Electrical Engineering Computer Science

Ranaji Krishna

*** **** **., *** *********, CA *************@*****.*** +1-424-***-**** https://github.com/ranajikrishna No Visa Required A quantitative and analytical background developed through a Ph.D. in Signal Processing and mathematical modelling experience in technology and financial sectors. Keen interest in applying skills and insights to the business needs of the company. Experience

Quantitative Analyst, Growth and Analytics Team, Intercom, San Francisco, CA Feb. 2015 – July 2015

• Worked in a small group at a young and fast growing company that provides a CRM platform.

• Built models to give deep insights and predict trends from data.

• Developed a negative binomial regression model using R to determine factors that influence conversion of trial customers to full-time customers. Created tables from relational databases using PostgreSQL in AWS Redshift.

• Developed a model in Python based on perceptron classification technique to estimate potential cancellation amongst customers who were going to be switched to a new pricing scheme. Quantitative Associate, Complex Securities Group, Stout Risius Ross Inc., Los Angeles, CA Feb. 2014 – Nov. 2014

• Provided modelling and algorithm development expertise to price financial instruments at a private investment bank.

• Developed models to price instruments such as Equity Derivatives and Interest rate Derivatives, portfolios of exotic options and swaps.

• Developed a model using non-linear estimation techniques to estimate the credit rating of private companies.

• Implemented algorithms in R for Monte Carlo simulation analysis. Research Intern, Strategic Asset Allocation Group, Research Affiliates Llc., Irvine, CA Jul. 2013 - Sep. 2013

• Interned at an asset management firm and replicated journal papers to carry out simulation-based analysis in R of turbulence in financial markets; developed portfolios that were robust to turbulent return of assets.

• Proposed a new technique to quantify turbulence and to create portfolios with marginal performance increment.

• Developed linear regression models in R to explore the explanatory power of growth in GDP towards major indices of countries. Queried large data sets acquired from Wharton Research Data Services (WRDS) using SAS. Software Research Scientist, Emerging Technologies Group, Xyratex Ltd., U.K. Feb. 2011 – Oct. 2012

• Worked in a research group focussed on improving system efficiency of high performance computing clusters and parallel file systems. Recognized for contributions and promoted after first year.

• Developed a model to detect time and location of anomalies in HPC clusters using information entropy of message terms from large volumes of system log messages.

• Developed models in Python (SimPy) and C to predict the performance of caches in parallel file systems working at exa- scale computing levels.

Mathematical Research Scientist, Deep Visuals Ltd., Cambridge Science Park, U.K. Jul. 2010 – Nov. 2010

• Joined a start-up that develops technology for smart browsing of digital image collections.

• Developed a model to rank images of interest to users browsing a database of images. The model extracted the ontology of the past-images viewed and formulated an adaptive weighting scheme to predict the images that were relevant to the users.

• The model was implemented as algorithms in .NET environment using C Sharp. Statistical Signal Processing Research Engineer, Dialog Devices Ltd., Loughborough Innovation Park, U.K. Mar. 2010 - Jul. 2010

• Worked at a digital health start-up that developed technology to monitor the health of patients with diabetes.

• Developed a model and an algorithm in MATLAB for a bio-medical device to detect the occurrence of arterial diseases in patients. Designed filtering techniques to clean the signals acquired for detection purposes.

• Employed statistical methods such as Leave-One-Out-Cross-Validation and developed simulation techniques to draw Receiver Operating Characteristic curves.

Relevant Skills

• Mathematics: Time Series Analysis, Bayesian Inference, Machine Learning, Hidden Markov Models and Optimization.

• Programming languages: Developed algorithms in R, Python, MATLAB and C. Familiar with C Sharp. See code examples at: https://github.com/ranajikrishna

• Querying languages: SQL/PostgreSQL and SAS.

Education

Master of Financial Engineering, Anderson School of Management, UCLA. Dec. 2012 - Dec. 2013

• Finance: Econometrics (Time Series Analysis), Stochastic Calculus, Derivative Pricing and Computational Methods.

