Gordon Institute Virtual Information Session
2024-08-14 17:30 (America/New_York) | MA
Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty-three Master of Science degrees offered through Northeastern’s College of Engineering, College of Science, or Khoury College of Computer Sciences.
The National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for its innovative curriculum that combines technical education, leadership capabilities, and the “Challenge Project”: an opportunity for students to receive master’s level credit while working in industry.
By aligning technical proficiency with leadership capabilities, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completing the program, more than 88% of the 2022 class reported increased leadership responsibility, while more than 50% of the 2022 class reported being promoted within one year of graduation.
Our Director of Admissions will answer your application questions for Fall 2024.
You will have the opportunity to hear from Alumni on how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions.

Mock Interview: CommLab Drop-In Workshops
2024-08-15 16:00 - 17:00, 2024-08-22 16:00 - 17:00, 2024-08-29 16:00 - 17:00 | MA
Join the CommLab any Thursday from 4-5pm ET, we’ll delve into the intricacies of interviews, unveiling effective preparation strategies for any interview scenario. Engage in an interactive setting as we dissect the overall interview experience, discuss common interview scenarios, and share insights on what to do during critical moments. Join this hybrid workshop series through Zoom.

Poster Design and Presentation: CommLab Drop-In Workshops
2024-08-15 17:00 - 18:00, 2024-08-22 17:00 - 18:00, 2024-08-29 17:00 - 18:00 | MA
The CommLab will host drop-in workshops for poster design and presentation to focus on crafting the best visual communication of your research and telling your research story! We will discuss techniques and implement communication strategies to successfully showcase your work. No matter where you are in the process, whether it is just in the idea phase or you are trying to polish your final poster, we are happy to help you. Join us any Thursday from 5-6pm, on Zoom.

Faruk Volkan Mutlu PhD Dissertation defense
2024-08-16 10:30 - 11:30 | MA
Name:
Faruk Volkan Mutlu
Title:
Cost-aware Joint Caching and Forwarding in Networks with Diverse Cache Resources
Date:
8/16/2024
Time:
10:30:00 AM
Committee Members:
Prof. Edmund Yeh (Advisor)
Prof. Stratis Ioannidis
Prof. Elif Uysal
Abstract:
The rapid growth of data-intensive applications is testing the limitations of today’s data distribution networks. Caching is a crucial tool for high performance in such networks, and a core principle in emerging paradigms like information-centric networking (ICN). In this dissertation, motivated by the needs of a landmark initiative addressing the networking challenges faced by large-scale scientific research programs, we focus on the key issue of expanding cache capacities in a cost-effective manner. While DRAM is still the standard cache device today due to its high transfer rates, its capacity is very limited and subject to contention by other networking functions. Large DRAM modules are also expensive, making wide area networks with cache-enabled routers costly to deploy. On the other hand, technologies like flash storage offer larger capacities at lower costs; observing recent advancements in this domain, we expect devices like NVMe SSDs to feature as additional cache tiers in networks supporting data-intensive applications. However, the slower transfer rates of such devices and the added operational costs they introduce pose some challenges.
This dissertation primarily focuses on the open problem of developing cost-aware caching policies that can effectively manage multiple types of cache available to routers. We begin by introducing an object-level multi-tiered caching model that incorporates the diverse characteristics of cache devices, namely their transfer rates and utilization costs. We then integrate this model with an established optimization framework to develop a joint caching and forwarding policy that uses caching resources available in the network intelligently to improve performance, minimize costs of cache utilization and avoid congestion. To highlight the advantages of our approach against adapted baselines, we conduct an exhaustive experimental evaluation of this policy under a large variety of simulation settings; we also provide a discussion of the event-driven object-level ICN simulator we built to facilitate this evaluation and support future research. Lastly, we also present a variation on our strategy that attempts to improve its efficiency under certain conditions by introducing new control variables into the aforementioned optimization framework.
This dissertation also discusses our work on the effective use of transmission and cache resources in wireless networks. While this work investigates a different context than that outlined above, it serves to complement our primary scope with a perspective on how caching can be leveraged in settings where resource constraints have a different type of interplay with network performance. Specifically, in the context of multi-hop wireless networks with arbitrary topologies and interfering transmissions, we study the open problem of delay minimization via caching subject to transmission power limitations. We cast this scenario as an optimization problem and highlight its analytical properties. We identify the challenges in finding a global optimum to the problem under general conditions, and propose an algorithm that converges to a local optimum. We conclude this discussion with numerical results that demonstrate the effectiveness of our approach.

