URL: /topics/ai-ml/computer-vision.html PATH: _topics/ai-ml/computer-vision.md Computer Vision Hands-free tracking of chemical substances in research lab Scientists can register their lab samples by holding up bottle to the camera. Captured images of the bottle labels are pre-processed locally, then processed for OCR and registration in the cloud. Use custom CNN, Amazon Textract, Amazon Rekognition. Serial number recognition Read serial numbers (SNs) from machine parts. Part of the SN is cast in mold, part is engraved as dot peen. Use pre-trained DNN models for object detection and object recognition. Post processing corrects SNs based on format rules to 100% accuracy. Use augmentation of labeled images to increase training set 100 fold. Item counting on shopfloor Quality control to validate shipping label with stack of materials before loading on the truck. OCR to read shipping label, custom image transformations to count number of items in stack, determine dimensions of materials. Defect detection in manufacturing Qaulity control for lamination process of sheet metal. Image classification to detect work material. Unsupervised annomaly detection of defects. Multi-lane vehicle tracking for high-way traffic flow modeling Data collection along 5-lane Inter-state highway to measure vehicle speed, spacing between vehicles, accelleration, and lane change frequency. Use commercially available technology for traffic control. Data is used to calibrate traffic flow models that predict travel time and risk of congestions. Crowd tracking at public event to detect disturbances Data on pedestrian movement at public gatherings was collected using commercially available tracking devices, which was subsequently utilized to calibrate the social force model. Image categorization of vendor-uploaded photos on e-commerce site for improved search experience The e-commerce site introduced a new comparative view of listed products categorized by their images. Because many images that were provided by vendors are not properly categorized, about 1 million images needed to be processed before launch. Use pre-trained CNN for image classification. Add extra layers to align with application specific classes. Fraud detection on delivery site for raw materials using surveillance video Delivery trucks are weighted upon entrance and exit of the site. Use surveillance video at weight stations to verify proper position of track on scale, type raw material, and that same truck enters and exists under the shipping number. Use custom feature generation, classification models, and image similarity. Target recognition on hyper-spectral infrared imagery Object detection in hyper-spectral infrared images with over 500 channels. Object size ranges from sub-pixel to few pixels. Develop image quality measure to compare performance of automated target recognition systems. Synthesize image data with multi-spectral ray tracer. URL: /topics/ai-ml/demand-forecasting.html PATH: _topics/ai-ml/demand-forecasting.md Demand Forecasting, Time-series Analysis Demand forecasting for launch of new consumer product Forecast demand of new product prior to launch. Custom DNN time-series model trained on similar products. Weekly demand forecasting for retail merchandise with wide ranging velocities Demand forecasting for weekly re-ordering of about 60,000 products. Use ensemble of multiple models with product level selection on previous weeks. Cost optimization of shipping materials leveraging time-series forecasting Use probabilistic demand forecasting. Model cost of inventory and increased spending when using larger packaging. Understand temporal patterns on e-commerce sites Time series modeling for seasonal patterns: daily and weekly patterns of site visits, seasonal trends and impact of holidays. Seasonal demand of products. Clinical Trials Ensure meeting timelines for licensing and distribution in clinical trials leveraging time-series modeling and supply-chain simulation. URL: /topics/ai-ml/indoor-localization.html PATH: _topics/ai-ml/indoor-localization.md Indoor Localization Collect customer data in brick-and-mortar retail spaces: duration of stay, return visits, zones. Develop IoT devices for smart-phone tracking, laboratory and field study, evaluate accuracy, feasibility, and privacy. URL: /topics/ai-ml/inventory-estimation.html PATH: _topics/ai-ml/inventory-estimation.md Inventory Estimation Counting inventory of non-countable products, such as materials cut from roles or spools, or taken from containers or tanks, can be time-consuming and disruptive to operations if wall-to-wall counting is conducted. At the cut-order fulfillment center, we addressed this challenge by developing a machine learning model based on a probabilistic hidden Markov chain. This model, trained using reinforcement learning with an evolutionary algorithm, significantly reduced inventory errors by over 50%. URL: /topics/ai-ml/knowledge-graphs.html PATH: _topics/ai-ml/knowledge-graphs.md Knowledge Graphs Cross-organizational data index Build comprehensive data index across functional domains: research, manufacturing, distribution, marketing etc. Implement index as labeled property graph on graph database. Design collaborative tool for subject matter experts to define ontology and mapping of data sources to graph entities. Implement ETL workflow to update data index. Improved document search Use entity recognition on text documents to build knowledge graph. Combine semantic queries with graph search. Rule-based learning and reasoning Map knowledge graph to predictate logic. Use inductive logic programming to learn rules from examples. URL: /topics/ai-ml/natural-language-processing.html PATH: _topics/ai-ml/natural-language-processing.md Natural Language Processing & Understanding Build search index for electronic lab notebooks with attached documents Scientists need to search through electronic lab