, memory and I/O resources well in excess to what is currently available on current enterprise server platforms. In this talk, we will explore the use of current high-performance computing systems to simulate the future of data-intensive scalable computing platforms using multi-resolution methods that combine cycle-based and heuristic models into a scalable simulation and co-design infrastructure.
Biography
Dr. John Leidel is the chief scientist and founder of Tactical Computing Laboratories, where he leads efforts in developing advanced architectural and programming model techniques for scalable high-performance and data-intensive computing platforms. He founded Tactical Computing Labs in 2016 under the premise that commodity high-performance computing hardware and software architectures would not be sufficient to meet the ever-increasing demands of scalable, data-intensive computing. Leidel is currently researching the use of RISC-V in developing advanced accelerators for high-performance computing, network processing and irregular algorithms. He also leads efforts at Tactical Computing Labs in advanced compiler architectures and programming models. Leidel currently serves as a RISC-V Foundation’s Technology Horizontal chairperson. He holds a Ph.D. in computer science from Texas Tech University.
Faculty Contact: Roger Pearce
Multi-fidelity Bayesian Optimization of Nanoporous Materials for Gas Separations
Monday, April 17
Dr. Cory Simon
Assistant Professor, Oregon State University
Abstract
Nanoporous materials are useful for the energy-efficient, adsorptive separation/purification of gas mixtures. Modular, tunable classes of nanoporous materials offer a large number of candidate structures for any given gas separation task.
Often, we wish to search for the nanoporous material giving the optimal property for a gas separation while incurring the minimal cost.
Multi-fidelity Bayesian optimization (MFBO) constitutes an “experiment-update-plan” closed loop to efficiently find the optimal nanoporous material while leveraging multiple experiments that trade-off fidelity and cost differently. The two components of MFBO are: (1) the surrogate model, a supervised machine learning model that predicts the outcome of each experiment on each material with quantified uncertainty, and (2) the acquisition function, which scores each material and experimental fidelity according to their appeal for the next experiment, balancing cost, exploration and exploitation.
In this talk, we employ MFBO to efficiently find, among a database of approximately 600 covalent organic frameworks, the structure with the highest adsorptive selectivity for xenon over krypton while leveraging both high-fidelity and low-fidelity molecular simulations.
[Joint work with: Nick Gantzler at Oregon State University, and Jana Doppa and Aryan Deshwal at Washington State University.]
Biography
Dr. Cory Simon is an assistant professor of chemical engineering at Oregon State University. He earned his Ph.D. in chemical engineering from the University of California, Berkeley. Simon’s research group develops mathematical models, trains machine learning models and conducts computer simulations to tackle or deliver insights into problems in chemistry and materials science.
Faculty Contact: Ricardo Gutierrez-Osuna
The Many Dimensions of “Identity”
Monday, April 10
Dr. Radia Perlman
Computer Programmer and Network Engineer
Abstract
People usually assume that “the identity problem” is well-understood and that, given how long Internet authentication has been deployed, the world must have solved how to do that (whatever “that” is) securely. This talk describes various issues, for instance: how does a website get a name, how does a website get a certificate, how does a browser know what to trust to sign certificates, how a human finds a website, how a human acquires a name, and how a user proves they own their name. Surprisingly, there are issues with all of these aspects as deployed today. As with most security problems, some people propose “blockchain” as being “the solution.” This talk will describe what aspects of identity and authentication blockchain might address and compare a “blockchain” approach with what is deployed today. If the talk spurs spirited debate, all the better.
Biography
Dr. Radia Perlman will begin her association with Texas A&M University in September as a Hagler Fellow and will teach a network security class in the fall of 2023. Her specialties include network routing protocols and network security. Perlman developed the technology for making network routing self-stabilizing, largely self-managing, and scalable. She also invented the spanning tree algorithm, which transformed Ethernet from a technology that supported a few hundred nodes within a single building to something that could support large networks. Perlman also has made contributions in network security, including scalable data expiration, distributed algorithms despite malicious participants, distributed denial-of-service prevention techniques and user authentication. She is the author of the textbook “Interconnections” (about network layers two and three) and coauthor of “Network Security: Private Communication in a Public World” (about cryptography, quantum computing, quantum-safe public key algorithms and more). She holds an S.B. and S.M. in mathematics and a Ph.D. in computer science, all from the Massachusetts Institute of Technology.
