جستجوی استاد راهنمای دانشگاهی

Mark Libermanمشاهده پروفایل

استاد

• Corpus-based Phonetics
• Speech and Language Technology
• Clinical Applications of Linguistics
• +۶ مورد دیگر

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 .

Bernd Möbiusمشاهده پروفایل

استاد

• Speech Technology
• Phonetics
• Phonology
• +۸ مورد دیگر

Professor Bernd Möbius is a leading academic in Phonetics and Phonology at the Department of Language Science and Technology, Saarland University. His research bridges phonetic theory with speech technology applications, focusing on text-to-speech systems, prosody modeling, and computational simulations of speech processes. Current research projects: DFG SFB 1102, C1: Information density and phonetic structure predictability DFG SFB 1102, C4: Slavic intercomprehension and surprisal theory (INCOMSLAV) Research Themes: Key areas include text-to-speech synthesis, speech prosody analysis, experimental methods in speech production/perception, information density in phonetics, and cross-linguistic studies of Slavic-Germanic languages. Scientific Contributions: Recent work explores Parkinson-induced dysarthria detection, breath noise acoustics, surprisal-driven speech behaviors, multilingual BERT models for idiomaticity, and perceptual consequences of acoustic adjustments.

Un J. Kangمشاهده پروفایل

استاد

• Parkinson's disease
• Movement disorders
• Basal ganglia circuit
• +۶ مورد دیگر

Un J. Kang, MD is the Founders Professor of Neurology at NYU Grossman School of Medicine and Professor in the Department of Neuroscience and Physiology. He serves as Director of Translational Research at the Marlene and Paolo Fresco Institute for Parkinson's and Movement Disorders and Co-Director of the Parekh Center for Interdisciplinary Neurology. His clinical practice focuses on Parkinson's disease and related movement disorders including tremor, dystonia, and Huntington's disease. Dr. Kang earned his MD from Johns Hopkins University in 1982, completed residency in Neurology at NY Presbyterian - Columbia University Medical Center in 1986, and specialized in Movement Disorders through fellowship training at the same institution in 1987. His research investigates the pathogenesis of Parkinson's disease and mechanisms of therapy, with particular focus on basal ganglia circuit plasticity, neural adaptation to dopamine loss, and development of biomarkers for neurodegenerative disorders. He pioneered experimental gene therapy approaches for Parkinson's disease and now directs the Kang Lab where his team studies how brain circuitry is perturbed by dopamine loss and pharmacological treatment in mouse models. Dr. Kang's research portfolio includes multiple NIH-funded projects examining striatal cholinergic interneuron dysfunction, freezing of gait mechanisms, and biomarker development for Parkinson's disease. His laboratory has published extensively on Parkinson's disease mechanisms with 178 publications to date, including significant recent work on alpha-synuclein detection methods and striatal pathway alterations in Parkinsonian motor deficits. Before joining NYU Langone in 2019, Dr. Kang created and led a multidisciplinary research team at the University of Chicago for two decades and previously served as chief of the movement disorders division at Columbia University. He leads the BioFIND consortium as principal investigator, banking biospecimens for Parkinson's disease biomarker discovery.

Aleksandar Jeremicمشاهده پروفایل

دانشیار

• Biomedical Signal Processing
• Statistical Signal Processing
• Biometrics
• +۷ مورد دیگر

Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.

