Aadharsh Kannan
Principal Group Manager Data & Applied Science and Engineering at Microsoft
Hiring India SWE | Web3 neophyte | Incubating research that empowers individuals and organizations on the planet to achieve more
Office of the Chief Economist: We are an interdisciplinary organization involved in bringing research incubation to fruition. Modern pricing, demand forecasting, causal measurement products are all incubation areas we invest in. We research, develop, and deliver novel statistical models that combine machine learning with econometrics to infer accurate counterfactuals. We build solutions and products that use predictive analytics to produce actionable strategic insights. We work closely with stakeholders of different roles (ex. Finance, Business Planners, PMs, Software Engineers, and Data Scientists) within various business divisions (Azure, XBOX, Office, Bing, Dynamics) to make it all happen.
Microsoft
https://www.linkedin.com/company/microsoft
Office of the Chief Economist leading a cross-geo organization of Software Engineers, Data & Applied Scientists, and Economist. Engaged in discovering research incubations rooted in economic analysis, confirming traction, and delivering technology to add long-term value to our customers at Microsoft. Involved in strategic planning and directing trade-offs of research agenda or technology transfer attempts. We virtually work with every part of Microsoft on a variety of problems all the way from strategy prescriptions using data & economics to incubating ML-based pricing products (including the v1 build-outs). Overall, I am enthused about zero to one landscape (be it building out organizations/teams or solutions/products).
Redmond, Washington, United States
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Principal Group Manager Data & Applied Science and Engineering
Microsoft
https://www.linkedin.com/company/microsoft
Office of the Chief Economist working on applied Economic problem. We work on cloud pricing (spot, reserve, workloads, datacenter), sales promotion effectiveness, search auctions, machine learning predictions and forecasting, and market design.  The work involves theoretical models but focuses on applied statistics like causal inference and a wide variety of both supervised and unsupervised machine learning.  The Office of the Chief Economist is part of the Artificial Intelligence and Research (AI+R) Organization
Greater Seattle Area
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Principal Data and Applied Scientist Manager
Amazon
https://www.linkedin.com/company/amazon
Was employed in Amazon Econ Tech, an incubation division comprising of Economists, Data Scientists, Software Engineers, and Program Managers working on innovative disruptive technologies. I was working on products in the domain of private brand pricing and Amazon search.
Greater Seattle Area
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Senior Data Scientist
Microsoft
https://www.linkedin.com/company/microsoft
Data Scientist specializing in Cloud Economics. Develops Big Data Analytics and Predictive Models that enable effective pricing, optimize capacity management, reduce operating costs and provide strategic business insights in the Office of Chief Economist, Microsoft.
Redmond, Washington
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Senior Data Scientist
Microsoft
https://www.linkedin.com/company/microsoft
Data Scientist specializing in Cloud Economics. Develops Big Data Analytics and Predictive Models that enable effective pricing, optimize capacity management, reduce operating costs and provide strategic business insights in the Office of Chief Economist, Microsoft.
Greater Seattle Area
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Data Scientist 2
Microsoft
https://www.linkedin.com/company/microsoft
Office 365 - Office Extensibility delivering features for Share Point Online
Greater Seattle Area
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Software Engineer 2
Microsoft
https://www.linkedin.com/company/microsoft
Office 365 - Office Extensibility delivering features for Share Point Online
Greater Seattle Area
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Software Engineer
Microsoft
https://www.linkedin.com/company/microsoft
Microsoft India R&D Pvt. Ltd.
Hyderabad Area, India
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Software Engineer
MurGanT
Brought a Software as a Service (Content Management Solution for Education Institutions) Platform to market.
Garage - Parent's Home
Co-Founder, Chief Architect and Developer
Microsoft
https://www.linkedin.com/company/microsoft
Entertainment and Devices Division (Xbox and Kinect)
Hyderabad Area, India
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Software Engineering Intern
Master of Science - MS
