Dr. Alexander Mantzaris is an Associate Professor in the Department of Statistics & Data Science at the University of Central Florida, College of Sciences. His research bridges physics and sociology through Social Physics frameworks, focusing on statistical mechanics and thermodynamic analogies to model social phenomena. Current research explores criticality points in social systems Developing computational tools for NLP and big data Former work on Graph Convolutional Networks in social analysis Specializes in entropy-based modeling of polarization and segregation His publications emphasize interdisciplinary approaches combining network science, computational modeling, and sociological dynamics. Recent articles address thermodynamic formulations of political cycles, energy states in Schelling models, and memory-efficient data processing algorithms. Dr. Mantzaris teaches graduate courses in big data analytics and statistical learning theory. He maintains active research in computational social science with applications to political dynamics, media influence, and complex systems analysis.
Yuan-Fang Li is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He also serves as Associate Dean International. His research focuses on knowledge graphs, natural language processing, multimodality, and graph representation learning. He holds a PhD from National University of Singapore (2006) and a Bachelor of Computing (Honours) from the same institution (2002). Affiliations: Monash University (since 201?), National University of Singapore (PhD 2002-2006) Key Projects: Leading research on neuro-symbolic systems (HARNESS project), large-scale multimodal knowledge management, and maritime knowledge graphs Teaching: Taught courses including FIT4002, FIT4004, and supervised over 20 PhD students Research interests include complex question answering over knowledge graphs, knowledge extraction from text/images, and structural/temporal graph learning. He has published 152+ works with notable contributions to scene graph generation, event extraction, and LLM-based reasoning. Key awards include the 2020 Best Student Paper Award and 2017 Kurzweil Prize. Grants: ARC Discovery Projects, industry collaborations (e.g., Outotec Oy) Labs/Teams: Active in Monash's Data Science & AI research groups, leading neuro-symbolic AI initiatives
Dr. Nidhi Hegde is an Associate Professor in the Department of Computing Science at the University of Alberta and a Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii). Her research focuses on privacy-preserving machine learning, algorithmic fairness, and robust algorithm design for networked systems. Dr. Hegde's current research investigates differential privacy in bandit algorithms, debiasing frameworks for language models, and long-term fairness guarantees for minority groups. Her work combines theoretical foundations with practical applications in distributed systems and multi-agent learning environments. Recent publications address covariate shift effects in optimization, private matroid optimization, and reinforcement learning with functional noise. She teaches graduate courses on Responsible AI and Ethical Issues in Data Analytics, covering topics including data privacy, fairness in algorithms, interpretability, and accountability. Dr. Hegde maintains active research collaborations and previously led privacy research at Borealis AI (RBC's research institute).
Siddharth Garg is the Institute Associate Professor of Electrical and Computer Engineering at NYU Tandon School of Engineering, leading the EnSuRe Research Group. He holds a Ph.D. from Carnegie Mellon University (2009) and a B.Tech. from IIT Madras. His research focuses on secure and energy-efficient computing systems, integrating machine learning, cybersecurity, and hardware design. He previously held roles as Assistant Professor at NYU Tandon (2014-2020) and the University of Waterloo (2010-2014). Key affiliations include NYU Center for Cybersecurity (CCS), NYU Wireless, and the Center for Advanced Technology in Telecommunications. His work has been recognized with prestigious awards like the NSF CAREER Award (2015) and inclusion in Popular Science’s 'Brilliant 10' (2016). Notable research includes private inference optimization, secure hardware IP protection, and adversarial machine learning defenses. Publications highlight advancements in zero-knowledge proofs, AI-driven chip design, and mitigating backdoor attacks in neural networks. His grants include funding from NYU Wireless and NSF initiatives like the Chips4All project. The EnSuRe group emphasizes bridging software and hardware design gaps using AI and fostering cybersecurity education.
