ECE Graduate and Postdoctoral Studies Student Perspective Stories

Explore the vast range of research happening at ECE and read through student perspective profiles! To learn more please visit Graduate and Postdoctoral Studies

Hill, Ian

Optimal experiment design for accelerated testing of integrated circuit wear-out reliability
Computer chips are expected to last for tens of years, yet for most markets engineers only have a few months from product sample to release to test whether their designs will last long enough before failing. My research aims to improve the design of accelerated aging tests through quantifying the expected information gain of different test procedures and the development of on-chip sensors for monitoring degradation in semiconductor devices.
My supervisor! I talked to many professors at other universities in Canada but felt that my current supervisor would provide the best fit graduate experience for me based on how I work and learn.

Thompson, Grady

Analysis of stochastic directed acyclic graphs
My research involves determining different analytical techniques to solve for and approximate end-to-end distribution times of stochastic directed acyclic graphs. This has many applications in high performance computing and cloud computing. The specific process we are attempting to improve is determining the end-to-end time distribution of a task, where the task can be broken into subtasks and distribution of the subtasks is known. The challenging aspect of the problem is when the subtasks are dependent on each other, with some requiring others to finish before they can start. In this problem the time is often very difficult to calculate exactly, which leads to the need for good approximations.
When I was deciding on what program I wanted to do for my undergraduate degree I looked for which one had the most computer science and math courses. I decided to pursue Computer Engineering and take math and computer science for as many electives as I could. Graduate school in computer engineering offers the same benefit of being math and computer science focused, which naturally drew me to it.

Black, David

Novel approaches to teleoperation and human computer interaction in medical robotics
My research involves improving teleoperation by leveraging mixed reality (MR), high speed communication, and haptics. First, we are developing a “human teleoperation” system in which an expert remotely guides a novice person in the field via an MR interface over 5G. While the deployment of robots in the field to carry out tasks such as tele-ultrasound or maintenance of aircraft faces myriad problems, our system takes advantage of the flexible, intelligent, inherently safe nature of people, using a novel MR approach to achieve near robot-like precision and latency in "teleoperating" humans instead of robots. Visual, audio, and force feedback to the expert provides them with an intuitive, realistic experience akin to carrying out the task personally. Our primary focus is on the application of this technology to tele-medicine to improve healthcare for rural and Indigenous communities in Canada and beyond. In a closely related project, our lab has developed a novel force sensor for the patient side manipulators of the da Vinci surgical robot, and I have helped implement force sensing on the surgeon side interface as well. This dual force sensing setup on by far the most popular robotic surgical system is to our knowledge unique, and we are using it to implement haptic feedback for the surgeon, so they can feel the forces applied in the patient. This has the potential to decrease tissue damage, improve suturing, and ease the learning curve for novice surgeons on the robot.
During one of my co-op work placements, I worked with my now PhD supervisor, Tim Salcudean, at the Robotics and Control Lab (RCL). I developed a strong interest in some of the research being carried out in the RCL, and found that Tim’s style of supervision and the way the lab was run really clicked with how I like to work. Tim is a world leader in this field, with many great connections and opportunities for collaboration and internships, and the UBC Electrical and Computer Engineering graduate program and its ecosystem inside engineering and also computer science has a number of other fantastic faculty, staff, and students. Not only does this form a great network of talented people who collaborate and help each other with research, but it is also inspiring and motivating seeing the many interesting projects and spin-offs coming out of this program.

Roorda, Esther

Esther Roorda's image
Machine learning acceleration on FPGAs
Machine learning is a branch of computing that is increasingly used in various applications, from medical research to social media websites. Training and using machine learning models can require a massive amount of computation however, which may be prohibitively time consuming and energy intensive. My current research focus is on performing these types of computation using Field Programmable Gate Arrays (FPGAs), which can be configured to execute machine learning workloads faster and with less energy.
I was specifically interested in working with my advisor, Dr. Steve Wilton, who is an expert in the field of reconfigurable computing, and a very experienced and supportive supervisor. I also liked the rest of the research group, and the lab had a fun, friendly working environment.

