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Ruchit AgrawalView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +7 more

Dr Ruchit Agrawal is an Assistant Professor and Head of CS Outreach at the School of Computer Science, University of Birmingham Dubai. He holds a PhD in Computer Science from Queen Mary University of London and was a postdoctoral research scientist at the University of Oxford, where he worked on Artificial Intelligence for Healthcare. His research is centered on advancing AI systems with applications in healthcare and natural language understanding. Research Interests: Dr Agrawal specializes in Artificial Intelligence, Machine Learning, Natural Language Processing, Deep Learning, and Clinical Machine Learning. His work explores Multimodal Deep Learning and Adaptive and Contextual AI , aiming to develop intelligent systems that can understand and respond to complex, real-world environments, particularly in medical contexts. His research bridges theoretical innovation with practical healthcare applications. His scholarly contributions include numerous publications in leading journals and top-tier international conferences in AI and computer science. The body of his work demonstrates a strong trajectory in healthcare-oriented AI, multimodal data integration, and context-aware learning models. Scientific Awards: Marie-Curie Fellow Dr Agrawal has experience supervising students at the PhD, MSc, and BSc levels, contributing to academic training and research mentorship. While specific grants are not listed, his Marie-Curie Fellowship and postdoctoral position at Oxford indicate a strong record of competitive research funding. He currently leads computer science outreach initiatives at the University of Birmingham Dubai, promoting engagement and education in computing fields. He is affiliated with the School of Computer Science at the University of Birmingham’s Dubai campus, contributing to both research and academic leadership in an international academic environment.

• Learning Engineering
• Technology-Enhanced Learning
• Question-Based Learning
• +12 more

Olof Bälter is a Professor in Computer Science at KTH Royal Institute of Technology, affiliated with the Division of Media Technology and Interaction Design within the School of Electrical Engineering and Computer Science. He is the founder of the Technology-Enhanced Learning research group and holds a focus on learning engineering and human-computer interaction. His research interests center on technology-enhanced learning , question-based learning , learning analytics , AI in education , and inclusive pedagogy . A consistent theme in his work is improving efficiency in education and daily life through digital tools. He developed the Pure Question-Based Learning (Pure QBL) methodology, a digital Socratic approach that enhances student engagement and learning outcomes. His work extends to wellness in education through initiatives like walking seminars, and he investigates digital interventions for mental health, such as online Cognitive Behavioral Therapy (CBT) courses. His recent publications highlight trends in AI-generated educational content , learning efficiency , digital pedagogy , and inclusive course design , with applications in computer science education, language instruction, and global development. His research often employs experimental and data-driven methods, including randomized controlled trials and learning analytics. Teacher of the Year at the Surveying program KTH's Pedagogical Prize Higher Education Hero STINT Excellence in Teaching Scholarship (2008 and 2013) Olof Bälter has supervised numerous courses in programming, computer science, media technology, and learning engineering. He has collaborated with institutions such as Stanford University, Williams College, Region Stockholm, Stockholm University, and organizations like Promobilia and Begripsam. His projects aim to scale effective learning methods globally and make education more accessible and efficient. He leads research on the effectiveness of Pure QBL for students with ADHD and is involved in developing digital tools for health literacy and professional development in Ethiopia and Rwanda. His work bridges theory and practice, aiming to transform educational delivery through innovation and evidence-based design.

Brad KnoxView profile

Associate Professor

• Reinforcement Learning
• Human-Computer Interaction
• Artificial Intelligence
• +4 more

Brad Knox is a Research Associate Professor in the Department of Computer Science at the University of Texas at Austin . His work bridges machine learning, human-computer interaction, and computational cognitive science, with a focus on developing systems that learn from human feedback. Key research areas: Reinforcement Learning, Human-AI Interaction, Reward Design, Autonomous Systems Notable contributions: TAMER framework for human-guided learning, empirical studies on reward misdesign, and human preference modeling for autonomous agents Research Trends : His recent work (2023-2025) emphasizes reward alignment, safety in autonomous systems, and preference-based learning frameworks. Earlier studies (2012-2020) established foundational methods for integrating human feedback into reinforcement learning architectures and exploring behavioral signatures in decision-making. Scientific Honors : Bert Kay Dissertation Award (2013) Victor Lesser Distinguished Dissertation Award (IFAAMAS, Runner-up, 2013) NSF SBIR Grant (PI, 2016) NSF Graduate Research Fellowship (2008-2011) IEEE Intelligent Systems AI 10 to Watch (2013) Teaching & Leadership : Knox served as Principal Lecturer for MIT's Interactive Machine Learning course (2013) and held organizational roles at major conferences including Reinforcement Learning Conference (Scheduling Chair, 2025) and RLDM workshop (Co-chair, 2022).

