Magnus Bång is a Senior Associate Professor at the Department of Computer and Information Science (IDA) at Linköping University, affiliated with the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His research focuses on advancing human-AI collaboration, automation systems, and AI applications in domains like cyberphysical production, air traffic management, and process industries. He has contributed to interdisciplinary projects involving real-time human-automation interfaces, explainable AI dashboards for industrial processes, and safety-critical systems integration. His work bridges theoretical AI advancements with practical implementations in sectors such as aviation and maritime logistics. Notable collaborations include research with the Swedish Maritime Administration to enhance shipping efficiency through AI and interactive visualization. He actively participates in EU-funded initiatives like the Horizon 2020 projects targeting autonomous systems and air traffic control. Research interests span MLOps for industrial systems, glyph-based communication design for human-automation teams, and operator modeling across traffic management domains. His publications emphasize cross-disciplinary solutions to challenges in automation and human-centric AI design.
Negin Alemazkoor is an Assistant Professor at the University of Virginia's School of Engineering and Applied Science, specializing in interdisciplinary research on infrastructure resilience. Her work focuses on developing AI-driven methodologies for analyzing interconnected systems like power grids, urban flood models, and transportation networks under uncertainty. Key areas include enhancing grid reliability through multi-fidelity modeling, hurricane evacuation equity analysis, and precision-compression techniques for large-scale data. She co-leads a NSF-funded initiative to democratize AI education in high schools. Her research integrates graph neural networks, physics-informed models, and machine learning to address challenges in energy systems, environmental monitoring, and disaster response. Notable projects include hurricane-induced power outage risk analysis under climate change and precision guarantees for smart-meter data analytics. She emphasizes computational efficiency and multi-fidelity approaches to balance accuracy with resource constraints. Recent contributions span AI applications in flood forecasting, renewable energy integration, and infrastructure cybersecurity. Her NSF grant aims to create inclusive AI curricula, reflecting her commitment to education and societal impact. She is affiliated with UVA Engineering’s research initiatives on resilient systems and data-driven decision-making.
Jinhan Kim is a Postdoctoral Researcher at the Università della Svizzera italiana (USI) in the Faculty of Informatics, working in the TAU lab under Prof. Paolo Tonella. He earned his Ph.D. from KAIST under Prof. Shin Yoo, focusing on software engineering research in mutation testing, fault localization, and deep learning system testing. His work bridges traditional software engineering techniques with AI-driven methodologies, emphasizing AI4SE and SE4AI paradigms. Education: Ph.D. in Software Engineering, KAIST, 2023 Research Interests: Mutation Testing Deep Learning System Testing Autonomous Systems Testing Adversarial Attack Detection Empirical Software Engineering Service and Leadership: Organized SBFT 2026 and DeepTest 2026 (co-located with ICSE 2026) Program Committee Member for ASE, ISSTA, Mutation, and DeMeSSAI Board of Distinguished Reviewers for TOSEM (2024–2025) Labs and Teams: Active contributor to the TAU Lab at USI, focusing on advanced software testing and AI integration.
Jeyavijayan 'JV' Rajendran is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He is an ASCEND Fellow and leads the Secure and Trustworthy Hardware (SETH) Lab. His research focuses on hardware security, computer security, and novel applications of AI in secure hardware design. Education: PhD in Electrical Engineering (NYU 2015), MS in Computer Engineering (NYU Tandon 2010), BE in Electronics and Communication Engineering (Anna University 2008). Research Interests: Hardware Security, Computer Security, Logic Locking, Hardware IP Protection, and Reinforcement Learning for Security. He explores AI-driven approaches to detect vulnerabilities, protect intellectual property, and enhance secure hardware design through fuzzing, obfuscation, and formal verification. Notable Awards: 2022 Office of Naval Research Young Investigator Award, 2021 IEEE CEDA Ernest Kuh Early Career Award, 2017 NSF CAREER Award. Lab and Teams: The SETH Lab focuses on trustworthy hardware design, developing techniques to secure integrated circuits against reverse engineering and IP theft. Current projects include LLM-based hardware code generation, formal approaches for hardware fuzzing, and AI-driven vulnerability detection.