Faculty Contact: Scott Schaefer
All You Need to Know to Win a Cybersecurity Adversarial Machine Learning Competition and Make Malware Attacks Practical
Monday, April 3
Dr. Marcus Botacin
Visiting Assistant Professor, Texas A&M University
Abstract
The use of machine learning (ML) techniques for malware detection has been a trend in the last two decades and is considered state-of-the-art in the literature. More recently, researchers started to investigate adversarial approaches to bypass these ML-based malware detectors. These adversarial attacks became so popular that some companies have launched a public challenge called the Machine Learning Security Evasion Competition (MLSEC) to encourage researchers to develop and attack ML-based static malware detectors. In this talk, Marcus presents a winning experience (three times in a row) on both sides of attacking and defending ML-based malware detectors while participating in the first three MLSEC editions (2019-2021).
Biography
Marcus Botacin is a visiting assistant professor in the computer science and engineering Department at Texas A&M University. He holds a computer science Ph.D. (Federal University of Paraná, Brazil, 2021), a master’s degree in computer science (University of Campinas, Brazil, 2017) and a bachelor’s degree in computer engineering (University of Campinas, Brazil, 2015). Botacin’s main research interests are malware analysis, reverse engineering and the science of security.
Faculty Contact: Ricardo Gutierrez-Osuna
Addressing Challenges of Core Microarchitecture Research
Wednesday, March 22
Dr. Daniel A. Jiménez
Professor, Texas A&M University
Abstract
Core microarchitecture research has been studied for decades but remains crucial due to the evolving demands of modern computing workloads. Growing instruction footprints, the influx of massive data into the processor, the overhead of modern programming languages and the emphasis on productivity over performance all require innovative approaches. As Moore’s Law reaches its end, the onus of improving performance and efficiency falls on microarchitecture research. Additionally, with more and more companies opting to design their own processors, academia is tasked not only with developing new processing technologies but also training the workforce to design these new chips.
In this talk, I will motivate the need for continued core microarchitecture research, give some recent examples of topics we study such as instruction fetch, address translation and cache management, and give some insight into the challenges we face in this kind of work. For example, branch prediction has been a well-studied topic for decades, but recent trends in software design have caused huge growth in instruction footprints, putting pressure on other areas of instruction fetch as well as overwhelming the capacity of modern branch predictors and ultimately leading to performance degradation.
Biography
Daniel A. Jiménez is a professor in the Department of Computer Science and Engineering at Texas A&M University. He received his Ph.D. in computer sciences from The University of Texas at Austin. His research is in microarchitecture, including microarchitectural prediction and cache management. Jiménez pioneered the development of neural-inspired branch predictors that have been implemented in millions of processors sold by IBM, AMD, Oracle and Samsung.
He designed the neural branch predictors used in the popular Samsung Galaxy S7/8/9/10/20. His 2001 paper on perceptron-based branch prediction won the Institute of Electrical and Electronics Engineers (IEEE) International Symposium on High-Performance Computer Architecture (HPCA) Test of Time Award in 2019. Jiménez won the 2021 IEEE CS B. Ramakrishna Rau Award for his contributions to neural branch prediction. He is an IEEE Fellow, an Association for Computing Machinery (ACM) Distinguished Scientist, a National Science Foundation CAREER award winner and a member of the International Symposium on Computer Architecture (ISCA), IEEE/ACM International Symposium on Microarchitecture and HPCA halls of fame. Jiménez is the chair of the IEEE Computer Society Technical Committee on Computer Architecture (TCCA) and co-chair of the ISCA Steering Committee. He was general chair of IEEE HPCA in 2011, program chair for IEEE HPCA in 2011 and selection committee chair for IEEE Micro “Top Picks” 2020.
