Aaditya Ramdas
Curriculum Vitae
1835 Cedar St, Apt B
Berkeley, CA 94703
H (+1) 773-234-3277
B aramdas@cs.berkeley.edu
Í cs.berkeley.edu/∼aramdas
DOB: May 3, 1988
Research Interests
My research spans theory, algorithms and applications in machine learning, nonparametric statistics
and stochastic optimization. Broad areas of interest include
{ Sequential Learning
{ Kernel Methods
{ Nonparametric Hypothesis Testing
{ Statistical Learning Theory
Specific recent subareas of interest within these include
{ Independence & Two-sample Testing
{ Statistics on Non-abelian Groups
{ Stochastic Convex Optimization
{ False Discovery Rate
{ Markov Chain Monte Carlo
{ Systems of Equations & Inequalities
Current Position
2015–now Postdoctoral Researcher in EECS and Statistics.
University of California, Berkeley (UCB), Berkeley (USA)
Advisors: Michael Jordan & Martin Wainwright
Academic Background
2013–15 Doctor of Philosophy in Statistics and Machine Learning.
Carnegie Mellon University (CMU), Pittsburgh (USA)
PhD Thesis: Computational and Statistical Advances in Testing and Learning
Advisors: Larry Wasserman & Aarti Singh
2010–12 Master of Science in Machine Learning.
Carnegie Mellon University (CMU), Pittsburgh (USA)
GPA – 4.2 out of 4
Masters Data Analysis: Statistical Modeling of Crime in Pittsburgh
2005–09 Bachelor of Technology in Computer Science and Engineering.
Indian Institute of Technology (IIT), Bombay (India)
GPA – 9.44 out of 10 (Institute Rank 9/600, Department Rank 3/60)
IIT Joint Entrance Exam, Rank 47/400,000.
Work Experience
2009-10
Sum’14
Sum’12
Sum’09
Sum’08
Sum’07
Algorithmic Trader, Tower Research Capital, Gurgaon (India) & New York (USA).
Research Intern (ML), Gatsby Neuroscience Unit (UCL), London (UK).
Research Intern (AI), Microsoft Research (MSR), Cambridge (UK).
Quantitative Analyst, Deutsche Bank, Mumbai (India).
Research Intern (Geometry), INRIA, Sophia-Antipolis (France).
Research Intern (Logic), LaBRI, Bordeaux (France).
1/7
Peer-Reviewed Publications
∗
= equal contribution, sub. = submitted, ar. = Arxiv preprint available
(only full-length papers at competitive venues; excludes workshops and short papers at conferences with minimal refereeing)
Hypothesis Testing
sub., ar. Aaditya Ramdas, D. Isenberg∗ , A. Singh, L. Wasserman.
Minimax Lower Bounds for Linear Independence Testing
sub., ar. R. F. Barber, Aaditya Ramdas.
The p-filter: multi-layer FDR control for grouped hypotheses
ar. Aaditya Ramdas, A. Balsubramani∗ .
Sequential Nonparametric Testing using the Law of the Iterated Logarithm
sub., ar. Aaditya Ramdas, A. Singh, L. Wasserman.
Classification Accuracy as a Proxy for Two Sample Testing
ar. Aaditya Ramdas, N. Garcia∗ , M. Cuturi.
Wasserstein Two Sample Testing and Related Families of Nonparametric Tests
sub., ar. Aaditya Ramdas, S. Reddi, B. Poczos, A. Singh, L. Wasserman.
Adaptivity & Computation-Statistics Tradeoffs for High Dimensional Two-Sample Testing
NIPS K. Chwialkowski, Aaditya Ramdas, D. Sejdinovic, A. Gretton.
Fast Two-Sample Testing with Analytic Representations of Probability Measures
29th Conference on Neural Information Processing Systems, 2015.
IJCAI Aaditya Ramdas, L. Wehbe∗ .
On the Increase in Power of Kernel Independence Testing due to Shrinkage
24th International Joint Conference on Artificial Intelligence, 2015. oral talk
AISTATS Aaditya Ramdas, S. Reddi∗ , B. Poczos, A. Singh, L. Wasserman.
High-Dimensional Power of Linear-Time Two Sample Tests vs. Mean-Shift Alternatives
18th International Conference on Artificial Intelligence & Statistics, 2015.
AAAI Aaditya Ramdas, S. Reddi∗ , B. Poczos, A. Singh, L. Wasserman.
