Computer Engineering on Computer Engineering | BOUN

Mission Statements

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Boğaziçi University

Boğaziçi University’s vision is to be a leading institution of higher education, one that shapes the future through being a pioneer in education, teaching, and research. The primary tenets of our vision are to

Enrich the education and teaching experiences with innovative and creative approaches;
Strengthen the culture of science, research, creativity, and innovation to become one of the leading research universities in the world; and to
Contribute to shaping a better future through our academic, scientific, and cultural activities.

Boğaziçi University’s mission is to educate individuals who embrace it’s institutional values, who are able to think both creatively and critically, who are independent and egalitarian, who appreciate ethical values, who respect nature and are aware of environmental issues, who are rooted in the local and open to the global, and who can, with their self-confidence and scholarly, social and cultural foundations, successfully take on leadership positions anywhere in the world.

Overview

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The undergraduate program in Computer Engineering is designed so that students have a balanced background in computer hardware, software, and computer applications, and that they can adapt themselves to the rapidly changing technologies in their professional careers. Emphasis is on the engineering aspects of the field such as the analysis and design of both hardware and software systems.

The course requirements for the Bachelor of Science in Computer Engineering total a minimum of 145 credit hours of formal course work. The first five semesters of the program are common to all students. Starting in the sixth semester, students take elective courses in order to specialize in the subject according to their interests. During their study students must complete an industrial training of a minimum of 40 work days.

Curriculum (Pre-2025)

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First Semester
Code	Course Title	Prerequisites	Credits	ECTS
MATH101	Calculus I	—	4	6
PHYS121	Introductory Mechanics & Thermodynamics	—	4	8
CHEM105	Fundamentals of Chemistry	—	4	6
CMPE150^[a]	Introduction to Computing	—	3	5
EC101	Principles of Microeconomics	—	3	6
			18	33
Second Semester
Code	Course Title	Prerequisites	Credits	ECTS
MATH102	Calculus II	MATH101	4	6
PHYS201	Physics III	PHYS121	4	6
HSS^[j]	Humanities and Social Sciences Elective	—	3-4	5-6
CMPE160	Introduction to Object Oriented Programming	CMPE150	4	8
EC102	Principles of Macroeconomics	EC101	3	6
			18-19	31-32
Third Semester
Code	Course Title	Prerequisites	Credits	ECTS
MATH201	Matrix Theory	—	4	5
PHYS202^[h]	Physics IV	PHYS201	4	6
EE210	Introduction to Electrical Engineering	—	3	6
CMPE220	Discrete Computational Structures	—	3	5
CMPE250	Data Structures and Algorithms	CMPE160	4	6
TK221^[k1]	Turkish for Native Speakers I	—	2	3
			20	32
Fourth Semester
Code	Course Title	Prerequisites	Credits	ECTS
MATH202	Differential Equations	MATH201	4	7
EE212	Introduction to Electronic Engineering	EE210	3	4
CMPE230	Systems Programming	CMPE160	4	6
CMPE240^[b]	Digital Systems	—	4	6
CMPE260^[e]	Principles of Programming Languages	CMPE250	3	5
TK222^[k1]	Turkish for Native Speakers II		2	3
			20	32
Fifth Semester
Code	Course Title	Prerequisites	Credits	ECTS
CMPE300	Analysis of Algorithms	CMPE250	3	5
CMPE322	Operating Systems	CMPE250	4	6
CMPE343	Introduction to Probability and Statistics for Computer Engineers	MATH101	3	6
CMPE344^[d]	Computer Organization	CMPE240	4	6
IE310	Operations Research	MATH201	4	5
HTR311^[k2]	History of the Turkish Republic I		2	3
			20	32
Sixth Semester
Code	Course Title	Prerequisites	Credits	ECTS
CMPE321^[c]	Introduction to Database Systems	CMPE250	4	6
CMPE350	Formal Languages and Automata Theory	CMPE220	3	5
CMPE352^[g]	Fundamentals of Software Engineering	—	2	4
CMPE362	Introduction to Signal Processing for Computer Engineers	MATH202	3	6
IE306	Systems Simulation	CMPE343	4	7
HTR312^[k2]	History of the Turkish Republic II		2	3
			18	29
Seventh Semester
Code	Course Title	Prerequisites	Credits	ECTS
CMPE443^[f]	Principles of Embedded Systems Design	CMPE240	4	6
CMPE451^[g]	Project Development in Software Engineering	CMPE321, CMPE352	2	5
CC^[i]	Complemetary Course Elective		3-4	6
CC^[i]	Complemetary Course Elective		3-4	6
CC^[i]	Complemetary Course Elective		3-4	6
			15-18	29
Eighth Semester
Code	Course Title	Prerequisites	Credits	ECTS
CMPE492	Computer Engineering Design Project	SENIOR	4	8
HSS^[j]	Humanities and Social Sciences Elective		3	6
CC^[i]	Complemetary Course Elective		3-4	6
CC^[i]	Complemetary Course Elective		3-4	6
CC^[i]	Complemetary Course Elective		3-4	6
			16-19	32

Curriculum Notes

^[a] The course CMPE101 will replace CMPE150 starting from the Autumn 2026 semester.
^[b] The course CMPE142 will replace CMPE240 starting from the Spring 2027 semester.
^[c] The course CMPE222 will replace CMPE321 starting from the Spring 2028 semester.
^[d] The course CMPE244 will replace CMPE344 starting from the Spring 2027 semester.
^[e] The course CMPE320 will replace CMPE260 starting from the Spring 2027 semester.
^[f] The course CMPE346 will replace CMPE443 starting from the Autumn 2028 semester.
^[g] The course CMPE354 will replace CMPE352 and CMPE451 starting from the Spring 2026 semester.
^[h] Science Elective courses will replace PHYS202 starting from the Spring 2026 semester. These courses must be selected from the List of Science Elective Courses.
^[i] Complementary Course Elective courses must be selected from the List of Complementary Courses. At least three Complementary Course Elective courses must be from the CMPE department.
^[j] Humanities and Social Sciences Elective courses must be selected from the List announced by the university each semester.
^[k1] Foreign students must take TKF courses in place of TK221/TK222.
^[k2] Foreign students must take HTR405/HTR406 in place of HTR311/HTR312.

