This page is created to inform any interested participants regarding the seminars to be conducted for the EE690 Graduate Seminars course. The schedule and the content will be updated throughout the semester.
2024-2025 Fall Graduate Seminars
| Date | Time | Location | Speaker | Institution & Country | Topic |
|---|---|---|---|---|---|
| Oct 18 | 12:40 | D-231 | Aykut Koç | Bilkent University, Ankara, Türkiye | Devising Transformers as an Autoencoder for Unsupervised Multivariate Time Series Imputation |
| Nov 8 | 17:30 | Online | Kaan Sel | Massachusetts Institute of Technology, Cambridge, MA, USA | Digital Medicine for Cardiovascular Health |
| Nov 15 | 12:40 | D-231 | Halil Ersin Söken | METU, Ankara, Türkiye | Fault-tolerant Attitude Determination and Control System Design for Small Satellites |
Title: Devising Transformers as an Autoencoder for Unsupervised Multivariate Time Series Imputation
Abstract: Time-series data processing is essential across various fields, including healthcare, transportation, and weather forecasting. Multivariate time-series data, in particular, exhibit a correlation pattern over a common independent variable. This is illustrated by concurrent sensor readings in applications like autonomous driving or multiple channels in data collection devices used in medical diagnoses. However, the increasing incidence of data acquisition failures, including sensor malfunctions and human errors, results in gaps and substantial loss of information. We propose a novel method called Multivariate Time-Series Imputation with Transformers (MTSIT) to tackle these challenges. This method employs an unsupervised autoencoder model with a transformer encoder to leverage unlabeled observed data for simultaneous reconstruction and imputation of multivariate time series. The MTSIT strategy presents an input sequence with gaps (missing patterns) to the transformer encoder. The final encoder block produces an output sequence that is linearly transformed into the imputed sequence. The Mean Squared Error (MSE) is subsequently computed between the missing values and their predicted imputations, guiding the network’s training toward minimizing the MSE.
Bio: Aykut Koç received the B.S. degree from the Electrical and Electronics Engineering Department, the M.S. and Ph.D. degrees in electrical engineering from Stanford University, Stanford, CA, USA, in 2005, 2007, and 2011, respectively. He is a Faculty Member with the Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey. He has authored or co-authored more than 80 research papers and one book chapter and issued five patents. His research interests are machine learning and signal processing, extending into natural language and graph signal processing. He currently serves as an Associate Editor of IEEE SIGNAL PROCESSING LETTERS, IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, and IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY. He is the current Chair of IEEE Signal Processing Society (SPS) Turkiye Chapter. He received the 2023 Science Academy Young Scientists Award (BAGEP).
Title: Digital Medicine for Cardiovascular Health
Abstract: Over the past decade, the concept of real-time sensing has seen unprecedented advances, where a myriad of individuals has now access to wide choices of smart commercial off-the-shelf (COTS) wearables that can easily monitor vital biometrics such as heart rate on a continuous basis. Despite these advancements, extracting complex cardiovascular parameters from wearable device measurements for precision medicine remains a challenge, due to several unmet needs; limited availability of advanced sensing paradigms, substantial physiological heterogeneity among individuals, limited access to ground truth data at personalized levels for disease states, the data-intensiveness of artificial intelligence (AI)-based modeling of complex input (sensor measurements) and output (complex cardiovascular parameters) relationships, the disparity between computational model parameters and sensor measurements.
This seminar presents several topics towards developing advances sensors and algorithms coupled with human physiology to enable continuous and unobtrusive monitoring of complex cardiovascular health parameters. We will present simulations on electrical models of biological tissues to drive the design, optimization, and placement of sensors. Additionally, we will discuss novel sensing paradigms utilizing novel form-factors: rings, electronic tattoos, distributed patches, harnessing the deep tissue sensing capabilities of bioimpedance, achieving medical-grade accuracies in blood pressure (BP) estimation. Furthermore, we will introduce the concept of physiology-driven AI modeling, which leverages our existing knowledge of human physiology and real-time measurements to uncover hidden and complex BP information, reducing the dependence on ground truth data.
The integration of next-generation wearables and AI will have a significant impact on precision medicine, revolutionizing traditional medical practices that heavily rely on outdated, bulky, and invasive systems. By motivating the use of AI and advanced wearable sensors, in view of human physiology, we aspire to usher in a new era of personalized, effective, and accessible medical care. With this seminar we will cover key challenges of extracting clinical parameters from wearable device measurements and potential solutions through bioimpedance sensors, physiology-driven sensing paradigms, and AI modeling, to provide an understanding of the advancements in cardiovascular health monitoring and the potential for personalized, accessible medical care.
Bio: Dr. Kaan Sel is a postdoctoral associate at the Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA. He earned his Ph.D. degree in computer engineering from Texas A&M University, as well as his B.Sc. and M.Sc. degrees in electrical and electronics engineering, and a B.Sc. (double major) degree in physics from Middle East Technical University in Ankara, Turkey. His research focuses on wearable and mobile systems, computational cardiovascular modeling, and scientific machine learning for digital health. Dr. Sel has authored over 20 papers published in reputable journals and conferences and holds multiple patent applications. Dr. Sel has served as a reviewer for prestigious journals including Nature, ACM, and IEEE, as well as for various IEEE conferences. He has received the runner-up award for the IEEE Engineering in Medicine and Biology Society student paper competition in 2019. Additionally, Dr. Sel has been recognized for his academic excellence, receiving the Charles Bowman and Lynn Holleran International Endowment Academic Excellence Award in 2022 and the Shibata International Memorial Scholarship in the same year.
Title: Fault-tolerant Attitude Determination and Control System Design for Small Satellites
Abstract: Today, we are seeking faster progress in space activities. New mission concepts, led by cheap and affordable small satellites, are expanding the possibility of space research to more people. As a consequence, spacecraft attitude determination and control (ADC) have become an even more attractive research field. Despite the shrinking sensors and actuators, we need to propose solutions for ADC systems that are as accurate as those for larger spacecraft. One of the major challenges for small satellite ADC systems is surely the vulnerability of the system to faults. A small satellite is more open to system faults due to its small, commercial off-the-shelf equipment, which is used without comprehensive testing procedures. The aim of this talk is to give an overview of the challenges we face when designing the ADC system for small satellites and then review the approaches for designing a fault-tolerant ADC system. Specifically, we will discuss the multi-algorithmic hybrid approaches that integrate the ADC algorithms with the Fault Detection Isolation and Recovery (FDIR) schemes.
Bio: Personal webpage.