Alberto Rodrigues da Silva is a Professor at the Institute Superior Técnico , part of the University of Lisbon . He teaches Fundamentals of Information Systems , primarily during the 1st Semester of the 2025/2026 academic year. His scientific interests revolve around Information Systems , Model-Driven Engineering (MDE), Requirements Engineering (RE), Social Computing , and Software Engineering . He has extensively contributed to the development of rigorous requirements specification languages like RSL (Requirements Specification Language) and its extensions (e.g., RSL-IL4Privacy for privacy policies). His collaborative work spans automated acceptance testing, GDPR compliance, and domain-specific languages (DSLs) for applications such as mobile development , digital twins , and legal contexts (e.g., LegalLanguage ). His research trends focus on integrating model-driven engineering with privacy policies , IoT applications , and low-code platforms . He has also explored tools like Maestro for data classification and usability testing, and RiverCure for flood simulation. Email: alberto.silva@tecnico.ulisboa.pt .
Tyler Simko is an Assistant Professor of Political Science at the University of Michigan, specializing in US state and local politics, political geography, and computational social science. His research focuses on understanding and addressing inequality in American public policy through innovative methodological approaches. Education: Ph.D. in Government, Harvard University (2024) A.B. in Politics, Princeton University Simko's research examines state and local politics in the United States with particular focus on political geography and subnational policymaking. His active research agendas include legislative redistricting ("gerrymandering"), local public meetings, school segregation, affordable housing, and data privacy. Methodologically, he develops new techniques in computational social science and machine learning to evaluate subnational inequality and how it can be reduced. His work regularly involves partnerships with federal, state, and local officials to improve the design of public policy. His recent publications demonstrate a strong focus on applying computational methods to address real-world policy challenges, particularly in school desegregation, redistricting, and local government transparency. His research often leverages large-scale data collection efforts, such as LocalView (the largest database of local government meetings in the US), to analyze patterns of political behavior and policy outcomes across different jurisdictions. Awards and Recognition: APSA 2024-25 Best Paper in Education Politics and Policy Award APSA 2024-25 Best Paper in Urban and Local Politics, Honorable Mention MPSA 2024 Robert H. Durr Award for "the best paper applying quantitative methods to a substantive problem" Derek C. Bok Award for Excellence in Graduate Student Teaching of Undergraduates (2023) Simko teaches graduate and undergraduate courses in American Politics and Political Methodology at the University of Michigan. His teaching experience spans multiple institutions, including Harvard University and Princeton University. He has designed innovative courses on US Local Policymaking, data science, and computational social science. As a Data Scientist at the Office of Evaluation Sciences, he partners with federal, state, and local officials to improve program design and reduce administrative burdens. He is a co-PI of the Algorithm-Assisted Redistricting Methodology (ALARM) Project and co-creator of LocalView, the largest audio, video, and text database of local government meetings in the United States. These projects represent significant contributions to the field of computational social science and provide valuable resources for researchers studying local governance and policy-making.
Professor Paul Rayson is a Professor of Natural Language Processing in the School of Computing & Communications at Lancaster University, UK. He serves as Director of the UCREL (University Centre for Computer Corpus Research on Language) interdisciplinary research centre and is affiliated with multiple research institutes including Security Lancaster, the Lancaster Centre for Digital Humanities, and the Data Science Institute. Education: PhD in Computer Science, Lancaster University (2003) BSc (Hons) Computer Science and Mathematics, Lancaster University (1990) Professor Rayson's research focuses on semantic multilingual Natural Language Processing (NLP) in challenging linguistic environments with noisy language data, including historical texts, learner language, speech, email, and other computer-mediated communication. His work spans applications in dementia detection, mental health analysis, online child protection, cyber security, learner dictionaries, and text mining of biomedical literature, historical corpora, and financial narratives. He has developed semantic tagging tools like USAS (UCREL Semantic Analysis System) and Wmatrix for corpus analysis. Major Awards and Honors: FHEA (Fellow of the Higher Education Academy) MBCS (Member of the British Computer Society) Professor Rayson has supervised numerous PhD students in NLP and corpus linguistics, with eight current students and seven completed doctorates. He has led or co-investigated multiple major research projects including the £3.5m ESRC-funded Centre for Corpus Approaches to Social Science (CASS), the National Corpus of Contemporary Welsh, and projects related to mental health forums, financial narrative analysis, and cyber security. His research has been supported by ESRC, EPSRC, and other funding bodies. As Director of UCREL, he oversees research in corpus linguistics and NLP. He is also active in the Cyber Security Research Centre, Digital Health Group, and multiple Data Science Institute initiatives. His lab has developed several widely-used NLP tools including CLAWS for English POS tagging, USAS semantic analysis system, Wmatrix corpus analysis tool, and the Variant Detector (VARD) for historical texts.
