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Mark LibermanView profile

Professor

• Corpus-based Phonetics
• Speech and Language Technology
• Clinical Applications of Linguistics
• +6 more

Mark Liberman is a Trustee Professor at the University of Pennsylvania , holding appointments in the Department of Linguistics and Department of Computer and Information Science . He serves as Director of the Linguistic Data Consortium and Faculty Director of Ware College House . His career spans linguistics, speech technology, and computational methods. Education: Harvard University (1965-1969), MIT (M.S. 1972, Ph.D. 1975) Professional Experience: AT&T Bell Laboratories (1975-1990), University of Pennsylvania (1990-present) His research interests include: Corpus-based Phonetics : Analyzing speech patterns via large-scale datasets. Clinical Applications : Developing speech biomarkers for neurodegenerative diseases. Tonal Phonology : Studying lexical tone and intonation in languages like Yoruba and Mandarin. Formal Annotation Models : Creating frameworks for linguistic data standardization. Recent publications highlight automated speech analysis, cross-linguistic prosody, and digital biomarkers for conditions like ALS-FTD and Alzheimer’s. His collaborations span computational linguistics , neurology , and cognitive science . Scientific awards include the IEEE James L. Flanagan Award (2017), Antonio Zampolli Prize (2010), and fellowships from the AAAS and Linguistic Society of America . He advises PhD students May Chan and Jonathan Him Nok Lee and contributes to editorial boards for journals like Cognition and Annual Review of Linguistics . His work bridges speech science , language technology , and neurocognitive research .

• Human-Computer Interaction
• Learning Sciences
• Educational Technology
• +2 more

Jennifer Olsen, PhD, is an Assistant Professor of Computer Science at the University of San Diego since 2020. She holds a PhD, MS, and BS in Human-Computer Interaction and Cognitive Science from Carnegie Mellon University, followed by postdoctoral research at the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. Her research focuses on the intersection of human-computer interaction, cognition, and education, emphasizing collaborative learning and educational technology design from both learner and instructor perspectives. Education: PhD in Human-Computer Interaction, Carnegie Mellon University MS in Human-Computer Interaction, Carnegie Mellon University BS in Cognitive Science, Carnegie Mellon University Research Interests: Dr. Olsen explores how collaboration supports learning, designs technologies to enhance educational practices, and investigates gaze-based metrics for understanding collaborative problem-solving. Her work spans gamified robotics, AI-driven orchestration systems, and virtual reality applications in vocational training. She emphasizes learner-centered design and the integration of social robots and virtual agents in pedagogical settings. Grants/Advising: While no specific grants or advisees are listed, her prolific publication record indicates active involvement in educational technology research and development. Her work addresses challenges in classroom orchestration, multimodal data analysis, and accessibility in educational robotics. Labs/Teams: Collaborates with interdisciplinary teams focused on educational technology, human-robot interaction, and adaptive learning systems. Her research leverages tools like FROG orchestration graphs and eye-tracking technologies to develop practical classroom solutions.

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +8 more

Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines

Alexandra HorowitzView profile

Adjunct Associate Professor

• Dog Cognition
• Canine Behavior
• Olfactory Experience
• +3 more

Alexandra Horowitz is a Senior Research Fellow and Adjunct Associate Professor at Barnard College. She has been teaching since 2004, specializing in dog cognition and olfactory research. Her academic roles span the Departments of English and Psychology, reflecting her interdisciplinary focus on animal behavior and human-animal interactions. Education: M.S. and Ph.D. in Cognitive Science from UC San Diego; B.A. in Philosophy from the University of Pennsylvania. Research interests include naturalistic observations of dog social play, testing anthropomorphic assumptions, and characterizing canine olfactory abilities. She has authored influential books like Inside of a Dog and Being a Dog , exploring how dogs perceive the world. Her work frequently appears in top journals and media outlets, including the New York Times and TED-Ed. Recent articles highlight studies on dog navigation, play behavior, and olfactory perception. She has been recognized as among the Top 2% of Cited Authors globally. Her presentations and lectures span academic conferences, museums, and public forums, emphasizing science communication.

