Chitta Baral is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU), part of the Ira A. Fulton Schools of Engineering. He joined ASU in 1999 as an Associate Professor and was promoted to Full Professor in 2002. His research focuses on artificial intelligence, natural language processing, vision-language systems, and neuro-symbolic approaches. He directs the Cognition and Intelligence Lab (COGINT Lab) and has authored influential works like the book Knowledge Representation, Reasoning and Declarative Problem Solving (Cambridge University Press). His academic journey includes a B.Tech from IIT Kharagpur, and M.S./Ph.D. from the University of Maryland, College Park. Baral's work spans theoretical contributions (e.g., Answer Set Programming, logical reasoning) and applied domains like cybersecurity, biomedical informatics, and robotics. He has held editorial roles at top AI journals, led KR Inc., and collaborated with organizations like the Mayo Clinic. His recent research emphasizes LLM instruction engineering, bias mitigation, and multimodal reasoning benchmarks. He teaches advanced courses in NLP and advises students in AI-related areas.
Sandra Ricardo is an Assistant Professor at the University of Trás-os-Montes and Alto Douro (UTAD), Portugal, with a strong academic foundation in Mathematics, holding a PhD from the University of Rouen, France, and a Master’s from the University of Coimbra, Portugal. She is actively engaged in research and educational projects with international impact. PhD in Mathematics, National Institute of Applied Sciences of Rouen, University of Rouen, France (2008) Master's in Mathematics, University of Coimbra, Portugal (2000) Her research interests include Mathematics Education, History of Mathematics, Special Matrices, k-Bronze Fibonacci Numbers, and Mechanical Control Systems. She emphasizes innovative teaching strategies and the integration of historical context into mathematical instruction. Her work bridges theoretical mathematics with practical applications in education and biomedical signal analysis. The most recent publications reflect a dual focus: one stream on advanced algebraic structures such as quaternion Gaussian Bronze Fibonacci numbers and matrix theory, and another on pedagogical innovations in teaching fractions, statistics, and problem-solving in early education. These works highlight her commitment to both pure mathematical research and transformative educational practices. Sandra is involved in significant international projects: TeachersMOD (Erasmus+, EACEA): Modernizing elementary school teacher training in Kurdistan (2023–2025) Mais Conhecimento Melhor Futuro (Calouste Gulbenkian Foundation): Enhancing math, Portuguese, and digital literacy in Guinea-Bissau (2022–2023) These initiatives aim to improve educational access and quality in underserved regions, focusing on curriculum development and teacher capacity building. Sandra has advised and collaborated on numerous research projects, particularly in mathematics education reform and control theory. While no formal list of advisees is provided, her publications indicate strong mentorship and collaborative leadership. She has not received publicly listed scientific awards in the provided text. She contributes to academic outreach through the CIIE (Center for Research in Educational Innovation) and LabDERE (Laboratory of Digital Experimentation and Research in Education), promoting digital tools and innovative pedagogies in educational settings.