Ion Androutsopoulos is a Professor of Artificial Intelligence in the Department of Informatics at Athens University of Economics and Business (AUEB), where he also serves as Head of Department. He is founder and co-director of AUEB's Natural Language Processing Group and an Adjunct Researcher at the Digital Curation Unit and "Archimedes" Research Unit of the Research Centre "Athena". His research spans multiple dimensions of Artificial Intelligence with a focus on Natural Language Processing. Key interests include: Machine learning in NLP, particularly deep learning and large language models Question answering and retrieval augmented generation for document collections Dialog systems for new languages and knowledge domains Sentiment analysis and emotion recognition from text and speech Detecting toxic posts and disinformation online Image-to-text generation for medical diagnostics NLP applications in biomedical, legal, and financial domains His recent publications demonstrate strong activity across medical AI (particularly ImageCLEFmed Caption competitions where his group consistently ranks 1st-2nd), legal NLP (LexGLUE benchmark), financial NLP (EDGAR-CRAWLER), and multilingual challenges. His work shows increasing emphasis on large language models, explainability, and practical applications. Notable awards include: Top 2% scientist worldwide (Stanford University database, 2023) Multiple AUEB Excellent Teaching Awards (2017-18, 2021-22, 2023-24) Three consecutive BioASQ awards (2018-2020) Multiple 1st/2nd place rankings in ImageCLEFmed Caption competitions (2021-2025) He actively organizes major events including the Athens Natural Language Processing Summer School (AthNLP) and SemEval tasks. His group maintains strong industry and research collaborations, particularly in medical AI applications where they've developed systems that generate diagnostic captions from medical images with state-of-the-art performance.
Bryan Kian Hsiang Low serves as Associate Professor in the Department of Computer Science at the National University of Singapore's School of Computing, while simultaneously holding leadership positions as Director of AI Research at AI Singapore and Deputy Director of the NUS AI Institute. His academic journey includes a B.Sc. (2001) and M.Sc. (2002) in Computer Science from NUS, followed by a Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University (2009). His research spans probabilistic machine learning, multi-agent systems, and trustworthy AI, with particular focus on Bayesian optimization , federated learning , and data-efficient methodologies . The Low Lab develops frameworks for collaborative AI, automated machine learning, and AI applications in scientific domains through the Group of Learning and Optimization Working in AI (GLOW.AI), which maintains a multi-disciplinary approach bridging computer science, mathematics, and engineering disciplines. Analysis of his recent publications reveals a consistent emphasis on data valuation , privacy-preserving collaborative learning , and robust optimization techniques , with increasing integration of large language models into his research framework. His work demonstrates strong theoretical foundations coupled with practical applications in computational sustainability and robotics. Andrew P. Sage Best Transactions Paper Award (2006) NUS Overseas Graduate Scholarship (2004-2009) Faculty Teaching Excellence Award (2017-2018) IEEE RAS Distinguished Lecturer (2019) World Economic Forum Global Future Councils Fellow (2016-2018) Dr. Low actively mentors PhD students including Rachael Sim, Quoc Phong Nguyen, and Zhongxiang Dai, while leading major initiatives like the AI Phenome Platform for plant breeding optimization. His research group GLOW.AI operates at the intersection of theory and practice, with strong industry engagement through AI Singapore. Current projects focus on scalable AI systems for scientific discovery and developing frameworks for equitable collaborative machine learning with robust privacy guarantees.
