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Reza Jafari is a Collegiate Associate Professor of Computer Science at the Virginia Tech Innovation Campus. He previously served as faculty and program director of the ECE program at East Coast Polytechnic University and as a professional lecturer in the data science program at George Washington University. His research focuses on data mining, statistical modeling, machine learning/deep learning for prediction/classification, recurrent neural networks, and stability analysis of neural-based systems. Education: Ph.D., Computer and Electrical Engineering, Oklahoma State University (2012) M.S., Applied Mathematics, Oklahoma State University (2011) M.S., Mechatronics, American University of Sharjah (2005) B.S., Engineering, University of Tehran (2001) Research Interests: Jafari's work spans data analytics, statistical forecasting, machine learning, and neural networks. He emphasizes practical applications, such as temporal forecasting using deep learning and stability analysis of control systems. His contributions include advancements in recurrent neural networks and their hardware implementations. Publications: His recent work includes comparative studies of deep learning architectures for temporal forecasting (2025), precision robotics control (2024), and stability analysis of neural network-based controllers (2023). These studies highlight a trend toward integrating traditional methods with modern machine learning techniques. Awards: Innovator of the Year Award (2014) Innovator of the Year Award (2016) Professional Roles: Jafari has served as a peer reviewer for IEEE Transactions , IJCNN , and Automatic Control . His research bridges theoretical foundations and applied systems, with a focus on interdisciplinary solutions in data science and engineering.

• Data mining and data analytics
• Statistical modeling & forecast
• Machine learning and deep learning for prediction & classification
• +2 more

Dr. Dilek Hakkani-Tür is a Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. Her primary research focuses on language understanding, spoken language processing, and dialogue systems, with a recent emphasis on large language models (LLMs) and their applications in conversational AI. She has led collaborative efforts in dialog system technology challenges, including DSTC9, and has published extensively in top venues such as ACL, EMNLP, and IEEE SLT. Education: Implied via affiliation and research focus (PhD in Computer Science or related field) Her work bridges theoretical advancements and practical implementations in areas like dialogue state tracking, user preference alignment, and code debugging via LLMs. Collaborations span industry and academia, addressing challenges in human-AI interaction and multimodal dialogue systems. Recent projects include leveraging LLMs for individual preference alignment in conversations, confidence estimation in dialogue systems, and Socratic code debugging frameworks. These contributions highlight her role in advancing conversational AI's technical and applied dimensions.

• Language Understanding
• Spoken Language Processing
• Dialogue Systems
• +3 more

Prof. Dr. Alexandre de Spindler is a Professor of Information Systems at the Zurich University of Applied Sciences (ZHAW) within the School of Management and Law . He co-leads the Center for Information Systems and Technologies and focuses on application development frameworks for conversational interactions with information systems, leveraging multimodal foundation models while enhancing their controllability and reliability. His work extends to improving social skills in digital interactions and teaching agile requirements engineering. University: Zurich University of Applied Sciences School: School of Management and Law Department: Institute of Business Information Technology Academic Rank: Professor His research explores conversational systems , generative AI , and stateful prompt orchestration for complex interactions, particularly in healthcare and financial sectors. Recent work includes frameworks like PROMISE for model-driven prompt management and Science Fiction Prototyping for responsible AI innovation. Teaching contributions span data science programs (BSc, MSc, CAS) covering machine learning, generative AI, and full-stack application validation. He collaborates across disciplines including gerontology and forensic phonetics, with a focus on applying AI to societal challenges.

• Conversational Agents
• Generative AI
• Information Systems
• +8 more

Xuefu Zhou is an Associate Professor in the Department of Electrical Engineering at the University of Cincinnati, where he joined as an Assistant Professor in 2005 and was promoted in 2010. He holds an M.S. and Ph.D. in Electrical Engineering from the same university. Prior to academia, he worked in industry from 1995 to 2000 as an R&D Engineer, Senior Engineer, and Project Manager, specializing in distributed computer control systems and real-time embedded systems. His research interests span Deep Learning, Wireless Communications, IoT, Biomedical Signal Processing (e.g., ECG analysis), and Non-Destructive Evaluation. He directs the Wireless and Mobile Communication Lab (WMCL) and oversees the Master of Engineering (MENG) Graduate Program. Zhou has received notable awards including the 2011 and 2012 William E. Restemeyer Teaching Excellence Award, and the 2013 and 2018 CEAS Master Educator Award. He is a Senior Member of IEEE. He has led or collaborated on grants totaling over $1M, focusing on topics like ECG screening for cardiomyopathy, smart transportation systems, and edge AI classifiers. Recent projects include collaborations with Procter & Gamble, Medical Interface Solutions, and the Federal Highway Administration.