Zixiang Xiong is a Professor and Associate Department Head in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Robert M. Kennedy '26 Endowed Professorship II. He earned his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 1996. His career includes roles at Princeton University, University of Hawaii, and Texas A&M since 1999. Education: Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign, 1996 Visiting Research Associate, Princeton University, 1995–1997 University of Hawaii, 1997–1999 Research Interests: Focuses on machine learning, image/video processing, federated learning, network information theory, biomedical engineering, and communications. His work spans distributed source coding, genomic signal processing, and energy-efficient systems. Publications & Awards: Over 200 publications, including seminal works on distributed video coding and network information theory. Notable awards include the NSF Career Award (1999), ONR Young Investigator Award (2001), IEEE Fellow (2006), and the ECE Outstanding Faculty Award (2024). His research has led to patents in video compression and multimedia systems. Grants & Advising: Active in NSF-funded projects on coding theory and energy-delay tradeoffs. Advises numerous PhD and MS students, with over 50 alumni in academia and industry. Collaborates on biomedical imaging, remote sensing, and federated learning initiatives. Labs & Teams: Leads a dynamic research group at Texas A&M, focusing on cutting-edge projects in signal processing and machine learning applications. Collaborates with industry and governmental agencies on applied research.
Nabil Aouf is a Professor of Robotics and Autonomous Systems in the Department of Electrical and Electronic Engineering at City, University of London, a position he has held since January 2019. Previously, from 2006 to 2018, he was Professor of Autonomous Systems at Cranfield University’s Defence and Security campus, where he also served as Head of the System and Autonomy Group and Research Lead of the Centre of Electronic Warfare, Information and Cyber. He earned his PhD in Electrical Engineering from McGill University Faculty of Engineering between 1999 and 2002. His research focuses on Robotics, Autonomous Systems, UAV Navigation, Computer Vision, and Fault-Tolerant Control . Key areas include visual odometry, sensor fusion (vision/IMU, RGBD, thermal-visible), robust control for UAVs, fault diagnosis in inertial systems, 3D perception, and autonomous landing. His work integrates theoretical control methods with real-time implementation in aerospace and defense contexts. His recent publications reflect a strong emphasis on robust optimization, multispectral vision, and real-time autonomous navigation. Trends indicate a focus on enhancing autonomy under uncertainty—through illumination-invariant stereo matching, L∞ optimization, and robust feature matching—particularly for UAVs operating in challenging environments. Nabil Aouf has collaborated extensively with researchers such as M. Richardson, O. Araar, T. Mouats, and M. Boulekchour across numerous projects in UAV control, sensor fusion, and autonomy. While no scientific awards are listed in the provided text, his leadership roles and sustained publication record in high-impact journals and conferences underscore his academic contributions. He has supervised or collaborated with several advisees including S.H. Almutairi, L. Chermak, I. Vitanov, and D. Nam, contributing to both theoretical developments and practical implementations in autonomous systems. His work has applications in aerospace, defense, planetary exploration, and critical infrastructure inspection.
Tongguang Li is a Research Fellow at the Department of Human Centred Computing, Monash University. His research focuses on learning analytics, self-regulated learning, and AI applications in education. He has contributed to the development of the FLoRA engine, an AI tool designed to enhance hybrid human-AI regulated learning. Li’s work explores adaptive scaffolding, large language model (LLM) feedback systems, and the integration of multimodal data for educational insights. His recent studies investigate how LLMs like ChatGPT can provide effective feedback to students, analyze rhetorical patterns in writing, and measure the impact of scaffolding on learning processes. Li has been recognized for his research with the Conference Best Full Student Paper Award from the Australiasian Society for Computers in Learning in Tertiary Education (2022). Key themes in his work include understanding self-regulated learning strategies through trace data, optimizing adaptive systems for learner engagement, and leveraging AI for educational innovation. His research bridges cognitive science, data analytics, and educational technology to improve learning outcomes and pedagogical practices.
Sanjana Mudduluru is an Assistant Professor in the School of Computer Science at the University of Oklahoma (OU). She holds a BS from Jawaharlal Nehru Technological University (India), an MS in Data Science & Analytics, and a PhD in Computer Science, all from OU. Her research focuses on applying computer vision and machine learning to biomedical imaging, particularly in cancer research and medical diagnostics. She has extensive experience in software development and programming. Education: Ph.D., Computer Science, University of Oklahoma M.S., Data Science & Analytics, University of Oklahoma B.Tech, Computer Science, Jawaharlal Nehru Technological University Her research interests span machine learning, medical image processing, data analytics, and computer science education. She explores innovative deep learning models for medical image segmentation, classification, and synthetic data generation to enhance AI efficacy in healthcare. Recent work includes hybrid models for computer-aided diagnosis and self-supervised learning for rock image analysis. Awards: Dissertation Excellence Award (2023) Tomorrows Engineer Scholarship (2021–2022) CS Alumni Graduate Fellowship (2021–2022) Dr. Mudduluru has no listed advisees but has contributed to grants related to biomedical imaging research. She is affiliated with OU’s Devon Energy Hall and actively publishes in interdisciplinary fields blending computer science with healthcare applications.