Jean-Philippe Thiranمشاهده پروفایل

استاد

• Computational Imaging
• Medical Image Analysis
• Diffusion MRI
• +۵ مورد دیگر

Prof. Jean-Philippe Thiran is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he serves as Director of the Signal Processing Laboratory (LTS5) and Director of the Institute of Electrical and Micro Engineering. He also maintains a part-time Associate Professor position with the Department of Radiology of the University Hospital Center (CHUV) and University of Lausanne (UNIL). Born in Namur, Belgium in 1970, he received his Electrical Engineering degree and PhD from the Université catholique de Louvain (UCL), Belgium, in 1993 and 1997 respectively. He joined EPFL in 1998 and has established himself as a leading researcher in computational imaging. His research focuses on computational imaging , with significant contributions to medical image analysis (particularly diffusion MRI, ultrasound imaging, and digital pathology) and computer vision . His recent work integrates advanced modeling, simulation, and machine learning techniques to extract microscopic tissue information from macroscopic MRI signals. This approach combines hyper-realistic synthetic tissue models, advanced Monte-Carlo simulations, and ML-based estimation techniques for brain microstructure analysis with potential applications to other tissues. Senior Member of IEEE Fellow of the European Association for Signal Processing (EURASIP) Prof. Thiran has authored or co-authored 1 book, 9 book chapters, 250 journal papers and over 270 peer-reviewed conference papers, and holds 12 international patents. He previously served as Co-Editor-in-Chief of the Signal Processing journal (2001-2005) and associate editor of IEEE Transactions on Image Processing. He has chaired major conferences including EUSIPCO 2008 and IEEE ICIP 2015. His laboratory at EPFL brings together interdisciplinary researchers to develop innovative imaging techniques that bridge macroscopic measurements and microscopic tissue properties, with significant potential for medical diagnostics and treatment planning applications.

Andre Levchenkoمشاهده پروفایل

استاد

• Systems Biology
• Signal Transduction
• Cell Communication
• +۵ مورد دیگر

Andre Levchenko is the John C. Malone Professor of Biomedical Engineering at Yale University, with secondary appointments in the Department of Neurosurgery and affiliations with the Cancer Signaling Networks, Immunology, and the Yale Program in Neurodevelopment and Regeneration. His research focuses on systems biology, signal transduction, and cell-cell communication, utilizing microfluidics and computational modeling to study cancer progression, stem cell behavior, and neurological disorders. PhD, Columbia University MEng, Moscow Institute of Physics and Technology Levchenko's work explores how cells process dynamic signals to make critical decisions, particularly in glioblastoma migration, organoid development, and cardiovascular tissue engineering. His lab develops innovative microfluidic platforms and mathematical models to dissect multicellular communication and signaling networks. Recent publications highlight his contributions to understanding YAP-driven cancer invasion , NOTCH signaling in angiogenesis , and metabolic regulation of hypoxia responses . He has pioneered methods for organoid modeling and single-cell analysis , advancing precision in biological signaling studies. Scientific Awards : Computational Molecular Biology Post-Doctoral Fellowship (Burroughs Wellcome Fund) National Academies Keck Futures Conference Invitee Distinguished Guest Lecturer, University of Virginia American Asthma Foundation Early Excellence Award Fellow, American Institute for Medical and Biological Engineering Levchenko leads the Levchenko Lab at the Yale Systems Biology Institute, collaborating with institutions like Mayo Clinic and Yale Cancer Center. His research has received recognition in Faculty of 1000 and multiple journal highlights.

Birgit Frauscherمشاهده پروفایل

استاد

• Epilepsy
• Sleep Medicine
• Electroencephalography
• +۴ مورد دیگر

Dr. Birgit Frauscher is the Lincoln Financial Group Distinguished Professor in Neurobiology at Duke University School of Medicine, where she serves as Professor of Neurology and holds a secondary appointment in the Department of Biomedical Engineering at the Duke Pratt School of Engineering. She is currently the Director of the Duke Comprehensive Epilepsy Center and leads the Analytical Neurophysiology (ANPHY) Lab. Her clinical and research work focuses on epilepsy and sleep medicine, utilizing both invasive and non-invasive electrical recordings to study brain activity in humans. Dr. Frauscher completed her medical training, neurology residency, and subspecialty training in electroencephalography, epilepsy, and sleep medicine at the Medical University of Innsbruck in Austria. After completing her clinical training in 2008, she earned her habilitation degree in 2011. She further specialized in intracranial EEG and signal analysis during a visiting professorship at the Montreal Neurological Institute and Hospital, McGill University (2013-2015), where she later served as an Attending Epileptologist and Group Leader of Epilepsy. Her research interests focus on developing novel seizure-independent EEG markers for the epileptogenic zone, investigating sleep-epilepsy interactions, and using intracranial EEG to study brain physiology during wakefulness and sleep. Her work aims to improve epilepsy diagnosis, prognosis, and treatment outcomes by better localizing the epileptic focus. Dr. Frauscher's recent publications demonstrate her continued leadership in epilepsy research, with over 170 peer-reviewed papers and an H-index of 62. Dr. Frauscher has received several prestigious awards including the Clinician-Scientist awards of the FRSQ (2018-2023), the Michael Prize of the International League against Epilepsy (2019), and the Ernst Niedermeyer Prize from the Austrian Epilepsy Society (2015). Her scholarly work has significantly advanced clinical knowledge in epilepsy and sleep medicine, establishing her as a leading figure in the field. As Director of the Duke Comprehensive Epilepsy Center and head of the ANPHY lab, Dr. Frauscher oversees a research program dedicated to advancing neuroscience through innovative approaches to studying brain activity. Her lab employs quantifiable tools to investigate neurophysiological and pathological processes related to epilepsy and sleep, with the ultimate goal of improving patient outcomes through better understanding of brain function.