Dr. med. univ. Elisabeth Daniela Olbert is a senior physician in the Department of Neurology at Tulln University Hospital, affiliated with the Karl Landsteiner Private University of Health Sciences. She actively contributes to clinical research through the Forschungsimpulse program, focusing on autonomic nervous system dysfunction in stroke, multiple sclerosis, and other cerebrovascular conditions. She leads multiple prospective studies and collaborates with Prim. Assoc. Prof. PD Dr. Walter Struhal. Medical University of Vienna – Medical Studies Residency in Neurology – Zurich and Austria Technical College for Mechatronics – Early Education Elisabeth Olbert's research centers on the autonomic nervous system's role in neurological diseases, particularly its impact on stroke, cerebral autoregulation, and cognitive function in multiple sclerosis. She utilizes real-world clinical data and leads prospective studies on vascular interventions. Her work bridges clinical neurology with data science, leveraging her background in mechatronics for signal processing and data analysis. Her recent publications reflect a strong focus on autonomic dysregulation following acute neurological events like stroke and hemorrhage, as well as rare neuroinflammatory conditions such as MOG antibody disease and NMOSD. She also contributes to broader topics like chronic fatigue syndrome and gender-specific stroke management, demonstrating a comprehensive approach to clinical neurology and patient-centered research. While no formal scientific awards are listed in the provided texts, her leadership as principal investigator on multiple funded research projects highlights her active and recognized role in the academic community. Dr. Olbert supervises or advises no named students in the available data. However, she is deeply involved in research projects such as AFFRICATE and studies on autonomic dysfunction in MS, which likely involve collaboration with junior researchers and clinicians. Her work is supported by institutional research funding from Forschungsimpulse. She is a key contributor to the autonomic neuroscience research group at Tulln University Hospital, participating in the European Programme of Clinical Autonomic Neuroscience (EPOCAN). Her work involves multidisciplinary collaboration with neurologists, data scientists, and clinical researchers across multiple Austrian hospitals in multicenter studies.
Prof. Hasan BULUT is a full-time faculty member at Ege University's Faculty of Computer and Information Sciences, Department of Computer Engineering. His primary research focuses on software engineering, parallel algorithms, computer networks, artificial intelligence, and algorithm design. He has contributed to fields like distributed systems, data structures, and bioinformatics through innovative algorithmic solutions. His academic work spans over two decades, with notable contributions to machine learning applications in energy forecasting, DNA sequence analysis, and cloud computing optimization. Key areas of expertise include hybrid machine translation models, real-time data clustering, and optimization techniques for computational problems. Prof. BULUT's recent publications emphasize interdisciplinary approaches, combining deep learning with traditional methods to solve challenges in healthcare informatics, financial prediction, and bioengineering. His work on network slicing techniques for 5G and beyond networks highlights cutting-edge contributions to modern communication systems. Despite an extensive publication record and collaborations within Ege University, no formal scientific awards or grant information is explicitly mentioned in the provided texts. His academic career includes supervising numerous research projects but specific student advisee details are not documented here.
Constantino Reyes-Aldasoro is a Senior Lecturer in Biomedical Image Analysis at the Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London. His research focuses on the analysis, interpretation, and visualization of biomedical data, particularly in the context of cancer, inflammation, and neurodegenerative diseases. PhD in Computer Science – University of Warwick (2004) MSc in Electrical Engineering – Imperial College London (1994) Bachelor’s in Mechanical and Electrical Engineering – Universidad Nacional Autónoma de México (1993) His research spans image analysis, machine learning, and computational modeling applied to biomedical imaging, with emphasis on electron microscopy, histopathology, and radiology. He has developed algorithms for cell segmentation, vessel tracing, and tumor microenvironment analysis, contributing significantly to open-source tools in the field. His recent publications show a strong trend toward integrating deep learning with traditional image analysis, especially in cancer diagnostics and Alzheimer’s disease assessment. He has also explored topological data analysis and persistent homology for evaluating dataset consistency in colorectal cancer research. Senior Member, IEEE Member Level 1, Sistema Nacional de Investigadores CONACYT (Mexico) He has supervised multiple PhD students in areas such as HeLa cell analysis, coronary plaque detection, and Alzheimer’s imaging. He has secured grants from the Leverhulme Trust, Australian Research Council, and Cancer Research UK. He is an academic editor for journals including PLOS ONE and Journal of Imaging , and has chaired conferences like MIUA and BMVA. He is part of the giCentre research group at City, and actively promotes interdisciplinary collaboration in AI for healthcare.
