Heroes of Machine Learning – Top Experts and Researchers you should follow
- The path to democratizing machine learning has been blazed by experts and researchers determined to make the world a better place
- We celebrate these heroes of machine learning today. Feel we should include anyone else? Let us know!
- We have put together this list using a simple 3-step framework
What a time this is to be working in the machine learning field! The last few years have been a dream run for anyone associated with machine learning as there have been a slew of developments and breakthroughs at an unprecedented pace.
There’s just one thing to keep in mind here – these breakthroughs did not happen overnight. It took years and in some cases, decades, of hard work and persistence.
We are used to working with established machine learning algorithms like neural networks and random forest (and so on). We tend to lose sight of the effort it took to make these algorithms mainstream. To actually create them from scratch. The people who lay the groundwork for us – those are the true heroes of machine learning.
We at Analytics Vidhya salute these heroes who have blazed a trail for this modern era of machine learning. Come join us as we celebrate these experts and their groundbreaking achievements!
Our Framework for picking these Machine Learning Experts
This isn’t your typical “influencers” list. Our selection isn’t based on who has more followers on social media, or some similar banal metric. Here’s our simple framework:
- We have kept this list to machine learning experts who are active in this field right now
- Each expert has made at least one significant contribution to the machine learning and deep learning field
- The names we have picked out are in no particular order since we cannot objectively differentiate the impact of these wonderfully complex and diverse innovations
Why should you follow these experts?
Machine Learning and Deep Learning is a fast evolving area. We expect this to continue in the coming years. If you want to learn about the latest developments in the field, gain perspective from the best brains and be future proof – there cannot be a better way but to follow these experts!
In order to make it easy for you – we have provided links to their profiles which you should follow! Just click on each name to head over to their profiles.
Now, coming to the Hall of Fame..
Who else would be top of any machine learning list? Geoffrey Hinton is an Emeritus Distinguished Professor at the University of Toronto and a Google Brain researcher.
He is best known for his work on artificial neural networks (ANNs). His contributions in the field of deep learning are a key reason behind the success of the field and he is often called the “Godfather of Deep Learning” (with good reason). His research on the backpropagation algorithm brought about a drastic change in the performance of deep learning models.
Mr. Hinton’s other notable research works are Boltzmann machines and Capsule neural networks. Both major breakthroughs in our field.
Hinton recently won the 2018 Turing Award for his groundbreaking work around deep neural networks, along with Yann LeCun and Yoshua Bengio. He has also won the BBVA Foundation Frontiers of Knowledge Award (2016) and IEEE/RSE Wolfson James Clerk Maxwell Award.
Michael Jordan is a professor at the University of California, Berkeley. His areas of research are machine learning, statistics, and deep learning. He has been a major advocate of Bayesian networks and has made a significant contribution towards probabilistic graphical models, spectral methods, natural language processing (NLP), and much more.
He has won many well-known awards, including the IEEE Neural Networks Pioneer Award, the best paper award (with R. Jacobs) at the American Control Conference (ACC 1991) and the ACM – AAAI Allen Newell Award. He has also been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics.
The below talk he gave at SysML is a MUST-WATCH for anyone in machine learning. It gives a good overview of the field and puts the recent hype into perspective.
3. Andrew Ng
Andrew Ng is probably the most recognizable name in this list, at least to machine learning enthusiasts. He is considered as one of the most significant researchers in Machine Learning and Deep Learning in today’s time.
He is the co-founder of Coursera and deeplearning.ai and an Adjunct Professor of Computer Science at Stanford University. Professor Andrew also co-founded the Google Brain project and was previously the Chief Scientist at Baidu.
His aim is to democratize deep learning and give everyone in the world access to high-quality education for free. His online courses on machine learning and deep learning are highly sought after.
Andrew has an exceptional track record as an academic researcher – he has over 300 published papers in machine learning and robotics! He is also a recipient of prestigious awards like IJCAI Computers and Thought award, ICML Best Paper Award, ACL Best Paper Award and many, many more.
4. Yann LeCun
Yann LeCun is another iconic name in machine learning. He is a professor, researcher, and R&D manager with academic and industry experience in machine learning, deep learning, computer vision, and robotics.
Mr. LeCun is currently the Chief AI Scientist and VP at Facebook.
