Clayton Morrison is an Associate Professor at the University of Arizona, specializing in artificial intelligence and machine learning. His research focuses on machine learning, probabilistic modeling, causal inference, knowledge representation, and automated planning. He is affiliated with the Core Faculty and PhD Faculty in Artificial Intelligence and Machine Learning programs. His recent work emphasizes information extraction from scientific literature, legal documents, and code binaries, leveraging neural networks and generative models. Notable projects include the Eidos, INDRA, and Delphi systems for causal model recovery and the Tomcat dataset for benchmarking. His research bridges theoretical advancements with practical applications in 3D texture generation, lightweight object detection, and code synthesis. He has contributed to over 60 peer-reviewed articles spanning machine learning, computer vision, and computational linguistics. His work often addresses interdisciplinary challenges such as linking mathematical formulas to textual descriptions (MathAlign) and modeling biological event contexts in biomedical texts. Morrison's lab focuses on creating AI systems that can autonomously learn from complex data, with applications in automated model assembly (Automates) and federated learning frameworks. His research has been supported by grants exploring reinforcement learning, multi-hop inference, and explainable AI.
Vipin Chaudhary is the Department Chair of the Department of Computer and Data Sciences and holds the Kevin J. Kranzusch Professorship at the Case School of Engineering, Case Western Reserve University. He specializes in High Performance Computing, Artificial Intelligence, Quantum Computing, and Computer-Aided Diagnosis. With over $80 million in external research funding, his work bridges theoretical advancements and practical applications in healthcare, quantum systems, and machine learning. His academic career includes a PhD in Electrical and Computer Engineering from The University of Texas at Austin (1992) and a Bachelor of Technology in Computer Science from IIT Kharagpur (1986). Research interests span quantum algorithms for molecular modeling, AI-driven medical diagnostics, and optimizing large language models. Notable awards include the 2019 Director's Superior Accomplishment Award and Honorary Professor status at Amity University (2018). He has published extensively in top-tier conferences and journals, addressing topics like quantum circuit optimization, long-context language models, and MRI segmentation techniques. His leadership in creating intelligent cyberinfrastructure aims to democratize AI access for scientific discovery. Education: PhD, Electrical and Computer Engineering, The University of Texas at Austin (1992) Bachelor of Technology, Computer Science & Engineering, IIT Kharagpur (1986) Awards: Director's Superior Accomplishment Award (2019) NSF Honorary Professor (2018) at Amity University His publications reflect a focus on advancing quantum computing efficiency, improving medical imaging through AI, and creating scalable machine learning frameworks. Current projects include quantum noise mitigation, long-context model optimization, and knee MRI segmentation using SAM2. He serves as a key figure in interdisciplinary research, integrating computational and biomedical sciences.
Dr. Yun Liu is a DECRA Fellow (2023-2025) at the School of Chemical Engineering, University of Adelaide, within the Faculty of Sciences, Engineering and Technology. She is eligible to supervise Masters and PhD students and leads an active research program in biomedical engineering and drug delivery systems. Dr. Liu completed her PhD at the Australian Institute for Bioengineering and Nanotechnology (AIBN) at the University of Queensland in April 2020 under the supervision of Prof. Chun-Xia Zhao (NHMRC Leadership Fellow) and Prof. Anton Middelberg (Former Federation Fellow). Her research focuses on multifunctional nanomaterials with customizable drug loading and regulated release capabilities. She has pioneered innovative approaches including sequential nanoprecipitation technology and salt-induced nanoprecipitation platforms that create nanoparticles with exceptionally high drug loading capacity (up to 66.5%). Her work integrates principles from chemical engineering, materials science, and biomedical engineering to develop advanced drug delivery systems. Recent publications demonstrate her expertise in nanoparticle mechanics, biomimetic coatings, and microfluidic fabrication techniques for therapeutic applications. DECRA Fellow (2023-2025) Dr. Liu has published 35 papers, including 14 as first or corresponding author, in prestigious journals such as PNAS and Angewandte Chemie, accumulating over 2,100 citations (H-index 23). She is the key inventor on three patents (one licensed) and currently supervises six PhD students working in the drug delivery field within Prof. Chun-Xia Zhao's research group. Her research has resulted in technologies that have been successfully licensed to industry. Her laboratory specializes in developing advanced drug delivery systems using microfluidic techniques to produce nanomaterial libraries and tumor-on-a-chip models for nanoparticle assessment, with particular focus on mechanical properties of nanoparticles and their impact on cellular interactions.
