جست‌وجوی استاد و پژوهشگر

Daniela Vallentinمشاهده پروفایل

پژوهشگر

• Neural circuits
• vocal communication
• vocal learning
• +۶ مورد دیگر

Dr. Daniela Vallentin serves as a Group Leader at the Max Planck Institute for Ornithology in Seewiesen, Germany, and holds a temporary or junior faculty position with the Graduate School of Systemic Neurosciences (GSN) at Ludwig Maximilian University of Munich. Her research focuses on neural circuit mechanisms underlying vocal communication in songbirds, utilizing advanced intracellular recording techniques in behaving animals. Her primary research interests encompass vocal learning processes, social interaction dynamics in songbirds, premotor circuit functionality, and sensorimotor transformations during vocal behavior. She investigates how neural circuits generate precise temporal patterns in vocal communication, with particular emphasis on inhibition-excitation balance in premotor areas and its role in vocal turn-taking and song production. Her work bridges systems neuroscience and behavioral biology to uncover fundamental principles of learned vocal communication. Analysis of her recent publications reveals a consistent trajectory in songbird neuroscience, spanning cellular-level intracellular recordings to population dynamics. Her research demonstrates how premotor circuits encode temporal sequences, how inhibition shapes vocal timing during social interactions, and the neural mechanisms protecting learned song segments. This body of work establishes critical links between neural circuit dynamics and complex vocal behaviors, with implications for understanding vocal learning across species. Dr. Vallentin actively mentors graduate students through the GSN program, including Linda Bistere and Giacomo Costalunga. Her position as Group Leader at the Max Planck Institute indicates substantial research funding supporting her investigations into neural circuit mechanisms of vocal communication. She leads the Research Group Vallentin at the Max Planck Institute for Ornithology, which specializes in neural circuit analysis of vocal communication using zebra finches as a model system. The group employs cutting-edge electrophysiological techniques combined with behavioral analysis to dissect the neural basis of vocal learning, social communication, and sensorimotor integration in naturalistic contexts.

Anne-Claude Gavinمشاهده پروفایل

استاد

• Lipid Metabolism
• APOE Lipidation
• Alzheimer's Disease Mechanisms
• +۴ مورد دیگر

Anne-Claude Gavin is a Professor at the University of Geneva's Faculty of Medicine within the Department of Cell Physiology and Metabolism. Her research group, based at CMU office C05.1535.a, focuses on lipid metabolism with particular emphasis on APOE lipidation mechanisms and their implications in Alzheimer's disease. The laboratory actively recruits PhD candidates for high-level collaborative projects, with a recent opening targeting autumn 2025 start dates for research on APOE polymorphism and metabolic stressors. Her research interests center on lipid-protein interactions, particularly APOE homeostasis across isoforms and cell states, ceramide transport mechanisms, and the role of lipid dysregulation in neurodegenerative diseases. Recent work integrates structural biology, cellular imaging, and systems approaches to dissect molecular mechanisms of lipid transfer proteins like CERT and STARD4. Her group has made significant contributions to understanding how APOE variants differentially modulate cellular cholesterol metabolism and inflammatory responses in astrocytes. Analysis of recent publications (2023-2025) reveals strong thematic continuity in lipid-centric research, with increasing focus on Alzheimer's disease mechanisms through APOE studies, structural characterization of lipid transfer proteins, and cellular aging processes in hematopoietic systems. Key methodologies include molecular dynamics simulations, fluorescent biosensor development, and system-wide lipid transfer protein mapping. While no specific awards are listed in the provided materials, her research has garnered media attention including coverage in RTS's CQFD program (March 2022) discussing protein contributions to Alzheimer's disease. Her laboratory maintains active recruitment for doctoral positions, indicating ongoing grant funding for projects like the collaborative study on APOE lipidation mechanisms. Active recruitment for PhD positions in lipid metabolism Media features on Alzheimer's research Collaborative projects with clinical implications The laboratory operates within the Lipid Metabolism research group at the University of Geneva's Centre Médical Universitaire (CMU), utilizing advanced techniques including molecular dynamics, chemoproteomics, and genetically encoded fluorescent probes to investigate lipid dynamics in cellular systems. Current work emphasizes translational connections between fundamental lipid biochemistry and neurodegenerative disease pathways.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +۳ مورد دیگر