• Deep Learning
• Wireless Communications and Networking
• Internet of Things (IoT)
• +4 more

Albert Gatt is a Professor of Natural Language Processing at Utrecht University's Department of Information and Computing Sciences, where he also serves as Programme Director for AI & Data Science. He holds an Associate Professor position (on leave) at the University of Malta's Institute of Linguistics and Language Technology. His research focuses on Natural Language Generation (NLG), multimodal models, and under-resourced language support, particularly for Maltese. He leads projects like NL4XAI and MASRI, addressing challenges in explainable AI and speech recognition. Education: Advanced degrees in computational linguistics and AI (not explicitly detailed in text). Key Projects: Multilingual NLG, Vision-Language benchmarks, Maltese ASR, and NLP evaluation methodologies. Research interests span data-to-text generation, vision-language interfaces, and evaluation practices. His work bridges computational linguistics with cognitive science, emphasizing human-AI collaboration. Notable contributions include the TUNA corpus, SimpleNLG toolkit, and foundational studies on referring expression generation. Publications (2025-2024) explore robust fine-tuning, LLM evaluation, and visual-linguistic grounding. Collaborations span academia and industry, addressing ethical AI and language equity. Supervises a global team of researchers and PhD students across multiple institutions, fostering innovation in NLG, multimodal AI, and Maltese language tech.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +5 more

Robyn Choi is a Senior Lecturer in Audiology at the School of Human Sciences, The University of Western Australia, and an honorary researcher at the Telethon Kids Institute. She also serves as Senior Audiologist at Fiona Stanley Hospital, demonstrating strong connections between academic and clinical practice. Her educational background includes BSc, MAudSt, MAudA (CCP), and PhD qualifications, with Fellowship of the Higher Education Academy (FHEA). She coordinates and teaches in the Master of Clinical Audiology and Master of Hearing Sciences programs, with specific unit coordination responsibilities across multiple courses. PHYL5502 Basic Clinical Audiology Part 1 PHYL5512 Basic Clinical Audiology Part 2 PHYL5513 Speech, Language and Communication PHYL5515 Hearing Devices and Adult Aural Rehabilitation PHYL5610 Advanced Hearing Aids and Rehabilitation Part 1 PHYL5611 Advanced Clinical Audiology Part 2 PHYL5612 Community and Workplace Audiology PHYL5613 Audiology Practice Management PHYL5615 Advanced Clinical Audiology Part 1 PHYL5617 Advanced Hearing Aids and Rehabilitation Part 2 Dr. Choi's research spans several interconnected areas focused on improving pediatric hearing health outcomes. Her work examines the relationship between hearing loss, auditory processing disorders, and learning difficulties in school-aged children. She investigates novel diagnostic approaches for middle ear disorders using wideband absorbance technology and develops tele-audiology solutions to increase access to services. Her educational research focuses on simulation-based clinical training methods for audiology students. Her recent publications demonstrate consistent focus on pediatric audiology, with emphasis on learning difficulties, middle ear assessment, and telehealth applications. The research shows progression from foundational epidemiological work (like the 2017 systematic review on hearing loss rates) toward more specialized diagnostic and therapeutic interventions. School of Human Sciences Early-Career Research Award (2022) Citation for Outstanding Contribution to Student Learning (2021) Australian Research Training Program Scholarship (2015) QIMR Honours & Coursework Masters Scholarship (2013) Ko Awatea Scholarship (2009) Dr. Choi supervises Higher Degree Research students in the Master of Clinical Audiology program and PhD candidates. Her current research projects include evaluating auditory function in children with learning difficulties, exploring rehabilitative methods for auditory processing disorders, assessing young children's auditory processing abilities, and investigating multisensory signals for safety alerts. She has secured significant funding from sources including Channel 7 Telethon Trust, Medical Research Future Fund, and Defence Science Centre.

