Northeastern University College of Engineering

Yufei Feng MS Thesis Defense
2024-08-13 14:00 - 15:00 (America/New_York)
Name:
Yufei Feng
Title:
Beam Management in Operational 5G mmWave Networks
Date:
8/13/2024
Time:
2:00:00 PM
Committee Members:
Prof. Dimitrios Koutsonikolas (Advisor)
Prof. Josep Jornet
Prof. Mallesham Dasari
Abstract:
Due to the directional nature of mmWave signal propagation, beam management plays a critical role in the performance of 5G mmWave deployments. However, the details of beam management in commercial deployments and its performance in real-world scenarios remain largely unknown. In this paper, we fill this gap by performing a comparative measurement study of the beam management procedure of two major US operator in Boston, MA. We study a number of beamforming parameters including beamwidth, number of beams, beam switching delay, and their impact on performance, and we explore the interplay between beam management and rate adaptation. We also investigate for first time Rx beam management on the UE side. Finally, we study the beam tracking performance and the quality of the selected beams for two operators.

Gözde Özcan PhD Dissertation Defense
2024-08-12 10:00 - 11:00
Name:
Gözde Özcan
Title:
Learning and Optimizing Set Functions
Date:
8/12/2024
Time:
10:00:00 AM
Location:
EXP 601
Committee Members:
Prof. Stratis Ioannidis (Advisor)
Prof. Jennifer Dy
Prof. Evimaria Terzi
Abstract:
Learning and optimizing set functions play a crucial role in the artificial intelligence research as various problems of interest can be characterized with set inputs and/or outputs. Submodular functions, i.e., set functions with a diminishing returns property, are an important subcategory of such functions. They naturally present themselves in applications such as sensor placement, data summarization, feature selection, influence maximization, hyper-parameter optimization, and facility location, to name a few. In a lot of these compelling problems, the objective is to maximize a submodular function subject to matroid constraints, which is known to be NP-hard. For problems of this nature, the continuous greedy algorithm provides a (1 − 1/e)-approximation guarantee in polynomial-time. It does so by estimating the gradient of the so-called multilinear relaxation of the objective function via sampling. However, for the general class of submodular functions, the number of samples required to achieve this theoretical guarantee can be computationally prohibitive.
In this dissertation, we address deterministic submodular maximization problems with matroid constraints, specifically those with objectives expressed through compositions of analytic and multilinear functions. We introduce a novel polynomial series estimator to approximate the multilinear relaxation of such functions and demonstrate that the sub-optimality introduced by our polynomial expansion can be minimized by increasing the polynomial order. By utilizing this estimator, a variant of the continuous greedy algorithm achieves an approximation ratio close to (1 − 1/e) ≈ 0.63 through deterministic gradient estimation. In numerical experiments, our polynomial estimator outperforms the sampling estimator, offering reduced errors in less time.
We extend our study to the stochastic submodular maximization setting with general matroid constraints, where objectives are defined as expectations over submodular functions with an unknown distribution. Adapting polynomial estimators to this context reduces the variance of the gradient estimation while introducing a controlled bias term. For several notable stochastic submodular maximization problems, we demonstrate that this bias decays exponentially with the degree of our polynomial approximators. Furthermore, for monotone functions, a stochastic variant of the continuous greedy algorithm attains an approximation ratio (in expectation) close to (1 − 1/e) ≈ 0.63 using these polynomial estimators. Our experimental results validate the advantages of our approach across synthetic and real-life datasets.
Finally, we turn our attention to the learning set functions under a so-called optimal subset oracle setting. A recent approach approximates the underlying utility function with an energy-based model. Approximating this energy-based model yields iterations of fixed-point update steps during mean-field variational inference. However, these fixed-point iterations are not guaranteed to converge and as the number of iterations increases, automatic differentiation quickly becomes computationally prohibitive due to the size of the Jacobians that are stacked during backpropagation. We address these challenges by examining the convergence conditions for the fixed-point iterations and utilizing implicit differentiation over automatic differentiation. We empirically demonstrate the efficiency of our method on synthetic and real-world subset selection applications.

Yifan Gong PhD Dissertation Defense
2024-08-09 11:00 - 12:00
Name:
Yifan Gong
Title:
Towards Energy-Efficient Deep Learning for Sustainable AI
Date:
8/9/2024
Time:
11:00:00 AM
Committee Members:
Prof. Yanzhi Wang (Advisor)
Prof. David R. Kaeli
Prof. Xue Lin
Prof. Huaizu Jiang
Prof. Stratis Ioannidis
Abstract:
The rapid advancements in deep learning (DL) and artificial intelligence (AI) have led to transformative applications across various domains, such as community virtual reality experiences, autonomous systems, and climate change prediction. Edge devices including mobile and embedded systems play a vital role in carrying these applications, facilitating the widespread adoption of machine intelligence. Along with the great success of DL and AI is the huge energy consumption for both training and inference. With the breakthrough of large-scale models for AI-generated content (AIGC) such as large language models and diffusion models, the energy consumption issue intensifies, causing the urgent need for sustainable AI solutions. In this talk, I will talk about how to facilitate deep learning on various edge devices in an energy-efficient manner for the goal of sustainable AI. Specifically, I will start by introducing my two system-level approaches to tackling the challenge. The first approach is named bottom-up, which conducts AI algorithm-aware efficient system design. The second approach is a top-down approach that achieves hardware-driven efficient AI algorithm design. Then, I will share my recent works addressing the efficiency issues for large-scale models. Finally, I will show the applications of my methods and pointers to the future direction.

Galante Program Virtual Info Sessions
2024-08-08 16:30 - 17:00
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.

Peiyan Dong PhD Dissertation Defense
2024-08-08 15:00 - 16:00
Name:
Peiyan Dong
Title:
Software-Hardware Co-Design: Towards Ultimate Efficiency in Deep Learning Acceleration
Date:
8/8/2024
Time:
3:00:00 PM
Committee Members:
Prof. Yanzhi Wang (Advisor)
Prof. David R. Kaeli
Prof. Devesh Tiwari
Prof. Cheng Tan
Abstract:
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 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.

