Inside Our ProgramProgram Events
Events
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Mar29
EVENT DETAILS
As AI techniques continue to advance, the efficient deployment of deep neural networks on resource-constrained devices becomes increasingly appealing yet challenging. Simultaneously, the proliferation of powerful AI technologies has raised significant concerns about sustainability and fairness, demanding increased attention from the community. This talk presents two novel software-hardware co-designs for improving the efficiency and sustainability of deep learning models. The first part introduces a hardware-efficient adaptive token pruning framework for Vision Transformers (ViTs) on embedded FPGA, HeatViT, which achieves significant speedup under similar model accuracy compared to the state-of-the-art. HeatViT is the first end-to-end accelerator for ViT on embedded FPGA and also achieve practical speedup by data-level compression for the first time. The second presents PackQViT and Agile-Quant, a paradigm of the efficient implementation for transformer-based models by sub-8-bit packed quantization and SIMD-based optimization for computing kernels. Our framework can achieve better task performance than state-of-the-art ViTs and LLMs with significant acceleration and power saving on edge processors, such as mobile CPU, Raspberry Pi and RISC-V. This work not only marks the first successful implementation of the LLM on the edge but also addresses the previous limitation where edge processors struggled to efficiently handle sub-8-bit computations. At the conclusion of the presentation, the speaker will discuss today's challenges related to AI sustainability and fairness and outline her research plans aimed at addressing these issues.
TIME Friday, March 29, 2024 at 11:00 AM - 12:00 PM
LOCATION L440, Technological Institute map it
CONTACT Catherine Healey catherine.healey@northwestern.edu EMAIL
CALENDAR Department of Electrical and Computer Engineering (ECE)
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Apr2
EVENT DETAILS
MemComputing is a new approach to computation that employs memory (time non-locality) to both process and store information on the same physical location. Memory is a remarkable feature since it can generate correlations between the machine units that do not decay exponentially, rather algebraically, both temporally and spatially. A memcomputing machine then navigates its phase space by following specific trajectories which showcase these long-range correlations, namely during dynamics the machine can change the values of just a few or as many variables in the problem specification as needed to reach the solution efficiently. Massimiliano will discuss the fundamental reasons behind this efficiency and show many applications of MemComputing to combinatorial optimization problems, machine learning, and quantum problems. He will finally discuss the path to its hardware realization. Work supported by DARPA, DOE, NSF, CMRR, and MemComputing, Inc.
TIME Tuesday, April 2, 2024 at 11:00 AM - 12:00 PM
LOCATION L440, Technological Institute map it
CONTACT Catherine Healey catherine.healey@northwestern.edu EMAIL
CALENDAR Department of Electrical and Computer Engineering (ECE)
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Apr4
EVENT DETAILS
The number of autonomous agents in everyday life is expected to scale dramatically over the next decade driven by advances in machine learning, compute power and data availability. These cyber-physical systems will need edge perception: i.e. the capability of being situationally aware in diverse environments and the ability to make real-time decisions exploiting data collected using a network of multi-modal sensors.
One omnipresent information carrier for edge perception is the thermal signal emanating from all objects at non-zero temperature. Here, we build the information theoretic foundations of thermal perception to prove that infrared radiation, even in pitch darkness, carries equivalent amounts of information per detected photon as daytime visible radiation. This opens a new frontier of heat-assisted detection and ranging (HADAR [1]) which can have societal impact similar to existing techniques of Radar, Sonar and Lidar. I will discuss the design and deployment of the thermal voyager : an autonomous navigation agent which exploits passive infrared thermal imaging for semantic segmentation, 3D depth perception and path planning.
Finally, I will explain how this research presents disruptive opportunities for collaborations cross-cutting all the core areas within ECE: computer engineering, signals and systems as well as photonics/solid-state devices.
TIME Thursday, April 4, 2024 at 11:00 AM - 12:00 PM
LOCATION L440, Technological Institute map it
CONTACT Catherine Healey catherine.healey@northwestern.edu EMAIL
CALENDAR Department of Electrical and Computer Engineering (ECE)
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Apr9
EVENT DETAILS
April 9th and 10th
Description:Real-world networks often exhibit a hidden structure, which we wish to infer. For example, many networks exhibit community structure. Inferring communities is a valuable tool in network analysis; community detection has been used in a wide array of applications including recommender systems (e.g. Netflix), webpage sorting, fraud detection, and neurobiology. Inspired by these real-world networks, researchers in probability, statistics, information theory, and machine learning have studied structure recovery problems in random graph models. In addition to community detection, problems of this type include graph matching, recovery of planted subgraphs, and inference of graph properties. This workshop will bring together leading experts in the field, and both local and external participants, with the goal of sharing the latest advances and launching new collaborations.
Form to register:
https://docs.google.com/forms/d/e/1FAIpQLSfZoMhFJMR00yQDSmYQYg33qQ-lpKtnGBm3jyV3XMXzq7Sgrg/viewform
Speakers:
Elchanan Mossel (MIT)
Tselil Schramm (Stanford University)
Alex Wein (UC Davis)
Jiaming Xu (Duke University)
Logistics:
Date: April 9th -10th
In-person Location: Northwestern University: Mudd Library 3rd floor, 2233 Tech Drive, EvanstonTIME Tuesday, April 9, 2024
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr10
EVENT DETAILS
April 9th and 10th
Description:Real-world networks often exhibit a hidden structure, which we wish to infer. For example, many networks exhibit community structure. Inferring communities is a valuable tool in network analysis; community detection has been used in a wide array of applications including recommender systems (e.g. Netflix), webpage sorting, fraud detection, and neurobiology. Inspired by these real-world networks, researchers in probability, statistics, information theory, and machine learning have studied structure recovery problems in random graph models. In addition to community detection, problems of this type include graph matching, recovery of planted subgraphs, and inference of graph properties. This workshop will bring together leading experts in the field, and both local and external participants, with the goal of sharing the latest advances and launching new collaborations.
