Claire Bowern is Professor of Linguistics at Yale University specializing in historical linguistics, language documentation, and Australian Indigenous languages. Her research employs computational phylogenetics to study language evolution and supports language revitalization through digital archives and fieldwork methodologies. Recent publications address: Phylogenetic signal in lexical evolution across language families Digital infrastructure for endangered language documentation (FLEx software analysis) Decolonizing linguistics pedagogy and research practices Her work consistently integrates linguistic, anthropological, and computational approaches to analyze language diversity and change. She contributes to global databases including Grambank and D-PLACE, examining links between linguistic, cultural, and environmental patterns.
Sumeet Kumar Gupta is an Associate Professor in the Department of Electrical and Computer Engineering at Purdue University. His academic career spans from his current role to a prior Assistant Professorship at Pennsylvania State University (2014-2017) and an engineering position at Qualcomm Inc. (2012-2014). He holds a PhD in Electrical and Computer Engineering from Purdue University (2012), an M.S. from the same institution (2008), and a B.Tech in Electrical Engineering from IIT Delhi (2006). B.Tech, Electrical Engineering, IIT Delhi (2006) M.S., Electrical and Computer Engineering, Purdue University (2008) PhD, Electrical and Computer Engineering, Purdue University (2012) Dr. Gupta's research focuses on neuromorphic computing, low power variation-aware VLSI design in emerging nanotechnologies, device-circuit co-design, and nano-scale device modeling/simulations. His work addresses challenges in ferroelectric materials, crossbar arrays for deep neural networks, and energy-efficient AI hardware. Recent publications (2025-2024) highlight trends in: Ferroelectric HfO2/HZO thin films Compute-in-memory architectures Variability/stochasticity analysis Machine learning for device optimization Interconnect resistance/temperature effects AI hardware fault tolerance Scientific Awards & Recognitions: DARPA Young Faculty Award (2016) Early Career Professorship, Penn State (2014) 6th TSMC Outstanding Student Research Bronze Award (2012) Magoon Award (Purdue) Outstanding Teaching Assistant Award (Purdue, 2007) Intel PhD Fellowship (2009) His professional journey includes academic appointments at Purdue University (2020-present, Associate Professor) and Pennsylvania State University (2014-2017, Assistant Professor) after industry experience at Qualcomm Inc. (2012-2014). He maintains IEEE and EDS membership while publishing over 100 refereed works.
Qi Alfred Chen is an Assistant Professor in the Department of Computer Science at the University of California, Irvine (UCI), within the Donald Bren School of Information and Computer Sciences. He also holds affiliations with the Department of Electrical Engineering and Computer Science (EECS), the Institute of Transportation Studies at UC Irvine (ITS-Irvine), the Center for Embedded and Cyber-physical Systems (CECS), the Institute for Software Research (ISR), and the UC Irvine Cybersecurity Policy & Research Institute (CPRI). His research focuses on network and systems security, with particular emphasis on autonomous vehicle and IoT security. Dr. Chen received his Ph.D. from the University of Michigan in 2018. His educational background has prepared him for his current research in security of critical computer systems. His work bridges theoretical security principles with practical implementations in real-world systems. Chen's research interests center on network and systems security , with a major focus on smart systems and IoT security , particularly in transportation and autonomous vehicle systems . His work addresses security challenges through systematic problem analysis and mitigation, discovering and mitigating security problems in next-generation transportation systems, smartphone OSes, network protocols, DNS, GUI systems, and access control systems. His research has high impact in both academic and industry contexts, with over 10 top-tier conference papers, a DHS US-CERT alert, multiple CVEs, and coverage in major news media like Fortune and BBC News. His publication record shows a clear evolution from foundational work on network protocols and smartphone security toward increasingly sophisticated security analyses of autonomous vehicle systems and AI-powered transportation technologies. The research trajectory demonstrates growing technical sophistication and real-world impact, with recent work focusing on physical-world adversarial attacks against autonomous driving perception systems, LiDAR spoofing, and security of multi-sensor fusion in autonomous vehicles. NSF CAREER Award (2022) on securing the AI stack in emerging autonomous and connected CPSs Chancellor's Award for Excellence in Undergraduate Research Mentorship, UC Irvine (2021) 5th place nation-wide at National CCDC competition (2021, as faculty advisor) 1st place (Gold Medal) at CCDC Western Regional competition (2021) ProQuest Distinguished Dissertation Award, University of Michigan (2019) Dr. Chen has mentored numerous successful students, including PhD candidates and undergraduates who have gone on to positions at major tech companies like Meta, Uber, Amazon, and Intel. His research group has secured significant funding, including an NSF CAREER award, and has made substantial contributions to the field through high-impact publications and vulnerability disclosures. He is also the co-founder of the ISOC VehicleSec Symposium and has organized the AutoDriving CTF contest at DEF CON. Chen leads the AV & IoAT Security Research Group at UCI, focusing on security challenges in autonomous vehicles and the broader Internet of Autonomous Things. His team has developed numerous attack demonstrations and security analyses that have received significant media attention and influenced industry practices. The group maintains an active YouTube channel showcasing their security research.