Curriculum 2025

Mon, 01 Jan 0001 00:00:00 +0000


First Semester
Code	Course Title	Prerequisites	Credits	ECTS
MATH101	Calculus I	—	4	6
PHYS121	Introductory Mechanics & Thermodynamics	—	4	8
CHEM105	Fundamentals of Chemistry	—	4	6
CMPE101	Introduction to Computer Engineering	—	4	6
EC101	Principles of Microeconomics	—	3	6
		Total	19	32
Second Semester
Code	Course Title	Prerequisites	Credits	ECTS
MATH102	Calculus II	MATH101	4	6
PHYS201	Physics III	PHYS121	4	6
CMPE142	Digital Systems	—	4	6
CMPE160	Object Oriented Programming	CMPE101	4	8
EC102	Principles of Macroeconomics	EC101	3	6
		Total	19	32
Third Semester
Code	Course Title	Prerequisites	Credits	ECTS
MATH201	Matrix Theory	—	4	5
EE210	Introduction to Electrical Engineering	—	3	6
CMPE220	Discrete Computational Structures	—	3	5
CMPE230	System Programming	CMPE160	3	6
CMPE250	Data Structures and Algorithms	CMPE160	3	6
TK221^[f1]	Turkish Language and Literature I		2	3
		Total	18	31
Fourth Semester
Code	Course Title	Prerequisites	Credits	ECTS
MATH202	Differential Equations	MATH201	4	7
EE212	Introduction to Electronic Engineering	EE210	3	4
CMPE222	Introduction to Database Systems	CMPE250	3	6
CMPE244	Computer Organization	CMPE142	4	5
XSCI^[c]	Science Elective		3	6
TK222^[f1]	Turkish Language and Literature II		2	3
		Total	19	31
Fifth Semester
Code	Course Title	Prerequisites	Credits	ECTS
CMPE300	Analysis of Algorithms	CMPE250	3	5
CMPE322	Operating Systems	CMPE250	3	6
CMPE343	Introduction to Probability and Statistics for Computer Engineers	MATH101	3	6
CMPE346	Principles of Embedded Systems Design	CMPE244	4	6
IE310	Operations Research	MATH201	4	5
HTR311^[f2]	History of Turkish Republic I		2	3
		Total	19	31
Sixth Semester
Code	Course Title	Prerequisites	Credits	ECTS
CMPE320	Principles of Programming Languages	CMPE250	3	5
CMPE350	Formal Languages and Automata Theory	CMPE220	3	5
CMPE354	Software Engineering	CMPE222	4	7
CMPE362	Introduction to Signal Processing or Computer Engineers	MATH202	3	6
IE306	System Simulation	CMPE343	4	7
HTR312^[f2]	History of Turkish Republic II		2	3
		Total	19	33
Seventh Semester
Code	Course Title	Prerequisites	Credits	ECTS
CMPE^[a]	Departmental Elective		3	6
CMPE^[a]	Departmental Elective		3	6
CC^[b]	Complementary Course Elective		3	6
HSS^[d]	Humanities and Social Sciences Elective		4	6
XSPX^[e]	Specified Elective		3	5
		Total	16	29
Eighth Semester
Code	Course Title	Prerequisites	Credits	ECTS
CMPE492	Computer Engineering Design Project	SENIOR	4	8
CMPE^[a]	Departmental Elective		3	6
CC^[b]	Complementary Course Elective		3	6
CC^[b]	Complementary Course Elective		3	6
HSS^[d]	Humanities and Social Sciences Elective		3	5
CMPE400	Engineering Internship		0	10
		Total	16	41

Curriculum Notes

^[a] Departmental Elective courses must be selected from courses with codes CMPE4XX or CMPE5XX. Eligibility conditions for CMPE5XX courses apply.
^[b] Complementary Course Elective courses must be selected from the List of Complementary Courses.
^[c] Science Elective courses must be selected from the List of Science Elective Courses.
^[d] Humanities and Social Sciences Elective courses must be selected from the list announced by the university each semester.
^[e] Any course listed under Complementary Course Elective, Science Elective, or Humanities and Social Sciences Elective courses is eligible to be counted as a Specified Elective course.
^[f1] Foreign students must take TKF courses in place of TK221/TK222.
^[f2] Foreign students must take HTR405/HTR406 in place of HTR311/HTR312.

History

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Founded in 1982, the Department of Computer Engineering has played a leading role in shaping technological advancement in Turkey. The department’s undergraduate program, designed to meet the rigorous standards of the IEEE/ACM Joint Curriculum Committee, graduated its first graduates in 1986. That same year, the department expanded its offerings with the launch of MS and PhD programs in Computer Engineering.

Milestones of Growth

1976: The groundwork for computer education is laid with the establishment of a Computer Science graduate program within the Faculty of Arts & Sciences.
1982: The Department of Computer Engineering is established within the Faculty of Engineering, marking a significant step towards specialized computer education. The department’s commitment to quality is evident from the outset, with the undergraduate program designed to meet the standards set by the IEEE/ACM Joint Curriculum Committee. To focus resources on this new program, admissions for the Computer Science program are temporarily paused.
1986: The department celebrates its first graduating class in the Computer Engineering undergraduate program. Recognizing the growing need for advanced degrees, the department launches MS and PhD programs in Computer Engineering.
1988: Reflecting its rapid development, the department moves to a new home in the “Educational Technology Building” This move facilitates not only research and education within Computer Engineering but also allows the department to expand its services to the entire university. This includes offering computer literacy education and associated PC laboratory services:

Five PC laboratories with a total of 60 computers, including a pilot classroom for Computer-Aided Education.
Educational laboratories focused on Software Engineering and Computer Hardware.
Research laboratories dedicated to Artificial Intelligence, Computer-Aided Design, and Robotics.
A minicomputer laboratory with a 7-terminal PDP 11/23 minicomputer.

1992: Demonstrating its commitment to continuous improvement, both undergraduate and graduate programs are revised to adhere to the latest guidelines published by the IEEE/ACM Joint Curriculum Committee. Additionally, service courses are reorganized to enhance their effectiveness. The department flourishes, reaching a total enrollment of 360 students, including 57 graduate students.
1998: The department achieves a significant milestone with international accreditation by the ABET board, a testament to the high quality of its educational programs.
2003: The Software Engineering MSc program is launched to address the growing industry demand for software professionals. This program provides an opportunity for four-year graduates to refine their skills and acquire the necessary expertise for successful careers in software engineering.
2004: The department successfully renews its ABET accreditation, demonstrating its ongoing commitment to maintaining its exceptional standards.
2006: The “Educational Technology Building” undergoes modernization and reinforcement to enhance earthquake safety. Following these renovations, the department, now boasting 19 full-time and 8 part-time faculty members along with 21 research and teaching assistants, returns to its renovated building.
2010, 2016 & 2022: The department’s commitment to excellence is further solidified with successful renewals of its ABET accreditation.

Department Statistics

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Latest Enrollment Data

2021 - 2022
Prep. Class	98
Freshman	108
Sophomore	103
Junior	91
Senior	250
Total	650
Graduated (2022)	N/A

Historical Enrollment Data

Academic Year	Total Enrolled including Prep. Class
2020 - 2021	650
2019 - 2020	629
2018 - 2019	615
2017 - 2018	589
2016 - 2017	570
2015 - 2016	522

Historical Graduation Statistics

Calendar Year	Total Graduated
2021	80
2020	96
2019	81
2018	76
2017	79
2016	67

Elective Courses

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Humanities and Social Sciences (HSS) courses are an integral part of the engineering curriculum at Boğaziçi University. These courses are designed to complement technical training by expanding students’ understanding of human society, culture, ethics, and critical thinking. They help engineering students develop a broader perspective on the social and ethical dimensions of their work.

Administration

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Chair

Arzucan Özgür

Professor

Vice Chairs

İnci Baytaş

Associate Professor

Double Major Degree Program

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Overview

A double major program enables Boğaziçi University students to concurrently obtain two separate undergraduate diplomas for two separate undergraduate programs.

Double Major Programs Directive at Boğaziçi University encompasses provisions regarding student admissions and registration procedures for double major programs conducted at Boğaziçi University, as well as regulations pertaining to the implementation of double major program practices. You can check the directive on the University’s page.

Academic Advisors

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All students enrolled in the Computer Engineering program are assigned an academic advisor, who is a full-time faculty member within the department. These advisors play a crucial role in providing guidance and support to students on a wide range of issues, including adjustment to university life, academic direction, and career planning.

Academic advisors are responsible for monitoring the courses students register for and approving their academic schedules. It’s important to note that course registration is only considered finalized once it receives approval from the student’s advisor.

Minor Degree Program

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Overview

A minor program enables Boğaziçi University students to obtain a non-degree certificate by completing a limited number of courses in a specific field within another undergraduate program at the University.

Minor Degree Programs Directive at Boğaziçi University encompasses provisions regarding student admissions and registration procedures for minor degree programs conducted at Boğaziçi University, as well as regulations pertaining to the implementation of minor degree program practices. You can check the directive on the University’s page.

