Faculty name

Assistant Professor This email address is being protected from spambots. You need JavaScript enabled to view it.
Hoehn 360
(205) 975-3388

Research and Teaching Interests: Engineering problems ranging from autonomous control and robotics, digital signal analysis, and neural recording/electrophysiology

Office Hours: By appointment

Education:

  • B.S., Bogazici Universitesi, Istanbul, Turkey, Electronics
  • M.S., Technishe Fachhochschule Berlin, Germany, Informatics
  • M.S., University of Alabama at Birmingham, Electrical Engineering
  • Ph.D., Texas Tech University, Lubbock TX, Mechanical Engineering

Abidin Yildirim (Dr. Abi) is Assistant Professor in Electrical and Computer Engineering, Biomedical Engineering (secondary appointment), and Education and Curriculum Development (secondary appointment). He has expertise in electronics, engineering systems design, including biomedical instrumentation, industrial control, robotics, and autonomous vehicles. He is a passionate instructor and mentor. He has developed various comprehensive STEM training curriculums and outreach programs for undergraduate college education as well as K-12. He is currently master instructor of various community STEM training programs for middle school and high school students.

Teaching Philosophy

My teaching philosophy is based on the belief that education is a solution to many problems that we confront in life. Appropriate and solid education provides us tools to make the right choices and informed decisions, synthesize ideas and develop critical thinking. Therefore, helping my students to pursue adequate education and teach them critical thinking is among my highest priorities in the classroom. Being able to help people is a big privilege that teachers have in their daily lives. Being able to help students that will become engineers and help others is even a bigger privilege that I have had through my professional career. Teaching is an opportunity to share insights, to grasp new ideas for research, and to enrich one’s understanding. As a teacher, first and foremost, my goal is to get students interested in the material they learn and then to provide them with enough knowledge to continue life-long self-education. It makes me happy to see students that generate new ideas and integrate information.

Although many of my students who come to me come with highly diverse levels of competence and preparedness, they all deserve guidance toward robust career development and academic success. To help further the mission of the university’s compliance program, I make it a point to prioritize the education and training of students from all walks of life to ensure their unconditional success without bias. Therefore, my main teaching goal is to provide the very best post-secondary education, academic advising, research training, and career development that we can to the general public and produce as strong a workforce as possible for the region and the nation.

Each of my students is offered a carefully designed plan according to their interests and background. Plans that specifically develop their academic ability and fill in their education gaps are designed to maximize each student’s potential and ensure their academic success. The highly diverse nature of the students in my lab — which includes students with a different part of the world and various level of quality of education — often presents serious challenges to synchronous collaboration. Given this, I am frequently required to do all of the time consuming, substantial face-to-face tutoring, preparing them to have a basic understanding of the nature of the research in my lab. Afterward, I work closely with the students so that they can reproduce the research I have already done. I also provide generous travel support to students, frequently allowing them to attend and participate in regional and national conferences using self-raised funds through grants and summer STEM programs for K-12 students.

I generally tutor my students on a daily basis and meet with them weekly to comprehensively assess their learning outcomes and research progress and advise them about their career development. Although I have official office hours for each course I am teaching, posted on my syllabuses, I have an open-door policy for my students and try to be there for them as much as possible. I typically keep my office door open daily, from early morning till late night, including most of the weekends. I usually have my undergraduate and graduate students available around my office so that there will always be someone there to help me meet my students’ needs.

I believe that the best way to learn is by doing. I emphasize the learning-by-doing approach all my courses. Align with experiential teaching methods; I use modest but fundamental hands-on, real-life-related examples in the classroom while I am lecturing the course material. That approach helps the students to understand the course material in-time and have the opportunity to ask questions or discuss problems regarding experiments and to the course subject right away. This approach seems to consume more instructor time to prepare course material; however, in terms of student’s success and learning outcome,it is much more dynamic in contrast to conventional “talk-chalk” classrooms with additional lab hours.

In particular, real-life examples help my students develop logical reasoning and critical thinking about why they are studying these subjects and why it is essential for their professional life. In addition to that, the hands-on classroom in-time real-life examples foster problem-solving activity, because they have to find solutions in a limited classroom time to find their solutions for a particular problem. Finally, only that way, my students can relate themselves to teaching material and content, will understand the presented topic comprehensively, and will be ready to use the problem-solving skills, if they need it. I believe that through this “learning by doing” approach, they will be more able to understand the topics they are learning, and they will have a desire to continue learning that will last them their entire lives.

Enhancing student academic performance is challenging, but the time and effort put into accomplishing this feat are well worth it. I find this to be an indispensable step for fast-tracking students, motivating their research interest, and carving out a clear and present pathway facilitating their research development. Through my teaching and mentoring process, I am pursuing the ultimate goal to help students excel in their studies as well as become contributing professionals in the fields of electrical and computer engineering, autonomous systems, and biomedical instrumentation.

Current Teaching Portfolio

I have the necessary background to teach courses within the Electrical and Computer Engineering curriculum as well as courses in general engineering topics and the honors curriculum. However, my area of specialty is in microprocessors and embedded systems, digital and analog circuits, robotics, autonomous systems, biomedical instrumentation, and sensors. I am also confident in teaching assembly programming, C#, Python, LabView, and MATLAB/R.

Research Interests

I firmly believe that teaching should be complemented with research if we are to train the future workforce well. It is evident that, with administrative duties and expectation, the course of engagement in the area of research could change. To me, there is nothing more natural than curiosity, which let me build my microscope from a mini light bulb and reading glasses from my dad and investigate first plant cells using onion epidermis as my first subject. NASA’s moon landing excited me so much I end up building my first Newtonian telescope as I was 15. My lifelong interest in science and engineering always guided my choices in what topics to learn and what research to do. Fascinated with single Ge-Diode radio circuit, I found my way in Electrical Engineering.