Shy Shoham is a Professor at New York University, with joint appointments in the Department of Ophthalmology and the Department of Neuroscience. He co-directs the Tech4Health Institute and leads the Neural Interface Engineering Laboratory, which focuses on developing bidirectional neural interfaces to study and manipulate neural activity patterns. His research bridges neuroscience and engineering , aiming to advance medical neurotechnology and decode sensory-motor information coding . His lab’s recent publications highlight innovative work in ultrasound neuromodulation , optoacoustic and fluorescence imaging , and optogenetics . Key trends include non-invasive techniques for functional neuroimaging , infrared neural stimulation , and multimodal imaging systems to study brain dynamics across scales. The lab also explores applications in psychiatry (e.g., treating schizophrenia) and sensory processing (e.g., olfactory circuit analysis). Students in his lab include PhD candidates Tim and Eric. The lab actively engages in collaborative research , with affiliations to NYU’s Tech4Health Institute and Neuroscience Institute . Their work integrates holographic systems and acousto-optic modulation to address challenges in neural activity mapping and therapeutic interventions .
Tanveer Hossain Bhuiyan is an Assistant Professor in the Mechanical, Aerospace, and Industrial Engineering Department at The University of Texas at San Antonio (UTSA), under the Margie and Bill Klesse College of Engineering and Integrated Design. He holds a Ph.D. in Industrial Engineering from the University of Tennessee-Knoxville (2021), an M.S. in Operations Research with a minor in Statistics from Mississippi State University (2018), and dual M.S. and B.S. degrees from Bangladesh University of Engineering and Technology (BUET). His research focuses on data-driven mathematical models and algorithms for decision-making under uncertainty, with applications in critical infrastructure protection, sustainable transportation, disaster mitigation, and power systems. Ph.D.: University of Tennessee, Knoxville M.S.: Mississippi State University (Operations Research) M.S.: Bangladesh University of Engineering and Technology (BUET) B.S.: Bangladesh University of Engineering and Technology (BUET) Bhuiyan’s research employs stochastic optimization, game theory, and high-performance computing for cybersecurity, microgrid planning, and drone logistics. Funded by the U.S. Department of Energy, Department of Homeland Security, and the U.S. Navy, his work spans cyber-physical systems, renewable energy integration, and supply chain risk management. He serves as an Associate Editor for the Cyber-Physical Energy Systems journal and reviews for top-tier publications like European Journal of Operational Research and Omega . His recent publications highlight trends in: Cybersecurity modeling via attack graphs Hydrogen microgrid resilience Drone delivery optimization Stochastic power system planning Botnet detection algorithms Material discovery frameworks Bhuiyan is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and has worked as a Postdoctoral Research Associate at Idaho National Laboratory and a Research Intern at Pacific Northwest National Laboratory and Argonne National Laboratory.
