Abhinav Dhall is an Associate Professor in the Department of Data Science & AI at Monash University. His research focuses on computer vision, affective computing, and human-centered AI, with a particular emphasis on deepfake detection, multimodal analysis, and ethical AI applications. He is actively involved in organizing workshops like the Multimodal and Responsible Affective Computing (MRAC) and chairs conferences such as ACCV. Dhall accepts PhD students and has contributed significantly to datasets like AV-Deepfake1M and EmotiW challenges. His work spans topics including HDR imaging, facial expression recognition, and AI ethics in multimedia systems.
Prof. Freek J. Beekman is a Full Professor and head of the Biomedical Imaging section within the Department of Radiation Science & Technology at Delft University of Technology (TU Delft), Faculty of Applied Sciences. He is a leading figure in biomedical imaging, with extensive contributions to nuclear imaging technologies, including SPECT, PET, and CT. His research spans detector development, image reconstruction algorithms, hybrid photonic imaging, and the application of artificial intelligence in medical imaging. Research Interests: His work focuses on advancing imaging modalities through innovations in hardware (e.g., multi-pinhole collimators) and software (e.g., deep learning for attenuation correction). He has pioneered ultra-high-resolution imaging systems, particularly for preclinical and clinical SPECT, and has developed integrated platforms like U-SPECT-BioFluo. His recent research explores glymphatic delivery of nanoparticles, infection imaging, and AI-driven reconstruction techniques, reflecting a strong translational focus. Publication Trends: His most recent publications (2021–2023) emphasize deep learning in SPECT, multi-isotope imaging, high-resolution ex vivo systems, and applications in neuroimaging and oncology. The articles demonstrate a consistent focus on improving image quality, resolution, and clinical utility through physics-informed and AI-enhanced methods. Scientific Awards: NWO Physics Valorization Prize Innovation of the Year Award by the World Molecular Imaging Society (2015, 2018) Edward Hoffman Memorial Award (2017) Bruce Hasegawa Memorial Award (2021) FOM Valorization Award (2013) TU Delft Entrepreneurial Award (2010) Advising and Grants: While specific student names are not listed, his leadership in large collaborative projects and supervision of numerous publications suggests active mentoring. He has secured significant funding through national and international grants, evidenced by his invention of over 20 patent families and successful technology transfer. His founding and leadership of MILabs BV (sold to Rigaku) highlights his impact on commercialization and industry-academia collaboration. Labs and Teams: He leads the Biomedical Imaging research group at TU Delft, which develops cutting-edge imaging systems such as VECTor (SPECT-PET) and EXIRAD-HE. His teams have produced technologies used globally in academic and pharmaceutical research, contributing to tracer development and therapeutic innovation.
Dr. Wan Renjie is an Assistant Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He holds a BEng in Network Engineering from the University of Electronic Science and Technology of China and a PhD from Nanyang Technological University (NTU), Singapore. Prior to joining HKBU, he was a Wallenberg-NTU Presidential Postdoctoral Fellow (2020–2022) and a guest researcher at Peking University (2019–2020). His research focuses on computational photography, 3D vision, AI security, digital watermarking, and neural representations . He explores robustness and security in vision models, especially concerning NeRFs and 3D Gaussian Splatting, and develops methods for low-light enhancement, reflection removal, and domain adaptation. Dr. Wan has published in top-tier venues including TPAMI, IJCV, CVPR, ICCV, NeurIPS, AAAI, and ECCV . His recent work emphasizes copyright protection for neural 3D models , adversarial attacks in multimodal and event-based systems, and medical image reconstruction. He is actively mentoring PhD students and research assistants. VCIP 2020 Best Paper Award Outstanding Reviewer, ICCV 2019 He teaches courses such as Introduction to AI and ML (COMP3057) , AI Application Development (COMP3065) , and Python for Data Analysis and Machine Intelligence (COMP7035) . Dr. Wan leads a dynamic research group with ongoing projects on watermarking, 3D reconstruction, and AI security, and he is currently recruiting new PhD students and research assistants.
Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.
Yan Chen is an Assistant Professor at the Virginia Tech College of Engineering , where he leads the PRIME Lab (Programming with Intelligent Machines & Environments) . His work focuses on creating interactive Human-AI systems to enhance real-time data analysis and programming education, particularly addressing barriers in collaborative learning environments. University of Toronto (Postdoctoral Fellow) University of Michigan (Ph.D., Information Science) University of Colorado, Boulder (BS/MS in Applied Math & Electrical & Computer Engineering) His research bridges Human-Computer Interaction (HCI) and Computer Science Education , with a focus on real-time data analysis , AI-driven programming assistance , and scalable learning tools . He employs LLMs and human-centered design to simplify complex computational processes, enabling data workers to detect critical patterns efficiently. Recent publications highlight trends in generative AI for education , proactive AI programming support , and collaborative analytics . Key themes include real-time classroom insights , intergenerational smartphone learning , and automated feedback systems . Scientific recognition includes: 🏆 Best Paper at L@S 2024 🏅 Best Paper Honorable Mention at CHI 2023 🏅 Best Paper Honorable Mention at UIST 2022 🏆 Best Short Paper at VL/HCC 2020 He mentors a team of PhD and MS students in projects spanning AI-assisted education, web automation, and collaborative coding tools, with active recruitment for future research directions.
Tasos Dagiuklas is a Professor in the Department of Computer Science and Technology within the School of Engineering and Technology at the University of Bedfordshire. With over 168 publications spanning from 1995 to 2025, he has established himself as a leading researcher in telecommunications and network systems. His extensive publication record demonstrates continuous scholarly contribution across multiple decades in the field. Professor Dagiuklas' research focuses on wireless communications, edge computing, 5G/6G networks, quality of experience (QoE), and federated learning . His work bridges theoretical networking concepts with practical applications, particularly in multimedia delivery and security. He has developed significant expertise in video streaming optimization, network security mechanisms, and resource management in emerging network architectures. His research consistently addresses the evolving challenges of modern communication systems, with recent work increasingly focusing on AI integration in networking. Analysis of his recent publications (2023-2025) reveals a strong trend toward edge computing, federated learning, and security applications in next-generation networks. His work demonstrates a strategic shift from traditional networking concerns to more complex systems involving AI integration, energy efficiency, and heterogeneous environments. The publications show consistent collaboration with researchers across multiple institutions, with particularly strong partnerships with Muddesar Iqbal, Ilias Politis, and Stavros Kotsopoulos. Professor Dagiuklas has made substantial contributions to the academic community through his extensive publication record in high-impact venues including IEEE journals and conferences. His work has evolved from foundational networking research to cutting-edge investigations of AI-enhanced communication systems, reflecting the broader trajectory of the field itself. His research demonstrates both technical depth in specific networking challenges and breadth across multiple application domains.
Dr. Andrzej Ożadowicz is a University Professor at the Department of Power Electronics and Automation of Energy Conversion Systems within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in room 510, building C-1, with contact details including phone +48 12 617 50 11 and email ozadow@agh.edu.pl. He holds PhD, DSc, and Engineering degrees, reflecting his dual expertise in academic research and practical engineering applications. His research spans Power Electronics, Building Automation, Smart Grids, and IoT-driven energy systems. Key interests include energy efficiency optimization through digital twins and BIM, distributed energy resource integration , and AI-enhanced demand management . Notably, he pioneers applications of deep reinforcement learning in home energy systems and develops frameworks for Smart Readiness Indicator implementation. His work bridges theoretical innovation with practical case studies in building thermal modeling and dynamic façade systems. Recent publications (2021-2025) reveal three dominant trends: (1) Convergence of digital twin technology with building automation for real-time energy management; (2) Critical analysis of IoT security and interoperability in smart infrastructure; (3) Pedagogical innovations in engineering education through blended learning methodologies post-COVID-19. His scholarly output demonstrates consistent focus on energy transition challenges and smart grid evolution. Professor Ożadowicz actively contributes to the Discipline Council for Automation, Electronics, Electrical Engineering and Space Technologies at AGH. He is instrumental in the AutBudNet initiative —a network of certified laboratories for energy efficiency assessment that implements "learning by doing" principles in building automation education. His work with this consortium emphasizes practical validation of smart grid technologies and demand response systems.
