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.

2023-2024 Spring Graduate Seminars

Seminar Schedule
Date Time Location Speaker Institution & Country Topic
March 4 13:40 D-231 Oğuz Ergin TOBB University of Economics and Technology (ETÜ), Ankara, Türkiye A Potpourri of Ideas and Results in DRAM, FPGA Infrastructures, and RISC-V Processors
March 11 13:40 D-231 Orhan Arıkan Bilkent University, Ankara,Türkiye Semantic Signal Processing and Communications
March 18 13:40 D-231 Y. Ahmet Dicle Middle East Technical University / ASELSAN, Ankara, Türkiye Research and Development for Original Products and the Importance of University-industry collaboration
April 1 13:40 Online (link) Zafer Doğan Koç University, İstanbul, Türkiye Demystifying the learning dynamics in high dimensional setting
April 15 13:40 Online (link) Sırma Örgüç Massachusetts Institute of Technology, Cambridge, MA, USA Programmable Interfaces for Biomedical and Neuroscience Applications
April 29 13:40 D-231 Ali Özgür Yılmaz Middle East Technical University, Ankara, Türkiye From Conceptualization to Implementation: Innovations in Communications and Radar Systems
May 6 13:40 Online (link) Burak Acar Boğaziçi University, İstanbul, Türkiye Brain Network Analysis With Application to Alzheimer’s Disease
May 20 13:40 D-231 Murat Göl Middle East Technical University, Ankara, Türkiye Role of Energy Communities in Modern Power Systems

Title: A Potpourri of Ideas and Results in DRAM, FPGA Infrastructures, and RISC-V Processors

Abstract: In this talk, I will give an overview of some recent ideas and results in DRAM based random number generation, FPGA based DRAM testing and evaluation infrastructures, undervolting in FPGAs, and RISC-V processor design. This presentation will cover innovative advancements in True Random Number Generators (TRNGs) and FPGA efficiency. Specifically, the talk describes QUAC-TRNG, a high-throughput TRNG that can be implemented in commodity DRAM chips, demonstrating substantial improvements in throughput and utilization compared to state-of-the-art DRAM-based TRNGs. It also introduces DR-STRaNGe, an end-to-end system design addressing the key challenges associated with DRAM-based TRNGs, achieving significant improvements in performance, fairness, and energy consumption. The talk will outline a new experimental infrastructure, DRAM Bender, and briefly discuss the Processing-in-DRAM framework (PiDRAM) to facilitate in-memory processing. The presentation will further explore the effects of undervolting FPGAs on the efficiency and accuracy of convolutional neural network benchmarks, including the surprising impact of temperature and humidity on fault rates. Finally, the ongoing work on utilizing the occurring faults in FPGA undervolting for random number generation (TuRaN) will be discussed, reflecting a broad exploration of cutting-edge techniques in TRNGs, DRAM technology, and FPGA efficiency.
Before we conclude our presentation, we will explain the RISC-V based processor core design efforts in our KASIRGA research group.

Bio: Oguz Ergin is a professor and chair in the department of computer engineering, TOBB ETÜ, Ankara, Turkey. He received his BS degree in electrical engineering from Middle East Technical University in 2000 and MS, PhD degrees from State University of New York at Binghamton in 2003 and 2005 respectively. He was a senior research scientist in Intel Barcelona Research Center during 2004 and 2005. For the last 18 years he has been a faculty member in TOBB ETÜ. He was a visiting associate professor in the University of Notre Dame in 2014, a visiting researcher in University of Edinburgh in 2015-2016 and a visiting professor in ETH Zürich, as part of EFCL and SAFARI in 2023.

Title: Semantic Signal Processing and Communications

Abstract: Recent advances in machine learning enabled real time extraction of semantic information at the sensor node enabling goal oriented semantic signal processing and communications. For efficient goal-oriented signal processing on the extracted semantic information, a hierarchical graph-based semantic language is proposed.  In this way, semantic filtering of the extracted information can be achieved at the sensor node with a dramatic reduction in the rate of communication in the network.  The proposed semantic signal processing and communications framework can easily be tailored for specific applications and goals in a diverse range of applications. As illustrated over simulated and real sensor data, the proposed framework enables several orders of magnitude reduction in the rate of communication.

