Understanding a person’s psychophysiological condition is crucial for different fields of applications, including health monitoring and cognitive stress measurement. Continuous measurement helps us understand the physical and cognitive condition of a person. Heart rate, breathing rate, blood pressure and heart rate variability helps to assess the affective nature of a person. This can help study stress level, attention, fatigue, discomfort, delirium, and productivity of a human being including a factory worker, or a driver. But Most of the measurement methods available in practice require instrumentation, which are often intrusive in nature, impossible to use for continuous monitoring and need experts to operate. Remote measurement eases the inconvenience associated with contact-based devices, reduces person hour, and enables safer alternative. The recent pandemic has further demonstrated the importance of contactless measurement methods. One major part of this tutorial will cover remote measurement of vital signs.
The tutorial will also discuss recent advances in ubiquitous health monitoring. Ubiquitous health monitoring refers to the continuous and seamless monitoring of an individual's health and physiological parameters using various interconnected and pervasive technologies. The goal of ubiquitous health monitoring is to provide real-time and non-intrusive data collection, analysis, and feedback to support healthcare and promote wellness. This concept leverages the widespread adoption of wearable devices, Internet of Things (IoT) sensors, and other smart technologies to monitor a person's health status constantly, regardless of their location or activity.

In this tutorial we would present how the community can take advantage of recent developments in wearables and remote measurement for continuous monitoring of vital signs. With increasing use of cyber physical systems, internet of things across industries including wearables, remote measurement is gaining more attention than ever. Due to the development of artificial intelligence and emergence of big data analysis in last decade, vital sign measurements are now very accurate and can extract different modalities of vital sign. This tutorial aims to provide a comprehensive detail of all such development, underlying technology, and their scope in human factor research.

This tutorial will discuss several important components of remote measurements and summarizes work from last two decades in a half-day session:

1. Scopes: First, we’ll discuss the scopes and promises of remote measurement of vital signs (heart rate, respiration rate, blood pressure, heart rate variability), and ubiquitous health monitoring across industry and discuss the benefits. This part will further discuss the scope of ubiquitous health monitoring, related challenges, sensors, and technologies. (Dr. Lynn Abbott) - 30 min
2. Application: Next, we’ll discuss the roles of vital sign in psychophysiological measures including arrythmia, cognitive stress, attention, fatigue, discomfort, and drowsiness. (Dr. Abhijit Sarkar) – 30 min
3. Existing Methods: Next, we’ll discuss promises and limitations of existing methods for remote measurement of vital signs. This includes methods that uses conventional cameras, RF cameras, radar, Wifi. This will highlight some of the major accomplishment for each of the methods. (Dr. Lynn Abbott) – 30 min
4. Break – 15 min
5. Ubiquitous health monitoring (UHM): This session will discuss what UHM is, components of UHM, current state of research in wearable technologies, cloud-based computing of health data, and how advanced data analytics techniques are used for UHM (Dr. Sarkar, Dr. Abbott).
6. Camera based method: (Dr. Abhijit Sarkar) – 60 minutes

a. First, we’ll discuss how data from RGB and NIR cameras contains blood volume pulse information from human face.
b. Next, we’ll discuss challenges from motion and ambient illumination and methods to address those challenges.
c. Next, we’ll show how advance computer vision, signal processing, and machine learning methods including deep learning are used to extract blood volume pulse, and respiration rate.
d. Next, we’ll discuss how thermal imaging can be used for the study of human psychophysiology.
e. Finally, we’ll discuss next frontiers in remote measurements, and current states.
7. Discussion: (Dr. Abhijit Sarkar, Dr. Lynn Abbott) – (15 minutes)

About the Speaker(s) Dr. Abhijit Sarkar is a Senior Research Associate in the Division of Data & Analytics at Virginia Tech Transportation Institute. He currently leads the computer vision and machine learning group. His research focuses on the application of computer vision, machine learning, and time series data analysis for transportation safety and mobility. His recent projects involve perception of autonomous systems, sensor fusion, driver distraction, data deidentification, cardiac biometrics, human psychophysiology, operation of heave vehicles, intersection safety, and naturalistic driving data. As a PI and Co-PI he has led projects with total value of more than $18 Million. These projects were funded by NHTSA, FHWA, NSF, FMCSA, Safe-D UTC, NASA, NCHRP, and multiple private sponsors. He earned his Ph.D. from Virginia Tech, USA, his master's from IIT Kharagpur, and his bachelor's from Jadavpur University, India, all in Electrical Engineering.
Dr. Abbott is a Professor at Virginia Tech, where he is a faculty member in the Bradley Department of Electrical and Computer Engineering. His primary research interests involve Computer Vision, Machine Learning, and Biometrics. In the area of biometrics, he has led efforts involving fingerprint analysis, authentication from cardiovascular signals, and facial expression recognition. His work is currently supported by the National Science Foundation (NSF) and by the Federal Highway Administration (FHWA). Dr. Abbott has authored or coauthored more than 160 technical publications and has been awarded one U.S. patent. He teaches graduate courses in the area of Computer Vision, and undergraduate courses in software development, microcontroller systems, and Artificial Intelligence.