notebooks and their attachments to retrieve data points from experiments. Use Amazon Textract and NLP processes to extract relevant data points from documents, use OpenSearch as searchable database. Key-term extraction from contracts and addendums Legal staff manually reviewed contract with business partners and addendums to keep an up-to-date database of the terms. Employ natural language processing (NLP) techniques to extract key terms, rule-based logic to update database with extracted key-terms from addendums. Data extraction from news releases, social media posts, and emails for financial advisory firm To keep an up-to-date database on mergers and acquisitions (M&A) deals, their terms, and their status, financial advisors rely on information from various sources such as news releases, social media posts, and emails. Employ natural language processing (NLP) techniques to efficiently process this information and disambiguate the relevant data. Identify parties, their roles, relationships and actions from court filings Build knowledge graph from narratives Building situational knowledge graph from data Classify and extract form data from health-care claims Reduce response time for processing health-care claim by improved routing to specialist and automated extraction of key terms. Use Amazon Textract for OCR and key term extraction. Custom rules for post-processing and triaging. Tagging of advertisements for improved marketing analysis Advertiser was limited in their marketing analysis due to incomlete and inaccurate tagging of ads. Use NLP multi-class labeling to automatically tag advertisement artifacts. URL: /topics/ai-ml/product-quality.html PATH: _topics/ai-ml/product-quality.md Product Quality, Manufacturing Processing Control Investigate deterioration of product quality leveraging data from control systems, equipment sensors, raw materials. Integrate data lake for pro-active analytics and visualization. Predict product quality based on process data. Prioritize alarm messages for operators leveraging machine learning models. Mobile app for operators with interactive dashboard and manual. URL: /topics/ai-ml/recommender-systems.html PATH: _topics/ai-ml/recommender-systems.md Recommender Systems Email campaign with personalized recommendations based on customers viewing history Collborative filtering based on page views. Multi-week campaign with customized recommendations. Product recommendations on e-commerce site leveraging visual similarities Product-similarity based recommendation based on visual features, including geometrical shapes, color distribution, and texture. URL: /topics/sharing/lectures-and-workshops.html PATH: _topics/sharing/lectures-and-workshops.md Lectures and Workshops Lectures Course Number Title Level Institution CCIS 105 Programming Principles I undergraduate CAU CCIS 106 Programming Principles II undergraduate CAU CCIS 121 Introduction to Computer Systems undergraduate CAU CCIS 223 Data Structures undergraduate CAU CCIS 227 Deductive Systems undergraduate CAU CCIS 371 Computer Algorithms undergraduate CAU CCIS 374 Database Systems undergraduate CAU CCIS 375 Artificial Intelligence undergraduate CAU CCIS 413 Introduction to Robotics undergraduate CAU CCIS 416 Introduction to High Performance Computing undergraduate CAU CCIS 671 Algorithm Design and Analysis graduate CAU CCIS 672 Computer Organization graduate CAU CCIS 673 Operating Systems Design graduate CAU CCIS 674 Database Design graduate CAU CCIS 675 Artificial Intelligence graduate CAU CCIS 676 Theory of Programming Languages Design graduate CAU CCIS 683 Algorithms for Parallel Computers graduate CAU CCIS 711 Image Processing graduate CAU MSDA 8010 Data Programming graduate GSU MSDA 8150 Machine Learning for Analytics graduate GSU MSDA 8650 Advanced Deep Learning with Business Applications graduate GSU Workshops Title Venue Audience Optimization Workshop GSU MS Data and Analytics students AWS for Data Scientists GSU MS Data and Analytics students Best Programming Practices for Data Scientists and Analysts GSU Professionals URL: /topics/simulation/adaptive-sampling-by-histogram-equalization.html PATH: _topics/simulation/adaptive-sampling-by-histogram-equalization.md Adaptive Sampling by Histogram Equalization Development of an adaptive sampling method that efficiently varies the sampling rate in local regions of a function based on the distribution of already collected samples. The algorithm does not rely on gradients in the parameter space and therefore allows to create accurate representation with less collected or computed sample points. In cases where he acquisition of samples is expensive, like computer simulations or experiments, ASHE has the advantage of requiring significantly less data points. URL: /topics/simulation/air-corridors.html PATH: _topics/simulation/air-corridors.md Air Corridors Air corridors are an integral part of the advanced air mobility infrastructure. They are the virtual highways in the sky for transportation of people and cargo in the controlled airspace at an altitude of around 1000 ft. to 2000 ft. above the ground level. This paper presents fundamental insights into the design of air corridors with high operational efficiency as well as zero collisions. It begins with the definitions of air cube, skylane or track, intersection, vertiport, gate, and air corridor. Then, a multi-layered air corridor model is proposed. Traffic at intersections is analyzed in detail with examples of vehicles turning in different directions. The concept of capacity of an air corridor is introduced along with the nature of distribution of locations of vehicles in the air corridor and collision probability inside the corridor are discussed. Finally, the results of simulations of traffic flows are presented. URL: /topics/simulation/dynamics-of-pedestrian-crowds.html PATH: _topics/simulation/dynamics-of-pedestrian-crowds.md Dynamics of