Hyunghoon Cho is an Assistant Professor at Yale School of Medicine in the Department of Biomedical Informatics & Data Science, with a secondary appointment in the Department of Computer Science. He received his PhD in Electrical Engineering and Computer Science from MIT (2019) and MS/BS in Computer Science from Stanford University (2013). His research focuses on computational challenges in biomedical data privacy, single-cell genomics, and network biology. Assistant Professor (Primary): Biomedical Informatics & Data Science Assistant Professor (Secondary): Computer Science Appointments: Yale School of Medicine | Broad Institute (Schmidt Fellow) Research Themes: Privacy-Enhancing Technologies for genomic and health data Scalable AI/ML tools for omics data analysis Structured biological modeling for system-level discovery His work includes secure GWAS, transcriptomic privacy assessment, and sfkit - a federated genomic analysis toolkit. He received the NIH Director's Early Independence Award and leads NSF-funded projects on confidential genome analytics. Awards: NIH Director's Early Independence Award Lab Members: Haris Smajlović (Postdoc), Vincent Angelo (CBB MS), Denis Loginov (Senior Software Engineer), Lucy Zheng (CBB PhD)
Pranav Rajpurkar is an Associate Professor at Harvard University, co-founder of a2z Radiology AI, and lead of the Rajpurkar Lab. His work pioneers AI systems that emulate physician-level expertise in medical tasks, with a focus on multi-modal medical AI and radiology. He joined Harvard faculty at age 25 and became Associate Professor at 30 after completing his Stanford PhD at 19. Education: Bachelor of Science (CS), Stanford University, 2015 Master of Science (CS), Stanford University, 2018 PhD (CS), Stanford University, 2021 Rajpurkar's research centers on building AI that thinks and communicates like doctors, with breakthrough work in ECG arrhythmia detection and chest X-ray interpretation. His lab develops foundational datasets (ReXGradient-160K, RadRevise) and benchmarks for medical AI, spanning computer vision, NLP, and multimodal systems. Current work focuses on generative AI for clinical workflows, voice-guided emergency assessment, and rigorous evaluation frameworks for medical LLMs. His 2025 publications reveal three dominant trends: (1) Generative medical AI for radiology report generation and editing, (2) Voice-enabled AI agents for prehospital care, and (3) Multimodal clinical monitoring systems. Key advancements include universal biomedical foundation models (UniBiomed), 3D CT segmentation from text reports, and frameworks for AI-human role separation in radiology. Scientific Awards: Forbes 30 Under 30 in Science (2022) MIT Tech Review Innovator Under 35 (2023) Nature Medicine Early-career Researcher To Watch (2022) Rajpurkar has mentored over 84,000 students through Harvard courses and Coursera's AI for Medicine program. His lab secures major NIH and NSF grants for medical AI development, with recent funding focused on multimodal emergency care systems (MC-MED) and radiologist-AI collaboration. He directs the Harvard-Stanford Medical AI Bootcamp and co-hosts The AI Health Podcast. The Rajpurkar Lab operates as a cross-institutional hub with collaborators at Stanford, MIT, and major hospitals. It drives initiatives like the MAIDA framework for global medical imaging data sharing and develops open-source tools including RadGraph for radiology report analysis. Current projects focus on voice AI for stroke assessment and generative models for non-invasive cancer management.
