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Events for March 18, 2024
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Human Factors in Aviation Safety HFH 24-3Mon, Mar 18, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
Humans design, build, operate, and maintain the aviation system. It is no wonder that the majority of aviation accidents and incidents have roots in human factors. With this realization comes the conclusion that quality human factors training is effective in improving safety. This course presents information on human factors in a manner that can be readily understood and applied by aviation practitioners. Emphasis is placed on identifying the causes of human error, predicting how human error can affect performance, and applying countermeasures to reduce or eliminate its effects. The course content follows the subjects recommended in FAA Advisory Circular 120-51E. The course also addresses topics recommended in the International Civil Aviation Organization’s Human Factors Digest No. 3: Training Operational Personnel in Human Factors. The emphasis is from the pilot’s perspective but applies to all phases of aviation operations. The course relies heavily on participation, case studies, demonstrations, self-assessment, and practical exercises.
Location: Century Boulevard Building (CBB) - 960
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AHFH3
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Advanced System Safety Analysis ADVSS 24-1
Mon, Mar 18, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
This course is a continuation of the <a href="https://aviationsafety.usc.edu/courses/system-safety/">System Safety</a> course focused on engineering aspects of the course. The objective is to address advanced issues in system safety analysis and broaden the trainees’ perspective on system safety issues. Engineering methods addressed in the System Safety course are reviewed, and special advanced topics are addressed. Additional methods for system safety analysis are addressed, focusing on the application of these methods.
Location: Century Boulevard Building (CBB) - 960
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AADVSS1
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EiS Communications Hub Drop-In Hours
Mon, Mar 18, 2024 @ 10:00 AM - 01:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi Ph.D. students are invited to stop by the EiS Communications Hub for one-on-one instruction for their academic and professional communications tasks. All instruction is provided by Viterbi faculty at the Engineering in Society Program.
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?authuser=0
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EiS Communications Hub Drop-In Hours
Mon, Mar 18, 2024 @ 10:00 AM - 01:00 PM
Engineering in Society Program
Student Activity
Drop-in hours for writing and speaking support for Viterbi Ph.D. students
Location: Ronald Tutor Hall of Engineering (RTH) - 222
Audiences: Everyone Is Invited
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
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CS Colloquium: TBA
Mon, Mar 18, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: TBA, TBA
Talk Title: TBA
Series: Computer Science Colloquium
Host: Heather Culbertson
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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ECE Seminar: Marcelo Orenes-Vera, "Navigating Heterogeneity and Scalability in Modern Chip Design"
Mon, Mar 18, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Marcelo Orenes-Vera, PhD Candidate, Dept of CS, Princeton University
Talk Title: Navigating Heterogeneity and Scalability in Modern Chip Design
Abstract: Abstract: The pursuit of continued improvements in performance and energy efficiency, following the end of Moore's Law and Dennard scaling, marks a pivotal moment in system architecture. As modern systems leverage parallelism and hardware specialization to achieve these goals, new challenges arise:
(1) The complexity of the system grows with the number of distinct hardware components, making it difficult to verify that it will behave correctly and securely;
(2) Parallelizing applications across more processing elements increases the pressure on the memory hierarchy and the network to supply data, which results in severe bottlenecks for data-and communication-intensive applications such as graph analytics and sparse linear algebra.
These challenges call for re-thinking our software abstractions and hardware designs to achieve scalable and efficient systems, as well as introducing robust methodologies to ensure their correctness and security. This talk presents my work on scalable data-centric architectures that co-design the hardware with a migrate-compute-to-the-data programming model to outperform the best results from the Graph500 list. Moreover, this architecture offers a chiplet-based design that enables post-silicon re-configuration of critical resources like the memory hierarchy or network-on-chip for a cost-efficient integration based on different deployment targets. In addition, this talk also introduces two formal-verification-based tools that assist the design of verifiably correct and secure hardware RTL by leveraging high-level abstraction primitives. In addition to facilitating the design process, my verification work also identified and fixed security vulnerabilities and correctness bugs in widely used open-source hardware projects.
Biography: Marcelo is a PhD candidate at Princeton University advised by Margaret Martonosi and David Wentzlaff. His research focuses on Computer Architecture, from hardware RTL design and verification to software programming models of novel architectures. He has previously worked in the hardware industry at Arm, contributing to the design and verification of three GPU projects; at Cerebras Systems, creating high-performance kernels for the Wafer-Scale Engine; and at AMD Research, contributing to design next-generation data centers optimized for large graph structure traversal. At Princeton, he has contributed in two chip tapeouts that aims to improve the performance, power and programmability of ML and Graph workloads. His contributions to scalable data-centric architectures were recognized with the gold medal at the ACM/SIGMICRO 2022 SRC and with an honorable mention at the IEEE Top Picks of 2023.
