Northeastern University College of Engineering

Julian Gutierrez PhD Dissertation Defense
2024-03-21 12:00 - 13:30 (America/New_York)
Announcing:
PhD Dissertation Defense
Name:
Julian Gutierrez
Title:
Towards Real-Time Safe Flight Paths for Urban Air Mobility
Date:
3/21/2024
Time:
12:00:00 PM
Location: Zoom
Committee Members:
Prof. David Kaeli (Advisor)
Prof. Pau Closas
Dr. Evan Dill (NASA)
Dr. Natasha Neogi (NASA)
Abstract:
The emergence and development of advanced technologies and vehicle types have created a growing demand for introducing new forms of flight operations. These new and increasingly complex operational paradigms, such as Advanced and Urban Air Mobility (AAM/UAM), present regulatory authorities and the aviation community with the challenge of finding methods to integrate these emerging operations without significant additional risk to pedestrians and infrastructure. Predictive and autonomous risk mitigation capabilities become critical to meet this challenge. However, urban environments experience effects that are computationally expensive to model, limiting conventional aviation concepts, policy, and risk prediction tools from being effectively translated into this space. With the emergence of High-Performance Computing (HPC) ecosystems in the last two decades, we can use these software and hardware capabilities to help bridge the gap between real-time predictive responses and modeling accuracy.
In this dissertation we first present a simulation framework to estimate the quality of Global Navigation Satellite System (GNSS) performance for autonomous aircraft in urban environments. We propose a new algorithm designed for HPC to accelerate modeling the characteristic effects of dense urban canyons on GNSS, allowing the extension of established GNSS integrity techniques into urban navigation. Additionally, we provide a thorough validation of the simulator, which proves high-accuracy modeling when compared to sensors in the real world. Second, we use this simulation framework to provide situational awareness when processing the raw output of a GNSS sensor. This effort focuses on multipath mitigation, which reduces the error in the estimated position solution. Third, we use this simulation framework as the input into a new 4D path-planning algorithm based on an adaptation of the Bellman-Ford algorithm. HPC techniques are employed to accelerate the algorithm to produce flight paths that minimize exposure to GNSS risks. We evaluate the computational cost of satellite availability fluctuations by prioritizing events when satellite availability changes as triggers for these updates.

Mock Interview: A CommLab Workshop Series
2024-03-21 14:00 - 15:00, 2024-03-28 14:00 - 15:00, 2024-04-04 14:00 - 15:00 | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
Join the CommLab every Thursday from 2-3pm ET, we’ll delve into the intricacies of interviews, unveiling effective tricks and 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 either in person in room 206 Egan or through Zoom.

Graduate School of Engineering Campus Tour (In Person)
2024-03-23 10:00 - 11:00
Want to learn more about the Graduate School of Engineering on the 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 both newly admitted and prospective students. We can’t wait to meet you!

CommLab Writing Hours
2024-03-25 11:00 - 13:00, 2024-04-01 11:00 - 13:00, 2024-04-08 11:00 - 13:00 | 335 CSC, 360 Huntington Ave, CSC, Boston, MA, 02115, United States
Graduate students, are you looking for a place for focused research writing time? Join the CommLab for writing hours on Mondays from 11 am-1 pm ET. Drop in any Monday and stay for a short time or the whole two hours. CommLab Fellows will be available to provide feedback on your writing. We will be meeting in 335 Curry Student Center.

Negotiation Fundamentals: 5 Steps to Negotiation Success
2024-03-26 12:00 - 13:00
Get ready for our next Graduate Greatness webinar focusing on the art of negotiation!
📅 Date: March 26, 2024
🕒 Time: 12:00 pm – 1:00 pm
📍 Location: Zoom Webinar
Negotiation is an essential skill that impacts every aspect of our lives. Whether it’s securing a job offer, navigating contracts, or even managing everyday situations, mastering negotiation can elevate your success.
Join us for an enlightening presentation by negotiation expert Roy Weissman, where you’ll gain invaluable insights into negotiation best practices and fundamental skills. Discover how to transition from basic bargaining to strategic negotiation tactics that yield optimal outcomes.
No matter your profession or level of experience, this webinar will empower you with practical strategies and hands-on experience to enhance your negotiation prowess.
Register for the event: http://tinyurl.com/2xythc38