Objectives
With a growing need for mass data visualization, most business and consumer applications must display compelling data Visualizations to improve their data's impact. One primary way to present an overview of the system status and content is by building a persuasive visualization that facilitates decision-making and augments cognition. What are the basic principles behind designing effective and intuitive visualization? This introductory/ intermediate course reviews the fundamentals of data visualization and evaluation of visualization. Participants will then evaluate several visualizations and practice building a compelling visualization.

Content and Benefits
The first section of the tutorial will be used to review the fundamental principles in designing visualization. Participants will then practice evaluating several example. Following this, the participants will work in teams to build an effective dashboard according to the guidelines and principles taught in the previous section.
The course will feature presentations, small group activities, and discussions to enhance learning. The presentations will examine the following topics:
• Introduction • Fundamental Principles of Visualization in Design • Visual Designs • Mass Data Visualization • Evaluating visualization • Building Effective Visualization

Target Audience
Potential beneficiaries of this course may be: • People who are involved with UI/UX design • People who have some experience with dashboard design • HCI professionals with an interest in UX design • Researchers already working in UX design

About the Speaker(s) Abbas Moallem, Ph.D., is a consultant and adjunct professor at San Jose State University, California, where he teaches human-computer interaction, cybersecurity, information visualization, and human factors. Dr. Moallem is the editor of HCI in Cybersecurity Handbook, Smart and Intelligent System and the author of Cybersecurity Awareness among College Students and Faculty, and Understanding Cybersecurity Technologies: A Guide to Selecting the Right Cybersecurity Tools, published by CRC Press.

How confident are you in your AI-assisted UX research today?In the ideal world, everyone using AI to facilitate UX research would carefully evaluate the results by comparing them to what a team of experienced researchers would do, and assess what is working and what isn’t based on solid research principles. Is it solid work…or just competent incompetence? In the real world, there usually isn’t the time or motivation to make this comparison.In this workshop, we are going to live in that ideal world, take that time, and make those comparisons.We will start with an assessment of the quality and challenges of AI-assisted research by sharing our own personal experiences, plus reviewing recent notable articles and research.The heart of this workshop will be to work in teams on three research challenges—designing personas for research targets, designing user interview questions (for our personas), and analyzing research results and user feedback to generate insights and recommendations—then compare what our teams create to what the best AI research tools come up with. These exercises will be timed both ways so that we can also compare the time and effort required.We will complete the workshop with a discussion of the insights we have learned: what AI-assisted research is good for (and good enough for) vs. where AI-assisted research falls short, plus how to best design prompts to get the best results.(Note: While there is a predetermined process and research challenges, no conclusions will be pre-determined. This workshop will be a genuine, unbiased hands-on learning opportunity.)

About the Speaker(s) Everett McKay is Principal of UX Design Edge and a UX design consultant and trainer with global clientele that includes Europe, Asia, South America, Australia, and Africa. Everett's specialty is finding practical, intuitive, simple, highly usable solutions quickly for web, mobile, and desktop applications. Everett has over 30 years' experience in user interface design—and even more programming UIs. (He loves React!)

Everett is author of "Intuitive Design: Eight Steps to an Intuitive UI", the definitive guide to designing intuitive interactions, and "UI Is Communication: How to Design Intuitive, User Centered Interfaces by Focusing on Effective Communication", a groundbreaking approach to UI design using human communication-based principles and techniques. While at Microsoft, Everett wrote the Windows UX Guidelines for Windows 7 and Windows Vista. Everett holds a master's degree in computer science from MIT.