Guido Perboli is a Full Professor in the Department of Management and Production Engineering (DIGEP) at the Polytechnic University of Turin, where he also serves as Logistics Coordinator and Project Coordinator for activities supporting relationships with government bodies. He is a member of the Interdepartmental Center CARS@PoliTO (Center for Automotive Research and Sustainable Mobility) and serves as Director of the ICT for City Logistics and Enterprises (ICElab@Polito) research center, which he founded in 2016. His research interests span a broad range of topics including Operations Research, Logistics, Last-mile Delivery, Sustainable Logistics, Combinatorial Optimization, Stochastic Programming, Business Development, and Lean Business methodologies. His work particularly focuses on City Logistics, Green Logistics, and the application of emerging technologies like Blockchain and AI in supply chain management. He has developed GUEST, a Lean Business methodology for innovation processes from early idea definition to implementation. Professor Perboli's recent publications demonstrate a strong focus on urban logistics, last-mile delivery optimization, blockchain applications in supply chains, and the integration of AI techniques in transportation systems. His work shows an increasing trend toward interdisciplinary research that combines optimization methods with emerging technologies to address sustainable urban mobility challenges. Professional Recognition: CASE Best Paper award from IEEE Conference on Automation Science and Engineering (2011) Effective member of INFORMS (2019-present) Effective member of EURO (1995-present) Effective member of AIRO (1995-present) Associate Editor for Journal of Applied Research and Technology (2020-present) Associate Editor for Sustainability (2018-present) Professor Perboli actively advises PhD students and has supervised numerous research projects, including EU-funded initiatives like SINFONICA, HESTER, and 5G-LOGINNOV. He serves as Scientific Director for multiple commercial research projects focused on blockchain, IoT, and AI applications in logistics. Beyond academia, he is Chief Scientific Officer of Arisk S.p.A., a fintech company specializing in business crisis prediction using AI and machine learning. His research group, ICElab@Polito, focuses on two main areas supporting urban growth: logistics and enterprises. The center collaborates with numerous companies including Amazon, DHL, and FCA, addressing real-world challenges in urban logistics and supply chain management through innovative research approaches.
Sanjay Srinivasan is a Professor of Petroleum and Natural Gas Engineering and the John and Willie Leone Family Chair in the Department of Energy and Mineral Engineering at Penn State University. He serves as Director of the EMS Energy Institute and leads the Penn State Initiative for Geostatistics and GeoModeling Applications. His research focuses on petroleum reservoir characterization, CO2 sequestration, and integration of seismic data in reservoir models through advanced geostatistical and machine learning methods. Ph.D., Petroleum Engineering, Stanford University M.S., Petroleum Engineering, University of Southern California B. Tech, Petroleum Engineering, Indian School of Mines Srinivasan’s work addresses reservoir recovery processes, unconventional reservoirs, and subsurface energy security. His methodologies include probabilistic modeling, data assimilation, and AI-driven workflows for fracture network mapping and porous media generation. Key applications span Gulf of Mexico deepwater plays and geological carbon storage. Recent publications highlight trends in: Reinforcement learning for geostatistical workflows and well optimization Physics-informed GANs for 3D porous media modeling Probabilistic integration of geomechanical and geostatistical inferences Machine learning approaches for seismic fracture identification CO2 sequestration in heterogeneous reservoirs Scientific awards include Distinguished Member (SPE, 2022), SPE Faculty Pipeline Award (2012), Cox Visiting Fellowship (Stanford, 2010), and SPE Southwest Region Reservoir Description Award (2009).
Kai Leonhard is an Adjunct Professor at the Chair of Technical Thermodynamics , RWTH Aachen University. His research focuses on computational chemistry, thermodynamics, and molecular modeling, particularly in solvent design and reactive chemical processes. Department: Chair of Technical Thermodynamics Email: kai.leonhard@ltt.rwth-aachen.de Prof. Leonhard's work integrates quantum chemistry with computer-aided molecular and process design (CAMD/CAPD), emphasizing solvation thermodynamics, reaction kinetics, and machine learning applications. His projects span biofuel combustion, microgel synthesis, and sustainable solvent development. Recent publications highlight advancements in COSMO-RS-based solvent screening, reaction network exploration via ChemTraYzer-TAD, and multi-fidelity modeling for partition coefficients. He employs machine learning to enhance predictive thermodynamic models and optimize chemical processes.