Anja Belz is a Professor at Dublin City University's School of Computing, specializing in Natural Language Processing research. She leads the Natural Language Processing Research Group and has established herself as a leading expert in human evaluation methodologies, reproducibility in NLP, and data-to-text generation systems. Her work bridges theoretical research and practical applications with significant contributions to medical text generation and evaluation standards. Her research interests center on creating robust evaluation frameworks for NLP systems, with particular focus on human evaluation methodologies, reproducibility assessment, and quality criteria standardization. She has pioneered work on the Human Evaluation Data Sheet (HEDS) and the QCET (Quality Criteria for Evaluation Taxonomy), addressing critical gaps in evaluation comparability across NLP research. Her work on reproducibility spans multiple shared tasks (ReproNLP, ReproGen) that have become benchmarks in the field, examining how different experimental conditions affect evaluation outcomes. Analysis of her recent publications reveals a clear research trajectory focused on making NLP evaluation more rigorous, transparent, and comparable. Her work increasingly incorporates large language models while maintaining critical scrutiny of their evaluation methodologies. She has made significant contributions to understanding when LLM-based evaluation correlates with human judgments, and has developed frameworks for assessing the reproducibility of NLP evaluation results in quantified terms. Professor Belz has organized numerous workshops and shared tasks focused on human evaluation and reproducibility in NLP, including multiple ReproNLP shared tasks that have attracted international participation. Her research has been consistently published in top-tier NLP conferences including ACL, EMNLP, and INLG, with a strong emphasis on methodological rigor and practical applicability to real-world NLP evaluation challenges. She has also contributed significantly to NLP research in under-resourced languages, particularly Irish, Welsh, Breton, and Maltese. Her work on medical text generation has practical implications for healthcare applications, particularly in automating clinical documentation and systematic reviews. She has developed methods for biomedical synthesis generation that could significantly reduce the time and cost of keeping medical practitioners updated with research. Her research on consultation checklists aims to standardize the human evaluation of medical note generation systems, addressing critical challenges in clinical safety and quality assessment.
Prof. Robert Burduk is a full-time professor at the Faculty of Information and Communication Technology , Wrocław University of Science and Technology , specializing in artificial intelligence and machine learning. His research focuses on supervised classification, ensemble learning, and decision support systems with applications in photovoltaic energy forecasting, insect rearing monitoring, and software bug assignment. Key research areas: Artificial Intelligence, Machine Learning, Business Intelligence Department: Systems and Computer Networks Recent Publications (2024-2023): Burduk's work addresses classifier fusion, photovoltaic power optimization, and agricultural monitoring using advanced AI techniques like convolutional neural networks and domain shift detection. Notable themes include ensemble learning strategies, energy system modeling, and interdisciplinary applications.
Colum MacKinnon is a Professor in the Department of Neurology at the University of Minnesota Medical School . His research is centered on motor control, Parkinson's disease, and neurodegenerative disorders, with a focus on gait abnormalities, deep brain stimulation (DBS), and transcranial magnetic stimulation (TMS). He leads multiple NIH-funded research projects and is actively involved in clinical and translational neuroscience. University: University of Minnesota School: Medical School Department: Department of Neurology Academic Rank: Professor His research interests include Parkinson's disease, motor control, neuroplasticity, freezing of gait, proprioception, and neuromodulation techniques such as DBS and TMS. He investigates the neural mechanisms underlying motor signs in neurodegeneration and develops technologies for monitoring and treating gait disorders. The recent publications highlight a strong trajectory in clinical neuroscience, particularly in understanding motor rigidity, gait initiation, and the application of wearable monitoring systems. His work increasingly integrates large-scale clinical trials and multi-institutional collaborations, such as the North American Prodromal Synucleinopathy (NAPS) Consortium. Scientific Awards and Recognitions: No specific awards mentioned in the provided text. Advising and Grants: Dr. MacKinnon serves as Principal Investigator (PI) on several NIH-funded projects, including: Neuroplasticity and Parkinsonian Motor Signs in iRBD Study in Parkinson Disease of Exercise Phase 3 (SPARX3) Continuous Wearable Monitor for Freezing of Gait Sleep-specific DBS therapy in Parkinson's disease (as Co-Investigator) These grants reflect his leadership in clinical trials, neurotechnology development, and pathophysiological research in Parkinson's disease. Labs and Research Teams: He collaborates with multidisciplinary teams across institutions, including Northwestern University and the NIH. His work is embedded in large consortia and involves engineers, neurologists, neurosurgeons, and data scientists focused on improving outcomes in movement disorders.