Data Mining, Data Science, Machine Learning and Big Data Analytics
Computer Science
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University of Washington
https://www.linkedin.com/school/university-of-washington/
Master of Science - MS
Advanced Theory Concentration
Economics
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Purdue University Krannert School of Management
https://www.linkedin.com/school/purdue-krannert/
Bachelor of Technology - BTech
Bachelor of Technology in Computer Science and Engineering
Computer Science and Engineering
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Amrita Vishwa Vidyapeetham
https://www.linkedin.com/school/amrita-vishwa-vidyapeetham-official/
This paper uses precise and novel data on country level Cloud IaaS and PaaS revenue to
measure the intensive margin of technology diffusion across countries and within countries over time. We
horse race diffusion models and find cloud diffusion exhibits both Log-Log and Logistic Growth Patterns.
We use cross-validation on nearly 100 features to determine what drives cross-country differences. We
find that features impacting GDP, Internet Connectivity, and Human Capital explain the intensity of
cloud growth. We finally compare the relative impacts of these variables using a random coefficients
model. While correlative, our algorithmic research design motivates data-driven hypothesis generation
and further causal work regarding how policymakers can encourage more cloud computing adoption
and technology adoption more broadly.
Characterizing the Usage Intensity of Public Cloud
ACM Transactions on Economics and Computation
https://vita.mcafee.cc/PDF/publiccloud.pdf
It is a well-known statistical property that learning tends to slow down with each additional data point. Thus even if scale effects are important in web search, they could be important in a range that any viable entrant could easily achieve. In this paper we address these questions using browsing logs that give click-through-rates by query on two major search engines. An ideal experiment would be to fix the query difficulty and exogenously provide more or less historical data. We approximate the ideal experiment by finnding queries that were not previously observed. Of these new queries, some grow to be moderately popular, having 1000{2000 clicks in a calendar year. We examine ranking quality during the lifespan of the query and finnd statistically significant improvement on the order of 2-3% and learning faster at lower levels of data. We are careful to rule out alternate explanations for this pattern. In particular, we show that the effect is not explained by new, more relevant documents entering the landscape, rather it is mainly shifting the most relevant documents to the top of the ranking. We thus conclude they represent direct scale effects. Finally, we show that scale helps link new queries to existing queries with ample historical data by forming edges in the query document bipartite graph. This indirect knowledge is shown to be important for deflating uniqueness and improving ranking.
Scale Effects in Web Search
WINE 2017: The 13th Conference on Web and Internet Economics
https://arxiv.org/submit/2109128/view
We examine the economics of demand and supply in cloud computing. The public cloud offers three main benefits to firms: 1) utilization can be scaled up or down easily; 2) capital expenditure (on-premises servers) can be converted to operating expenses, with the capital incurred by a specialist; 3) software can be “pay-as-you-go.” These benefits increase with the firm’s ability to dynamically scale resource utilization and thus point to the need for dynamic prices to shape demand to the (short-run) fixed datacenter supply. Detailed utilization analysis reveals the large swings in utilization at the hourly, daily or weekly level are very rare at the customer level and non-existent at the datacenter level. Furthermore, few customers show volatility patterns that are excessively correlated with the market. These results explain why fixed prices currently prevail despite the seeming need for timevarying dynamics. Examining the actual CPU utilization provides a lens into the future. Here utilization varies by order half the datacenter capacity, but most firms are not dynamically scaling their assigned resources at-present to take advantage of these changes. If these gains are realized, demand fluctuations would be on par with the three classic industries where dynamic pricing is important (hotels, electricity, airlines) and dynamic prices would be essential for efficiency.
Usage Patterns and the Economics of the Public Cloud
WWW 2017
http://vita.mcafee.cc/PDF/EconPublicCloud.pdf
A state space classifier using neural networks to classify a machine based on data derived from various sensors- achieved more than 99% true positives.