Suren Jayasuriya is an Associate Professor at Arizona State University's The GAME School, with joint appointments in the School of Electrical, Computer and Energy Engineering (ECEE) and the Department of Arts, Media and Engineering (AME). He is also an Affiliate Faculty Member at the Mary Lou Fulton College for Teaching and Learning Innovation. His lab, the Imaging Lyceum, focuses on transdisciplinary research bridging computational imaging, computer vision, sensors, and STEAM education. Education Ph.D. Electrical and Computer Engineering, Cornell University (2017) M.S. Electrical and Computer Engineering, Cornell University (2015) B.S. Mathematics, University of Pittsburgh (2012) B.A. Philosophy, University of Pittsburgh (2012) Research Focus Dr. Jayasuriya's work integrates optics, computational photography, and machine learning to develop novel imaging systems. His research spans: Computational cameras and light transport analysis Atmospheric turbulence modeling and video restoration Neural volumetric reconstruction for sonar/radar STEAM education frameworks for K-12 teachers Philosophical aspects of imaging and representation His lab emphasizes interdisciplinary collaboration across engineering, arts, and humanities. Publication Trends Recent publications demonstrate strong focus on computational imaging (45%), AI/ML applications (30%), and educational technology (25%). Dominant themes include turbulence mitigation in videos, neural rendering for sonar/radar, sensor fusion, and AI curriculum development for middle schools. Work frequently appears in top venues like CVPR, SIGGRAPH, and IEEE Transactions. Awards Image Electronics Technology Excellence Award (IIEEJ, 2021) Best Demo Awards: IEEE ICCP 2019, MIRU 2018 Best Paper Award: IEEE ICCP 2014 ASEE Diversity Paper Finalist (2020) Teaching Honors: Fulton Top 5% Award (2019, 2021), ASU Game Changing Faculty (2021) Teaching & Advising Teaches graduate/undergraduate courses including Machine Vision (EEE 515), Minds and Machines (AME 400), and thesis supervision. Leads NSF-funded projects on computational imaging education and AI teacher training. Mentors students through the Imaging Lyceum lab with projects spanning optics, philosophy, and educational technology. Lab & Collaborations Directs the Imaging Lyceum, emphasizing Aristotle-inspired collaborative research. The lab works on: computational cameras, STEAM education, sensor development, and philosophical inquiries into imaging. Collaborates with Carnegie Mellon Robotics Institute and international partners. Funded by NSF, NEH, and industrial partners for projects in sonar imaging, heat resiliency sensing, and educational AI.
George Vasilakopoulos is a Professor in the Department of Digital Systems at the University of Piraeus , where he also serves as Vice-Chancellor for Academic Affairs and Personnel. By law, he is President of the Quality Assurance Unit (MODIP) and the Employment and Career Structure (DASTA) of the university, overseeing the development of modern information systems. He earned his PhD from the University of London and has held leadership roles including Department President, Director of Postgraduate Programs, and Scientific Director of the Digital Health Services Laboratory. PhD: University of London Current Roles: Vice-Chancellor, Department of Digital Systems Professor Labs: Digital Health Services Laboratory His research focuses on Health Informatics , Cloud Computing , and Medical Data Security , with key contributions to: Emergency healthcare process automation Privacy-preserving personal health record systems Context-aware authorization models Cloud-based medical service frameworks Machine learning in clinical data analysis Interoperable health information systems The trends in his 15 most recent articles (2010-2015) reveal a consistent emphasis on integrating cloud infrastructure , semantic technologies , and mobile platforms to enhance emergency care, chronic disease management, and patient data security. His work bridges biomedical engineering , software architecture , and public health policy . He has held advisory roles for the Minister of Health on IT issues, served on hospital boards, and contributed to national committees for healthcare technology standards. His professional activities include project evaluation for Greek and European research programs and authoring three books on health informatics.
