About the Authors
Linh Tran
Graduate Student
Department of Mathematics
Yale University
New Haven, CT, USA
l.tran[ta]yale[td]edu
https://math.yale.edu/people/linh-tran
Graduate Student
Department of Mathematics
Yale University
New Haven, CT, USA
l.tran[ta]yale[td]edu
https://math.yale.edu/people/linh-tran
Linh Tran is currently fifth year
Ph.D. student in the Math department at Yale University, working
under the supervision of Professor Van Vu.
He went to special math classes for gifted children at the high school
division of the Vietnam National University in Ho Chi Minh City.
Encouraged and nurtured by his high school teachers, Tran found his
passion for Math and was determined to follow this path,
especially after having had little success in other subjects.
Under the guidance of his advisor, Prof. Van
Vu, he does research in random graph theory, particularly majority dynamics
and generalizations, and random matrix theory, particularly its application
in data analysis and machine learning methods. He is also interested in
combinatorics problems involving the probabilistic method, applications of
geometry in combinatorics, e.g., random fractals, and theoretical computer
science.
Van Vu
Percey F. Smith Professor of Mathematics and Professor of Statistics & Data Science
Yale University
New Haven, CT, USA
van.vu[ta]yale[td]edu
https://campuspress.yale.edu/vanvu/
Percey F. Smith Professor of Mathematics and Professor of Statistics & Data Science
Yale University
New Haven, CT, USA
van.vu[ta]yale[td]edu
https://campuspress.yale.edu/vanvu/
Van Vu is the P. F. Smith Professor of Mathematics and Professor of
Data Science at Yale University. Vu was born in Hanoi (Vietnam) in 1970.
He went to special math classes for gifted children at Chu Van An and Hanoi-Amsterdam high schools.
In 1987, he went to Hungary for his undergraduate studies, and obtained
his M.Sc. in mathematics at
Eötvös University, Budapest, in 1994.
He received his Ph.D. at Yale University, in 1998 under the direction
of László Lovász.
His research interest includes random structures (random matrices,
random graphs, random functions), probabilistic and additive
combinatorics, and randomized algorithms.
Recently, he has also been interested in real-life applications of
data science (such as NLP and computer vision).