Byron YuView profile

Professor

• Computational Neuroscience
• Neural Dynamics
• Motor Control
• +7 more

Byron Yu is a Professor in Electrical & Computer Engineering and Biomedical Engineering at Carnegie Mellon University, with affiliations to the Neuroscience Institute and Robotics Institute. He is a core faculty member of the Center for the Neural Basis of Cognition. Research focuses on computational neuroscience , neural dynamics , and brain-machine interfaces . Key contributions include dimensionality reduction techniques and neural population activity analysis. Recent publications explore topics such as neural dynamics during motor imagery, BCI optimization, and attentional processing. His work has appeared in Nature Neuroscience , Neuron , and eLife , often as cover articles. Awardees include the Gerard G. Elia Career Development Professorship and AIMBE Fellowship . His lab has mentored numerous PhD and postdoctoral researchers, many now in academic and industry leadership roles.

• Computational Neuroscience
• Artificial Intelligence
• Machine Learning
• +7 more

Raul Vicente Zafra is a Professor of Data Science at the University of Tartu, Faculty of Science and Technology, Institute of Computer Science, where he has been working since 2013. His research spans computational neuroscience, artificial intelligence, and data science, with a particular focus on bridging biological and artificial models of intelligence. Education: PhD in Physics (2001-2006), University of the Balearic Islands BSc in Physics (1997-2001) Professor Zafra's research interests center on computational neuroscience and artificial intelligence, with specific expertise in brain-computer interfaces, reinforcement learning, neural modeling, and explainable AI. His work bridges the gap between biological and artificial intelligence systems, exploring how neural principles can inform machine learning algorithms and vice versa. He has made significant contributions to understanding neural coherence, time interval learning in neural systems, and the application of information theory to brain-computer interfaces. His research often involves interdisciplinary collaboration between computer science, neuroscience, and medicine. Analysis of Zafra's recent publications reveals a strong focus on the intersection of artificial intelligence and neuroscience. His work spans explainable AI methods, brain-computer interfaces, reinforcement learning models that mimic cognitive processes, and neurophysiological studies of brain activity. A notable trend is his exploration of how biological principles of neural computation can inform and improve artificial intelligence systems, particularly in areas like time-based learning, consciousness modeling, and neural coherence. Scientific Awards: 2012: Attendee at the 62nd Lindau Nobel Laureate Meeting 2007: Quantum Electronics and Optics Division Prize of the European Physical Society for the best PhD Thesis in Applied Optics in Europe 2006: PhD Extraordinary Award of the Physics Department of the University of the Balearic Islands 2001: Physics Degree Extraordinary Award (First Class Honors, best GPA) 1997: Bronze Medal in the "8th Spanish Physics Olympiad" Professor Zafra has been principal investigator on numerous significant research projects including the Estonian Centre of Excellence in Artificial Intelligence, Cardiovascular Stress Impacts On Neuronal Function, and Bridging biological and artificial models of vision. His grant portfolio demonstrates strong funding support from the Estonian Research Council, European Commission, and other major funding bodies. He has supervised multiple PhD students and mentored early-career researchers in computational neuroscience and AI. His laboratory work focuses on developing computational models of neural systems and applying these insights to artificial intelligence. Current research directions include explainable AI methods, brain-computer interfaces, modeling of consciousness and cognitive processes, and the application of AI to healthcare challenges.