Chemical Engineering Spring Seminar Series: Dr. Christina Chan
2024-03-27 12:00 - 13:00 | 103 Churchill, 103 Churchill Hall, 360 Huntington Ave, Boston, MA, 02115, United States
Role of microenvironment on mediating diseases, DNA repair, and lipid alterations
Our group incorporates metabolic engineering and systems biology approaches in combination with biochemical and molecular biology measurements to identify targets and disease biomarkers. To modulate these targets and pathway we are concomitantly developing polymeric-based drug delivery systems.
We apply a multifaceted approach in investigating the role of soluble cues (e.g., elevated fatty acid levels, PFAS) in the microenvironment on modulating the signaling and regulatory pathways that contribute to diseases. These extracellular signals are mainly in the form of soluble factors that activate intracellular signaling cascades that drive changes in the cell. Our group has identified that saturated fatty acids (i.e., palmitate), which are well studied for their roles in metabolism, can also activate signaling pathways that affect proteostasis. Through biochemical and biophysical studies, we found that palmitate binds directly to proteins involved in proteostasis to modulate their activity and downstream signaling to alter DNA repair, which has implications on chemotolerance, lipid profile, and heart disease.

Christina Chan is a University Distinguished Professor and Interim Chairperson of Chemical Engineering at Michigan State University (MSU). She also has appointments in the Departments of Biochemistry and Molecular Biology, Biomedical Engineering, and Computer Science and Engineering. Prior to joining MSU in 2002, she was a post-doctoral fellow at the Center for Engineering in Medicine at the Harvard Medical School. Chan earned her B.S. in Chemical Engineering from Columbia University and her M.S. and Ph.D. in Chemical and Biochemical Engineering from the University of Pennsylvania. She spent 8 years in DuPont prior to returning to academia. Her laboratory applies a multifaceted approach in investigating the role of soluble cues in the microenvironment on modulating the signaling and regulatory pathways that contribute to diseases. To modulate these targets and pathways, her laboratory is developing polymeric-based drug delivery systems as well as tissue engineering platforms that capitalize on how scaffolds, cells, and biologically active molecules interact to form functional tissues. Her group has published more than 165 journal articles, reviews, book chapters and reviewed conference papers. She was elected Fellow of the American Institute of Medical and Biological Engineering (AIMBE), AIChE, and AAAS.

UMass Dartmouth Jobs, Internship & Graduate School Expo
2024-03-27 12:00 - 15:00
Calling all Corsairs! Meet a COE Graduate Admissions team member at the UMass Dartmouth Jobs, Internship & Graduate School Expo! Ask your questions about Northeastern University and the Graduate School of Engineering on March 27th.

LinkedIn, CV, Resume: A CommLab Workshop Series
2024-03-27 14:00 - 15:00, 2024-04-03 14:00 - 15:00 | 206 Egan, 360 Huntington Ave, Boston, MA, 02115, United States
Join our empowering LinkedIn, CV, and Resume Workshop series any Wednesday from 2 pm to 3 pm ET. This collaborative space offers valuable tips and peer feedback to enhance your online profile and professional presence. This is a community learning initiative, and together, we strive to make our profiles better. Join this hybrid workshop series either in person in room 206 Egan or through Zoom.

Poster Design and Presentation: A CommLab Workshop Series
2024-03-27 17:00 - 18:00, 2024-04-03 17:00 - 18:00
The CommLab will host a workshop series for poster design and presentation to focus on crafting the best visual communication of your research and telling your research story! With the upcoming RISE Expo, 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 Wednesday, between February 28th to April 10 from 5:00-6:00 PM on Zoom. See you there!