Prof. Dr. Christina Raasch is Professor of Digital Economy at Kühne Logistics University (KLU) and holds a joint appointment with the Kiel Institute for the World Economy (IfW) . Since 2017 she has led research and teaching on how digitalization reshapes innovation processes, enterprise crowdfunding, and customer-driven disruptive innovation. Education Habilitation (Dr. habil.) in Business Administration, Hamburg University of Technology (TUHH), 2012 PhD in Management, University of Erlangen-Nuremberg, 2006 MSc (lic. oec.) in Economics & Management, University of St. Gallen (HSG), 2002 Visiting Researcher, MIT Sloan School of Management, 2010-2012 Research Interests Prof. Raasch’s work centers on digital transformation of innovation . She investigates how firms leverage digital technologies—ranging from AI to crowdfunding platforms—to enhance idea generation, evaluation, and implementation. Core themes include: Open & User Innovation: understanding when and how users become valuable innovators inside and outside firms. Disruptive Innovation Dynamics: analyzing whether disruptive ideas stem from users or producers under varying environmental conditions. Enterprise Crowdfunding: designing decentralized decision-making systems that mitigate hierarchy-induced biases. Publication Trends Her 70+ publications reveal a systematic exploration of demand-side innovation . Early work modeled welfare impacts of user innovation; recent studies use large-scale field data from Siemens and other multinationals to uncover cognitive and social biases in idea evaluation. A consistent thread is bridging micro-level behavioral insights with macro-level policy and strategy implications. Scientific Awards & Honors Fellow of the Open and User Innovation (OUI) Society Host of the 2023 OUI Conference at KLU Research Funding & Industry Collaboration Current grants exceed €2 million and include: FabCity-Citizen Extension (2025-2026) – decentralized urban innovation funded by the German Federal Ministry of Education and Research. EvaluationShirking (2024-2026) – idea evaluation biases in collaboration with a global industrial manufacturer. Idea Evaluation in Democratized Innovation (2019-2023) – DFG-funded project on enterprise crowdfunding design. Labs, Teams & Knowledge Transfer Prof. Raasch leads the Open & User Innovation Research Group at KLU, supervising doctoral researchers and managing industry partnerships with firms in automotive, high-tech, and logistics sectors. She regularly contributes to policy panels and media outlets such as Harvard Business Manager and Springer Professional .
Jill M Castek is Professor of Literacy, Technology & Bi/multilingual Learners & STEM Education in the Department of Teaching, Learning, and Sociocultural Studies at the University of Arizona's College of Education. As Graduate Advisor for the Second Language Acquisition and Teaching (SLAT) program and Co-Director of the Digital Innovation and Learning Lab (DIALL), she bridges research and practice in digital literacy development. Dr. Castek earned her PhD in Education Psychology (Cognition and Instruction) from the University of Connecticut as a Neag fellow in the New Literacies Research Lab, followed by postdoctoral work at UC Berkeley's Lawrence Hall of Science. Her research examines how digital, visual, and audio storytelling fosters literate capacities across print and digital forms in classrooms and community spaces. Her work centers on digital problem solving , critical media literacy , and equity-centered making practices in STEM education. She investigates intersections of disciplinary literacy, new media, and learning for bi/multilingual students, with strong emphasis on community-based applications through libraries and non-profits like Literacy Connects where she serves on the board. Recent publications (2022-2025) reveal growing focus on AI-era teacher education , digital health literacy , and misinformation challenges , while maintaining core commitments to collaborative problem solving and expansive literacies frameworks. Her scholarship consistently addresses K-12 and adult learning contexts through interdisciplinary lenses. Scientific recognition includes: Neag fellowship during doctoral studies As Principal Investigator on NSF and IMLS grants, Dr. Castek advances digital equity through projects like the Making Equity Network (broadening STEM participation for underrepresented groups) and library-focused digital problem-solving initiatives serving economically vulnerable adults. Her leadership in the Technology Enhanced Language Learning (TELL) cluster hire demonstrates institutional impact. Through DIALL, she cultivates innovations connecting digital storytelling with self-expression at individual and community levels, promoting workforce readiness via creative problem-solving approaches. Her board membership with Literacy Connects extends this community-engaged scholarship to Tucson-based literacy initiatives.