Dr. Adarsh Valoor is a Research Fellow in AI For Good at the University of Southampton, working within the Agents, Interaction and Complexity Group in the Department of Electronics and Computer Science. He is part of the RAI UK initiative collaborating with Prof. Gopal Ramchurn. His research focuses on applying artificial intelligence to medical diagnostics with emphasis on interpretability and responsibility. Dr. Valoor completed his Bachelor of Science in Physics from NSS College Ottapalam, University of Calicut, followed by a Master of Science in Computer Science from Central University of Tamil Nadu. He earned his PhD from the National Institute of Technology, Thiruchirapalli (NIT-T), where he worked in the Department of Computer Applications under the supervision of Dr. G. R. Gangadharan. During his doctoral studies, he was a DST-Inspire Fellow. Dr. Valoor's research primarily centers on the application of artificial intelligence in medical diagnostics, with special emphasis on developing interpretable and responsible machine learning models for neurodegenerative diseases. His work aims to bridge the gap between complex AI technologies and practical healthcare applications by enhancing the transparency and ethical considerations of AI tools. His specific interests include explainable AI for medical diagnosis, mental health assessment using AI, and ethical considerations in AI deployment for healthcare. Analysis of Dr. Valoor's publication record reveals a strong focus on applying explainable artificial intelligence to healthcare challenges, particularly in the areas of mental health and neurodegenerative diseases. His work consistently emphasizes transparency in AI decision-making processes, with multiple publications addressing Alzheimer's disease diagnosis, depression detection, and stress monitoring. A notable trend in his research is the development of methods that not only improve diagnostic accuracy but also provide clinicians with understandable reasoning behind AI decisions. DST-Inspire Fellow Dr. Valoor has contributed to significant research in the field of AI for healthcare, particularly through his work on explainable models for medical diagnostics. While specific grant information isn't detailed in the provided text, his position as a PhD Research Fellow in AI For Good suggests involvement in funded research initiatives focused on ethical and transparent AI applications. Dr. Valoor works within the Agents, Interaction and Complexity Group at the University of Southampton, which is part of the broader RAI UK initiative. This research environment focuses on developing responsible and ethical AI systems, with particular emphasis on creating transparent decision-making processes in complex AI applications.
Daqing He is a Professor and Associate Chair of the Department of Informatics and Networked Systems (DINS) at the School of Computing and Information (SCI), University of Pittsburgh. He also holds appointments in the Intelligent Systems Program (ISP) and directs the Information Retrieval, Integration and Synthesis research lab. His academic journey began with a PhD from the University of Edinburgh, establishing a foundation for his distinguished career in information science. Dr. He's research focuses on information retrieval, natural language processing, adaptive web systems, and scholarly data management. His work bridges computer science and information science with practical applications in healthcare, education, and social information access. He has published over 300 articles in prestigious journals and conferences including Journal of the Association for Information Science and Technology, Information Processing and Management, and ACM SIGIR proceedings. His recent publications demonstrate a clear trajectory toward integrating advanced AI techniques with healthcare applications, particularly in clinical information retrieval, patient-centered recommender systems, and biomedical text processing. His work increasingly combines retrieval-augmented generation, large language models, and personalized health information systems to address real-world challenges in healthcare information access. ACM SIGIR CHIIR 2023 Best Short Paper Award ACM SIGIR CHIIR 2019 Best Poster Award iConference 2017 Best Poster Award ACM SIGIR CHIIR 2017 Best Student Paper Award iConference 2013 Best Paper Award Honorable Mention Dr. He has successfully mentored numerous PhD students who have become active researchers in information science. His research has been supported by significant grants from NIH, NSF, Amazon Research Award, and the University of Pittsburgh, including the $1.48 million NIH NLM-funded HELPeR project developing a personalized health information access system for patients. His lab maintains strong collaborations with healthcare institutions and technology companies to translate research into practical applications. His Information Retrieval, Integration and Synthesis lab brings together computer scientists, information scientists, and healthcare professionals to develop innovative solutions for information access challenges. Current projects focus on health information systems, clinical abbreviation resolution, and personalized learning technologies, reflecting his commitment to applying information science to improve human capabilities in information-rich environments.