Huan Wang PhD Dissertation Defense
2024-03-28 11:00 - 12:00
Announcing:
PhD Dissertation Defense
Name:
Huan Wang
Title:
Towards Efficient Deep Learning in Computer Vision via Network Sparsity and Distillation
Date:
3/28/2024
Time:
11:00:00 AM
Zoom
Committee Members:
Prof. Yun Fu (Advisor)
Prof. Octavia Camps
Prof. Zhiqiang Tao
Abstract:
AI, empowered by deep learning, has been profoundly transforming the world. However, the excessive size of these models remains a central obstacle that limits their broader utility. Modern neural networks commonly consist of millions of parameters, with foundation models extending to billions. The rapid expansion in model size introduces many challenges including training cost, sluggish inference speed, excessive energy consumption, and negative environmental implications such as increased CO2 emissions.
Addressing these challenges necessitates the adoption of efficient deep learning. The dissertation focuses on two overarching approaches, network pruning and knowledge distillation, to enhance the efficiency of deep learning models in the context of computer vision. Network pruning focuses on eliminating redundant parameters in a model while preserving the performance. Knowledge distillation aims to enhance the performance of the target model, referred to as the “student,” by leveraging guidance from a stronger model, known as the “teacher”. This approach leads to performance improvements in the target model without reducing its size.
In this defense presentation, I will start with the background and major challenges of leveraging these techniques to improve the efficiency of deep neural networks. Then, I shall present the proposed solutions for various vision tasks, including image classification, single-image super-resolution, novel view synthesis / neural rendering / NeRF / NeLF, text-to-image generation / diffusion models, and photorealistic head avatars. Extensive results and analyses will justify the efficacy of the proposed approaches, demonstrating that pruning and distillation make a generic and complete framework for efficient deep learning in various domains. Finally, a comprehensive summary (with takeaways) and outlook of the future work will conclude the presentation.

Interdisciplinary Thinking as a Professional Skill
2024-03-28 12:00 - 13:00
Our next Graduate Greatness webinar is here to broaden your horizons with Interdisciplinary Thinking as a Professional Skill.
📅 Date: Thursday, March 28th
🕒 Time: 12:00 – 1:00 p.m. EDT
📍 Location: Zoom Webinar
In today’s complex world, the ability to think across disciplines is more valuable than ever. Join us for an engaging session where we’ll explore the power of interdisciplinary thinking as a professional skill.
Led by David Dawson, this webinar will explore strategies for integrating diverse perspectives, fostering creativity, and solving complex problems. Interdisciplinary thinking can boost your career and drive innovation.
Register for the event: http://tinyurl.com/2tfsskat

3MT (Three Minute) Thesis
2024-03-28 16:00 - 17:30
This event is being presented by Graduate Women in Science and Engineering (GWISE) and Northeastern University Library. It’s a competition where PhD/ graduate students can share their thesis research under 3 minutes and compete for Cash prizes. It is a great opportunity for students to practice their communication skills and to share their research with a broader audience.

Matthew Wallace MS Thesis Defense
2024-03-29 10:00 - 12:00
Announcing:
MS Thesis Defense
Name:
Matthew Wallace
Title:
Model Predictive Planning
Date:
3/29/2024
Time:
10:00:00 AM
Location:
Room: HS 204. Link: Teams
Committee Members:
Prof. Laurent Lessard (Advisor)
Prof. Michael Everett
Prof. Derya Aksaray
Abstract:
This thesis presents Model Predictive Planning (MPP), a trajectory planner for low-agility vehicles such as a fixed-wing aircraft to navigate obstacle-laden environments. MPP consists of (1) a multi-path planning procedure that identifies candidate paths, (2) a raytracing procedure that generates linear constraints around these paths that enforce obstacle avoidance, and (3) a convex quadratic program that finds a feasible trajectory within these constraint if one exists. Low-agility aircraft cannot track arbitrary paths, so refining a given path into a trajectory that respects the vehicle’s limited maneuverability and avoids obstacles often leads to an infeasible optimization problem. The critical feature of MPP is that it efficiently considers multiple candidate paths during the refinement process, thereby greatly increasing the chance of finding a feasible and trackable trajectory. I begin by presenting a background on path planning, trajectory optimization, and Model Predictive Control. This is followed by a presentation of the MPP algorithm. Finally, I demonstrate the effectiveness of MPP on both a longitudinal and 3D aircraft model.