Northeast Regional Higher Education Neurodiversity Coalition Conference
2024-08-08 - 2024-08-09 | Interdisciplinary Science and Engineering Complex (ISEC), 805 Columbus Ave, Boston, MA, 02115, United States
Improving educational and professional outcomes for neurodivergent students
This conference is both timely and relevant. The neurodiverse population is increasing, while students have lower academic performance and higher college dropout and underemployment rates.
Join Together to Make a Difference

Increase your understanding of neurodiversity
Learn trends in neurodiversity employment
Understand best practices for neuroinclusive pedagogy and curriculum
Gain knowledge to create a neuroinclusive workplace and campus
Explore and share best practices with a community of higher education and industry professionals

Who Should Attend
First-year engineering faculty, deans, department chairs, teaching faculty, research assistants, academic and career advisors, DEI professionals, employers, and other college and workforce professionals.
Featured Keynote Speakers
Dr. Jeff Karp, a renowned biomedical engineer at Harvard Medical School and MIT grew up being “written off” for his learning differences. He consequently developed “Life Ignition Tools,” a process for embracing life that resulted from years of iteration and tinkering to make his unique thought patterns and behavior work for him.
John Elder Robison, a well-known author of Switched On: A Memoir of Brain Change and Emotional Intelligence, Look Me in the Eye, Be Different, and Raising Cubby, which details his life with Asperger syndrome. He’s a leading voice for autism and neurodiversity, imploring audiences to find strengths where others see weaknesses based on societal standards.
Learn More and Register
https://nucoe.madebyvital.com/nehenc.
Presented in partnership by Northeastern University, University of Connecticut, and the University of Rhode Island

Cobra Alemdar PhD Dissertation Defense
2024-08-07 11:00 - 12:00
Name:
Kubra Alemdar
Title:
Overcoming and Engineering Wireless Signals for Communication and Computation
Date:
8/7/2024
Time:
11:00:00 AM
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Josep Jornet
Prof. Marvin Onabajo
Abstract:
The phenomenal growth of connected devices, especially rapid expansion of IoT networks and the increasing demand for wireless services are the main driving forces for the evolution of wireless technologies. However, the realization of such technologies requires a radical transformation of existing infrastructures to satisfy the needs of changing wireless environments. The main limitation in delivering these systems stems from a vast diversity in their demands and constraints. To address this limitation, this dissertation shows how wireless signals and their interaction with and within the wireless propagation domain can be used as communication or computational tools that enable us to achieve certain novel tasks. Specifically, we build i) cross-functionality architectures to engineer the wireless channel to a) enable the operation of emerging technologies, and b) demonstrate a new paradigm for computing with wireless signals, and ii) intelligently shape the wireless channel to create reliable communication links. This dissertation presents an experimentally validated software-hardware systems with thorough analysis, delivering the following key advancements with distinct contributions:
First, We present an innovative physical layer solution for distributed networks that provides over-the-air (OTA) clock synchronization, known as RFCLOCK, to overcome the hurdle of implementing fine-grained synchronization for emerging technologies. We first develop the theory for such precision synchronization, and second implement it in a custom-design, compatible with commercial-off-the-shelf (COTS) software-defined radios (SDRs). We compare the performance of RFClock with popular wired and GPS-based hardware solutions, both in terms of clock performance as well as impact on distributed beamforming.
Next, we propose two novel approaches, utilizing reconfigurable intelligent surfaces (RISs) to ensure reliable connectivity in wireless networks by controlling the propagation environment: i) we present RIS-based spatio-temporal approach to enhance the link reliability for IoTs where sensors are small-factor designs with single-antenna in a rich multipath environment. We demonstrate the design of RIS and how it can effectively perturb the environment, generating multiple wireless propagation channels and achieving the performance of a multi-antenna receiver in a Single-Input Single-Output (SISO) link. We compare the performance of the system with a multi-antenna receiver in terms of channel hardening and outage probability. ii) We introduce REMARKABLE, an online learning based adaptive beam selection strategy for robot connectivity that trains kernelized multi-armed bandit (MAB) model directly in real-world settings of a factory floor. We show how RISs with passive reflective elements can create beamforming towards target robots, and provide a solution to the problem of adaptive beam selection in dynamic channel conditions. We experimentally demonstrate that REMARKABLE can achieve a significant reduction in beam selection time compared to classical approaches and adaptive beam selection in mobility settings.
Finally, we introduce AirFC, a system harnessing the capability of OTA computation to run inference on a neural network (NN) consisting of a set of fully connected layers (FC) by leveraging multi-antenna systems. We experimentally demonstrate and validate that such computation is accurate enough when compared to its digital counterpart.

Kubra Alemdar PhD Dissertation Defense
2024-08-07 11:00 - 12:00

Malith Jayaweera PhD Dissertation Defense
2024-08-06 10:00 - 11:00
Name:
Malith Jayaweera
Title:
Energy-Aware Transformations for Affine Programs on GPUs
Date:
8/6/2024
Time:
10:00:00 AM
Committee Members:
Prof. David Kaeli (Co-advisor)
Prof. Yanzhi Wang (Co-advisor)
Dr. Norman Rubin
Prof. Martin Kong (Ohio State University)
Abstract:
Graphics Processing Units (GPUs) have been increasingly used to accelerate workloads ranging from high performance computing to machine learning. Development of high-level programming languages, improved compilers, and runtime drivers have helped to accelerate the widespread adoption of GPUs. Given the wider adoption and ever-increasing computing capabilities, the power consumption of GPUs is quickly becoming a critical factor. Furthermore, the GPU micro-architecture differs from vendor to vendor, and even between hardware generations of the same vendor. Also, program variants with similar performance could differ in energy consumption due to the difference in utilization of GPU resources such as Streaming Multiprocessors (SMs) or memory. Despite performance improvements in compilation techniques, energy-aware code generation for heterogeneous GPUs has not been aggressively explored.
In this dissertation, we first identify the potential for energy-aware compilation techniques for GPUs. Next, we use these insights to study loop tiling, which is a popular loop transformation that has been successfully applied to computational domains such as linear algebra, deep neural networks and iterative stencils. We then propose an energy-aware tile size selection for affine programs to generate energy-efficient code targeting GPUs.
We also investigate the challenging problem of optimizing the scheduling of complex sparse tensor algebra and expressions on GPUs, with a focus on maximizing parallelism utilization to unlock optimal performance. We perform a comprehensive examination of the search space for sparse tensor expression scheduling, seeking to characterize the intricate inter-relationships between kernel characteristics, GPU architecture, and hardware constraints such as memory bandwidth limitations, to inform optimal scheduling decisions.