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

Hrushikesh Mhaskarمشاهده پروفایل

استاد پژوهشی

• Approximation Theory
• Computational Harmonic Analysis
• Machine Learning
• +۳ مورد دیگر

Hrushikesh Mhaskar is a Research Professor of Mathematics at Claremont Graduate University (CGU) since 2012, with a prior 32-year tenure at California State University, Los Angeles. He holds a PhD in Mathematics from Ohio State University, alongside an MS in Computer Science and an MSc from the Indian Institute of Technology, Mumbai. His research focuses on approximation theory, computational harmonic analysis, machine learning, and signal processing, with significant contributions to neural network theory and kernel-based methods. Mhaskar has authored over 150 papers, two books, and five edited volumes. His work includes pioneering studies on weighted polynomial approximation, Fourier domain conversions, and manifold learning. He currently serves on editorial boards for journals like Applied and Computational Harmonic Analysis and Journal of Approximation Theory , and collaborates with institutions like the University of California, Santa Barbara. Awards include five Alexander von Humboldt Fellowships and a John von Neumann Distinguished Professorship. His research is supported by the NSF and previously by the U.S. Air Force and intelligence agencies. Notable contributions include developing eignets for function approximation on manifolds and analyzing deep vs. shallow networks' approximation capabilities. His work bridges theoretical mathematics with practical applications in biomedical data analysis (e.g., blood glucose prediction) and signal processing.

• Developmental Psychology
• Educational Psychology
• Cognitive Neuroscience
• +۳ مورد دیگر

Susana Cid Fernández is a Permanent Labor Teacher at the Department of Developmental and Educational Psychology within the Faculty of Education Sciences at the University of Santiago de Compostela. She is affiliated with the Institute of Psychology (IPsiUS) and actively contributes to the NeuCogA - Aging, Applied Cognitive Neuroscience, and Psychogerontology research group. Research Interests : Her work focuses on neurocognitive aging, event-related potentials (ERPs), and brain stimulation techniques like tDCS/tACS for cognitive enhancement. She investigates attentional control, memory processing, and neurophysiological markers in healthy aging and mild cognitive impairment (MCI), with applications in aphasia rehabilitation and cognitive training interventions. Publications : Recent studies highlight her contributions to understanding age-related ERP changes, brain stimulation efficacy for memory improvement, and cross-modal information processing in aging. Her work bridges cognitive neuroscience with clinical applications in psychogerontology. Labs & Teams : Collaborates with the NeuCogA group on interdisciplinary projects integrating cognitive psychology, neurophysiology, and rehabilitation technologies.

• Signal Processing in Wireless Communications
• Multi-antenna systems (MIMO)
• Wireless Power Transfer
• +۱۴ مورد دیگر

