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Prof. Tiago H. Falk received the BSc degree from the Federal University of Pernambuco, Brazil, in 2002, and the MSc and PhD degrees from Queen’s University, Canada, in 2005 and 2008, respectively, all in electrical engineering. In 2007, he was a visiting Research Fellow at the Sound and Image Processing Lab, Royal Institute of Technology (KTH), Sweden, and in 2008 at the Quality and Usability Lab, Deutsche Telekom/TU Berlin, Germany. From 2009-2010 he was an NSERC Postdoctoral Fellow at Holland-Bloorview Kids Rehabilitation Hospital, affiliated with the University of Toronto. He joined the Institut National de la Recherche Scientifique (INRS) in Montreal, Canada in Dec. 2010 as a tenure-track Assistant Professor. In 2015, he was promoted to tenured Associate Professor and in 2021 to a Full Professor. He is also an Adjunct Professor at McGill University (ECE Dept.) and an Affiliate Researcher at Concordia University (PERFORM Centre). At INRS, he heads the Multimedia/Multimodal Signal Analysis and Enhancement (MuSAE) Laboratory.
Prof. Falk is a Senior Member of the IEEE, an alumnus of the Global Young Academy (GYA), a member of the Sigma Xi Research Society, Academic Chair of the Canadian Biomedical and Biological Engineering Society, Co-Chair of the IEEE SMC Brain-Machine Interface Technical Committee, a Board-of-Governor Member-at-Large of the IEEE SMC Society, and a voted member of the IEEE SPS Audio and Acoustic Signal Processing Technical Committee (2018-2020; 2021-2023). His research interests lie at the crossroads of signal processing and machine learning, with the former providing contextual cues and intelligence to the latter. Applications across numerous domains, including affective human-machine interfaces, human-inspired multimedia/immersive technologies, and in-the-wild human performance/health monitoring, have been explored. Together with his research team, he has published 300+ journal and conference papers, book chapters and patents in these domains.
1. In your own words, please tell us about your background.
I obtained my BSc in Electrical and Electronics Engineering from the Federal University of Pernambuco, Recife, PE, Brazil in 2002. Already in my 2nd year of undergraduate studies, I knew I wanted to be a Professor, perhaps by inspiration from my dad, who has been an academic for most of his life, and to this date still teaches classes in Business/Hospital Administration, well into his late seventies. To achieve this goal, I became involved in research via the university’s “Scientific Initiation” program several years in a row, I became involved in TA’ing 1st year courses, and taught physics and math to high school students. Soon upon graduation I moved to Canada to pursue my MSc in Electrical and Computer Engineering at Queen’s University in Kingston (mid-2005) where I continued into the PhD program and defended my thesis in the end of 2008. In 2009, I started my postdoctoral training at the Holland-Bloorview Kids Rehabilitation Hospital, a teaching hospital affiliated with the University of Toronto, building assistive technologies for children with severe disabilities. In December 2010, I started as an Assistant Professor at the Institut national de la recherche scientifique (INRS-EMT), University of Quebec, in Montreal where I was later promoted to Associate (2015) and Full Professor (2021). I have been directing the Multisensory/multimodal Signal Analysis and Enhancement (MuSAE) Lab since arriving at INRS.
2. What are the most important factors in your success?
I think success is a very subjective term and different academics will have different definitions of what it means – win awards/prizes, attract multi-million dollar grants, publish hundreds of papers, supervise hundreds of students, transfer patents to industry, etc. Ultimately, at the foundation of all of these are the students we supervise and the collaborations we establish with colleagues. Making an environment that is welcoming, multi-disciplinary, and multi-cultural, yet challenging scientifically and technically, will spark innovations – innovations that will eventually lead to such publications, awards and grants. Having a circle of colleagues to bounce off ideas from, to work together on exciting projects, to push us beyond our boundaries, as well as to have a beer with to unwind, are also extremely important. Many project ideas and collaborations have started as part of chats during an ICASSP conference and have often led to top publications, awards and grants. So ultimately, in my opinion, the most important factor in success is not to focus on our own, but that of our students and colleagues. Ours will come as a natural consequence.
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?
As academics, we are faced with rejections all the time – rejections from granting institutions, from journals, conferences, etc. As cliché as it may sound, the most important lesson to learn from failures is to learn from them, to get back up after falling, dust ourselves off, and try again in a different and improved manner. I see many young researchers and students facing a lot of stress to get papers accepted into major conferences with 10% acceptance rates. I see many new students showing signs of imposter syndrome, thinking they are not smart enough because they can’t get a paper accepted to conference X or conference Y. Many of the Best Paper Awards in the Lab have come from smaller Workshops, aimed at specific topics, with a smaller number of participants but with equally-high impactful work. The IEEE SPS has many such Workshops, including IWAENC, WASPAA, ASRU. I advise all students to consider such Workshops when submitting their work – the feedback they will receive will be useful and the connections they will make will last a lifetime.
