About me
I currently work as a machine learning engineer at Captricity.
Before that, I was a PhD student in the Medical Vision Group at CSAIL, where I worked with Polina Golland. For my PhD, I worked on applications of machine learning and artificial intelligence to problems in medical imaging. I was funded by the NSF Graduate Research Fellowship.
I was an undergraduate in EECS at UC Berkeley. While I was there I worked with Stan Klein's Visual Processing Lab on signal processing in vision science, and with Dan Garcia and Brian Harvey on a curriculum development project involving parallelism in introductory computer science courses.
You can also find me on github.
Publications

Adrian Dalca, Ramesh Sridharan, Natalia Rost, and Polina Golland.
"tipiX: Rapid Visualization of Large Image Collections."
In MICCAI Interactive Medical Image Computing (IMIC), 2014.
Best paper award for impact and usability.
[Paper] 
Ramesh Sridharan, Adrian Dalca, and Polina Golland.
"An Interactive Visualization Tool for Nipype Medical
Image Computing Pipelines." In MICCAI Interactive Medical Image Computing (IMIC), 2014.
[Paper] [Live demo] 
Adrian Dalca, Ramesh Sridharan,
Lisa Cloonan, Kaitlin Fitzpatrick, Allison Kanakis, Karen Furie,
Ona Wu, Jonathan Rosand, Natalia Rost, and Polina Golland.
"Segmentation of Cerebrovascular Pathologies in Stroke Patients with Spatial and Shape Priors."
In Proceedings MICCAI: International Conference on Medical
Image Computing and Computer Assisted Intervention (MICCAI)
2014.
[Paper] 
Sophie Chou*, William Li*, and Ramesh Sridharan*.
"Democratizing Data Science."
KDD@Bloomberg, 2014.
[Paper] 
Ramesh Sridharan*, Adrian Dalca*,
Kaitlin Fitzpatrick, Lisa Cloonan, Allison Kanakis, Ona Wu,
Karen Furie, Jonathan Rosand, Natalia Rost, and Polina
Golland. "Quantification and
Analysis of Large Multimodal Clinical Image Studies:
Application to Stroke." In Proceedings MICCAI
International Workshop on Multimodal Brain Image Analaysis
(MBIA), 2013.
[Paper] [Poster] 
Danial Lashkari, Ramesh
Sridharan, Ed Vul, PoJang Hsieh, Nancy Kanwisher,
and Polina Golland. "Search for
Patterns of Functional Specificity in the Brain: A
Nonparametric Hierarchical Bayesian Model for Group fMRI
Data." NeuroImage, 59(2):13481368, 2012.
[Paper]  Ramesh Sridharan. "A Generative Model for Activations in Functional MRI." Master's Thesis, MIT, 2011.

Danial Lashkari, Ramesh Sridharan, and Polina Golland.
"Categories and Functional Units: An Infinite Hierarchical
Model for Brain Activations." Accepted in Advances in Neural
Information Processing Systems (NIPS), 2010.
[Paper] [Poster] 
Danial Lashkari, Ramesh Sridharan, Ed Vul, PoJang
Hsieh, Nancy G. Kanwisher, and Polina Golland.
"Nonparametric Hierarchical Bayesian Model for Functional
Brain Parcellation." In Proceedings of MMBIA: IEEE
Computer Society Workshop on Mathematical Methods in
Biomedical Image Analysis, 2010.
[Paper] 
Matthew Johnson*, Ramesh Sridharan*,
Robert H. Liao, Alexander Rasmussen, Dan Garcia and Brian K.
Harvey.
"Infusing Parallelism into Introductory Computer Science
Curriculum using MapReduce." UC Berkeley Technical Report
No. UCB/EECS200834, 2008.
[Paper]
* Indicates equal contribution
Teaching
List of notes and writeups I've made: A series on introductory practical statistics, with a focus on how and when to use different statistical techniques:
 1: Introduction and definitions
 2: Confidence Intervals and Classical Hypothesis Testing
 3: Linear regression fundmentals
 4: Advanced linear regression: robustness and sparsity
 5: Nonparametric tests (with fewer assumptions) and model evaluation
 6: Categorical data: Chisquared tests and ANOVA
 7: Principles of experimental design
 Primers on a few key topics in probabilistic inference and machine learning:
 Notes for seminars, made in collaboration with my colleage Matt Johnson:
 Differential Geometry, from Kuhnel's Differential Geometry
 GoodTuring Estimators, from McAllester and Schapire's On the Convergence Rate of GoodTuring Estimators
 Streaming Algorithms: Part 1 and Part 2, from Muthukrishnan's Data Streams: Algorithms and Applications
 In 2015, I received the Goodwin Medal, awarded by MIT to one or two graduate students each year for teaching performance that is "conspicuously effective over and above ordinary excellence."
 I taught a short course titled 6.S085, Statistics for Research Projects in January 2012 (with my colleague Finale DoshiVelez), January 2013, and January 2014 (with my colleague George Chen), and again in January 2015. I also wrote up a series of notes (soon to be compiled into a small book), which you can find at the course website.
 In 2013, I received the Frederick C. Hennie III Teaching Award from the EECS department at MIT, awarded to a few graduate students in the department each year for teaching excellence.
 I was a TA for 6.s080: Introduction to Inference in Fall 2012. This was a brandnew undergraduate course on inference! Along with my fellow TAs, George Chen and Gauri Joshi, we made Khan Academyinspired videos summarizing recitation topics. If you're at MIT, you can view my teaching ratings. I also wrote up some notes on course topics, which you can find above.
 I was a TA for 6.438: Algorithms for Inference in Fall 2010. If you're at MIT, you can view my teaching ratings.
 I was a TA for 6.041: Probabilistic Systems Analysis and Applied Probability in Fall 2008. If you're at MIT, you can view my teaching ratings.
 In 2008, I received the Outstanding Graduate Student Instructor Award, granted to several TAs within each department across UC Berkeley each year. The same year, I received the EECS Outstanding Graduate Student Instructor Award, granted to one or two outstanding TAs within the department each year.
 I was a TA for CS61A: Structure and Interpretation of Computer Programs, an introductory computer science class, from Summer 2006 to Spring 2008. You can see my teaching ratings.