Bio: In 1986, Prof. Orhan Arıkan received his B.Sc. degree in Electrical and Electronics Engineering from the Middle East Technical University, Ankara. Following his graduation as the salutatorian, he started his graduate studies in Electrical Engineering at University of Illinois Urbana-Champaign. Focus of his graduate research was on radar signal processing. During his graduate studies he was awarded with the Schlumberger Research Fellowship. He received M.S. and Ph.D. degrees in Electrical and Computer Engineering in 1988 and 1990, respectively.

Following his graduate studies, Prof. Arıkan has worked for three years as a Research Scientist at Schlumberger-Doll Research Center, Ridgefield, CT. In 1993, he joined Electrical and Electronics Engineering Department of Bilkent University. His research interests are in the areas of semantic signal processing, statistical signal processing and remote sensing. He served as the chairman of the department in 2011-2019. Currently, Prof. Arıkan is the Dean of Engineering Faculty at Bilkent University.

In 1998, Prof. Arıkan received the Distinguished Teaching Award of Bilkent University. In 2002, he received the Young Investigator Award in Engineering from Turkish Scientific and Technical Research Foundation. He has served as the Chairman of IEEE Signal Processing Society, Turkey Section in 1995-1996 and served as the President of IEEE Turkey Section in 2000-2001.

Title: Research and Development for Original Products and the Importance of University-industry collaboration

Abstract: How can University and  Industry collaborate. Can they ever get along? The role government plays in university industry collaboration. Why would universities and industries collaborate? Societal Impact. Student outcomes. What helps university-industry collaborations succeed? How can a university find an industry partner? How does collaborating with industry affect citation impact?

METU Electrical and Electronics Department’s role in ASELSAN’s initial establishment and later development.

Importance of R&D work and University-industry collaboration for Turkey

Bio: Was born in Zonguldak in year 1957. After graduating from the Middle East Technical University-Department of Electrical & Electronical Engineering has worked for 4 years in various laboratories as a Researh Assistant in METU-EEE. Has worked in ASELSAN in Turkey and abroad together with various internatioanal and local companies in many projects and has managed various departments. Has errected three factories in Kazakhistan, in Jordan and Sivas (Turkey) which were owned by the Joint Venture Companies whose %50 share was owned by ASELSAN. Facilities Project Planing and Design, Construction, Supervision, Maintenance work was done during these installations. Besides Facilities Engineering has managed the departments of Human Resources, IT, Defence Security, Administrative Services as the Director of Facilities Engineering. Has worked in ASELSAN Macunköy Facilty during the second development phase between 1984- 1988 and has worked in ASELSAN Akyurt Facility since year 1986 since the master plan of the facility had been designed. After working 33 years as an engineer and engineering manager (Director there after) and as a member of ASELSAN Management Team has retired in 2017 completing 60 years age limit according to the ASELSAN company retirement rules. Is a registered TUBITAK Business Development Mentor for the Start up Firms.

MIDDLE EAST TECNICAL UNIVERSITY Part Time Instructor (Science and Technology Policies Institute- Technology Management and Globalisation Course (Msc) 2018-

BİLKENT UNIVERSITY Part Time Instructor (Department of Business Administration-Technology Management Course (Msc) 2018-2019 Fall Semester

    Director (ASELSAN Akyurt Facilities – Facility Engineering) 01/2005- 09/2017 (ret.)
    Director (Microelectronics Guidance and Electo Optics Division (MGEO) – Akyurt Facility Support Services) 04/2004 01/2005
    Manager (MGEO – Facilities and Maintenance) 01/2005- 03/1990
    Assist. Manager (Production) 03/1990- 08/1989
    Assist. Manager (Construction Department) 08/1989-10/1984