Hao Yang is an Assistant Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with dual affiliations at the Johns Hopkins Data Science and AI Institute and the Johns Hopkins Institute for Assured Autonomy. His research develops Trustworthy Machine Learning methods to enhance urban mobility systems, focusing on traffic safety, equity, and sustainability through ethical AI and human-machine cooperative systems. Yang earned dual bachelor's degrees in Electrical and Computer Engineering from Beijing University of Posts and Telecommunications and the University of London, followed by a Ph.D. in Civil Engineering (Transportation) from the University of Washington. His educational background bridges telecommunications, electrical engineering, and transportation systems. His research integrates spatio-temporal modeling, assured autonomous systems, and multimodal representation learning to address transportation equity and safety. Key projects include edge-AI-powered traffic surveillance, real-time crash identification, and cooperative signal assistance for vulnerable road users. His work emphasizes ethical AI deployment in cyber-physical infrastructure to create sustainable urban mobility solutions. Recent publications reveal a strategic shift toward large language models and multimodal AI for transportation challenges, with strong emphasis on explainability, reliability, and equity in traffic crash prediction, flow forecasting, and autonomous driving systems. This evolution demonstrates his commitment to adapting cutting-edge AI for real-world transportation problems. Yang's scientific contributions have earned significant recognition: Michael Kyte Outstanding Student of the Year Award (2022) High-Value Research Award from AASHTO (2022) Best Paper Award from TRB Information Systems Committee (2023) Best and Outstanding Dissertation Awards (2024) IEEE DTPI Outstanding Paper Award (2022) TRANSFOR22 Data Competition 2nd place (2022) ASCE Bridges Photo Contest First Place (2021) He actively mentors graduate researchers and seeks 2-3 PhD students for Fall 2025 to advance trustworthy AI in transportation. His research is supported by NSF, USDOT, and AASHTO grants including the Real-Time Truck Parking Information System project that received the High-Value Research Award. Current work focuses on edge-AI for traffic safety and multimodal data integration. Yang leads research within Johns Hopkins' Data Science and AI Institute and Institute for Assured Autonomy, collaborating with Transportation Research Board committees. His lab develops real-time perception systems using edge computing and representation learning, with active projects on non-motorized user safety and equitable traffic management for people with disabilities.