Oliver Brock is an Alexander von Humboldt Professor at the Technical University of Berlin, where he leads research in robotics and computational intelligence. His work focuses on developing algorithmic foundations that enable robotic agents to perform complex tasks in dynamic environments, with applications extending to structural molecular biology. Brock also serves as Track Director for the Cognitive Systems and Science of Intelligence study programs, and previously chaired the Department of Computer Engineering and Microelectronics from 2013-2015. Education: Ph.D. in Computer Science, Stanford University (2000) Master of Science in Computer Science, Stanford University (1994) Diplom in Computer Science, Technical University of Berlin (1993) Brock's research spans robotics, computer vision, and computational biology. His laboratory develops algorithms for motion planning, manipulation, and perception that allow robots to operate effectively in human environments. Notably, his team applies robotics principles to solve problems in protein structure prediction and molecular biology, creating a unique bridge between synthetic and biological intelligence research. His work emphasizes uncertainty modeling, adaptive behavior, and the integration of learning with physical constraints. Analysis of his recent publications reveals a strong interdisciplinary trajectory connecting robotics with cognitive science, neuroscience, and biology. His research increasingly explores how computational models from robotics can illuminate biological processes, while biological insights inform more robust robotic systems. Key themes include uncertainty modeling in perception and decision-making, the physics of manipulation, and the computational foundations of intelligence across different substrates. Selected Awards: Alexander von Humboldt Professorship (2009) NSF CAREER Award (2006) Best Systems Paper Award at Robotics: Science and Systems (2016) First Place in Amazon Picking Challenge (2015) Multiple awards at Critical Assessment of Structure Prediction (CASP) competitions Brock actively mentors students and postdoctoral researchers, with several of his advisees receiving best paper awards. His laboratory receives substantial funding from German research foundations, the European Union, and industry partnerships focused on advancing robotic manipulation and perception. He serves on editorial boards for leading robotics journals including Autonomous Robots and the International Journal of Robotics Research. The Robotics and Biology Laboratory (RBO) at TU Berlin, led by Brock, maintains strong collaborations with biological research institutions and focuses on developing physically grounded computational models that work across both robotic and biological domains. Current projects emphasize uncertainty-aware perception, adaptive manipulation strategies, and the computational principles underlying intelligent behavior in complex environments.
Todd Kelley is a Professor in the Department of Technology Leadership and Innovation at Purdue Polytechnic Institute. He joined Purdue in 2008 after completing his PhD at the University of Georgia and has focused on P-12 STEM education, engineering design, and biomimicry-inspired curricula. His roles include leading the second-year Design Thinking course and serving as program coordinator for the engineering/technology teacher education program (2014–2023). PhD in Workforce Education, University of Georgia (2008) M.A. in Industrial Technology Education, Ball State University (1997) B.S. in Technology Education, Oswego State University (1993) His research explores design cognition in young learners, integrated STEM education through engineering design, and ethnographic approaches to human-centered solutions. Recent work examines transdisciplinary teaching, rural education disparities, and biomimicry in entomology contexts. He leads NSF-funded projects like TRAILS and contributed to the SLED program for elementary STEM. Notable awards include the 2022 Outstanding Publication Award, 2018 Gerhard Salinger Award, and multiple Gerald R. Day Top Peer-Reviewed Article Awards. He has published extensively in journals such as International Journal of STEM Education and Journal of Engineering Design , with recent articles focusing on rural STEM equity, design assessment tools, and cognitive modeling in classrooms. Kelley actively engages in professional service, including the Council on Technology Teacher Education and the I-STEM Resource Network. His family includes four children and involvement at Cornerstone Baptist Church. In 2025, he hosted Purdue’s Technology, Leadership and Innovation podcast, featuring alumni like Greg Kelley.
Zachary J. Kastenberg, MD, MS, is a pediatric surgeon and Assistant Professor of Surgery at the University of Utah School of Medicine. He specializes in general, thoracic, and neonatal surgery with a focus on advanced minimally invasive techniques. His clinical work at Primary Children’s Hospital includes collaborative care for pediatric surgical oncology and hepatobiliary conditions alongside oncologists, gastroenterologists, and transplant teams. B.S. in Biology, University of Minnesota M.D. from Harvard Medical School M.S. in Health Services Research, Stanford University Residency and Chief Residency in General Surgery, Stanford University Clinical Fellowship in Pediatric Surgery, Primary Children’s Hospital/University of Utah Dr. Kastenberg’s research bridges clinical practice and health services, targeting surgical care efficiency and outcomes in pediatric populations. His recent studies examine trauma management, liver transplantation, and oncological interventions, reflecting his commitment to advancing pediatric surgical protocols. He actively participates in national societies (ACS, APSA, AAP, COG) and contributes to multidisciplinary guidelines for Ewing sarcoma treatment. His scientific accolades include Fellowships in the American College of Surgeons (FACS) and American Academy of Pediatrics (FAAP). Dr. Kastenberg educates medical students and surgical residents, ensuring knowledge dissemination in his field.
