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.
2021-2022 Fall Graduate Seminars
|Date||Speaker||Institution & Country||Topic|
|October 25||Elif Tuğçe Ceran||METU, Turkey||A Reinforcement Learning Approach to Age of Information|
|November 8||Erdinç Tatar||Bilkent University, Turkey||Finding Solutions to the MEMS Gyroscope Drift Problem|
|November 15||Bo Ji *||Virginia Tech, USA||Fair Resource Allocation and Learning: Combinatorial Sleeping Bandits with Fairness Constraints|
|December 6||Igor Kadota *||Columbia University, USA||WiFresh: Age-of-Information from Theory to Implementation|
|December 13||Selçuk Yerci||METU, Turkey||Green Energy Transition: A Photovoltaics Perspective|
|January 10||Maher Al-Greer||Teesside University, UK||System Identification Techniques in Digital Control Design for DC-DC Switch Mode Power Converters|
* Different from our usual schedule, this seminar will be held at 17:40.
A Reinforcement Learning Approach to Age of Information by Elif Tuğçe Ceran
Date: October 25
Bio: Elif Tugce Ceran Arslan received the B.S. and M.S. degrees in Electrical and Electronics Engineering from METU, Ankara, Turkey in 2012 and in 2014 respectively, and Ph.D. degree in electrical and electronics engineering from Imperial College London (ICL) in 2019. Between 2020-2021, she was a postdoctoral researcher at Communication Networks Research Group, METU. In 2021, she joined the Electrical and Electronics Engineering Department at METU, where she is currently working as an Assistant Professor.
Her research interests lie mainly in the intersection between machine learning, wireless communications and the Internet of Things in addition to the resource allocation, 5G networks, distributed/federated learning, reinforcement learning, performance evaluation and optimization of computer networks.
Abstract: In recent years, reinforcement learning (RL) methods have attracted significant attention thanks to ground-breaking achievements in this area of research. Age of Information (AoI), on the other hand, is a recently proposed semantic performance metric that captures the timeliness of the information. This talk provides a comprehensive overview of reinforcement learning, with a special focus on deep reinforcement learning (DRL), and its applications on the communication problems minimizing the AoI. More specifically, the scheduling of sampling and transmission of status updates in order to minimize the long-term average AoI at the destination is studied under resource constraints. It is assumed that the underlying statistics of the system are not known, and hence, several RL methods including deep Q-Network (DQN) are exploited and their performances are demonstrated.
Finding Solutions to the MEMS Gyroscope Drift Problem by Erdinç Tatar
Date: November 8
Bio: Erdinc Tatar received B.S. and M.S. degrees (with high honors) in Electrical and Electronics Engineering from Middle East Technical University (METU), Ankara, Turkey, and Ph.D. degree in Electrical and Computer engineering from Carnegie Mellon University, Pittsburgh, PA, in 2008, 2010, and 2016 respectively.
He was a Graduate Research Assistant with Micro-Electro-Mechanical Systems Research and Applications Center, METU, and with Carnegie Mellon University from 2008 to 2011, and 2012 to 2016, respectively. From 2016 to 2019 he worked as a MEMS Design Engineer responsible for the development of next generation gyroscopes in Analog Devices, Inc., Wilmington, MA. Currently he is an assistant professor with the Department of Electrical and Electronics Engineering and UNAM in Bilkent University. His research interests include MEMS sensors (specifically Inertial and Gas sensors), microfabrication and packaging technologies, and readout and control electronics for MEMS sensors.
Abstract: Drift is one of the major performance limiting factors for most of the sensors. Environmental stress and temperature effects are believed to be the major source of the drift and the latter is studied the most in the literature. Certain enhancements are achieved but the sensor drift cannot be removed completely by temperature compensation. My PhD thesis successfully addressed the drift problem for gyroscopes by incorporating the stress sensor and the gyroscope on the same die for the first time. Since stress compensation achieved promising results, I will talk about our current research in Bilkent University that includes integrating capacitive stress sensors with a circular MEMS gyroscope to suppress the drift. A circular topology for a gyroscope enables the device to have a central support and surrounding electrodes on the periphery. This circular structure allows us to place the stress sensors next to the central anchor and surrounding electrodes. This close to ideal stress sensor-gyroscope integration is expected to result in better gyroscope - stress correlation and stress compensation.
