Dr. Daphné Chopard is a Researcher affiliated with the Professorship for Medical Data Science at ETH Zürich. Her work focuses on advancing medical data science through machine learning, clinical informatics, and multimodal learning applications in healthcare. She specializes in areas such as time-series analysis in critical care, generative models for medical data, and natural language processing for clinical texts. Her research emphasizes improving healthcare outcomes through innovative data-driven approaches, including projects like the SwissPedHealth pediatric data network and foundational work on multimodal variational autoencoders. Dr. Chopard’s contributions span clinical decision support systems, adverse event detection in trials, and acronym disambiguation in medical narratives. Her recent projects include studies on ventilation protocols in pediatric critical care and weakly-supervised learning applied to medical imaging datasets like MIMIC-CXR. She collaborates on initiatives to enhance representation learning in multimodal healthcare contexts, reflecting her commitment to bridging AI advancements with practical clinical applications.
Olga Vechtomova is a Professor at the University of Waterloo, affiliated with the Information Systems research group and specializing in Search Engines and Natural Language Processing. Her work bridges computational creativity, multimodal systems, and AI-driven text generation. She leads projects like LyricJam , a real-time lyric generation system for live music, and explores applications in dynamic story generation, hate speech detection, and low-resource summarization. Her research emphasizes ethical AI, creative technologies, and leveraging large language models for diverse tasks. Research interests include natural language processing, machine learning, and multimodal interaction. Recent work focuses on artistic inspiration modeling, stylized text generation, and improving NLP efficiency through semi-supervised learning and distillation techniques. Her contributions span over 60 papers since 2000, with a strong emphasis on foundational NLP challenges and real-world applications. She collaborates on systems like Promptmix for model distillation and LyricJam sonic for music-audio lyric generation.
Dongming Xu is an Associate Professor in Business Information Systems at the University of Queensland Business School. She holds a PhD from the City University of Hong Kong in Information Systems and has established herself as a prominent researcher in the field of information systems with over 100 publications in top-tier journals and conference proceedings. Her educational background includes a PhD from City University of Hong Kong in Information Systems, though specific details about earlier degrees are not provided in the available text. Dr. Xu's research focuses on the confluence of information technology use and innovation, with particular emphasis on IT entrepreneurship, social media applications in business contexts, and business intelligence systems. Her work explores how information systems influence society and business performance, with applications spanning disaster management, eFinance, eHealth, and knowledge management. She combines theoretical model building with laboratory and field experiments, often developing prototype systems to validate her research. Her publication record demonstrates consistent high-quality output across multiple domains of information systems research, with recent work emphasizing digital disruption, platform ecosystems, social media in disasters, healthcare technology, and micro-learning applications. Her research shows a clear trajectory from foundational work on intelligent agents and decision support systems toward contemporary topics in digital transformation and platform-based innovation. Associate Editor, Information & Management Associate Editor, Journal of Electronic Commerce Research Associate Editor, Australasian Journal of Information Systems Dr. Xu has supervised numerous PhD students to completion, with research topics spanning digital disruption, IT startup development, social media in disasters, conceptual modeling, and environmental management. She has received multiple research grants, including current funding for 'Empowering Australia's Visual Arts via Creative Blockchain Opportunities' (2023-2026) and past projects on 'Smart micro learning with open education resources' (2018-2022). Her research has been supported by various agencies including the Hong Kong Government Research Grant Council, The National Natural Science Foundation of China, The University of Queensland, and City University of Hong Kong. She leads research in several key areas including IT entrepreneurship, business intelligence systems, and social media applications across multiple domains. Her work often involves developing innovative systems such as web-service-agent-based family wealth management systems, decision support systems for securities exception management, and knowledge management systems for disaster management.