Deepak Ganesan is a Professor at the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. His research focuses on low-power sensing and communication, networked systems, and machine learning applied to pervasive health monitoring and societal challenges. PhD, Computer Science, University of California, Los Angeles (2004) MS, Computer Science, University of California, Los Angeles (2000) BTech, Computer Science, Indian Institute of Technology, Madras (1998) Ganesan's work bridges wireless sensor networks, smart textiles, and healthcare applications. He designs ultra-low-power wearable devices for tracking health signals like drug use, smoking, and cognitive performance, often integrating machine learning for robust detection. His research emphasizes societal impact, particularly in aging and Alzheimer's care through the Massachusetts AI and Technology Center for Connected Care (MassAITC) and the Center for Personalized Health Monitoring (CPHM). Recent publications highlight innovations in edge-cloud collaboration, fabric-based sensors, and longitudinal health analytics. His NIH-funded MD2K Center for Excellence and affiliations with the Center for Data Science and Computational Social Science Institute further underscore his interdisciplinary approach. ACM Fellow NSF CAREER Award (2006) IBM Faculty Award (2008) UMass Junior Faculty Fellow (2008) UMass Lilly Teaching Fellow (2009) Best Paper at CHI 2013 Best Paper Runner-up at Mobicom 2014 Honorable Mentions at Ubicomp 2013 Ganesan leads the SENSORS: Wireless Sensor Networks Group and contributes to global initiatives like the Internet of Battlefield Things. His work spans academic research, industry partnerships, and policy development in AgeTech and digital health.
Ziran Wang is an Assistant Professor in the Department of Civil Engineering at Purdue University's College of Engineering, appointed as new faculty in 2022. His research bridges digital twin technologies, autonomous driving systems, and human-machine interaction to advance intelligent transportation solutions. Ph.D. in Mechanical Engineering, University of California, Riverside Prior role: Principal Researcher at Toyota North America His work focuses on creating personalized autonomous driving experiences through machine learning, emphasizing safety and efficiency in real-world applications. Key areas include multimodal large language model integration, federated learning for privacy-preserving data sharing, and cooperative perception frameworks. He develops novel approaches for digital twin-based traffic simulation, medical emergency detection in vehicles, and human behavior modeling in complex urban environments. Analysis of his 2024-2025 publications reveals a dominant trend toward generative AI applications in autonomous driving, particularly for perception-prediction-planning integration and real-world validation. His research increasingly incorporates digital twins for safety-critical testing and explores medical applications through in-vehicle health monitoring systems. Dr. Wang advises graduate students including Wenhui Huang and leads the Purdue Digital Twin Lab, which develops advanced simulation and testing platforms for autonomous systems. His lab maintains strong industry partnerships with Toyota for real-world deployment and validation of research成果.
M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.
Dr. Tatsuya Mori is a Professor at the Department of Computer Science and Communication Engineering , Faculty of Science and Engineering, Waseda University . He also holds visiting researcher positions at RIKEN Center for Advanced Intelligence Project (since 2018) and National Institute of Information and Communications Technology (since 2019). Education: Ph.D. in Information Science (2005), Waseda University Research Interests: Spanning information security and privacy across emerging technologies like autonomous driving , AI , 3D sensing , VR , biometric measurement , and Web3 . His work focuses on offensive security and interdisciplinary research , including physical-layer attacks on sensors and behavioral studies on phishing detection. Scientific Awards: Recipient of multiple prestigious awards, including the Distinguished Paper Award Runners-Up at IEEE EuroS&P 2024 , IPSJ Outstanding Paper Award 2024 , and CSS2024 Concept Research Prize . His research has been recognized in top conferences like USENIX Security , NDSS , and ACM CCS . Professional Leadership: Active in academic governance as Chief Investigator for NISC Working Groups and Committee Member for JST Research Areas . He serves on program committees for NDSS , IMC , and ACM CCS .