Chen, Zitao

Headshot of Zitao Chen
Making machine learning reliable, secure and private
Machine learning (ML) has achieved remarkable performance in many tasks like image classification, and already seen great prospects in many real-world applications. ML can facilitate precision medicine to empower clinical decision making, maneuver the driving vehicle without human intervention. On the other hand, existing ML technology is also brittle and prone to failure that could entail critical consequence: (1) a hardware mistake can translate to a software failure that causes an ML model to exhibit unexpected behavior, such as misclassifying a stop sign as a speed limit sign; (2) the ML model can also be easily fooled by the inputs that have been tampered with; and (3) struggle to provide both high quality of service and strong privacy protection when the model is trained on sensitive data. This engenders serious concern on the trustworthiness of ML. My research concentrates on three prominent challenges (reliability, security and privacy) in trustworthy ML and advocates a multi-faceted solution to improve the reliability, security and privacy of ML to fully deliver the promise of benefits of ML.

Zhang, Wenwen

Pathological gait pattern analysis
Gait disorders caused by aging or disease (e.g. Parkinson, stroke) will severely decrease the quality of life or even endanger life safety. Current gold standard to monitor and analysis gait pattern is Gaitrite pressure pad system. However, GaitRite is constrained to strictly-controlled lab environment, and also suffers from foot step occlusion and imbalanced foot detection problem, which happen a lot in Parkinson and stroke patients. If wearable sensors embedded with inertial measurement units (IMUs) are able to proceed gait information and provide as accurate result as GaitRite does, timely daily tracking and imbalanced foot detection would be possible, while occlusion is avoided in the meanwhile. Our goal is to develop low-cost wearable sensors to quantify pathological gait parameters.
The Department of Electrical and Computer Engineering at UBC is a diversified place where we are able to get touched with different kinds of excellent people. We can explore varied fields from circuit fabrication to model design. This precious experience can help us find out where exactly our academic research interest lays as well as give us guidance on future directions.

Kalia, Megha

Making Augmented Reality Feasible for Robot Assisted Surgery
My research aims to make Augmented Reality (AR) feasible for robot assisted surgery. In robot assisted surgery a surgeon makes minimal precise incisions by reaching inside a patient’s body using miniature instruments and a camera mounted on robotic arms. The system while providing the same dexterity as human hands enables a precise and safe surgery leading to reduced recovery time from months to weeks in comparison to open surgery. However, like open surgery, the concerns of incomplete cancer removal remain; primarily because of unclear boundaries between healthy and cancerous regions in the camera image. The boundaries, although, are visible in medical imaging data such as Magnetic Resonance Imaging (MRI), constant juggling between the MRI and the camera image, while performing surgery, is impractical. Here, Augmented Reality can help by projecting information like tumor locations, retrieved from medical data like MRI, directly on top of the organ in real-time. Based on this, the surgeon can make the most optimum incision. However AR is not yet mainstream in robot assisted surgery because of the very low margin of errors needed and practical restrictions of safety and sterility in an operating theater. Simply put, my research is two-pronged: First part is to come up with the most accurate and real-time algorithms that need no external equipment during surgery to superimpose the tumours detected through MRI (for example) directly on the surgical camera view. The second part of my research relates more with the manner in which we display the tumour in the camera. More precisely, we need to find the balance of what is the best way of showing the tumour in the surgical camera view so that it does not create visual clutter and block the surgeon's view itself of where to make the incision.

Hosseinirad, Sara

Automated Closed-Loop System of Anesthesia
In the literature, the closed-loop anesthesia control has been proven to outperform manual control. These systems will allow anesthesiologists to run several operating rooms simultaneously as well as to maintain a high standard of quality; however, some technological developments are missing. One of them is the lack of an integrative system that includes the impacts of changes in anesthesia, fluid, cardiac output, etc. on each other. In this research, we aim at automating the entire anesthesia process. In the first step, we will design a new depth of hypnosis and analgesia control system and its associated safety system based on a novel, universal pharmacokinetic model of propofol and remifentanil, known as Eleveld3. In the second step, we will investigate multivariable control of the many aspects of anesthesia beyond the depth of hypnosis and analgesia, e.g., cardiac output, arterial pressure, temperature, etc.
I gained an interest in control engineering when I was an undergraduate student in Aerospace Engineering. Here at UBC, my supervisors and their research group are one of the pioneers in control engineering, and I believe involving in their outstanding research projects on the closed-loop system for anesthesia will create a lot of opportunities for me in the future. Researching under the supervision of Prof. Dumont allowed me to meet many great researchers and engineers and learn from their experiences.