Advisory Board

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Sina Afra

[I18n] CEO & Founder

Semih Akar

[I18n] Director

Çiğdem İltemir Carino

[I18n] Assistant General Manager

İlker İnanç

[I18n] CEO & Founder

Günseli Karadere

[I18n] CEO

Ekrem Kocagüneli

[I18n] Research Scientist

Sema Oktuğ

Professor

Mehmet Subaşı

[I18n] Director

Exchange Programs

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International Student Mobility

The Computer Engineering Department encourages its students to consider studying abroad through exchange programs, which offer a unique opportunity to explore the field from a global perspective.

Studying abroad enriches the experience of students in computer engineering careers, preparing them to adapt to new environments and understand diverse problem-solving approaches in the field. Exposure to different educational and cultural contexts expands academic horizons and cultivates essential skills for success in the globalized tech industry.

Gender Equality

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Boğaziçi University aims to advance gender equality in research, academia, and administration. Several University departments work toward objectives including boosting the visibility and involvement of women in social and academic life, collecting and updating the relevant records, and expanding the database of female statistics in these areas.

Industrial Training

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Objective

The main objective of Industrial Training is to contribute to the education of the students of the Faculty of Engineering by providing them practical, technological, as well as managerial experience and knowledge in nonacademic organizations.

Regulations

Each student must complete a total of at least 40 working days of training as a partial requirement for an undergraduate degree in engineering.
Each department determines the areas and minimum durations of training according to its own engineering formation and educational program, and announces them to the students. The duration of each training cannot be less than 15 working days. The total requirement can be completed in one or more organizations. If the requirement is split into two internships, planning each internship as 20 working days is recommended.
It is the responsibility of the department’s training coordinator to coordinate and guide training activities in conformity with these bylaws.
Students who have successfully completed their undergraduate program, but have not fulfilled their training requirements properly are not allowed to register. These students have to complete their training by the end of the first semester after they complete their coursework in the undergraduate program.
At the end of each training, the documents listed below must be submitted to the training coordinator within one month after the beginning of the next academic semester. The total delay in days in the submission of these documents after the deadline will be subtracted from the duration of training.

Required Documents

Training Booklet: This refers to either a specially formatted booklet containing several issues related to training and available from the University Bookstore and from the Faculty website, or to a report the format of which is determined by the department. This booklet is to be filled in by the students in English and it consists mainly of the following three parts:

Academic Integrity

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Definitions

Cheating includes, but is not limited to, the following actions:

Copying using unauthorized materials, copying from someone else’s paper, allowing others to copy from his/her own paper, or giving unauthorized assistance to others during a test or examination.
Working with others in completing a take-home examination or assignment unless the instructor has allowed independent action or allowed any specific aid could be accepted by students.
Submitting of material in whole or part for academic evaluation that has been prepared by another student(s), individual(s) or community agency.
Submitting, without prior permission of the instructor, any work by a student which has at any time been submitted in identical or similar form by that student in fulfillment of any other academic requirement at any institution.
Marking or submitting an examination or evaluation material in a manner designed to deceive the grading system.
Obtaining in a fraudulent manner any material relating to a student’s academic work (theft of examination, collusion with a university employee, etc.).
Attempting to influence or change an academic evaluation, grade, or record by unfair means.
Permitting another student to substitute for one’s self in any academic evaluation.
Wilfully damaging the academic work or efforts of another student.
Trying to deceive the instructor and/or assistant by any means such as denying the submission of a homework/project.

Actions Against

The following actions will be taken against all sides involved in cheating:

Senior Projects

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Our senior-year students in computer engineering demonstrate their brilliance and skills through a variety of specialized senior projects. These projects showcase their proficiency in areas such as software/system development, hardware design, robotics, artificial intelligence, and machine learning. From applying cutting-edge algorithms and designing advanced computer systems to developing innovative software applications and addressing robotic challenges, our computer engineering seniors push the boundaries of technological innovation and make significant contributions to the field before embarking on their professional careers.

Graduation Cerenomies

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156th Graduation Cerenomy

155th Graduation Cerenomy

Faculty

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Full-time Members

Lale Akarun

Professor

Fatih Alagöz

Professor

Şefik Şuayb Arslan

Professor

Academic Staff

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Teaching Assistants

Furkan Akkurt

Teaching Assistant

Deniz Aksoy

Teaching Assistant

Arda Arslan

Teaching Assistant

Support Staff

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Administrative Staff

Çeşminaz Eser

Buket Konak

System and Network Administrator

Technical and Building Staff

Suna Keziban Baştuğ

Building Staff

PhD in Computer Engineering

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Overview

The PhD program at the Department of Computer Engineering consists of three stages:

Courses

This stage consists of eight technical elective courses and the PhD seminar course.

Qualifying Exam

M.S. in Computer Engineering

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Overview

The Master of Science program at the Department of Computer Engineering consists of two stages:

Courses

This stage consists of seven technical elective courses and the MS seminar course.

Thesis Stage

Affiliated Programs

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Cognitive Science

The Master of Arts Program in Cognitive Science aims at introducing the students to the investigation of cognitive processes of various dimensions. It is offered through the cooperation of the Computer Engineering, Linguistics, Philosophy and Psychology Departments.

Systems and Control Engineering

The Master of Science Program in Systems and Control Engineering is open to the graduates of engineering and science majors, who would like to get a graduate degree in the interdisciplinary area of Systems Analysis and Control Theory. It is offered through the cooperation of the Electrical & Electronics Engineering, Computer Engineering, Mechanical Engineering, and Industrial Engineering Departments.

Assoc. Prof. İnci Meliha Baytaş Receives the BAGEP 2026 Award

Sun, 05 Apr 2026 00:00:00 +0000

Assoc. Prof. İnci Meliha Baytaş has been awarded the 2026 Young Scientist Award (BAGEP) by the Science Academy. The BAGEP program supports early-career researchers and recognizes outstanding academic achievements across disciplines.

The award ceremony for the 2026 recipients is planned to take place on Saturday, April 18, at Sabancı University.

We congratulate Assoc. Prof. Baytaş on this achievement and wish her continued success in her research.

To learn more about the 2026 BAGEP results, you can visit the official announcement here: https://bilimakademisi.org/bagep-2026-sonuclari-aciklandi/

Gökberk Çelikmasat received the best paper award in PROFES '25 Conference.

Wed, 10 Dec 2025 00:00:00 +0000

Our master’s student, Gökberk Çelikmasat, received the best paper award at the PROFES ‘25 conference held in Salerno, Italy, from December 1-3, 2025, for his work titled “Generating Business Process Models with Open Source Large Language Models using Instruction Tuning”.

Congratulations Dr. Goshgar Can Ismayilov!

Fri, 02 May 2025 00:00:00 +0000

Decentralized Privacy-Preserving Collective and Multi-Objective Trading Protocols on Blockchain with Zero-Knowledge Proofs

In this thesis, we propose decentralized privacy-preserving cryptographic protocols on blockchain with zero-knowledge proofs for three essential problems. Firstly, the privacy-preserving payment problem refers to a specific group of transactions where a source address (i.e. sender) transfers a certain amount of tokens to a destination address (i.e. receiver) while still protecting the privacy of their balances and transaction details. We extend this problem to address multi-token payments as well. Secondly, the privacy-preserving aggregation problem refers to a multi-party computation where a group of blockchain addresses (i.e. aggregators) aggregate their individual data to reach the global aggregation by still protecting the privacy of their own data. We also extend this problem to address prefix aggregation and to support for arbitrary numbers of aggregators. Thirdly, privacy-preserving multi-token bartering problem refers to a multi-party computation where a group of blockchain addresses (i.e. barterers) collectively exchanges a set of tokens in return for another set of tokens through proposing bids by still protecting the privacy of their balances and bids. We extend this problem to address multi-objective bartering using Bellman-Ford algorithm and pareto-domination. We propose the PTTS protocol for the first problem, the PVSS and PRFX protocols for the second problem and the PMTBS and zkMOBF protocols for the third problem. We analyze these protocols in terms of their scalability (as computational, communication and storage overheads) and their security (as potential attacks and reduction proofs). We perform experimental evaluation on Ethereum and Avalanche to measure blockchain gas consumption, proof generation/verification times and proof artifact sizes.