• Audiology
• Middle Ear Assessment
• Auditory Processing Disorder
• +7 more

Josiah Wang is a Senior Teaching Fellow in the Department of Computing at Imperial College London, where he also serves as the degree coordinator and admissions tutor for the MSc Computing program. He teaches Python Programming and Introductory Machine Learning courses. Previously, he held postdoctoral research positions at Imperial College London and the University of Sheffield, focusing on Artificial Intelligence with specializations in Computer Vision and Natural Language Processing. He earned his PhD in Computer Science from the University of Leeds (2013), advised by Katja Markert and Mark Everingham. His research explored cross-modal learning, including pioneering work on learning object recognition from textual descriptions and multimodal machine translation. Notable contributions include the MultiSubs dataset and methods for phrase localization without paired training examples. Education: PhD in Computer Science, University of Leeds (2013) MSc in Computing, University of Leeds (2007), supervised by David Hogg Research Interests: Josiah's work bridges AI and human cognition, emphasizing systems that learn from limited data and integrate multiple modalities. His contributions span visually descriptive language annotation, image captioning, and multimodal translation. Though no longer active in active research, his legacy includes foundational papers on object recognition from text (BMVC 2009) and unsupervised phrase localization (ICCV 2019). Grants & Projects: MultiMT and MMVC projects (Imperial College London) CHIST-ERA ViSen project (University of Sheffield) Labs & Teams: Collaborated extensively with interdisciplinary teams at Imperial, Sheffield, Lyon, and Barcelona, including work on multimodal machine translation and visual grounding systems.

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +3 more

Dr. D. Kimbrough Oller is a Professor and Plough Chair of Excellence at the University of Memphis's School of Communication Sciences and Disorders. He holds affiliations with the Konrad Lorenz Institute for Evolution and Cognition Research (External Faculty Member since 2008) and the Scientific Advisory Board of the LENA Foundation (since 2005). His research focuses on infant vocal development, bilingualism, child phonology, and the evolution of language. Education: Ph.D. in Psycholinguistics (University of Texas at Austin, 1971); B.A. in Romance Languages (University of California, Berkeley, 1968). Experience: Directed projects on vocal development and language evolution since the 1970s; NIH-funded research totaling over $6 million. Research Interests: Bridging infant vocal behavior, evolutionary linguistics, and clinical applications in speech disorders (e.g., autism, Down syndrome). Key topics include the emergence of speech capacity and bilingual language acquisition in children. Grants: Recent funding includes NIH awards for 'Infant Vocal Communication: Typical Development and Autism Risk' ($3.2 million, 2015–2020) and 'Vocal Exploration and Interaction in the Emergence of Speech' ($2.9 million, 2011–2016). Awards: Notable honors include the Honors of the American Speech-Language-Hearing Association (2013) and the University of Memphis Presidential Lifetime Achievement Award (2018). Elected as a AAAS Fellow in 2023. Labs/Teams: Leads the Infant Vocal Communication Lab and collaborates with interdisciplinary teams on projects involving neurodevelopmental disorders and language evolution.

• Infant vocal development
• Bilingualism
• Child phonology
• +5 more

Albert Qiaochu Jiang serves as a Visiting Research Fellow at the Department of Computer Science and Technology, University of Cambridge. His research integrates machine learning with formal theorem proving, focusing on neural theorem provers and mathematical reasoning systems. He leads the reasoning team at Mistral AI while maintaining academic supervision at Cambridge. His research interests center on machine learning for theorem proving , with specific expertise in neural-symbolic integration, autoformalization, and large language models for mathematical reasoning. His work bridges artificial intelligence with formal verification, developing systems that enhance automated reasoning capabilities through neural networks. Current projects involve improving premise selection for theorem provers, multilingual mathematical formalization, and creating efficient architectures for mathematical language models. Analysis of his recent publications reveals a strong focus on advancing neural theorem proving through innovative architectures like Target-Based Automated Conjecturing and Magistral. His research trajectory shows increasing sophistication in integrating language models with formal verification systems, with significant contributions to datasets like Numinamath and frameworks like Llemma. Key trends include optimizing compute efficiency in proof generation, enhancing multilingual mathematical reasoning, and developing interactive human-AI collaboration systems for formal mathematics. While no scientific awards are currently documented in available sources, his research output demonstrates significant impact in the intersection of AI and formal methods. As leader of Mistral AI's reasoning team, Jiang directs research on neural theorem proving systems while contributing to academic supervision at Cambridge. His work involves substantial industrial-academic collaboration, leveraging resources from both institutional contexts to advance mathematical AI. Current projects focus on creating practical systems for mathematical automation with real-world verification applications. His research operates at the intersection of academia and industry through Mistral AI's reasoning team, where he develops neural theorem proving systems with practical applications in formal verification. This dual affiliation enables rapid translation of theoretical advances into deployable tools for mathematical automation.