Francisco A. Monteiro is an Associate Professor in the Department of Information Science and Technology at ISCTE – University Institute of Lisbon, and an Integrated Researcher at Instituto de Telecomunicações (IT), Lisbon, Portugal. He holds a PhD in Engineering from the University of Cambridge and prior degrees from Instituto Superior Técnico, University of Lisbon. His academic journey includes progressive roles at ISCTE-IUL, culminating in his promotion to Associate Professor in May 2024. Education: PhD in Engineering, University of Cambridge, UK (2012) MSc in Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon (2003) Licenciatura in Electrical and Computer Engineering, Instituto Superior Técnico, University of Lisbon (1998) His research focuses on signal processing in wireless communications, particularly in MIMO systems, network coding, quantum communications, and advanced decoding techniques like GRAND. He has made significant contributions to quantum error correction, entanglement purification, and low-latency decoding for 5G/6G systems. His recent work bridges classical wireless theory with emerging quantum networking paradigms, as evidenced by publications in Quantum , IEEE Access , and IEEE Transactions . The trend in his recent publications (2023–2024) shows a strong shift toward quantum information processing—especially quantum error correction via noise-guessing decoding (QGRAND), entanglement routing, and purification—while maintaining a foundation in classical MIMO, full-duplex communications, and network coding. His work is highly interdisciplinary, combining information theory, signal processing, and quantum computing. Scientific Awards: Best Paper Award, ECWT 2007 Best Student Paper Award, ECWT 2004 Exemplary Reviewer Award (2014, 2015), IEEE Wireless Communications Letters Innovation Young Engineer Prize (3rd place), Ordem dos Engenheiros, 2002 College Senior Scholarship, Fitzwilliam College, Cambridge, 2007 Fellowship from Fundación Endesa & Fundación Carolina, 2019 Francisco Monteiro has advised several MSc students, including Francisco Rosário and João Sande Lemos, both winners of the Luís Vidigal Prize. He has co-supervised PhD-level research and served on multiple thesis committees. His research has been funded through competitive grants and collaborations with institutions in Canada, Finland, Spain, and the UK. He has served as General Chair of ISWCS 2018, Lead Guest Editor for EURASIP Journal, and organizer of the Quantum Internet Alliance Workshop. He is actively involved in research groups such as the Radio Systems Group at IT and has led projects on MIMO, wireless energy transfer, and quantum networking. His lab work emphasizes algorithmic innovation for practical implementations in next-generation wireless and quantum systems.

• Action recognition
• Representational dynamics
• Conceptual knowledge
• +۳ مورد دیگر

Moritz Franz Nikolaus Wurm is a Researcher at the Interdepartmental Center for Mind/Brain Sciences (CIMEC) at the University of Trento, Italy , where he leads the Action Concepts and Recognition Group since 2022. He completed his PhD in Psychology (magna cum laude) at the Max Planck Institute for Neurological Research and the University of Münster in 2011, followed by postdoctoral work at Harvard University and the University of Trento. Research Interests : Dr. Wurm specializes in action recognition , conceptual knowledge representation , and neural decoding using fMRI , MEG , and MVPA/RSA techniques. His work explores how contextual, social, and physical cues shape neural processing of actions and objects, with recent projects on dynamic RSA for temporal neural analysis. Scientific Awards : Fondo Italiano della Ricerca (FIS) Starting grant (2024-2029) Piano Nazionale di Ripresa e Resilienza (PNRR) PhD Grant (2023-2027) University of Trento Starting Grant Giovani Ricercatori (2021) DFG Research Fellowship (2014–2017) Best Abstract Award at CAOs Workshop (2014) Other Activities : He serves as a scientific advisor for the MEG Laboratory at CIMEC, organizes the Concepts, Actions, and Objects workshop series, and leads the CIMeC Colloquia Series. He is an ad hoc reviewer for journals like Nature Communications , NeuroImage , and Journal of Neuroscience .

Ingrid Daubechiesمشاهده پروفایل

استاد

• Applied Mathematics
• Signal Processing
• Image Compression
• +۳ مورد دیگر

Ingrid Daubechies is a prominent mathematician and physicist known for her foundational work in wavelet theory. Born in Belgium, she earned her B.S. and Ph.D. from the Free University of Brussels. She transitioned from a physics background to applied mathematics, becoming a leading authority on wavelets while at AT&T Bell Laboratories. In 1993, she became the first woman tenured professor of mathematics at Princeton University, later joining Duke University as the James B. Duke Professor of Mathematics. Her research spans signal processing, image compression, and interdisciplinary applications in biology and art restoration. Daubechies' academic journey includes roles at Bell Labs, Princeton, and Duke, alongside extensive collaborations in computational biology and mathematics education. She has pioneered wavelet-based algorithms for data compression (JPEG 2000), developed metrics for biological morphology analysis, and contributed to art restoration techniques using mathematical imaging. Her leadership roles include presidency of the International Mathematical Union (2011–2014) and advocacy for women in mathematics. Recognized with prestigious awards like the National Academy of Sciences Award, John von Neumann Lecture Prize, and Nemmers Prize, her work bridges pure mathematics and applied sciences. She has supervised numerous research projects and authored influential texts, including Ten Lectures on Wavelets . Her legacy includes advancing computational tools for diverse fields, from medical imaging to evolutionary biology.