Silvestro MiceraView profile

Professor

• Neural Engineering
• Neuroprosthetics
• Robotics
• +3 more

Silvestro Micera is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) and holds the Bertarelli Foundation Chair in Translational Neuroengineering. He directs the Translational Neural Engineering Laboratory and teaches courses including Neural signals and signal processing and Translational neuroengineering . His research bridges neural interfaces, robotics, and neuroprosthetics to restore motor functions in spinal cord injuries, stroke, and amputations. Micera's research integrates implantable neural interfaces, robotic rehabilitation, and hybrid neuro-prosthetic systems. Key focus areas include: Robotic neurorehabilitation for mobility restoration Neural control mechanisms in movement CNS/PNS neural interface development Bioelectronic modulation for sensory feedback His recent publications emphasize machine learning-driven motor recovery prediction, closed-loop sensory feedback systems, and minimally invasive neuroprosthetics. Trends include AI-optimized stimulation protocols, multimodal data fusion for rehabilitation, and clinical translation of neural bypass technologies. Awards: IEEE EMBS Early Career Achievement Award (2009) IEEE EMBS Technical Achievement Award (2021) Micera leads EU-funded projects such as TIME, CLONS, and NeuWalk, focusing on neural prostheses. He advises 8 current and 18 former PhD students in neuroengineering. His lab collaborates with MIT, Harvard, and industry partners (e.g., Plexon) to advance translational neurotechnologies.

Najim DehakView profile

Associate Professor

• Machine Learning
• Speech Processing
• Audio Classification
• +7 more

Najim Dehak is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, part of the Whiting School of Engineering. His research focuses on machine learning applied to speech processing, audio classification, and health applications. He is renowned for developing the I-vector representation for speaker recognition, introduced in 2008 during a workshop at Johns Hopkins’ Center for Language and Speech Processing. Prior to this role, he was a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory. Dehak holds a PhD from the School of Advanced Technology in Montreal (2009). He is a Senior Member of IEEE and contributes to the IEEE Speech and Language Technical Committee. His work bridges AI, healthcare, and signal processing, with notable contributions to neurodegenerative disease detection via speech and handwriting analysis. Research interests include adversarial attacks on speech systems, multimodal biomarker discovery, and robust speech processing across demographics. His lab’s tools, like the Hermespeech Recorder, enable scalable data collection for clinical and research applications. Education: PhD in Advanced Technology (2009), Montreal Affiliations: Johns Hopkins University, IEEE Labs/Teams: Center for Language and Speech Processing (CLSP) His recent work explores AI’s role in aging research, including Alzheimer’s and Parkinson’s disease detection through speech, eye tracking, and handwriting analysis. Ongoing projects address fairness in speaker verification and robustness against adversarial attacks in ASR systems.

Mayank R. MehtaView profile

Professor

• Neurophysics
• Synaptic Plasticity
• Learning and Memory
• +3 more

Mayank R. Mehta is a Professor at the University of California, Los Angeles (UCLA), holding joint appointments in the Departments of Physics & Astronomy, Neurology, and Neurobiology. He is a member of the Brain Research Institute and the W. M. Keck Center for Neurophysics at UCLA. His research bridges experimental and theoretical neuroscience, focusing on how neuronal networks encode space-time, the role of brain rhythms in learning and memory, and the impact of sleep and virtual reality on neural dynamics. His recent publications highlight breakthroughs in understanding hippocampal spatiotemporal selectivity, dendritic activity during behavior, and the causal influence of visual cues on memory neurons. Notable findings include the discovery that dendrites generate ten times more spikes than neuronal cell bodies and the modulation of hippocampal theta rhythms in virtual reality. Research Themes: Neurophysics of spatial-temporal coding Dendritic contributions to learning Virtual reality and brain plasticity Neural oscillations in memory consolidation Key Collaborators: Bert Sakmann (Max Planck Florida Institute) Thomas Hahn (Bernstein Center Heidelberg/Mannheim) Maryam Ghorbani (UCLA) Mehta's lab at UCLA trains graduate and postdoctoral researchers in cutting-edge techniques combining hardware development, electrophysiological recordings, and biophysical modeling. His work has significant implications for treating learning and memory disorders like Alzheimer's disease.