On the Decreasing Power of Kernel & Distance based Hypothesis Tests in High Dimensions
29th AAAI Conference on Artificial Intelligence, 2015.
Statistical Learning
sub. M. Rabinovich, A. Ramdas, M. Wainwright, M. Jordan.
Function mixing times and MCMC concentration away from equilibrium
sub. A. El-Alaoui, X. Cheng, Aaditya Ramdas, M. Wainwright, M. Jordan.
Asymptotic behavior of `q -based Laplacian regularization in semi-supervised learning
sub. H. Mania, Aaditya Ramdas, M. Wainwright, M. Jordan, B. Recht.
Universality of Mallows’ and degeneracy of Kendall’s kernels for rankings
AISTATS Aaditya Ramdas, B. Poczos, A. Singh, L. Wasserman.
An Analysis of Active Learning with Uniform Feature Noise
17th Intl. Conference on Artificial Intelligence & Statistics, 2014. oral talk
ALT Aaditya Ramdas, A. Singh.
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization
24th International Conference on Algorithmic Learning Theory, 2013. oral talk
2/7
Convex Optimization
sub., ar. Aaditya Ramdas, A. Hefny∗ , D. Needell∗ .
Rows vs. Columns: Randomized Kaczmarz or Gauss-Seidel for Ridge Regression
OMS Aaditya Ramdas, J. Peña.
Towards a Deeper Geometrical, Analytical and Algorithmic Understanding of Margins
Optimization Methods and Software, 2015.
SIMAX Aaditya Ramdas, A. Ma∗ , D. Needell∗ .
Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz methods
SIAM Journal of Matrix Analysis and Applications, 2015.
ICML Aaditya Ramdas, J. Peña.
Margins, Kernels and Non-linear Smoothed Perceptrons.
31st International Conference on Machine Learning, 2014. oral talk.
ICML Aaditya Ramdas, A. Singh.
Optimal Rates for Stochastic Convex Optimization under Tsybakov Noise Condition
30th International Conference on Machine Learning, 2013. oral talk
Applications
JCGS Aaditya Ramdas, R. Tibshirani.
Fast & Flexible ADMM Algorithms for Trend Filtering
Journal of Computational and Graphical Statistics, 2015.
AoAS L. Wehbe, Aaditya Ramdas, R. Steorts, C. Shalizi.
Regularized Brain Reading with Shrinkage and Smoothing
Annals of Applied Statistics, 2015.
PLoS ONE L. Wehbe, B. Murphy, P. Talukdar, A. Fyshe, Aaditya Ramdas, T. Mitchell.
Simultaneously uncovering patterns of brain regions involved in story reading subprocesses
Public Library of Science ONE, 2014.
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Talks
Conferences
2016
2015
2015
2015
2014
2014
2013
2013
2013
Beyond Worst Case Mixing Times for Markov Chains (ITA Workshop)
Nonparametric Independence Testing for Small Sample Sizes (IJCAI)
Adaptivity & Computation-Statistics Tradeoffs in Two Sample Testing (JSM)
Sequential Testing using the Martingale LIL (Intl. W’shop on Sequential Methodology)
Margins, Kernels and Nonlinear Smoothed Perceptrons (ICML)
Active Learning with Uniform Feature Noise (AISTATS)
Connecting Convex Optimization and Active Learning (NIPS, OPT Workshop)
Algorithmic Connectiions between Convex Optimization and Active Learning (ALT)
Optimal Convex Optimization under Tsybakov Noise Condition (ICML)
Universities
2016
2015
2015
2014
2014
2014
2014
2014
2013
2013
2013
2013
p-Filter: FDR Control for Grouped Hypotheses (UC Davis)
Insights into why Kernel Methods work well in High Dimensions (CMU)
Margins - Algorithms, Geometry and Analysis (CMU)
Computation-Statistics Tradeoffs in Two Sample Testing (UC Berkeley)
Adaptivity for Two Sample Testing (Kyoto University)
High Dimensional Two Sample Testing (Institute for Statistical Mathematics, Tachikawa)
Algorithms for Trend Filtering (Gatsby Neuroscience Unit, University College London)
Algorithms for Trend Filtering (CMU)
Active Learning & Stochastic Optimization (Chennai Mathematical Institute)
Active Learning & Stochastic Optimization (IIT Madras)
Alg. Connections Between Active Learning and Stochastic Opt. (CMU)
Optimal Upper and Lower Bounds for Stochastic Optimization (CMU)
Industry
2016
2015
2015
2014
2013
2012
p-Filter: FDR Control for Grouped Hypotheses (AmpLab Retreat, Tahoe)
Sequential Nonparametric Testing (Alibaba, Seattle)
Sequential Nonparametric Testing (Google, Pittsburgh)
Algorithms for Trend Filtering (Microsoft Research, Cambridge)
Active Learning & Stochastic Optimization (IBM Research, Bangalore)
Connecting Statistical & Logical Inference (Microsoft Research, Cambridge)
Workshop Posters
2015
2015
2015
2015
2014
2014
2013
2013
NSF Workshop for Empirical Processes and Modern Statistical Decision Theory, Yale