Working on engineering problems ranging from autonomous control and robotics, digital signal analysis, and neural recording/electrophysiology might seem to be sometimes unconnected; however, I have enjoyed many years to research and enjoy enormously all of them. I believe that, if we understand the neural activities better, we will be able to create smart machines that will help us to advance our lives. Luckily, big data and deep learning, machine learning with AI, the autonomous systems, not only transportations and cars but also manufacturing and logistics will be more efficient. I do research and mentor my students currently in autonomous systems, machine learning, as well as investigating in brain-machine interface techniques.

My future research will continue in these fields and also concentrate more on the integration of these fields in an efficient and scalable way to achieve rapid results for society. Obviously, such systems should be able to work in noisy and unpredictable environments; thus, robust control is needed to handle such uncertainty in the mechanical and electrical models and the environment. These systems should sense the environment like we as humans does to — for example, navigate intelligently. For instance, the human brain is the best example of real intelligence. Studying the signals recorded from a human brain and connecting it to machines would make smart systems that can navigate autonomously and make intelligent decisions.

Apart from my continuous research on autonomous systems and research in neural recording, I always eager to collaborate with researchers from different areas, and I welcome any offers to contribute their research from my colleagues. I believe that the most exciting innovations and inventions in science and engineering are happening through the cross-section between different disciplines. In other words, significant breakthroughs could come out from interdisciplinary teams of experts when they share their experience with each other and work on joint projects. I was lucky to have the opportunity to work with such scientists on many research projects to enhance my knowledge as well as skills working with interdisciplinary teams.

My present research program consists of two main areas of study: robotics and autonomous vehicles, and bioinstrumentation. My goal is to apply mathematical tools (e.g., using advanced geometry, topology, and statistics) that to improve navigation accuracy and speed up the adapting process of these algorithms in order to facilitate discoveries in the field of autonomous systems and solve practical engineering problems in these areas.

Recent Courses

  • EGR 200, Introduction to Engineering: Entry-level general engineering course assigned for all undergraduate students. It is a project-based course that covers engineering design principles, ethics, and technical report writing. The total number of students per semester was 100-120.
  • EE 437/537, Application of Embedded Systems: Course covers the historical development of microprocessors, microcomputers, and embedded systems. Processor architectures, memory management, and system on chip architectures are covered. Students complete the course with real-word applications, such as IoT, robotics, biomedical applications, or smart home appliances. A detailed project reporting is mandatory.
  • EGR 602, Engineering Methods I: This is a 3-credit hour graduate course covers topics of statistical methods of engineering design and manufacturing. Course topic includes the basics of metallurgy, statistical data collecting, and analysis during mass production, uncertainty principles, tolerances in precision engineering, etc. Students complete the class with a project that requires to utilize in in-depth analysis and measurement techniques to manufacture complex mechanical components using 3D Printer.
  • EGR 603, Engineering Methods II: It is a continuum of the EGR 602 course, that mainly covers current manufacturing processes and product life cycle management. The primary emphasis of the course is Industry 4.0, networked manufacturing practices, and the importance of Big Data in rapid product development and customized manufacturing. A subsection of the course also covers the production safety assessment and analysis.

Select Publications

  • Tyler Dickerhoff, Abidin Yildirim, Timothy J. Gawne, Roboneuron, “A simple and robust real-time analog spike simulator and calibrator,” Journal of Neuroscience Methods, Volume 218, Issue 2, 15 September 2013, Pages 161-163, ISSN 0165-0270.
  • Walker, H. C., Huang, H., Gonzalez, C. L., Bryant, J. E., Killen, J., Knowlton, R. C., Montgomery, E. B., Cutter, G. C., Yildirim, A., Guthrie, B. L. and Watts, R. L. (2012), "Short latency activation of cortex by clinically effective thalamic brain stimulation for tremor," Mov. Disord., 27: 1404–1412. doi: 10.1002/mds. 25137
  • Abidin Yildirim, Zekai Demirezen, Atila Ertas, and Murat M. Tanik, “Application of the Perron Frobenius Theorem in Signal Classification, and Identification,” International Journal of Computers, Information, Technology and Engineering (IJCITAE), Volume 5, Number 2, December, 2011, pp. 55-68.
  • Stan Gatchel, Abidin Yildirim, Emrah Gumus, Faruk Gungor, Levent Caglar., “Use of Complex System Design to Improve Glaucoma Implants,” Integrated Design and Process Technology, IDPT 2007. June, 2007.
  • Gnadt, J.W. Echols, S.D., Yildirim, A., Honglei Zhang Paul, K., “Spectral cancellation of microstimulation artifact for simultaneous neural recording in situ,” IEEE Transactions on Biomedical Engineering, (October 2003, Vol. 50, Number 10. ISSN 0018-9294).
  • Amthor, FR, Tootle, JS, Yildirim, A., “A new transparent multi-unit recording array system fabricated by in-house laboratory technology,” Journal of Neuroscience Methods, 2003 Jun 30; 126(2):209-19.
  • Doganli, A. Ertas, A. Yildirim, “A Vibration Monitoring Method using Infrared Diodes,” The Seventh World Conference on Integrated Design & Process Technology Conference Proceedings, pp. 855-860, IDPT-2003.
  • A. Yildirim, M. N. Tanju, K. Abe, M. M. Tanik, and A. Ertas, “An Internet Based Interactive Course on Digital Signal Processing,” The Fifth Biennial World Conference on Integrated Design and Process Technology, SDPS (Proceedings in CD), Dallas, Texas, June 4-8, 2000.

Academic Distinctions and Professional Societies

  • New York Academy of Sciences
  • Sigma Xi – Texas A&M Chapter