Northeastern University College of Engineering

Poster Design and Presentation: CommLab Drop-In Workshops
2024-06-20 17:00 - 18:00, 2024-06-27 17:00 - 18:00, 2024-07-11 17:00 - 18:00, 2024-07-18 17:00 - 18:00 (America/New_York)
The CommLab will host drop-in workshops for poster design and presentation to focus on crafting the best visual communication of your research and telling your research story! We will discuss techniques and implement communication strategies to successfully showcase your work. No matter where you are in the process, whether it is just in the idea phase or you are trying to polish your final poster, we are happy to help you. Join us any Thursday from 5-6pm, on Zoom.

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!

CommLab Drop-In Writing Hours
2024-06-24 10:00 - 12:00, 2024-07-01 10:00 - 12:00, 2024-07-08 10:00 - 12:00, 2024-07-15 10:00 - 12:00
Graduate students, are you looking for a place for focused research writing time? Join the CommLab drop-in writing hours any Mondays from 10 am-12 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 13 International Village.

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

Small Talk for Networking: CommLab Drop-In Workshops
2024-06-25 15:00 - 16:00, 2024-07-02 15:00 - 16:00
Join the CommLab any Tuesday from 3-4 PM ET for our weekly Small Talk for Networking Drop-In Workshop in room 13 International Village or virtually through Zoom. This workshop series will offer you the ability to practice techniques and use essential tools to improve your conversational skills and connect with others at networking events.

LeetCode Mock Interviews – CommLab Drop-In Workshops
2024-06-25 16:00 - 17:00, 2024-07-02 16:00 - 17:00, 2024-07-09 16:00 - 17:00, 2024-07-16 16:00 - 17:00
Join the CommLab any Tuesday from 4-5 PM for our weekly LeetCode Mock Interview Workshop via Zoom. This workshop is tailored towards programming jobs and prior coding knowledge is expected. Boost your LeetCode problem-solving confidence for interviews by building your speaking skills while solving programming problems.

LinkedIn, CV, Resume: CommLab Drop-In Workshops
2024-06-26 16:00 - 17:00, 2024-07-03 16:00 - 17:00, 2024-07-10 16:00 - 17:00, 2024-07-17 16:00 - 17:00
Join the CommLab’s empowering LinkedIn, CV, and Resume Drop-In Workshops any Wednesday from 4 pm to 5 pm ET. This collaborative space offers valuable advice and peer feedback to enhance your online profile and professional presence. Join this workshop series through Zoom.

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.

Mock Interview: CommLab Drop-In Workshops
2024-06-27 16:00 - 17:00, 2024-07-11 16:00 - 17:00, 2024-07-18 16:00 - 17:00
Join the CommLab any Thursday from 4-5pm ET, we’ll delve into the intricacies of interviews, unveiling effective preparation strategies for any interview scenario. Engage in an interactive setting as we dissect the overall interview experience, discuss common interview scenarios, and share insights on what to do during critical moments. Join this hybrid workshop series through Zoom.

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!

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 Student Fellowship Writing Club-Hosted by NU CommLab and NetSI
2024-07-10 15:00 - 16:00, 2024-07-17 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.

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.

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.