Faculty Contact: Ricardo Gutierrez-Osuna
Human-in-the-Loop Creative AI
Monday, March 20
Takeo Igarashi
Professor, The University of Tokyo
Abstract
Generative models that apply deep learning to the generation of contents such as images and sound are attracting attention. However, generative process using deep learning is a black box, which makes it difficult for humans to understand and control. In this talk, I will introduce methods for human intervention and control of such generative processes and other computer-assisted content generation systems. I will show examples in computer-aided design using physical simulation, generative models of images, 3D models and acoustic signals.
Biography
Takeo Igarashi is a professor in the Department of Creative Informatics at The University of Tokyo. His research interests are in user interfaces and interactive computer graphics. Igarashi has received several awards, including the Association for Computing Machinery's (ACM) SIGGRAPH 2006 Significant New Researcher Award, the ACM CHI Academy award 2018 and the Asia Graphics 2020 Outstanding Technical Contributions Award. He served as a program co-chair for the ACM Symposium on User Interface Software and Technology (UIST) in 2013, a conference co-chair for ACM UIST in 2016, technical papers chair for SIGGRAPH ASIA in 2018 and technical program co-chair for ACM CHI in 2021.
Faculty Contact: Jeeeun Kim
Learning to Generalize to Out-of-Distribution Data
Monday, March 6
Dr. Jiebo Luo
Professor, University of Rochester
Abstract
Data-driven deep models will inherit the characteristics of the training data and thus can handle the in-distribution testing data well. However, in real-life applications, the distribution of testing data is likely different from that of training data because of many factors, including distribution shift, domain shift, isolated data server, noisy labels, and so on. Such challenging datasets may make the learned model unreliable and pose threats to the learned model's generalization capacity for unseen testing data. This talk will cover several related research topics. First, real-world data inevitably contains noise and bias. We adopt adversarial learning to obtain bias/domain invariant features so that the learned model can generalize to out-of-distribution testing data. Second, due to privacy concerns and transmission load, directly transferring data from edge devices to a centralized server to train a unified model is usually infeasible, while the data are often heterogeneously distributed among different insolated edge devices. We propose federated learning methods to alleviate the problem with applications in molecular graphs, medical images, and time series data.
Biography
Jiebo Luo is the Albert Arendt Hopeman Professor of Engineering and professor of computer science at the University of Rochester. His research focuses on computer vision, natural language processing, machine learning, data mining, social media, computational social science, and digital health. Luo has authored nearly 600 papers and over 90 U.S. patents. He is also an active member of the research community: a fellow of NAI, Association for Computing Machinery (ACM), Association for the Advancement of Artificial Intelligence (AAAI), Institute of Electrical and Electronics Engineers (IEEE), International Association for Pattern Recognition, SPIE, editor-in-chief of the IEEE Transactions on Multimedia (2020-2022), as well as a member of the editorial boards of the IEEE Transactions on Pattern Analysis and Machine Intelligence (2006-2011), IEEE Transactions on Multimedia (2004-2009, 2013-2016), IEEE Transactions on Circuits and Systems for Video Technology (2010-2012), IEEE Transactions on Big Data (2018-present), Pattern Recognition (2002-2020), ACM Transactions on Intelligent Systems and Technology (2015-present) and so on. In addition, Luo served as an organizing or program committee member for numerous technical conferences sponsored by IEEE, ACM, AAAI, ACL, IAPR and SPIE, including, most notably, program co-chair of the 2010 ACM Multimedia Conference, 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 ACM Conference on Multimedia Retrieval and 2017 IEEE International Conference on Image Processing.
Faculty Contact: Tianbao Yang
Cyber Warfare – Lessons Learned from Ukraine
Monday, Feb. 27
Dr. Martti Lehto
Professor of Practice, University of Jyväskylä
Abstract
Cyber warfare involves non-kinetic attacks on critical infrastructure. Critical infrastructure systems comprise a heterogeneous mixture of dynamic, interactive and non-linear elements. Cyber warfare has both offensive and defensive methods covering the own system defense, cyber intelligence and offensive operations to damage, disrupt, or destroy systems and processes. This presentation discusses the principles of cyber warfare, cyber-attacks against critical infrastructure and experiences of cyber warfare in Ukraine.
