SPS Pioneers in IEEE History: Member Highlight: Dr. Sanjit K. Mitra Elected to the Engineering Academy of Japan

Dr. Sanjit K. Mitra, Distinguished Professor Emeritus of Electrical and Computer Engineering at University of California, Santa Barbara, has recently been elected to the Engineering Academy of Japan (EAJ) as an International Fellow. EAJ is composed of leading experts from academia, industry, and government institutions who possess a wide range of knowledge and have made outstanding contributions in engineering and technological sciences, and closely related fields, according to the academy’s website. EAJ is a non-profit, non-governmental organization established to contribute to the advancement of engineering and technological sciences. Dr. Mitra was recognized for his contributions to signal processing. Since its establishment in 1987, the EAJ has grown to over 800 individual Japanese members and 27 foreign associates (nonresidents of Japan), including six from the United States. View Dr. Mitra’s full biography.

At IEEE’s 75th anniversary, we approached one of the SPS pioneers, Dr. Sanjit Mitra, with the following questions

1. In your own words, please tell us about your background.

I was born and brought up in a small town in India. I received my undergraduate and post-graduate education in India after which I worked as an engineer at a research institute during 1957 and 1958 in Kolkata (formerly known as Calcutta) for the maintenance of India’s first digital computer. I then decided to go to the United States for post-graduate studies at University of California (UC) Berkeley in 1958 where I carried out research in analog circuit design.  I received my Master’s and PhD degrees in 1960 and 1962, respectively.  I then joined Cornell University as an Assistant Professor where I was supported by an NSF Research Initiation Grant and after three years I joined Bell Laboratories, New Jersey and in January 1967 I joined UC Davis as an Associate Professor.  During summer of 1967 I worked at Lenkurt Electric Company in Redwood City, California where I gained practical experience in analog filter design. During my stay at UC Davis, I was supported by grants from NSF in analog circuit design, however, I desired a change in research direction and accepted a position at Stanford Linear Accelerator Center in Menlo Park, California during summer 1972 on a non-circuit type of problem.  I was able to solve this problem in about two weeks.  Then the supervisor asked me to give some lectures on digital signal processing, an emerging field at that time, of which I had little knowledge. Fortunately, Gold and Rader had just published their book “Digital Processing of Signals” and after reading this book, I gave three one-hour lectures.  In addition, I gave one lecture on fast Fourier transform by reading the paper “An Algorithm for the Machine Calculation of. Complex Fourier Series” of by Cooley and Tukey.  After returning to UC Davis, I introduced a graduate level course on digital signal processing which I team taught with another professor.

I took a sabbatical leave from UC Davis and joined the Indian Institute of Technology Delhi in September 1972 as a Visiting Professor and stayed there for about 8 months.  During my stay at IIT Delhi, I visited and presented seminars at many other universities and government laboratories of India. For the remaining four months of my sabbatical leave, I was a Visiting Professor at Kobe University, Kobe, Japan.  In 1977 I transferred to UC Santa Barbara where I am a Professor Emeritus of Electrical & Computer Engineering since June 2016 and served as the Chair of the department from July 1979 to June 1982. One of my proudest achievements was to take the department to the top 20 in the country at the end of my three-year appointment. During my active career at UC Santa Barbara, I went to Australia on a sabbatical leave for six months as a Visiting Professor at Australian National University in Canberra. During my stay at ANU, I visited many universities in the eastern part of the country and presented seminars.

I have graduated 41 Master’s students of whom 4 are from Germany, 5 from Italy and 5 from Norway.  I also have graduated 50 PhD students of whom 9 are Fellows of the IEEE and one is a member of the US National Academy of Engineering. I have held visiting appointments at 16 institutions in 12 countries and presented 31 keynote/plenary lectures at international conferences held in 18 countries and presented 463 invited lectures in 42 countries. I have served as an External Examiner of 17 doctoral dissertations from 7 countries.

2. What are the most important factors in your success? 

The most important factors are my parents who valued education followed by Professors Charles A. DeSoer and Ernst S. Kuh of UC Berkeley, and my supervisor Mr. M. Robert Aaron of Bell Labs.

3. Failures are an inevitable part of everyone’s career journey, what is the most important lesson you have learned during your career when dealing with failures?

My biggest failure has been not being able to restructure the electrical and computer engineering curriculum at UC Santa Barbara to keep up with the dramatic technological developments during the second half of last century. In my opinion, a student can specialize in at most one area in the undergraduate program. However, today’s economy requires an engineer who has specialized in more than one area. To this end, we need to offer a five-year combined BS/MS program which permits the student to specialize in more than one field. Hence, the undergraduate curriculum should be restructured into a multi-track program.  We should also offer an internship-in-industry program to provide the student with a meaningful and valuable real-world design experience before graduation. A student enrolled in five-year combined BS/MS program could work in an industry after receiving the BS degree during the summer before returning to work for the MS degree. I presented an invited paper describing my idea for restructuring the curriculum at the 1997 IEEE International Conference on Acoustics, Speech, & Signal Processing held at Munich, Germany.

