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Events for October 18, 2024
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EiS Communications Hub - Tutoring for Engineering Ph.D. StudentsFri, Oct 18, 2024 @ 10:00 AM - 02:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Come to the EiS Communications Hub for one-on-one tutoring from Viterbi faculty for Ph.D. writing and speaking projects!
Location: Ronald Tutor Hall of Engineering (RTH) - 222A
Audiences: Viterbi Ph.D. Students
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Fri, Oct 18, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: David Issadore, Ph.D., Professor of UPenn
Talk Title: Diagnosing disease on a microchip: Finding nanoscale needles in messy nanoscale haystacks
Abstract: The transformative growth in microelectronics in the latter half of the 20th century was fueled fundamentally by the ability to miniaturize complex circuits onto chips. The impact of this has been profound– computing is pervasive and portable and communication is instant and global. My research aims to harness this same engineering approach to solve high impact problems in medical diagnostics. To accomplish this goal my lab develops hybrid microchips, where microfluidics are built directly on top of semiconductor chips. In this talk I will focus on recent work at Penn on 'digital asays.' Digital assays — in which ultra-sensitive molecular measurements are made by performing millions of parallel experiments in picoliter droplets — have generated enormous enthusiasm due to their single molecule resolution. These assays have incredible untapped potential for disease diagnostics but are currently confined to laboratory settings due to the instrumentation necessary to generate, control, and measure tens of millions of droplets. To overcome this challenge, we are developing a hybrid microelectronic / microfluidic chip to ‘unlock’ droplet-based assays for mobile use. Our microDroplet Megascale Detector (µMD) takes inspiration from cellular networks, in which phones are identified by their carrier frequency and not their particular location. In collaboration with physicians at The Abramson Cancer Center, we are demonstrating the power of this approach by developing a multiplexed extracellular vesicle-based diagnostic for the early detection of pancreatic cancer. I will also discuss ongoing projects on the early diagnosis of lung cancer, treatment guidance for traumatic brain injury, and the differential diagnosis of Alzheimer's versus Lewy body dementia.
Biography: The Issadore lab combines microelectronics, microfluidics, nanomaterials, and machine learning to solve big problems in healthcare. We create miniaturized platforms for the diagnosis of disease, we develop new platforms to manufacture micro and nanomaterials, and we dip our toes into an assortment of other areas where we can leverage our engineering training to improve healthcare. This work requires an interdisciplinary approach in which engineers, scientists, and physicians work together in teams. David received his PhD in applied physics from Harvard and his BS in both electrical engineering and physics from Penn State. Before coming to Penn, where he is now a Professor of Bioengineering, he was a postdoctoral fellow at MGH's Department of Systems Biology.
Host: Maral Mousavi
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Carla Stanard
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AI Seminar- Why Are Human Laws So Difficult For AI to Follow?
Fri, Oct 18, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: John Licato, University of South Florida
Talk Title: Why Are Human Laws So Difficult For AI to Follow?
Abstract: Join Zoom Meeting: https://usc.zoom.us/j/96076927864?pwd=tOuC1grLlyiRgcwicpm9e7XziHgE0R.1 Meeting ID: 960 7692 7864 Passcode: 810249 Register in advance for this webinar: https://usc.zoom.us/webinar/register/WN_ANEShGxrSfeTwa5sFZsRag Although it is now incredibly easy to create and deploy a chatbot for almost any application, powered by highly capable LLMs, even the best systems still tend to perform poorly when they need to interpret and reason about rules---specifically, rules expressed in the kind of language found in laws, contracts, regulations, and the like. Why does this problem still exist, and how can it be overcome? Dr. Licato argues that the problem is rooted in a feature (not a bug) of human languages called open-texturedness. And this open-texturedness, because it is an inevitable feature of normative rule systems, must be addressed by any agent-level AI system, especially if we want it to be able to follow our laws.
Biography: John Licato, PhD is an Associate Professor of Computer Science and Engineering at USF, Director of the USF Advancing Machine and Human Reasoning (AMHR) Lab, and founder of AI startup Actualization AI, LLC. He designed and teaches the natural language processing course (the field that created ChatGPT) at USF, and his lab's mission is to not only make AI smarter, but to use those advances to make people reason better as well. His research expertise lies in AI, NLP, human reasoning, cognitive modeling, and legal / regulatory reasoning, with over 100 peer-reviewed publications. He has been featured in outlets such as NPR's Marketplace Tech, ABC Action News, and the Tampa Bay Business Journal. If speaker approves to be recorded for this AI Seminar talk, it will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI.