Yann LeCun is the founding father of convolutional nets. He made convolutional neural networks work with backpropagation, which is widely used in computer vision applications. And that’s just scraping the surface of what this expert is capable of.
He has over 150 papers published under his name and has received a number of awards for his contributions. He has won the 2014 IEEE Neural Network Pioneer Award and the 2015 PAMI Distinguished Researcher Award. LeCun also won the 2018 Turing award, along with Geoffrey Hinton and Yoshua Bengio.
Yoshua Bengio is a professor at the Department of Computer Science and Operations Research at the Université de Montréal. He is also the co-founder of Element AI, a Montreal-based business incubator that seeks to transform AI research into real-world business applications.
Yoshua is well known for his work on artificial neural networks and deep learning in the 1980s and 1990s. He co-created the prestigious ICLR conference with Yann LeCun. He is one of the most-cited computer scientists in the areas of deep learning, recurrent networks, probabilistic learning, and natural language.
There is probably no topic in deep learning that Yoshua hasn’t touched and that’s why his contribution to the field of deep learning is quite diverse as compared to his contemporaries.
He has received the prestigious award of Canada Research Chair in Statistical Learning Algorithms and also won the 2018 Turing award. Do check out his talk on Deep Learning below:
If you haven’t heard of Jürgen Schmidhuber yet – rectify that immediately! He is a computer scientist, known for his work around artificial neural networks and deep learning. His lifetime goal is to build a self-improving Artificial Intelligence smarter than himself.
He, along with some of his students, published sophisticated versions of long short-term memory (LSTM), an improved version of recurrent neural networks. His research work also included the speeding up of convolutional neural networks using GPUs.
Mr. Schmidhuber is the recipient of numerous awards, author of over 350 peer-reviewed papers, and Chief Scientist of the company NNAISENSE, which aims at building the first practical general-purpose AI. He is also advising various governments on AI strategies.
Terry is a professor at the Salk Institute for Biological Studies and the author of The Deep Learning Revolution (MIT Press). He is one of the pioneers of neural networks back in the 1980s. Along with Geoffrey Hinton, he demonstrated that simple neural networks could be useful and made to learn certain tasks.
He is the co-inventor of the Boltzmann machine along with Geoffery Hinton and contributed immensely in solving problems related to speech and vision. Terry is also the co-creator of the algorithm for Independent Component Analysis that has been widely used in machine learning and signal processing.
He received the Hebb Prize for his contributions to learning algorithms by the International Neural Network Society in 1999. He also received IEEE’s Neural Network Pioneer Award in 2002. In 2017, he was elected to the National Academy of Inventors.
David is a professor of Statistics and Computer Science at Columbia University. His research interests lie in topic models, probabilistic modeling, and approximate Bayesian inference.
He was one of the original developers of the popular topic modeling technique, Latent Dirichlet Allocation (LDA), along with Andrew Ng and Michael I. Jordan. His research work revolves around recommendation systems, neuroscience, computational social sciences, and natural language.
He is the recipient of the ICML Test of Time Award (for “Dynamic Topic Models”) 2016, the Presidential Award for Outstanding Teaching, and many more. Apart from that, he has published over 100 papers.
This lecture by Blei on Topic Models is a gem. I have already bookmarked it.
Daphne Koller is a Professor in the Department of Computer Science at Stanford University and one of the founders of Coursera. She received both her bachelor’s degree and a master’s degree from the Hebrew University of Jerusalem. Then she went on to complete her Ph.D. at Stanford in 1993.
Her areas of interest are computer vision and computational biology. She even co-authored a book on probabilistic graphical models along with Nir Friedman. After leaving Coursera in 2016, she founded a drug discovery startup called Insitro
The online education model of Stanford was her idea that she initiated in 2010. It has led to the formation of the open-for-all online courses that are being offered by Stanford.
She was awarded the Arthur Samuel Thesis Award in 1994, Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999 and in 2011 was elected as a member of the National Academy of Engineering, an American non-governmental organization.
Zoubin is a professor of Information Engineering at the University of Cambridge. His research interests include Bayesian approaches to machine learning, statistics, information retrieval, bioinformatics, and artificial intelligence.
He completed his Ph.D. from the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology, under Michael I. Jordan and Tomaso Poggio. In 2014, he co-founded a startup, Geometric Intelligence that focusses on object or scenario recognition.