CommLab Drop-In Writing Hours
2024-08-16 11:00 - 12:30, 2024-08-23 11:00 - 12:30, 2024-08-30 11:00 - 12:30 | 13 International Village, 360 Huntington Ave, 13 INV, Boston, MA, 02115, United States
Graduate students, are you looking for a place for focused research writing time? Join the CommLab drop-in writing hours any Friday from 11 am-12:30 pm ET. Drop in any Friday and stay for a short time or the whole hour and a half. CommLab Fellows will be available to provide feedback on your writing. We will be meeting in 13 International Village.

Yanyu Li PhD Dissertation Defense
2024-08-16 11:00 - 13:00 | MA
Name:
Yanyu Li
Title:
Accelerating Large Scale Generative AI: a Comprehensive Study
Date:
8/16/2024
Time:
11:00:00 AM
Committee Members:
Prof. Yanzhi Wang (Advisor)
Prof. David Kaeli
Prof. Kaushik Chowdhury
Abstract:
We have witnessed the great success of deep learning in various domains, such as the emerging large language models (LLMs) and Artificial General Intelligence (AGI), diffusion models for image and video generation, and classic vision tasks including classification, segmentation, detection, etc. Built with linear, convolution, and attention blocks, Deep Neural Networks (DNNs) play a vital role in the performance revolution. However, powerful DNNs often call for tremendous computation and storage size, which hinders their wide adoption. For instance, LLMs and diffusion models generally have billions of parameters and hundreds of GMACs, which is prohibitive for edge deployment. As a result, Efficient AI has become a hot research area. In this work, with algorithm optimizations and co-designs with hardware platform, we pursue the appealing features of edge or user-end AI, where we cut down energy consumption, shorten response latency, shrink model storage size, eliminate the need for cloud server access and protect user privacy. Firstly, we systematically investigate quantization, pruning, and architecture search techniques for efficient vision backbones. We do a comprehensive study on quantization number system and precision, and propose a novel mix-scheme mix-precision quantization technique to maximize hardware utilization and minimize performance loss. Regarding network pruning, we propose a novel indicator-based approach, named Pruning-as-Search, that is fully differentiable and automatically decides pruning policies, outperforming human tuning methods in terms of performance and efficiency. Further, we address the long-existing issue of rigid network width design, proposing a family of flexible-width pruned networks with minimal per-layer redundancy. As for architecture search, we formulate a joint optimization objective of both size and latency, releasing a series of efficient Vision Transformers, named EfficientFormer (V1 and V2), to serve as strong vision backbones with MobileNet-level size and millisecond-level latency on mobile phones.
Secondly, we make dedicated optimizations for large-scale generative tasks, i.e., Stable Diffusion (SD) for text-to-image generation, which serves as pioneer work to enable their mobile deployment. With the proposed efficient architecture design and novel step distillation, we shrink the generation latency of SD by a magnitude, from more than 1 minute to generate a 512$times$512 image to 1~2 seconds, while preserving the stunning generative quality. We extend our work to the even more challenging video generation task, enabling 2-bit inference and single step adversarial distillation to speedup video diffusion models by a magnitude.