Host: Dr. Massoud Pedram, pedram@usc.edu
Webcast: https://usc.zoom.us/j/98003769115?pwd=Sm5JU2RUN1N4Qnd6UkZSOTFEdFpzZz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/98003769115?pwd=Sm5JU2RUN1N4Qnd6UkZSOTFEdFpzZz09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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NL Seminar-Do Androids Know They're Only Dreaming of Electric Sheep?
Mon, Mar 18, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Sky Wang, Columbia University
Talk Title: Do Androids Know They're Only Dreaming of Electric Sheep?
Series: NL Seminar
Abstract: REMINDER: This talk will be a live presentation only, it will not be recorded. Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you’re an outside visitor, please provide your: Full Name, Title and Name of Workplace to (nlg-seminar-host(at)isi.edu) beforehand so we’ll be aware of your attendance. Also, let us know if you plan to attend in-person or virtually. More Info for NL Seminars can be found at: https://nlg.isi.edu/nl-seminar/ We design probes trained on the internal representations of a transformer language model that are predictive of its hallucinatory behavior on in-context generation tasks. To facilitate this detection, we create a span-annotated dataset of organic and synthetic hallucinations over several tasks. We find that probes trained on the force-decoded states of synthetic hallucinations are generally ecologically invalid in organic hallucination detection. Furthermore, hidden state information about hallucination appears to be task and distribution-dependent. Intrinsic and extrinsic hallucination saliency varies across layers, hidden state types, and tasks; notably, extrinsic hallucinations tend to be more salient in a transformer's internal representations. Outperforming multiple contemporary baselines, we show that probing is a feasible and efficient alternative to language model hallucination evaluation when model states are available.
Biography: If speaker approves to be recorded for this NL Seminar talk, it will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI. Subscribe here to learn more about upcoming seminars: https://www.isi.edu/events/ Sky is a Ph.D. candidate in Computer Science at Columbia University advised by Zhou Yu and Smaranda Muresan. His research primarily revolves around Natural Language Processing (NLP), with broad interests in the area where NLP meets Computational Social Science (CSS). Here, his research primarily revolves around three major areas: (1) revealing and designing for social difference and inequality, (2) cross-cultural NLP, and (3) mechanistic interpretability. His research is supported by a NSF Graduate Research Fellowship and has received two outstanding paper awards at EMNLP. He has previously been an intern at Microsoft Semantic Machines, Google Research, and Amazon AWS AI.
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://www.youtube.com/watch?v=Pm0ljFMg0cwLocation: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=Pm0ljFMg0cw
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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Machine Learning Center Seminar: Lily Weng (UC San Diego) - Towards Interpretable Deep Learning
Mon, Mar 18, 2024 @ 12:00 PM - 01:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Lily Weng, UC San Diego
Talk Title: Towards Interpretable Deep Learning
Series: Machine Learning Center Seminar Series
Abstract: Deep neural networks (DNNs) have achieved unprecedented success across many scientific and engineering fields in the last decades. Despite its empirical success, however, they are notoriously black-box models that are difficult to understand their decision process. Lacking interpretability is one critical issue that may seriously hinder the deployment of DNNs in high-stake applications, which need interpretability to trust the prediction, to understand potential failures, and to be able to mitigate harms and eliminate biases in the model.
In this talk, I'll share some exciting results in my lab on advancing explainable AI and interpretable machine learning. Specifically, I will show how we could bring interpretability into deep learning by leveraging recent advances in multi-modal models. I'll present two recent works [1,2] in our group on demystifying neural networks and interpretability-guided neural network design, which are the important first steps to enable Trustworthy AI and Trustworthy Machine Learning. I will also briefly overview our other recent efforts on Trustworthy Machine Learning and automated explanations for LLMs [3].
