https://joaocarlos.github.io/Hugo-academia/Recent content on Dr. Jane SmithHugoenSat, 15 Jun 2024 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/deep-learning-for-urban-traffic-prediction/Sat, 15 Jun 2024 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/deep-learning-for-urban-traffic-prediction/Abstract We present a novel deep learning architecture for predicting urban traffic patterns using multi-modal sensor data. Our approach combines convolutional neural networks with attention mechanisms to capture both spatial and temporal dependencies in traffic flow. Experiments on real-world datasets from three major cities demonstrate that our method outperforms existing approaches by 15% in prediction accuracy while reducing computational requirements by 40%. Citation @article{smith2024traffic, title = {Deep Learning for Urban Traffic Prediction}, author = {Smith, Jane and Doe, John and Johnson, Alice}, journal = {IEEE Transactions on Intelligent Transportation Systems}, year = {2024}, doi = {10.https://joaocarlos.github.io/Hugo-academia/courses/digital-design/Mon, 01 Jan 2024 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/courses/digital-design/Course Description This course provides a comprehensive introduction to digital logic design, covering combinational and sequential circuits, finite state machines, and FPGA implementation. Students will gain hands-on experience with hardware description languages (VHDL/Verilog) and modern design tools. Learning Objectives By the end of this course, students will be able to: Design and analyze combinational and sequential digital circuits Implement digital systems using hardware description languages Use FPGA development tools for synthesis and simulation Apply systematic design methodologies to complex digital systems Debug and verify digital circuits using industry-standard techniques Prerequisites Introduction to Programming Basic Circuit Analysis Discrete Mathematics Topics Covered Weeks 1-2 Digital Fundamentals Number systems, Boolean algebra, logic gates, and truth tables.https://joaocarlos.github.io/Hugo-academia/papers/adaptive-sensing-for-smart-cities-a-reinforcement-learning-approach/Wed, 20 Sep 2023 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/adaptive-sensing-for-smart-cities-a-reinforcement-learning-approach/Abstract Urban sensing networks face the challenge of balancing data quality with energy consumption. We propose an adaptive sensing framework that uses reinforcement learning to dynamically adjust sampling rates based on environmental conditions and application requirements. Our system reduces energy consumption by 60% while maintaining 95% of the data quality compared to fixed-rate sampling approaches. Citation @inproceedings{smith2023adaptive, title = {Adaptive Sensing for Smart Cities: A Reinforcement Learning Approach}, author = {Smith, Jane and Garcia, Maria}, booktitle = {Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys)}, year = {2023}, doi = {10.https://joaocarlos.github.io/Hugo-academia/books/ml-smart-cities/Fri, 15 Sep 2023 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/books/ml-smart-cities/Description This comprehensive textbook provides a thorough introduction to machine learning techniques and their applications in smart city environments. Written for graduate students and practitioners, the book covers both theoretical foundations and practical implementations. Table of Contents Introduction to Smart Cities Data Collection and Sensor Networks Machine Learning Fundamentals Deep Learning for Urban Data Reinforcement Learning for Control Systems Privacy and Security Considerations Case Studies and Applications Future Directions Reviews “An essential resource for anyone working at the intersection of AI and urban computing.https://joaocarlos.github.io/Hugo-academia/papers/edge-computing-for-real-time-environmental-monitoring/Thu, 10 Mar 2022 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/edge-computing-for-real-time-environmental-monitoring/Abstract Real-time environmental monitoring requires processing large volumes of sensor data with minimal latency. This paper presents an edge computing architecture that performs local data processing and anomaly detection, reducing cloud bandwidth requirements by 80% while enabling sub-second response times for critical events. Citation @article{smith2022edge, title = {Edge Computing for Real-Time Environmental Monitoring}, author = {Smith, Jane and Chen, Robert and Wang, Lisa}, journal = {IEEE Internet of Things Journal}, year = {2022}, doi = {10.https://joaocarlos.github.io/Hugo-academia/about/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/about/Biography I am an Associate Professor at the Department of Computer Science at Example University. I received my Ph.D. in Computer Science from Stanford University in 2015, where I worked on distributed systems and machine learning. My current research focuses on developing