Joshua Groen PhD Proposal Review
2024-08-05 14:00 - 15:00
Name:
Joshua Groen

Title:
Optimizing and Securing Open RAN with Experimental System Validation
Date:
8/5/2024
Time:
2:00:00 PM
Location:
ISEC232;
Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Stratis Ioannidis
Prof Engin Kirda
Dr. Christopher Morrell
Abstract:
5G and beyond cellular networks promise remarkable advancements in bandwidth, latency, and connectivity, with the emergence of Open Radio Access Network (Open RAN) representing a pivotal direction. O-RAN inherently supports machine learning (ML) for network operation control, with RAN Intelligence Controllers (RICs) utilizing ML models developed by third-party vendors based on key performance indicators (KPIs) from geographically dispersed base stations or user equipment (UE). Realistic and robust datasets are crucial for developing these ML models. We collect a comprehensive 5G dataset using real-world cell phones across diverse scenarios and replicate this traffic within a full-stack srsRAN-based O-RAN framework on Colosseum, the world’s largest radio frequency (RF) emulator. This process produces a robust, O-RAN compliant KPI dataset reflecting real-world conditions, enabling the training of ML models for traffic slice classification with high accuracy.
The O-RAN paradigm introduces cloud-based, multi-vendor, open, and intelligent architectures, enhancing network observability and reconfigurability. However, this also expands the threat surface, exposing components and ML infrastructure to cyberattacks. We examine O-RAN security, focusing on specifications, architectures, and intelligence proposed by the O-RAN Alliance. We identify threats, propose solutions, and experimentally demonstrate their effectiveness in defending O-RAN systems against cyberattacks, offering a holistic and practical perspective on O-RAN security.
We investigate the impact of encryption on two key O-RAN interfaces: the E2 interface and the Open Fronthaul, using a full-stack O-RAN ALLIANCE compliant implementation within the Colosseum network emulator and a production-ready Open RAN and 5G-compliant private cellular network. Our findings provide quantitative insights into the latency and throughput impacts of encryption protocols, and we propose four fundamental principles for security by design within Open RAN systems.
Finally, we address the security of Time-Sensitive Networking (TSN) in O-RAN. The O-RAN framework encourages multi-vendor solutions but increases the exposure of the open fronthaul (FH) to security risks, especially when deployed over third-party networks. Synchronization is crucial for reliable 5G links, with attacks on synchronization mechanisms posing significant threats. We demonstrate the impact of spoofing and replay attacks on Precision Time Protocol (PTP) synchronization, causing catastrophic failures in a production-ready O-RAN and 5G-compliant private cellular network. To counter these threats, we design an ML-based monitoring solution detecting various malicious attacks with over 97.5% accuracy, and outline additional security measures for the O-RAN environment.

CommLab Drop-In Writing Hours
2024-08-02 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.

Kyle Lockwood PhD Dissertation Defense
2024-07-30 13:30 - 14:30
Name:
Kyle Lockwood
Title:
Leveraging Submovements for Prediction and Trajectory Planning in Human-Robot Handover
Date:
7/30/2024
Time:
1:30:00 PM
Location:
ISEC 532 –
Committee Members:
Prof. Deniz Erdogmus (Advisor)
Prof. Eugene Tunik (Co-Advisor)
Prof. Mathew Yarossi
Prof. Tales Imbiriba
Abstract:
Collaborative physical interactions between humans and robots pose difficult modeling challenges. To create natural interactions, engineers must consider human inference of intent, anticipation of action, and coordination of movement. Humans can handle these challenges effortlessly when interacting with one another, but they are very difficult to overcome in robot implementations. Although human-human handover is a seemingly simple task, it requires a complex perception-action coupling to determine when and where the handover will happen, as well as choosing an appropriate trajectory to receive the object. Critically, modeling human-robot handover requires incorporating knowledge about human inference and trajectory planning to obtain seamless interactions. Despite recent advancements in sensing and control, human-robot handovers are far from approaching the fluidity and flexibility of human-human collaboration. Existing predictive models applied to human-robot handover often utilize classification methods and other approaches that suffer in accuracy when encountering noisy human trajectories that are not captured during their training. To address these challenges, this work presents two models that act as robotic surrogates for human inference and trajectory planning in a handover task. This approach delivers promising results while remaining grounded in a physiologically meaningful feature of human motion: Gaussian-shaped submovements in velocity profiles. This thesis analyzes human-human handover kinematics to establish a baseline for model evaluation and investigate the influence of handover role, it presents models for human inference and trajectory planning, and it applies the inference model in human-robot handover experiments.

Yunus Bicer PhD Dissertation Defense
2024-07-29 15:00 - 17:00
Name:
Yunus Bicer
Title:
Novel Methods for Electromyographic Hand Gesture Recognition: Expressive Gestures Sets with Minimal Calibration
Date:
7/29/2024
Time:
3:00:00 PM
Location:
ISEC 632 –
Committee Members:
Prof. Deniz Erdogmus (Advisor)
Prof. Mathew Yarossi (Co-Advisor)
Prof. Eugene Tunik
Prof. Tales Imbiriba
Abstract:
Gesture recognition, the process of interpreting hand gestures through computational algorithms and devices, is essenatial for enhancing human-computer interaction(HCI). This thesis focuses on surface electromyography (sEMG)-based gesture recognition, where the signals generated by muscles are analyzed to identify hand gestures. sEMG systems provides more natural and intuitive interactions compared to traditional input methods and hold significant potential in assistive technology, prosthetics, and immersive environments such as virtual and augmented reality. Despite these advantages, sEMG-based methods face challenges including user-specific variability in signals, limited gesture expressivity, and the need for extensive calibration time. This research aims to address these issues by proposing novel methods for minimizing calibration time and expanding expressivity of gesture recognition capabilities. Key innovations include a real-time probability feedback mechanism to facilitate user adaptation and techniques to recognize a wider range of gestures with minimal training data. This work seeks to enhance the usability and versatility of sEMG-based systems, making them more accessible and effective for various applications.