4. How does your work affect society?
I am a signal processing researcher at heart and my interests have shifted over the years, but one thing has remained consistent: to build tools that can have an impact. As a PhD student, I was interested in building non-intrusive quality and intelligibility metrics from speech that could be used by telecommunication service providers to improve the quality of their calls, by hearing aid manufacturers to improve the quality-of-life of their users, and to assist clinicians in monitoring the progress of their speech therapies from children to stroke survivors. As a postdoc, I was interested in building assistive technologies, ranging from hum detectors for non-verbal communications to brain-computer interfaces. More recently, my team and I have been working on using signal processing to provide additional intelligence to machine learning algorithms to make them more reliable in everyday settings. Such intelligence can include extracting context from the environment and/or from signal noise, measure sensor quality, or extract new signal representations that can serve as improved inputs to e.g., deep learning algorithms. This aspect has been crucial in the development of tools that have practical use; some examples of tools we have developed include the use of IoT devices to monitor beehive health, wearables to monitor the stress/workload/anxiety levels of first responders in the field and nurses in the hospital, and more recently, the use of multisensory immersive environments and brain-computer interfaces to better train Olympians and police officers.
5. In your opinion, what are some of the most exciting areas of research for students and upcoming researchers?
In our lab, we are investing heavily in the marriage between signal processing and machine learning (as mentioned above) to allow for practical solutions to be developed. Advances in neurotechnologies and brain-machine interfaces (BMIs) will also have great societal impact in the no-so-distant future and numerous innovations are needed, from sensors, to signal processing, to machine learning. Lastly, we have a vision of the future internet being the so-called Internet of Senses, where not only audio-visual content is transmitted, but also touch, smells, and taste, combined with affective computing, BMIs, and machine learning to enable truly immersive experiences. This is a virgin territory with lots of area for innovation.
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?
One challenge has been mentioned above - competitiveness, from internships to conferences. Once upon a time having a PhD is all you needed to enter academia. At some point, multi-year postdocs became the new norm. Now students are required to have papers in conference X and Y, awards, scholarships, postdocs in university X and Y, supervisor so-and-so, etc. If you don’t have the resources available to you (one simple example is access to multiple GPUs for deep learning research) you are automatically at a disadvantage. To these students, I suggest doing as much as you can with the resources you have available to you. Try to make a difference locally. Start an IEEE student chapter in your university, invite lecturers and get to know them personally, invite members from industry, reach out to professors around the world and see if there are any summer internship openings (in our Lab, we host between 3-5 international summer interns every year), organize journal reading clubs, reach out to professors in your own university and see if there are research projects available that you can assist with. Many courses are now available online - take as many as you can, from deep learning, to signal processing, to python coding. And … don’t forget to network. As I mentioned above, having a strong network of colleagues and friends around you should be seen as a requirement for your success – you cannot get to the top alone, so surround yourself by likeminded individuals so you can grow together.
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?
In our lab, many experiments rely on human subject experimentation, so, of course, this took a hit during the pandemic. We had to pivot and develop remote monitoring packages and dropping kits off at participant homes. This reduced the number of participants drastically, while increased the variability of the experimental conditions. This has, on the other hand, enabled new research questions to be answered, so a silver lining of the pandemic. I think the pandemic has also shown the world the importance of open-source data, as well as the challenges run by many different organizations, including IEEE SPS. It has given people access to data and allowing them to continue with their research even while in lockdown. I encourage all researchers to make their data available open-source, not only for replication purposes, but to allow those with reduced resources to also make significant contributions to research. A major downside of the pandemic to newer researchers has been the limited exposure to networking at conferences and during internships. In the Lab, all senior PhD students are encouraged to do one or more internships in industry to be exposed to new environments and to corporate research. This does not have the same outcomes when done virtually. On the other hand, virtual conferences have dropped many barriers and given the opportunity for participation to many around the world. I encourage researchers to, whenever possible, push for hybrid events in the future, thus allowing those who can attend in-person to fully benefit from the face-to-face interactions, while giving the opportunity to learn and gain exposure from those not capable of travelling to the conference site, for any number of reasons.
8. If there is one take home message you want the readers of this interview to have, what would it be?
Try to make the best use of the resources you have available to you and don’t forget to network! Extra curriculars are an excellent way to meet new colleagues. As the pandemic becomes a thing of the past, go to conferences and local events, make an effort to go to some smaller Workshops and meet the key people in your field of research, from both academia and industry, and network, network, network! Become involved in challenges, hackathons, your local student chapter. Academia is an always-learning environment, so take advantage of the many courses that are now available online, the many videos from experts now available on YouTube, and the many virtual conferences with free registrations. It is certain that you will encounter many rejections along the way, we all do – it’s part of academia. But don’t doubt yourself, dust yourself off, learn from your mistakes (many reviews have very useful ideas and tips!), and keep moving forward. Surround yourself by likeminded individuals and don’t be ashamed to ask for help. And take care of your mental health. As goes the saying “you can’t pour from an empty cup.”
To learn more about Tiago H. Falk, please visit his webpage.
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