    Middle East Technical University-Electrical Electronics Engineering Department (Research Assistant) (High Voltage, System, Electrical Machinery, Energy Systems Analysis Laboratories) 11/1980-10/1984
    Middle East Technical University-Electrical Electronics Engineering Department (Student Assistant- Digital Electronics) 03/1977-06/1977

MSc: Middle East Technical University-Electrical Electronics Engineering Department / 1984 (Thesis: Transformer Circuits Under Unbalanced Conditions
BSc: METU / Electrical Electronics Engineering Department / 1980
High School: TED Ankara College/ 1975

Facultyof Politcal Science/Marketing Foundation –Management Program (6 Months)/1993

METU-Center of Education (SEM) Human Resources Education (3 Months)/1998

MAJOR Projects and work done in ASELSAN:
1-ASELSAN Macunköy Facilities Construction Project (Project, Construction, Control, Supervision, Maintenance Phases)
2- Preperation of ASELSAN Akyurt Facilities Master Plan. Construction and Foundation of ASELSAN Akyurt Facilities (Project, Construction, Control, Supervision, Maintenance Phases)
3-Stinger Project Clean Room, De-Ionised Water Plant, Liquid Nitrogen Plant Project and Constuction Phases together with General Dynamics and Tsuchiyama Kaino Engineers Supervision, Operation and Maintenance of the whole Facilities.
4- Installation of Akyurt Facilities Local Area Network and IT Infrastructure and being in charge of Department of Data Systems and IT.
5- Working as the MGEO Division Coordinator in the Project “ASELSAN’s Transition and upgrade from Enterprise Management Software MANMAN to SAP” .
6- Foundation of KAE (Kazakhistan –ASELSAN Engineering) Facilities in Kazakhistan.
7- MEA – ASELSAN Middle East (Jordan) Facilities Construction and Commissioning.
8- Erection of ASELSAN Sivas Optics Company Facility in Sivas.
9- Akyurt Integrated Management System (EYS) Foundation. (ISO 18001 OHSAS and ISO14001 Enviroment Management Systems, Energy Management Systems Foundtion with Bureau Veritas)
10- Worked in various Human Resources, IT, Industrial Security Programs and Projects.
11-Worked as the General Coordinator of MGEO (Microelectronics Guidance and Electro Optics) Division Executive Board. MGEO Representative of Board of Inovation and New Ideas. Was a member of ASELSAN Directors Board.
12- TUBİTAK ‘Business Development Mentor for Startup Firms’ (METU Research and Development Students Union “University4society 2017” Mentor. METU Business Development Union “NASA Space Apps Challenge 2017” Mentor)

Title: Demystifying the learning dynamics in high dimensional setting

Abstract: The remarkable success of deep learning algorithms has revolutionized the field of machine learning. However, theoretical understanding of their learning dynamics is still limited. Statistical physics and random matrix theory emerge as two powerful tools to unravel the mystery of the empirical success of these models. Statistical physics aims to map large dimensional learning problems into physics-inspired problems of interacting with many particles. Such methods have been tremendously successful in understanding the macroscopic properties of many-body interactive systems. As such, the generalization ability of neural networks can be analyzed for a range of typical-case scenarios unlike the worst-case type bounds which are provided by traditional learning theory approaches. Therefore, they are gradually recognized as indispensable tools in the study of large-dimensional learning problems. Yet, new analysis frameworks are required to capture the full dynamics of deep networks. Alternatively, many new random matrix ensembles arise in learning problems. As growing interest indicates, the spectral properties of these matrices may help answer crucial questions regarding the training and generalization performance of deep networks, and the fundamental limits of high-dimensional learning. Unlike their classical counterparts, these new random matrices are often highly structured and are the result of nonlinear transformations. This combination of structure and nonlinearity leads to substantial technical challenges when applying existing tools from random matrix theory to these new ensembles. In this talk, I will provide an overview of the two approaches with the ultimate goal of achieving a complete understanding of the theory of deep learning. Specifically, I will provide some recent examples of an exact characterization of the asymptotic training and generalization errors of high-dimensional learning by exploiting a general universality concept in high dimensional setting.