Shijie Yan PhD Proposal Review
2024-07-29 12:00 - 13:30
Name:
Shijie Yan
Title:
Efficient Monte Carlo light transport algorithms in complex scattering media
Date:
7/29/2024
Time:
12:00:00 PM
Committee Members:
Prof. Qianqian Fang (Advisor)
Prof. Steven Jacques
Prof. David Kaeli
Prof. Edwin Marengo
Abstract:
Modeling light-tissue interactions is crucial for many optical imaging modalities, for which the Monte Carlo (MC) method has been widely recognized as the gold-standard. Despite dramatic speed improvements gained via the use of graphics processing units (GPUs), MC simulations remain computationally intensive. Efficient and accurate MC algorithms are needed to further consider physiologically realistic tissue models, especially for emerging optical imaging techniques. Voxel-based MC (VMC) and mesh-based MC (MMC) are two major MC methods for modeling complex tissues with their respective strengths and weaknesses. While VMC offers higher computational efficiency due to the simple data structure, its accuracy suffers from the terraced boundary shape especially in low-scattering medium; on the other side, MMC offers improved boundary fidelity but can be slow and memory-intensive, particularly at high mesh density. Furthermore, emerging wide-field diffuse optical imaging systems using structured light require more efficient modeling to handle numerous illumination patterns. Additionally, niche applications such as polarized light imaging could also benefit from many of the recent advances from modern MC simulations such as GPU acceleration and handling of complex heterogeneous media.
This proposal is aimed to push the frontiers of modern MC simulation algorithms to fundamentally enhance their utilities in diverse applications. To reduce the staircase effect in VMC, we have developed a hybrid MC algorithm, named split-voxel MC (SVMC), where sub-voxel oblique surfaces are extracted using a marching-cubes algorithm and are incorporated into a memory-efficient voxelated data structure. SVMC allows VMC to handle curved surfaces while remaining computationally efficient. A GPU-accelerated marching-cubes algorithm was also developed to further accelerate SVMC domain preprocessing. On the other hand, to further improve MMC computational efficiency, a dual-grid MMC (DMMC) algorithm was developed to perform fast ray-tracing inside a coarse tetrahedral mesh while saving fluence data over a dense voxelated grid, simultaneously achieving improved speed and output accuracy. To accommodate increasing needs of modeling wide-field pattern based sources, we have developed a “photon sharing’’ MC algorithm that performs simulations of all illumination and detection patterns in parallel, improving computational speed by an order of magnitude. Additionally, we have developed a GPU-accelerated massively-parallel algorithm capable of modeling Mie scattering of sphere particles in three-dimensional media for polarized light imaging, achieving nearly 1000$times$ speed acceleration compared to sequential implementation.
Lastly, we have also investigated a hardware-accelerated MMC algorithm using the NVIDIA OptiX ray-tracing framework, leveraging modern GPU ray-tracing (RT) cores extensively optimized for graphics rendering. Preliminary results demonstrate comparable accuracy and significantly improved simulation speed compared to conventional tetrahedral MMC.

Ruyi Ding PhD Proposal Review
2024-07-29 09:00 - 10:30
Name:
Ruyi Ding
Title:
Towards Robust and Secure Deep Learning: From Training through Deployment to Inference
Date:
7/29/2024
Time:
9:00:00 AM
Committee Members:
Prof. Yunsi Fei (Advisor)
Prof. Aidong Ding
Prof. Lili Su
Abstract:
In recent years, deep learning has experienced rapid advancement, leading to the development of numerous commercial deep neural network (DNN) models across diverse fields such as autonomous driving, healthcare, and recommendation systems. However, this wide adoption has intensified concerns about AI security throughout a neural network’s lifecycle — from training to deployment, and inference. Various vulnerabilities have emerged, threatening confidentiality, privacy, and intellectual property (IP) rights: poisoned training datasets facilitate privacy leakage and backdoor injection; after deployment, models may be misused through unauthorized transfer learning, a new form of IP infringement, and weights and parameters are subject to side-channel assisted model extraction attacks; during inference, adversarial attacks may compromise DNN functionality, causing misclassifications.
This dissertation addresses new security challenges across the neural network lifecycle through several novel contributions. We identify a new poisoning vulnerability in graph neural networks, where injecting poisoned nodes exacerbates link privacy leakage, allowing attackers to steal adjacent information from private training data, highlighting the necessity of robust AI training. To prevent model misuse after deployment, we introduce EncoderLock and Non-transferable Pruning, employing innovative training schemes and pruning methods to restrict the malicious use of pre-trained models through transfer learning, effectively implementing applicability authorization. Towards secure deep learning implementations, we adopt a software-hardware co-design approach to address DNN vulnerabilities. Specifically, we leverage the electromagnetic emanations from DNN accelerators in a new approach called EMShepherd, which detects adversarial examples (AE) on edge devices in a ‘black-box’ manner. To protect deployed DNNs against side-channel-based weight-stealing attacks, we develop PixelMask, which leverages the characteristics of DNN for side-channel defense by masking out unimportant inputs and dropping related operations to obfuscate side-channel signals. Lastly, we explore the use of Trusted Execution Environments (TEE) to safeguard model weights and data privacy against model stealing and membership inference attacks.
This proposal identifies key challenges of robust and secure deep learning, tackles vulnerabilities at various stages of the AI lifecycle, and provides comprehensive protection mechanisms, from securing the training process to safeguarding deployed models, paving the way for more resilient and reliable AI technologies in real-world applications.