Bing Liuمشاهده پروفایل

استاد مدعو

• Natural Language Processing
• Large Language Models
• Dialogue Systems
• +۴ مورد دیگر

Bing Liu serves as Director of Applied Research at Scale AI and Adjunct Professor in the Computer Science and Engineering department at the University of California, Santa Cruz. Previously, he held leadership roles at Meta (GenAI and Reality Labs), Google Research, and Capio.ai (acquired by Twilio), with expertise spanning generative AI, NLP, and spoken dialogue systems. His educational background includes: Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University B.Eng in Electrical and Electronic Engineering (First Class Honors) from Nanyang Technological University, Singapore Exchange studies at KTH Royal Institute of Technology, Sweden Liu's research focuses on advancing large language models, dialogue systems, and reinforcement learning for conversational AI. His work bridges theoretical innovation with industrial-scale applications, particularly in zero-shot learning, multilingual capabilities, and evaluation frameworks for generative AI. He has pioneered techniques in dialogue state tracking, knowledge-enriched task-oriented systems, and continual learning to address catastrophic forgetting in neural dialogue models. Analysis of his 15 most recent publications reveals a clear trajectory toward industrial-scale generative AI: early work (2018-2021) established foundational methods in task-oriented dialogue systems, while recent publications (2022-2024) focus on LLM post-training, multimodal integration, and evaluation benchmarks like Humanity's Last Exam. Key thematic clusters include cross-lingual transfer, knowledge grounding in dialogue, and robust evaluation frameworks for frontier models. As an educator, Liu guides graduate research at UC Santa Cruz in LLM and multimodal AI while serving in critical conference roles including Publication Chair for ACL 2024 and Area Chair for ACL 2023's Large Language Models track. His industry leadership includes building Scale AI's 40+ member research team and driving Meta's Llama3 development, managing multimillion-dollar data roadmaps that scaled the Meta AI Assistant to 700M MAU. Liu directs Scale AI's applied research lab focused on GenAI data and evaluation, having previously built Meta's NLU team for AR/VR voice assistants deployed across Portal, Oculus, and Ray-Ban Smart Glasses. His current work centers on creating evaluation leaderboards adopted by top AI labs and developing the data engine for next-generation generative models.

• Data Mining
• Machine Learning
• Text Summarization
• +۶ مورد دیگر

Mark Last is a Professor at Ben-Gurion University in Beersheba, Israel, with a distinguished career spanning over three decades in computer science research. His work primarily focuses on data mining, machine learning, and natural language processing applications. His research interests encompass stream data mining, text summarization, fuzzy logic systems, and classification algorithms. Last has made significant contributions to developing techniques for analyzing dynamic data streams, multilingual text processing, and applying machine learning to real-world problems in healthcare, social media analysis, and security informatics. His work often bridges theoretical advancements with practical applications, particularly in handling non-stationary data and developing interpretable models. Recent research trends show a continued focus on stream data analysis, with applications expanding into social media monitoring, healthcare prediction systems, and multilingual content analysis. His work demonstrates consistent innovation in adapting machine learning techniques to evolving data environments and practical challenges. Mark Last has maintained a prolific publication record with over 175 publications documented in DBLP, collaborating extensively with researchers including Abraham Kandel, Marina Litvak, and Oded Maimon. His work has been published in top venues including IEEE Access, Machine Learning journal, and Expert Systems with Applications.