Seid Koric is an Adjunct Associate Professor in the Department of Mechanical Science and Engineering at the University of Illinois, affiliated with the National Center for Supercomputing Applications (NCSA) as Senior Technical Associate Director. He holds a PhD in Engineering from the University of Illinois. His research focuses on integrating artificial intelligence with high-performance computing (HPC), particularly in materials science, computational mechanics, and multiphysics modeling. Key research areas include deep learning for metamaterial design, finite element analysis, and real-time monitoring of complex systems using digital twins. He has pioneered applications of deep operator networks (DeepONets) for predicting solution fields in materials processing and additive manufacturing. His work bridges computational methods with industrial challenges, such as steel solidification and nuclear system monitoring. Notable achievements include the HPC Innovation Excellence Award (2011 and 2020), recognizing his contributions to HPC and AI integration. Collaborations span academia and industry, addressing challenges in aerospace, energy systems, and smart manufacturing. His lab develops scalable computational frameworks and open-source tools for exascale computing, emphasizing interdisciplinary problem-solving. Education: PhD in Engineering, University of Illinois Affiliations: NCSA (Senior Technical Associate Director), Mechanical Science and Engineering Department Labs/Teams: Leads computational research groups focused on AI-driven HPC applications at NCSA
Eng-Jon Ong is a Research Fellow at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP) within the Faculty of Engineering and Physical Sciences. His work spans computer vision, machine learning, and human-computer interaction, with a focus on facial feature tracking, lip reading, sign language recognition, and biomedical applications of deep learning. University: University of Surrey Department: Centre for Vision, Speech and Signal Processing (CVSSP) Affiliations: CVSSP research group Research Interests: Ong specializes in advanced computer vision techniques, including real-time 3D reconstruction, facial expression analysis, and automated lip-reading systems. His work bridges theoretical machine learning with practical applications like medical imaging (protein localization) and media production (virtual studios). Notable contributions include: Development of robust facial feature tracking algorithms using linear predictors and AAMs Pioneering methods for sign language recognition via sub-unit analysis Deep learning architectures for single-cell protein localization (HCPL system) Real-time lip-reading systems with state-of-the-art accuracy Publications reflect a strong focus on temporal pattern recognition, ensemble learning, and cross-modal interaction analysis. Recent work emphasizes biomedical applications, such as improving protein localization accuracy through novel deep learning ensembles. Labs/Teams: Active contributor to CVSSP's multidisciplinary research projects in computer vision and machine learning.
Meng Xu is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Her research focuses on computer vision, deep learning, and biomedical imaging applications within robotics and healthcare domains. She contributes to advancing localization systems, image processing techniques, and neural network architectures for real-world challenges. Her work bridges theoretical advancements with practical implementations, such as end-to-end visual localization networks (e.g., Bev-locator) and unsupervised methods for biomedical image enhancement. She also explores multi-modal fusion strategies (e.g., MCAPR) and optimization techniques for efficient deep learning models. Key research directions include improving camera pose estimation accuracy, developing robust feature matching algorithms, and applying deep learning to body shape classification and human pose estimation. Her publications reflect a strong emphasis on solving technical challenges in both robotics and medical imaging through innovative computational approaches.
Elahe Arani is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. She also holds external positions as Head of AI Research at Wayve (since October 2023) and previously served as Senior AI Manager and Senior Research Scientist at Wayve from September 2020 to September 2023. Her research interests are centered around Continual Learning, Self-Supervised Learning, and Learning under Noisy Labels. She focuses on developing algorithms for efficient, reliable, and adaptable AI models, particularly in the context of Scene Understanding and Multi-Task Learning. Her work also explores the application of AI in Autonomous Vehicles and integrates insights from Neuroscience to design more biologically plausible AI systems. Key areas include improving generalization in neural networks, mitigating catastrophic forgetting, and leveraging shape-awareness for robust model training. Elahe Arani's recent publications highlight advancements in Continual Learning and Self-Supervised Learning techniques, with a focus on improving model efficiency and adaptability. Her work also delves into applications for Autonomous Vehicles, such as vision-language alignment in SimLingo and generative world models in Gaia-2. She has contributed to frameworks addressing catastrophic forgetting and neural network optimization, often combining biological plausibility with computational methods. No scientific awards are explicitly mentioned in the provided text. No supervised students or specific grant information is listed. Her research emphasizes practical applications and theoretical contributions to AI, with a notable focus on reducing data dependency and environmental impact through efficient model designs. While no specific lab or team names are mentioned, her research involves collaborations in AI-driven systems, including work on road maintenance inspection and autonomous driving technologies. These collaborations aim to bridge academic and industry applications of AI.