Derry Wijaya is an Associate Professor and Program Coordinator for the Data Science Program at Monash University Indonesia. She also co-directs the Monash Data and Democracy Research Hub, focusing on analyzing data's impact on democracy. Previously, she served as an Assistant Professor at Boston University's Department of Computer Science. Her research spans multilingual NLP, low-resource language technologies, and combating AI-driven misinformation. Education: PhD in Language Technologies, Carnegie Mellon University (2013) Postdoctoral Fellowship, University of Pennsylvania (2013–2015) Bachelor's & Master's in Computing, National University of Singapore Research Interests: Improving language model performance via self-consistency and reasoning Analysis of bias, toxicity, and framing in AI outputs Preservation of Indonesian indigenous scripts and languages Development of tools like OpenFraming AI for multilingual framing analysis Recent Work Trends: Her publications (2023–2025) emphasize ethical AI, low-resource language solutions, and social media analysis. Notable contributions include frameworks for metric calibration (MetaMetrics), debiasing generative models, and surveys on Indonesian language technology needs. Awards & Roles: Fulbright Scholarship recipient Serves on program committees for ACL, EMNLP, NeurIPS, and ICLR Co-created OpenFraming AI for multilingual framing analysis Grants & Labs: Leads the Monash Data and Democracy Hub, focusing on tech's societal impact. Active in grant-funded projects preserving Indonesia's linguistic heritage through digitization efforts.
Jay Pujara is a Research Associate Professor in the Department of Computer Science at the University of Southern California (USC), affiliated with the Viterbi School of Engineering and the Information Sciences Institute (ISI). He directs the Center on Knowledge Graphs and leads research in artificial intelligence, specializing in knowledge graph construction, scalable machine learning, and probabilistic models. Education : PhD in Computer Science (University of Maryland, 2016), MS and BS in Computer Science from Carnegie Mellon University (2005 and 2004), with minors in Robotics, Mathematical Sciences, and Logic & Computation. Research Interests : His work focuses on probabilistic models for dynamic data, knowledge graph construction, entity resolution, and applications in NLP and social network analysis. He emphasizes scalable algorithms and real-world impact in domains like finance, climate science, and healthcare. Awards : Includes the SWSA Ten-Year Award (2023), Best Paper Awards at IUI 2019 and ISWC 2013, and grants totaling over $9M from DARPA, NSF, and industry partners. Grants & Mentorship : Principal Investigator on projects like "Artificial Domain-Understanding and Collaborative Agency" (DARPA) and "Explainable and Robust AI Agents" (NSF). Mentored over 50 students in PhD, MS, and undergraduate programs, focusing on knowledge graphs, NLP, and machine learning. Labs & Teams : Leads ISI’s Knowledge Graph and Neurosymbolic AI teams, coordinating the Open Knowledge Network (OKN) and tools like KGTK. Active in academic service, including roles on PhD admissions committees and ISI’s Space Management Committee.
Yuzhe Yang is an Assistant Professor of Computational Medicine and Computer Science at UCLA, with a visiting research scientist role at Google Health. He holds a PhD in Computer Science from MIT (2024), advised by Dina Katabi, and a B.S. with honors from Peking University. Research Focus: Machine learning for healthcare, medical AI fairness, and AI-driven biomedical discovery Key Contributions: Ten Notable Advances (Nature Medicine) and Ten Crucial Advances (The Lancet Neurology) His lab develops Trustworthy Learning Algorithms and Generalist Health Models that integrate Multimodal Data for personalized health coaching. Notable projects include AI-based Parkinson's Disease Biomarkers via nocturnal breathing and Foundation Models for Equitable Medicine . Recent publications at ICLR 2025 (wearable foundation models), Nature Medicine 2024 (medical AI fairness), and Science Advances 2025 (vision-language medical bias) highlight his interdisciplinary work. He serves on ML4H workshops and reviews for top conferences like NeurIPS and ICML. Awards include Forbes 30 Under 30 , Takeda Fellowship , and Baidu PhD Fellowship . Advising opportunities: Recruiting PhD students (CS/CompMed) and postdocs in AI for health. Lab: Health Intelligence Lab (HAIL)
Abolfazl Asudeh is an Associate Professor in the Department of Computer Science at the University of Illinois Chicago and director of the Innovative Data Exploration Laboratory (InDeX Lab) . He is a Senior Member of ACM and IEEE , serving as Associate Editor for IEEE Transactions on Knowledge and Data Engineering , VLDB Ambassador , and VLDB Endowment Liaison to NSF . His research focuses on Algorithm Design for Data and AI problems , emphasizing efficient, accurate, and responsible solutions through Approximation Algorithms , Randomized Methods , and Computational Geometry . Recent work explores LLM optimization ( Needle ), fair data structures ( FairHash ), and responsible AI frameworks ( Chameleon ). Scientific awards include Communications of the ACM Research Highlight Google Research Scholar Award SIGMOD 2019 Research Highlight Best of VLDB 2020 SIGMOD 2017 Reproducibility Award Grants: NSF IIS-2348919 (2024-2027): Fairness-aware Data Structures NSF IIS-2107290 (2021-2024): Collaborative Fairness Research The InDeX Lab develops systems like Needle (image retrieval) and RSR (matrix multiplication). His work integrates fairness , reliability , and computational efficiency across data structures , LLMs , and responsible AI implementations.