Congratulations Dr. Barış Yamansavaşçılar

Fri, 21 Mar 2025 00:00:00 +0000

Efficient Orchestration Methods in Air Computing Using Deep Reinforcement Learning

Abstract

After the proliferation of smart devices and diverse Internet of Things (IoT) requirements, we observe the dominance of cutting-edge applications with ever-increased user expectations in terms of mobility, pervasiveness, and real-time response. Over the years, to meet the requirements of those applications, cloud computing provides the necessary capacity for computation, while edge computing ensures low latency. However, these two essential solutions would be insufficient for next-generation applications due to the highly dynamic load. Therefore, we put forward a novel, next generation paradigm called air computing that presents a dynamic, responsive, and high-resolution 3D computation environment for all spectrum of applications. To this end, we first investigate efficient task orchestration through deep reinforcement learning (DRL) considering terrestrial resources via edge computing. Afterwards, to evaluate the corresponding air computing environments correctly, we design and develop our novel discrete event simulator, AirCompSim. Subsequently, we focus on the unknown user locations in an infrastructure-less environment in which users cannot connect to any communication device or computation-providing server, which is essential to task offloading in order to achieve the required quality of service. Thus, we propose a novel DRL-based scheme, DeepAir, which uses four main phases including sensing, localization, resource allocation, and multi-access edge computing. Performance evaluation results show that our DRL-based methods outperform the benchmark methods including heuristics and fuzzy logic. Thus, problems related to dynamic capacity enhancement and orchestration can be overcome in air computing environments.

A EuroHPC Success Story

Wed, 05 Mar 2025 00:00:00 +0000

Professor Akarun and her research team have been granted computing hours through EuroHPC, the European High-Performance Computing Joint Undertaking. In a recent interview, she discusses how access to supercomputers has propelled her research forward and explores the potential of Artificial Intelligence (AI) to make interactions with public services more seamless for the Deaf community.

In the interview, Professor Akarun explains:

Access to the EuroHPC infrastructure enables us to carry our research to a different level. Large language models have revolutionised the whole field of machine learning, but they are very costly to train and even fine-tune. With our previous supercomputing resources, training would take weeks or months, and we would exceed our allocated quotas before the models converged. Therefore, before the access now granted to us by EuroHPC, this kind of work would be out of the question.

Congratulations Dr. Yiğit Yıldırım

Fri, 10 Jan 2025 00:00:00 +0000

Abstract

The current raison d’être of robots is their potential to be utilized in tasks deemed suitable by humans and for the benefit of humanity. To ensure that robots perform designated tasks as successfully as humans, it is logical to aim for human intelligence or functionality. The research presented in this thesis is a collection of attempts to bring robot functionality one step closer to the human level. Learning from Demonstration (LfD), which essentially signifies teaching robots new skills by demonstrating them, provides invaluable resources for progressing in this direction. With the advances in data-driven approaches, LfD methods have undergone a significant transformation. Despite the improvements, there remains a substantial gap between humans and robots in terms of intelligence. To help bridge this gap, we first proposed increasing the sociability of mobile robots. We introduced a data-driven navigation framework that learns navigation-related movement primitives from real-world human data. The proposed model leverages spatial metrics of proxemics and trajectory characteristics to minimize disturbance to surrounding individuals. Building on this work, we proposed a new LfD framework called Conditional Neural Expert Processes (CNEP). CNEP learns movement primitives of diverse skills simultaneously in an unsupervised manner. Leveraging the entropy concept, CNEP paves the way for capturing the multimodalities in demonstrations. Finally, many real-world skills require the rhythmic application of the same primitive, such as hammering a nail. For such skills, we proposed the Position- Enhanced Movement Primitives (PEMP) model that learns to utilize the angular phase instead of the linear phase. Our experiments showed that PEMP has great potential in modeling and synthesizing periodic primitives.

Fatih Alagöz received the Excellence in Research Award

Sun, 01 Sep 2024 00:00:00 +0000

Boğaziçi University Foundation (BÜVAK) rewards scientists who make a difference with their research activities and publications at Boğaziçi University every year. This year, seven scientists in the senior and junior categories received the BÜVAK Academy Excellence Award. The Academic Board, which takes part in the determination of the scientists who receive the award, evaluates the national and international publication performances of the academicians. In this context, where different types of publications are published, their number, continuity and the number of citations they receive according to leading indexes are examined.

Congratulations Dr. Sümeyye Ağaç!

Tue, 27 Aug 2024 00:00:00 +0000

Enhancing Lightweight Models for Efficient Sensor-based Human Activity Recognition

Abstract

This thesis presents a comprehensive study on enhancing the performance of lightweight models for efficient sensor-based Human Activity Recognition (HAR) on resource-constrained devices, particularly in memory-constrained environments such as wearables with microcontrollers. By integrating convolutional block attention modules into the lightweight versions of the DeepConvLSTM model, originally proposed for sensor-based HAR, and the highly efficient SqueezeNet architecture, the study aims to boost recognition accuracy without increasing computational demands. The thesis examines the impact of channel and spatial attention mechanisms across various model sizes. Results demonstrate that attention-enhanced lightweight models can achieve performance levels comparable to larger, more resource-intensive models while maintaining minimal resource usage. Subsequently, combinations of knowledge distillation and attention mechanisms were applied to further improve model efficiency. It was found that attention-based distillation could significantly enhance the accuracy of lightweight models even in the absence of attention modules in the teacher model. The effectiveness of the developed models was then compared with model compression techniques such as quantization and pruning. Furthermore, this thesis provides the most comprehensive study to date on sensor-based HAR in resource-constrained environments, offering insights and strategies for the practical application of efficient and high-performance sensor-based HAR systems, thereby addressing a significant gap in the field.

BAP PhD Thesis Award for Dr. Alper Ahmetoğlu

Tue, 02 Jul 2024 00:00:00 +0000

Our doctoral graduate Alper Ahmetoğlu’s PhD dissertation titled “Neurosymbolic Representations for Lifelong Learning” has been awarded the DOCTORAL THESIS AWARD by the Boğaziçi University Scientific Research Projects Commission due to its outstanding scientific quality. Dr. Ahmetoğlu, a former member of the CoLoRs Lab, conducted his thesis under the supervision of Assoc. Prof. Dr. Emre Uğur and co-supervision of Prof. Dr. Erhan Öztop, and is currently continuing his postdoctoral research at Brown University.

Congratulations Dr. Berrenur Saylam!