Prof. dr. Enoch O. Aboh is Professor of Linguistics at the University of Amsterdam , Faculty of Humanities, Department of Literature and Linguistics. His office is located at Spuistraat 134, room 647, and he can be contacted at e.o.aboh@uva.nl . Research Interests: Prof. Aboh’s work lies at the intersection of formal syntax, language contact, and learnability. He investigates creole formation , multilingual ecologies , Gbe and Kwa syntax , cartographic approaches to clause structure , and sign language morphosyntax . A recurring theme is the emergence and evolution of grammatical systems under contact, approached through both descriptive fieldwork and formal theoretical modelling. His publications reveal a methodological breadth spanning experimental studies with kindergarteners on statistical learning, computational detection of loanwords, and fine-grained syntactic analyses of serial verb constructions, predication patterns, and determiner systems. This spectrum underscores his commitment to integrating cognitive, typological, and formal perspectives on language. Awards & Recognition: While no specific prizes are listed in the current material, Prof. Aboh’s extensive editorial and collaborative work—evidenced by numerous co-edited volumes and Festschrift contributions—attests to his standing in the field. Students & Grants: Details on PhD advisees or funded projects are not provided in the source text. Labs & Teams: No dedicated laboratory or research group names are mentioned, yet his affiliation with the Amsterdam Center for Language and Communication (ACLC) can be inferred from the institutional context.
Shuangquan (Peter) Wang is an Assistant Professor of Computer Science at Salisbury University. He holds a PhD in Computer Science from the College of William & Mary (2020) and a PhD in Pattern Recognition and Intelligent Systems from Shanghai Jiao Tong University (2008), along with earlier degrees from Wuhan University of Technology and Wuhan Institute of Technology. His research focuses on mobile/wearable computing, activity recognition, smart health, and machine learning. He has over 10 years of experience in academia and industry, including roles at Philips Research East Asia and Nokia Research Center (Beijing). His work emphasizes wearable sensor-based health monitoring, such as fall detection, mastication analysis, and Parkinson’s disease monitoring. He leads the WISH Research Lab and serves as an Associate Editor for Elsevier's Smart Health Journal. Recent contributions include papers on salinity anomaly detection (2024), LLM-based user requirement analysis (2024), and socially acceptable food recognition (2022). His research trends emphasize interdisciplinary applications of machine learning in healthcare and sensor-driven human activity analysis. Professional service roles include coordinating Salisbury University’s Center for Applied Mathematics and Science (2021–2024) and chairing ACM/IEEE CHASE conferences. He has delivered invited talks on artificial intelligence and its societal impacts to diverse audiences, including the Institute of Retired Persons at Salisbury University. His lab, WISH Research Lab, explores innovative solutions in smart health and mobile computing, integrating wearable technologies with machine learning for real-world health applications.
Laurent Matthey is an Associate Professor at the University of Geneva's Department of Geography and Environment since 2014. He directs the Urban Planning orientation of the joint UNIGE-HES-SO Master's in Territorial Development and co-directs the UNIGE-EPFL MAS in Urban Planning. His work bridges ethnography and literary studies, examining political issues in urban narrative production through sensitive registers and attentional regimes. Current affiliations: CRESSON team (UMR AAU) since 2023 Active in EspaceSuisse committee and RAMAU network management Research focuses on epistemology of urban planning knowledge , organized into two streams: Planning discourses and narrative studies Ethnography of urban projects His research-creation explores intersections between: Communication and urban planning Fiction and urban planning Narrative and sensitive methods Urban social quality Current projects include "Participez! Neighborhood makers" (FNS Division 1 funding) and completed works like "The narrative making of the city" (FNS-COST 18126) and "Developing housing cooperatives in Greater Geneva" (Interreg V France-Switzerland). He has supervised doctoral students in Geneva, including Hugo Campi ("Flowers to Convince"), Nicole Peccoud ("Social emergency in Geneva"), and Morgane Rudaz ("Feminist collages"). Key scientific contributions appear in GeoHumanities , Planning Theory , and Social Inclusion , with recent emphasis on: AI's role in urban narrative (2024) Decolonial planning approaches (2023) Affective urbanism (2018) Teaching encompasses: Urban geography and ecology (T206059 BGEO) Research-creation seminar (T416007 MDT) Creative methods in metropolitan foresight Professional engagements include urban quality studies for Geneva's Urban Planning Office and Lausanne's Roads Department. He has co-authored foundational texts on Swiss urbanism with Christophe Mager and David Gaillard, exploring topics like "Urbanism fictionnel" (2011) and "Being an urban planner" (2016).