Pedestrian Crowds Development of a microscopic simulation model based on the concept of social forces. Study of the emergence of self-organization phenomena in crowds, and formation of trails. URL: /topics/simulation/highway-traffic.html PATH: _topics/simulation/highway-traffic.md Vehicular Traffic Predict highway traffic congestions and travel times, data collection for model calibration. Traffic data collection to verify macroscopic traffic models and calibrate them to the behavior of local drivers. The data collection uses a computer vision system that track vehicles across a five-lane highway. URL: /topics/simulation/micro-sensor-management-and-cooperative-sensor-fusion.html PATH: _topics/simulation/micro-sensor-management-and-cooperative-sensor-fusion.md Micro-sensor Management and Cooperative Sensor Fusion Unattended sensor networks may comprise devices of various modalities, such as magnetic, electrostatic, acoustic, seismic and infrared. Sensor nodes may function as a trip-wire, pointer, identifier or tracker. All these nodes contribute to greater situational awareness by fusing the various data sources. At any given time, however, not all nodes are need in the sensor fusion process. The objective of this project is to develop a self-organized resource management system that allows to nodes to manage their level of activity with respect to data collection, processing and communication in order to reduce power consumption and to extend the lifetime of the network. URL: /topics/simulation/public-transit.html PATH: _topics/simulation/public-transit.md Transportation (Vehicular, Pedestrian, Public Transit) Plan capacity of public transit systems leveraging computer simulation. URL: /topics/strategy/data-science-competency-assessment.html PATH: _topics/strategy/data-science-competency-assessment.md Data Science Competency Assessment Data Science Competency Assessment workshops play a vital role in ensuring the successful implementation of AI/ML solutions in data science projects, which in turn leads to optimized day-to-day operations. One of the key advantages of Data Science Competency Assessment workshops is its ability to bring together key stakeholders responsible for crucial aspects of data science integration. This includes individuals overseeing data sources, accessibility, security, compliance, technical expertise, and compute infrastructure, encompassing both on-premises and cloud environments. By involving these stakeholders, the workshops foster collaboration and enable a comprehensive evaluation of the organization’s data science capabilities. During the workshop, observations are carefully analyzed to identify the company’s strengths and gaps in terms of data science competency. This analysis is pivotal in understanding the organization’s current state and determining the groundwork required to implement effective data science solutions that address specific business needs. By gaining insights into the strengths and weaknesses, informed decisions can be made to align data science initiatives with the organization’s goals. Considering the groundwork needed for any data science solution that addresses a business need is of utmost importance. The Data Science Competency Assessment workshop provide a structured approach to assess the organization’s readiness, ensuring that the necessary foundation is in place before embarking on data science projects. By considering the identified strengths and gaps, the workshop facilitates informed decision-making, resource allocation, and strategic planning, ultimately leading to successful implementation and maximum impact. URL: /topics/strategy/working-backwards.html PATH: _topics/strategy/working-backwards.md Working Backwards Amazon’s Working Backwards method is a unique and innovative approach to product development and decision-making. The method, which has been widely adopted within Amazon, encourages teams to start with the end in mind and work their way backward to determine the steps needed to achieve the desired outcome. This customer-centric approach prioritizes understanding customer needs and preferences before diving into the development process. At the heart of the Working Backwards method is the creation of a press release and a Frequently Asked Questions (FAQ) document. The process begins by writing a hypothetical press release that captures the essence of the product or feature being envisioned. This press release serves as a concise and compelling description of the product’s benefits, features, and value proposition. It forces teams to clarify their vision and think about how the product will be received by customers and the market. Following the press release, the team then develops a detailed FAQ document that anticipates questions customers might have about the product. This exercise helps identify potential challenges and provides valuable insights into customer needs, concerns, and expectations. By thoroughly addressing these questions, teams can refine their product idea and make informed decisions about its feasibility and viability. The Working Backwards method fosters a disciplined and customer-focused approach to product development. It aligns teams around a shared vision and ensures that all stakeholders have a clear understanding of the desired outcome. This approach also promotes collaboration and communication within the team, as members work together to refine the product concept and identify the necessary steps to bring it to life. URL: /topics/systems/data-provenance-and-workflow-automation.html PATH: _topics/systems/data-provenance-and-workflow-automation.md Data Provenance and Workflow Automation Maintain integrity of knowledge base with provenance tracking of data points generated from multiple processing steps on text documents. Automate workflows for data processing, managing machine-learning models, and scientific computing tasks.