Chao Zhang is an Assistant Professor at the School of Computational Science and Engineering within the College of Computing at Georgia Institute of Technology. He holds affiliations with the Institute for Data Engineering and Science (IDEaS) and the Machine Learning Center (ML@GT). His research focuses on making it easier to build domain-customized foundation models and AI agents for task-solving and decision-making, with technical emphasis on data efficiency, computation efficiency, and model robustness. Dr. Zhang's research interests span multiple cutting-edge areas of AI, with particular focus on four main themes: Data-Centric LLMs for adapting language models to target domains through data-efficient methods; LLM Agents & Reasoning for improving language model capabilities through environmental interaction; AI Alignment for ensuring responsible deployment through uncertainty quantification and factuality; and AI for Science for leveraging foundation models in material science, biomedical research, and urban science. His work emphasizes weakly-supervised learning, out-of-distribution generalization, and interpretable machine learning approaches. His publication record shows consistent high-impact contributions across top AI conferences including NeurIPS, ICML, KDD, ACL, and ICLR, with recent work focusing on enhancing LLM capabilities, improving model robustness, and applying AI to scientific discovery. The trend in his publications demonstrates a clear trajectory from foundational machine learning techniques toward increasingly sophisticated applications of large language models and their integration with scientific domains. NSF CAREER Award (2022) Google Faculty Research Award (2020) Amazon AWS Machine Learning Research Award (2020) Facebook Faculty Research Award (2021) Kolon Faculty Fellowship (2021) ACM SIGKDD Dissertation Runner-up Award (2019) Georgia Tech CoC Outstanding Junior Faculty Award (2024) Dr. Zhang actively mentors a large group of graduate students across computer science, machine learning, and computational science programs, with alumni securing positions at leading tech companies and research institutions. His research has been generously supported by NSF grants (IIS CAREER-2144338, IIS-2106961, IIS-2008334), ONR MURI funding, and industry partnerships with Kolon, HomeDepot, ADP, and Adobe. His lab focuses on developing practical AI systems that address real-world challenges while advancing fundamental understanding of machine learning principles. Dr. Zhang leads research efforts in the Georgia Tech AI ecosystem that bridge theoretical advances with practical applications, particularly in scientific domains. His group develops methods for data-efficient LLM adaptation, LLM agent reasoning capabilities, uncertainty quantification, and domain-specific foundation models, with active collaborations across material science, biomedical research, and urban science domains.
Greeshma Agasthya is an Assistant Professor at the Nuclear & Radiological Engineering and Medical Physics Program within the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. She leads the Computational Medical Physics Laboratory and holds an adjunct appointment in the Radiation Oncology Department at Emory University . Doctorate in Biomedical Engineering from Duke University Postdoctoral training at Emory University's Winship Cancer Institute Her research focuses on multiscale digital twins for personalized radiation dosimetry, AI frameworks for cancer surveillance, and computational modeling of radiation protocols. Key areas include: Radiation dosimetry for imaging, therapy, and theranostics Medical imaging systems (neutron imaging, CT, tomosynthesis) Machine learning for clinical decision support Interdisciplinary collaboration across medical physics, radiology, and computer engineering Recent publications highlight her work on: Machine learning models for pediatric anxiety detection Temporal semantic drift in clinical terminology Dosimetric impacts of radiopharmaceutical extravasation Environmental exposure analysis for lung cancer risk Chromosome conformation-based radiosensitivity modeling She secured a DOE-BER grant for "Bridging the gap between low dose exposures and emergent physiology" and co-founded a Woodruff Innovation Nexus seed grant for multi organ-on-a-chip development in radiopharmaceutical therapy.
