Lessons learnt Fall 2009

I believe I have come a long way since I taught my first class of the semester. I have always tried to make my teaching sessions lively and interesting by making them interactive and I have achieved this by using active learning strategies such as group discussions, question answer sessions, one minute papers in my class. This way the students can learn more effectively than what would result from just plain old lecture sessions. But this is only a part of all the new things I have learnt about teaching this semester.

In applying these ideas, I was really fortunate to get such helpful feedbacks from the peer observation and the teaching observation projects. The feedbacks were encouraging but had lots of important suggestions. For example, as mentioned earlier, the practice of breaking the pace of the lecture from time to time, the practice of encouraging the students when solving a difficult problem, by being part of their team in the endeavor rather than just giving out the solution, from a position of superior knowledge. I believe that all these things have been really helpful in improving my teaching and should continue to help me, when I teach this course next time.

Also the complementary experience to being observed was observing faculty members and peers from whom I learnt a lot. Observing some of my peers was almost like being able to observe myself, so it gave a good grip on what works and what does not. For example, if a problem is posed for the students to solve, it might be a good idea to collect the answers, even if only for show, because it imposes a minimal sense of official importance to the procedure and encourages better participation. Also it helps to be cheerful and encouraging, but that varies from person to person.

However, these are only details, important only for preparing for next time. But the memories that I take away from this experience has nothing to do with how hard the course was to teach or how much time I spent preparing notes - rather this has been about being involved with so many amazing young persons, each of whom is as different from the other as possible, and watch with an amused smile, as the scene plays out.

Teaching Experiences

It would be untrue to say that I was not apprehensive when I first started teaching this semester. I am in charge of two lab sessions one of which has strength of 20 and the other 6 students. What made the process more interesting was that several of the students were from diverse backgrounds. Some were students who majored in EE while others were from Bioinformatics or biology. While teaching I have been trying to mixture my passion for exploring new ideas with experiences gained during past sessions. This helps to make the sessions interactive, which not only helps us to explore the content interactively but also the student’s comprehensibility of the content increases with such interactions. For me it was also eye-opening to see the capabilities of my students in such collaborative environment. The students had different level of expertise in different domains and the course matter involves data structure and organization, a core requirement for students in computer science and interesting for those who wants to pursue the understanding of data organization and optimization in computer programs.

Future Goals

The recent years in the technological education has witnessed a tremendous activity in all interdisciplinary fields. Elements of quantum physics and computer science were amalgamated in quantum information processing, molecular biology and Computer Science joined hands to create Bioinformatics, and Mechanical engineers worked with biologists to develop artificial limbs of extraordinary precision and maneuverability. Computer Science in particular, covering an array of fields such as Bioinformatics, Bio-mechanics, Electrical Engineering and Bioengineering, Systems Analysis, 3-D modeling, etc, has shown a tremendous potential for research and also an increasing number of applications in today's world of medicine and technology. So it is well known that future research in the varied fields of science, engineering and humanities will heavily depend on computers in general. As a result of which I look forward to a career of research after my graduation from U.T.E.P.

In view of the above I am currently involved in a lot of research activities to make that goal feasible. I have been working on problems related to Computer Vision, Machine Learning which is my area of interest and have managed to develop some excellent algorithms which are both efficient and cost effective to solve them. My recent research on surveillance motion detection has resulted in a publication which was accepted at the International Conference on Image Analysis and Recognition. Also as a sequel to this work I have developed a Foveal Visual System which simulates the behavior of a system that detects motion in real time using low resolution images and then foveates the area of interest similar to a human eye. I will be submitting this work very recently in a renowned Computer Vision Conference.

Apart from the above I have been also worked in biological sequence analysis research such as RNA structure prediction mechanisms using Machine Learning algorithms formally known as "Optimizing cut in RNA sequences using Machine Learning" or “Predicting the 3D structures of protein molecules using Electron Microscopic Cryo images” etc. Some of the other vision related projects include “Face Detection using Principal Component Analysis”, “Face Recognition and Tracking" and “Automatic Image Enhancement using a Genetic Algorithm”.

My motivation in research and ability for independent thinking is amply vindicated through my projects and my continued interest in them and a lifetime devoted to research seems to be the most exciting proposal for me. Being a researcher in computer science would enable me to solve real world problems first hand and thereby serve the society most directly. I feel that this is a most rewarding vocation and definitely one that I intend to pursue after my graduation.

Resources at UTEP

While writing research papers in scientific journals a significant portion of the work is dedicated to literature review. As a student of UTEP this has proved to be quite simple due to the numerous facilities that exist for students who have to simply login to the system and download related material of choice. Especially the library database that has a well established collection of journals, scientific magazines, e-books reduces much of the time and effort in actually retrieving a copy of the required material. Also since all of these materials are licensed under the University of Texas system Copyright Act the legal hazards associated also are taken care of.
Another facility which many students and I myself have used extensively is the my utep system which has a large number of resources listed under it for online services. These includes the Goldmine for registration purposes, the mspace for allowing students to upload and download documents, and the Blackboard learning system which helps students to take online courses. For example I have often made payments for my courses online using the Goldmine credit payment system which has in fact proved to be quite useful since I did not have to turn up at the Academic Services building to pay my tuition. Similarly in the case of mspace I have often uploaded documents and important material related to my T.A. work or my research and instead of having the fear of losing it all the time simply downloaded the necessary from the Internet. On the other hand Blackboard was a facility that I recently learnt of since I have not taken any online courses before. I was hoping we could make use of it even for regular courses like in the case when the faculty uploads an assignment and we finish the assignment on time and submit it online.

Apart from all the resources mentioned above I have also learnt about a recent one known as the 'Turnitin' where as a T.A. or faculty it is easy to detect plagiarized documents submitted by the students. But I am not sure about the rate of accuracy involved in such predictions or how much useful it would prove to be in the case when there is a match with documents belonging to students of the previous year.