Rui Lou PhD Dissertation Defense
2024-07-25 14:00 - 15:30
Announcing:
PhD Dissertation Defense
Name:
Rui Luo
Title:
Shared Assistance Methods for Human-in-the-loop Robot Systems
Date:
7/25/2024
Time:
2:00:00 PM
Location:
EXP 701A.
Committee Members:
Prof. Taskin Padir (Advisor)
Prof. John Peter Whitney
Prof. Yanzhi Wang
Dr. Mark Zolotas
Abstract:
Fully autonomous robot systems, though highly desired, face substantial theoretical and practical challenges when being deployed into a dynamic environment where human co-exists. To tackle this challenge, this thesis investigates the concept of human-in-the-loop (HITL) systems, which incorporate human input to enhance robot functionality. HITL systems offer a pragmatic alternative, combining human versatility with robotic precision.
This research aims to address critical questions in one specific HITL system which prioritizes the dominant role of human within the system, positioning the robot primarily in an assistive capacity that adheres to human commands to facilitate the achievement of a shared goal. It explores two primary paradigms of shared assistance methods—Shared Control (SC) and Shared Autonomy (SA)—and discuss the system designs as well as specific algorithms to implement the three critical components in a HITL systems: human intention estimation, modulation of human inputs and robot autonomy, and the human-robot communication channel.
Due to the variety of use cases and their specific challenges, four distinct HITL systems are developed and analyzed to exemplify how shared assistance methods could be incorporated to assist human operators: an assistive wheelchair for indoor navigation, a human-centered robot system design for industrial tasks, a mobile bi-manual robot for tele-manipulation, and a VR-based customizable shared control system for fine teleopeartion. Although each system represents a comprehensive robotic solution, the research contributions for each work vary.
In the assistive wheelchair navigation system, the focus was on human intent estimation via low-throughput interface utilizing a recursive Bayesian filter, with significant efforts dedicated to developing a real-time user interface serving as the communication channel. In the human-robot collaboration system for industrial setting, the emphasis was on human state estimation through camera-based posture tracking and exploring the interplay between robot behavior and human ergonomics. For the two teleoperation systems, the primary focus was on the real-time modulation of human inputs and robot autonomy to aid in achieving dexterous manipulation tasks. A novel VR-based user interface was developed to enable users to customize the level of robotic autonomous assistance. Each system was validated through a pilot study involving 10-20 human subjects, accompanied by extensive data analysis to provide insights into designing HITL systems for various applications.
In conclusion, this thesis contributes to a deeper understanding of HITL systems, highlighting their potential to enhance human productivity, ergonomics, and quality of life in various applications through concrete examples. The integration of human intent estimation and real-time shared control methods into robotic systems demonstrates the feasibility and benefits of HITL approaches. Our extensive experimental analysis underscores the critical role of human feedback in designing practical HITL systems that can be deployed in real-world scenarios.

Zhenglun Kong PhD Dissertation Defense
2024-07-23 21:00 - 22:00
Name:
Zhenglun Kong
Title:
Towards Efficient Deep Learning for Vision and Language Applications
Date:
7/23/2024
Time:
9:00:00 PM
Committee Members:
Prof. Yanzhi Wang (Advisor)
Prof. David Kaeli
Prof. Dakuo Wang
Prof. Weiyan Shi
Abstract:
Machine learning and AI have been advancing rapidly in recent years, leading to numerous applications across diverse fields such as autonomous vehicles, entertainment, science, healthcare, and assistive technologies—significantly enhancing daily life. However, this advancement has been accompanied by a significant increase in the size of deep neural network (DNN) models, which poses considerable economic challenges. The substantial costs associated with the training, inference, and deployment of large vision and language models require extensive computational resources and time, proving especially taxing for smaller entities and individuals. This also complicates deployment on resource-constrained devices and in areas with limited infrastructure.
A major challenge is deploying AI models on devices with limited capacity, such as wearables, sensors, and mobile phones. These edge devices, often operating offline and requiring real-time processing, are critical for many applications but struggle to support large models. My dissertation research addresses these pressing issues with the aim of enabling the practical implementation of AI. We ensure the effectiveness of AI models while adapting them for use in constrained environments by tackling fundamental AI challenges from four angles:
1. Managing Massive Computation: We introduce a novel token pruning framework that reduces the latency of Vision Transformers (ViT) by up to 41% compared to existing works on mobile devices. Additionally, we propose a quantization framework for large language models (LLMs), achieving an on-device speedup of up to 2.55x compared to FP16 counterparts across multiple edge devices.
2. Mitigating Training Costs: We develop fast, accurate, and memory-efficient training methods by utilizing a hierarchical data redundancy reduction scheme, which achieves up to a 40% speedup in ViT pre-training with minimal accuracy loss.
3. Merging Multiple Models: We propose an efficient way to merge multiple LLMS, yielding a more advanced and robust LLM while maintaining the model size, as well as reducing knowledge interference.
4. Co-designing Speed-aware Deep Neural Networks: We consider memory access cost, the degree of parallelism, and practical latency in the design of 2D and 3D object detection models for practical deployment.  By addressing these areas, my research aims to enable the effective and efficient use of AI models in constrained environments, ensuring their practical implementation across various applications.

Andrea Lacava PhD Proposal Review on 7/23
2024-07-23 11:30 - 12:30
Name:
Andrea Lacava
Title:
Enabling Intelligent nextG Cellular Networks through the Open RAN Architecture
Date:
7/23/2024
Time:
11:30:00 AM
Location:
EXP 501
Committee Members:
Prof. Tommaso Melodia (Advisor)
Prof. Francesca Cuomo (Advisor)
Prof. Stefano Basagni
Prof. Ioannis Chatzigiannakis
Abstract:
The 5th generation (5G) and beyond of cellular networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities, customized to the needs of individual users. However, achieving such fine-grained control over the Radio Access Network (RAN) is unfeasible with the current cellular architecture.
To bridge this gap, the Open RAN paradigm and its specification introduce an “open” architecture with abstractions that facilitate closed-loop control and enable data-driven, intelligent optimization of the RAN at the user-level. This thesis focuses on the design and development of system-level solutions to enable intelligent control in the next generation of cellular networks through the Open RAN architecture. The main research areas explored in this thesis include (i) the design and evaluation of platforms for the creation, datasets generation and testing of the Open RAN architecture solutions; (ii) the development of Artificial Intelligence (AI)/Machine Learning (ML) models for various deployments and networking scenarios; and (iii) innovative methodologies for agile spectrum, infrastructure, and AI management within Open RAN. Among the significant contributions of this thesis are ns-O-RAN, the first open-source simulation platform that integrates a functional 5G protocol stack in Network Simulator 3 (ns-3) with an O-RAN-compliant E2 interface, and the pioneering architectural design and implementation of the dApps, the real-time controllers for the O-RAN architecture. Furthermore, the solutions proposed in this thesis are leveraged to investigate various network optimization use cases deemed critical in cellular networks. The results demonstrate that our approach outperforms traditional Radio Resource Management (RRM) heuristics, enhancing overall RAN conditions at scale in both simulations and state-of-the-art experimental testbeds.