Steven S. Gross is Professor of Pharmacology at Weill Cornell Medicine and a key faculty member in the Graduate School of Medical Sciences. He leads a research laboratory focused on nitric oxide (NO) signaling and directs the Mass Spectrometry Core Facility, underscoring his dual role in research and institutional infrastructure. His work bridges pharmacology, biochemistry, and cellular signaling, with significant contributions to understanding NO in vascular regulation and disease. Dr. Gross earned his Ph.D. in Biomedical Science from Mount Sinai School of Medicine. His research interests include nitric oxide signaling, molecular pharmacology, cellular regulation, vascular biology, and metabolic pathways. His lab investigates the synthesis and action of NO, its role in septic shock, and the development of NO-based therapeutics, leading to the founding of ArgiNOx Inc. His recent publications reveal a strong trend in cancer metabolism, redox signaling, and gene-environment interactions, with high-impact work on vitamin C in KRAS-mutant cancers, fructose and tumor growth, and epigenetic regulation by TET1. These studies span molecular oncology, metabolomics, and bioinformatics, often involving interdisciplinary collaborations. Active member of NIH Study Sections Founder and Board Director, Nitric Oxide Society Author of over 90 research papers and 40 book chapters Dr. Gross actively mentors graduate students, including Tal Nuriel, Alex Hansler, Pamela Wille, Qiuying Chen, and Yuliang Ma. His lab is central to research on NO biology and mass spectrometry applications. He has no listed scientific awards in the provided texts, but his sustained publication record and leadership roles indicate significant scientific impact.
Juhee Bae is a Senior Lecturer in the Department of Information Technology at the University of Skövde, Sweden. She is affiliated with the AI research group, focusing on machine learning, visual analytics, and explainable AI. Her work bridges research and education, with courses in advanced data science and experience in teaching coordination. Ph.D. in Computer Science, North Carolina State University Merited Teaching Award (2023) Mobility Grant for Belgian University Collaboration (2020) Her research spans machine learning , visual analytics , and explainable AI , with applications in Steel industry process optimization Climate-adaptive water management Migration intention forecasting Smart production logistics Wearable biosensor analysis Recent publications highlight predictive models for weather-driven migration, causal discovery frameworks, and interactive data mining techniques. She contributes to editorial boards and conference organization. Juhee Bae serves as course coordinator for Explainable AI and has been active in international collaborations. Scientific awards include 2023 Merited Teaching Award 2020 Mobility Grant Her projects STRATUS (AI for climate adaptation), INSITE-X (steel industry AI), and Understanding Human Migration demonstrate practical AI applications. She works with interdisciplinary teams and has presented talks on predictive machine learning and explainable AI.
Jie Tang is a Professor at the Department of Computer Science, Tsinghua University , and a Fellow of ACM, AAAI, IEEE . His research focuses on Artificial General Intelligence (AGI) , with significant contributions to large pre-trained models like GLM-130B, ChatGLM, CogView, CogVideo, and CodeGeex. Research Trends : Jie Tang's work spans AGI development with human-like reasoning Graph Neural Networks for network representation Social network mining and influence modeling Academic knowledge graph construction (AMiner system) Advancing foundation models for cross-modal tasks Scientific Awards : SIGKDD Test-of-Time Award SIGKDD Service Award NSFC Distinguished Young Scholar 2nd National Award for Science & Technology Advising and Grants : He mentors highly-motivated students and postdocs in AGI research. His work has received extensive funding and recognition, including over 400 publications in top conferences (IJCAI, AAAI, NeurIPS, KDD) and journals (TPAMI, TKDE).