Bio: Zafer Doğan is currently working as an assistant professor of electrical and electronics engineering at Koç University, where he leads the Machine Learning Information Processing (MLIP) Research Group under the KUIS Artificial Intelligence Research Center. His research is focused on the dynamics of learning algorithms for large-scale non-convex optimization problems, the interpretability and explainability of artificial learning models, and the theoretical understanding of well-behaved non-convex optimization and deep learning frameworks. He also explores specific applications in artificial learning frameworks to provide stability, tractability, and reproducibility features. This comprehensive understanding of these models will be key to achieving continuous advancement in the field and to the widespread acceptance of data-driven systems in the future.

Before his current position, he was a postdoctoral research fellow at the John A. Paulson School of Engineering and Applied Sciences at Harvard University from 2016 to 2019, and at the Campus Biotech of Ecole Polytechnique Federale de Lausanne (EPFL) in Geneva from 2015 to 2016. He received his Ph.D. and M.Sc degrees in electrical engineering from EPFL, Switzerland, in 2015 and 2011, respectively. His Ph.D. work focused on sparse signal representation in data processing, including sparse signal representations, sampling and approximation, multiresolution analysis, and the theory of finite rate of innovation, as well as inverse problems in nonlinear tomography and neuroimaging. He obtained his B.Sc. degree in Electrical and Electronics Engineering from Middle East Technical University (METU) in 2009.

Title: Programmable Interfaces for Biomedical and Neuroscience Applications

Abstract: The rapidly changing fields of biomedical sciences and neuroscience increasingly adopt scientific and technological innovations to advance the diagnosis and treatment of disease. This talk explains the design of various interface systems at the intersection of electronics, algorithms, and material science to enable innovations in biomedical engineering and neuroscience previously undiscovered by isolated investigation.

Bio: Sirma Orguc is a postdoctoral associate at the Institute of Medical Engineering and Science at the Massachusetts Institute of Technology (MIT). She received her M.S. in 2016, and Ph.D. in 2021 from Electrical Engineering and Computer Science Department at MIT. During her Ph.D., she worked on developing wearable and implantable devices for medical and neuroscience applications, focusing on overcoming challenges around efficiency and size. After her Ph.D., as a Schmidt Science Fellow, she pivoted her research to build optimized control systems in closed-loop neuroscience to allow the study of various neuroscience problems such as depth of unconsciousness under anesthesia and psychiatric disorders.

Title: From Conceptualization to Implementation: Innovations in Communications and Radar Systems

Abstract: In this presentation, Prof. Yılmaz will offer a retrospective exploration of his career spanning over two decades in the fields of communications and radar systems. He will discuss how these disciplines, though not recognized as intrinsically linked until recently, have continually enriched each other through the cross-pollination of ideas and methodologies. Prof. Yılmaz will highlight key milestones and breakthroughs that have characterized his journey as both a faculty member and a company founder, demonstrating how innovations in one field have consistently spurred advancements in the other.

The presentation will focus on innovative applications of low-resolution ADCs that are crucial in today's hardware-constrained environments, illustrating how they significantly enhance system efficiency and reliability. Additionally, Prof. Yılmaz will address the escalating importance of computing and software in the age of artificial intelligence, emphasizing the pivotal roles of GPUs, TPUs, and DPUs in advancing edge computation capabilities for signal processing tasks.

Prof. Yılmaz will conclude by sharing his insights into the future directions of communications and radar technologies, highlighted by real-world applications and the potential of machine learning and integrated approaches.