Form to register:
https://docs.google.com/forms/d/e/1FAIpQLSfZoMhFJMR00yQDSmYQYg33qQ-lpKtnGBm3jyV3XMXzq7Sgrg/viewform
Speakers:
Elchanan Mossel (MIT)
Tselil Schramm (Stanford University)
Alex Wein (UC Davis)
Jiaming Xu (Duke University)
Logistics:
Date: April 9th -10th
In-person Location: Northwestern University: Mudd Library 3rd floor, 2233 Tech Drive, EvanstonTIME Wednesday, April 10, 2024
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr11
EVENT DETAILSmore info
Want to join the decision-makers? Register for Northwestern University's Master of Science in Information Technology (MSIT) Program Information Session and learn how you can enhance your knowledge of IT and gain the business management skills you need to direct effective IT strategy.
TIME Thursday, April 11, 2024 at 12:00 PM - 1:00 PM
CONTACT Svetlana Korzeniowski msit@northwestern.edu EMAIL
CALENDAR MS in Information Technology (MSIT) Program
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Apr13
EVENT DETAILSmore info
Want to join the decision-makers? Register for Northwestern University's Master of Science in Information Technology (MSIT) Program Information Session and learn how you can enhance your knowledge of IT and gain the business management skills you need to direct effective IT strategy.
TIME Saturday, April 13, 2024 at 1:00 PM - 3:00 PM
LOCATION McCormick Education Center STE 1400
CONTACT Svetlana Korzeniowski msit@northwestern.edu EMAIL
CALENDAR MS in Information Technology (MSIT) Program
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Apr15
EVENT DETAILS
In collaboration with the Kellogg Operations Department
Monday / CS Seminar
April 15th / 12:15 PM
In Person / Kellogg Global Hub 1120Speaker
Cynthia Rudin, Duke UniversityTalk Title
Simpler Machine Learning Models for a Complicated WorldAbstract
While the trend in machine learning has tended towards building more complicated (black box) models, such models have not shown any performance advantages for many real-world datasets, and they are more difficult to troubleshoot and use. For these datasets, simpler models (sometimes small enough to fit on an index card) can be just as accurate. However, the design of interpretable models is quite challenging due to the "interaction bottleneck" where domain experts must interact with machine learning algorithms.I will present a new paradigm for interpretable machine learning that solves the interaction bottleneck. In this paradigm, machine learning algorithms are not focused on finding a single optimal model, but instead capture the full collection of good (i.e., low-loss) models, which we call "the Rashomon set." Finding Rashomon sets is extremely computationally difficult, but the benefits are massive. I will present the first algorithm for finding Rashomon sets for a nontrivial function class (sparse decision trees) called TreeFARMS. TreeFARMS, along with its user interface TimberTrek, mitigate the interaction bottleneck for users. TreeFARMS also allows users to incorporate constraints (such as fairness constraints) easily.
I will also present a "path," that is, a mathematical explanation, for the existence of simpler-yet-accurate models and the circumstances under which they arise. In particular, problems where the outcome is uncertain tend to admit large Rashomon sets and simpler models. Hence, the Rashomon set can shed light on the existence of simpler models for many real-world high-stakes decisions. This conclusion has significant policy implications, as it undermines the main reason for using black box models for decisions that deeply affect people's lives.
This is joint work with my colleagues Margo Seltzer and Ron Parr, as well as our exceptional students Chudi Zhong, Lesia Semenova, Jiachang Liu, Rui Xin, Zhi Chen, and Harry Chen. It builds upon the work of many past students and collaborators over the last decade.
Here are papers I will discuss in the talk:
Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo Seltzer, Cynthia Rudin
Exploring the Whole Rashomon Set of Sparse Decision Trees, NeurIPS (oral), 2022.
https://arxiv.org/abs/2209.08040Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo Seltzer
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization, IEEE VIS, 2022.
https://poloclub.github.io/timbertrek/Lesia Semenova, Cynthia Rudin, and Ron Parr
On the Existence of Simpler Machine Learning Models. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2022.
https://arxiv.org/abs/1908.01755Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin
A Path to Simpler Models Starts With Noise, NeurIPS, 2023.
https://arxiv.org/abs/2310.19726Biography
TBATIME Monday, April 15, 2024 at 12:15 PM - 1:15 PM
LOCATION KGH 1120, Kellogg Global Hub map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr25
EVENT DETAILS
TBA
TIME Thursday, April 25, 2024 at 9:00 AM - 11:00 AM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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Apr29
EVENT DETAILS
TBA
TIME Monday, April 29, 2024 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)
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May1
EVENT DETAILS
TBA
TIME Wednesday, May 1, 2024 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Wynante R Charles wynante.charles@northwestern.edu EMAIL
CALENDAR Department of Computer Science (CS)