Tue, 02 Jul 2024 00:00:00 +0000

Machine Learning for Well-being Assessment: Exploring Digital Biomarkers

Abstract

This thesis investigates the potential of wearable sensors and self-reported questionnaires by exploring different factors of human well-being using digital biomarkers. The primary goal is to identify and validate these biomarkers, comparing them against traditional psychological studies. Various machine learning models are trained and validated for analyzing different well-being factors, including sleep, mental well-being (stress, anxiety, depression, positive and negative affect), and academic achievement. Two extensive datasets are utilized in these explorations: NetHealth, which is collected from more than 700 college students over 4 years, and Tesserae collected over a year of 757 office workers. Advanced techniques, incorporating the analysis of time-lagged data to capture temporal patterns and multitask learning, are employed to unravel complex relationships among well-being parameters. The research unfolds systematically, progressing from single well-being factor exploration to time-based and multi-task methodologies. This thesis emphasizes the importance of incorporating temporal dimensions and multi-task learning strategies for a more comprehensive understanding of well-being and the factors influencing it. Our findings offer valuable insights into the identification of reliable biomarkers and the relationships between various well-being aspects within two different target groups of university students and office workers. It aims to provide a new perspective, moving beyond single-factor exploration to enhance the comprehensiveness and applicability of well-being studies.

Congratulations Dr. Beytullah Yiğit!

Mon, 03 Jun 2024 00:00:00 +0000

A Security Framework for Software-Defined Networks

Abstract

Software-Defined Networking (SDN) emerges as a transformative technology, offering the flexibility and scalability crucial for modern digital services. While SDN presents opportunities to address security shortcomings in traditional networks, it also introduces new vulnerabilities, particularly in data and control plane communications.

Congratulations Dr. Nuriye Özlem Özcan Şimşek!

Tue, 21 May 2024 00:00:00 +0000

Genomic Data Analysis Using Machine Learning Methods For Disease and Disease-Gene Prediction

Abstract

Genomic diseases arise due to certain mutations or combinations of mutations in the DNA. This combination can be different for each patient and the effect of each mutation is different for the disease. In this study, we are focussing on the genomic causes of diseases. We defined two research problems. One is to detect the disease from the genetic code, represented as list of mutations. The second is to detect disease-gene associations.

SIU 2024 Alper Atalay Awards

Sun, 19 May 2024 00:00:00 +0000

Two of our graduate students, Merve Gül Kantarcı and Timoteos Onur Özçelik, won the second and third prizes for the best student paper at the Signal Processing and Communication Applications conference held in Tarsus between May 14-18.

The Alper Atalay Best Student Paper Award at the Signal Processing and Communications Applications Conference is given in memory of Alper Atalay, who graduated with honors from Boğaziçi University, Department of Electrical and Electronics Engineering in 1993 and lost his life in a tragic car accident on August 16, 1998.

Congratulations Dr. Binnur Görer!

Fri, 08 Mar 2024 00:00:00 +0000

A Tutoring Framework with Robotic and Virtual Agents for Requirements Elicitation Interview Training

Abstract

Requirements elicitation interviews are a widely adopted technique where the interview success depends on the interviewer’s preparedness and communication skills. Scaling practice sessions for many students is challenging due to the need for stakeholder involvement. To address this, we introduce a robotic tutor for requirements elicitation interview training. It has components to support both the interview phase, wherein students act as interviewers while the system assumes the role of an interviewee, and the feedback phase, during which the system assesses students’ performance and offers contextual feedback. We empirically evaluated the system with a group of graduate students. Building on this study, we introduce a generic architecture to leverage multiple technologies for the tutoring agent. It also features a behavioral feedback evaluator to enhance students’ soft skills. We demonstrate the architecture’s applicability through two implementations: with a physical robotic agent and a virtual voice-only agent. Through empirical studies with another group of graduate students, we found that the participants appreciated both systems. They demonstrated higher learning gains when trained with the robotic agent but found the voice-only agent more engaging and easier to use. Our findings indicate that each system has distinct benefits and drawbacks, suggesting a customizable approach for educators’ preferences and resources. Additionally, we explore large language models to generate interview scenarios, aiming to augment the interview tutoring system with various scenarios. We assessed the quality of generated scripts with language quality metrics and an expert judgment study and confirmed their applicability.

Turna Turkish Language Model

Tue, 06 Feb 2024 00:00:00 +0000

Congratulations Dr. Alper Ahmetoğlu

Wed, 31 Jan 2024 00:00:00 +0000

Neurosymbolic Representations for Lifelong Learning

Abstract

This thesis presents a novel framework for robot learning that combines the advantages of deep neural architectures in processing high-dimensional vectors with classical AI search techniques to bridge the gap between continuous sensorimotor data of the robot and domains consisting of finite entities. The aim is to convert information about the environment collected through interactions into an appropriate symbolic form on which a search tree can be built to reach a desired state. The framework consists of an encoder-decoder type of network with binarized activations in the bottleneck layer. The state of the environment, represented as a set of object features, is given to the encoder as input. The output is a discrete vector, treated as the object’s symbol, given to the decoder together with the action vector. The decoder predicts the effect observed by the agent due to the executed action. Once the network is trained, we can transform the continuously represented environment definition into symbolic vectors using the encoder. This allows us to build rules defining the transitions in the environment defined over these symbols. These rules can be translated into planning domain definition language (PDDL), allowing domain-independent off-the-shelf planners to be used to search for a goal state. Our experiments on tabletop object manipulation setups show that the system can learn appropriate symbols of the environment that allow it to build object towers with desired heights and complex object structures that require modeling the relations between objects by reasoning through the rules defined over the symbols learned in an unsupervised manner. As the framework is built with differentiable blocks, it affords appending recent advances in deep learning with ease, allowing it to be extensible in multiple directions.

CMPE Graduation Ceremony 2023!

Fri, 07 Jul 2023 00:00:00 +0000

The CMPE Graduation Ceremony 2023 was held on July 7. After the diploma ceremony on the south campus in the morning, the new graduates, their family members, and members of CMPE came together in the garden of the CMPE building in the afternoon.

We congratulate the new graduates of CMPE and their family members. As members of CMPE, we are so proud of you and honored to celebrate your graduation with you. We wish you the best of luck with your new beginnings!

Yavuz Köroğlu defended his Phd thesis

Mon, 25 Jul 2022 00:00:00 +0000

Underestimating the value of software testing had catastrophic results in recent history. Automated Test Generation (ATG) is an approach that aims to minimize the manual effort required for testing. This thesis aims to improve the effectiveness and performance of ATG approaches via Machine Learning (ML) based guidance, and focuses on Android Graphical User Interface (GUI) testing using Reinforcement Learning (RL), specifically. We propose four solutions, Q-learning Based Exploration (QBE), Test Case Mutation (TCM), Fully Automated Reinforcement LEArning Driven (FARLEAD), and FARLEAD2 test generators. QBE uses RL to crawl a set of applications and learns an action generation policy while exploring. Then, it uses this learned policy to either detect more unique crashes or cover more activities in new applications. TCM takes the tests QBE generates and replaces the well-behaving actions in those tests with bad-behaving ones to detect even more crashes. FARLEAD uses RL to learn how to verify a functional behavior that is given as a high-level test scenario in the form of a monitorable formal specification. FARLEAD learns by trial-and-error like QBE but it learns app-specific patterns instead of QBE’s app-generic patterns. To the best of out knowledge, FARLEAD is the first engine fully automating the functional testing of GUI applications. Finally, FARLEAD2 improves FARLEAD with Generalized Experience Replay (GER) and human-readable Staged Test Scenario (STS) language.Experimental results show that, QBE outperforms state-of-the-art test generators in crash detection and coverage. Furthermore, executing QBE first and then switching to TCM detects even more unique crashes. FARLEAD and FARLEAD2 expand the scope of automated testing to verifying functional behavior. Overall, these test generators elevate automated GUI testing closer to replacing manual GUI testing.

CMPE Graduation Ceremony 2022!