Lisa Wu Wills is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at Duke University, leading the APEX Lab (Application-driven Programmable Efficient Accelerated Systems Lab). Her research focuses on hardware acceleration for big data analytics in genomics, graphs, and databases to advance healthcare and natural sciences. Education: Ph.D. in Computer Science, Columbia University, 2014 Research Interests: Dr. Wills pioneers computer architecture and hardware-software co-design to create efficient accelerators for emerging applications. Her work targets genomics , graph analytics , and database systems , emphasizing simplified hardware deployment and energy efficiency for scientific breakthroughs in healthcare and AI. Publication Trends: Her 2022-2025 publications reveal a strong focus on open-source frameworks (Beethoven, PyTFHE) for accelerator development, hardware acceleration in privacy-preserving computing, and optimization for large language models. Key themes include transfer learning for EDA, domain-specific architectures for genomics, and energy-efficient image processing. Scientific Awards: Google ML and Systems Junior Faculty Award (2025) Advising and Grants: Dr. Wills mentors three PhD students: Chris Kjellqvist (Beethoven framework architect), Mason Ma (PyTFHE lead for FHE applications), and Mansi Choudhary (COCOSSim simulator creator). Her 2025 Google award funds research on accelerating vector databases and retrieval-augmented generation for LLMs. Labs and Teams: She directs the APEX Lab at Duke, developing tools like Beethoven (open-source accelerator composer) and PyTFHE for hardware-software integration, enabling domain scientists to leverage custom acceleration with minimal hardware expertise.
Alessandro Aliakbargolkar is a Professor at the Department of Space Systems Design under the School of Aerospace Engineering at Skolkovo Institute of Science and Technology (Skoltech). His research focuses on Federated Satellite Systems, CubeSat constellations, and Spacecraft Systems Architecture, with applications in Earth observation, messaging services, and networked satellite systems. He has an extensive publication record in these areas, including work on technology roadmapping and digital twin implementation. Key Research Areas: Satellite federation and resource sharing CubeSat constellation design Network performance optimization Integration of systems engineering models with AI Selected Trends: Recent work explores digital twin technologies for CubeSats, federated satellite network analysis, and large language model applications in spacecraft design. Publications often combine theoretical frameworks (e.g., network theory) with practical implementations (e.g., LoRa-based messaging services). ORCID Profile: 0000-0001-5993-2994
Andrei Kutuzov is an Associate Professor in the Language Technology Group (LTG) within the Department of Informatics at the University of Oslo. He serves as the Norwegian on-site manager of the High-Performance Language Technology (HPLT) project and has made significant contributions to computational linguistics and natural language processing. His research primarily focuses on computational linguistics and natural language processing, with specialized expertise in semantic change detection, diachronically aware language models, distributional semantics, and large language models. Kutuzov has been instrumental in developing Norwegian language resources including NorBERT, NorELMo models, and the very large-scale NORA.LLM generative models. He created WebVectors, a web service for exploring neural distribution models for Norwegian and English texts. Analysis of his recent publications reveals a strong focus on semantic change modeling, multilingual dataset development, and Norwegian language technology. His work spans from theoretical linguistic analysis to practical applications in language modeling, with significant emphasis on low-resource and Nordic languages. Kutuzov's research demonstrates a consistent trajectory toward improving language models' understanding of semantic evolution and developing robust evaluation frameworks for Norwegian language processing. Norwegian Artificial Intelligence Research Consortium (NORA) award as Distinguished Early Career Researcher (2022) Kutuzov teaches several advanced courses including IN5550 - Neural Methods in Natural Language Processing (2019-2025) and IN3050 - Introduction to Artificial Intelligence and Machine Learning (2024-2025). He has received research funding through the HPLT project which focuses on developing high-performance language technologies. His laboratory work centers around the Language Technology Group at UiO, where he collaborates on developing Norwegian language resources and models, with particular emphasis on diachronic semantic analysis and multilingual capabilities.