This tutorial looks at how Artificial Intelligence is changing the fields of Design, User Experience, and Usability (DUXU), focusing especially on ergonomics and user-centered interaction. As intelligent systems increasingly shape our everyday digital spaces, it's crucial that interfaces are functional, adaptable, ethical, and ergonomically supportive. Many products fail to provide meaningful user experiences, not because they lack features, but because users find them hard to interpret, use, or trust. This tutorial aims to help participants understand how design choices impact user well-being, performance, and satisfaction by combining AI-driven insights with usability and ergonomic principles.•Participants will learn to spot and avoid common design and usability issues while gaining practical skills for improving user experience in AI-enabled systems. The session will cover:•Key concepts and current challenges in AI-driven DUXU and adaptive ergonomics•The connection between intelligent algorithms, interface components, and human cognitive and physical needs•Practical design and evaluation principles supported by predictive, conversational, and generative AI technologiesContent and Benefits:This tutorial is suitable for both beginners and experienced practitioners. It mixes discussions with hands-on design and evaluation exercises. Participants will analyze real-world AI-powered interfaces, identify usability and ergonomic problems, and suggest improvements based on human-centered AI guidelines. Key learning benefits include:•Understanding how DUXU and ergonomic design influence AI-enabled product development•Hands-on experience using AI-supported evaluation and design methods•Practical guidelines for creating adaptive, clear, and trustworthy interfacesTarget Audience:This tutorial is open to researchers, practitioners, and students in Human-Computer Interaction, Ergonomics, AI design, Interface Engineering, and related fields, including:•Designers: Interaction, Product, UX, UI, Visualization•Usability and User Experience Evaluators•AI and HCI Researchers•Software and System Engineers•Web and Application Developers•Human Factors and Ergonomics ProfessionalsBy the end of the session, participants will be ready to use AI responsibly to design ergonomic, adaptive, and meaningful user experiences. This will support safer, more intuitive, and user-friendly technological ecosystems.

About the Speaker(s) Dr.Javed Anjum Sheikh, Associate Profesor/Director CS&IT in the University of Minhaj University Lahore – before that, I was the Assistant Professor/Campus Director/Associate Dean of the University of Lahore, Gujrat Campus and was the Assistant Professor (Associate Director) of the faculty of Computing and IT.

Objectives
AI has changed the game for all things UX, prototyping included. Before AI, UXers knew that prototyping was expensive and time consuming, and that there were many benefits to low-fidelity prototypes over high-fidelity, fully functional mockups. That we can now use AI to build a fully functional prototype in mere minutes changes everything.Or does it? While AI clearly reduces the time and effort to develop functional prototypes, much of the conventional thinking about prototypes still applies. It is still possible to waste a great deal of time prototyping with AI tools if the process isn’t grounded in UX-based best practices.The goal of this course is to rethink prototyping from the ground up, starting by exploring conventional prototyping best practices and why they were needed. We will then explore modern AI-based prototyping possibilities, current practices (best or otherwise), and discuss their pros and cons.Once this foundation is established, we will work in teams to update prototyping best practices and as a class, apply those best practices to a real prototyping challenge.

About the Speaker(s) Everett McKay is a UX design consultant, trainer, full-stack developer, and founder, with over 30 years' experience and world-wide clientele.

Every day, the number of ransomware attacks, identity thefts, credit card fraud, email message hacking, etc. grows, and costs individuals and institutions both short-term and long-term losses.The press is full of reports of data center breaches that result in loss of intellectual property, trade secrets, and/or customer data and affect the company’s reputation. Successful cyber protection at the individual or enterprise level is not possible without well-trained people who are aware of security risks and knowledgeable enough to make sound judgments when confronted with cyber-attacks such as phishing or fraudulent phone calls. The active involvement of employees and their awareness are paramount to a company’s security compliance.The objective of this tutorial is to cover 10 important areas of cybersecurity risks and teach attendees about protective measures.After completing this training session, participants will learn practical ways to deal with cyberattacks and a list of actions to protect themselves at both the individual and company levels. 1.Trust2.Authentication3.Privacy4.Ransomware5.Identity Theft6.Phishing7.Application Access8.Social Media9.Home Networking10.SurveillanceTarget AudiencePrior knowledge or experience in cybersecurity is not required. Therefore, potential beneficiaries of this course may be:  •Students at all levels•All Academics •Professional and Practitioners