Biography
Dr. Martti Lehto, (Military Sciences), Col. (GS) (ret.) works as a professor of practice (cybersecurity) in the University of Jyväskylä in the Faculty of Information Technology (IT). He has over 30 years of experience as a developer and leader of the C4ISR Systems in the Finnish Defence Forces. Lehto served for 30 years in the Finnish Air Force, where his last position was Deputy Chief of Staff for Logistics and Armaments in Air Force Command. Now he is a cybersecurity and cyber defense researcher and teacher in the IT Faculty. He is also an adjunct professor at the National Defense University in air and cyber warfare. Lehto’s research areas are cybersecurity strategy, cyber warfare, cybersecurity in aviation and maritime, and critical infrastructure protection. He has over 200 publications, research reports and articles from the above-mentioned areas. Since 2001 he has been the editor-in-chief of Military Magazine.
Faculty Contact: Paula deWitte
Wearable Acoustic and Vibration Sensing and Machine Learning for Human Health and Performance
Monday, Feb. 20
Dr. Omer Inan
Professor, School of Electrical and Computer Engineering, Georgia Tech
Abstract
Recent advances in digital health technologies are enabling biomedical researchers to reframe health optimization and disease treatment in a patient-specific, personalized manner. This talk will focus on my group’s research in three areas of relevance to digital health: (1) cardiogenic vibration sensing and analytics; (2) musculoskeletal sensing with joint acoustic emissions and bioimpedance; and (3) non-invasive neuromodulation for stress. Our group has extensively studied the timings and characteristics of cardiogenic vibration signals such as the ballistocardiogram and seismocardiogram and applied these signals for cuffless blood pressure measurement, heart failure monitoring, and human performance. We have also leveraged miniature contact microphones to measure the sounds emitted by joints, such as the knees, in the context of movement and have examined how these acoustic characteristics are altered by musculoskeletal injuries and disorders (e.g., arthritis). Finally, we have developed non-pharmacological treatment paradigms for posttraumatic stress disorder based on non-invasive vagal nerve stimulation and have performed extensively validation of this approach with collaborators in psychiatry and radiology. We envision that these technologies can all contribute to improving patient care with lower cost.
Biography
Omer Inan is a professor and Linda J. and Mark C. Smith Chair in Bioscience and Bioengineering in the School of Electrical and Computer Engineering, and adjunct associate professor in the Coulter Department of Biomedical Engineering at Georgia Tech. He received his B.S., M.S. and Ph.D. in electrical engineering from Stanford University in 2004, 2005 and 2009, respectively. From 2009 to 2013, Inan was the chief engineer at Countryman Associates, Inc., a professional audio manufacturer of miniature microphones and high-end audio products for Broadway theaters, theme parks and broadcast networks. His research focuses on non-invasive physiological sensing and modulation for human health and performance and is funded by the Defense Advanced Research Projects Agency, National Science Foundation (NSF), Office of Naval Research (ONR), National Institutes of Health, Centers for Disease Control and Prevention and industry. Inan has published more than 300 technical articles in peer-reviewed international journals and conferences and has twelve issued patents. He has received several major awards for his research including the NSF CAREER award, the ONR Young Investigator award, and the Institute of Electrical and Electronics Engineers’ Sensors Council Early Career award. Inan also received an Academy Award for Technical Achievement from The Academy of Motion Picture Arts and Sciences (The Oscars). He is an elected fellow of the American Institute for Medical and Biological Engineering. While at Stanford as an undergraduate, Inan was the school record holder and a three-time NCAA All-American in the discus throw.
Faculty Contact: Theodora Chaspari
Debugging the Fragmented DNS Infrastructure at Scale
Wednesday, Feb. 15
Dr. Zhou Li
Assistant Professor, Department of Electrical Engineering and Computer Engineering, University of California, Irvine
Abstract
Domain Name System (DNS) is a fundamental infrastructure that supports almost all sorts of internet activities. However, service failures and breach of DNS are not rare, and some even led to the shutdown of large data centers, though DNS was designed under the goals like resiliency from the very beginning. We argue that the root causes are that DNS infrastructure has become too fragmented and its protocols have become much more complex, so new research efforts are needed to harden the DNS infrastructure. In this talk, I'll describe our efforts in this direction. First, I'll talk about two new DNS attacks we identified under the settings of domain revocation and conditional resolution, and their implications. Second, I'll talk about how we measure the operational status of DNS-over-Encryption at a large scale. Finally, I'll conclude the talk with an outlook for DNS-related research.