Cristian Gómez Canelaمشاهده پروفایل

استاد

• Environmental Chemistry
• Analytical Chemistry
• Mass Spectrometry
• +۵ مورد دیگر

Cristian Gómez Canela is a Full Professor in the Department of Analytical and Applied Chemistry at the School of Engineering, Ramon Llull University (IQS). He serves as Coordinator of the Master's Degree in Analytical Chemistry and is an active member of the Catalan Chemical Society (SCQ), representing SCQ in EuChems-EYCN. His academic journey includes a PhD in Chemistry from the University of Barcelona (2014), followed by postdoctoral research at IDAEA-CSIC and King's College University. Dr. Gómez Canela's research focuses on environmental analytical chemistry, particularly the optimization and validation of analytical methods based on liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and high-resolution mass spectrometry (HRMS) for determining organic pollutants in environmental samples. His work extends to metabolomics applied to aquatic organisms and the analysis of neurotoxic compounds in water systems. His research fingerprint reveals strong expertise in zebrafish models (100%), neurotransmitter analysis (66%), Daphnia magna studies (64%), and neurotoxicity assessment (21%). His recent publications (2024-2025) demonstrate a clear trend toward environmental neurotoxicology, with emphasis on the effects of pharmaceuticals and industrial pollutants on aquatic organisms. His work integrates advanced analytical techniques with biological endpoints to assess environmental risks, particularly focusing on neurological and cardiovascular impacts. The research spans method development for pollutant detection, environmental monitoring, and mechanistic studies of neurotoxic effects. Dr. Gómez Canela leads multiple significant research projects including CHEMIPARK (2024-2027) on passive sampling methodologies for environmental pollutants, GESPA (2022-2025) as part of the Environmental Process Engineering and Simulation Group, and several projects on neuroactive compounds in water systems. He has an impressive research output with 91 scientific publications from 2011-2025 and an h-index of 27 with over 2,000 citations. As a dedicated educator, he contributes to multiple academic programs including the Master in Analytical Chemistry, Master in Pharmaceutical Chemistry, and undergraduate degrees in Chemistry and Chemical Engineering. His research group GESPA represents a multidisciplinary team combining chemical engineering, biotechnology, and chemical analysis to advance environmental sustainability through theoretical and experimental approaches.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.

Tim Q. Duongمشاهده پروفایل

استاد

• Medical Imaging
• MRI
• Image Analysis
• +۹ مورد دیگر

Tim Q. Duong, Ph.D., is a Professor at Albert Einstein College of Medicine, affiliated with the Departments of Radiology, Biochemistry, Ophthalmology & Visual Sciences, and Neuroscience. His research focuses on medical imaging, MRI, image analysis, machine learning, and predictive modeling for studying diseases like COVID-19 , neurodegeneration (Alzheimer's, multiple sclerosis), brain injuries , and breast cancer . Develops AI-driven MRI techniques for early disease detection Investigates neuroplasticity in glaucoma and diabetic retinopathy Leads grants from NIH and National Eye Institute Research Trends : Recent publications emphasize AI integration in medical imaging, long-term effects of SARS-CoV-2, and advanced MRI applications for ocular and neurological disorders. Grants include multiple R01 awards for diabetic retinopathy and glaucoma studies. Training Opportunities : Actively recruits postdocs, research coordinators, and faculty. Offers research positions for graduate, medical, and high school students, including Regeneron Scholar programs. Labs & Teams : Leads the Duong Lab at Montefiore Medical Center, focusing on translational research for clinical imaging solutions.