Host: Abel Salinas and Pete Zamar
More Info: https://www.isi.edu/events/5149/why-are-human-laws-so-difficult-for-ai-to-follow/
Webcast: https://www.youtube.com/watch?v=CmNz7hAAtLsLocation: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://www.youtube.com/watch?v=CmNz7hAAtLs
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://www.isi.edu/events/5149/why-are-human-laws-so-difficult-for-ai-to-follow/
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PhD Thesis Proposal - Soumya Sanyal
Fri, Oct 18, 2024 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Presentation Title: Demystifying and Improving Large Language Models on Consistent Reasoning
Date and Time: 18th October, 11 AM - 12 PM
Location: OHE 114
Committee Members: Prof. Xiang Ren (Chair), Prof. Morteza Dehghani, Prof. Robin Jia, Prof. Jieyu Zhao
Presentation Abstract: Large Language Models (LLMs) have demonstrated remarkable performance on a variety of language tasks. Yet, a significant shortcoming of LLMs lies in their lack of consistency and generalization across diverse reasoning tasks. My thesis proposal aims to address this gap by systematically uncovering the limitations of LLMs in reasoning and developing methods to improve their reasoning consistency. The proposed research focuses on three core areas: (1) benchmarking the consistency of LLMs on deductive reasoning tasks, (2) accurately detecting inconsistencies in LLM reasoning across different language tasks, and (3) developing novel techniques to enhance the consistency and reliability of LLM reasoning. Through extensive research in these areas and proposed future thesis works, my proposal aims to make LLMs more consistent reasoners, ultimately minimizing the reasoning gap between humans and machines.Location: Olin Hall of Engineering (OHE) - 114
Audiences: Everyone Is Invited
Contact: Soumya Sanyal
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FBI STEM Career Paths Recruiting Session
Fri, Oct 18, 2024 @ 12:30 PM - 01:30 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
This event is for Viterbi engineering students only. Please register on Handshake.
Join us to learn about opportunities in STEM, the Honors Internship Program, navigating the FBI's hiring portal and preparing a comprehensive federal resume. Bring your questions!
Links:
Access to federal resume and core competencies:
Special Agent Documents and Downloads | FBIJOBS
FBI – Federal Bureau of Investigation - YouTube
All Engineering majors invited!
U.S. Citizens only
Individuals with disabilities who need accommodations to attend this event may contact Viterbi Career Connections at vcareers@usc.edu">vcareers@usc.edu or (213) 740-9677. It is requested that individuals requiring accommodations or auxiliary aids such as sign language interpreters and alternative format materials notify us at least 7 days prior to the event. Every reasonable effort will be made to provide reasonable accommodations in an effective and timely manner.Location: Virtual Event
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
Event Link: https://usc.joinhandshake.com/
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MHI ISSS Seminar - Dr. Ioannis Savidis, Friday, October 18th at 2pm in EEB 132
Fri, Oct 18, 2024 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ioannis Savidis, Associate Professor, Drexel University
Talk Title: AI/ML for EDA: Learning Algorithms in Analog and Digital Design
Series: Integrated Systems
Abstract: In the ever-evolving landscape of Electronic Design Automation (EDA), the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms with traditional heuristic optimization algorithms has emerged as a transformative force in automated circuit design. This presentation delves into the dynamic intersection of AI/ML and EDA, exploring state-of-the-art techniques shaping the analog and digital physical design space. Machine learning, specifically deep learning, has the potential to significantly improve the accuracy, speed, efficiency, and reliability of EDA tasks such as circuit modeling, simulation, layout design, and optimization. Delving into such cutting-edge advancements, I will describe current AI/ML research performed by the ICE Lab that promises to transcend traditional paradigms, with the goal of enabling designers to navigate complexities with unparalleled efficiency and accuracy. Specifically, a focus on state-of-the-art learning and optimization techniques for the modeling and design of mixed-signal ICs will be presented and discussed. Practical considerations, challenges, and opportunities of ML algorithms for analog and digital circuit design will be discussed, with a focus on the use of such algorithms for prediction and optimization tasks within the EDA design flow.
Biography: Dr. Ioannis Savidis (S'03-M'13-SM'18) is an Associate Professor in the Department of Electrical and Computer Engineering at Drexel University, where he directs the Integrated Circuits and Electronics (ICE) Design and Analysis Laboratory. He received his B.S.E. from Duke University in 2005, and his M.Sc. and Ph.D. from the University of Rochester in 2007 and 2013, respectively. Dr. Savidis has authored over 130 technical papers in peer-reviewed journals and conferences, including a book on Three-Dimensional Integrated Circuit Design and holds 16 issued and five pending patents. His research interests include high-performance digital and mixed-signal integrated circuits, power management for SoC and microprocessor circuits, hardware security, AI/ML algorithms for circuit optimization, and electro-thermal modeling for 2-D and 3-D circuits. Dr. Savidis is a senior member of IEEE and has received two Best Paper Awards, the 2018 NSF CAREER Award, and the 2019 DoD DURIP Award. He serves on organizing committees for several conferences including IEEE HOST, ACM GLSVLSI, and IEEE ISCAS, and on technical program committees for DAC, ICCAD, MLCAD, and others. Dr. Savidis is a member of the VLSI Systems and Applications Technical Committee of the IEEE Circuits and Systems Society and serves on the editorial boards of IEEE Transactions on VLSI Systems, Microelectronics Journal, and ACM Transactions on Design Automation of Electronic Systems.
Host: Hossein Hashemi, Mike Chen and Constantine Sideris
Webcast: https://usc.zoom.us/j/94304141343More Information: MHI_Seminar_Flyer_Savidis_Oct18_2024.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://usc.zoom.us/j/94304141343
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