Fair Resource Allocation and Learning: Combinatorial Sleeping Bandits with Fairness Constraints by Bo Ji
Date: November 15
Bio: Dr. Bo Ji received his B.E. and M.E. degrees in Information Science and Electronic Engineering from Zhejiang University, Hangzhou, China, in 2004 and 2006, respectively, and his Ph.D. degree in Electrical and Computer Engineering from The Ohio State University, Columbus, OH, USA, in 2012. Dr. Ji is an Associate Professor in the Department of Computer Science at Virginia Tech, Blacksburg, VA, USA. Prior to joining Virginia Tech, he was an Associate Professor in the Department of Computer and Information Sciences and a faculty member of the Center for Networked Computing at Temple University, where he was an Assistant Professor from July 2014 to June 2020. He was also a Senior Member of the Technical Staff with AT&T Labs, San Ramon, CA, from January 2013 to June 2014. His research interests are in the modeling, analysis, control, optimization, and learning of computer and network systems, such as wired and wireless networks, large-scale IoT systems, high performance computing systems and data centers, and cyber-physical systems. He has been the general co-chair of WiOpt 2021 and the technical program co-chair of ITC 2021, and he has also served on the editorial boards of various IEEE/ACM journals (IEEE/ACM Transactions on Networking, IEEE Transactions on Network Science and Engineering, IEEE Internet of Things Journal, and IEEE Open Journal of the Communications Society). Dr. Ji is a senior member of the IEEE and a member of the ACM. He is a National Science Foundation (NSF) CAREER awardee (2017) and an NSF CISE Research Initiation Initiative (CRII) awardee (2017). He is also a recipient of the IEEE INFOCOM 2019 Best Paper Award.
Abstract: The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters. The goal of the player (i.e., the decision maker) here is to maximize the cumulative reward in the face of uncertainty. However, the basic MAB model neglects several important factors of the system in many real-world applications, where multiple arms (i.e., actions) can be simultaneously played and an arm could sometimes be "sleeping" (i.e., unavailable). Besides reward maximization, ensuring fairness is also a key design concern in practice. To that end, in this talk we will first introduce a new Combinatorial Sleeping MAB model with Fairness constraints, called CSMAB-F, aiming to address the aforementioned crucial modeling issues. The objective is now to maximize the reward while satisfying the fairness requirement of a minimum selection fraction for each individual arm. To tackle this new problem, we extend an online learning algorithm, called Upper Confidence Bound (UCB), to deal with a critical tradeoff between exploitation and exploration and employ the virtual queue technique to properly handle the fairness constraints. By carefully integrating these two techniques, we develop a new algorithm, called Learning with Fairness Guarantee (LFG), for the CSMAB-F problem. Further, we rigorously prove the feasibility-optimality and a regret upper bound of LFG. Finally, we will present simulation results that corroborate the effectiveness of the proposed algorithm. Interestingly, the simulation results reveal an important tradeoff between the regret and the speed of convergence to a point satisfying the fairness constraints.