About the Speaker(s) Abbas Moallem, Ph.D., is a consultant and adjunct professor at San Jose State University, California, where he teaches human-computer interaction, cybersecurity, information visualization, and human factors. He is the program chair of HCI-CPT, the International Conference on HCI for Cybersecurity, Privacy, and Trust. Dr. Moallem is the editor of the HCI in Cybersecurity Handbook and the author of Cybersecurity Awareness among College Students and Faculty. His two recent books, Smart and Intelligent System and Understanding Cybersecurity Technologies: A Guide to Selecting the Right Cybersecurity Tools, were published by CRC Press. He is also the editor of The Human Element in Smart and Intelligent Systems, a book series from CRC Press. He currently serves as Communication Chair of the HCI International Conference program chair of the International Conference on HCI for Cybersecurity, Privacy, and Trust (HCI-CPT), and program chair of the Human Factors in Cybersecurity Conference

Welcome to a practical and engaging journey into the world of Python and Data Science. This tutorial is designed to guide you step by step as you learn how to extract meaningful insights from data. You will see how predictive models can support smarter decisions. Together, we will go beyond raw numbers and explore how data can tell a compelling story through clear visualization and analysis.In today’s data-driven world, we need a programming language that is both powerful and easy to work with, especially when dealing with complex math and statistics. Python has become one of the most versatile tools for data science. Its wide range of scientific libraries, simplicity, and flexibility make it a natural choice for anyone entering this field.This tutorial will help you build a strong foundation by exploring:- Key ideas and challenges within Data Science- How these ideas connect with Python- Main principles, methods, and tools used in predictive modeling- Practical exposure to popular Python libraries used in real-world data analysisContent and BenefitsThis tutorial is designed for:- Beginners with no programming experience- Programmers who are new to PythonYou will learn the essentials of working with data using Python and Pandas. Hands-on exercises will support each concept. By the end, you will be able to design and evaluate a basic data analysis workflow.Topics CoveredBy participating in this tutorial, you will:- Understand the fundamental steps involved in a typical data science process- Gain hands-on experience with predictive data analysis- Apply your learning through a combination of presentations and practical exercises- Get helpful guidelines for future research and continued learning in Data ScienceThis tutorial is not meant to turn you into a full Python developer, but it will give you the confidence and skills to continue your journey in Python and Data Science on your own.Join us and discover how Python can help you unlock insights, build predictive models, and truly understand the power of data. The path to smarter, data-informed decision-making begins here.

About the Speaker(s) Dr.Javed Anjum Sheikh, Associate Profesor/Director CS&IT in the University of Minhaj University Lahore – before that, I was the Assistant Professor/Campus Director/Associate Dean of the University of Lahore, Gujrat Campus and was the Assistant Professor (Associate Director) of the faculty of Computing and IT.

Objectives
Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement. Eye trackers are used in research on the visual system, in psychology, in psycholinguistics, marketing, as an input device for human-computer interaction, and in product design. Eye trackers are also being increasingly used for rehabilitative and assistive applications (related for instance to control of wheel chairs, robotic arms and prostheses). There are a number of methods for measuring eye movement. The most popular variant uses video images from which the eye position is extracted. Other methods use.

About the Speaker(s) Jan Watson, Drexel University, Jan Watson is a researcher at the School of Biomedical Engineering, Science and Health Systems in Philadelphia Pennsylvania USA.

Heuristic evaluation is a well-known technique that evaluates a design based on its compliance with recognized usability principles. Heuristic evaluations have the benefit of being very efficient and focused (for example, an accessibility evaluation is focused on accessibility problems.) However, most practitioners prefer user-based testing because they have more confidence in the results. Ideally, teams should use both, as effective heuristic evaluations make user-based testing more productive by focusing on hard-to-find problems.
But a heuristic evaluation is only as good as the set of heuristics used, and the most popular heuristics are well past their “best by” dates. Arguably the most popular usability heuristics were devised by Jakob Nielsen and Rolf Molich—in 1990! Considering how rapidly UI design has changed, the relevance and practical value of even 5-year-old heuristics should be suspect. Less popular heuristics are often vague and hard to apply meaningfully (example: “…check whether the user has enough control…” What does that even mean?)

This tutorial will consist of two parts. In Part 1, we will quickly review the most well-known usability heuristics, plus a summary of the top design principles recommended by the most popular platforms (iOS, Android, Windows, and Mac). The class will break into three teams (representing desktop, web, and mobile), and devise their own usability heuristics using a structured process. The focus of the results will be on their practical value. At the end of this part, each team will present their results to the class.
For Part 2, we will review the ground rules for effective heuristic evaluations, then as apply our newly created heuristics to desktop, web, and mobile designs (at least one for each platform). The tutorial will end with a discussion about the effectiveness of the evaluations and how to further improve the process.