Miead Tehrani Moayyed PhD Dissertation Defense
2024-07-22 13:00 - 14:30
Name:
Miead Tehrani Moayyed
Title:
RF Channel Models for Static and Mobile Scenarios: From Simulations to Models for Large-scale Emulations and Digital Twins
Date:
7/22/2024
Time:
1:00:00 PM
Location:
Room: EXP-601A
Committee Members:
Prof. Stefano Basagni (Advisor)
Prof. Tommaso Melodia
Prof. Milica Stojanovic
Abstract:
The extremely high data rates provided by communications at higher frequency bands, such as mmWave, can address the unprecedented demands of next-generation wireless networks. However, several impairments limit wireless coverage at higher frequencies, necessitating accurate models of wireless scenarios and large-scale testing to test and realize the potential of these new technologies. Large-scale accurate simulations and wireless network emulators now offer a time- and cost-effective solution for performing these tests in a lab before field deployment. This dissertation focuses on modeling, calibration, and validation of realistic RF scenarios for wireless network emulation at scale. The contributions of this work include: (i) Investigating the characteristics of the wireless channel at higher frequencies (mmWave) and evaluating the performance of mmWave communications on top of the NR standard for 5G cellular networks; (ii) developing a streamlined framework to create realistic RF scenarios with mobility support for Finite Input Response (FIR)-based emulators like Colosseum, starting from rich inputs such as precise ray tracing methods or real-field measurements, and (iii) creating an accurate AI-assisted propagation model that integrates joint measurements and simulations, achieving the desired accuracy and reasonable computational requirements for real-time Digital Twin (DT) wireless networks. Particularly:
(i) We derive channel propagation models via ray tracing simulations for mmWave transmissions with applications to V2X communications. We analyze aspects related to blockage modeling, the effects of antenna beamwidth, beam alignment, and multipath fading in urban scenarios, emphasizing the importance of capturing diffuse scattered rays for improved large-scale and small-scale radio channel propagation models. Furthermore, we compare the performance of mmWave 5G NR with the 4G Long-Term Evolution (LTE) standard in a realistic environment and demonstrate the impact of MIMO technology on improving the performance of 5G NR cellular networks. As transmitted radio signals are received as clusters of multipath rays, identifying these clusters provides better spatial and temporal characteristics of the channel. We address the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. We analyze how the clustering solution changes with narrower-beam antennas and provide a comparison of the cluster characteristics for different types of antennas.
(ii) Our framework for modeling wireless scenarios for large-scale emulators optimally scales down the large set of channel input to the fewer parameters allowed by the emulator using efficient clustering techniques and Channel-Impulse Response (CIR) re-sampling. We demonstrate the effectiveness of the proposed framework by modeling realistic scenarios for Colosseum, starting with rich input from commercial-grade ray tracing software, Wireless InSite (WI) by Remcom. To support mobility, we implement a mobile channel simulator on top of the WI ray-tracer, consisting of two steps: (a) spatially sampling the mobile channels using the ray-tracer, and (b) parsing the ray tracing outputs to extract the channels for each time instant of emulation. We also develop a Software-Defined Radio (SDR)-based channel sounder to precisely characterize emulated RF channels. The sounder framework is fully containerized, scalable, and automated to capture the gains and delays of the channel CIR taps.
(iii) We extend these efforts to develop the first Digital Twins for Mobile Networks (DTMN) on Colosseum, using the RF testbed Arena as a use case. This use case demonstrates the scope and capabilities of Colosseum as a DT, providing the research community with a set of tools to replicate real-world environments. We compare key network performance metrics, namely throughput and SINR, of the Arena/Colosseum DTMN to validate the fidelity of our twinning process. Furthermore, we present an AI-assisted propagation model to generate realistic, real-time, and scalable scenarios for DTMNs. This model seamlessly integrates measurements with ray tracing, providing a high-resolution, realistic channel model. We study the computational complexity and configuration trade-offs associated with ray tracing for high-fidelity prediction, generating a large dataset to train this enhanced AI model. Our proof of concept highlights the accuracy and generalization capabilities of our AI model across previously unseen transmitter (TX) locations and unfamiliar environments, outperforming state-of-the-art approaches and achieving significant improvements in accuracy. We analyze the computational complexity of our AI model, comparing it to high-fidelity ray tracing. Profiling reveals a three-order-of-magnitude acceleration, enabling real-time propagation prediction with reasonable accuracy. We explore key ray tracing parameters contributing to the discrepancy between measurements and simulations and demonstrate the integration of measurements into channel prediction, thereby calibrating the model.

