Events

Job Search Strategies
For Tech Industry Jobs
Wednesday, March 26 – 12pm
ITE 459
Virtual meeting link

Faculty Candidate Presentation
Neha Singh
Wednesday, March 12 – 12-1pm
ITE 406
Virtual meeting link

From Heuristics to AI: The Evolution of Sentiment Analysis

Dr. Neha Singh is a Lecturer in the Department of Computer Science & Engineering at Washington University in St. Louis (WashU). She earned her Ph.D. in Information Systems from the University of Maryland, Baltimore County (UMBC), where she developed a scalable, holistic physical and social sensing framework for disaster management. Her research focuses on leveraging multi-modal data sources to build efficient predictive models that extract critical insights and information in real time to aid disaster response. Dr. Singh has extensive experience designing and delivering technical curricula at top institutions, including the University of California, Berkeley (UC Berkeley), WashU, UMBC, and Pioneer Academics. She has developed and taught courses that emphasize hands-on learning and real-world problem-solving. In addition to her academic roles, she serves as a Research Faculty Advisor at Pioneer Academics, mentoring high school students in applied data science research and guiding them through advanced computational modeling and AI-driven decision-making techniques.

Faculty Candidate Presentation
Subigya Nepal
Tuesday, March 25 – 10-11am
ITE 406
Virtual meeting link

Leveraging Digital Behavioral Signals for AI-powered Mental Health Assessment, Prediction, and Intervention

Mental health conditions affect millions globally, yet traditional assessment methods rely on sporadic clinical visits that may miss important behavioral changes. This talk presents a technological approach to mental wellness through three components: behavioral understanding, prediction, and intervention. I will discuss findings from a longitudinal mobile sensing study that tracked college students’ behaviors through the COVID-19 pandemic, and present MoodCapture, a system for studying naturalistic facial expressions during phone use. The talk will conclude by demonstrating two intervention platforms: mSITE, designed for enhancing social interaction in serious mental illness, and MindScape, a context-aware journaling tool that combines behavioral sensing with AI. These studies collectively illustrate the potential of ubiquitous computing and AI in creating scalable, privacy-conscious solutions for mental healthcare.

Dr. Subigya Nepal is a Postdoctoral Fellow at Stanford Institute for Human-Centered AI working at the intersection of AI, ubiquitous computing and mental health. His research focuses on developing technologies that understand and enhance mental wellbeing through everyday devices. He has led several foundational studies in mobile sensing – including the longest-running study of its kind – to uncover patterns in mental health and behavior, while developing tools for passive sensing and context-aware interventions. His work, published in top venues like ACM UbiComp, CHI, and CSCW, has received distinguished paper awards and been featured in The Washington Post, Bloomberg, and The Times UK. His research aims to bridge the gap between technological innovation and accessible mental healthcare through evidence-based, human-centered approaches. He completed his PhD in Computer Science from Dartmouth College in 2024 and has spent two summers as a research intern at Microsoft Research working on workplace wellbeing.

WISS Presentation
Dr. Pedro Pedrosa Rebouças Filho
Wednesday, April 2 – 12-1pm
ITE 459
Free lunch for those who RSVP

Bridging AI Theory and Practice: Deep Learning, Explainability, and Real-World Applications

Artificial Intelligence has become a fundamental pillar in modern technological advancements, with deep learning leading breakthroughs across multiple domains. However, despite the remarkable progress, AI models often remain “black boxes,” raising concerns about transparency, reliability, and trust.

In this talk, we will explore how AI research bridges theory and practice, emphasizing the significance of Deep Learning and Explainable AI (XAI) in real-world applications. The discussion will include foundational concepts, current challenges, and innovative solutions developed at LAPISCO — ranging from AI-driven surveillance systems to healthcare applications that enhance medical diagnosis.

This session aims to foster discussions on collaborative research opportunities by presenting key research contributions and industry-driven projects, bridging the gap between academia and applied AI solutions.

Dr. Pedro Pedrosa Rebouças Filho is an AI and Machine Learning researcher with over 15 years of expertise in academia and industry. He is currently a Postdoctoral Research Fellow at UMBC under the supervision of Professor Houbing Song. Since 2008, Dr. Pedrosa has served as an Associate Professor at the Federal Institute of Ceará (IFCE), and since 2016, as a Visiting Professor at the Federal University of Ceará (UFC).

Dr. Pedrosa leads AI research at LAPISCO, where he has managed over 30 major projects focused on AI-driven solutions for surveillance, healthcare, and energy. He holds a PhD in Teleinformatics from UFC and has completed postdoctoral work at the University of Porto. A distinguished member of the Brazilian Academy of Sciences and IEEE Senior Member, Dr. Pedrosa has received prestigious awards such as the BRAGFOST Young Scientist Award and BRICS Young Scientist Award. He has published over 200 papers, contributing significantly to both academic research and real-world AI applications.

Personal Website: reboucasfilhopedropedrosa.github.io
LinkedIn: linkedin.com/in/reboucasfilho-pedropedrosa