Classification of Machine Fault using Neural Networks
A business case study to help identify if implementing 5s concept in HR results in more productivity and employee morale. This is a case study on a local business.
Implementation of 5s in HRM
ISSN: 0973-9785 Vol. 5 No. 4 Oct-Dec 2010, Journal of Contemporary Research in Management
A SVM based classifier that understands characters from pictures of a document. The software could automatically account for skew and rotation.
Optical Character Recognition for Document Scanning
Education
Microsoft
https://www.linkedin.com/company/microsoft
Data Science Fundamentals Session for Microsoft Employees
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Instructor
Disaster and Humanitarian Relief
Friends of Youth
https://www.linkedin.com/company/friends-of-youth
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Food Distribution
Civil Rights and Social Action
The Prison Scholar Fund
https://www.linkedin.com/company/the-prison-scholar-fund
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Strategy Consulting
https://www.linkedin.com/in/zhuxi-yang-549b7846
Greater Seattle Area
Zhuxi Yang
Data Scientist at Microsoft
https://www.linkedin.com/in/kevin-hannon-598a4b65
Seattle, WA
Kevin Hannon
Software Engineer 2 at Microsoft
https://www.linkedin.com/in/quentin-simoneaux-103160130
Seattle, WA
Quentin Simoneaux
Software Engineer at Microsoft
https://www.linkedin.com/in/maria-rivero-44941b79
Seattle, WA
Maria Rivero
Software Engineer at Microsoft Remote Desktop
https://www.linkedin.com/in/miguelgarcia05
Kirkland, WA
MIGUEL GARCIA
Software Engineer ll at Microsoft
https://www.linkedin.com/in/noahsutter
Seattle, WA
Noah Sutter
Software Engineer at Microsoft
https://www.linkedin.com/in/tompcarpenter
Seattle, WA
Tom Carpenter
Analytics and Data Science | Social Psychologist & People Researcher | Quant/Qual Behavioral Science, Surveys, Metrics, Measurement
https://www.linkedin.com/in/drew-cross-01398328
Bothell, WA
Drew Cross
Software Engineering Lead at Microsoft
https://www.linkedin.com/in/winston-bui-39655950
Seattle, WA
Winston Bui
Software Engineer at Microsoft
https://www.linkedin.com/in/siddhesh-surve-41033a18
Greater Seattle Area
Siddhesh Surve
Senior Software Engineer at Microsoft
https://www.linkedin.com/in/jing-su
Seattle, WA
Jing Su
Software engineer with 4+ years work experience of web development, having a good knowledge of algorithms, web programming and software development.
https://www.linkedin.com/in/nayomitchell
Greater Seattle Area
Nayomi M.
Software Engineer 2 at Xbox | Microsoft
https://www.linkedin.com/in/tarunsejwal
Greater Seattle Area
Tarun S.
Software Development Engineer at Microsoft
https://www.linkedin.com/in/yue-joyce-xu-12931996
Everett, WA
Yue (Joyce) Xu
Software Engineer at Microsoft
https://www.linkedin.com/in/yifan-n-1b213566
Greater Seattle Area
Yifan N.
Software Engineer II at Microsoft Azure
https://www.linkedin.com/in/elaine-y-661791190
Bellevue, WA
Elaine Y.
Software Engineer at Microsoft
https://www.linkedin.com/in/kevintaha
Greater Seattle Area
Kevin Taha
Software Engineer @ Microsoft
https://www.linkedin.com/in/deal
United States
Austin Deal
Software Engineer
https://www.linkedin.com/in/vinay-nagarajan
Greater Seattle Area
Vinay Nagarajan
Software Engineer at Microsoft
https://www.linkedin.com/in/evilches
Redmond, WA
Erika Vilches
Senior Data & Applied Scientist at Microsoft
R. Preston McAfee
      
          



            
              
        
              
  

    
        Aadharsh Kannan is a fantastic data scientist, on a steep trajectory in ability and skills.  When he joined the Microsoft chief economist's team, he was already proficient in statistical and data science techniques.  He collaborates very effectively with economists and has absorbed much of our expertise, both through learning by doing and by formal education, so that he can perform regression discontinuity analyses, complicated dif-in-difs, double ML approaches and other econometric techniques for causal inference.  In one instance, he sigle-handedly built a knowledge graph that was used in a 2017 WINE paper.  He has been promoted rapidly because of his out-sized contributions.  I recommend him without reservations.
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My 8 year old son received this appreciation from his elementary school teacher. Each week teacher recognizes one student for this award. I am very…
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XKCD yesterday was so good.  :-)
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Over the last couple of years, one area that I have been focusing on with my team is AI Explainability. How we can bring the latest techniques to…
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Chennai
Aadharsh Kannan
UI UX | Visual Designer