Masters of Computer Science Candidate with around 3 years of work experience in Java EE, Spring MVC, Hibernate, JavaScript, JQuery, Back End Software Development. Looking for an opportunity as a Full Stack Developer
Science base usage analysis - AGU2016 - in21d08Sky Bristol
ScienceBase is a research infrastructure developed and operated by the U.S. Geological Survey with users and uses across a number of other agency and organization partners. Over four years ago, we released an Application Programming Interface (API) as the foundation of the system and took on the mindset that our progress would be measured by the uptake of the API by others beyond ourselves in developing interesting applications. We now measure success more by someone finding ScienceBase, organizing their data and information, developing an innovative API-driven application and then serendipitous discovery through a science meeting. Because of the way we built the RESTful API, we can characterize what parts of the system are employed. Analysis of usage data helps us take the supposition out of what works and guides design and funding decisions. This analytics-based process facilitates regular adjustments to our thinking and allows us to test design decisions as hypotheses rather than untestable aspirations.
Masters of Computer Science Candidate with around 3 years of work experience in Java EE, Spring MVC, Hibernate, JavaScript, JQuery, Back End Software Development. Looking for an opportunity as a Full Stack Developer
Science base usage analysis - AGU2016 - in21d08Sky Bristol
ScienceBase is a research infrastructure developed and operated by the U.S. Geological Survey with users and uses across a number of other agency and organization partners. Over four years ago, we released an Application Programming Interface (API) as the foundation of the system and took on the mindset that our progress would be measured by the uptake of the API by others beyond ourselves in developing interesting applications. We now measure success more by someone finding ScienceBase, organizing their data and information, developing an innovative API-driven application and then serendipitous discovery through a science meeting. Because of the way we built the RESTful API, we can characterize what parts of the system are employed. Analysis of usage data helps us take the supposition out of what works and guides design and funding decisions. This analytics-based process facilitates regular adjustments to our thinking and allows us to test design decisions as hypotheses rather than untestable aspirations.
I am a Computer Science Graduate with Master of Science degree from University at Buffalo, State University of New York. Working as Software Engineer at Intel.
EDUCATION
M.S. Computer Sciences State University of New York, Buffalo, New York December 2016
B.Tech Computer Sciences HMRITM, I.P. University, New Delhi, India May 2014
TECHNICAL SKILLS
• Languages: Proficient: Java Experienced : C++, Python, JavaFx, R
• Database: MySQL, mongo DB, Oracle
• UI Technology: HTML, JavaScript, JQuery, JSON, CSS, PHP, Twitter Bootstrap, WordPress
• Software/Tools: SolrLucene, MS Office, MATLAB, Eclipse, Adobe Photoshop, NetBeans, IntelliJ
COURSES: Machine Learning, Data Intensive Computing, Distributed Systems, Information Retrieval, Analysis of Algorithms, Computer Security, Algorithms for Modern Computing
RELEVANT EXPERIENCE
Software Engineer Intel Corporation, OR February 2017 - Present
• Automated scripts to analyze defects data and model the root cause analysis to decrease defect rate significantly (~84%) with Python, MySQL in two quarters.
• Worked on creating an innovative software to predict major customer issues by analyzing Tech Forum data (Natural Language Processing) with sentiment analysis and predicting trend for issues with internal products. Found correlation between customers reported major issues and community data and recognition for this effort, which could save money and effort by predicting and analyzing sentiments and working on issues before the issues are actually filed. Used Python, R, mongo DB, machine learning algorithms and JavaScript.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. ALAKESH MANI
azm6495@psu.edu | +1 (484) 995 8240 | linkedin.com/in/alakesh-mani-468b2b150 |Malvern
EDUCATION
Pennsylvania State University, USA Aug 2019 – Dec 2020
Master of Professional Studies in Data Analytics
Courses taken: Applied Statistics, Database Design Concepts, Data-Driven Decision Making, Analytics
Programming in Python, Large Scale Database and Warehouse, Data Mining, Predictive Analytics, Deep
Learning, Data Visualization.
Madras Institute of Technology, India Aug 2013 – Jun 2017
Bachelor of Engineering in Electronics and Communication Engineering
Relevant Courses taken: Object Oriented Programming, Data Structures and Algorithms, Database Management
Systems.
TECHNICAL SKILLS
Programming languages: Java, Scala, C++
Scripting languages: Python, Shell
Querying languages: MySQL, HiveQL
Operating systems: Linux, Windows
Big data stack: Hadoop, Spark, Hive, Hbase, Kafka
PROFESSIONAL EXPERIENCE
Aspire Systems | Chennai, India Jul 2017 – Jun 2019
Software Analyst
Automation of Payroll tax calculation
Worked for one of the big four companies to make a product which can provide an automated way of Payroll
calculation from different source of data files.
Using Apache Spark-Python/SQL, validated the data and applied multiple business rules.
Using Microsoft’s HDInsight cluster, managed Spark jobs.
Using Apache Livy, integrated the whole process to make it interactive for the process to be called by other
applications using REST API.
Building Recommendation Engine
Worked for one of the leading e-distributor of chemicals to build a recommendation engine which will suggest
similar products to the users.
Using Apache Spark-Scala/SQL, cleansed and preprocessed the data
Applied item-item similarity method which is one of the methods of Collaborative filtering algorithm.
Item-item similarity matrix was built using Spark’s MLlib library to extract the similar items and filtered out
the recommended products for each product.
Scrapping data from Twitter
Worked for one of the leading fine dining restaurant chain in US to collect statistical details from their twitter
page.
The metrics were calculated and then was loaded into Apache Hive database.
The process was done on daily basis based on automation scheduled by CRON job.
2. AWARDS AND ACTIVITIES
Received Penn State Great Valley Chancellor’s Scholarship for $10,000.
Secured first place in a Microcontroller programming event named “Mupro” of LiveBeat’15, an intra-college
technical symposium, organized by Department of Instrumentation Engineering.
Runner-up in an intra college Business plan competition in 2015 held in MIT, Anna University.
Active member of National Sports Organization (NSO) of MIT, Anna University. Organized many sports
events and has helped grooming juniors to shine at the sports events.