Biography
Zhou Li is an assistant professor at the University of California, Irvine, Department of Electrical Engineering and Computer Engineering, leading the Data-driven Security and Privacy Lab. Before joining UC Irvine, he worked as a principal research scientist at RSA Labs from 2014 to 2018. His research interests include DNS, graph security analytics, privacy enhancement technologies and side-channel analysis. Li has received the National Science Foundation’s Faculty Early Career Development Award, Microsoft Security AI award and Internet Research Task Force’s Applied Networking Research Prize.
Faculty Contact: Yupeng Zhang
The Sense of Structure
Monday, Feb. 6
Dr. Ricardo Gutierrez-Osuna
Professor, Texas A&M University
Abstract
This talk will discuss how readers of English tend to make decisions concerning what a given document means. It is based on the existence of recognizable patterns in the interpretative process of most readers. Readers take the greatest percentage of their clues not from word choice but rather from the location of words within the structure of a sentence or a paragraph. The talk is based on the book “The sense of structure: writing from the reader’s perspective” by George D. Gopen. It will describe and provide examples on reader’s expectations at the sentence and paragraph levels. It will also challenge pieces of advice that writers often receive on how to improve their writing.
Biography
Ricardo Gutierrez-Osuna received a Bachelor of Science degree in electrical engineering from the Polytechnic University of Madrid (Spain) in 1992, and Master of Science and Ph.D. degrees in computer engineering from North Carolina State University in 1995 and 1998, respectively. He is currently a professor in the Department of Computer Science and Engineering at Texas A&M University. He has broad research interests in speech processing, machine learning and models of human perception.
Faculty Contact: Ricardo Gutierrez-Osuna
"Writing Science," a book by Joshua Schimel
Monday, Jan. 30
Dr. Ricardo Gutierrez-Osuna
Professor, Texas A&M University
Abstract
This talk describes the basic structure of a scientific paper. The talk is based on Professor Joshua Schimel’s book titled “Writing science, how to write papers that get cited and proposals that get funded.” We will describe four core story structures (OCAR, ABDCE, LDR, and LR) and when to use them in storytelling. We will then focus on how to map the OCAR (Opening, Challenge, Action, and Resolution) structure, with its hourglass shape, into the traditional sections of a research article: Introduction, Methods, Results and Discussion. We will also discuss the basic structure of paragraphs and sentences, and how to use them effectively to improve flow.
Biography
Ricardo Gutierrez-Osuna received a Bachelor of Science degree in electrical engineering from the Polytechnic University of Madrid (Spain) in 1992, and Master of Science and Ph.D. degrees in computer engineering from North Carolina State University in 1995 and 1998, respectively. He is currently a professor in the Department of Computer Science and Engineering at Texas A&M University. He has broad research interests in speech processing, machine learning and models of human perception.
Faculty Contact: Ricardo Gutierrez-Osuna
"Houston, We Have a Narrative," a book by Randy Olson
Monday, Jan. 23
Dr. Ricardo Gutierrez-Osuna
Professor, Texas A&M University
Abstract
This talk describes the important role that storytelling plays in science communication. The talk is based on Randy Olson’s book titled “Houston, we have a narrative: why science needs story.” The talk will describe the basic structure of research articles and how that structure relates to narrative, going back to classical Greece and mythology. The talk will also present three simple techniques, the Word, Sentence and Paragraph templates, that can be used to inject a narrative component into science writing.
Biography
Ricardo Gutierrez-Osuna received a Bachelor of Science degree in electrical engineering from the Polytechnic University of Madrid (Spain) in 1992, and Master of Science and Ph.D. degrees in computer engineering from North Carolina State University in 1995 and 1998, respectively. He is currently a professor in the Department of Computer Science and Engineering at Texas A&M University. He has broad research interests in speech processing, machine learning and models of human perception.
Faculty Contact: Ricardo Gutierrez-Osuna