Yayun Duمشاهده پروفایل

استادیار

• Bioelectronics
• Intelligent Systems
• Wearable and Implantable Sensors
• +۷ مورد دیگر

Dr. Yayun Du is an Assistant Professor in the Department of Electrical and Computer Engineering at Vanderbilt University School of Engineering. She holds a Ph.D. in Robotics and System Control (Minor: Solid Mechanics) from UCLA (2022) and was a postdoctoral scholar at Northwestern University's Rogers Group through 2024. Current faculty at Vanderbilt University Ph.D. from University of California, Los Angeles Postdoctoral experience at Northwestern University Her research integrates bioelectronics and robotics through three core directions: 1) Developing multimodal wearable/implantable sensors for health monitoring, 2) Creating human-in-the-loop interaction systems using brain-computer interfaces, and 3) Applying machine learning to medical environment robotics. She has deployed four sensor types across seven hospitals globally, serving users from neonates to elderly patients. Dr. Du's recent publications focus on wireless bioelectronic devices ( PNAS ), sustainable sensor materials ( ACS Sustainable Chemistry & Engineering ), and agricultural robotics ( ICRA , IROS ). She serves as Associate Editor for ICRA 2025 and has received two Best Paper Award final nominations at IROS 2021. Finalist - Best Paper Award in Agri-Robotics (IROS 2021) Finalist - Best Paper Award in Robot Mechanisms and Design (IROS 2021) As head of the Du Group, she leads interdisciplinary research with applications in both healthcare and agricultural contexts, collaborating with Vanderbilt Institute for Surgery and Engineering (VISE) and clinical partners. Her work emphasizes deployable systems that transition from academic research to real-world implementation in medical and industrial environments.

Yasamin Mostofiمشاهده پروفایل

استاد

• Wireless Systems
• Robotics
• Control Systems
• +۱۰ مورد دیگر

Professor Yasamin Mostofi is a faculty member in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. She is also affiliated with the Department of Computer Science and the Center for Control, Dynamical Systems and Computation. Her work bridges wireless systems, robotics, and machine learning. PhD, Stanford University MS, Stanford University MS, Sharif University of Technology Research Interests : Her lab focuses on wireless systems and autonomous agents , developing novel mathematical models for RF sensing and communication-aware robotics. Current directions include WiFi/millimeter wave/6G-based imaging and analytics, networked robotics for cellular systems, human-robot collaboration, and machine learning integration. Recent Publications : Her work spans through-wall crowd analytics , robot-assisted connectivity , and vision-aided wireless sensing , with applications in smart health, security, and retail optimization. Key trends include cross-modal integration (WiFi and vision), synthetic data generation, and real-world clinical validation. Scientific Recognition : Presidential Early Career Award for Scientists and Engineers (PECASE) Antonio Ruberti Prize, IEEE Control Systems Society NSF CAREER Award IEEE Region 6 Outstanding Engineer Award IEEE Fellow (2020) Advising and Leadership : She mentors PhD students in systems research, with graduates joining leading companies like Qualcomm and Google. She co-founded NPJ Wireless Technology (Nature Portfolio) and serves on editorial boards including IEEE Transactions on Control of Network Systems .

Canan Dagdevirenمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Piezoelectric Devices
• Wearable Electronics
• +۶ مورد دیگر