Alanson SampleView profile

Associate Professor

• Human Computer Interaction
• Cyber-Physical Systems
• Wireless Technology
• +4 more

Alanson Sample is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan (since 2018), leading the Interactive Sensing and Computing Lab. His research focuses on Human-Computer Interaction, Cyber-Physical Systems, and wireless technology innovations. He holds a PhD in Electrical Engineering from the University of Washington (2011), with prior postdoctoral work there developing implantable medical devices. Before academia, Sample held senior roles at Disney Research (2013-2018) as Executive Lab Director and Principal Research Scientist, leading teams in Robotics, AI, Computer Vision, and HCI. Earlier, he worked at Intel Labs (2008-2013) on energy harvesting for wearables and IoT. Key research themes include wireless power systems, embedded sensing, and privacy-aware technologies. Over 150+ publications span topics like magnetic sensing (MagDesk), acoustic gesture recognition (HandSAW), and privacy-preserving activity tracking (PrivacyMic). His work bridges academic and industry innovation, particularly in scalable interactive systems and medical technology. Notable projects include the Quasistatic Cavity Resonance wireless charging system, RFID-based sensing platforms (WISP), and wearable health monitoring devices. His lab develops hardware-software co-design frameworks for energy-efficient embedded systems. Current research emphasizes medical applications, smart infrastructure, and ubiquitous computing interfaces.

Ray BobrownickiView profile

Lecturer

• Sport Psychology
• Coaching Instruction
• Motor Learning
• +4 more

Dr. Ray Bobrownicki is a Lecturer in Sport Psychology and Co-Programme Director of the BSc (Hons) Applied Sport Science at the University of Edinburgh's Moray House School of Education and Sport. He holds affiliations with the Institute for Sport, Physical Education and Health Sciences (ISPEHS) and the Human Performance Science Research Group. As a chartered psychologist (BPS) and licensed athletics coach, his work bridges academic research and real-world sport practice. Educations: PhD (Sport Psychology and Coaching), University of Edinburgh MSc (Performance Psychology), University of Edinburgh AB (Psychology), Brown University PGCert (Academic Practice), University of the West of Scotland His research focuses on optimizing coaching instruction, motor learning, and performance under pressure. He explores how verbal instructions, analogies, and technology (e.g., VR) influence skill acquisition and athlete motivation. Secondary interests include the societal impacts of sporting policies on athlete welfare and identity. Recent work critiques traditional sport science methodologies and advocates for interdisciplinary, applied research. His publications span 2015–2025, emphasizing critical analysis of coaching practices, motor learning mechanisms, and systemic issues in sport. Key themes include instructional design, constraints-led approaches, and translational research validity. Awards: Fellow of the Higher Education Academy Chartered Psychologist (British Psychological Society) Associate Fellow (British Psychological Society) Teaching responsibilities include programme leadership for BSc Applied Sport Science and contributions to MSc Performance Psychology modules. He currently supervises PhD student Tongyu Liu on esports support taxonomies and welcomes inquiries on coaching instruction, performance psychology, and athlete policy impacts. Dr. Bobrownicki’s career integrates athletic experience (Commonwealth Games finalist, high jumper) with coaching expertise (e.g., mentoring record-breaking athletes) to inform evidence-based practice in sport science.

• Neurosciences
• Biomedical Engineering
• Cognitive Sciences
• +4 more

Dr. Juan Alvaro Gallego is a Senior Lecturer (equivalent to Associate Professor) in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He leads the Behaviour and Neural Dynamics Lab (Be.Neural), a multidisciplinary team focused on understanding neural mechanisms underlying motor control and spinal cord learning, with applications in developing neural interfaces to restore movement in conditions like Parkinson’s disease and paralysis. His research integrates behavioral experiments, neural recordings, data analysis, and computational models, funded by the ERC, EPSRC, ARIA, and industry partners like InBrain Neuroelectronics and Meta Reality Labs. Research interests include motor control, neural dynamics, and clinical applications of neural engineering. The lab collaborates across systems neuroscience and biomedical engineering, aiming to translate fundamental discoveries into therapeutic technologies. Key areas of focus include neural manifolds, synaptic plasticity in motor learning, and closed-loop neuroprosthetics for tremor management. Funding sources include the European Research Council, Engineering and Physical Sciences Research Council, and industry collaborations. The Be.Neural Lab’s work is showcased on their dedicated website (https://beneural.ic.ac.uk).