Later, Uber acquired Geometric Intelligence and Zoubin joined Uber’s A.I. Labs in 2016. He has published over 250 research papers and was elected Fellow of the Royal Society (FRS) in 2015.
11. Sebastian Thrun
Another very popular name on our list. Sebastian Thrun is currently the CEO of Kitty Hawk Corporation and the co-founder of Udacity. But we’re sure you’ve heard his name before all these things.
Sebastian founded the Google X Lab and Google’s self-driving team. He led the project from the start and is widely considered a leader when it comes to autonomous vehicles. He has developed multiple autonomous robotic systems in his career.
As you might expect from a person of Sebastian’s stature, he is deeply integrated into the academic side of machine learning as well. He is the Adjunct Professor at Stanford University and at Georgia Tech.
Sebastian was named one of ‘Brilliant 5’ by Popular Science magazine in 2005. He has also been awarded the Max-Planck-Research Award (2011).
If you’ve gone through Andrew Ng’s videos, there’s a good chance you would have come across Yaser Abu-Mostafa’s lectures too. His ability to break down complex topics into easy-to-understand bytes is really incredible. There’s a lot each of us could learn from him.
Professor Yaser is a Professor of Electrical Engineering and Computer Science at the California Institute of Technology. He co-founded the most renowned machine learning conference for researchers – NIPS, or the Conference on Neural Information Processing Systems.
He was awarded the Richard Feynman award prize for excellence in teaching (which is no surprise to anyone who has seen his talk about machine learning) and has numerous technical publications.
13. Peter Norvig
Peter Norvig is among the godfathers of modern-day AI. There are no two ways about it – he has inspired the current work that is happening around the world in machine learning. We owe him a huge debt of gratitude.
He is currently the Director of Research at Google. Before his current role, Peter was head of Google’s core search algorithms group, and of NASA Ames’s Computational Sciences Division. He won the NASA Exceptional Achievement Award in 2001.
Peter is also a bestselling author and has written numerous books on the field of artificial intelligence. We loved his article titled ‘Teach Yourself Programming in Ten Years” where he put forth an impassioned argument against introductory books that promised to teach you programming in one go.
A cool fact about Peter Norvig – he was employee #8 at Junglee!
14. Trevor Hastie
Does the name sound familiar? Trevor Hastie is the co-author of the popular books “Introduction to Statistical Learning” and “Elements of Statistical Learning”. Professor Trevor is well known for his contributions to the field of applied statistics (published over 200 articles and written over 5 books in this field).
He is currently the Professor of Mathematical Sciences and the Professor of Statistics at Stanford University. He has a wonderful way of engaging with the audience and making statistics and machine learning concepts fun to learn.
Professor Trevor is a member of some of the most highly distinguished societies in academia, such as the Royal Statistical Society, American Statistical Association, National Academy of Sciences, among others.
Have you heard of LASSO regression? Well – you should. It’s an integral part of a data scientist’s toolbox. The most influential person involved in creating and developing this LASSO method? Robert Tibshirani!
He is currently a Professor in the Departments of Statistics, Health Research, and Policy at Stanford University. He has recently been working extensively in the healthcare field, developing statistical tools to analyze complex genomic datasets.
Professor Robert is also a popular author. In fact, he is the other co-author of the two books we mentioned under Traveor Hastie’s profile – Introduction to Statistical Learning and Elements of Statistics Learning.
Like Trevor Hastie, he is also a member of prestigious academic societies, such as the Institute of Mathematical Statistics, the American Statistical Association, Royal Society of Canada, among others.
16. Anil K. Jain
Anil K. Jain is a University Distinguished Professor in the Computer Science and Engineering Department at Michigan State University. He is an IIT-Kanpur graduate in electrical engineering.
Professor Anil is known for his contributions in the fields of computer vision, pattern recognition, and biometric recognition and is a highly cited machine learning Google Scholar profile.
He has been awarded a plethora of awards based on his work in computer science and machine learning. He received the W. Wallace McDowell Award in 2007 from the IEEE Computer Society, the Humboldt Research Award, among various others. He also received the best paper awards from the IEEE Transactions on Neural Networks (1996) and the Pattern Recognition journal (1987, 1991, and 2005).
17. Jitendra Malik
Jitendra Malik is currently a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He also plays a pivotal role at Facebook as part of their AI Research division.