Shuo Jiang PhD Dissertation Defense
2024-08-16 14:00 - 16:00 | MA
Name:
Shuo Jiang
Title:
Tactile Intelligence in Robotics
Date:
8/16/2024
Time:
2:00:00 PM
Location:
EXP-701A
Committee Members:
Prof. Lawson Wong (Advisor)
Prof. Robert Platt
Prof. Alireza Ramezani
Prof. Taskin Padir
Abstract:
In recent years, the evolution of robot electronic skin technology has introduced a novel avenue for robots to perceive their external environment and internal state. In contrast to conventional visual perception methods, tactile perception enables the discernment of additional physical properties of objects, such as friction and mass distribution, or even observes contact with higher resolution. Importantly, tactile perception is resilient to challenges posed by inadequate illumination or environmental occlusion. However, it presents inherent challenges, including a limited sensing range, compulsory physical interaction with the environment, and intricate coupling with robot control, rendering data collection and utilization challenging. Addressing these challenges and devising effective, efficient, and interpretable methods for processing tactile signals have emerged as pivotal issues in robot tactile perception.
With the development of artificial intelligence technology, we are now able to interpret tactile information from a new perspective beyond traditional sensor technology and signal processing methods, thereby expanding a wider range of robotic applications. With our continuous efforts over the past few years, we have comprehensively addressed the following challenges in enhancing robot tactile perception through the application of advanced artificial intelligence and control methods: enabling robots to explore object shapes through tactile feedback; developing tactile-based safety mechanisms for human-robot collaboration; enhancing the locomotion adaptability of snake robots on irregular terrains through tactile perception; utilizing whole-body exteroceptors and proprioceptors for accurate body schema estimation; and implementing tactile gesture recognition in human-robot interactions. At the same time, we developed a modular full-body electronic skin system for robots and its accompanying software, which can accurately detect forces applied to the robot’s entire body and perform high-speed tracking of the real-time kinematics of the robot’s sensor array.
In conclusion, this dissertation explores how robot tactile perception can accomplish complex tasks in various scenarios or achieve performance improvements in traditional tasks through the integration of sensor technology, machine learning, control theory, and robotics. Through extensive theoretical and experimental analysis, we have demonstrated the critical role of tactile perception in embodied intelligence for robots and established a fundamental knowledge framework for future academic research in this field.

Graduate School of Engineering Campus Tour - In Person
2024-08-17 13:00 - 14:00 | MA
Want to learn more about Northeastern’s Boston campus? Then we welcome you to sign up for a Graduate School of Engineering campus tour! Led by one of our expert Graduate Student Ambassadors, we’ll show you key locations on campus, in addition to resources specific to Engineering, and answer your questions about Boston. Please complete the registration form linked below to select the date and time that works best for you. Tours are open to all students interested in learning more about the Graduate School of Engineering. We can’t wait to meet you!

Yuexi Zhang PhD Dissertation Defense
2024-08-19 10:00 - 11:00 | MA
Name:
Yuexi Zhang
Title:
Human Action and Event Detection by Leveraging Multi-modality Techniques
Date:
8/19/2024
Time:
10:00:00 AM
Committee Members:
Prof. Octavia Camps (Advisor)
Prof. Mario Sznaier
Prof. Sarah Ostadabbas
Abstract:
Human Action and Event Analysis with multi-modalities has emerged as a critical area of research in computer vision and machine learning, driven by the need to understand complex human behaviors in diverse environments.
A significant advantage of multi-modal analysis is its application in cross-view action recognition, where activities are observed from different viewpoints. To tackle such a problem, we propose a flexible frame which is able to integrate diverse modalities(RGB pixels, 2D/3D key points, etc.) to overcome the limitations of single-modal approaches. It consists of two branches where a Dynamic Invariant Representation branch (DIR) concentrates on identifying view-invariant properties through key points trajectories while Context Invariant Representation branch(CIR) is to capture the pixel-level view-invariant features. In the meantime, our approach leverages contrastive learning techniques to enhance the effectiveness of recognition accuracy, where it enables the model to learn more discriminative and view-invariant features by contrastive positive pairs against negative pairs. The fusion of multi-modal data, coupled with contrastive learning, leads to improved accuracy in recognizing actions across various views and environments. Extensive experiments demonstrate the effectiveness of our approach on diverse modalities. Furthermore, another promising application with multi-modal techniques is zero-shot action detection, which aims to recognize actions that the model has not been explicitly trained on. Recently, with language models are quickly developed, leveraging LLMs in this context has shown significant potentials, as these models can bridge the gap between seen and unseen actions by understanding and generalizing from textual descriptions. To further explore the problem, we propose a transformer encoder-decoder architecture with global and local text prompt, which allowing the model to infer the characteristics of unseen actions based on different textual attributes. We evaluate our approach on different benchmarks to demonstrate advantages.

