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Nancy M. Amato is the Abel Bliss Professor of Engineering and Director of the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC), within the Grainger College of Engineering. She holds degrees from Stanford (B.S. in Mathematical Sciences and Economics), UC Berkeley (M.S. in Computer Science), and UIUC (Ph.D.). Her leadership roles include CRA Board Chair, IEEE RAS President Elect, and ACM Council Member-at-Large. Amato is a Fellow of AAAI, AAAS, ACM, and IEEE, and a Member of the American Academy of Arts and Sciences. Her research focuses on robotics (task/motion planning, multi-robot systems), computational biology (protein-drug interactions), parallel computing (STAPL project collaboration with Lawrence Rauchwerger), and computational geometry. She pioneered sampling-based motion planning for robotics and computational biology, including protein folding studies. Current projects include the NSF Expeditions 'Mind in Vitro' exploring biological neuron-based computing. Amato has mentored 29 PhD students (many in academia/industry), 30+ masters, 100+ undergrads, and 10+ high school students, prioritizing underrepresented groups. She co-leads the CRA-WP Distributed REU program, fostering diversity in computing. Awards include the IEEE RAS Saridis Leadership Award (2019) and UC Berkeley's Distinguished CS Alumnus Award (2024).

• Robotics
• Computational Biology
• Parallel and Distributed Computing
• +3 more

Yue Cao, Ph.D., is a Professor in the Department of Radiation Oncology BME at the University of Michigan. His work focuses on integrating advanced imaging techniques with radiation therapy to improve cancer treatment outcomes. He leads research in MRI-guided radiotherapy, adaptive therapy optimization, and AI-driven predictive modeling for personalized oncology. Key research areas include real-time motion tracking via MRI, biomarker-driven response assessment, and multi-modal imaging fusion. His clinical trials investigate de-escalation strategies using PET/MRI biomarkers in HPV-related oropharyngeal cancers and hepatocellular carcinoma. He has pioneered techniques like sparse sampling MRI for volumetric tracking and neural network-based image reconstruction. Publications emphasize innovations in radiotherapy planning quality assurance, survival prediction using longitudinal imaging, and overcoming interobserver variability in tumor segmentation. His team collaborates on large-scale datasets for brain metastasis detection (e.g., nnU-Net extensions) and liver function assessment through dynamic contrast-enhanced MRI. Current efforts include developing AI-enhanced knowledge-based systems for NRG Oncology trials and exploring synergies between 90Y SIRT and SBRT therapies. His work bridges cutting-edge imaging science with clinical implementation to reduce treatment toxicity while maximizing tumor control.

• Radiation Oncology
• Medical Imaging
• Artificial Intelligence in Healthcare
• +5 more

Dr. Ying Zhang is an Assistant Professor of Radiation Oncology at UT Southwestern Medical Center’s Department of Radiation Oncology, specializing in the Division of Medical Physics & Engineering. Previously, she served as an Assistant Professor and therapeutic medical physicist at the Medical College of Wisconsin. Board certified in therapeutic medical physics by the American Board of Radiology (DABR), her expertise lies at the intersection of artificial intelligence and radiation therapy. Dr. Zhang holds a Ph.D. in biomedical engineering from Tianjin University, with advanced training at Yale University’s Therapeutic Radiology Department as a Ph.D. exchange student. She completed postdoctoral fellowships and residency training in medical physics at the Medical College of Wisconsin. Her research focuses on leveraging AI to enhance MRI-guided adaptive radiotherapy, including developing auto-segmentation tools, contour quality management systems, and deep learning-driven adaptive treatment planning. Her work has produced over 15 peer-reviewed publications and garnered recognition through prestigious conference awards. Dr. Zhang’s contributions span clinical usability evaluations, contour refinement techniques, and machine learning applications for treatment optimization. She is affiliated with leading professional organizations in radiation oncology and medical physics.

• Medical Physics
• Radiation Oncology
• Artificial Intelligence in Healthcare
• +3 more

Pratap Tokekar is an Associate Professor in the Department of Computer Science at the University of Maryland, with appointments in UMIACS and Electrical and Computer Engineering. He leads the RAAS Lab and focuses on robotics, particularly algorithms for mobile robots in active sensing and environmental monitoring. His research spans AI, machine learning, and multi-robot systems. Education: Ph.D., Computer Science, University of Minnesota (2014); B.Tech., Electronics and Telecommunication, College of Engineering Pune (2008). Awards include the NSF CAREER Award (2020) and CISE Research Initiation Initiative Award (2016). Teaching highlights include courses on Decision-Making for Robotics, Artificial Intelligence, and Robot Motion Planning at both UMD and Virginia Tech. He advises six Ph.D. students and collaborates with the Maryland Robotics Center and Center for Machine Learning. Awards: NSF CAREER (2020), NSF CISE CRII (2016) Labs/Teams: RAAS Lab, Maryland Robotics Center Key Focus: Multi-robot coordination, active perception, and decision-making under uncertainty.