Maarten Sap is an Assistant Professor at Carnegie Mellon University's Language Technologies Institute with a courtesy appointment in the Human-Computer Interaction Institute. He also holds a part-time research scientist position at the Allen Institute for AI (AI2) as an AI safety lead. Current affiliations: CMU (2022–present), AI2 (2022–present) Prior: Postdoctoral Researcher at AI2 (2021–2022), Research Intern at AI2 (2018–2019) and Microsoft (2019) His research focuses on enhancing AI systems with social intelligence and addressing social biases in language technology. Key themes include: Ethical AI and Human-Centric Design Narrative Dynamics and Social Context Analysis AI Agents and Social Intelligence Toxic Language Detection and Cultural Bias Mitigation Recent publications examine: AI safety frameworks like HAICOSYSTEM Clinical reasoning alignment (ALFA) Multilingual moderation (PolyGuard) Cultural sensitivity in non-verbal AI (Mind the Gesture) Personality shaping in LLMs (BIG5-CHAT) Scientific Recognition: 2025 Okawa Research Grant Best Paper Runner Up - NAACL 2025 Outstanding Paper - EMNLP 2023 Best Paper - FAccT 2023 Best Paper - WeCNLP 2020 He advises a diverse group of PhD students across CMU and MIT, and has served on multiple program committees including ACL, EMNLP, and FAccT. His work appears in top venues like Nature Machine Intelligence, PNAS, and ACL.
Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, specifically within the Language Technologies Institute (LTI). He leads the L3 Lab and serves as an advisor for the AI for Math Fund. His academic journey includes a PhD from New York University under Kyunghyun Cho and postdoctoral positions at the Allen Institute for Artificial Intelligence and the University of Washington with Yejin Choi. Dr. Welleck's educational background shows a strong foundation in computer science. He earned his PhD in Computer Science from New York University, where he worked under the mentorship of Kyunghyun Cho and Zheng Zhang. Prior to this, he completed his MSE and BSE in Computer Science from the University of Pennsylvania, demonstrating a long-standing commitment to the field. Dr. Welleck's research focuses on bridging informal and formal reasoning with AI, with particular emphasis on developing learning, inference, and evaluation algorithms for large language models. His work spans multiple cutting-edge areas including mathematical reasoning , code generation , inference algorithms , and AI reasoning agents . A significant portion of his recent work involves combining AI with formal methods for mathematics, where he has developed frameworks like Llemma (an open-source language model for mathematical reasoning) and meta-generation (for inference-time algorithms). His research is characterized by a strong theoretical foundation coupled with practical applications that push the boundaries of what AI systems can achieve in formal reasoning domains. Analysis of Dr. Welleck's recent publications reveals a clear research trajectory focused on enhancing language models' capabilities in formal reasoning and mathematical problem-solving. His work demonstrates an evolution from foundational research in neural text generation to increasingly sophisticated approaches that integrate formal methods with deep learning. Key trends include the development of inference-time algorithms that improve model performance without additional training, frameworks for mathematical reasoning that connect informal and formal proofs, and novel evaluation methodologies for language models. His publications consistently appear in top-tier conferences including NeurIPS, ICLR, ICML, and ACL, reflecting the high impact of his contributions to the field. Dr. Welleck's scientific achievements have been recognized with several prestigious awards: NAACL 2025 Best Paper Award ICLR 2025 Oral Presentation (Top 2%) ICLR 2025 Spotlight Presentation (Top 5%) NeurIPS 2021 Outstanding Paper Award (Top 0.1%) for MAUVE NVIDIA AI Labs Pioneering Research Award (2017 and 2018) As an educator and mentor, Dr. Welleck actively guides the next generation of AI researchers. He currently advises multiple PhD students including Pranjal Aggarwal, Weihua Du, Andre He, and Seungone Kim (some co-advised with other faculty), along with MS students Riyaz Ahuja, Jiewen Hu, Qinyue Tan, and Thomas Zhu, and undergraduate Tate Rowney. At CMU, he teaches advanced courses such as Neural Code Generation and Advanced NLP, and has previously taught at New York University and the University of Washington. His commitment to education extends to creating resources like the Thesis Review Podcast and developing tutorials on neural theorem proving that have been presented at major conferences. Dr. Welleck leads the L3 Lab at CMU, which focuses on the intersection of language, learning, and logic. The lab brings together students and researchers to tackle challenging problems in AI reasoning, with particular emphasis on mathematical reasoning and code generation. Recent initiatives include the development of Llemma, an open-source language model specialized for mathematical reasoning, and work on inference-time algorithms that enable language models to improve their performance through additional computation during inference rather than through additional training.