• Music Cognition
• Virtual Reality Applications
• Social Connectedness
• +5 more

Dr. Kelsey Onderdijk is a Post-doctoral Researcher at Tilburg University in the Department of Communication and Cognition within the Tilburg School of Humanities and Digital Sciences. Her research focuses on the intersection of music cognition, virtual reality, and social connectedness, with publications spanning neuroscience, psychology, and human-computer interaction domains. Dr. Onderdijk completed her doctoral studies at Ghent University with a dissertation titled "Come together: Exploring unity in music interaction through agency and social connectedness." Her academic journey reflects a strong commitment to understanding human interaction through musical experiences, particularly examining how technological mediation affects social bonding. Her research interests include: Music cognition and embodied music interaction Virtual reality applications in musical contexts Social connectedness in digital music environments Effects of technological constraints on musical collaboration Physiological measures of emotional engagement in music Impact of external constraints on creative expression Dr. Onderdijk's publication record demonstrates a clear trajectory examining how people connect through music in both physical and virtual spaces. Her work bridges psychology, musicology, and human-computer interaction, with particular attention to the impact of the COVID-19 pandemic on musical collaboration. Recent publications have focused on concert experiences in virtual reality environments and the physiological correlates of emotional engagement during musical performance. Her research has garnered significant attention, with multiple publications receiving citations and mentions in news outlets, policy sources, and social media platforms. The "Impact of Lockdown Measures on Joint Music Making" paper has been particularly influential, accumulating 39 citation indexes and 50 Mendeley readers. Dr. Onderdijk actively collaborates with researchers across institutions, particularly with scholars from Ghent University including Marc Leman and Pieter-Jan Maes, indicating a strong network within the music cognition research community. Her work contributes to UN Sustainable Development Goals, particularly those related to well-being and innovation.

• Brain Plasticity
• Motor Learning
• Neurorehabilitation
• +5 more

Dr. Cassandra Sampaio Baptista is a Lecturer at the University of Glasgow's School of Psychology & Neuroscience. Her research focuses on brain plasticity in adulthood, particularly exploring how experiences like skill learning or rehabilitation influence structural and functional changes in the brain. She employs neuroimaging techniques such as fMRI neurofeedback and MRI to investigate mechanisms of myelin and white matter plasticity. Her work emphasizes translational applications, including stroke rehabilitation and promoting healthy aging. Key contributions include demonstrating myelin's role in motor learning and developing MRI protocols for white matter analysis. She collaborates on projects funded by the BIAL Foundation (2025–2026) and has supervised multiple postgraduate students. Recent publications highlight studies on oligodendrocyte dynamics, neurofeedback interventions for stroke survivors, and cross-species neuroscience approaches. While no specific awards are listed, her extensive publication record reflects her leadership in neuroplasticity research.

Alexander MathisView profile

Assistant Professor

• Computational Neuroscience
• Machine Learning
• Sensorimotor Control
• +6 more

Alexander Mathis is an Assistant Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Brain Mind Institute (School of Life Sciences). His research bridges computational neuroscience and machine learning to decode sensorimotor behaviors and develop AI tools for behavioral analysis. Pure Mathematics MSc, Ludwig Maximilians University Munich PhD in Computational Neuroscience, Ludwig Maximilians University Munich His work focuses on understanding how the brain generates behavior through computational models and algorithms. Key contributions include the DeepLabCut toolbox, hBehaveMAE , and other frameworks for pose estimation, action segmentation, and brain-inspired AI. His group also explores proprioception, motor control, and neural coding theories. Recent publications include unsupervised hierarchical behavior modeling via masked autoencoders (ECCV 2024), synthetic basketball benchmarks (Shot7M2), and extensions of BABEL into hBABEL. These works highlight his interest in temporal hierarchies in behavior and scalable AI solutions for neuroscience. Scientific Honors Robert Bing Prize (2024) Eric Kandel Young Neuroscientists Prize (2023) Frontiers of Science Award (2023) Marie Skłodowska-Curie Postdoctoral Fellowship Studienstiftung des deutschen Volkes scholarship He advises PhD candidates in neuroscience and life sciences, leads courses on brain-like computation and software engineering for life sciences , and collaborates across AI4Science initiatives. His group actively participates in competitions, such as NeurIPS' MyoChallenge, where brain-inspired reinforcement learning algorithms have won awards.