Anomadarshi Barua is an Assistant Professor in the Department of Cyber Security Engineering at George Mason University, leading the System Design and Security research group. His work spans hardware-software co-design for securing cyber-physical systems (CPS), robotics, and sensors. Prior Affiliation: PhD from University of California, Irvine (2023) Industry Experience: Intel Corporation, Solidigm, Nordic Semiconductor, IDEAS Research Themes: Focuses on multimodal system security (audio, visual, electromagnetic data), analog-digital signal integrity, and quantum-inspired defenses in CPS. Key applications include healthcare systems, smart grids, and industrial control systems (ICS). Recent ACSAC 2024 paper acceptance Best Paper Award at ACSAC 2022 NSF panel reviewer (2024) Labs & Collaborations: Collaborates with University of Louisville on robotics and works on Commonwealth-funded UG research (2024). Publications in ACM CCS, USENIX, CHES, and IEEE Transactions (TDSC, TIFS).
Prof. Ryan Keith Shosted is a full-time tenured Professor at the University of Illinois at Urbana-Champaign , affiliated with the Department of Linguistics , Spanish and Portuguese , American Indian Studies Program , Beckman Institute , Lemann Center for Brazilian Studies , Center for Latin American and Caribbean Studies , and Center for African Studies . He serves as Director of the Program in Translation and Interpreting Studies and leads the Chin-Woo Kim Phonetics Laboratory . Education: Ph.D. , Linguistics, University of California, Berkeley (2006) M.A. , Linguistics, University of California, Berkeley (2003) B.A. , Linguistics, Brigham Young University (2000) Shosted's research focuses on the intersection of phonetics , phonology , and historical linguistics . He pioneered the application of ultrafast dynamic MRI to study the vocal tract's physiological-acoustic mapping in diverse languages, including Hittite cuneiform , Deseret Alphabet , and endangered languages like Q'anjob'al. His work spans speech production modeling , nasalization mechanisms , and cross-linguistic articulatory analysis . The 15 most recent publications demonstrate his leadership in dynamic speech imaging , phonetic-aerodynamic modeling , and historical sound change analysis . Key trends include advanced MRI techniques for speech study, phonetic universals , and historical writing systems as tools for linguistic reconstruction. Scientific Awards: Campus Award for Excellence in Undergraduate Teaching (2021) Dean's Award for Excellence in Undergraduate Teaching (2021) Arnold O. Beckman Award (2009, 2010) Jacob K. Javits Fellowship (2001-2005) Shosted's grant portfolio includes NSF funding for nasalization research (BCS-1651197, BCS-1121780) and NIH collaboration (1R01DE027989-01A1) on cleft palate speech. He has directed 12 graduate students and taught courses ranging from Hittite language to quantitative phonetic methods . The Chin-Woo Kim Phonetics Laboratory , under his directorship since 2007, expanded in 2010 to include articulatory phonetics facilities with EPG, ultrasound, and MRI analysis capabilities. He continues to lead Beckman Institute collaborations in speech imaging technology.
Irem Boybat is a Researcher in the In-Memory Computing Group at IBM Research - Zurich, Switzerland, focusing on advanced AI hardware solutions. She holds a Ph.D. in Electrical Engineering from EPFL (2020) and prior degrees from EPFL and Sabanci University. Ph.D., Electrical Engineering, EPFL (2020) M.Sc., Electrical Engineering, EPFL (2015) B.Sc., Electronics Engineering, Sabanci University (2013) Her research bridges in-memory computing and AI, targeting energy-efficient hardware for deep learning and neuromorphic systems. Recent work explores analog AI accelerators, heterogeneous architectures, and scalable models for edge computing. Publications highlight cross-disciplinary innovation in materials, circuits, and system design. She has received the IBM Pat Goldberg Memorial Best Paper Award and EPFL PhD Thesis Distinction. Her invited talks span prestigious venues including the European Phase-Change Symposium, IEEE CICC, and HiPEAC. Collaborations include EU H2020 projects like MANIC and WiPLASH.