Jordan Boyd-Graber is a Full Professor at the University of Maryland, affiliated with the Department of Computer Science and the University of Maryland Institute for Advanced Computer Studies (UMIACS). His research focuses on machine learning, natural language processing, computational linguistics, topic models, and question answering. He has made significant contributions to the development of interactive topic modeling systems and evaluations of large language models. His work often bridges theoretical advancements with practical applications, such as improving human-AI collaboration and enhancing the interpretability of machine learning models. Boyd-Graber’s research also addresses challenges in adversarial examples, calibration of models, and the ethical implications of AI systems. His recent publications span cutting-edge topics like evaluating topic models through ProxAnn, mitigating hallucinations in vision-language models, and exploring human-AI complementarity in question answering. He collaborates extensively with researchers in computer science, linguistics, and social sciences to advance interdisciplinary applications of NLP and ML.
Mariya Toneva is a tenure-track faculty member at the Max Planck Institute for Software Systems , conducting groundbreaking research at the intersection of Machine Learning , Natural Language Processing , and Neuroscience . She leads the Bridging AI and Neuroscience (BrAIN) group , focusing on computational models that align AI systems with human brain processes. Her work aims to enhance both AI capabilities and neuroscience understanding through this cross-disciplinary approach. Actively recruiting postdocs, PhDs, and research interns in areas like code/text representation, brain-AI alignment, and neuroimaging data analysis Collaborator on NIH-funded projects using fMRI and neuropixel data Research Themes : Her group explores neural mechanisms of language processing, event segmentation in narratives, memory reactivation via music, and effective human-AI collaboration frameworks. Key methods include LLM analysis, cross-modal similarity metrics, and naturalistic task-based fMRI studies. Key Publications (2024-2025): Brain-tuned speech models (INTERSPEECH 2025) Cognitive event boundaries in LLMs (Behavioral Research Methods 2025) Music-induced memory reactivation (biorxiv 2024) LLM-brain alignment reasons (EMNLP 2024) Advising : Mentors PhD candidates Omer Moussa (speech processing), Camila Kolling (representational similarity), and Gabriele Merlin (LLM alignment). Collaborates with institutions like MIT, NYU, and ETH Zurich.
Dr. Kate Farrahi is an Associate Professor in the ECS department at the University of Southampton, where she leads research in the Vision, Learning and Control (VLC) Group. Previously, she was a Research Assistant at the Idiap Research Institute and earned her PhD in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her work focuses on the intersection of machine learning and digital health, particularly in developing human sensing methods using vision and wearable technologies. She currently supervises four PhD students in Computer Science and actively accepts new PhD applications. Her research interests span machine learning applications in healthcare, including wearable device analytics, epidemiological modeling via AI, and drug discovery through generative methods. She has been recognized with a Best Paper Award (2022) and contributes to interdisciplinary research groups such as the Institute for Life Sciences and Centre for Machine Intelligence. Her work bridges computational methods with real-world health challenges, emphasizing practical deployment of AI solutions in clinical and public health contexts. Research Groups: Vision, Learning and Control; Institute for Life Sciences; Centre for Health Technologies; Centre for Machine Intelligence Key Collaborations: Cross-disciplinary projects combining computer science with biomedical engineering and public health
Yoshua Bengio is a Full Professor at the Université de Montréal, affiliated with the Department of Computer Science and Operations Research at the Faculty of Arts and Sciences. He is a pioneer of deep learning and a leading figure in AI safety. He co-founded Mila – Quebec Institute of Artificial Intelligence and serves as its scientific director. His work focuses on advancing AI technology while addressing ethical and safety challenges, including AI governance and catastrophic risk mitigation. Education: Ph.D. in Computer Science from McGill University (1991), postdoctoral studies at MIT. Research interests include deep learning, causal inference, AI ethics, and responsible AI development. He contributed to the Montreal Declaration for Responsible AI and leads the International Scientific Report on AI Safety. Recent articles emphasize AI safety frameworks, governance, and technical advancements in machine learning. Awards include the Turing Award (2018), Killam Prize (2019), and recognition as TIME's Most Influential Person (2024). He holds prestigious fellowships and is a member of the UN Scientific Advisory Board for Breakthrough Science and Technology. Affiliations include Mila, IVADO (as founding scientific director), and CIFAR programs. His work bridges academia, industry, and policy to ensure AI benefits humanity while minimizing existential risks.