Stephen D. Christmanمشاهده پروفایل

استاد

• Cognitive Psychology
• Handedness
• Hemispheric Asymmetry
• +۲ مورد دیگر

Stephen D. Christman is a Professor in the Department of Psychology at the University of Toledo, affiliated with the College of Arts and Letters. He holds a Ph.D. from the University of California, Berkeley (1988) and leads research in the Cognitive Neuropsychology Lab. Education: Ph.D., University of California, Berkeley, 1988 His research focuses on Cognitive Psychology , particularly Handedness , Hemispheric Asymmetry , Episodic Memory , and Visual Perception . He investigates how interhemispheric interaction influences memory, decision-making, and personality traits. Recent publications highlight trends in handedness research spanning Political Psychology , Memory Encoding , and Interhemispheric Communication . Articles from 2020–2021 examine links between handedness and political behavior, while 2017–2018 studies focus on memory retrieval using fNIRS and motor manipulation. Key co-authors include John D. Jasper, Andrew Geers, and R. E. Propper. His work has been cited extensively in Psychology , Neuroscience , and Public Health domains.

• Endocannabinoid System
• Dopamine Signaling
• Motivation
• +۸ مورد دیگر

Joseph F. Cheer, PhD, is a Professor in the Department of Anatomy and Neurobiology at the University of Maryland School of Medicine with a secondary appointment in Psychiatry. He serves as the CARTI Basic Science Research Training Track Leader and directs multiple NIH and private foundation-funded projects focused on neurophysiological and neurochemical aspects of endogenous cannabinoid signaling in intact systems. His educational background includes: B.S. in Biology from Universidad de los Andes, Bogotá, Colombia (1996) Ph.D. in Neuroscience from University of Nottingham, Nottingham, UK (2000) Postdoctoral training at Wake Forest University (2000-2002) Postdoctoral training at University of North Carolina, Chapel Hill (2002-2006) Cheer's research primarily investigates the physiological function of the endogenous cannabinoid system, particularly its role in motivated behaviors and potential therapeutic applications in pathological states like addiction. His work employs state-of-the-art electrophysiological (ensemble recordings) and electrochemical (fast-scan cyclic voltammetry) techniques to extract neurobiological correlates of behavior in real-time. His laboratory examines how endogenous cannabinoids modulate the encoding of motivated behavior, with specific interest in the dopaminergic projection from the ventral tegmental area to the nucleus accumbens. Analysis of his 15 most recent publications reveals consistent focus on endocannabinoid-dopamine interactions in reward processing, addiction mechanisms, and neurodevelopmental effects of cannabinoid exposure. His work spans from basic mechanisms of neurotransmitter release to translational studies in animal models of psychiatric and neurodegenerative disorders including Huntington's disease, depression, and schizophrenia. His significant scientific achievements include: Acceptance into the American College of Neuropsychopharmacology (2014) Multiple featured articles in high-impact journals including Nature Neuroscience (2019) Faculty of 1000 recognition for several publications Multiple plenary and keynote speaking engagements at major conferences Several prestigious mentoring awards including GPILS/OPS Postdoctoral Mentor Award (2017) and STAR-PREP Excellence in Mentoring Award (2018) Cheer has successfully secured multiple NIH grants as Principal Investigator, including five major R01 awards totaling over a decade of continuous funding. His research program has successfully recruited and mentored numerous tenure-track faculty members and has established collaborations across multiple institutions. His laboratory serves as a training ground for postdoctoral fellows, graduate students, and undergraduates interested in neuroscience research. His research group, the Cheer Lab, employs a comprehensive suite of neuroscience techniques including fast-scan cyclic voltammetry, ensemble single-unit recordings, optogenetics, chemogenetics, and calcium imaging to investigate endocannabinoid-dopamine interactions in real-time during behavior.

