Current Fellows

The Lane Fellows Program recognizes and supports scientists of outstanding intellect who are dedicated to a career at the interface of computational and biological sciences so that they can pursue postdoctoral research in the rich computational environment at Carnegie Mellon. Candidates may be nominated by their thesis advisor, department head, or another faculty member from their Ph.D. granting institution who is familiar with their qualifications.

Xin Gao
University of Waterloo
Thesis Advisor: Ming Li

Xin Gao received his Bachelor of Science degree from the Computer Science and Technology Department at Tsinghua University, China in 2004. He then applied to David R. Cheriton School of Computer Science at the University of Waterloo where he began working on his doctoral thesis in the area of bioinformatics and algorithms. His doctoral work mainly focuses on fully automated NMR protein structure determination and protein structure prediction. Xin’s research interests include computational methods and machine learning techniques in structural biology, sequence analysis, and system biology. He is particularly interested in developing highly-efficient algorithms and high-quality systems that really work on noisy and large-scale biological data sets.

Peter Huggins
University of California, Berkeley
Thesis Advisors: Lior Pachter and Bernd Sturmfels

A mathematician turned computational biologist, Peter earned his PhD in math at UC Berkeley with a designated emphasis in computational/genomic biology.  While at Berkeley, Peter applied polyhedral geometry to analyze Needleman--Wunsch sequence alignment, fitness landscapes, and the performance of neighbor-joining.  In particular he helped construct the first genome-wide parametric alignment.  Peter's current research interests include sequence analysis, proteomics and phylogenetics, with a focus on probabilistic models and machine learning techniques. He's particularly interested in applications to HIV sequence analysis and disease association studies.  In his spare time, Peter enjoys poker, backgammon, and fishing.

Hiroyuki Kuwahara
University of Utah
Thesis Advisors: Chris J. Myers

Hiroyuki Kuwahara obtained his B.S. and Ph.D. in computer science from the University of Utah. His Ph.D. thesis describes systematic and automatic model abstraction methodology to efficiently estimate temporal behaviors of genetic regulatory networks. To further pursue his research in the multidisciplinary field of computational biology, He worked for Microsoft Research – University of Trento Centre for Computational and Systems Biology. Among Hiro’s research interests are stochastic modeling, analysis, and control of biochemical systems. In particular, he is currently interested in analysis of rare deviant behaviors in the presence of stochastic fluctuations and analysis of reliable behaviors with unreliable elements.

Arvind Rao
University of Michigan
Thesis Advisors: Alfred Hero, David States and James Engel

Arvind received his Bachelor of Engineering degree (with distinction) in Electronics and Communications from Bangalore University, India in 2001. In 2003, he received the Master of Science in Engineering degree from the Electrical and Computer Engineeringg department at the University of Texas at Austin, with a specialization in Communications, Networks and Systems. He earned a A.M. in Statistics from the University of Michigan in 2007 and was a Rackham Predoctoral Fellow. For his doctoral work at the University of Michigan, he worked on understanding long -range transcriptional regulation in higher eukaryotes. His research interests lie at the intersection of signal processing, machine learning, experimental and computational systems biology.

Le Song
University of Sydney
Thesis Advisor: Alex Smola

Le Song completed his B.S. degree majoring in computer science from the South China University of Technology. Then he went to Australia and did his Master's and PhD studies both at the Univeristy of Sydney. He was also an endorsed student from the National ICT Australia. His main research interests are statistical machine learning, kernel methods, information visualization and their applications to biological and social problems. He has worked on various projects such as visual analysis of complex networks, identification of discriminative neuromarkers from EEG data, and selection of informative genes from microarray data. His goal is to bring modern machine learning tools into biology and generate real impact in the biology community.