• Robotics
• Artificial Intelligence
• Machine Learning
• +4 more

Dr. Jay Pscheidt is a Professor in the Department of Botany and Plant Pathology at Oregon State University (OSU), affiliated with the College of Agricultural Sciences. His work focuses on diagnosing and managing plant diseases in woody perennials, including tree fruits, nuts, berries, grapes, ornamentals, and turf. He leads the Fruit and Ornamental Disease Management Testing Program and co-edits the Pacific Northwest Plant Disease Management Handbook . His research emphasizes applied diagnostics, fungal/bacterial disease control, and pesticide efficacy testing, often using innovative technologies like intelligent spray systems. Dr. Pscheidt holds multiple leadership roles, including editorial work for peer-reviewed journals and extension publications. His research group includes faculty assistants like Brent Warneke, Nicole DiManno, and Martin James Whitney. He has received prestigious awards such as the APS Excellence in Extension Award (2023) and the Briskey Award (2022). His outreach efforts prioritize delivering practical solutions to growers via websites, courses (e.g., the Phytophthora Online Course ), and extension guides. Key research areas include eastern filbert blight management, mummy berry disease dynamics, and bacterial blight in hazelnuts. He collaborates on projects like sensor-based spray technology to optimize pesticide use and reduce environmental impact. His recent publications address genetic diversity of pathogens, grower decision-making patterns, and disease-resistant cultivar development. Dr. Pscheidt’s grants and collaborations span federal and institutional funding, with a focus on specialty crops. His lab’s work directly impacts Oregon’s agricultural economy, particularly in hazelnut, wine grape, and blueberry industries. He maintains active involvement in both field research and educational outreach, ensuring science translates into actionable farming practices.

• Plant Pathology
• Disease Management
• Agricultural Extension
• +4 more

Sean Luke is a Professor in the Department of Computer Science at George Mason University (GMU) and serves as Associate Director of the Center for Social Complexity at the Krasnow Institute. He leads the GMU Evolutionary Computation Laboratory (ECLab) and is actively engaged in research at the intersection of artificial intelligence, evolutionary computation, and multi-agent systems. His work spans robotics, music technology, simulation, and knowledge representation. Position: Professor, Department of Computer Science, GMU Leadership: Associate Director, Center for Social Complexity Laboratory: ECLab – GMU’s Evolutionary Computation Laboratory Research Focus: Multi-agent systems, evolutionary algorithms, swarm robotics, computational creativity in music, agent-based modeling Sean Luke's research interests center on evolutionary computation, genetic programming, and multi-agent simulation. He has made foundational contributions to the development of MASON, a widely used Java-based multi-agent simulation toolkit, and ECJ, a general-purpose evolutionary computation framework. His work in computational creativity explores how AI can assist in music synthesis and patch design, culminating in the open-access textbook Computational Music Synthesis . He also investigates swarm robotics, human-agent collaboration, and optimization in complex systems such as supply chains. His most recent publications (2020–2024) reflect a strong trend toward interdisciplinary applications of AI and simulation. These include co-creative music systems, planner-guided robot swarms, and agent-based modeling for supply chain resilience. His work frequently appears in top venues such as GECCO, AAMAS, and IJCAI, and emphasizes open science through freely available software and textbooks. Sean Luke has mentored numerous students and researchers, including William Squires, Drew Wicke, Ermo Wei, and Matteo D'Auria, who have co-authored significant works in multi-agent learning, swarm robotics, and model calibration. He has also been involved in the RoboPatriots RoboCup team, demonstrating practical applications of AI in robotics competitions. Key Projects: MASON, ECJ, RoboPatriots, Computational Music Synthesis Open Resources: Essentials of Metaheuristics , Computational Music Synthesis Luke’s work is supported by a strong track record of publications and technical reports, with recent efforts focusing on improving fairness, scalability, and usability in AI systems. He continues to advance the state of the art in evolutionary algorithms and multi-agent learning, with a growing emphasis on real-world applications and human-AI collaboration.