Roux Institute Campus Spotlight
2024-08-20 09:30 - 10:30 | The Roux Institute, 100 Fore Street, Portland, ME, 04101, United States
Northeastern University’s Portland, Maine campus is a vibrant community of graduate learners from around the world, along with tech startup founders, researchers, and business partners.
Join us on Tuesday, August 20, 2024, at 9:30 a.m. ET, as the College of Engineering hosts a virtual campus spotlight showcasing the engineering programs at the Roux Institute in beautiful Portland, Maine.
During this webinar you will:

Learn about our programs and co-op opportunities from a College of Engineering Recruiting Specialist
Explore the campus and its impressive resources with insights from the Associate Director of Recruitment and Employer Relations.
Gain insight into generous scholarship opportunities and the application process

Galante Program Virtual Info Sessions
2024-08-20 12:00 - 12:30, 2024-08-22 09:00 - 09:30 | MA
Learn how the Galante Engineering Business Program and Engineering Business Certificate can complement your technical engineering education with essential business skills.
Join us for an informational session to learn more about the Galante Engineering Business Program on one of the following dates:

Thursday, August 8 at 4:30 p.m. EDT – Virtual
Monday, August 12 at 8:30 a.m. EDT – Virtual
Tuesday, August 20 at 12:00 p.m. EDT – Virtual
Thursday, August 22 at 9:00 a.m. EDT – Virtual

RSVP Here
During the session, we will cover the details of the Galante Program and Engineering Business Certificate, including the application process and eligibility requirements.

LeetCode Mock Interviews – CommLab Drop-In Workshops
2024-08-20 16:00 - 17:00, 2024-08-27 16:00 - 17:00 | MA
Join the CommLab any Tuesday from 4-5 PM for our weekly LeetCode Mock Interview Workshop via Zoom. This workshop is tailored towards programming jobs and prior coding knowledge is expected. Boost your LeetCode problem-solving confidence for interviews by building your speaking skills while solving programming problems.

Graduate Student Fellowship Writing Club-Hosted by NU CommLab and NetSI
2024-08-21 15:00 - 16:00 | MA
Join the NU CommLab and NetSI sponsored weekly graduate student fellowship writing club for support in writing your fellowship application! The fellowship writing club meets virtually on Wednesdays from 3-4pm from July 10- August 21. We will offer you an opportunity to ask questions to faculty, staff and students who have reviewed, mentored or applied and received fellowships. We will provide fellowship writing tips and guidance as well as offer writing and draft review sessions. Register to join our Zoom Sessions.

