Resume

• 2009

  Doctor of Philosophy (PhD)

  PhD Computer Science\nFocus: Machine Learning (Reinforcement Learning

  Evolutionary Computation

  Nurturing-Robotics)

  Computer Science

  Computer Science Graduate Student Association (CSGSA)

  University of Oklahoma

• 2008

  IEEE (Institute of Electrical and Electronics Engineers) - Member

  IEEE Robotics and Automation Society Membership\nIEEE Pattern Analysis and Machine Intelligence\nIEEE Big Data Community\nIEEE Systems Engineering\nIEEE Internet of Things

  Professional

  ACM (Association for Computing Machinery) - Member

  AAAI (Association for the Advancement of Artificial Intelligence) - Member

  INNS (International Neural Network Society) - Member

  Punjabi

  English

  Urdu

  Arabic

  MS

  Masters in Technology

  Technology Management

  Research on Face Recognition at School of Technology

  Eastern Illinois University

• 2002

  BS

  Bachelors in Computer Science

  Computer Science

  - Speed Programming competition\n- Programming Quiz competition participant and winner

  COMSATS Institute of Information Technology

  Application Security and Secure Coding Training course in \"C/C++\"

  Codebashing

  185dd4cdc0febb38239bb5c5c07d5a8c7c099dc2

• Microsoft SQL Server

  Genetic Algorithms

  Web Applications

  Software Development

  Nurturing Robotics

  C#

  Databases

  Web Services

  Reinforcement Learning

  HTML

  Visual Studio

  Software Engineering

  XML

  Evolutionary Computation

  C++

  Algorithms

  SQL

  Machine Learning

  Evolutionary Robotics

  C

  Gabor Filter Based Efficient Thermal and Visual Face Recognition Using Fusion

  Usman Ali

  Fusion architecture for efficient visual and thermal face recognition biometric system is presented in this paper. Both Data fusion and decision fusion are employed in the architecture to improve the individual fusion performance. Gabor filter technique is used for recognition of features from input image and the database images. To our knowledge this is the first visual

  thermal and fused-data (fusion of visual and thermal data) face recognition fusion recognition system

  which utilizes Gabor filter for feature extraction. We have achieved the accuracy of above 98%. Paper also discusses the performance issues of memory and response time and defines new frontiers for fast and efficient recognition system.

  Gabor Filter Based Efficient Thermal and Visual Face Recognition Using Fusion

  An agent interacting with its environment may learn to perform complex tasks through reinforcement learning. Reinforcement learning requires exploration of unfamiliar situations

  which necessarily involves unknown and potentially dangerous or costly outcomes. Various sorts of external support for the learning agent are possible through investments of time or other resources. Nurturing

  one individual investing in the development of another individual with which it has an ongoing relationship

  is widely seen in the biological world

  often with parents nurturing their offspring. In artificial intelligence

  nurturing can be seen as an opportunity to develop both better machine learning algorithms and robots that assist or supervise other robots. Although research into robot-to-robot nurturing is at a very early stage

  the hope is that this approach can result in more sophisticated learning systems. The research presented here demonstrates the effectiveness of nurturing through the evolution of the parameters of a reinforcement learning algorithm that is capable of finding good policies in a changing environment.

  Nurturing promotes the evolution of reinforcement learning in changing environments

  Over the past several decades

  reinforcement learning has emerged as one of the major paradigms in machine learning because it allows an agent to learn through interaction with its environment

  so long as there is some mechanism by which the agent can gain evaluative feedback on the effects of its actions. However

  there are still many open questions as to the most appropriate reinforcement learning approach

  particularly for difficult problems such as those dealing with delayed reward

  unknown reward structures

  continuous state and/or action spaces

  perceptual aliasing

  and/or environmental change. Here we present a new learning algorithm for these types of difficult problems. It combines the eligibility traces approach to reinforcement learning with artificial neural networks for generalization and pushes the idea of stochastic computations down to the level of the synapse. A proof-of-concept experiment in the domain of robotics demonstrates that the approach has promise.