Mehrshad Zandigohar PhD Proposal Review
2024-07-18 11:30 - 12:30
Announcing:
PhD Proposal Review
Name:
Mehrshad Zandigohar
Title:
Deployable and Multimodal Human Grasp Intent Inference in Prosthetic Hand Control
Date:
7/18/2024
Time:
11:30:00 AM
Location: https://teams.microsoft.com/l/meetup-join/19%3ameeting_N2QyNzc1MWMtOWJmMi00NGNmLThlNzctN2JlNjU2Y2I1MmI1%40thread.v2/0?context=%7b%22Tid%22%3a%22a8eec281-aaa3-4dae-ac9b-9a398b9215e7%22%2c%22Oid%22%3a%22de13c261-ac42-49d7-8950-6dec3adaca4e%22%7d
ISEC 532 –
Committee Members:
Prof. Gunar Schirner (Advisor)
Prof. Deniz Erdogmus
Prof. Mallesham Dasari
Prof. Mariusz P. Furmanek
Abstract:
For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. Among robotic control methods for prosthetic hand actuators, coarse-grained grasp types are a common means of effortless yet effective control. However, to advance next-generation prosthetic hand control design, it is crucial to address current shortcomings in robustness to out of lab artifacts, generalizability to new environments and deployment of such compute-intensive grasp estimators.
First and foremost, current control methods based on physiological modality such as electromyography (EMG) are prone to yielding poor inference outcomes due to motion artifacts, muscle fatigue, and many more. Similarly, methods based on visual modality are also susceptible to its own artifacts, most often due to object occlusion, lighting changes, etc. To address such drawbacks of single modality approaches, we present a multimodal evidence fusion framework for grasp intent inference using eye-view video, eye-gaze, and EMG from the forearm processed by neural network models. Given the lack of a synchronized multimodal dataset for evaluating multimodal grasp estimation, we propose our own customized HANDSv2 dataset with the most complete EMG profile and visual data synchronized in time. Our experimental results indicate that fusing both modalities, on average, improves the instantaneous upcoming grasp type classification accuracy while in the reaching phase by 13.66% and 14.8%, relative to EMG (81.64% non-fused) and visual evidence (80.5% non-fused) individually, resulting in an overall fusion accuracy of 95.3%.
Although visual grasp classification has shown promising results, the generalizability to unseen object classes remains a significant challenge within the research community. This limitation arises from the fixed number of grasp types available in existing models, contrasted with the virtually infinite variety of objects encountered in the real world. The poor performance of grasp detection models on unseen objects negatively affects users’ independence and quality of life. To address this, we propose Grasp Vision Language Model (Grasp-VLM). Grasp-VLM takes advantage of the zero-shotness capability of large vision language models and teach them to perform human-like reasoning to infer the suitable grasp type estimate based on the object’s physical characteristics suitable for previously unseen objects, resulting in better generalizability in real-life scenarios. Our initial results show a significant 49% accuracy of Grasp-VLM over unseen object types compared to 15.3% accuracy of the current State-of-the-Art.
Lastly, given the computational intensity of such models, which often contain billions of parameters, deploying them to edge devices poses a serious challenge. To mitigate this, we investigate Hybrid Grasp Network (HGN), a deployment infrastructure that combines an edge-specialized model for low-latency operations with a cloud-based universal model ensuring high generalization, effectively balancing performance and resource constraints.
The holistic approach presented in this dissertation tackles four essential areas of robotic prosthetic hand control design. Handsv2 provides a customized dataset filling the gap for a multimodal synchronized dataset. Our multimodal fusion approach effectively outperforms single modality approaches providing accurate and robust grasp type estimations during the entire grasping timeline. In addition, Grasp-VLM addresses the lack of generalizability to new object types providing a more realistic grasp estimation. Lastly, our HGN design aims at providing a real-time solution investigating both speed and accuracy objectives.

Jagatpreet Nir PhD Dissertation Defense
2024-07-18 11:00 - 12:00
Announcing:
PhD Dissertation Defense
Name:
Jagatpreet Nir
Title:
Low Contrast Visual Sensing and Inertial-Aided Navigation in GPS-Denied Environments
Date:
7/18/2024
Time:
11:00:00 AM
Committee Members:
Prof. Hanumant Singh (Advisor)
Prof. Martin Ludvigsen
Prof. Michael Everett
Prof. Pau Closas
Abstract:
Field robots perform complex tasks, necessitating high autonomy and reliable navigation capabilities. Integrating complementary sensors at the hardware level is crucial to maintaining navigation estimates even during sensor failure. This work is motivated by the need for robust and accurate navigation systems for robotic field applications, particularly in diverse and challenging environments. The development of such systems involves balancing design requirements with constraints such as size, weight, power, computational capacity, and cost. Underwater navigation exemplifies navigation in Visually Degraded Environments (VDEs), where Autonomous Underwater Vehicles (AUVs) and Remote Operated Vehicles ( ROVs) navigate in challenging conditions. This thesis focuses on exploring methods to enhance the robustness of visual-inertial odometry systems in VDEs.
The current state-of-the-art Visual Inertial Odometery (VIO) techniques provide high-accuracy navigation estimates in texture-rich scenes. However, robots operating in harsh and unpredictable environments, such as underwater, often encounter VDEs due to low texture, uneven illumination, or backscatter. During prolonged visual degradation, the Inertial Measurement Units (IMUs) become the primary sensor as visual measurements are unreliable. In this reserach, we address the problem of designing an underwater VIO navigation system and algorithmic pipelines to ensure reliable navigation estimates during several seconds of visual degradation, emphasizing the importance of selecting better Micro Electro Mechanical Systems (MEMS) IMUs for dependable performance within a cost budget.
A robust VIO system designed for underwater settings is introduced. Our contributions include a general system design approach for underwater VIO, an algorithmic formulation for fusing deep learning-based Visual Odometry (VO) with IMUs data. The underwater datasets depict visual degradation in real-world settings with a time-synchronized 8-bit grayscale camera and IMU. Our hybrid VIO pipeline integrates IMU measurements with VO estimates from a deep-learning VO engine, combining deep learning with classical sensor fusion techniques to achieve accurate metric and gravity-aligned trajectory estimates even in visually degraded conditions. The proposed system outperforms traditional VIO methods, demonstrating robustness with consistent trajectory estimates and minimal drift during complete visual outages. The extensible design allows for the incorporation of new sensors, addressing various underwater navigation challenges.
To conclude, this thesis focuses on environments where exteroceptive sensing, like cameras, is compromised for extended periods, relying on proprioceptive sensors such as IMUs to navigate. The aim is to quantify navigation accuracy in harsh environments and improve system design at both hardware and software levels. Specifically, underwater visual-inertial navigation for small vehicles is used to demonstrate the principles and algorithms developed. The outlined methodology showcases sensor selection, sensor-fusion algorithms, and individual improvements to build enhanced visual-inertial systems for VDEs and the applicability of the proposed approach from controlled settings to field tests.

Graduate Student Fellowship Writing Club-Hosted by NU CommLab and NetSI
2024-07-10 15:00 - 16:00
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.