Reza Vafaee PhD Proposal Review
2024-04-01 10:30 - 11:30
Announcing:
PhD Proposal Review
Name:
Reza Vafaee
Title:
Efficient Algorithms for Sparse Sensor Scheduling in Large-Scale Dynamical Systems with Performance Guarantees
Date:
4/1/2024
Time:
10:30:00 AM
Location: Zoom
Committee Members:
Prof. Milad Siami (Advisor)
Prof. Eduardo Sontag
Prof. Laurent Lessard
Prof. Alex Olshevsky (Boston University)
Abstract:
This research proposal introduces innovative frameworks for sparse sensor scheduling in large-scale dynamical networks. The first framework addresses sensor scheduling in discrete-time linear time-invariant dynamical networks, presenting a novel learning-based rounding method to convert weighted sensor schedules into sparse, unweighted schedules while maintaining comparable observability performance. The second framework extends the approach to dynamically select sensors for linear time-varying systems, utilizing an online sparse sensor scheduling framework with randomized algorithms to approximate fully-sensed systems with a constant average number of active sensors at each time step. Finally, a myopic approach within a Kalman filtering framework is adopted in the third framework, addressing non-submodular sensor scheduling in large-scale linear time-varying dynamics. A simple greedy algorithm is employed, providing approximation bounds based on submodularity and curvature concepts. Simulation results validate the theoretical foundations and demonstrate the proposed approach’s superiority over existing methods.

Learn about engineering program opportunities in Seattle, WA
2024-04-02 09:00 - 10:00
The Graduate School of Engineering is proud to offer programs on many of Northeastern University’s multiple global campuses. In this webinar, we focus on spotlighting the Seattle, WA campus. You’ll have an opportunity to learn more about the programs and opportunities available on this campus from admissions and campus representatives.

Learn about engineering program opportunities in Oakland, CA
2024-04-02 10:00 - 11:00
The Graduate school of Engineering is proud to offer programs on many of Northeastern University’s multiple global campuses. In this webinar, we focus on spotlighting the Oakland, CA campus. You’ll have an opportunity to learn more about the programs and opportunities available on this campus from admissions and campus representatives.

Batool Salehihikouei PhD Dissertation Defense
2024-04-03 11:00 - 12:30
Announcing:
PhD Dissertation Defense
Name:
Batool Salehihikouei
Title:
Leveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins
Date:
4/3/2024
Time:
11:00:00 AM
Location: EXP-601A
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Hanumant Singh
Prof. Josep Jornet
Dr. Mark Eisen
Abstract:
With the widespread Internet of Things (IoT) devices, a wide variety of sensors are now present in different environments. For example, self-driving vehicles and automated warehouses depend on sensor information for navigation and management of the robots, respectively. In this dissertation, we present methods, where these sensors are re-purposed to assist network management in wireless communication, especially when classic approaches fall short to provide the required quality of service (QoS). This thesis presents data-driven and AI-based methods, where the multimodal sensor information is used for two applications: (i) beamforming at the mmWave band and (ii) joint optimization of the navigation and network management in warehouse environments. In the first part, we study multimodal beamforming methods for mmWave vehicular networks. First, we present deep learning fusion algorithms, where the inputs from a multitude of sensor modalities such as GPS (Global Positioning System), camera, and LiDAR (Light Detection and Ranging) are combined towards predicting the optimum beam at the mmWave band. We prove that fusing the multimodal sensor data improves the prediction accuracy, compared to using single modalities. Second, we study the trade-off between the accuracy and cost of different learning strategies and demonstrate that federated learning is the most successful learning strategy, with respect to the communication overhead. Third, we propose algorithms to further optimize the communication overhead by incorporating a pruning strategy tailored to the disturbed nature of the federated learning systems. Fourth, we propose a modality-agnostic deep learning paradigm that operates on any possible combination of sensor modalities. In part two, we propose using digital twins to overcome the challenges of scarcity of data and close-world assumption in deep learning algorithms. A digital twin is a replica of a real world entity, which is typically used for studying the impact of any configuration settings in a safe, digital environment. In this dissertation, we propose a framework that operates by harmonic usage of the DL models and running emulations in the twin. Moreover, we use digital twins to generate training labels and fine-tune the models for unseen scenarios. Finally, we study a robotic industrial setting, where the path planning policy is continuously updated by monitoring the dynamics of the real world, constructing the digital twin, and updating the policy. The constructed twin captures the features of both physical and RF environments in the digital world and includes a reinforcement learning algorithm that jointly optimizes navigation and network resource management.