Zohreh Azizi PhD Proposal Review
2024-08-22 13:00 - 14:00 | MA
Name:
Zohreh Azizi
Title:
Exploring SIM for monomer orientation
Date:
8/22/2024
Time:
1:00:00 PM
Location: https://northeastern.zoom.us/j/7318775019
Meeting ID: 731 877 5019
Committee Members:
1. Prof. Charles DiMarzio (Advisor)
2. Prof. Carey Rappaport
3. Dr. Sangyeon (Fred) Cho
Abstract:
Collagen fibrils, the most abundant protein polymers in animals, protect cells from mechanical forces such as stress, tension, compression, and shear. Each collagen molecule, approximately 300 nm long and 1.5 nm in diameter, consists of three polypeptide chains forming a supercoiled triple helix. These fibrils self-assemble, with diameters ranging from 20 nm to several hundred nanometers. Large collagen fibrils are visible with scanning electron microscopy (SEM) and optical microscopy. Electron microscopy damages samples during preparation, limiting observations to static, non-living conditions. The natural self-assembly behavior, spatial arrangement, and dimensions of collagen fibrils play a vital role in shaping the structure, strength, and function of tissues. These factors are essential in determining how tissues are organized, how they withstand physical forces, and how effectively they perform their biological functions.
Detecting the orientation and location of collagen monomers is essential for understanding their role in collagen spontaneous formation and their interactions with fibril surfaces. To study collagen monomers in a dynamic, living state, high-resolution optical microscopy is preferred, as it allows for detailed imaging beyond the diffraction limit of light.We introduce a new technique using structured illumination to determine the spatial separation of punctuate objects to super–resolution limits that is amenable to both scattering and fluorescent objects. We call the technique Structured–Illumination Point–Separation (SIPS) Microscopy. We apply it to determine the orientation of a collagen monomer by imaging two fluorescent tags at different locations on the monomer. Experimentally, we show that our approach effectively resolves the orientation of collagen monomers with a resolution surpassing the diffraction limit.
In this illumination technique we are employing time-multiplexed binary patterns with a DMD based SIM while the camera shutter is open, mitigating undesired diffractions from the DMD. Three different phases of sinusoidal patterns generated by the DMD 3000 DLP series are projected onto the fluorescent sample in a 4f system. After data acquisition of the effect of the structured patterns on sample in three different phases images multiplied by a phase factor (1, 𝑒−𝑖2𝜋/3,𝑒−𝑖4𝜋/3 ) and then combined. By implementing the Radon transform of Fourier transform of phase of complex image, and evaluate the Radon transform in center of x’=0 , direction of pairs obtain. we enhance image reconstruction and analysis, utilizing their characteristics for detailed imaging and data extraction in Structured–Illumination Point–Separation. With this strategy a non-destructive and effective technique that allows researchers to measure orientation of collagen monomer in a way that preserves the sample and offers high-resolution imaging, which is crucial for studying the structure and properties of collagen.

Northeastern’s Global Study Expo-India (Hyderabad) 2024
2024-08-24 12:00 - 17:00 | MA
You are cordially invited to Northeastern’s Global Study Expo in Hyderabad Saturday August 24th, 2024. This is your opportunity to:

Take advantage of our limited-time offer to expedite the application process.
Connect with a Northeastern representative
Deepen your understanding of Northeastern programs.
Learn about The College of Engineering admissions requirements.
Discover potential scholarship opportunities.
Hear about the benefits of our global campus network with locations in the U.S., U.K., and Canada.
Learn about our students’ educational and professional outcomes.
Hear first-hand from our successful students and alumni.

We would love to see you in Hyderabad at the ITC Kohenur!

Northeastern’s Global Study Expo-India (Mumbai) 2024
2024-08-25 12:00 - 18:00 | MA
You are cordially invited to Northeastern’s Global Study Expo in Mumbai, Sunday August 25, 2024. This is your opportunity to:

Take advantage of our limited-time offer to expedite the application process.
Connect with a Northeastern representative
Deepen your understanding of Northeastern programs.
Learn about The College of Engineering admissions requirements.
Discover potential scholarship opportunities.
Hear about the benefits of our global campus network with locations in the U.S., U.K., and Canada.
Learn about our students’ educational and professional outcomes.
Hear first-hand from our successful students and alumni.