  Stochastic synapse reinforcement learning (SSRL)

  An agent may interact with its environment and learn complex tasks based on evaluative\n feedback through a process known as reinforcement learning. Reinforcement\n learning requires exploration of unfamiliar situations

  which necessarily involves unknown\n and potentially dangerous or costly outcomes. Supervising agents in these\n situations can be seen as a type of nurturing and requires an investment of time usually\n by humans. Nurturing

  one individual investing in the development of another\n individual with which it has an ongoing relationship

  is widely seen in the biological\n world

  often with parents nurturing their o spring. There are many types of nurturing

  \n including helping an individual to carry out a task by doing part of the task for\n it. In arti cial intelligence

  nurturing can be seen as an opportunity to develop both\n better machine learning algorithms and robots that assist or supervise other robots.\n Although the area of nurturing robotics is at a very early stage

  the hope is that this\n approach can result in more sophisticated learning systems. This dissertation demonstrates\n the e ectiveness of nurturing through experiments involving the evolution of\n the parameters of a reinforcement learning algorithm that is capable of nding good\n policies in a changing environment in which the agent must learn an episodic task\n in which there is discrete input with perceptual aliasing

  continuous output

  and delayed\n reward. The results show that nurturing is capable of promoting the evolution\n of learning in such environments.

  [Ph.D. Dissertation] NURTURING PROMOTES THE EVOLUTION OF LEARNING IN CHANGING ENVIRONMENTS

  Syed

  Naveed

  Ph.D.

  UNIVERSITY OF OKLAHOMA (OU)

  OKLAHOMA

  USA

  MIDWEST TROPHY MANUFACTURER (MTMRECOGNITION)

  OKLAHOMA

  USA

  Eastern Illinois University

  Zhongxing Telecom Corporation

  Web site maintenance and development experience at Department of Educational Leadership in the Eastern Illinois University. Department had multiple interactive websites and data maintenance requirements. Websites were used by the department administration and students to perform various tasks including but not limited to registration of courses

  surveys to analyze instruction and program strengths and weaknesses. The list of websites created and maintained included: department main website

  Illinois Council of Professors of Educational Leadership (ICPEA)

  Leaders Assistance Program (NLA)

  Teacher Induction Program (TIP)

  Illinois Superintendent Survey \n(ISS).

  Eastern Illinois University

  Software Engineer

  Design and development experience for one of the major IT projects implemented in Pakistan

  PTCL Billing and Customer Care (PTCL B&CC) developed and deployed by ZTE CORPORATION Pakistan (R&D center)

  largest listed telecommunications manufacturer and wireless solutions provider. I worked as a part of System Integration team. My experience includes but not limited to

  requirements gathering

  system analysis

  operations

  and testing for various integration modules. I also assisted in Integration test plan development and execution

  GAP and PAT analysis.

  Zhongxing Telecom Corporation

  MIDWEST TROPHY MANUFACTURER (MTMRECOGNITION)

  OKLAHOMA

  USA

  Midwest city OK

  Mobile web application design and development experience at MTMRecognition. WeCaddie

  a database driven and live update featured book keeping golf web app

  was designed and developed for use in golf course. Several core features were developed including

  but not limited to

  traditional user logins with Facebook integration

  scorecards with live updates

  interactive metro user interface with slick design suitable for most mobile devices on all popular web browsers. Other features included various game types comprising of Individual

  Best ball

  Four ball

  and Foursome with betting types of No bet

  Nassau

  Skins

  and Per Match.

  Summer intern (Web Application Developer)

  Norman

  OK

  Web site design

  development

  and maintenance experience at integrated PoroMechanics Institute (iPMI) of Mewbourne School of Petroleum and Geological Engineering (MPGE) at the University of Oklahoma. Department had multiple websites (such as iPMI and BIOT 2005 conference) and scientific industrial software development requirements. Main website was used by the department to reflect their organizational hierarchy and related information

  research efforts

  and to provide software downloads and their detailed documentation to the industrial users and consortia members. I mainly developed Quantitative Geomechanics Gas Shale Mineralogy Simulator (QGGSMS) using .NET. I also worked on Poromechanics Rock Testing Simulator (PCORE-3Dnet).

  Graduate Research Assistant (Webmaster/Software Developer)

  UNIVERSITY OF OKLAHOMA (OU)

  OKLAHOMA

  USA

  Research and development experience at School of Technology in the Eastern Illinois University

  USA. Department had research project on live Face Recognition using camera snaps and videos. Project included Application analysis/development

  data collection

  and statistical analysis. Performed data collection and analysis for enhanced face recognition using Neurotechnology SDK.

  Eastern Illinois University