About the Speaker(s) Everett McKay is Principal of UX Design Edge and a UX design consultant and trainer with global clientele that includes Europe, Asia, South America, Australia, and Africa. Everett's specialty is finding practical, intuitive, simple, highly usable solutions quickly for web, mobile, and desktop applications. Everett has over 30 years' experience in user interface design—and even more programming UIs. (He loves React!)

Everett is author of "Intuitive Design: Eight Steps to an Intuitive UI", the definitive guide to designing intuitive interactions, and "UI Is Communication: How to Design Intuitive, User Centered Interfaces by Focusing on Effective Communication", a groundbreaking approach to UI design using human communication-based principles and techniques. While at Microsoft, Everett wrote the Windows UX Guidelines for Windows 7 and Windows Vista. Everett holds a master's degree in computer science from MIT.

This tutorial focuses on the principles and practices of human-centered digital technology, emphasizing their integration into Artificial Intelligence (AI) modeling to create systems that are effective, transparent, and aligned with human values. Led by Dr. Rayan Ebnali Harari, a faculty member at Harvard Medical School and an expert in human-centered AI, this session provides a detailed, step-by-step approach to designing AI solutions that are both technically robust and user-oriented.

Participants will explore: Foundational Principles: Key concepts in human-centered design and their importance in AI development across fields.

Data Practices: Methods for curating, preprocessing, and annotating data to ensure relevance, quality, and alignment with human expertise and real-world scenarios.
Model Building: Practical steps for incorporating human insight into AI modeling, including feature engineering, algorithm selection, and explainability techniques.
Evaluation and Validation: Strategies for assessing AI systems, focusing on transparency, user trust, and performance metrics in practical applications.

Drawing on insights from NIH-funded projects, including applications in medical imaging (MRI, ultrasound, TEE) and other domains, the tutorial highlights real-world use cases that demonstrate the importance of aligning AI development with human decision-making processes. By integrating examples from diverse fields, participants will learn how to create AI systems that improve decision-making, enhance trust, and deliver impactful results.

This session is ideal for professionals, researchers, and developers across industries who are interested in building AI systems that effectively integrate human expertise, ensuring practical and ethical outcomes in real-world settings.

About the Speaker(s) Abbas Moallem, Ph.D., is a consultant and adjunct professor at San Jose State University, California, where he teaches human-computer interaction, cybersecurity, information visualization, and human factors. Dr. Moallem is the editor of HCI in Cybersecurity Handbook, Smart and Intelligent System and the author of Cybersecurity Awareness among College Students and Faculty, and Understanding Cybersecurity Technologies: A Guide to Selecting the Right Cybersecurity Tools, published by CRC Press.

Objectives
Interactive presentations and prototypes are essential tools for conveying ideas and gaining stakeholder buy-in. In this tutorial, you’ll learn how to leverage Figma’s powerful features to create professional, polished, and fully interactive deliverables that make a lasting impression.
We’ll begin by exploring Figma’s core interface and tools, ensuring participants of all skill levels feel confident navigating the platform. Then, we’ll dive into creating engaging presentations by combining text, images, and animations. You’ll discover how to use layers, components, and design systems to maintain consistency and streamline your work.

The tutorial also covers the creation of clickable prototypes, enabling you to simulate user interactions and showcase functionality effectively. You’ll learn how to:

• Use interactive components and transitions to bring your designs to life.
• Create user flows and link screens to guide stakeholders through a cohesive story.
• Optimize designs for real-time collaboration and feedback using Figma’s sharing features.
By the end of this tutorial, you’ll have built a complete interactive prototype and presentation, ready to impress your audience in both academic and professional settings.

About the Speaker(s) Iryna Kunytska is a seasoned design professional with over 12 years of experience in product design and entrepreneurship. As a lead product designer and the founding designer for multiple successful startups (Logitech, Streamlabs, Amous, Quandri, etc.), Iryna specializes in designing and launching innovative products from 0 to 1. In addition to running her own design business, Iryna is a mentor, investor, and entrepreneur dedicated to empowering others to succeed in their design and business journeys. Her extensive experience spans a variety of industries, where she has crafted user-centric, visually compelling, and highly functional designs. Known for her ability to break down complex design processes into actionable steps, Iryna’s tutorials provide practical, hands-on knowledge for students, aspiring designers, and professionals alike. Whether you’re just starting out or looking to refine your skills, Iryna’s expertise will inspire and equip you to achieve your design goals.

