Michael I Jordan | Citations | Biography | Publications | Curriculum-vitae
Michael I Jordan is a renowned figure in the fields of electrical engineering, computer science, and statistics. As the Pehong Chen Distinguished Professor at the University of California, Berkeley, his research interests span across various disciplines, including computational science, statistics, cognitive science, biology, and social sciences. With a distinguished career and numerous accolades, Jordan has made significant contributions to the academic world through his groundbreaking research and innovative methodologies.
Early Life and Education
Michael I Jordan’s passion for mathematics and science was evident from an early age. He completed his Bachelor of Science in Psychology from Louisiana State University in 1978, laying the foundation for a future career in academia. His thirst for knowledge led him to pursue a Master’s degree in Mathematics from Arizona State University in 1980, where he honed his analytical skills and developed a deep understanding of mathematical principles.
Determined to push the boundaries of his knowledge, Jordan embarked on a journey to earn his Ph.D. in Cognitive Science from the University of California, San Diego in 1985. This interdisciplinary field allowed him to explore the intersection of cognitive psychology, computer science, linguistics, and philosophy, setting the stage for his future research endeavors.
Academic Career
After completing his doctoral studies, Michael I Jordan’s academic career took off when he joined the Massachusetts Institute of Technology (MIT) as a professor in 1988. During his tenure at MIT, Jordan continued to expand his research portfolio, delving into diverse areas such as machine learning, graphical models, and optimization. His work pushed the boundaries of these fields, earning him recognition as a leading expert and paving the way for future breakthroughs.
In 1998, Jordan joined the University of California, Berkeley, where he currently holds the prestigious position of the Pehong Chen Distinguished Professor. This role allows him to bridge the gap between electrical engineering and computer science, as well as statistics, leveraging his multidisciplinary expertise to advance research and inspire the next generation of scholars.
Research Contributions
Michael I Jordan’s research contributions have had a profound impact on various fields, including machine learning, statistical modeling, and artificial intelligence. His pioneering work in latent Dirichlet allocation (LDA) has revolutionized natural language processing and topic modeling. LDA provides a probabilistic framework for uncovering underlying themes and patterns in large textual datasets, enabling researchers to extract valuable insights and make informed decisions.
Another notable contribution by Jordan is his work on spectral clustering, a powerful technique for grouping data points based on their spectral properties. Spectral clustering has applications in image segmentation, community detection in social networks, and data compression. Jordan’s analysis and algorithmic advancements in this field have significantly enhanced the accuracy and efficiency of spectral clustering methods.
Additionally, Jordan has made significant contributions to the fields of graphical models and variational inference. His research has provided valuable insights into the foundations of graphical models and their applications in diverse domains, including computer vision, genetics, and natural language processing. Variational inference, a technique for approximating complex probabilistic models, has been a key focus of Jordan’s research, enabling efficient and scalable inference in large-scale problems.
Publications
Throughout his prolific career, Michael I Jordan has published numerous influential papers in top-tier journals and conferences. His publications have not only expanded the boundaries of knowledge but also served as a catalyst for further research and innovation in the field. Some of his notable publications include:
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“Understanding the acceleration phenomenon via high-resolution differential equations” – This paper, co-authored with B. Shi, S. Du, W. Su, explores the acceleration phenomenon in optimization algorithms by analyzing high-resolution differential equations. The insights from this work have implications for the design of efficient optimization techniques.
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“Learning equilibria in matching markets from bandit feedback” – In collaboration with M. Jagadeesan, A. Wei, and J. Steinhardt, Jordan presents a novel approach to learning equilibria in matching markets using bandit feedback. This research has implications for designing fair and efficient matching systems in various domains.
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“Interleaving computational and inferential thinking: Data science for undergraduates at Berkeley” – In this article, co-authored with A. Adhikari and J. DeNero, Jordan discusses the importance of integrating computational and inferential thinking in data science education. The paper presents a curriculum developed at Berkeley that equips undergraduate students with the necessary skills for data analysis and inference.
Michael I Jordan Awards and Recognitions
Michael I Jordan’s contributions to the academic community have been recognized with numerous awards and accolades. His groundbreaking research and dedication to advancing scientific knowledge have earned him the following honors:
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Leonardo da Vinci Lecture (2023): Jordan was invited to deliver the prestigious Leonardo da Vinci Lecture, honoring his exceptional contributions to the fields of electrical engineering, computer science, and statistics.
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Foreign Member of the Royal Society (2021): Jordan was elected as a Foreign Member of the Royal Society, a testament to his global impact and recognition as a leading scholar.
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Ulf Grenander Prize in Stochastic Theory and Modeling (2021): This esteemed prize was awarded to Jordan for his outstanding contributions to stochastic theory and modeling, highlighting his expertise in probabilistic modeling and statistical analysis.
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IEEE John von Neumann Medal (2020): The IEEE John von Neumann Medal was awarded to Jordan in recognition of his pioneering contributions to machine learning and statistical modeling.
These are just a few of the many awards and honors that Michael I Jordan has received throughout his illustrious career. His dedication to advancing scientific knowledge and interdisciplinary research has made him a highly respected figure in academia.