Behnami, Delaram

Overview of the INFUSE framework for AI-driven assistance with echo.
A machine learning framework for automated diagnostic assistance in echocardiographic images
My research involves developing artificial intelligence (AI)-based frameworks for analysis, interpretation, and enhancement of medical images of various modalities, including ultrasound, CT and MR. My Ph.D. thesis focuses on developing machine learning solutions for analysis and understanding of echocardiographic (echo) images (ultrasound images of the heart), as part of the RCL’s Information Fusion for Echo (INFUSE) project. Such AI-based solutions can assist and augment clinical decision making for disease diagnosis and management, streamline the clinical workflow, and subsequently increase the patient throughput in the healthcare system.
I particularly love working in the intersection of biomedical engineering, computer vision, and machine learning--it is a research area that can have and potential large-scale impacts on people's health and well; and, it has a little bit of everything I like! My research group (Robotics and Controls Laboratory - RCL) focuses exactly on. We have access to a network of engineering and clinical professionals and state-of-the-art equipment for conducting research. Also, our department (ECE) offers courses relevant to this field.

Ahmed, Khaled

UBC graduate student Khaled Ahmed
UBC graduate student Khaled Ahmed
Protecting Mobile Phone Users Against Malware
My research is about protecting mobile phone users from malware. In particular, I aim to protect users from malware that uses logic bombs, a technique by which malware hides its malicious intents from malware analysis. This requires developing efficient code analysis techniques for monitoring app behaviours, malware detection technique that can operate on a slice of the code which contains the untested behaviour, and develop hardware accelerators that can run the analysis efficiently.
The chance to work with and learn from top professors in my field. There's also a good mix of offered courses.

Abdelaal, Alaa Eldin

UBC doctoral student Alaa Eldin Abdelaal
UBC doctoral student Alaa Eldin Abdelaal
Multi-Modal Teaching and Learning via Demonstration for Robot-Assisted Surgery
Medical errors are the third cause of death in the United States leading to around 250,000 deaths every year. The limited amount of training time available for residents and the outdated methods currently used for training, give rise to the need for a more efficient training framework to facilitate the acquisition of surgical skills and overcome the medical errors problem. The proposed research addresses this problem in robot-assisted surgeries. In these surgeries, a surgical robot is directly controlled by a surgeon to perform the procedure inside the patient's abdomen. This setup allows us to record valuable surgical data such as the surgeon's movements and eye gaze. We propose using the collected data to improve the training for novice surgeons in robot-assisted surgeries. Analyzing the collected data will enable us to understand what distinguishes experts from novices. Furthermore, this will enable us to understand the underlying principles of surgical skills. This understanding will then be used to develop innovative training methods that will allow experts to remotely transfer their skills to novices. The proposed research has the potential to revolutionize the way surgeons are currently trained. We collaborate with the leading firm producing surgical robots to deploy our training framework into their training facilities. The findings of this research can serve as new guidelines for companies producing surgical robot systems to improve their design to facilitate the acquisition of surgical skills. Beside surgical applications, the developed training methods have the potential of being applied in other areas of robotics such as training persons with disabilities to control their robotic assistance devices.
My supervisor, Prof. Septimiu E. Salcudean is a well-known figure in the area of medical robotics. Our lab (The Robotics and Control lab) has three da Vinci Surgical systems (from Intuitive Surgical Inc.); they all have "read" interfaces that allow one to track the positions of the instruments, while one of them has a da Vinci Research Kit (dVRK) that enables researchers to hack the system for their own purposes. Only 30 research groups worldwide have access to the dVRK interface, and only four of them are in Canada. The lab also has strong ties with the Vancouver General Hospital (with Drs. Peter Black, Larry Goldenberg and Chris Nguan). This facilitates the observation of real robotic surgeries and getting input from clinicians about my proposed work. Our lab also collaborates with other established groups including Dr. Gregory Hager's group at Johns Hopkins University. This makes it easy to have Dr. Hager as my co-supervisor.