American Statistical Association (ASA), Pittsburgh Chapter, Best Poster Award
UC San Diego, Information Theory & Applications (ITA) Workshop
AIM Workshop on Inference in High Dimensional Regression
UC Los Angeles, IPAM Stochastic Gradient Methods Workshop
UC London, High-Dimensional Sensing and Inference Workshop
GlobalSIP, IEEE Global Conference on Signal & Information Processing
IISc Bangalore, Indo-US Machine Learning and Optimization Workshop
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Professional Service
Organization
2015 Workshop on Active Learning: Bridging Theory and Practice (ICML), Organizer.
with Nina Balcan, Aarti Singh, Akshay Krishnamurthy.
2014 Workshop on Optimization in Machine Learning (NIPS), Organizer.
with Alekh Agarwal, Suvrit Sra, Miro Dudik, Zaid Harchaoui, Martin Jaggi.
2014 ML Department Student Research Symposium (CMU), Lead Organizer.
2012-15 Weekly Lunch Seminar Series on Machine Learning (CMU), Organizer.
Journal Reviewing
2016
2014,16
2015
2015
2015
2015
2014,15
2014
2014
2013
Annals of Applied Statistics (AoAS)
Journal of Machine Learning Research (JMLR)
Numerical Algorithms (NA)
Machine Learning Journal (MACH)
Journal of Artificial Intelligence Research (JAIR)
Transactions on Pattern Analysis and Machine Intelligence (T-PAMI)
Biometrika
Data Mining and Knowledge Discovery (DAMI)
Optimization Methods and Software (OMS)
IEEE Transactions on Information Theory (IEEE-TIT)
Conference Reviewing
2016
2015
2015
2014,15,16
2013,14
2013,16
2012
AAAI Conference on Artificial Intelligence (AAAI)
European Conference on Machine Learning (ECML)
International Joint Conference on Artificial Intelligence (IJCAI)
International Conference on Machine Learning (ICML)
Conference on Neural Information Processing Systems (NIPS)
Conference on Artificial Intelligence & Statistics (AISTATS)
Conference on Uncertainty in Artificial Intelligence (UAI)
Department Service
2015-16
2014-15
2014-15
2013-14
2013-14
2008-09
2007-08
Graduate Admissions Committee, CS Department (UCB)
Teaching Faculty Hiring Committee, ML Department (CMU)
Graduate Student Assembly Representative (CMU), Outstanding Representative Award
Graduate Admissions Committee, ML Department (CMU)
Education Review Committee Founder, ML Department (CMU)
Industry Job Placement Coordinator, CS Department (IITB)
Sports Coordinator, CS Department (IITB)
University Service
2015-16
2014-15
2014-15
2012-13
2011-12
2008-09
2008-09
2007-08
2007-08
2007-08
2006-07
Steward for Postdoctoral Union (UCB)
SafeZone Allies for LGBTQ Safety, Trained Member (CMU)
Campus Smoking Policy Review Committee, Member (CMU)
Explorer’s Club Core Officer (CMU)
Indian Graduate Students Association Treasurer (CMU)
Campus Radio Cofounder (IITB)
Job Fair Placement Representative, CS Department (IITB)
Google Campus Ambassador (IITB)
Institute Secretary for Academic Affairs (IITB)
Sports Secretary, CS Department (IITB)
Sports Secretary, Hostel 3 (IITB)
5/7
Teaching
2015 Alan J. Perlis Graduate Student Teaching Award, School of Computer Science.
2014 Best Teaching Assistant Award, Machine Learning Department.
2014 Machine Learning (MS), Review Videos, for all introductory graduate courses.
12 YouTube videos, 10-15 minutes each, on multivariate calculus, probability/statistics,
real/functional analysis, linear algebra; review for the 400+ sized ML introductory course.