Emma Mercier is an Associate Professor and Associate Head & Director of Graduate Programs in the Department of Curriculum & Instruction at the University of Illinois, Urbana-Champaign's College of Education. She also holds a secondary appointment in the Department of Educational Psychology, demonstrating her interdisciplinary approach to educational research. Dr. Mercier's research focuses on the relationship between social interaction and learning, with particular emphasis on collaboration and computer-supported collaborative learning (CSCL) in classroom settings. Her work examines how technology influences group interactions and learning, especially through the use of multi-touch tables in classrooms. She investigates between-group and whole-class interactions, device ecologies, teacher tools, and classroom contexts that shape learning opportunities in technology-enhanced environments. Her research spans K-12 and higher education settings, with significant contributions to engineering education and the design of collaborative learning spaces. Analysis of Dr. Mercier's recent publications reveals a strong focus on orchestration tools that support instructors in facilitating collaborative learning, the role of technology (particularly augmented and virtual reality) in collaborative problem solving, and the design of effective collaborative tasks in engineering education. Her work bridges educational theory with practical classroom applications, often employing design-based implementation research methodologies. A notable trend is her increasing focus on machine learning applications to analyze and support collaborative interactions in real-time classroom settings. Dr. Mercier has been actively involved in mentoring graduate students and teaching courses related to educational research methods, child development and technology, and advanced study of education. Her work has involved significant collaboration with researchers across institutions and disciplines, particularly in the fields of educational technology, learning sciences, and engineering education. Her research has been supported through various projects, including the CSTEPS (Collaborative Support Tools for Engineering Problem Solving) initiative, which has developed and evaluated tools to support collaborative learning in engineering classrooms. This work has involved partnerships with teaching assistants, course assistants, and faculty to implement and refine collaborative learning approaches in undergraduate engineering courses.
Dr. Patrick Kung serves as Associate Professor and Associate Department Head for Undergraduate Programs in the Department of Electrical and Computer Engineering at the University of Alabama's College of Engineering. His research spans nanotechnology, quantum computing, and terahertz photonics with significant contributions to metamaterials and optical systems. Research Focus: Dr. Kung specializes in terahertz spectroscopy, polarization-sensitive imaging, and nanoscale material engineering. His work integrates machine learning with optical systems for applications in underwater imaging, quantum networking, and biodegradable polymers. Recent projects include $1 million Department of Energy funding for quantum networking research (2024) and development of materials for slowing light propagation. Publication Trends: His recent publications (2022-2025) demonstrate a clear trajectory toward multimodal sensing systems combining terahertz technology, polarization control, and AI-driven image processing. Key themes include underwater object recognition using single-photon LiDAR, compact drone-compatible imaging platforms, and cryogenic photonic components for quantum applications. The work consistently bridges fundamental nanophotonics with practical engineering solutions. Department of Energy Funding ($1 Million for Quantum Networking Research, 2024) Dr. Kung actively mentors students in EPA-funded water disinfection projects using UV-LED technology and collaborates with industry partners through the Southeast Executives-on-Roster program. His laboratory work focuses on nanowire-based thin films and metamaterial absorbers, with applications in environmental monitoring and quantum communication hardware.