Gaël Georges Marcel Le Mens is a Full Professor at Pompeu Fabra University (UPF), holding a position in the Department of Economics and Business. He is also affiliated with the Barcelona School of Economics and serves as academic co-director of the Executive Master in Business Administration (EMBA) at the UPF Barcelona School of Management. His academic journey includes teaching roles at INSEAD, London Business School, ESADE, and the University of Lugano, alongside positions at the universities of Southern Denmark and New York. Education: Doctor in Business Administration, Stanford Graduate School of Business MSc in Management Science and Engineering, Stanford University Diploma in Engineering, Supélec Bachelor of Economics, University of Paris XI His research focuses on decision-making processes, information sampling, machine learning applications in semantics, and organizational behavior. Key themes include cognitive heuristics, social media impact on political expression, and the interplay between popularity and evaluation dynamics. He has explored how feedback mechanisms shape political communication and developed methodologies to compare human and machine conceptual judgments using models like BERT. His publications span journals such as PNAS , Psychological Review , and Industrial and Corporate Change , reflecting his interdisciplinary approach. Though no explicit awards are noted, his prolific output highlights sustained academic impact. He has advised multiple institutions on curriculum design and executive education, leveraging his cross-university teaching experience. Le Mens is affiliated with the Barcelona School of Management’s research teams and contributes to initiatives bridging artificial intelligence and social sciences. His work often addresses practical challenges in organizational decision-making and digital communication strategies.
Parisa Kordjamshidi is an Associate Professor of Computer Science and Engineering at Michigan State University (MSU), leading the Heterogeneous Learning and Reasoning (HLR) Lab. Her research focuses on Neuro-Symbolic AI, spatial language understanding, and structured learning, with notable contributions to frameworks like Saul for declarative programming. She joined MSU in 2019 after roles at Tulane University and the Florida Institute for Human and Machine Cognition. Education: Ph.D. in Computer Science from KU Leuven (2013), postdoctoral research at UIUC's Cognitive Computation Group, and work in the KnowEng project. Research Interests: Artificial Intelligence, Machine Learning, Natural Language Processing, Neuro-Symbolic systems, spatial semantics extraction, structured output learning, and multimodal reasoning. Key projects include NSF CAREER awards for spatial language understanding and ONR grants for integrating domain knowledge into AI. Awards: NSF CAREER (2019), Amazon Faculty Research Award (2022), Fulbright Scholar (2025), and Rising Stars at MIT EECS (2015). Grants: Active projects on Neuro-Symbolic compositional generalization (ONR), spatial language learning (NSF), and collaborations with the Department of Media and Information for health misinformation management. Professional Activities: Editorial roles at JAIR, TACL, and Frontiers journals; service on program committees for ACL, EMNLP, and AAAI; organization of workshops like Spatial Language Understanding (SpLU) and CLeaR. Lab and Software: HLR Lab develops Saul (declarative learning-based programming framework) and tools for spatial role labeling. Her team emphasizes mentoring, with structured weekly meetings, reading groups, and conference participation for students.
Dr. Gunel Jahangirova is a Lecturer in Computer Science within the Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences at King's College London. Her research focuses on software testing, software engineering for AI, and search-based software engineering. She earned her PhD through a joint program at Fondazione Bruno Kessler (Italy) and University College London (UK), followed by postdoctoral work on the ERC-funded 'Precrime' project at Università della Svizzera italiana (Switzerland). Software Testing AI Engineering Search-Based Optimization Deep Learning Verification Her recent publications explore fault localization in neural networks, ethical testing of autonomous systems, and environmental impacts of AI code development. Current projects include ITEA GENIUS and ITEA GreenCode , focusing on AI testing and sustainable software practices.