• Machine Learning
• Theorem Proving
• Artificial Intelligence
• +3 more

Dr. Jing Zhang is a Lecturer in the School of Computing at the Australian National University (ANU) , within the ANU College of Systems & Society . Previously, he served as a Research Fellow at ANU (2021–2022) and earned his PhD in 2021 under the supervision of Nick Barnes. His academic journey includes a Master’s (2010) and Bachelor’s (2007) from Northwestern Polytechnical University . Education: PhD, Australian National University, 2021 (Supervisor: Nick Barnes) Master’s Degree, Northwestern Polytechnical University, 2010 Bachelor’s Degree, Northwestern Polytechnical University, 2007 Research Interests: Jing Zhang’s research focuses on computer vision and machine learning , with a particular emphasis on generative AI . His work addresses challenges in image, video, and audio generation/editing; explainable model adaptation; out-of-distribution detection; and adversarial attacks/defenses. He explores techniques to enhance model robustness and generalization across diverse domains. Awards & Recognition: CVPR 2020 Paper Award Nominee for “UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders” Teaching & Supervision: Jing Zhang teaches COMP8536: Advanced Topics in Computer Vision (Semester 2, 2024) and ENGN4528/6528: Computer Vision (Semester 1, 2025). He currently supervises 4 PhD students as their primary advisor, focusing on cutting-edge research in computer vision and AI. Professional Contributions: Co-organizer of tutorials on salient object detection (ACCV 2019) and uncertainty estimation (ICCV 2021) Regular reviewer for top venues: CVPR, ICCV, ECCV, TPAMI, IJCV, and others Research Groups: While not explicitly stated, his work is likely affiliated with ANU’s computer vision research groups, focusing on generative AI and robust machine learning systems.

• Computer Vision
• Machine Learning
• Generative AI
• +5 more

Gabriela Ferraro is a Research Fellow at the School of Cybernetics, Australian National University , specializing in Natural Language Processing (NLP) and Computational Linguistics . Her work focuses on multidisciplinary applications for social good, including: Text simplification and language learning technology Mental health detection through language analysis Preservation of endangered languages E-health systems development In 2021, she was awarded the Woman in Science Career Award by the Commonwealth Scientific and Industrial Research Organization (CSIRO). She actively participates in academic leadership as a member of the Australasian Language Technology Association Committee . Her recent research spans: Transfer learning for hate speech detection Continual learning with noisy data Transformer-based semantic parsing Context-aware neural optimization Lexical taxonomy construction Scientific Awards : Woman in Science Career Award (CSIRO) Gabriela combines technical expertise with ethical priorities, leading projects that intersect NLP, healthcare, and environmental monitoring. She also emphasizes teaching and student supervision as integral parts of her academic practice.

• Natural Language Processing (NLP)
• Computational Linguistics
• Machine Learning for Text Processing
• +6 more

Scott Burris is a Professor of Law at Temple Law School and Professor in Temple's School of Public Health, where he directs the Center for Public Health Law Research. He has been a faculty member at Temple since 1991 and is internationally recognized for his pioneering work in public health law, particularly in legal epidemiology, policy surveillance, and the intersection of law with HIV/AIDS policy and substance abuse. Professor Burris received his J.D. from Yale Law School and his B.A. from Washington University in St. Louis. His career began during the early days of the HIV/AIDS epidemic, where he served as an attorney at the American Civil Liberties Union, lobbying and litigating on behalf of people with HIV. He edited the first systematic legal analysis of HIV in the United States, "AIDS and the Law: A Guide for the Public" (Yale University Press, 1987). Burris's research focuses on how law influences public health and health behavior, with particular emphasis on developing theory and methods for effective local health governance. His work spans international public health law, legal epidemiology, policy surveillance, substance abuse policy, and housing law. He has made significant contributions to understanding the impact of law on HIV prevention, opioid overdose prevention, and pandemic response. His recent work increasingly examines methodological rigor in legal epidemiology, the application of artificial intelligence to legal analysis, and the relationship between housing law and health equity. His extensive publication record includes over 200 books, book chapters, articles, and reports. Recent publications demonstrate his continued leadership in advancing the science of legal epidemiology, with particular attention to methodology, policy surveillance systems, and innovative applications of technology to legal analysis. American Public Health Association Law Section Lifetime Achievement Award (2014) Jay Healey Health Law Professors award (2018) Professor Burris has served as a consultant to numerous international organizations including the United Nations Development Programme, the World Health Organization, and the United Nations Office on Drugs and Crime. He founded the Public Health Law Research Program for the Robert Wood Johnson Foundation in 2009, which has supported over 80 empirical studies of the impact of law on health. His research has been funded by major organizations including the Robert Wood Johnson Foundation, the Open Society Institute, the National Institutes of Health, the Bill and Melinda Gates Foundation, and the Centers for Disease Control and Prevention. He is the founder of Legal Science, LLC, a private company dedicated to improving access to legal information and supporting policy surveillance practice. He also serves as an advisor to several international institutions including the Tsinghua University AIDS Institute, the Shanghai Academy of Social Sciences Research Center for HIV/AIDS Public Policy, and the Program in Bioethics at Monash University. His work bridges academic research, practical application, and policy development in the field of public health law.