Learn about engineering program opportunities in Silicon Valley , CA
2024-04-03 12:00 - 13:00
The Graduate school of Engineering is proud to offer programs on many of Northeastern University’s multiple global campuses. In this webinar, we focus on spotlighting the Silicon Valley, CA campus. You’ll have an opportunity to learn more about the programs and opportunities available on this campus from admissions and campus representatives.

Chemical Engineering Spring Seminar Series: Dr. Sindia M. Rivera Jiménez
2024-04-03 12:00 - 13:00 | 103 Churchill, 103 Churchill Hall, 360 Huntington Ave, Boston, MA, 02115, United States
Professional Organizations and Social Responsibility in Chemical Engineering Education
Professional organizations (POs) are established communities that significantly influence the competencies and values of engineers, but the impact of their interaction with academia on undergraduate education is not fully understood. This study addresses this gap by exploring how engineering faculty in POs strategically incorporate social responsibility into their teaching. Relying on Paulo Freire’s critical consciousness and the Transformational Agency framework, it examines faculty reflections on societal and power dynamics for curriculum change.
Conducted over eight months, the study focuses on a Community of Practice (CoP) within the American Institute of Chemical Engineering’s Education Division, engaging faculty from multiple institutions. We employed qualitative methods, analyzing interview data through thematic analysis with In-Vivo and Axial coding. Preliminary results highlight how the CoP influences faculty’s reflective practices and understanding of societal structures, suggesting it enhances educators’ critical awareness and ability to integrate social responsibility into their teaching.
The findings deepen our understanding of POs’ role in evolving engineering education. They showcase how educators’ involvement in POs can shape socially responsible engineers, addressing the complex societal roles engineers face. This seminar aims to inspire educators with strategies for creating transformative learning environments.

Dr. Rivera-Jiménez is an Assistant Professor in the Department of Engineering Education at the University of Florida and is affiliated with the Department of Chemical Engineering and the Institute of Higher Education. Her research group focuses on community-driven methods to improve practices and policies that enhance the professional formation of engineers and impact the success of diverse engineering communities, including faculty, undergraduate and graduate students, and transfer students. Current projects include faculty support via professional societies, student motivation and emotions in blended learning, and studying diverse transfer student success within organizational contexts.
Additionally, she hosts “The Engineering Professor Speaks Education Podcast,” a bilingual series exploring the nuances of being an effective engineering educator. Her most recent accolades include the AIChE IDEAL Star Award (2021), the AIChE Education Division Service Award (2022), and the ASEE Education Research Methods Apprentice Faculty Grantee Award (2023).