ChE PhD Dissertation Defense: Mohammad Hamrangsekachaee
2024-07-01 13:00 - 15:00
PhD Dissertation Defense: Endothelial Glycocalyx: Response to Fluid and Solid Mechanics in its Environment
Mohammad Hamrangsekachaee
Location: Snell Library 033 and Zoom
Abstract: Atherosclerosis, a precursor to cardiovascular diseases (CVDs), accounts for 37% of deaths in individuals under 70 years old, primarily due to endothelial cell (EC) dysfunction. The glycocalyx (GCX), a carbohydrate-rich structure on ECs lining the vessel luminal surface, is crucial for EC function and vascular health by regulating vascular tone, hemostasis, permeability, and mechanotransduction. Therefore, cellular models emulating the vascular mechanical environment are vital for understanding GCX’s role and its interaction with mechanical surroundings. This dissertation introduces an innovative in vitro model to investigate the combined effects of tissue stiffness and shear stress on endothelial cell function.
Tunable non-swelling gelatin-methacrylate (GelMA) hydrogels were fabricated with stiffnesses of 2.5 and 5 kPa, representing healthy vessel tissues, and 10 kPa, corresponding to diseased vessel tissues. Immunocytochemistry analysis showed that on hydrogels with different levels of stiffness, the GCX’s major polysaccharide components exhibited dysregulation in distinct patterns. For example, there was a significant decrease in heparan sulfate expression on pathological substrates (10 kPa), while sialic acid expression increased with increased matrix stiffness.
GelMA hydrogels were then integrated into a flow chamber designed to generate physiological flow conditions. The combined effects of fluid shear stress and substrate stiffness were analyzed for heparan sulfate, sialic acid, hyaluronic acid, syndecan-1, CD44, and YAP. Under shear stress, heparan sulfate’s coverage was reduced at 10 kPa, while sialic acid and CD44 expression increased at 10 kPa. YAP activation
showed increased nuclear translocation and decreased phosphorylation at 10 kPa. Our findings revealed that substrate stiffness and mechanical forces significantly influence GCX expression and endothelial cell function.
This research highlights the critical role of the mechanical environment on GCX in vascular health, particularly in the context of atherosclerosis. By developing an innovative in vitro model that integrates tissue rigidity and shear stress, we have provided a more precise simulation of the vascular environment. This model offers a valuable tool for further understanding EC mechanotransduction and developing targeted treatments for cardiovascular diseases.

Graduate School of Engineering Campus Tour - In Person
2024-06-29 13:00 - 14:00
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!

Gordon Institute Virtual Information Session
2024-06-26 17:30 - 18:30
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.

Chemical Engineering Summer Sustainability Seminar Series: Ewa Marek
2024-06-24 12:00 - 13:00
Chemical looping for sustainable production of chemicals
The chemical industry is centred on the processing of crude oil and is responsible for the lion’s share of greenhouse gas emissions. Running industrial-scale installations requires huge energy inputs, employing high temperatures, pressures, and, often, explosive mixtures. The current industry is designed to work continuously, using cheap, petrochemical feedstock. Switching the starting point and moving from oil to renewable feedstock, whether it is bio-derived carbonaceous material, captured CO2, or renewable electricity, requires new decentralised and flexible operations, as all these resources are spatially distributed and available intermittently. Our work focuses on small-scale, dynamic processes, developing and employing new reaction and process pathways to produce key chemicals. This talk will present our work using a chemical looping approach where solid particles of metal oxides are the sole source of oxygen to catalytic reactions. In particular, I will discuss the chemical looping epoxidation (CL-E) of ethylene to produce ethylene oxide (EO) and chemical looping oxidative dehydrogenation (CL-ODH) of ethanol to produce acetaldehyde.

Bio
Dr. Ewa Marek is an associate professor at the Department of Chemical Engineering and Biotechnology, University of Cambridge and a fellow of Jesus College. She leads the Energy Reactions and Carriers Group, working on the production of value-added chemicals from intermittently available renewable feedstock and electricity, incorporating non-thermal plasma, ultrasounds and chemical looping to drive efficient, transient processes. This work led to 2021 Hinshelwood Prize and 2023 Energy&Fuels Rising Star Award for early-career academics. Before setting up her group, she was a post-doctoral associate in the Engineering Department, Cambridge, and earlier, she worked for six years on industrial R&D and advanced measurement methods in the UK, Netherlands and Poland. She studied energy and chemical processing (BEng, MSc) in Cracow and carbon capture (PhD) in Katowice (both in Poland).
Links
https://www.ceb.cam.ac.uk/staff/dr-ewa-marek
https://www.jesus.cam.ac.uk/people/ewa-marek

2024 ASEE Annual Conference
2024-06-23 - 2024-06-26
Join COE Graduate Admissions and the CHME Faculty at the 2024 ASEE Annual Conference in Portland, Oregon! Ask your questions about our graduate engineering programs across the U.S. and Canada during the conference from June 23rd-June 26th. We look forward to meeting you there!

5th Annual Bioengineering Symposium
2024-06-20 09:00 - 17:00 | ISEC Auditorium, 805 Columbus Ave, Boston, MA, 02115, United States
Join us for the 5th Annual Bioengineering Symposium in the ISEC Atrium and Auditorium. Every year, our bioengineering PhD students present their work at the Bioengineering Symposium in the form of Oral Presentations, Poster Sessions, and 5-minute Presentations. All are welcome to attend.
Tentative Schedule:
9:00am: Breakfast and Welcome from Abraham Joy, Professor and Chair
9:15am: Keynote Address by Faculty member
9:45am: Oral Presentations by PhD Students
10:45am: Poster Session 1
11:30am: Oral Presentations by PhD Students
12:30pm: Lunch
1:15pm: Poster Session 2
2:00pm: Oral Presentations by PhD Students
3:15pm: 5-minute thesis competition
4:50pm: Awards

Business Lessons in Technology and Leadership
2024-06-14 14:45 - 17:45 | East Village, 17th floor, 360 Huntington Ave, East Village 17th floor, Boston, MA, 02115, United States
Event for Faculty and Staff Only
Featuring Dunton Family Dean David De Cremer and Chess Grandmaster Garry Kasparov
Friday, June 14 | 2:45-5:45 p.m. | East Village Residence Hall, 17th Floor 291 St Botolph Street, Boston, MA 02115
Fireside chat between Dunton Family Dean David De Cremer and Chess Grandmaster Garry Kasparov, followed by a panel of practitioners and scholars leading digital transformation.
Garry Kasparov is widely considered the greatest chess player who ever lived. He gained global notoriety for his matches against the world’s strongest computers, including IBM’s Deep Blue.

Galante Luncheon Info Session
2024-06-13 12:00 - 13:00
Join us for the Galante Engineering Business Program Luncheon Info Session on Thursday, June 13th from 12 pm to 1 pm in Shillman 320. Features an overview, panel discussion, exclusive giveaways, and lunch! Please RSVP and email galante@northeastern.edu with any questions.