intelligent systems for urban environments, combining expertise in artificial intelligence, IoT, and data analytics. Research Interests Artificial Intelligence Developing intelligent systems that can learn and adapt to complex environments, with applications in urban planning and resource optimization.https://joaocarlos.github.io/Hugo-academia/courses/advanced-machine-learning/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/courses/advanced-machine-learning/Course Description This graduate-level course covers advanced topics in machine learning, including deep learning architectures, reinforcement learning, generative models, and their applications to computer vision, natural language processing, and robotics. Learning Objectives Master advanced deep learning architectures (CNNs, RNNs, Transformers) Understand and implement reinforcement learning algorithms Design and train generative models (GANs, VAEs, Diffusion) Apply ML techniques to research problems Read and critique ML research papers Prerequisites CS401: Introduction to AI Strong programming skills in Python Graduate standing or instructor approval Evaluation Component Weight Paper Reviews 20% Programming Assignments 30% Research Project 40% Presentation 10%https://joaocarlos.github.io/Hugo-academia/projects/ai-for-traffic-optimization/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/projects/ai-for-traffic-optimization/Project Overview This project developed novel machine learning algorithms for real-time traffic signal optimization. By analyzing historical traffic patterns and real-time sensor data, our system dynamically adjusts signal timing to minimize congestion and reduce emissions. Key Achievements Reduced average commute times by 12% in pilot districts Decreased vehicle emissions by 8% through improved traffic flow Deployed system in 3 cities (San Francisco, Oakland, San Jose) Technology licensed to commercial traffic management companies Publications Smith et al.https://joaocarlos.github.io/Hugo-academia/supervision/alex-chen/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/supervision/alex-chen/https://joaocarlos.github.io/Hugo-academia/supervision/emily-brown/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/supervision/emily-brown/https://joaocarlos.github.io/Hugo-academia/courses/introduction-to-artificial-intelligence/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/courses/introduction-to-artificial-intelligence/Course Description This course provides a comprehensive introduction to artificial intelligence, covering fundamental concepts, algorithms, and applications. Students will learn about search algorithms, knowledge representation, reasoning, machine learning, and neural networks. Learning Objectives By the end of this course, students will be able to: Understand the history and foundations of AI Implement search algorithms for problem-solving Apply machine learning techniques to real-world problems Design and train neural networks Evaluate AI systems ethically and critically Prerequisites CS201: Data Structures and Algorithms MATH301: Linear Algebra Basic programming experience in Python Evaluation Component Weight Assignments (4) 30% Midterm Exam 25% Final Project 30% Class Participation 15% Schedule Week Topic Reading 1 Introduction to AI Chapter 1 2-3 Search Algorithms Chapters 3-4 4-5 Knowledge Representation Chapters 7-8 6-7 Machine Learning Basics Chapters 18-19 8 Midterm Exam - 9-10 Neural Networks Chapter 21 11-12 Deep Learning Supplementary 13-14 AI Applications & Ethics Chapter 27 15 Project Presentations - Resources Textbook: Russell & Norvig, Artificial Intelligence: A Modern Approach, 4th Edition Software: Python 3.https://joaocarlos.github.io/Hugo-academia/supervision/james-park/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/supervision/james-park/https://joaocarlos.github.io/Hugo-academia/supervision/maria-garcia/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/supervision/maria-garcia/https://joaocarlos.github.io/Hugo-academia/projects/smart-urban-monitoring-network/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/projects/smart-urban-monitoring-network/Project Overview The Smart Urban Monitoring Network (SUMN) is a large-scale research project aimed at deploying and managing a city-wide network of environmental sensors. The project combines cutting-edge IoT hardware with advanced machine learning algorithms to provide real-time insights into urban environmental conditions. Objectives Deploy 1,000+ environmental sensors across San Francisco Develop adaptive sensing algorithms to optimize data collection Create predictive models for air quality and noise pollution Provide open data access to researchers and city planners Key Results Successfully deployed 500 sensors in pilot phase Achieved 99.https://joaocarlos.github.io/Hugo-academia/supervision/tom-wilson/Mon, 01 Jan 0001 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/supervision/tom-wilson/