Žiga Emeršičمشاهده پروفایل

استادیار

• Biometrics
• Deep Neural Networks
• Computer Vision
• +۵ مورد دیگر

Žiga Emeršič is an Assistant Professor at the University of Ljubljana, Faculty of Computer and Information Science , affiliated with the Computer Vision Laboratory . His work bridges biometrics , deep learning , and computer vision , with a focus on ear-based recognition systems and explainable AI. IEEE Member #98052610 Email: ziga.emersic@fri.uni-lj.si Office: R2.33, LRV Laboratory Office Hours: Tuesdays 12:30 or by arrangement Research interests include biometric recognition , deep neural networks , object detection , and privacy-preserving AI . He pioneered ear biometrics research, developing tools like the Ear Biometric Database in the Wild and ContexedNet for context-aware recognition. Recent publications analyze biometric model performance ( Neural Computing & Applications, 2018 ), explore k-Same-Net for face deidentification ( Entropy, 2018 ), and advance ear alignment using two-stack hourglass networks ( IET Biometrics, 2023 ). His 2021 work on context-aware ear detection addresses real-world variability. Awards include the European Association for Biometrics Award (2021) , SDRV Excellence Plaque (2023) , and multiple University of Ljubljana recognitions for teaching and research (2016, 2018, 2022). He co-organized the 1st Machine Learning Summer School in Central America (2018) . As co-founder of OOSM Ltd (2014-2015), he applied deep learning to smart city solutions. His 2017 doctoral thesis on visual ear detection in unconstrained environments solidified his expertise in biometric AI .

Shuting Hanمشاهده پروفایل

پژوهشگر ارشد

• Neuroscience
• Cortical Dynamics
• Sensory Processing
• +۳ مورد دیگر

Dr. Shuting Han leads a Junior Research Group at the University of Zurich under the Helmchen Lab, funded by the SNSF Ambizione Fellowship since 2024. She holds a Research Fellow position focusing on cortical dynamics underlying sensory processing and memory. Her research examines how distributed cortical areas interact during sensory processing and memory formation, utilizing multi-area two-photon calcium imaging, virtual reality behavior paradigms, electrophysiology, and advanced data analysis techniques. Key projects include investigating sensory representation in cortical areas, predictive processing in neural circuits, cortico-cortical interactions, memory consolidation across the neocortex, and developing high-throughput imaging methodologies. Her recent publications demonstrate expertise in cross-modal predictions, cortical microstates during consciousness alterations, and neural ensemble dynamics. She directs research on top-down predictive signals in neocortex and develops tools for volumetric neural imaging. SNSF Ambizione Fellowship Dr. Han mentors PhD students Maï Ly Leclair and Saidong Ma in the Helmchen Lab. Her group develops custom multi-area two-photon microscopes and applies machine learning for neural data analysis, bridging experimental neuroscience with computational approaches to decode cortical information processing.

• Philology
• Russian Literature
• Theater Studies
• +۵ مورد دیگر

Evgenya Abelyuk serves as Associate Professor at the School of Philological Studies within HSE University's Faculty of Humanities. She concurrently holds the positions of Chief Expert at the Centre for Digital Archival Studies and Programme Head for Contemporary Philology in Russian Language and Literature Instruction at Secondary Schools. Having joined HSE University in 2010, she maintains active roles in research, teaching, and academic leadership. Her educational background includes: 1975: Philological Degree from Lomonosov Moscow State University 1999-2001: Qualification Improvement in Philological Text Analysis (MIROS) 2005: Modern Text Analysis Methods (NIIRO) 2012: Work with Gifted Children (Moscow State University) 2015: Professional Internship in France (French Embassy Program) 2018-2021: Multiple advanced training programs in literary research and Russian language pedagogy Professor Abelyuk's research centers on philology , Russian literature (particularly Soviet-era works), and theater studies with specialized focus on Taganka Theater. Her work explores mythological language in art , digital commentary systems , and historical contexts of literary production. She pioneered the Digital Universal Commentary project, creating visual annotations for Soviet-era literary texts. Her recent publications (2020-2025) reveal three dominant trajectories: Bulgakov studies examining socio-political dimensions of his work; Taganka Theater analysis focusing on Yuri Lyubimov's directorial legacy; and digital humanities innovations in literary commentary. These intersect with her educational mission through textbooks and teacher training materials. Her scientific recognition includes: Best Teacher Award (2025) Best Teacher Award (2021-2022) Best Teacher Award (2017) Best Teacher Award (2013-2015) Professor Abelyuk supervises master's theses in philology while developing educational programs like the "Mentor's Seminar" and "Research Seminar on Reading Practices." Her grant work centers on digital projects including the Visual Commentary initiative and distance learning methodologies. She leads collaborative efforts through the Centre for Digital Archival Studies and maintains active partnerships with Taganka Theater researchers and international philological conferences.