Saud Alhusaini MD PhD is an Assistant Professor of Neurology at the Warren Alpert Medical School of Brown University and serves as a Neurologist/Movement Disorders Specialist at Rhode Island Hospital. His research integrates imaging genomics and multimodal brain imaging approaches to investigate neurological disorders including Parkinson's disease, essential tremor, and epilepsy. He is affiliated with the Carney Institute for Brain Science and collaborates extensively with clinicians, geneticists, electrophysiologists, MRI specialists, neuropsychologists, and data scientists. Education: PhD from the Royal College of Surgeons in Ireland (RCSI) MSc in Neuroscience from Trinity College Dublin MD from University of Dublin, School of Medicine Adult neurology residency at McGill University/Montreal Neurological Institute Clinical research fellowship at Yale School of Medicine Clinical fellowship at Stanford University Medical Center Dr. Alhusaini's research focuses on identifying key endophenotypes and subclinical biomarkers to elucidate the underlying mechanisms of complex neurological conditions. His work spans multiple areas including movement disorders, epilepsy, and brain structure genetics. He has made significant contributions to understanding the genetic architecture of brain structures through his involvement with the ENIGMA consortium, which conducts large-scale collaborative analyses of neuroimaging and genetic data across institutions worldwide. An analysis of his publication record reveals a consistent pattern of high-impact research at the intersection of neurology, genetics, and advanced imaging techniques. His recent work demonstrates particular expertise in Parkinson's disease genetics, epilepsy network analysis, and movement disorder diagnostics. The breadth of his research, spanning from basic genetic mechanisms to clinical applications, highlights his comprehensive approach to understanding neurological disorders. Dr. Alhusaini has received funding from the Rhode Island Research Foundation, Brown Physicians, Inc., and Advance RI-CTR to support his research initiatives. His collaborative approach is evident through his numerous multi-institutional projects and extensive co-author network across Brown University departments including Neurology, Neurosurgery, and Pathology and Laboratory Medicine.
Mehrdad Salehi is a researcher at the Chair of Computer Science Applications in Medicine at the Technical University of Munich (TUM) . His work focuses on the intersection of computer science and medical imaging, with expertise in ultrasound technology, deep learning, and surgical navigation systems. Key research areas include sonification of medical data, 3D ultrasound reconstruction, and machine learning-based segmentation. He has contributed to innovative projects like PRO-TIP calibration phantoms and ColibriDoc autonomous docking systems. His publications highlight trends in acoustic feedback mechanisms, neural radiance fields for medical imaging, and real-time image analysis. He can be reached at mehrdad.salehi@tum.de .
Stéphane Doncieux is a University Professor in Computer Science at Sorbonne University, where he is affiliated with the Institute of Intelligent Systems and Robotics (ISIR), a joint research laboratory with CNRS. Since January 2024, he has served as Director of ISIR, following a term as Deputy Director from 2019 to 2023. He leads the ASIMOV research team and is based at the Pierre and Marie Curie Campus in Paris. His primary research interests lie in cognitive and developmental robotics, with a strong focus on open-ended learning, evolutionary algorithms, and adaptive systems. He investigates how robots can autonomously learn diverse skills through mechanisms such as novelty search, quality-diversity optimization, and intrinsic motivation. His work bridges theoretical foundations in artificial life and practical applications in robotic manipulation, perception, and control. The recent publications highlight a consistent trend in advancing robotic learning under sparse rewards and in open-ended environments. Key themes include quality-diversity optimization for grasping, state representation learning, sim-to-real transfer, and the development of behavioral repertoires. These works are published in high-impact journals such as IEEE Transactions on Robotics, Evolutionary Computation, and Frontiers in Robotics and AI. Coordinator, DREAM FET H2020 project (2015–2018) Principal Investigator, ANR projects on Creative Adaptation by Evolution, Learning Movement Skills, and Grasping with Multimodal Feedback Involved in European initiatives including VeriDREAM and HumanE-AI-Net He has supervised numerous PhD and Master’s students, including Leni Le Goff, Giuseppe Paolo, Alban Laflaquière, and Achkan Salehi, often in collaboration with leading researchers like Olivier Sigaud and Jean-Baptiste Mouret. He teaches computer science and robotics at both undergraduate and graduate levels at Sorbonne University. Doncieux has been instrumental in shaping research directions in evolutionary and developmental robotics, notably through his leadership in the IEEE Task Force on Evo-Devo-Robotics and his editorial contributions. His lab, ASIMOV, fosters interdisciplinary research integrating computer science, neuroscience, and engineering to create more autonomous and intelligent robotic systems.