Jitendra is another pioneer in the computer vision field. He has mentored over 60 Ph.D. students and has been a part of well-known algorithms and concepts in machine learning, such as high dynamic range imaging, shape context, and R-CNN. The latter, R-CNN, is a popular type of neural network.
Per Wikipedia, “he was awarded the Longuet-Higgins Prize in 2007 and 2008 and the Helmholtz Prize twice in 2015 for contributions that have stood the test of time (awarded to papers after 10 years of publication)”.
18. Vladimir Vapnik
Vladimir Vapnik is one of the primary developers of the Vapnik-Chervonenkis theory of statistical learning. But he’s made a name of himself in the machine learning community for co-creating one of the most popular classification algorithms.
Vladimir is currently involved at Facebook AI Research where he is working with, you guessed it, Yann LeCun. His publications have been cited close to 180,000 times according to Wikipedia, an astonishing number.
Vladimir is also the co-creator of the support vector clustering algorithm. The number of awards he was won is staggering and too long to list here. Some notable ones are the Gabor Award in 2005, the Neural Networks Pioneer Award in 2010, and the Benjamin Franklin Medal in Computer an Cognitive Science in 2012.
19. Ian Goodfellow
If you’re remotely interested in computer vision, you should know the name – Ian Goodfellow. He is best known for inventing Generative Adversarial Networks (GANs). GANs have become ubiquitous in deep learning and are popularly used at companies like Facebook and Google.
Ian is currently a Director of Machine Learning at Apple. He is a researcher at heart and has previously worked as a research scientist at Google Brain and OpenAI.
Ian’s list of mentors is enviable. He completed his MS in computer science under Andrew Ng and his Ph.D. under Yoshua Bengio and Aaron Courville.
20. Andrej Karpathy
Andre Karpathy is already a legend in the AI community. He is currently working as the Director of AI at Tesla but has long been involved in the machine learning domain. His interest and specialization lies in deep learning, computer vision and image recognition.
He completed his Ph.D. from Stanford University under the supervision of the great Dr. Fei-Fei Li. He has previously worked at OpenAI as a research scientist as well. Talk about working in elite company!
21. Fei-Fei Li
Dr. Fei-Fei Li is an iconic name in the machine learning community. Her resume speaks for itself:
- Co-Director of Stanford University’s Human-Centered AI Institute and the Stanford Vision and Learning Lab
- Director of the Stanford Artificial Intelligence Lab (SAIL) from 2013 to 2018
- Co-founded AI4ALL in 2017, a non-profit organization aiming to increase diversity and inclusion in AI
- Leading scientist and principal investigator of ImageNet, a wildly popular deep learning dataset
- Google Cloud’s Chief Scientist of AI/ML and Vice President in 2017
The list goes on. She is an expert and thought leader in the fields of machine learning, computer vision, artificial intelligence, and cognitive neuroscience. She has published 170+ peer-reviewed research papers and continues to be a shining light for women in tech, data science and frankly, for all data scientists.
22. Jeremy Howard
If you’re a programmer by profession or at heart, you’ll love the work Jeremy Howard does. His Twitter timeline is a treasure trove of information and resources for programmers and developers interested in machine learning.
He is a founding researcher at fast.ai along with Rachel Thomas (profiled below). Howard started his professional career in management consulting before he jumped into entrepreneurship. He is a big advocate of open source library and packages and ahs contributed to several of them over the years.
Jeremy has mentored and advised many startups and is a Young Global Leader with the World Economic Forum.
23. Rachel Thomas
Rachel Thomas is the other co-founder of fast.ai. She is a mathematics whiz and a strong advocate for using AI for good. She was actually one of the earliest engineers at Uber during it’s foundational days.
She regularly features ain the top AI conference in the world and was selected by Forbes in their “20 Incredible Women in AI” list.
We personally love the way Rachel breaks down complex math equations into simple terms so that programmers are able to follow along. Her talk on why AI needs everyone is riveting and important in equal measure:
This is by no means an exhaustive list. That’s primarily the reason we put a framework in place before we created the list. We have been inspired by these heroes of machine learning and continue to look up to their work every day we work with their algorithms!
Who is your favorite expert from this list? Or is there anyone we should have included? Let’s discuss in the comments section below!