|Address:||Room 316 Lally, CS Department, 110 8th St, Troy, NY 12180|
|Office:||316 Lally Building|
|Email:||gittea at rpi dot edu|
I am an assistant professor in the Computer Science department of Rensselaer Polytechnic Institute. My research focuses on using randomization to reduce the computational costs of extracting information from large datasets. My work lies at the intersection of randomized algorithms, numerical linear algebra, high-dimensional probability, and machine learning.
I earned my PhD in applied and computational mathematics at CalTech under the supervision of Prof. Joel Tropp, in 2013. From 2013 until 2015, I was a member of the machine learning research group at eBay. Following that I was a postdoctoral fellow in the AMPLab at UC Berkeley and a member of the International Computer Science Institute. I joined RPI in January of 2017.
I am currently seeking PhD students.
In the spring of 2018, I am reteaching CSCI6971/CSCI4971, "Large Scale Matrix Computation and Machine Learning". See the website for the syllabus and assignments.
In the fall of 2017, I taught CSCI6220/4030, "Randomized Algorithms". See the website for the syllabus and assignments.
In the spring of 2017, I taught a course related to my research, CSCI6971/CSCI4971, "Large Scale Matrix Computation and Machine Learning". See the syllabus.
- S. Tu, S. Venkataraman, A. Wilson, A. Gittens, M. Jordan, B. Recht. Breaking Locality Accelerates Block Gauss-Seidel. ICML 2017
- S. Wang, A. Gittens, M. Mahoney. Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging. ICML 2017
- A. Gittens, D. Achlioptas, M. Mahoney. Skip-Gram – Zipf + Uniform = Vector Additivity. ACL 2017
- A. Gittens, A. Devarakonda, E. Racah, et al. Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+ MPI Using Three Case Studies. IEEE BigData 2016
- A. Gittens, J. Kottalam, J. Yang, et al. A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark. IPDPS ParLearning Workshop 2016
- A. Gittens, M. Mahoney. Revisiting the Nystrom Method for Improved Large-scale Machine Learning. JMLR. 2016
- C. Boutsidis, P. Kambadur, A. Gittens. Spectral Clustering via the Power Method-Provably. ICML 2015
- D. Kuang, A. Gittens, R. Hamid. Hardware compliant approximate image codes. CVPR 2015
- R. Hamid, Y. Xiao, A. Gittens, D. DeCoste. Compact Random Feature Maps. ICML 2014
- C. Boutsidis, A. Gittens. Improved matrix algorithms via the subsampled randomized Hadamard transform. SIMAX. 2013
- R. Y. Chen, A. Gittens, J. A. Tropp. The masked sample covariance estimator: an analysis using matrix concentration inequalities. Information and Inference. 2012
- J. Yang, A. Gittens. Tensor machines for learning target-specific polynomial features. 2015.
- D. Kuang, A. Gittens, R. Hamid. piCholesky: Polynomial Interpolation of Multiple Cholesky Factors for Efficient Approximate Cross-Validation. 2014
- A. Gittens, J. A. Tropp. Tail bounds for all eigenvalues of a sum of matrices. 2011
- A. Gittens. The spectral norm error of the naive Nystrom extension. 2011