Bio: Prof. Yılmaz earned his B.S. degree in Electrical Engineering and his M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, MI, USA, in 1999, 2001, and 2003, respectively. His research spans multi-antenna systems, low-complexity transceiver design, the impact of transceiver non-idealities, radar system design, integrated communication and sensing, and the application of machine learning in radar detection problems. He is actively engaged in cross-disciplinary collaborations and maintains strong ties with various industry partners to develop high-technology products, leveraging his extensive theoretical and practical expertise to foster innovations that address real-world problems. 

Title: Brain Network Analysis With Application to Alzheimer’s Disease

Abstract: Human brain is undoubtedly the most mysterious part of our body. Its mysteries have been subject to countless efforts from physiological and psychological perspectives, yet we still know very little. In this talk, we will first briefly go over modern network modeling of the brain using MRI data. More specifically, we will see how structural and functional network models, a.k.a. connectomes, are built, while highlighting the weak points in these approaches. In the second part, following a brief introduction of Alzheimer’s Disease Dementia (ADD), we will talk about some of the AI/ML research our group has conducted on brain connectomes for ADD diagnosis.

This seminar is not meant to be a comprehensive review of the vast0 research domain of connectiomics but rather aims at igniting some interest in the subject matter which poses a very rich set of research questions yet to be solved.

Bio: Dr. Acar was born in 1972 in Izmir, Turkey. He received his BS, MS and PhD degrees, all in electrical and electronics engineering, from Bilkent University, Ankara, Turkey, in 1994, 1996 and 2000 respectively. His PhD thesis was on Electrocardiogram (ECG) signal processing for diagnostic decision making and risk stratification, during which he spent a year at St. George’s Hospital Medical School, Dept. of Cardiac and Vascular Sciences. He was at the Stanford University, Medical School, Dept. of Radiology, 3D Lab. as a post-doc researcher between 2000 and 2003. He joined Bogazici University, Electrical and Electronics Eng. Dept, in 2003, where he is currently professor of  Electrical and Electronics Eng. He was a Mercator Visiting Professor at TU Munich, Chair for Computer Aided Medical Procedures & Augmented Reality, Germany, between 2012-2013. Dr. Acar is affiliated with Bogazici University, Center for Life Sciences and Koc University, KUIS AI Lab. His research interests are in the fields of signal & image processing and analysis in multiple domains (inc. medicine, industrial, finance), machine learning applications towards diagnostics and prognostics. Dr. Acar is the recipient of the Excellence in Research Award from Bogazici University Foundation (2006) and the Young Researcher Award from Turkish Academy of Sciences (2008).

Title: Role of Energy Communities in Modern Power Systems

Abstract: Energy generation and consumption trends have been changing for the last couple of decades because of both the technological development and political/social progress. Energy communities can be an effective means of re-structuring our energy systems, by empowering citizens to drive the energy transition locally and directly benefit from better energy efficiency, lower bills, reduced energy poverty and more local green job opportunities, according to the European Commission.

In this talk, role of electrical engineering in green transition from energy communities’ perspective will be presented. In addition to smart grid and micro-grid approaches, place of distributed generation, storage systems, IBRs and heat pumps in realization of energy community concept will be covered. Finally, the role energy communities and its components in the modern power systems will be discussed.

Bio: Murat Göl received his B.Sc. and M.Sc. degrees from Orta Doğu Teknik Üniversitesi- ODTÜ, Department of Electrical and Electronics Engineering in 2007 and 2009 respectively. He received his Ph.D. in 2014 from Northeastern University, Boston, USA.

He is currently a faculty member at ODTÜ. He was elevated to ‘IEEE Senior Member’ status in 2017 and has been awarded the title of 'Associate Professor' in 2018. He received the Young Scientists Award Program Funding (BAGEP) in 2021. He serves as an associate editor for the IEEE Transactions on Smart Grid, and as a reviewer for various journals. Dr. Göl is a member of the Advisory Board of Turkish Electricity Transmission Corporation. His areas of expertise are power systems analysis, modeling, and real time monitoring and control of power systems.





Son Güncelleme:
17/05/2024 - 01:40