AI-powered ergonomics combines machine learning, computer vision, and wearable sensors with digital simulation to improve our understanding of human performance. It does not replace traditional ergonomics; rather, it enhances it with ongoing, detailed data about posture, workload, and movement patterns. An AI system can quickly identify subtle signs of strain, count task repetitions, or detect hazardous movements. This shifts ergonomics from reactive to proactive, predicting and preventing injuries in advance. By using technology to support human welfare, AI expands the range of effective, personalised ergonomic solutions and makes them more comfortable.Issues and Challenges Despite its potential, several important issues need careful consideration when integrating AI into organizations responsibly and effectively:Data Privacy Risks: It is challenging to consent, monitor, and securely store sensitive data generated by wearables, video feeds, and biometric systems.Technical Skills Gap: Most organizations lack trained personnel who can operate or interpret the results of AI-based ergonomic systems.High Implementation Costs: Installing technologies such as digital twins, advanced sensors, and computer vision can lead to significant initial expenses.Compatibility and Integration Issues: AI technologies might not work well with existing systems or equipment, necessitating updates or redesigns.Algorithmic Bias: AI models trained on small datasets may misinterpret certain movements or unfairly label some groups as slow.Employee Trust and Acceptance: Workers may resist new technologies that invade their privacy or fear being replaced by machines.Applications AI is transforming ergonomic practices through its practical applications, including:Computer Vision for Posture & Movement Analysis: This includes detecting unsafe postures and providing real-time corrective feedback. Wearable Sensors for Biomechanical Tracking: Monitoring joint stress, fatigue, repetition, and force exertion in real time.Digital Twin Simulations: These involve creating virtual models of workstations, workflows, and equipment layouts before real-world implementation.Collaborative Robots: Cobots share the physical demands of human workers to reduce strain associated with production, transportation, and caring for the sick.AI-powered Adaptive Workstations: Smart desks and chairs adjust automatically based on ergonomic needs and user preferences.Predictive Analytics Dashboards: These tools allow for early detection of musculoskeletal risks, enabling timely and personalized safety strategies.Conclusion Introducing artificial intelligence to the workplace does not diminish ergonomics as a discipline; it elevates it. The combination of a human-centered approach and data-driven insight creates workplaces that are not only efficient but also safer and more welcoming. While challenges regarding privacy, cost, and trust must be addressed, the benefits are substantial. This workshop aims to equip participants with the knowledge and tools needed to navigate these changes and help design the future of smart, adaptable, and human-centered workplaces.Target AudienceThis tutorial is intended for ergonomics professionals, human factors practitioners, safety officers, workplace designers, and others seeking to apply AI and user-centered design to enhance workplace environments. Participants will gain clear, practical guidance on incorporating these advanced approaches into their ergonomic assessments and interventions.By the end of the session, attendees will understand how AI and user-centered design work together to support smarter, healthier, and more productive workspaces.

About the Speaker(s) Dr.Javed Anjum Sheikh, Associate Profesor/Director CS&IT in the University of Minhaj University Lahore – before that, I was the Assistant Professor/Campus Director/Associate Dean of the University of Lahore, Gujrat Campus and was the Assistant Professor (Associate Director) of the faculty of Computing and IT.

Neuroscience is the scientific study of the nervous system. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, computer science and mathematical modeling to understand the fundamental and emergent properties of neurons and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "ultimate challenge" of the biological sciences. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales and the techniques used by neuroscientists have expanded enormously, from molecular and cellular studies of individual neurons to imaging of sensory, motor and cognitive tasks in the brain.

About the Speaker(s) Adrian Curtin is a researcher with Shanghai Jiao Tong University and Drexel University. His research background focuses on the neuroergonomic application of neuroimaging, particularly in mental health, neurostimulation, and in analysis method development.

Neuroscience (or neurobiology) is the scientific study of the nervous system. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, computer science and mathematical modeling to understand the fundamental and emergent properties of neurons and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "ultimate challenge" of the biological sciences. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales and the techniques used by neuroscientists have expanded enormously, from molecular and cellular studies of individual neurons to imaging of sensory, motor and cognitive tasks in the brain.

About the Speaker(s): Dr. Adrian Curtin, Drexel University

Neuroscience (or neurobiology) is the scientific study of the nervous system. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, computer science and mathematical modeling to understand the fundamental and emergent properties of neurons and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "ultimate challenge" of the biological sciences. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales and the techniques used by neuroscientists have expanded enormously, from molecular and cellular studies of individual neurons to imaging of sensory, motor and cognitive tasks in the brain.

About the Speaker(s): TBD