• Evolutionary Computation
• Genetic Programming
• Multi-agent Systems
• +13 more

George D Konidaris serves as Associate Professor of Computer Science at Brown University, where his research bridges artificial intelligence, machine learning, and robotics with emphasis on autonomous decision-making systems. His work focuses on developing algorithms that enable robots and AI agents to learn hierarchical structures, discover reusable skills, and operate effectively in complex environments. Education: 2010: PhD, University of Massachusetts, Amherst 2003: MS, University of Edinburgh 2001: BS, University of the Witwatersrand 2000: BS, University of the Witwatersrand His research spans reinforcement learning , robotic motion planning , and hierarchical abstraction , with significant contributions to skill discovery, temporal abstraction, and model-based methods. Current work integrates visuo-haptic perception for manipulation tasks and explores language-guided robotics using large language models. His approach emphasizes creating systems that learn compact world representations for efficient long-horizon planning in partially observable environments. Analysis of his 2025 publications reveals strong trends in model-based reinforcement learning with focus on memory mechanisms, uncertainty quantification, and hierarchical skill composition. Key themes include temporal abstraction for planning efficiency, visuo-haptic fusion for robotic manipulation, and language grounding for task specification. His work increasingly connects cognitive science concepts like theory of mind with AI capabilities. Teaching responsibilities include CSCI 1410 (Artificial Intelligence) and CSCI 2951X (Reintegrating AI), where he bridges theoretical foundations with practical robotics applications.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +2 more

Nicholas C. Jacobson is an Associate Professor of Biomedical Data Science and Psychiatry at the Geisel School of Medicine, Dartmouth College. He serves as the Director of the Treatment Development & Evaluation Core within the Center for Technology and Behavioral Health (CTBH) and leads the AI and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Laboratory. His work bridges computational methods with clinical applications to transform mental healthcare through technology. Dr. Jacobson earned his PhD in Psychology from Pennsylvania State University in 2019, following an MSc in Psychology from the same institution in 2015. He completed his Postdoctoral and Clinical Fellowships in Psychology at Massachusetts General Hospital/Harvard Medical School in 2019. Dr. Jacobson's research focuses on harnessing artificial intelligence and passive sensor data from smartphones and wearable devices to develop scalable, personalized interventions for anxiety and depression. His work has three main pillars: (1) enhancing precision assessment of anxiety and depression using intensive longitudinal data, (2) conducting multimethod assessment utilizing passive sensor data from smartphones and wearable devices, and (3) providing scalable, personalized technology-based treatments utilizing smartphones. As a computational psychologist, he created the Differential Time-Varying Effect Model (DTVEM), an innovative statistical package in R that allows researchers to discover and model optimal lag times in intensive longitudinal data. His methodological expertise encompasses machine learning, structural equation modeling, multilevel modeling, time-series techniques, and dynamical systems modeling. His recent publications demonstrate a strong focus on digital phenotyping, machine learning applications in mental health, and personalized interventions. The research spans multiple domains including depression symptom networks, anxiety disorder assessment, eating disorder prevention, and the use of passive sensing to understand mental health conditions. A notable trend is the application of advanced computational methods to create more precise and personalized mental health assessments and interventions, with increasing emphasis on real-world implementation and accessibility. Principal Investigator of an R01 Award from the National Institute of Mental Health studying personalized deep learning models to predict rapid changes in major depressive disorder symptoms Secured over $6 million in funding as Principal Investigator and over $20 million as a co-Investigator Featured on NBC Nightly News and CBS Morning News for pioneering work in AI-powered mental health applications Dr. Jacobson has developed several impactful digital tools including Therabot, a generative AI therapy chatbot that demonstrated substantial reductions in symptoms of major depressive disorder, generalized anxiety disorder, and feeding and eating disorders in its first randomized controlled trial. He also developed Mood Triggers, a smartphone sensing platform that integrates ecological momentary assessment and intervention to help users identify and manage anxiety and depression triggers. His suite of smartphone applications has reached over 50,000 users in more than 100 countries. Dr. Jacobson is actively recruiting team members and encourages interested individuals to contact him through his personal website. He directs the AIM HIGH Laboratory, which focuses on advancing AI applications in mental healthcare. The lab develops innovative computational approaches to enhance mental health assessment and treatment through technology. Current projects include using passive sensor data to predict symptom changes, developing personalized just-in-time adaptive interventions, and creating quantitative tools that enable precision mental healthcare.

• Digital Phenotyping
• Digital Mental Health
• Anxiety and Depression
• +6 more

Gretchen Hansen is an Assistant Professor at the University of Minnesota, specializing in freshwater ecosystems and environmental change. Her work bridges ecological research with practical management solutions, focusing on fisheries ecology, climate change impacts, aquatic invasive species, and adaptive conservation strategies. Education: Ph.D. and B.S. from University of Wisconsin-Madison; M.S. from Michigan State University Research Methods: Statistical analysis of historical data, field observations, simulation modeling, and large-scale experiments Her research explores: Climate change effects on coldwater habitats and fish populations Aquatic invasive species impacts on food webs Water clarity trends and littoral zone dynamics Landscape-scale fish habitat modeling Adaptive management frameworks for sustainable fisheries Recent publications highlight her expertise in: Physiologically guided climate modeling for poikilotherms Deep learning applications for lake temperature analysis Long-term invasion biology studies Food web manipulations through invasive crayfish control Sustainable recreational fisheries management

• Fisheries Ecology
• Climate Change
• Aquatic Invasive Species
• +3 more