James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.
Cheung Ngai-Man is an Associate Professor and Associate Head of Pillar (Education) at Singapore University of Technology and Design (SUTD), part of the Information Systems Technology and Design (ISTD) pillar. He holds a Ph.D. in Electrical Engineering from the University of Southern California (2008) and has held research positions at Stanford University, Texas Instruments, IBM, and others. His research focuses on image and signal processing, computer vision, machine learning, and artificial intelligence. Education: Ph.D., Electrical Engineering, University of Southern California (2008); Postdoctoral research at Stanford University (2009–2011). Research Interests: Develops algorithms for multimedia data processing, explores interdisciplinary applications of signal processing and AI, and addresses challenges in computer vision and generative models. Recent work includes fairness in generative models, few-shot image generation, and adversarial robustness. Publications: Over 100+ peer-reviewed papers in top venues (CVPR, NeurIPS, IEEE TIP, TPAMI) focusing on computer vision, generative models, and AI security. Notable 2023 work includes studies on label-only model inversion attacks and fairness metrics in generative systems. Awards: Best Paper Finalist (CVPR 2019), SAIL Award Finalist (WAIC 2019), Outstanding Associate Editor (IEEE T-MM), Croucher Foundation Fellowship. Students: Supervised postdocs (Hossein Nejati, Fang Lu), research assistants (Mohammad Rostami), and visiting students (Ma Rui). Labs/Teams: Leads research groups in AI, computer vision, and multimedia systems at SUTD. Has spun off AI initiatives for wound care and contributed to Singapore’s National AI Strategy.
Benjamin Eysenbach leads the Princeton Reinforcement Learning Lab, where he designs algorithms that enable artificial intelligence systems to learn intelligent behaviors through trial-and-error, specializing in self-supervised methods that eliminate the need for human labels. He joined Princeton after completing his PhD in machine learning at Carnegie Mellon University under Ruslan Salakhutdinov and Sergey Levine, supported by the NSF Graduate Research Fellowship and Hertz Fellowship. His research bridges fundamental machine learning principles with practical applications in robotics and decision-making systems. Eysenbach's research focuses on developing self-supervised reinforcement learning algorithms that enable autonomous skill acquisition without external rewards. His investigations span contrastive learning methods, temporal abstraction techniques, and scalable architectures for goal-conditioned behaviors. These innovations aim to create more efficient and generalizable learning systems that can discover useful behaviors from unlabeled experience. Eysenbach's publications demonstrate consistent advancement in self-supervised RL methodologies, with recent work focusing increasingly on temporal abstraction and representation learning theory. His research shows progression from foundational contrastive RL frameworks toward more sophisticated analyses of generalization properties and uncertainty quantification. The 2025 works indicate expanding investigation into hierarchical control, probabilistic alignment, and hyper-deep network architectures. Eysenbach has been recognized with prestigious awards including the Hertz Fellowship and NSF Graduate Research Fellowship, supporting his doctoral research in self-supervised RL methodologies. His work has been presented at top machine learning conferences including NeurIPS, ICML, and ICLR. As director of the Princeton Reinforcement Learning Lab, Eysenbach oversees research initiatives in self-supervised RL, including projects on intention-conditioned modeling, horizon generalization, and contrastive learning frameworks. He has secured funding from the Princeton AI Lab to study neural correlates of temporal contrast in decision-making. Eysenbach teaches courses in reinforcement learning and has developed new benchmarks like JaxGCRL to accelerate research in goal-conditioned RL.