Wed, 06 Jul 2022 00:00:00 +0000

The CMPE Graduation Ceremony 2022 was held on July 6. After the diploma ceremony on the south campus in the morning, the new graduates, their family members and members of CMPE came together in the garden of the CMPE building in the afternoon.

We congratulate the new graduates of CMPE and their family members. As members of CMPE, we are so proud of you and honoured to celebrate your graduation with you. We wish you the best of luck with your new beginnings!

Arzucan Özgür received the Excellence in Research Award

Tue, 25 Jan 2022 00:00:00 +0000

Lale Akarun received the Excellence in Research Award

Mon, 24 Jan 2022 00:00:00 +0000

Algorithmic Ethics for Reinforcement Learning Agents

Mon, 01 Jan 0001 00:00:00 +0000

Alper Şen

Mon, 01 Jan 0001 00:00:00 +0000

Arda Yurdakul

Mon, 01 Jan 0001 00:00:00 +0000

Artificial Learning Laboratory

Mon, 01 Jan 0001 00:00:00 +0000

Arzucan Özgür

Mon, 01 Jan 0001 00:00:00 +0000

Atay Özgövde

Mon, 01 Jan 0001 00:00:00 +0000

Berk Gökberk

Mon, 01 Jan 0001 00:00:00 +0000

Birkan Yılmaz

Mon, 01 Jan 0001 00:00:00 +0000

Can Özturan

Mon, 01 Jan 0001 00:00:00 +0000

Cem Ersoy

Mon, 01 Jan 0001 00:00:00 +0000

Cem Say

Mon, 01 Jan 0001 00:00:00 +0000

CMPE101

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE101
Course Title	Introduction to Computer Engineering
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 1, PS:2, Labs: 2
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

Catalog Description

The aim of the course is twofold:

To give the student literacy on computers and data processing, and to make him/her a computer user either as a programmer or as an application software packages user.

CMPE140

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE140
Course Title	Introduction to Computing for Economics and Management
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 2, PS:1, Labs: 2
Course Credits	3
ECTS	5
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

Catalog Description

CMPE 140 introduces basic concepts of exploratory data analysis and programming using the R programming language and environment for statistical computing and graphics.

CMPE142

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE142
Course Title	Digital Systems
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:1, Labs: 2
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

Catalog Description

Number systems. Boolean algebra, logic networks and their simplification. Logic design with gates. MSI and LSI technologies. Combinatorial circuits and sequential circuits. Counters, shift registers, computer organization, arithmetic logic, memory and control units, mini and microcomputer systems. Laboratory experiments and applications of digital logic design.

CMPE150

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE150
Course Title	Introduction to Computing
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 1, PS:2, Labs: 2
Course Credits	3
ECTS	5
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

Catalog Description

The aim of the course is twofold:

To give the student literacy on computers and data processing, and to make him/her a computer user either as a programmer or as an application software packages user.

CMPE160

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE160
Course Title	Introduction to Object Orianted Programming
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 2
Course Credits	4
ECTS	8
Grading Mode	Letter Grade
Prerequisites	CmpE150
Corequisites	None

Catalog Description

Programming methodology: specification, design, coding, program correctness. Review of data types: scalar types, structured types. Data Structures: linear structures (stacks, queues, linear lists), nonlinear structures (trees, sets), files (sequential, random access). Implementation of data structures: linked list implementation, multidimensional array implementation, record implementation, character string, stack, queue, tree and set implementations. Recursion.

CMPE210

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE210
Course Title	Fundamentals of Object Oriented Programming
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:0, Labs: 2
Course Credits	4
ECTS	5
Grading Mode	Letter Grade
Prerequisites	CMPE150
Corequisites	None

Catalog Description

Review of scalar and structured data types. Object oriented programming concepts: classes and abstract data types, inheritence, polymorphisms, operator overloading. Classical and object oriented program specification, design, coding and testing. Linear (stacks, queues, linear lists) and nonlinear data structures (trees, sets). Sequential and random access files. Implementation of data structures: Linked lists, multidimensional arrays, records, character strings, stacks, queues, trees and sets. Recursion.

CMPE220

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE220
Course Title	Discrete Computational Structures
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	5
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

Catalog Description

Propositional logic and proofs. Set theory. Functions and relations. Algebraic structures. Groups and semi-groups. Graphs, lattices and Boolean algebra.

CMPE222

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE222
Course Title	Introduction to Database Systems
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 2
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE250
Corequisites	None

Catalog Description

Logical organization of data: Entity-relationship modeling of data. Hierarchical, network and relational models. Data description and query languages. Normal forms and database design. Physical design and access strategies. Security, integrity and reliability. Design and implementation of a simple database management system that includes file security and a simple query language.

CMPE230

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE230
Course Title	Systems Programming
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:2, Labs: 0
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE160
Corequisites	None

Catalog Description

Overview of compilers, interpreters, assemblers, linkers and loaders. Unix environment and system calls. Shell programming. Signals and exceptions. Localization and Unicode. PERL and CGI programming. Assembly language programming. Introduction to multithreading. Introductory Graphical User Interface (GUI) programming.

CMPE240

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE240
Course Title	Digital Systems
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:1, Labs: 2
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

Catalog Description

CMPE244

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE244
Course Title	Computer Organization
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 2
Course Credits	4
ECTS	5
Grading Mode	Letter Grade
Prerequisites	CMPE142 or EE240 or EE 243
Corequisites	None

Catalog Description

Register transfer level design. Basic computer layout. Instruction sets and their implementation. Addressing techniques. ALU, hardwired and microprogrammed controllers. Memory unit. I/0 structures and interrupt handling. Improvements on von Neumann machine. Bus structures. Assembly programming.

CMPE250

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE250
Course Title	Data Structures and Algorithms
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:2, Labs: 0
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE160
Corequisites	None

Catalog Description

Graphs. Advanced Sorting. Hashing. Heap Structures. Search Structures. Complexity. Parallel algorithms. File organization.

CMPE260

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE260
Course Title	Principles of Programming Languages
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	5
Grading Mode	Letter Grade
Prerequisites	CMPE250
Corequisites	None

Catalog Description

This course is intended to introduce the student to the main paradigms of computation, languages representative of these paradigms, techniques of implementing various programming language constructs, as well as basic concepts relating to the specification of programming languages.Syntax and semantics of programming languages, basic constructs that are common to all languages (control structures, subprograms, etc.), and basic programming paradigms (functional, logic, etc.) will be covered. The intent of the course is not teaching a specific programming language; instead, using several languages to exemplify the main concepts and constructs.

CMPE300

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE300
Course Title	Analysis of Algorithms
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:0, Labs: 2
Course Credits	3
ECTS	5
Grading Mode	Letter Grade
Prerequisites	CMPE250
Corequisites	None

Catalog Description

Analysis of computer science algorithms: Sorting, searching, paging and parallelism.
Analysis of mathematical algorithms: games and puzzles, network algorithms, and probabilistic algorithms.

Course Learning Outcomes

This course is intended to introduce the student to the main paradigms of algorithm analysis, methods and mathematical tools used for analyzing the performance of algorithms, the theory of parallel algorithms, as well as known sequential and parallel algorithmic solutions to frequently encountered problems. The theory of complexity analysis, basic techniques that are commonly used in analyzing the performance, basic classes of algorithms (comparison-based, recursive, divide-and-conquer, dynamic, greedy, numerical, graph), and lower bound theory will be covered. Parallel architectures and parallel algorithms will be studied in detail. Meanwhile, mathematical tools like interpolation, master theorem, etc. will be introduced. The last part of the course will be the study of the topic of probabilistic algorithms, which is a rapidly growing area of research.