Rahman, Ehsanur

Work function reduction of Carbon Nanotube ensemble through surface treatment for enhanced thermionic electron emission and its application in electron source and energy harvesting.
Thermionic electron emission from CNTs has recently been an emerging field of research due to the heat trap effect, which has significantly reduced the optical power intensity required to reach a thermionic emission temperature in CNTs compared to the bulk materials. The heat trap effect observed in CNTs is due to the highly anisotropic thermal conductivity along the nanotube axis. Moreover, the heat trap effect is observed over a wide spectrum of incident light which has made the CNTs particularly suitable for harvesting solar energy. Our research group at UBC has already demonstrated a simple yet effective thermionic solar converter based on CNTs, where we achieved a current and power density comparable to that of the state-of-the-art solar photovoltaic cells. If properly designed, CNT based thermionic solar converter can significantly exceed the efficiency of the conventional photovoltaic solar cell. However, the efficiency of the device reported by our group was quite low which can be substantially enhanced by reducing the work function of the CNTs. My PhD research is focused on reducing the work function of the CNT array by treating the surface of the ensemble with different low work function materials. I have shown that the work function of the CNTs can be reduced by introducing specific adsorbate materials using physisorption or chemisorption process. However, since the thermionic emission of electrons requires a very high temperature, not all the adsorbates can form a stable layer over the CNT surface at an elevated temperature. Therefore, while choosing an adsorbate for work function reduction, we need to consider the strength of the bond that the adsorbate makes with the CNTs. In my research, I will investigate the thermal stability of the bonds that the adsorbates form with the CNT and compare it with the extent to which the CNT work function is reduced by different adsorbates. Therefore, I will develop a mechanism to reduce the work function of the CNTs using thermally stable adsorption of specific materials, which would significantly enhance the performance of the CNT based thermionic electron emitters and make the cathode structure robust to high-temperature operation as necessary in thermionic emission.
I specifically chose to pursue my PhD in Electrical and Computer Engineering at the University of British Columbia as it has offered me a perfect combination of state-of-the-art research facilities and distinguished research faculties. More precisely, the resources available at UBC for experimental and computational research in my field of interest and collaboration with world-class researchers all around the world have provided me with a unique opportunity to excel here as a researcher and contribute to the scientific society.

Mohammad, Rafiuzzaman

Mohammad Rafiuzzaman, International Doctoral Fellow
Mohammad Rafiuzzaman, International Doctoral Fellow
Towards an adaptive, robust and efficient resource allocation system for highly dynamic resource constrained IoT environment.
Because of the Internet of Things or IoT in short, every day, more and more objects are getting online and there isn’t a single area of our life that won’t be touched by IoT devices in the next decade. Day by day, the advancement of microtechnology is enabling embedded IoT devices to perform more complex jobs written with high-level languages to get more productivity. But despite the increase of job complexity and advancement of emended devices, in terms of the “elasticity of available resources”, the IoT devices are still far more behind compared to the available resources in the cloud. Because of this, when we send programs to run on any IoT devices, we must be extra careful in terms of allocating resources, as the result of a small fraction of misallocation might be devastating, especially for the safety-critical jobs. Hence, we are in a great need for a new, efficient and fault tolerable resource management system for resource-constrained IoT devices in the forthcoming days where the classical and existing techniques won't work. My research focuses on finding a solution for this scenario.

Arshad, Rabe

User mobility management in wireless networks
Supporting user mobility is considered to be an intrinsic feature of wireless networks. With the massive deployment of wireless nodes to support increasing traffic demands, there exists a number of challenges in offering streamless services to users with mobility. Such user mobility issues need to be incorporated in the capacity planning phase and there arises a need to have mobility management techniques that could minimize the effect of user mobility on the desired data rate. As a part of my PhD, I am working on such novel mobility management techniques using tools from stochastic geometry to quantify and reduce the effect of user mobility in various wireless networks.
I came here with an industrial background and had the hope that I would go back to the industry after finishing my PhD. The best thing about my program is the opportunity to work in collaboration with the industry. I feel that this would really help me to land back in the industry in future.

Halawa, Hassan

Artemis: Defending Against Automated Large-Scale Cyber Intrusions by Focusing on the Vulnerable Population
State-of-the-art defenses against automated mass-scale cyber-attacks are mostly reactive and generally follow a ‘first-detect-then-prevent’ approach. This gives attackers the ability to evade detection by adjusting their tactics in order to circumvent the employed defenses and still reach the end-users. My research advocates for a proactive approach of identifying the vulnerable users, and employing this information to better protect them by building more robust and efficient system-wide defenses. Specifically, my work investigates novel defenses at the level of the system/infrastructure as well as at the level of individual users in large socio-technical systems. The goal is to develop techniques to identify the population of users vulnerable to various types of large-scale automated attacks. Then, using this knowledge to improve the robustness and efficiency of system-wide defenses, as well as to uncover ways to influence the behaviour of vulnerable users in order to decrease their susceptibility to large-scale attacks. For up-to-date information and links to recent publications, please visit the Artemis project webpage.