[1] Oikarinen and Weng, CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks, ICLR 23 (spotlight)
[2] Oikarinen, Das, Nguyen and Weng, Label-Free Concept Bottleneck Models, ICLR 23
[3] Lee, Oikarinen etal, The Importance of Prompt Tuning for Automated Neuron Explanations, NeurIPS 23 ATTRIB workshop
Biography: Lily Weng is an Assistant Professor in the Halicioglu Data Science Institute at UC San Diego. She received her PhD in Electrical Engineering and Computer Sciences (EECS) from MIT in August 2020, and her Bachelor and Master degree both in Electrical Engineering at National Taiwan University. Prior to UCSD, she spent 1 year in MIT-IBM Watson AI Lab and several research internships in Google DeepMind, IBM Research and Mitsubishi Electric Research Lab. Her research interest is in machine learning and deep learning, with primary focus on trustworthy AI. Her vision is to make the next generation AI systems and deep learning algorithms more robust, reliable, explainable, trustworthy and safer. For more details, please see https://lilywenglab.github.io/.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: CS Events
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AME Seminar
Mon, Mar 18, 2024 @ 01:30 PM - 02:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: Rachel Holladay, MIT
Talk Title: Dexterous Decision-Making for Real-World Robotic Manipulation
Abstract: For a robot to prepare a meal or clean a room, it must make a large array of decisions, such as what objects to clean first, where to grasp each ingredient and tool, how to open a heavy, overstuffed cabinet, and so on. To enable robots to tackle these tasks, I decompose the problem into two interdependent layers: generating a series of subgoals (i.e., a strategy) and solving for the robot behavior that achieves each of these subgoals. Critically, to accomplish a rich set of manipulation tasks, these subgoal solvers must account for force, motion, deformation, contact, uncertainty and partial observability.My research contributes models and algorithms that enable robots to reason over both the geometry and physics of the world in order to solve long-horizon manipulation tasks. In this talk, I will first discuss how this approach has enabled robots to perform tasks that require reasoning over and exerting force, like opening a childproof medicine bottle with a single arm. Next, I will present an abstraction for the complex physics of frictional pushing and demonstrate its application within the context of in-hand manipulation. Finally, I will illustrate how robots can make robust choices in the face of uncertainty. For example, this empowers robots to reliably chop up fruit of unknown ripeness!
Biography: Rachel Holladay is a Ph.D. student in the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology. Her research focuses on developing algorithms and models that enable robots to robustly perform long-horizon, contact-rich manipulation tasks in everyday environments. She received her B.S. in Computer Science and Robotics from Carnegie Mellon University.
Host: AME Department
More Info: https://ame.usc.edu/seminars/
Webcast: https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09Location: Olin Hall of Engineering (OHE) - 406
WebCast Link: https://usc.zoom.us/j/95892885119?pwd=QXZOZUhrcTJRYk5qZzZwVThrTytVZz09
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://ame.usc.edu/seminars/
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CSC/CommNetS-MHI Seminar: Nickolay Atanasov
Mon, Mar 18, 2024 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Nickolay Atanasov, Assistant Professor of Electrical and Computer Engineering | University of California, San Diego
Talk Title: Elements of generalizable mobile robot autonomy
Abstract: This seminar will discuss mobile robot autonomy in novel, unstructured, changing environments. It will argue that successful generalization requires motion, environment, and task models that can be constructed and adapted from streaming sensor observations and interaction among multiple robots. Four elements of generalizable mobile robot autonomy will be presented: 1) physics-informed motion-model learning using neural ordinary differential equations, 2) online mapping using object and semantic information, 3) multi-robot coordination using distributed optimization, and 4) task modeling and planning using automata labeled with object semantics.
Biography: Nikolay Atanasov is an Assistant Professor of Electrical and Computer Engineering at the University of California San Diego, La Jolla, CA, USA. He obtained a B.S. degree in Electrical Engineering from Trinity College, Hartford, CT, USA in 2008, and M.S. and Ph.D. degrees in Electrical and Systems Engineering from University of Pennsylvania, Philadelphia, PA, USA in 2012 and 2015, respectively. Dr. Atanasov's research focuses on robotics, control theory, and machine learning with emphasis on active perception problems for autonomous mobile robots. He works on probabilistic models and inference techniques for simultaneous localization and mapping (SLAM) and on optimal control and reinforcement learning techniques for autonomous navigation and uncertainty minimization. Dr. Atanasov's work has been recognized by the Joseph and Rosaline Wolf award for the best Ph.D. dissertation in Electrical and Systems Engineering at the University of Pennsylvania in 2015, the Best Conference Paper Award at the IEEE International Conference on Robotics and Automation (ICRA) in 2017, the NSF CAREER Award in 2021, and the IEEE RAS Early Academic Career Award in Robotics and Automation in 2023.
Host: Dr. Lars Lindemann, llindema@usc.edu
More Info: https://csc.usc.edu/seminars/2024Spring/atanasov.html
More Information: 2024.03.18 CSC Seminar - Nikolay Atanasov.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 132
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://csc.usc.edu/seminars/2024Spring/atanasov.html