Zhu-Tian Chen is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, where he leads research in data visualization, human-computer interaction, and augmented reality. Prior to this, he held postdoctoral positions at Harvard University and UC San Diego, working with leading researchers in visual computing and interactive design. Ph.D. in Computer Science, Hong Kong University of Science and Technology B.Eng. in Software Engineering, South China University of Technology His research focuses on augmenting human intelligence through hybrid human-AI systems, particularly in everyday and outdoor environments. He specializes in designing intelligent AR interfaces, embedded visualizations, and language-oriented interactions for applications in sports analytics, education, and data analysis. His work integrates human-centered design with applied machine learning to create intuitive and effective visualization tools. The recent trend in his publications shows a strong emphasis on intelligent AR systems for dynamic scenes, LLM-based code generation interfaces, and real-time augmentation of sports videos using natural language and gaze-based interactions. His work frequently appears in top-tier venues such as IEEE VIS, ACM CHI, and UIST. Best Paper Award, ACM CHI'23 Best Short Paper Honorable Mention, EuroVis'23 Best Paper Honorable Mention, IEEE VIS'22 (twice) Certificate of Distinction and Excellence in Teaching, Harvard University Hong Kong Ph.D. Fellowship Dr. Chen actively mentors undergraduate, master’s, and PhD students, as well as visiting scholars and interns, and is building a new research lab focused on visualization for intelligent AR systems. He has served on program committees for major conferences including ACM CHI, IEEE VIS, and EuroVis, and has been invited to speak at institutions such as Apple, JP Morgan, and multiple universities worldwide. He also contributes to the academic community through grant reviewing for NSF and the Department of Energy. He leads research projects in intelligent AR systems for sports, language-oriented interactions with LLMs, and immersive data visualization, often in collaboration with institutions like Harvard, UC San Diego, and HKUST. His lab welcomes students and collaborators interested in visualization, HCI, and applied AI.
Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.
Shuvendu K. Lahiri is a researcher at Microsoft Research, focusing on formal verification, program synthesis, and software testing. His work bridges artificial intelligence with formal methods, particularly in blockchain security and automated code generation. 2025 : Published LLM-Vectorizer (verified loop vectorizer) and neural synthesis for SMT-assisted proof-oriented programming 2024 : Explored LLM-based test-driven code generation and natural precondition inference 2023 : Developed resource management specifications and contributed to test generation with pre-trained models 2022 : Advanced Solidity type systems and merge conflict resolution using language models His research combines large language models with formal verification tools to improve software correctness. He actively contributes to conferences like ICSE, PLDI, and ISSTA as author and committee member.
Jean-François Godbout is a Professor in the Department of Political Science at the Université de Montréal and an Associate Academic Member of Mila - the Quebec AI Institute. He directs the undergraduate program in Big Data Analytics in Social Sciences and Humanities at UdeM and conducts interdisciplinary research through the Complex Data Lab. Affiliated with IVADO (AI Consortium) Member of CÉRIUM (International Research Centre) and CECD (Democratic Citizenship Centre) His research focuses on: Data Science applications in political institutions AI Safety and generative AI's impact on political attitudes Misinformation Mitigation through large language models Comparative Political Development in Canadian and Lower Canada contexts Legislative Institutions and voting records analysis Political Polarization in online societies Recent publications analyze social media disinformation, AI persuasion on harmful topics, and education-focused text simplification. His articles frequently combine graph mining , machine learning , and political science methodologies. Scientific collaborations include: Mila researchers (Andreea Musulan, Maximilian Puelma Touzel) IVADO data science initiatives McGill University interdisciplinary projects He supervises students in: Political science (Julien Robin, Matthew Taylor) Artificial Intelligence (Kellin Pelrine, Camille Thibault) Computational social science applications
David Condon is a Courtesy Assistant Professor in the Department of Psychology at the University of Oregon , affiliated with the College of Arts and Sciences . His research focuses on the structure and measurement of psychological individual differences , including temperament, personality, cognitive abilities, interests, values, and motivation. Specializes in large-scale datasets via the SAPA-Project , collecting data from over 250,000 participants annually. Develops measurement tools for clinical settings and advocates for open science practices , including open data, reproducible methods, and freely accessible publications. Current research explores empirically-informed prediction tools and taxonomic models linking individual differences across the lifespan. His scholarly work spans personality science , creativity research , and natural language processing , with publications analyzing personality structures at multiple levels of abstraction. While no longer accepting new graduate students, he continues to lead the PIE Lab , fostering interdisciplinary collaboration.