• Applied Finance Project: Pricing Options with Double Exponential Jump Diffusion Process. Ph.D. – Signal Processing, Loughborough University, U.K. Sep. 2006 – Dec. 2009 Thesis: Mathematical optimization and signal processing techniques in wireless relay networks. Advisors: Professor S. Lambotharan and Professor J.A. Chambers.

• Specialized in the development and analysis of signal processing algorithms for wireless MIMO communications. Emphasis on collaborative beamformer design using wireless relays with applications in wireless networks.

• Proposed novel models based on MMSE optimization and Convex optimization techniques to provide enhanced quality of services in wireless communication systems.

• Developed conceptual models from first principles by applying mathematical concepts of statistics and probability theory, stochastic processes, linear algebra, MMSE optimization and Convex optimization techniques.

• Designed efficient algorithms in MATLAB to implement the models for Monte Carlo simulation analysis. M.Eng. (Hons.) – Electronic Engineering, Cardiff University, U.K. Sep. 2001 – Jul. 2006 Degree Classification: First Class (Hons.).

• 5-year Integrated Master’s degree, which included 1-year of Industrial Placement.

• Modules: Digital Signal Processing, Adaptive Signal Processing, Artificial Intelligence and Real-Time Signal Processing. Online Courses

• “Learning from Data”, an online course in Machine Learning taught by Professor Y. A. Mostafa, Professor of Electrical Engineering and Computer Science, California Institute of Technology.

• “Machine Learning”, an online course in Machine Learning and Statistical Pattern Recognition taught by Professor Andrew Ng, Professor of Electrical Engineering and Computer Science, Stanford University.

• “A full course in Econometrics”, an Econometrics course taught by Ben Lambert, Tutor, Oxford University. Selective Publications

Journals

1. Krishna, R., Cumanan, K., Xiong, Z., Lambotharan, S., “A Novel Cooperative Relaying Strategy for Wireless Mesh Networks,” accepted for publication in Transactions in Vehicular Technology, IEEE. 2. Krishna, R.; Xiong, Z.; Lambotharan, S., "A Cooperative MMSE Relay Strategy for Wireless Sensor Networks," Signal Processing Letters, IEEE, vol.15, no., pp.549-552, 2008. 3. Cumanan, K.; Krishna, R.; Musavian, L.; Lambotharan, S., "Joint Beamforming and User Maximization Techniques for Cognitive Radio Networks Based on Branch and Bound Method. Accepted for publication in IEEE Transactions on Wireless Communications, June 2010.

4. Cumanan, K.; Krishna, R.; Xiong, Z.; Lambotharan, S., “Multiuser Spatial Multiplexing Techniques with Constraints on Interference Temperature for Cognitive Radios.” accepted for publication in IET Signal Process. Nov., 2009. 5. Xiong, Z.; Krishna, R.; Cumanan, K.; and Lambotharan, S., “Grassmannian Beamforming and Null Space Broadcasting Protocols for Cognitive Radio Networks” accepted for publication in IET Signal Processing, 2010. Conference Proceedings

6. Krishna, R.; Cumanan, K.; Xiong, Z.; Lambotharan, S., "A Semidefinite Programming Based Cooperative Relaying Strategy for Wireless Mesh Networks with Relay Signal Quantization," Vehicular Technology Conference, 2009. VTC Spring 2009. IEEE 69th, vol., no., pp.1-4, 26-29 April 2009.

7. Krishna, R.; Cumanan, K.; Sharma, V.; Lambotharan, S., "A robust cooperative relaying strategy for wireless networks using semidefinite constraints and worst-case performance optimization," Information Theory and Its Applications, 2008. ISITA 2008. International Symposium on, vol., no., pp.1-5, 7-10 Dec. 2008. 8. Krishna, R.; Cumanan, K.; Lambotharan, S., “Complexity reduction through uplink-downlink Beamformer decomposition”, in Proc. IEEE WCSP 2009.

9. Cumanan, K.; Krishna, R.; Sharma, V.; Lambotharan, S., "Robust Interference Control Techniques for Multiuser Cognitive Radios Using Worst-case Performance Optimization," Asilomar Conference Signals, Systems and computers, Pacific Grove, CA, Oct. 2008.

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