We would love to see you in Mumbai at the JW Marriott Mumbai Juhu

Yuhui Bao PhD Dissertation Defense
2024-09-03 10:00 - 11:00 | MA
Name:
Yuhui Bao
Title:
A Design Methodology for Producing Highly-Adaptable and High-Performance Simulation Frameworks
Date:
9/3/2024
Time:
10:00:00 AM
Committee Members:
Prof. David Kaeli (Advisor)
Prof. Ningfang Mi
Prof. Yifan Sun (William and Mary)
Abstract:
Computer architecture simulators play an essential role in the development and optimization of computer hardware. A variety of simulators have been developed to explore the design space of CPUs, GPUs, and customer accelerators. As GPUs continue to grow in popularity for accelerating demanding applications, such as high-performance computing and machine learning, GPU architects have been pushing the envelope of GPU performance in every new GPU generation. GPU vendors (e.g., NVIDIA and AMD) have been introducing subsequent generations of GPU architectures and products with updated instruction set architectures (ISAs) and new microarchitectural features every 2-3 years. Modeling the state-of-the-art architecture is a crucial feature of GPU simulators, which are used to characterize and accelerate challenging workloads facilitating performance evaluation and design exploration. However, the effort required to design and construct an accurate and performant simulator is huge. Due to the rapid rate of innovation in GPU technology, any simulator that is over-customized to capture the design of a specific architecture will quickly become outdated. Thus, we need to develop a design methodology for simulators that can guard against this trend, embracing future architectures.
In this dissertation, we propose a design methodology for producing highly-adaptable and high-performance simulation frameworks. We aim to design simulators featuring high adaptability, being able to accommodate future alterations or extensions, high performance and high fidelity. We leverage the Akita simulator framework to enable the modular and extensible design of various GPU components. To fulfill the goal of high fidelity, we design a set of microbenchmarks to evaluate individual GPU subsystems. We demonstrate how we follow our design methodology to achieve a highly-adaptable and accurate simulator — NaviSim, which provides the flexibility to support simulation of three different ISAs. To demonstrate the full utility of the NaviSim simulator, we conduct a performance study of the impact of individual architecture features revealing the high flexibility and configurability of NaviSim. In addition, we showcase how NaviSim’s high adaptability contributes to design space exploration, offering solutions to enhance the performance of real-world demanding applications.
Fast simulation speed is one of the key requirements of any simulators. NaviSim is designed to support multi-threaded execution, which is able to leverage the parallel capabilities offered by today’s multi-core CPUs, enabling parallel simulation. In this thesis we identify key performance bottlenecks in terms of both serial and parallel simulation execute modes and optimize simulation speed. We also present lessons learned about efficient simulator design and provide guidance for future simulator developers.

Graduate Student Drop-In Writing Hours
2024-09-04 09:00 - 11:00 | Curry Student Center, 360 Huntington Ave., Boston, MA, 02115, United States
Graduate students, are you looking for a place for focused research writing time? Join the CommLab drop-in writing hours any Wednesday between 9 -11 am ET. Drop in any Wednesday and stay for a short time or two hours. A CommLab Fellow will also be available to provide feedback on your writing. We will be meeting in weekly in 334 Curry Student Center. Note we will meet in 336 Curry Student Center on Wednesday, September 25th.

Graduate Student Fellowship Writing Club
2024-09-04 15:00 - 16:00 | MA
Join the NU CommLab and NetSI sponsored weekly graduate student fellowship writing club for support in writing your fellowship application! The fellowship writing club meets virtually on Wednesdays from 3-4 pm from September 4 – October 16. We will offer you an opportunity to ask questions to faculty, staff and students who have reviewed, mentored or applied and received fellowships. We will provide fellowship writing tips and guidance as well as offer writing and draft review sessions. Register to join our Zoom Sessions.

Bioengineering Fall Seminar Series
2024-09-06 12:00 | 105 Shillman Hall, 360 Huntington Ave, 105 SH, Boston, MA, 02115, United States
Wednesdays 12-1pm in 105 Shillman Hall, unless otherwise stated. All are welcome.
Speakers

9/6: Sara Rouhanifard, PhD
Assistant Professor of Bioengineering at Northeastern University
9/11: Mingyang Lu, PhD
Assistant Professor of Bioengineering at Northeastern University
9/18: Joanna Dahl, PhD
Assistant Professor of Engineering at the University of Massachusetts Boston
9/25: Stirling Churchman, PhD
Professor of Genetics at Harvard Medical School
10/2: Sujit Datta, PhD *in collaboration with CHME* (168 SN)
Professor of Chemical Engineering, Bioengineering and Biophysics at California Institute of Technology
10/9: Yizeng Li, PhD
Assistant Professor of Biomedical Engineering, Binghamton University
10/16: Song Li, PhD
Chancellor’s Professor and Department Chair of Bioengineering at
the University of California Los Angeles
10/23: Seemantini Nadkarni, PhD
Associate Professor of Dermatology at Harvard Medical School
10/30: Ahmad “Mo” Khalil, PhD
Professor of Biomedical Engineering at Boston University
11/6: Ramkumar “Ram” Annamalai, PhD
Assistant Professor of Biomedical Engineering at the University at Buffalo
11/13: Brad Hyman, MD PhD
John B. Penney, Jr. Professor of Neurology at Harvard Medical School and Massachusetts General Hospital
11/20: Lingchong You, PhD
James L. Meriam Distinguished Professor of Biomedical Engineering and the Director of the Center for Quantitative Biodesign at Duke University
11/27:
None, Thanksgiving
12/4: Jeffrey Moffit, PhD
Assistant Professor in Microbiology at Harvard Medical School
11/6: Jeffrey Fredberg, PhD, *Friday*
Professor of Bioengineering and Physiology at Harvard School for Public Health