4. How does your work affect society?

My work has led to today’s leaders at major industries in the Bay area and professors at top universities who are graduating next generation leaders.

5. In your opinion, what are some of the most exciting areas of research for students and upcoming researchers?

As computing power has increased dramatically, one is now able to solve computationally intensive problems easily. The exciting areas of research now are artificial intelligence, bio-medical engineering, fast data-base search, machine learning, human-machine interaction, behavioral informatics, nanotechnology and quantum computing.

6. What challenges do you think our current student population faces as far as preparedness in these areas is concerned? What would you suggest to these upcoming researchers?

Students need to be trained in new ways to not only be prepared for the above-mentioned technologies but in addressing the technological challenges facing humanity. They need to be educated with depth as well as breadth; they need to develop excellent verbal and written skills; they need to develop skills in working with multidisciplinary and multicultural teams; they need to be cognizant of the impact that their work has on society and finally they should be able to operate with the highest form of ethics.

7. During these COVID times, the teaching and learning has become online for some time as of now. What do you think are some of the challenges being faced in carrying out quality teaching as well as quality research? Do you have any suggestions for students and faculty?

COVID-19 has definitely affected teaching and learning as students were forced to take courses online. In addition, for almost two years one could not attend conferences as all of them became virtual.  However, almost all journals were available either as hard copies or soft copies, along with conference proceedings.  I am not sure whether COVID-19 had affected carrying out quality research. It appears that the virus problem is now over, and most universities are back to offering regular classes and it will be possible to attend all conferences in person.

8. What were the main changes during the last 25-30 years?

• Move from domain and model-based approaches to data driven machine learning approaches,   a huge paradigm shift, largely due to deep learning.
• Advances in sensors, computing hardware/software and communications have enabled connectivity at a massive scale.
• Global Positioning System.
• Smart Phones.
• Medical non-invasive diagnostics and wireless health.
• Greater ability to perform increasingly advanced space missions through intelligent robotics, resulting in a greater understanding of the universe through space exploration.
• Autonomous vehicles: unmanned ground vehicles and unmanned aerial vehicles.
• Advances in computing - from mainframes to servers to laptops, CPU to GPU.

9. What technological problems in signal processing were addressed in the last 25-30 years?

• JPEG 2000 (a discrete wavelet transform based compression standard).
• Integration of wireless technologies and signal processing.
• Signal processing for medical imaging.
• Low cost, miniaturized, sensing technologies enabled by microelectromechanical systems (MEMS) and nano technologies. This made the use of sensors and sensing technologies ubiquitous in our daily lives.
• Increased density, miniaturization and lower cost of semiconductor memories.
• Cloud computing.
• Heterogeneous integration and performance scaling of chips to overcome limitations of Moore's Law.
• Advances in Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are shifting the research attention towards intelligent, data-driven, signal processing.
• Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves.
• Graph Signal Processing (GSP): Classical signal processing tools developed in the Euclidean domain have been generalized to irregular domains such as graphs.
• Passive radar systems for both target detection and ground imaging.
• Applications of radar techniques span from ocean current monitoring to Earth digital elevation mapping, from automotive to biomedicine, from industrial monitoring in IoT scenarios to through the wall imaging, and from the detection of vital signs and discerning the activities of daily living.
• Waveform optimization for system parameter estimation, an emerging topic in signal processing with applications in radar and remote sensing.
• Cognitive radar, a recent and growing research area, offers substantial benefits for defense and civilian radar systems.
• Explosive growth in wireless communications, especially for applications in the Industrial Internet of Things (IIoT).
• Advances in speech processing and natural language processing leading to Siri, a virtual assistant of Apple, that adapts to and recommends how to operate within different emotional, social, and cultural norms
• Machine translation – Automatic translation of speech into multiple languages.
• High fidelity audio.
• Compressive Sensing and Sparse Reconstruction. This allowed going around Nyquist Sampling Theorem by utilizing the sparsity of the sensed data. Broad applications covering all sensing modalities and applied to both signals and images.
• Self-driving cars and integrated sensing and communications.
• Convex optimizations and iterative-based techniques for optimum solutions to constrained minimization problems.

10. If there is one take home message you want the readers of this interview to have, what would it be?

Even though I worked for a short time at Bell Labs, Lenkurt Electric Company, and Stanford Linear Accelerator Center, I believe teaching is the best profession I have had. The primary advantages being a teacher was the freedom to choose the courses which allowed me to learn new topics by teaching them.  The second advantage of being a teacher was to change the areas of my research. My advice to the younger generations is that they should always try to be innovative and not hesitate to work on problems that no one has worked before and take chances on new problems which sometimes may not yield interesting results. Starting a career after graduation does not mean the end of learning and one must continue to learn throughout his/her career by reading new papers and taking short courses offered by university extension.