Dr. Mihail Bogojeski is a postdoctoral researcher at the Technical University of Berlin , affiliated with the Machine Learning Group and the Berlin Institute for the Foundations of Learning and Data (BIFOLD) . His research focuses on advancing machine learning techniques for applications in the physical sciences and medical sciences , particularly in quantum chemistry , computational biology , brain-computer interfaces , and sequential data analysis . B.Sc. in Software and Information Engineering (2014), Vienna University of Technology M.Sc. in Computer Science (2017), TU Berlin Ph.D. in Computer Science (2023), TU Berlin His research integrates geometric deep learning with quantum chemistry to develop models for predicting electronic structure of molecular systems and electron densities . Recent work includes equivariant neural networks for molecular wavefunction prediction, generative time-series models for industrial aging processes, and explainable AI frameworks for catalyst discovery. Publications highlight his expertise in machine learning , quantum chemical accuracy , and multimodal data augmentation . His 15 most recent articles focus on machine learning applications in chemistry , neuroscience , and industrial process modeling , with subfields such as generative models , equivariant networks , density functional theory , and neural signal processing .
Dr. Lining Yao is a Cooper-Siegel Associate Professor of Human-Computer Interaction at Carnegie Mellon University's School of Computer Science, directing the Morphing Matter Lab. She holds courtesy appointments in Mechanical Engineering and Materials Science & Engineering, and is part of the Softbotics initiative. With a PhD from MIT Media Lab (2017), her research focuses on programmable materials, sustainable design, and interdisciplinary fabrication. Yao is a UNIDO eco-design instructor, CMU Inclusive Teaching Fellow, and NSF CAREER Award recipient. Education: PhD in Media Arts & Sciences, MIT Media Lab (2017) Bachelor's/Master's (unspecified in text) Research Interests: Yao's work bridges material science, computation, and design to create adaptive systems. Key themes include morphing materials for sustainability, soft robotics, food engineering, and human-plant interaction. Her lab explores biodegradable actuators, self-folding devices, and energy-harvesting systems. Key Contributions: Notable projects include self-burying seed carriers (Nature 2023), morphing pasta (Science Advances 2021), and the Thermorph 4D printing system (CHI 2018). She co-founded MorphingMatter4Girls, promoting STEM engagement. Awards: NSF CAREER Award 9 Best Paper/Talk Awards Wired UK Fellowship Labs & Future Work: The Morphing Matter Lab develops eco-conscious materials and systems. Upcoming directions include LLM-driven generative design, programmable degradation mechanisms, and interdisciplinary physical AI systems.
Abdeslam Boularias is an Associate Professor in the Department of Computer Science at Rutgers, The State University of New Jersey. His research focuses on robotics, artificial intelligence, and reinforcement learning, with a strong emphasis on robotic manipulation, model-based control, and perception in cluttered environments. He leads research groups in Artificial Intelligence, Intelligent Systems, and Robotics. Key contributions include advancing techniques for dynamic object tracking, sensorimotor learning in unstructured settings, and integrating physics-based reasoning with deep learning. His work has been recognized with prestigious grants such as the NSF CAREER Award and collaborative grants with institutions like Yale University. Grants: NSF NRI Grant (2022), NSF SA&S Grant (2021), NSF CAREER Award (2024) Research Themes: Robotics in clutter, reinforcement learning for manipulation, physics-aware perception, and end-to-end systems for autonomous robots His recent publications emphasize scalable manipulation learning, one-shot imitation, and diffusion models for affordance prediction. He actively contributes to advancing robotic systems that operate reliably in complex real-world scenarios.