2013 Convex Optimization (PhD), Teaching Assistant, Ryan Tibshirani & Barnabas Póczós.
Helped redesign course syllabus (content+schedule), restructured homeworks to increase
flexibility, experimented with mastery questions, peer-grading, optional questions and project.
2012 Convex Optimization (PhD), Teaching Assistant, Geoff Gordon & Ryan Tibshirani.
Created and graded homeworks and exams, gave recitations, held office hours, mentored
course projects, maintained slides, scribes and videos.
Guest Lectures (University)
2016 Sparse Linear Models, Statistical Learning Theory.
(80 minutes class; course by Martin Wainwright)
2015 Active Learning (Distilled Sensing and Classification), Statistical Machine Learning.
(80 minutes class; course by Larry Wasserman and Ryan Tibshirani)
2015 Modern Fast Stochastic Optimization for ML, Optimization.
(80 minutes class; course by Ryan Tibshirani)
2014 Lower Bounds in Optimization, Advanced Optimization.
(80 minutes class; course by Alex Smola and Suvrit Sra)
2012 ADMM and Mirror Descent, Convex Optimization.
(80 minutes class; course by Geoff Gordon and Ryan Tibshirani)
Outreach (Schools)
2016 Robots that run, Prescott Elementary, Oakland.
(60 mins, once; special class for elementary students.)
2015 SVD, Random Graphs and Random Walks, PACT, Princeton.
(90 mins, thrice; summer program for high school students.)
2015 Introduction to Computer Science, Technights, CMU.
(90 min volunteering, twice; program to introduce middle school girls to computer science.)
2015 Introduction to Machine Learning, ISG, Muscat.
(30 minutes class, twice; to introduce high school children to machine learning.)
2014 Mechanism Design: Auctions and Voting Theory, Andrew’s Leap, CMU.
(80 minutes class; summer program for high school students)
2013 Online Learning: Multi-armed Bandits, Andrew’s Leap, CMU.
(80 minutes class; summer program for high school students)
Future Faculty Program
Completed program run by the Eberly Center for Teaching Excellence (transcript available).
2014-15 Seminars.
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Course & Syllabus Design
Syllabus Design Workshop
Promoting Peer Learning
Planning & Delivering Effective Lectures
Leveraging Diversity & Promoting Equity
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Crafting a Teaching Statement
Building a Teaching Portfolio
Conducting Productive Discussions
Engaging Students in Active Learning
Good Assessment Practices
2014 Observations.
{ Classroom Teaching
{ Microteaching Workshop
2014 Projects.
{ Designed syllabus of UG course Mathematical Foundations of ML.
{ Pedagogical aspects of learning through videos.
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Awards & Honors
(includes a few that are both rarely awarded and competitive)
2013-14 Doug Beeferman PhD Fellowship, CMU.
2006-09 Inlaks Scholarship, IIT Bombay.
Awarded to the single best all-round student out of all 600 undergraduates; given full tuition
waiver plus additional stipend for three years.
2005-09 Academic Scholarships, CS Department and IIT Bombay.
For securing top cumulative grade point average (CGPA), ending with department rank 3
(out of 60) and institute rank 9 (out of 600 undergrads).
2005 Guest of Indian Prime Minister’s Office.
Invited to view India’s Annual Republic Day Parade from the PM’s Box, for academic
excellence, including All India Rank 10 (out of 300,000) in Central Board’s Class 12 Final
Exams with 97.4% and All India Rank 47 (out of 500,000) in the IIT Joint Entrance Exam.
2005-09 Institute Cultural Citation, IIT Bombay.
Awarded to 5 out of 1000 graduating UG and PG students; for winning over 20 intercollegiate
competitions in speaking, debating and literary arts, and several interhostel competitions.
2001-03 Sultanate of Oman, U-13 and U-15 Cricket Team.
Represented Oman in the U-13 Gulf Cup, finishing runners-up, and in the U-15 Asia Cup,
finishing 5th out of 14 teams. Also played for IIT Bombay and CMU cricket teams.
2001-03 Duke of Edinburgh Bronze and Silver Awards.
Awarded the International Award for Young People under the Duke of Edinburgh Award
Scheme, for social service, skill development, physical training and adventurous expeditions.
2013 Ironman Triathlon.
Finishing medalist of the 3.8km swim, 180km bike, 42km run Ironman triathlon at Louisville,
Kentucky (Aug 25, 2013). Also completed many other triathlons and marathons.
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