Membership and Fellowships
Michael I Jordan’s expertise and contributions to various academic disciplines have earned him membership in prestigious organizations and fellowships. Some notable memberships and fellowships include:
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American Academy of Arts and Sciences Member (2010): Jordan was elected as a member of the American Academy of Arts and Sciences, an esteemed organization that recognizes individuals who have made significant contributions to academia, arts, and sciences.
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National Academy of Engineering (NAE) Member (2010): Jordan’s exceptional contributions to the field of engineering earned him membership in the National Academy of Engineering, a prestigious organization dedicated to advancing engineering knowledge and innovation.
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National Academy of Sciences (NAS) Member (2010): Jordan’s groundbreaking research and scientific contributions led to his induction as a member of the National Academy of Sciences, one of the highest honors in the scientific community.
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American Association for the Advancement of Science (AAAS) Fellow (2011): Jordan’s outstanding contributions to the advancement of science and research earned him the distinction of being named a Fellow of the AAAS.
These memberships and fellowships reflect the recognition and respect that Michael I Jordan has garnered from his peers and the academic community for his exceptional contributions to multiple disciplines.
| Michael I Jordan GOOGLE CITATIONS |
CITED BY
|
YEAR |
|---|---|---|
| Latent dirichlet allocation
DM Blei, AY Ng, MI Jordan
Journal of machine Learning research 3 (Jan), 993-1022
|
51217 | 2003 |
| On spectral clustering: Analysis and an algorithm
A Ng, M Jordan, Y Weiss
Advances in neural information processing systems 14
|
12191 | 2001 |
| Machine learning: Trends, perspectives, and prospects
MI Jordan, TM Mitchell
Science 349 (6245), 255-260
|
7562 | 2015 |
| Trust region policy optimization
J Schulman, S Levine, P Abbeel, M Jordan, P Moritz
International conference on machine learning, 1889-1897
|
7413 | 2015 |
| Adaptive mixtures of local experts
RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton
Neural computation 3 (1), 79-87
|
5657 | 1991 |
| Sharing clusters among related groups: Hierarchical Dirichlet processes
Y Teh, M Jordan, M Beal, D Blei
Advances in neural information processing systems 17
|
5297 | 2004 |
| Graphical models, exponential families, and variational inference
MJ Wainwright, MI Jordan
Foundations and Trends® in Machine Learning 1 (1–2), 1-305
|
5286 | 2008 |
| Learning transferable features with deep adaptation networks
M Long, Y Cao, J Wang, M Jordan
International conference on machine learning, 97-105
|
5284 | 2015 |
| An introduction to variational methods for graphical models
MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul
Machine learning 37, 183-233
|
5215 | 1999 |
| Hierarchical mixtures of experts and the EM algorithm
MI Jordan, RA Jacobs
Neural computation 6 (2), 181-214
|
4166 | 1994 |
| An internal model for sensorimotor integration
DM Wolpert, Z Ghahramani, MI Jordan
Science 269 (5232), 1880-1882
|
4105 | 1995 |
| Distance metric learning with application to clustering with side-information
E Xing, M Jordan, SJ Russell, A Ng
Advances in neural information processing systems 15
|
3956 | 2002 |
| On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
A Ng, MI Jordan
Advances in Neural Information Processing Systems 14, 841
|
3517 | 2002 |
| An introduction to MCMC for machine learning
C Andrieu, N De Freitas, A Doucet, MI Jordan
Machine learning 50, 5-43
|
3456 | 2003 |
| Optimal feedback control as a theory of motor coordination
E Todorov, MI Jordan
Nature neuroscience 5 (11), 1226-1235
|
3434 | 2002 |
| High-dimensional continuous control using generalized advantage estimation
J Schulman, P Moritz, S Levine, M Jordan, P Abbeel
arXiv preprint arXiv:1506.02438
|
3166 | 2015 |
| Learning the kernel matrix with semidefinite programming
GRG Lanckriet, N Cristianini, P Bartlett, LE Ghaoui, MI Jordan
Journal of Machine learning research 5 (Jan), 27-72
|
3089 | 2004 |
| Kalman filtering with intermittent observations
B Sinopoli, L Schenato, M Franceschetti, K Poolla, MI Jordan, SS Sastry
IEEE transactions on Automatic Control 49 (9), 1453-1464
|
2912 | 2004 |
| Deep transfer learning with joint adaptation networks
M Long, H Zhu, J Wang, MI Jordan
InternaAll Poststional conference on machine learning, 2208-2217
|
2530 | 2017 |
| Kernel independent component analysis
FR Bach, MI Jordan
Journal of machine learning research 3 (Jul), 1-48
|
2498 | 2002 |
Conclusion
Michael I Jordan’s multidisciplinary approach to research and his groundbreaking contributions to fields such as machine learning, statistical modeling, and artificial intelligence have solidified his position as a pioneer in the academic world. His work on latent Dirichlet allocation, spectral clustering, and graphical models has transformed the way researchers analyze and understand complex datasets, paving the way for future advancements in these fields. With numerous awards, memberships, and fellowships, Jordan continues to inspire and guide future generations of scholars, leaving an indelible mark on the world of science and academia.