• Public Health Law
• International Public Health Law
• Legal Epidemiology
• +7 more

Prof. Tim Fingscheidt is a Professor at the Technical University of Braunschweig, heading the Signal Processing and Machine Learning Group within the Institute for Communications Technology. His career spans academia and industry, including roles at AT&T Labs (USA), Siemens, and leadership in research teams. He specializes in speech processing, audio technologies, and communication systems quality assessment. Education : 1987-1993: Dipl.-Ing. in Electrical Engineering (RWTH Aachen University, with distinction) 1998: PhD (RWTH Aachen University, with distinction) Research Interests : His work focuses on signal processing fundamentals, machine learning applications in audio/speech systems, error concealment in mobile transmissions, and automotive hands-free system standards. He actively contributes to ITU-T recommendations and chairs ITG committees on speech communication and audiotechnology. Awards : Received prestigious recognition including the IEEE Fellow (2025), ITG Fellow (2023), and multiple ITG/VDE prizes. Professional Activities : Leadership roles in EURASIP, VDE ITG, and IEEE. Standardization expertise in automotive communication systems (ITU-T P.1100/P.1110). Labs & Teams : Leads the Signal Processing and Machine Learning Group, collaborating on cutting-edge projects in audio/speech technologies and machine learning.

• Signal Processing
• Machine Learning
• Speech Processing
• +6 more

Kenny Joseph is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Associate Director of the AI and Society Institute for Artificial Intelligence and Data Science. PhD in Societal Computing from Carnegie Mellon University (2016) MS in Societal Computing from Carnegie Mellon University (2012) BS in Computer Science from Carnegie Mellon University (2010) His research focuses on computational social science , examining stereotypes and prejudice dynamics , social inequality through computational tools , and gender disparities in organizations . He develops machine learning frameworks for neighborhood change prediction and predictive models for child welfare systems , with work featured in the New York Times . Scientific contributions include: UB Exceptional Scholar—Young Investigator Award (2021) He advises students Yuhao Du (Meta data scientist), Jason Yan (Michigan Ph.D. student), Arjunil Pathak (Amazon researcher), and Navid Madani (former Ph.D. student). His Computation and Equity Lab (cubelab) produces interdisciplinary work spanning social media analysis, urban equity, and computational methods for marginalized communities.

• Computational Social Science
• Social Media Analysis
• Machine Learning for Social Good
• +6 more

Zhiyao Duan is a Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Rochester, with affiliations to the Goergen Institute for Data Science. He holds a B.S. and M.S. from Tsinghua University and a Ph.D. from Northwestern University. Primary: Department of Electrical and Computer Engineering Secondary: Department of Computer Science Affiliated: Goergen Institute for Data Science His research focuses on Computer Audition, exploring audio-visual scene understanding, source separation, and human-computer collaborative music systems. He has received prestigious awards including an NSF CAREER Award and best paper honors at SMC 2017. His work is funded by NSF, NIH, Adobe, Kwai, and others. Recent publications emphasize audio-visual integration, speech synthesis, and deep learning architectures. He actively participates in academic service, serving on editorial boards and technical committees. The Audio Information Research (AIR) lab, which he leads, has hosted numerous students and collaborated with institutions like Northwestern University, Tsinghua University, and Stanford University. Scientific Awards: NSF CAREER Award Best Paper Award at SMC 2017 Best Paper Nomination at ISMIR 2017 Grants: NSF BIGDATA Grant No. 1741472 NSF Grant No. 1846174 for Human-Computer Collaborative Music Making University of Rochester AR/VR and Health Analytics Grants Students: Yapeng Tian Lele Chen Sefik Emre Eskimez Bochen Li Rui Lu Yujia Yan

• Computer Audition
• Audio-Visual Scene Understanding
• Machine Learning
• +3 more