Panagiotis Papapetrou is a Professor of Data Science and Deputy Head of Department at the Department of Computer and Systems Science , Stockholm University (since 2017). He also serves as Head of the Data Science Research Group and holds an Adjunct Professor position at Aalto University (Finland). As a Board Member of the Swedish Association for Artificial Intelligence (SAIS) , he contributes to shaping AI research directions in Sweden. Research Pillars: Algorithmic data mining, interpretable machine learning, time series classification, and health informatics Key Projects: AI for societal fairness, digital twins for smart buildings, EXTREMUM for explainable medical AI, and e-learning personalization Teaching Legacy: Developed courses in Data Mining (HT2013-2022), Machine Learning (VT2022-2024), and Health Informatics (VT2018-2021) His work focuses on interpretable AI for healthcare applications, particularly through counterfactual explanations for time series classification and forecasting. This includes developing methods like Glacier for constrained counterfactuals and Ijuice for k-justified explanations. His research also explores multimodal clustering of sepsis patient records and federated learning approaches for ICU mortality prediction. Recent scientific contributions include: CounterFair (2024): Group fairness analysis via counterfactual burden metrics M-ClustEHR (2024): Multimodal clustering for electronic health records COMET (2024): Constraint-based glucose forecasting explanations Temporal pattern mining (2024-2025): Enhanced forecasting models through decomposition Z-Time (2024): Interpretable multivariate time series classification His editorial leadership includes: Action Editor at Machine Learning Journal (since 2024) Action Editor at Data Mining and Knowledge Discovery (since 2018) Guest Editorial Board for ECML/PKDD Journal Track (2014-2019)
Professor Trina Myers serves as the Head of School for the School of Information Technology at Deakin University's Faculty of Science Engineering and Built Environment. With extensive experience in academia and research leadership, she plays a pivotal role in shaping IT education and research directions at Deakin. She is also an active member of the Australian Council of Deans of ICT (ACDICT), having served as its immediate past President. Her educational background includes: Doctor of Philosophy in Computer Science from James Cook University Master of Business Administration from James Cook University Master of Information Technology from James Cook University Professor Myers' research focuses on semantic technologies, ontology engineering, Internet of Things, knowledge management, natural language processing, and human-computer interaction . Her work emphasizes interdisciplinary collaboration, bridging technology with fields such as healthcare, marine science, environmental conservation, and business. She has pioneered approaches in academagogy (academic gamification) to enhance online learning engagement, particularly for adult learners. Her IoT research has significant applications in healthcare space optimization, environmental monitoring, and resource management. Her recent publications demonstrate a strong trajectory in applying AI and IoT technologies to solve real-world problems, particularly in healthcare, education, and resource optimization. There's a clear pattern of interdisciplinary work connecting computer science with healthcare, education, and environmental science. Her research increasingly focuses on human-centered technology design, especially for vulnerable populations like adolescents with autism spectrum disorder. Her notable achievements include: Fellow of the Australian Computer Society (2023) Australian Awards for University Teaching (AAUT) Teaching Award (2020) Women in IT Professional Leadership Award Finalist (2020) Asia-Pacific International Triple E Entrepreneurial Educator of the Year Award (1st runner-up, 2020) Australian Computer Society, National Digital Disruptor ICT Educator of the Year (2019) Professor Myers actively supervises doctoral students across diverse research areas including gamification in language learning, brain tumor analysis using deep learning, AI in higher education, AI for refugee resilience, data integrity in edge environments, and quantum-driven satellite networking. She has secured significant research funding, including a recent grant for "Indiginizing ICT Curriculum: A Starter Framework for the Community of Practice" through the Australian Council of Deans of ICT. Her teaching philosophy emphasizes active learning methodologies, Process Oriented Guided Inquiry Learning (POGIL), blended learning, and collective intelligence approaches.
Massachusetts Institute of TechnologyUnited States
Akash Srivastava is a Research Scientist and Principal Investigator (PI) at the MIT-IBM Watson AI Lab in Cambridge, MA, and Chief Architect of Large Language Model Alignment at IBM Research. His work focuses on generative modeling , Bayesian inference , and machine learning for constrained engineering design . He previously conducted PhD research at the University of Edinburgh under Dr. Charles Sutton and Dr. Michael U. Gutmann on variational inference for generative models using deep learning. His research spans Neuro-Symbolic AI , Language Model Alignment , and Synthetic Data Generation , with applications in 3D modeling , urban logistics , and material science . Recent publications highlight advancements in diffusion models , continual learning , and privacy-preserving data synthesis . As a PI, he collaborates with MIT faculty like Prof. Faez Ahmed and Prof. Rafael Gomez-Bombarelli on projects such as generative modeling for mechanical systems , synthetic data in decision-making , and greener delivery networks . He has received funding through a DARPA grant for machine common sense research.