WiFresh: Age-of-Information from Theory to Implementation by Igor Kadota
Date: December 6
Bio: Igor Kadota received the B.S. degree in electronic engineering from the Technological Institute of Aeronautics (ITA), Brazil, in 2010, the S.M. degree in telecommunications from ITA in 2013, the S.M. degree in communication networks from the Massachusetts Institute of Technology (MIT) in 2016, and the Ph.D. degree from the Laboratory for Information and Decision Systems (LIDS) at MIT in 2020. He is currently a Postdoctoral Research Scientist in the Department of Electrical Engineering at Columbia University. His research is on modeling, analysis, optimization, and implementation of emerging communication networks, with the emphasis on wireless networks and time-sensitive traffic. Igor was a recipient of several awards, including the Best Paper Award at IEEE INFOCOM 2018, the MIT School of Engineering Graduate Student Extraordinary Teaching and Mentoring Award of 2020, and the 2019-2020 Thomas G. Stockham Jr. Fellowship. For additional information, please see: http://www.igorkadota.com
Abstract: Emerging applications, such as autonomous vehicles and smart factories, increasingly rely on sharing time-sensitive information for monitoring and control. In such application domains, it is essential to keep information fresh, as outdated information loses its value and can lead to system failures and safety risks. The Age-of-Information (AoI) is a recently proposed performance metric that captures the freshness of the information from the perspective of the destination. In this talk, we consider a wireless network with a base station receiving time-sensitive information from a number of nodes through unreliable channels. We formulate a discrete-time decision problem to find a transmission scheduling policy that optimizes the AoI in the network. First, we derive a lower bound on the achievable AoI performance. Then, we develop three low-complexity scheduling policies with performance guarantees: a randomized policy, a Max-Weight policy and a Whittle’s Index policy. Leveraging our theoretical results, we propose WiFresh: a simple yet unconventional architecture for wireless networks that achieves near optimal AoI. To demonstrate the impact of WiFresh in real operating scenarios, we deploy and validate our architect
Green Energy Transition: A Photovoltaics Perspective
Date: December 13
Bio: Dr. Selçuk Yerci received his B.S. and M.S. in physics from Middle East Technical University and Ph.D. in Electrical Engineering from Boston University. His M.S. and Ph.D. research was mainly focused on silicon photonics, in particular silicon compatible light sources. After completing his Ph.D. he worked as a post-doctoral associate at the Massachusetts Institute of Technology on thin-film crystalline silicon solar cells. Dr. Yerci is currently an assistant professor in the Micro and Nanotechnology Programme, and Electrical and Electronics Engineering at Middle East Technical University. Dr. Yerci continues his research activities in his research group (app.mnt.metu.edu.tr) under the Center for Solar Energy Research and Applications (GUNAM) laboratories (gunam.metu.edu.tr). His recent research is mainly focused on high-efficiency solar cells including material growth, device simulation, and device fabrication aspects. Dr. Yerci has authored/co-authored over 50 articles and holds an h-index of 21 according to WOS. Dr. Yerci has received the Young Researcher Awards from the Turkish Academy of Science in 2017, Parlar Vakfı in 2019, and Science Academy in 2020, Incentive Award from TUBITAK in 2021.
Abstract: Solar power offers the cheapest electricity in history. The roar of PV has been mainly based on the implementation of well-developed know-how on silicon microelectronics at a lower cost. Recently, new ideas have been suggested to further increase the conversion efficiency of PV cells. This talk will start with an overview of the green energy transition and PV research. Then, I will discuss the methods suggested to increase the efficiency of PV cells, more specifically, to reduce the cost/kWh. Finally, I will focus on light management in photovoltaics to boost efficiency.
System Identification Techniques in Digital Control Design for DC-DC Switch Mode Power Converters
Date: January 10
Bio: Maher Al-Greer Received the B.Sc. degree in electrical engineering and the M.Sc. degree in computer engineering from the University of Mosul, Iraq, in 1999 and 2005, respectively, and Ph.D. degree in Electrical and Electronic Engineering from Newcastle University, Newcastle upon Tyne, U.K, in 2012. In 2016 he joined the Electrical Power Research Group, as a Research Associate at Newcastle University worked on Town & Country Hybrid Powertrain (TC48) Innovate UK project. He joined Teesside University in 2017 as a Lecturer, currently, he is a Senior Lecturer in Electrical power engineering at the school of computing, engineering, and digital technologies at Teesside University. His main research interests include battery systems management and control, signal processing, AIs, ML in condition monitoring and control, digital power control and adaptive control techniques for power applications, diagnosis and monitoring, smart energy systems, and energy management. Currently, he is an Associate Editor of IET Power Electronics Journal, Guest member of staff in the school of engineering at Newcastle University, co-chair of Smart Energy Management, Conversion and Control research group at Teesside University. In 20019, he received the university star award for research performance.