Graduating soon or recently graduated and feeling stuck in your job search? Join us for Job Search Jumpstart: Finding Your First Job, a two-day virtual mini conference designed to help you move forward with confidence and clarity. Taking place over two half-days, this interactive event offers targeted support, practical strategies, and insider tips to help you launch your career. Whether you're just beginning or have already been applying without much success, you'll gain tools and insights to make real progress. Day 1: Getting Started and Building Your Toolkit Thursday, June 5 | 9:30 AM – 12:15 PM Start with self-reflection and career exploration, then move into creating strong application materials and maximizing your online presence. Sessions include: - Exploring your options and identifying a career path - Writing a strong résumé with what you already have - Creating a personalized job search routine - Leveraging LinkedIn to search and network like a pro Day 2: From Interview to Offer Friday, June 6 | 9:30 AM – 12:30 PM Focus on the later stages of the search process: nailing interviews, evaluating offers, and learning from real employers what may be holding your applications back. Sessions include: - Interview prep and communication strategies - Understanding job offers and negotiation basics - An employer panel on how to troubleshoot if you're not hearing backLearn more and register here. (https://uconn.co1.qualtrics.com/jfe/form/SV_73yX0FURqfD5CHs)

Sponsored by the Kim Family FundNo Neuroscience Seminar Series Today

Write your memoirs to share in class. New members are welcome!

Soumyashree Sahoo, a Ph.D. candidate in the Department of Computer Science and Engineering at the University of Connecticut, will present and defend her doctoral dissertation titled "Predicting Depression Treatment Outcomes Using Smartphone Data: An Exploration of Self-Ratings, Location, and Medication Data." Her research leverages mobile sensor data, self-reported surveys, and medication-related features to develop machine learning models that predict depression symptom improvement status in individuals during treatment.

Abstract: Depression is a prevalent mental health disorder, and identifying effective treatment early remains a key challenge. Traditional assessments rely on clinical questionnaires, which are costly, infrequent, and prone to biases. With the rise of smartphones, new opportunities have emerged to monitor mental health using active self-reports and passive sensor data. To address the shortcomings of traditional assessments, we explore the use of smartphone-collected data—specifically, self-reported daily mood and anxiety ratings, medication survey and passive location-based sensory data—to predict depression treatment outcomes. In the first part, we evaluated 5-point Likert-scale mood and anxiety self-ratings as alternatives to clinical questionnaires. These self-reports significantly correlate with clinical scores (QIDS), achieving an F1 score of 0.65 using machine learning models like SVM, XGBoost, and Random Forest, compared to 0.71 using clinical questionnaires. In the second part, we investigate the use of passively collected location data for predicting treatment outcomes. To address the heterogeneity between Android and iOS data collection, we apply a domain adaptation technique (Correlation Alignment - CORAL) to align data from both platforms, significantly improving prediction performance. Our results show that combining location features with baseline clinical scores yields an F1 score of up to 0.67—comparable to periodic self-reported questionnaires—highlighting location data as a promising predictor of depression treatment outcomes.   In the third part, we examine whether smartphone data collected early in treatment, specifically during the initial 2–4 weeks, can reliably predict outcomes at the 12th week (the end of the treatment course). Early prediction of treatment outcomes is critical for enabling timely clinical decisions. This study integrates weekly medication surveys, daily mood and anxiety self-ratings, and location-based sensory data to predict depression treatment outcomes 8 to 10 weeks before treatment completion with help of deep learning models (GRU-D and BRITS). Our findings indicate that smartphone data collected in the early phases of treatment can achieve prediction accuracies comparable to weekly clinical questionnaires.

For the full class schedule, descriptions, and to register, please visit the UConn Recreation website (https://recreation.uconn.edu/group-fitness-schedule/).