Tanel Alumäe is an Associate Professor of Speech Processing at Tallinn University of Technology's School of Information Technologies, Department of Software Science. With over 15 years of academic experience, he has held various research and teaching positions at the university since 2006, progressing from Research Fellow to Tenured Associate Professor. His work focuses on speech and language technologies with a particular emphasis on Estonian language applications. PhD in Information and Communication Technology (2006), Tallinn University of Technology Research Master's Degree in Informatics (2002), Tallinn Technical University MSc studies at Tallinn Technical University (1999-2002) and Universität Erlangen-Nürnberg, Germany (1999-2000) Diploma in Computer and Systems Engineering (1994-1999), Tallinn Technical University Alumäe's research spans automatic speech recognition, speaker recognition, natural language processing, and computational linguistics with a focus on Estonian language technology. His work addresses challenges in multilingual speech processing, deep learning applications for speech technologies, and developing practical systems for real-world applications including broadcast media processing and accessibility solutions. He has made significant contributions to low-resource language processing and specialized applications for children's speech and emotion recognition. His recent publications demonstrate a strong focus on cutting-edge speech processing techniques including deepfake detection, multi-speaker systems, speech-to-speech translation, and applying large language models to speech applications. The research shows a consistent pattern of addressing both theoretical challenges in speech processing and practical implementations for Estonian language technology. Award 'Keeletegu 2019' from the Ministry of Education and Research Award 'Keeletegu 2011' from Estonian Ministry of Education and Research 3rd award at the Tallinn University of Technology contest for applied scientific projects (2011) Boris Tamm stipend (2007) First prize at the national contest of students' scientific works (2007) Ustus Agur stipend of Estonian Information Technology and Telecommunications Association (2005) Alumäe has supervised postdoctoral researchers including Rena Nemoto (2012-2015) on pronunciation modeling for speech recognition. He serves in editorial and review capacities for major journals including Nature, Computer Speech & Language, and IEEE Transactions. His administrative roles include Secretary of the Northern European Association for Language Technology Board and membership on the Department of Software Science Council at TalTech. His research group at Tallinn University of Technology actively participates in international challenges (IWSLT, Interspeech, Odyssey) and collaborates with institutions worldwide. The team has developed open-source platforms for Estonian speech transcription and created systems for automatic closed captioning of Estonian broadcasts, demonstrating strong practical applications of their research.
Scott Staniewicz is a researcher at the University of Texas at Austin in the Department of Aerospace Engineering and Engineering Mechanics. His work focuses on geophysical applications of computer vision and remote sensing, particularly using Interferometric Synthetic Aperture Radar (InSAR) to detect surface deformation and tropospheric noise features. Academic Affiliation: University of Texas at Austin Research Focus: Surface deformation analysis, InSAR data processing, tropospheric noise mitigation Email: scott.stanie@utexas.edu Staniewicz's research employs computer vision techniques like Laplacian of Gaussian (LoG) filtering to identify spatially coherent deformation features (e.g., subsidence/uplift in oil-producing regions). His methods integrate noise spectrum estimation from real data and simulations to distinguish true deformation signals from atmospheric artifacts. Recent work includes software development for automated InSAR analysis and large-scale studies of anthropogenic deformation in the Permian Basin. He has contributed to open-source tools such as Blobsar (2025a) and Troposim (2025b) for deformation detection, and collaborated on studies analyzing seismic sequences (Skoumal et al., 2020), tropospheric delay corrections (Li et al., 2019; Yang et al., 2024), and statewide seismic networks (Savvaidis et al., 2019). His publications demonstrate expertise in combining computer vision with geophysical data analysis.