Richard J. Senghas is a Professor of Anthropology at Sonoma State University . His work bridges linguistic anthropology and Deaf studies , focusing on the evolution of Nicaraguan Sign Language (ISN) and sociocultural dynamics in intentional communities. Education BA, University of Massachusetts (Amherst), 1978 MA, University of Rochester, 1994 PhD, University of Rochester, 1997 His research examines how language emergence intersects with identity formation , community development , and political factors . He has published extensively on ISN’s development, sociolinguistic variation in sign languages, and cybernetic models of language change. Key publications include works in The Routledge Handbook of Linguistic Anthropology (2017), Annual Review of Anthropology (2002), and edited volumes on Deaf communities. His 2017 chapter explores communicative practices in sign languages, while older works analyze ISN’s birth and sociolinguistic diversity in ASL. Scientific Awards Edward Sapir Book Prize, Society for Linguistic Anthropology (2001) As a California State University Academic Senate representative, he advocates for academic governance. Collaboratively, he works with his sister Ann Senghas (Barnard College) and other scholars like Judy Pyers and Lorraine Monaghan .
Zheng (Tracy) Ke is an Associate Professor of Statistics at Harvard University. She holds a Ph.D. from Princeton University (2014) and a B.S. from Tsinghua University (2009). Her research focuses on high-dimensional statistics, machine learning, social network analysis, text mining, and bioinformatics. Notable contributions include advancements in network data analysis (e.g., SCORE normalization), text analysis methodologies, and statistical genetics pipelines. Dr. Ke has received prestigious awards such as the COPSS Emerging Leader Award (2024) and the Sloan Research Fellowship (2023). She has organized major conferences like the Workshop on Statistical Network Analysis and Beyond (2024) and contributed to the MADStat dataset analyzing statisticians' co-authorship networks. Her research interests span theoretical and applied domains, with a focus on developing scalable algorithms and rigorous statistical frameworks for complex data. Recent work emphasizes challenges like severe degree heterogeneity in networks and rare/weak signal detection in high-dimensional settings. Dr. Ke is an Associate Editor for the Journal of the American Statistical Association and actively collaborates on interdisciplinary projects.
Jonas Beskow is a Professor and Head of Division at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. His research focuses on multimodal interaction, speech synthesis, robotics, and human-robot interaction. He leads the Learning style variation in nonverbal behaviour for social robots and agents project as part of Digital Futures, a cross-disciplinary research center. His work involves developing social robots like the Furhat head and advancing technologies for gesture synthesis, audio-driven motion, and adaptive intelligent systems. He holds roles as Co-PI for the Advanced Adaptive Intelligent Systems (AAIS) and Adaptive Intelligent Homes (AIH) projects. His research spans robotics, computer graphics, and clinical applications such as dementia detection through multimodal patient behavior analysis. Beskow also contributes to educational initiatives, supervising courses in computer science and engineering, including degree projects in machine learning and systems engineering. Publications highlight innovations in gesture generation, speech-driven animation, and socially-aware robotics. Collaborations with institutions like Stockholm University and RISE Research Institutes drive interdisciplinary solutions. His work bridges artificial intelligence, human-computer interaction, and assistive technologies, emphasizing ethical and societal impacts of emerging digital systems.