Kaustubh Shivdikar PhD Dissertation Defense
2024-04-03 15:30 - 17:00
Announcing:
PhD Dissertation Defense
Name:
Kaustubh Shivdikar
Title:
Enabling Accelerators for Graph Computing
Date:
4/3/2024
Time:
3:30 PM
Location: Zoom
Committee Members:
Prof. David Kaeli (Advisor)
Prof. Devesh Tiwari
Prof. Ajay Joshi (Boston University)
Prof. John Kim (KAIST)
Prof. José Luis Abellán (University of Murcia)
Abstract:
The advent of Graph Neural Networks (GNNs) has revolutionized the field of machine learning, offering a novel paradigm for learning on graph-structured data. Unlike traditional neural networks, GNNs are capable of capturing complex relationships and dependencies inherent in graph data, making them particularly suited for a wide range of applications including social network analysis, molecular chemistry, and network security. The impact of GNNs in these domains is profound, enabling more accurate models and predictions, and thereby contributing significantly to advances in these fields.
GNNs, with their unique structure and operation, present new computational challenges compared to conventional neural networks. This requires comprehensive benchmarking and a thorough characterization of GNNs to obtain insight into their computational requirements and to identify potential performance bottlenecks. In this thesis, we aim to develop a better understanding of how GNNs interact with the underlying hardware and will leverage this knowledge as we design specialized accelerators and develop new optimizations, leading to more efficient and faster GNN computations.
A pivotal component within GNNs is the Sparse General Matrix-Matrix Multiplication (SpGEMM) kernel, known for its computational intensity and irregular memory access patterns. In this thesis, we address the challenges posed by SpGEMM by implementing a highly optimized hashing-based SpGEMM kernel tailored for a custom accelerator. This optimization is crucial to enhancing the performance of GNN workloads, ensuring that the acceleration potential of custom hardware is fully realized.
Synthesizing these insights and optimizations, we design state-of-the-art hardware accel-erators capable of efficiently handling various GNN workloads. Our accelerator architectures are built on our characterization of GNN computational demands, providing clear motivation for our approaches. Furthermore, we extend our exploration to emerging GNN workloads in the domain of graph neural networks. This exploration into novel models underlines our comprehensive approach, as we strive to enable accelerators that are not just performant, but also versatile, able to adapt to the evolving landscape of graph computing.

Conflict Resolution and Effective Communication Skills
2024-04-04 12:00 - 13:00
Join us for our upcoming Graduate Greatness webinar on “Conflict Resolution and Effective Communication” presented by Kimberly Wong.
📅 Date: Thursday, April 4th
🕒 Time: 12:00 – 1:00 p.m. EDT
📍 Location: Zoom Webinar
Conflict is an inevitable part of any academic journey, but with effective communication skills, challenges can be transformed into opportunities for growth. In this virtual workshop, we’ll dive into strategies for navigating conflict during graduate school.
Participants will have the opportunity to examine their own approaches to conflict, identifying strengths and barriers along the way. Together, we’ll explore methods to foster trust and understanding in professional relationships, providing you with concrete strategies for improving dialogue with faculty, staff, and classmates.
Register for the event: http://tinyurl.com/mw79jbhz

Anu Jagannath PhD Dissertation Defense
2024-04-04 13:00 - 14:00
Announcing:
PhD Dissertation Defense
Name:
Anu Jagannath
Title:
Deep Learning at the Edge for Future G Networks: RF Signal Intelligence for Comprehensive Spectrum Awareness
Date:
4/4/2024
Time:
1:00:00 PM
Committee Members:
Prof. Tommaso Melodia (Advisor)
Prof. Kaushik Chowdhury
Prof. Yanzhi Wang
Abstract:
Future communication networks must address the scarce spectrum to accommodate extensive growth of heterogeneous wireless devices. Efforts are underway to address spectrum coexistence, enhance spectrum awareness, and bolster authentication schemes. Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, secure communications, among others. Consequently, comprehensive spectrum awareness at the edge has the potential to serve as a key enabler for the emerging beyond 5G (fifth generation) networks. State-of-the-art studies in this domain have (i) only focused on a single task – modulation or signal (protocol) classification or radio frequency fingerprinting – which in many cases is insufficient information for a system to act on, (ii) consider either radar or communication waveforms (homogeneous waveform category), and (iii) does not address edge deployment during neural network design phase. In this dissertation, deep learning is applied to the various signal recognition problems from a multi-task perspective with an emphasis on edge deployment. To address edge deployment, various techniques are applied to solve the signal recognition problem under consideration (modulation, wireless protocol, emitter fingerprint recognition) to design scalable and computationally efficient framework. While designing the edge deployable architectures, the generalization capability of the architectures are evaluated under various circumstances to quantify their performance under real-world settings such as emissions from actual emitters (commercial emissions wherever applicable), training with a different propagation scenario and testing under a never-before-seen setting.
The study was sectioned into different stages where multi-task learning is first applied to solving wireless standard and modulation recognition, followed by applying deep compression for CBRS radar waveform classification, next radio frequency fingerprinting for commercial WiFi and Bluetooth emissions were studied utilizing novel multi-task attentional architectures, and finally the multi-task learning together with deep compression was employed to deploy the architectures in a real-time streaming radio testbed for real-time inferencing of wireless standard and modulation recognition. The feasibility of employing deep compression techniques are carefully evaluated in a real-world deployment setting to quantify the performance from a computational and inference capacity perspective.