CMPE320

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE320
Course Title	Principles of Programming Languages
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	5
Grading Mode	Letter Grade
Prerequisites	CMPE250
Corequisites	None

Catalog Description

CMPE321

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE321
Course Title	Introduction to Database Systems
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 2
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE250
Corequisites	None

Catalog Description

CMPE322

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE322
Course Title	Operating Systems
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:0, Labs: 2
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE250
Corequisites	None

Catalog Description

Evolution of operating systems. Multiprogramming and time sharing concepts. User and program interfaces. Concurrent processes, CPU scheduling, process synchronization, critical section problem. Deadlock prevention, avoidance, detection and recovery. Memory management, swapping, multiple partitions. Paging, segmentation, virtual memory, page replacement algorithms. File system structures, allocation methods, directory implementation. I/O interfaces, secondary storage structure. Protection and Security: Access matrix and rights, capabilities, security issues. LINUX case studies.

CMPE343

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE343
Course Title	Introduction to Probability and Statistics for Computer Engineers
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:1, Labs: 0
Course Credits	3
ECTS	5
Grading Mode	Letter Grade
Prerequisites	MATH101
Corequisites	None

Catalog Description

Introduction to probability theory. Random variables, expectation, variance and moment generating functions. Distributions: Bernoulli, binomial, uniform, Gaussian, exponential, Poisson, gamma. Introduction to statistical concepts. Sampling and sample statistics. Point and interval estimation. Hypothesis testing. Regression. Numerical and computational aspects of random variable generation, sampling, and estimation.

CMPE344

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE344
Course Title	Computer Organization
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:0, Labs: 2
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE240 or EE240 or EE 243
Corequisites	None

Catalog Description

CMPE346

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE346
Course Title	Principles of Embedded Systems Design
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:1, Labs: 2
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE244
Corequisites	None

Catalog Description

Embedded systems design flow. Hardware-software co-design. Real-time software design. Real-time debugging techniques and tools. Hardware-software integration: interface design. Examples on the design of data acquisition and control systems. Laboratory.

CMPE350

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE350
Course Title	Formal Languages and Automata Theory
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	5
Grading Mode	Letter Grade
Prerequisites	CMPE220 or MATH162 or MATH111
Corequisites	None

Catalog Description

Strings and languages. Classification of grammars. Finite-state automata and regular expressions. Pushdown automata and context-free grammars. Context sensitive grammars and linear bounded automata. Turing machines.

CMPE352

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE352
Course Title	Fundamentals of Software Engineering
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 2, PS:1, Labs: 0
Course Credits	2
ECTS	4
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

Catalog Description

Introductory concepts in software engineering. The software life cycle and the phases in software development: feasibility study, analysis, specification, design, implementation and testing, documentation, and maintenance. Tools, techniques, environments, and methodologies. Management issues: planning, organization and control. Professional ethics.

CMPE354

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE354
Course Title	Software Engineering
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:1, Labs: 2
Course Credits	4
ECTS	7
Grading Mode	Letter Grade
Prerequisites	CMPE222
Corequisites	None

Catalog Description

CMPE362

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE362
Course Title	Introduction to Signal Processing for Computer Engineers
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	MATH202
Corequisites	None

Catalog Description

Introduction to discrete and continuous time signals and systems with computer engineering applications. Time-domain signal representations, impulse response of linear time-invariant systems; convolution. Fourier series. Spectrum representation of signals. Fourier transform. Digital signals and sampling. Reconstruction, Filtering, Z-transform representation, discrete Fourier transform. Algorithms for signal processing. Floating point and quantization errors. Exercises with applications in audio and image processing.

CMPE425

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE425
Course Title	Compiler Design
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE 260
Corequisites	None

Catalog Description

Overview of the compilation process. Lexical analysis, regular expressions, finite automata and lexical analyzer generators. Symbol tables. Grammars. Top-down and bottom-up syntax analysis. Recursive-descent. SLR, canonical LR, LALR and operator precedence parsing techniques. Parser generators. Semantic analysis and attribute grammers. Abstract machines ad intermediate code. Syntax directed translation schemes. Implementation of a compiler for a simple imperative language.

CMPE434

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE434
Course Title	Introduction to Robotics
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

Catalog Description

Designing, building and programming mobile robots; sensors, effectors, locomotion, basic control theory, control architectures, path planning, localization, mapping, learning. Teamwork; robot contest.

CMPE443

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE443
Course Title	Principles of Embedded Systems Design
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:1, Labs: 2
Course Credits	4
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE240
Corequisites	None

Catalog Description

CMPE446

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE446
Course Title	Introduction to Computer Architecture
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE344 and CMPE322
Corequisites	None

Catalog Description

Performance limits of Von Neumann architecture. Reduced Instruction Set Computer (RISC) architecture. Pipelined processor design. Instruction pipeline and arithmetic pipelines. Array processors. Dynamic and static interconnection networks. Shared memory multiprocessor systems. Message passing multiprocessor systems.

CMPE451

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE451
Course Title	Project Development in Software Engineering
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 1, PS:0, Labs: 2
Course Credits	2
ECTS	5
Grading Mode	Letter Grade
Prerequisites	CMPE352 and CMPE321
Corequisites	None

## Catalog Description

Management and implementation of a large software development as a group. Application of software engineering principles in a project setting.

Course Learning Outcomes

To experience the application of software development techniques and methodologies throughout various stages of the software development lifecycle including planning, requirements gathering, design, implementation, testing, deployment, and delivery.
To acquire team-based software development skills such as communication, planning, and time management.
To gain communication skills required to interact with customers and the project team members.
To develop skills in planning and managing software development projects.
To gain the ability to use software development tools, techniques, environments, and methodologies for effective and productive software development.
To apply various Computer Engineering knowledge and algorithms, as learned in earlier courses, to software development.

Current Instructor

Suzan Üsküdarlı

Assistant Professor

CMPE460

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE460
Course Title	Introduction to Computer Graphics
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

## Catalog Description

Computer Graphics deals with the synthesis of images from geometric models and textures; which may be acquired form the real world or may be synthetically generated as well. The course will focus on modeling; which is the geometric description of 3D scenes; and rendering, which creates realistic images from such models. An important component of the course is the use of mathematics. The course uses linear algebra, and other areas of math. We will review mathematical basics used in the course. Programming experience in C++ and a basic knowledge of calculus and linear algebra is assumed. Apart from the lecture topics listed below, some OpenGL programming will also be covered.

CMPE462

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE462
Course Title	Machine Learning
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

## Catalog Description

Overview of artificial learning systems. Supervised and unsupervised learning. Statistical models. Decision trees. Clustering. Feature extraction. Artificial neural networks. Reinforcement learning. Applications to pattern recognition Overview of artificial learning systems. Supervised and unsupervised learning. Statistical models. Decision trees. Clustering. Feature extraction. Artificial neural networks. Reinforcement learning. Applications to pattern recognition and data mining.

CMPE470

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE470
Course Title	Computer Performance Evaluation
Language of Instruction	English
Course Semester	Not Avaliable at the moment
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

## Catalog Description

Nature of computer performance evaluation. Job processing models. Analytical techniques. Simulation. Management and control of system hardware and software resources. Performance of multiprocessing systems.

CMPE475

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE475
Course Title	Computer Networks
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

## Catalog Description

Overview of computer networks: Network architecture and the ISO model. Network topology: Connectivity analysis, delay analysis and backbone analysis. Physical layer: Transmission and multiplexing, terminal handling, errors. Data link layer and link protocols. Network layer: Routing and congestion, satellite and packet radio networks, local networks. Transmission and session layer. Presentation layer. Application layer.

