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Collaborating Authors


Director, Machine Learning & Data Science

#artificialintelligence

Design and build personalization engines/learning systems using advanced machine learning and statistical techniques Help the company in identifying tools and components, and building the infrastructure for AI/ML Research and brainstorm with internal partners to identify advanced analytics opportunities to advance automation, help with knowledge discovery, support decision-making, gain insights from data, streamline business processes, and enable new capabilities Perform hands-on data exploration and modeling work on massive data sets. Perform feature engineering, train the algorithms, back-test models, compare model performances and communicate the results Work with senior leaders from all functions to explore opportunities for using advance analytics Provide technical leadership mentoring to talented data scientists and analytics professionals Guide data scientists and engineers in the use of advanced statistical, machine learning, and artificial intelligence methodologies Provide thought leadership by researching best practices, extending and building new machine learning and statistical methodologies, conducting experiments, and collaborating with cross functional teams Develop end-to-end efficient model solutions that drive measurable outcomes. These technical skills include, but not limited to, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory, stochastic systems, Bayesian inference, statistical techniques, deep learning, supervised learning, unsupervised learning Solid understanding and hands on experience working with big data, and the related ecosystem, both relational and unstructured. Executing on complex projects, extracting, cleansing, and manipulating large, diverse structured and unstructured data sets on relational – SQL, NOSQL databases Working in an agile environment with iterative development & business feedback Providing insights to support strategic decisions, including offering and delivering insights and recommendations Experience in statistics & analytical modeling, time-series data analysis, forecasting modeling, machine learning algorithms, and deep learning approaches and frameworks. Deliver robust, scale and quality data analytical applications in a cloud environment.


Data Scientist - IoT BigData Jobs

#artificialintelligence

Position: Data Scientist Location: Chicago, IL/ SFO, CA Long term contract Client is looking for an advanced data science thinker, team leader, doer and expert who loves to dive into new and different problems, push the boundaries of innovation, and rapidly design, build and help implement machine learning and knowledge discovery solutions. Ideal candidate enjoys learning new contexts and areas of applications to help clients across industries and functions build ROI positive solutions. Creative thinking, problem solving and on-your-feet dot-connecting is very important. All your information will be kept confidential according to EEO guidelines.


New Directions for Scientific Discovery Research: Applications

AAAI Conferences

Much past AI research in scientific reasoning has fallen within the paradigm of historical study: we research some historical instance of scientific reasoning, and create a computer program (and associated theory) that automates that reasoning. This historical paradigm of studying scientific reasoning is of little or no further use to AI; the field must focus on building programs that make truly novel scientific discoveries. The first reason is that the historical para iigm is not convincing to those who doubt that computers can in fact make scientific discoveries. It is high time to prove that computers are capable of making novel scientific discoveries by exhibiting many examples of such discoveries. The second reason is that scientific-discovery researchers are in danger of being beaten to the gold by computational scientists: while we leisurely work on historical problems, they are already racing forward to make real discoveries.


Artificial Intelligence, Augmented Reality & Automation: Technology For Change

#artificialintelligence

Melvin Greer is Chief Data Scientist, Americas, Intel Corporation. He is responsible for building Intel's data science platform through graph analytics, machine learning and cognitive computing to accelerate transformation of data into a strategic asset for Public Sector and commercial enterprises. His systems and software engineering experience has resulted in patented inventions in Cloud Computing, Synthetic Biology and IoT Bio-sensors for edge analytics. He significantly advances the body of knowledge in basic research and critical, highly advanced engineering and scientific disciplines. Mr. Greer is a member of the American Association for the Advancement of Science (AAAS) and U.S. National Academy of Science, Engineering and Medicine, GUIRR.