Antoine Doucet is a Full Professor at the University of La Rochelle, where he teaches in the Computer Science department of the University Institute of Technology (IUT). He conducts his research at the Computer Science, Image and Interaction Laboratory (L3i) within the 'Images and Content' team, which he has led since 2015. He is also a member of the Franco-Vietnamese laboratory ICTLab and serves as Director of the ICT Department at the University of Science and Technology of Hanoi since 2016. His research focuses on information retrieval, natural language processing, text mining, and artificial intelligence, with emphasis on automatic analysis of text in all forms across languages. His work prioritizes generic methods that work across languages without relying on language-specific linguistic resources. This approach is particularly valuable for under-resourced languages and noisy texts from sources like social media or OCR output. As coordinator of the Horizon 2020 NewsEye project, he led efforts to improve access to European historical newspapers through semantic enrichment and advanced search capabilities. His research has practical applications in epidemic surveillance, document fraud detection, and historical content analysis. The NewsEye project involved 11 teams across Europe, including 3 national libraries and multiple research groups. Best paper award from IMIA Yearbook 2016 (among 1,272 candidates) Best paper award at HCI International with Ilona Nawrot Press coverage for ACL 2013 paper in major publications Recipient of French scientific excellence award (Prime d'Excellence Scientifique) Doucet actively supervises PhD and Master's students, with recent advisees including Chloé Artaud (Document fraud detection), Paul Martin (Photograph Time-Stamping), Ilona Nawrot (Temporal and Multilingual Text Analysis), and Gaël Lejeune (Multilingual Epidemic Surveillance). His research has been funded through multiple projects including ANR Digistory, AmeliOCR, PHC Nusantara, and USTH SWARMS. He has also coordinated significant European projects like NewsEye and Embeddia. At L3i, he leads a research group of approximately 40 persons focused on Images and Digital Content. His work bridges theoretical advances in multilingual text processing with practical applications in historical document analysis, epidemic surveillance, and document security.
Nikos Aletras is a Professor of Natural Language Processing at the University of Sheffield's School of Computer Science, where he serves as Head of the Natural Language Processing research group and is co-affiliated with the Machine Learning group. His academic journey began with a Bachelor's degree in Computer Science from the University of Crete, followed by a PhD in Natural Language Processing at the University of Sheffield. Prior to his current position, he worked as a research scientist at Amazon (Core ML and Alexa) and as a research associate at UCL's Department of Computer Science. Aletras' research spans multiple domains within AI, with particular emphasis on Natural Language Processing applications across social science, legal contexts, and data science. His work demonstrates a consistent focus on practical implementations of NLP techniques to solve real-world problems, especially in computational social science and legal technology. He has developed innovative text analysis methods that bridge traditional disciplinary boundaries, creating tools applicable across multiple scientific domains. His recent publications reveal a strong trend toward efficient and responsible AI, with significant work on model compression, hallucination mitigation in language models, and ethical considerations in computational social science research. The publications also show deep engagement with multilingual NLP challenges, explainable AI, and applications of NLP to social media analysis and legal contexts. Area Chair Award: Society and NLP (2023) Aletras has secured substantial research funding as both Principal Investigator and Co-Principal Investigator, including grants from EPSRC, ESRC, Leverhulme, EC Horizon 2020, and industrial partners like Amazon. His current projects focus on efficient deployment of large language models, addressing socio-technical limitations of LLMs for medical and social computing, and developing speech and language technologies. He actively supervises PhD students and collaborates with researchers across multiple disciplines. He leads the Natural Language Processing research group at Sheffield, which focuses on advancing NLP methodologies while applying them to diverse domains including computational social science, legal informatics, and healthcare technologies. The group maintains strong industry connections, particularly with technology companies working on language technologies, and collaborates with legal scholars and social scientists on interdisciplinary projects.
Zhou Tong serves as an Assistant Professor in the Computer Science Department at Wheaton College in Norton, MA. His academic foundation includes a Ph.D. in Computer Science from Florida State University and a B.S. in Computer Science from Millsaps College. His educational background: Ph.D. in Computer Science, Florida State University B.S. in Computer Science, Millsaps College Dr. Tong specializes in parallel computing and high performance computing (HPC), with significant contributions to performance modeling of HPC applications, workload characterization, and interconnect topology design. His secondary research domains include Machine Learning and Natural Language Processing, where he explores computational efficiency in data-intensive systems. His publication record reveals a concentrated focus on HPC networking innovations from 2016-2021, particularly in adaptive routing algorithms for dragonfly topologies, software-defined networking integration, and MPI application classification using logical clocks. These works consistently address performance optimization challenges in large-scale parallel computing environments. No scientific awards are documented in the available materials. Information regarding student advising, research grants, and laboratory facilities remains unspecified in current records.