Dr. Canan Dagdeviren is an Associate Professor and LG Career Development Professor of Media Arts and Sciences at the Massachusetts Institute of Technology, where she directs the Conformable Decoders research group at the MIT Media Lab. She joined the MIT faculty in January 2017 and has established herself as a leading innovator in conformable biomedical devices. Education: Ph.D. in Materials Science and Engineering, University of Illinois at Urbana-Champaign M.Sc. in Materials Science and Engineering, Sabanci University, Istanbul, Turkey B.Sc. in Physics Engineering, Hacettepe University, Ankara, Turkey Dr. Dagdeviren's research focuses on creating mechanically adaptive electromechanical systems that can intimately integrate with biological surfaces for sensing, actuation, and energy harvesting. She believes vital information from nature and the human body is 'coded' in various physical patterns, and her work develops 'conformable decoders' to translate these patterns into beneficial signals and energy. Her research spans wearable and implantable medical devices, with particular emphasis on piezoelectric systems that can be twisted, folded, stretched, wrapped, and implanted onto curvilinear surfaces of the human body without damage or significant alteration in performance. Analysis of her recent publications reveals a strong focus on medical applications of conformable electronics, particularly in ultrasound technology for breast cancer detection, deep brain stimulation, and bladder monitoring. Her work consistently bridges materials science, electrical engineering, and medical applications, with increasing emphasis on practical healthcare solutions that can be deployed outside clinical settings. Major Scientific Awards: NSF CAREER Award (2021) 3M Non-Tenured Faculty Award (2021) MIT Technology Review's Top 35 Innovators Under 35 (2015) Forbes' Top 30 Under 30 in Science (2015) National Academy of Engineering US Frontiers of Engineering Symposium participant (2019) Frank E. Perkins Award for Excellence in Graduate Advising Aziz Sancar Science Award Dr. Dagdeviren actively mentors graduate students and has received recognition for her advising excellence. Her research is supported by significant grants including the NSF CAREER award and has resulted in numerous patents and commercialization opportunities. She has developed innovative cleanroom-based courses at MIT that train students in microfabrication techniques for biomedical devices. The Conformable Decoders research group operates a specialized cleanroom facility at the MIT Media Lab, enabling the development and fabrication of novel conformable electronic systems. The group's work has attracted attention from major media outlets including BBC, CNN, and Nature, and has potential applications across multiple medical specialties including neurology, oncology, and urology.

Frank Rudziczمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Healthcare
• +۱۲ مورد دیگر

Dr. Frank Rudzicz is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research lies at the intersection of artificial intelligence, natural language processing, and healthcare, with a focus on developing machine learning systems that improve clinical decision-making, patient outcomes, and accessibility in medicine. He holds a BSc from Concordia University (2004), an MEng from McGill University (2006), and a PhD from the University of Toronto (2011). His research interests include Natural Language Processing, Machine Learning, Healthcare, Speech Technologies, Explainable AI, and Fairness in ML. Dr. Rudzicz's recent publications span a wide range of topics, including Alzheimer's detection through speech analysis, surgical outcome prediction, mental health monitoring, privacy in AI, and the application of large language models in clinical settings. His work consistently emphasizes ethical AI, patient privacy, and real-world clinical integration. He has received several awards, including a Best Paper award at EMNLP 2020, a Best Student Paper award at ICASSP 2021, and the ISCA Best Student Paper award in 2013. His research has been published in top-tier journals such as Nature Scientific Reports , JAMA Network Open , IEEE Access , and Frontiers in Human Neuroscience , as well as leading conferences including NeurIPS, ACL, ICML, and Interspeech. Dr. Rudzicz supervises a dynamic research group working on AI for health, with active projects in voice-based diagnostics, ambient clinical documentation, explainable AI for surgery, and wearable-based monitoring for chronic diseases. He collaborates widely across disciplines, including with clinicians, neuroscientists, ethicists, and public health experts. He is also involved in major initiatives such as the Genetics Navigator study and Bridge2AI-Voice, aiming to build ethically sourced, diverse biomedical datasets. His lab actively explores the societal implications of AI in healthcare, including fairness, trust, and resistance to malicious fine-tuning.

Gerold Schneiderمشاهده پروفایل

دانشیار

• Computational Linguistics
• Digital Humanities
• Corpus Linguistics
• +۳ مورد دیگر

Gerold Schneider is an Associate Professor at the University of Zurich , affiliated with the Department of Computational Linguistics under the Faculty of Arts and Social Sciences and Faculty of Business, Economics and Informatics . He leads the Text Crunching Center (TCC) , focusing on interdisciplinary research at the intersection of NLP, Digital Humanities, and Health Data Science. Research Interests His work spans Text Analytics , Digital Humanities , Corpus Linguistics , and Health Data Science , with applications in: Biomedical NLP (e.g., Alzheimer’s detection, clinical trials) Digital Humanities projects (e.g., analyzing Charles Dickens, UN archives) Migration discourse framing across languages Adversarial data collection for hate speech detection Interdisciplinary methodologies for digital unstructured data Recent Publications 2025–2024 research highlights include annotated corpora for preclinical and neurological studies, AI-driven analysis of historical linguistic variation, and innovative tools for language learners. His NLP applications address health diagnostics, ethical AI, and cross-lingual political discourse. Labs & Teams As TCC leader, he spearheads collaborative projects within the Digital Society Initiative (DSI) communities (AI & Law, Health, Ethics, etc.), integrating computational methods with humanities and health research.