CMPE476

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE476
Course Title	Distributed Systems
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

Catalog Description

Transport layer services and protocols. Internet protocols TCP and UDP. ATM Adaptation Layer protocols. Client-server and peer-to-peer programming models. Network programming. Remote procedure call. Application layer issues and protocols. Network security. Domain name system. Network management. Electronic mail and news services. Overview of distributed systems and structures. Distributed file and directory systems. Distributed coordination, concurrency control, deadlock detection and election algorithms.

CMPE478

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE478
Course Title	Parallel Processing
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	CMPE322
Corequisites	None

Catalog Description

Parallel machine and performance models: PRAM, speedup, work efficiency, scalability, Brent’s theorem. Parallel programming with message passing and multi-threading libraries. Parallel algorithms: Prefix computation, pointer jumping, list ranking, Euler tours on trees, sorting. Parallel architectures: Multiprocessors, multicomputers and Flynn’s taxonomy, SIMD, MIMD, SPMD, interconnection topologies. Load balancing and graph partitioning methods.

CMPE480

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE480
Course Title	Introduction to Artificial Intelligence
Language of Instruction	English
Course Semester	Fall
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	3
ECTS	6
Grading Mode	Letter Grade
Prerequisites	None
Corequisites	None

## Catalog Description

Representation of knowledge. Search and heuristic programming. Logic and logic programming. Application areas of artificial intelligence: problem solving, games and puzzles, expert systems, planning, learning, vision, and natural language understanding. Exercises in an artificial intelligence language.

CMPE491-492

Mon, 01 Jan 0001 00:00:00 +0000

Course Information


Faculty	Faculty of Engineering
Course Code	CMPE491-492
Course Title	Project In Computer Engineering
Language of Instruction	English
Course Semester	Spring
Course Hours	Lecture: 3, PS:0, Labs: 0
Course Credits	4
ECTS	7
Grading Mode	Letter Grade
Prerequisites	Senior in CMPE
Corequisites	None

Catalog Description

Instructions for 491/492 Project Selection and Registration

Read project descriptions posted by CmpE project advisors, discuss projects you are interested in with project advisors and decide on a project.
Note that your GPA must be over 2.00 to register for a 491 project.
Fill the project registration form and get the approval of your project advisor.
Return the registration form, approved by your project advisor, to the CmpE 491/492 coordinator before the registration period ends.
Enroll to CmpE 491/492 through the Registration system.
Repeat the above steps, if you want to make a change in your project selection

Cognitive Learning and Robotics Laboratory

Mon, 01 Jan 0001 00:00:00 +0000

Computer Architecture and Systems Laboratory

Mon, 01 Jan 0001 00:00:00 +0000

Computer Networks Laboratory

Mon, 01 Jan 0001 00:00:00 +0000

Conditional Computation Techniques in Deep Neural Networks with Conditional Information Gain

Mon, 01 Jan 0001 00:00:00 +0000

Cost-Effective and DDoS-Aware Network Function Sharing Models for Next-Generation Networks

Mon, 01 Jan 0001 00:00:00 +0000

Dependable Systems Research Group

Mon, 01 Jan 0001 00:00:00 +0000

Docs

Mon, 01 Jan 0001 00:00:00 +0000

Doğan Ulus

Mon, 01 Jan 0001 00:00:00 +0000

EblocBroker: An Autonomous Blockchain-based Computational Broker for e-Science

Mon, 01 Jan 0001 00:00:00 +0000

Emre Uğur

Mon, 01 Jan 0001 00:00:00 +0000

Fatih Alagöz

Mon, 01 Jan 0001 00:00:00 +0000

Fikret Gürgen

Mon, 01 Jan 0001 00:00:00 +0000

Graduation Ceremony 2024

Mon, 01 Jan 0001 00:00:00 +0000

Graduation Ceremony 2024

Mon, 01 Jan 0001 00:00:00 +0000

Graduation Ceremony 2025

Mon, 01 Jan 0001 00:00:00 +0000

Graduation Ceremony 2025

Mon, 01 Jan 0001 00:00:00 +0000

How Evolutionary Algorithms Can Efficiently Explore and Exploit the Search Space?

Mon, 01 Jan 0001 00:00:00 +0000

İnci Baytaş

Mon, 01 Jan 0001 00:00:00 +0000

Lale Akarun

Mon, 01 Jan 0001 00:00:00 +0000

Learning Robotic Navigation and Manipulation Primitives from Demonstration

Mon, 01 Jan 0001 00:00:00 +0000

Mehmet Turan

Mon, 01 Jan 0001 00:00:00 +0000

Muhammed Göleç

Mon, 01 Jan 0001 00:00:00 +0000

Nanonetworking Research Group

Mon, 01 Jan 0001 00:00:00 +0000

Predicting Psychological Attributes from User-Generated Content using Machine Learning Techniques

Mon, 01 Jan 0001 00:00:00 +0000

Satelllite Network Research Laboratory

Mon, 01 Jan 0001 00:00:00 +0000

Şefik Şuayb Arslan

Mon, 01 Jan 0001 00:00:00 +0000

Senior Projects Day

Mon, 01 Jan 0001 00:00:00 +0000

Senior Projects Day

Mon, 01 Jan 0001 00:00:00 +0000

Senior Projects Day

Mon, 01 Jan 0001 00:00:00 +0000

Suzan Üsküdarlı

Mon, 01 Jan 0001 00:00:00 +0000

System Verification Research Group

Mon, 01 Jan 0001 00:00:00 +0000

Text analytics and Bioinformatics Laboratory

Mon, 01 Jan 0001 00:00:00 +0000

Tuna Tuğcu

Mon, 01 Jan 0001 00:00:00 +0000

Tunga Güngör

Mon, 01 Jan 0001 00:00:00 +0000

Verifying Debates and Proofs Using Constant Space and Constant Private Randomness

Mon, 01 Jan 0001 00:00:00 +0000

Computer Engineering on Computer Engineering | BOUN

Mission Statements

Boğaziçi University

Overview

Curriculum (Pre-2025)

First Semester

Second Semester

Third Semester

Fourth Semester

Fifth Semester

Sixth Semester

Seventh Semester

Eighth Semester

Curriculum Notes

Curriculum 2025

First Semester

Second Semester

Third Semester

Fourth Semester

Fifth Semester

Sixth Semester

Seventh Semester

Eighth Semester

Curriculum Notes

History

Milestones of Growth

Department Statistics

Latest Enrollment Data

Historical Enrollment Data

Historical Graduation Statistics

Elective Courses

Humanities and Social Sciences (HSS)

Administration

Chair

Arzucan Özgür

Vice Chairs

İnci Baytaş

Double Major Degree Program

Overview

Academic Advisors

Minor Degree Program

Overview

Advisory Board

Sina Afra

Semih Akar

Çiğdem İltemir Carino

İlker İnanç

Günseli Karadere

Ekrem Kocagüneli

Sema Oktuğ

Mehmet Subaşı

Exchange Programs

International Student Mobility

Gender Equality

Industrial Training

Objective

Regulations

Required Documents

Academic Integrity

Definitions

Actions Against

Senior Projects

Graduation Cerenomies

156th Graduation Cerenomy

155th Graduation Cerenomy

Faculty

Full-time Members

Lale Akarun

Fatih Alagöz

Şefik Şuayb Arslan

Academic Staff

Teaching Assistants

Furkan Akkurt

Deniz Aksoy

Arda Arslan

Support Staff

Administrative Staff

Çeşminaz Eser

Buket Konak

System and Network Administrator