Nicolas Bohm Agostini PhD Proposal Review
2024-04-04 15:30 - 17:00
Announcing:
PhD Proposal Review
Name:
Nicolas Bohm Agostini
Title:
Hardware/Software Codesign and Compiler Techniques for Efficient Hardware Acceleration of Dense Linear Algebra Kernels and Machine Learning Applications
Date:
4/4/2024
Time:
3:30:00 PM
Location: Zoom
Committee Members:
Prof. David Kaeli (Advisor)
Prof. Gunar Schirner
Prof. José Luis Abellán (University of Murcia)
Antonino Tumeo (PNNL)
Abstract:
Today’s linear algebra and machine learning applications (ML) continue to grow in size and complexity, placing rapidly increasing demands on the underlying hardware and software systems. To address these issues, hardware designers have proposed using custom accelerators explicitly designed for accelerating these demanding workloads. What needs to be improved is the ability to perform efficient hardware/software (HW/SW) co-design in order to reap the full benefits from these platforms. This thesis presents an integrated solution to facilitate HW/SW accelerator design. We also address issues in accelerator deployment, enabling rapid prototyping, integrated benchmarking, and comprehensive performance analysis of custom accelerators.
In this thesis, we demonstrate the value of a lightweight system modeling library integrated into the build/execution environment, leveraging TensorFlow~Lite for deployment. We also explore efficient design space exploration of different classes of accelerators while considering the impact of parameters. Secondly, we employ the Multi-Level Intermediate Representation (MLIR) compiler framework to automatically partition host code from accelerator code, pre-optimizing the latter for improved high-level synthesis designs and high-quality accelerated kernels. Lastly, we propose compiler extensions to automate the generation and optimization of communication between the host CPU and AXI-based accelerators.
We present novel solutions that enable more efficient and effective design space exploration, optimization, and deployment of custom accelerators. The utility of these approaches is demonstrated through experiments with specific accelerator designs and key linear algebra and ML workloads. Most importantly, these solutions empower high-level language users, such as domain scientists, to participate in the design of new accelerator features.

Soft Matter Days
2024-04-08 14:15 - 14:30
Soft Matter Days: April 8-17, will feature invited guest speakers discussing a variety of interdisciplinary topics in soft matter and complex fluids. These topics sit at the interface of chemical & mechanical engineering, materials science, physics, chemistry, and biology. Guest speakers will discuss real-world phenomena found in food, blood flow, and granular materials. Two talks are guest lectures in CHME5179: RSVP required for those not in the class.
Monday, April 8, 2:15pm, Curry 340
Capillary Rise and Thin Films Near Edges: New Insights from Self-similarity
Howard Stone, Princeton University
Host: Xiaoyu Tang x.tang@northeastern.edu
Tuesday, April 9, 9:50am, Zoom (Guest Lecture for CHME 5179)
“Complex Fluids & Soft Matter in Food”
Dave Weitz, Harvard University
RSVP: Sara Hashmi s.hashmi@northeastern.edu
Thursday, April 11, 1:30pm, HS 210
Dynamics of blood flow at the cellular level in health and disease
Michael Graham, University of Wisconsin
Host: Sara Hashmi s.hashmi@northeastern.edu
Friday, April 12, 9:50am, Zoom (Guest Lecture for CHME 5179)
Nonlinear Rheology of Complex Fluids: Exploring Microstructure
Kate Honda, Northeastern University
RSVP: Sara Hashmi s.hashmi@northeastern.edu
Wednesday, April 17, 1:30pm, HS 210
Universality and scaling in shear thickening suspensions
Bulbul Chakraborty, Brandeis University
Host: Sara Hashmi s.hashmi@northeastern.edu