Rajiv Singh PhD Dissertation Defense
2024-09-09 09:00 - 10:00 | MA
Name:
Rajiv Singh
Title:
Interpolation and Convexification Methods for Tractable Learning of Dynamic Systems
Date:
9/9/2024
Time:
9:00:00 AM
Location: https://northeastern.zoom.us/j/97729968899?pwd=[REDACTED]
Committee Members:
Prof. Mario Sznaier (Advisor)
Prof. Lennart Ljung
Prof. Octavia Camps,
Prof. Stratis Ioannidis
Abstract:
In this thesis, we present interpolation and convexification based system identification techniques that are geared towards producing, practical, engineering-friendly models. The models are either linear, or close to being linear – they are either weakly nonlinear, are described by a switching among linear models, or linear models whose parameters are allowed to depend upon certain states or inputs or the system. A common objective in all the proposed approaches is to determine the lowest-order models that are consistent with the information available in the form of data and available priors. We leverage ideas from the rational interpolation community in order to create tractable algorithms that are efficient and often scale well with the amount of data. In addition, we present control-oriented learning methods extend the basic approaches by directly incorporating the closed-loop objectives. The resulting models are self-certified in that they produce certificates of guaranteed closed-loop behavior.
A summary of the essential ideas presented in this thesis follows next.
1. Using the rank-revealing properties of Loewner and Hankel matrices, we develop a convex algorithm for identification of low order stable transfer functions using time and frequency domain data. This results are guaranteed to meet prescribed worst case bounds.
2. We propose a set of techniques geared towards control-oriented identification of potentially unstable linear models using open-loop data. These models come with a certification of robust stabilizability which greatly aids the control design procedure. The first technique leverages the concept of coprime factors of a linear system while the second technique uses robust identification of a system’s predictor as a vehicle towards identification of the plant model. The latter technique also directly incorporates the closed-loop objective of νgap minimization into the identification procedure.
3. We present convex approaches to identification of nonlinear polynomial models with time-varying coefficients. The model coefficients evolution is governed by scheduling maps that are described by low-order linear differential equations. A first approach uses a Hankel matrix rank minimization technique towards a joint identification of the model’s parameters and the scheduling map. A second approach leverages the atomic norm minimization framework to extend the first approach to bilinear systems, and also support easy incorporation of scheduling priors.
4. We present some results regarding sparse identification of Nonlinear ARX models incorporating bounded nonlinear maps. We present approaches to achieve sparsity with respect to the number of regressors used, and with respect to the maximum lag employed by any of the contributing regressors. The proposed algorithm leverages ideas from sparse learning and ensemble learning for sparse NARX models.
5. We present a new framework for identification of switched linear and parameter-varying systems based on rational interpolation. We develop multivariate interpolation procedures based on the recent “block-AAA” algorithms. We demonstrate that this modeling framework leads to fast, accurate, and scalable algorithms that can be used in various settings where the data domain is described by correlations, frequency, or scheduling variables.

Vancouver, BC Campus Spotlight
2024-09-09 10:00 - 11:00 | MA
Northeastern University’s Vancouver, BC campus is a vibrant community of graduate learners from around the world, along with tech startup founders, researchers, and business partners.
Join us Monday September 9, 2024 at 10:00 a.m. ET as the College of Engineering hosts a virtual campus spotlight showcasing the engineering programs at the Vancouver campus.
During this webinar, you will:

Learn about our programs and co-op opportunities from a College of Engineering Recruiting Specialist
Explore the campus and its impressive resources with insights from the Associate Director of Recruitment and Enrollment Management.
Gain insight into generous scholarship opportunities and the application process
