Seminerler

Bu sayfa, EE690 Lisansüstü Seminerleri dersi kapsamında gerçekleştirilecek seminerler hakkında ilgili katılımcıları bilgilendirmek amacıyla oluşturulmuştur. Program ve içerik dönem boyunca güncellenecektir.


2023-2024 Bahar Dönemi Lisansüstü Seminerleri

Seminer Programı
Tarih Saat Yer Konuşmacı Kurum & Ülke Konu
4 Mart 13:40 D-231 Oğuz Ergin TOBB Ekonomi ve Teknoloji Üniversitesi (ETÜ), Ankara, Türkiye A Potpourri of Ideas and Results in DRAM, FPGA Infrastructures, and RISC-V Processors
11 Mart 13:40 D-231 Orhan Arıkan Bilkent Üniversitesi, Ankara, Türkiye Semantic Signal Processing and Communications
18 Mart 13:40 D-231 Y. Ahmet Dicle Orta Doğu Teknik Üniversitesi / ASELSAN, Ankara, Türkiye

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

1 Nisan 13:40 Çevrimiçi (bağlantı) Zafer Doğan Koç Üniversitesi, İstanbul, Türkiye Demystifying the learning dynamics in high dimensional setting

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

Özet: 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.

Özgeçmiş: 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.


Başlık: Semantic Signal Processing and Communications

Özet: 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.

Özgeçmiş: 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.


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

Özet: 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

Özgeçmiş: 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

ASELSAN
    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

Before ASELSAN
    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)


Başlık: Demystifying the learning dynamics in high dimensional setting

Özet: 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.

Özgeçmiş: 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.


Son Güncelleme:
29/03/2024 - 00:56