Aswin SankaranarayananView profile

Professor

• Computational Photography
• 3D Shape Estimation
• Imaging Systems
• +4 more

Aswin Sankaranarayanan is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU) , where he leads the Image Science Lab . His research focuses on computational photography , 3D shape estimation , and novel imaging system design . He earned his Ph.D. in Electrical and Computer Engineering (2009) from the University of Maryland and completed a postdoctoral fellowship at Rice University (2012) . Research Themes: Developing imaging systems that exploit low-dimensional signal models to overcome traditional sensing limitations Co-design of optics and processing algorithms for efficient sensing Application of non-linear signal models to high-dimensional data Advancing compressed sensing and big data processing techniques Scientific Recognition: SIGGRAPH 2023 Best Paper Award (Split-Lohmann Multifocal Displays) CVPR 2019 Best Paper Award (Fermat Paths for NLOS Reconstruction) NSF CAREER Award (2017) Dean’s Early Career Fellowship (2018-2021) Herschel Rich Invention Award (2016) Technical Contributions: His recent publications reveal expertise in non-line-of-sight shape reconstruction , VR/AR display systems , and biomedical imaging . Collaborations span institutions like University College London and University of Toronto.

• Innovation Management
• Entrepreneurship
• Healthcare Innovation
• +4 more

Olga Kokshagina serves as an Associate Professor in Innovation & Entrepreneurship at The University of Sydney, with adjunct research appointments at Monash University's Emerging Technology Lab and the UNU Hub - Learning Planet Institute. She is also an active member of the French Digital Council. Her research program investigates technology-mediated collaboration in complex innovation systems, focusing on healthcare transformation, deep tech commercialization, and co-design methodologies. Kokshagina has led high-impact projects with global institutions including the World Health Organization, OECD, STMicroelectronics, Vall d’Hebron Hospital, and Roche, demonstrating strong translational research capabilities. Her scholarly work centers on value-based healthcare innovation, digital platform governance, and AI-enhanced collaborative systems. She examines how organizational capabilities evolve during technological transitions, particularly in healthcare ecosystems, and investigates regulatory frameworks for algorithmic control in digital markets. Kokshagina's research bridges theoretical innovation management with practical applications, evidenced by her co-founding of Ninti—an initiative advancing women's health in workplace environments—and her Open Covid-19 crowdsourcing campaign that mobilized global expertise during the pandemic. Analysis of her 2021-2025 publications reveals a cohesive trajectory examining innovation in socio-technical systems. Key themes include value digitalization in healthcare, mission-oriented interdisciplinary collaboration, and the impact of big data on technology management. Her work consistently addresses grand challenges through mixed-methods approaches, spanning conceptual frameworks in journals like Research Policy to applied studies in Technovation and R&D Management, with increasing focus on quantum readiness and AI-augmented learning systems. No scientific awards are documented in the provided materials. Kokshagina currently holds a 2025 research grant for "Co-designing societal readiness and scenario building for quantum" through the University of Sydney Nano Institute/Catalyst program. While no student supervision activities are mentioned, her collaborative projects involve multi-institutional teams across industry, government, and academic sectors. Kokshagina maintains active roles within the University of Sydney Nano Institute and contributes to international policy discourse through the French Digital Council. Her Ninti initiative exemplifies her commitment to human-centered innovation, while ongoing collaborations with healthcare providers like Roche and Vall d’Hebron Hospital demonstrate sustained engagement with real-world implementation challenges in value-based care systems.

Ruohan GaoView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Multisensory Machine Intelligence
• +4 more

Ruohan Gao is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park , with affiliate appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) , Maryland Robotics Center (MRC) , and Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) . His research focuses on Computer Vision and Machine Learning , emphasizing Multisensory Machine Intelligence that integrates sight, sound, and touch . He aims to enable machines to perceive, understand, and interact with the world as humans do, with applications in robotic manipulation , audio-visual localization , and differentiable rendering . Article Trends : Span 2018–2025 , centering on audio-visual perception , multisensory datasets , and robotics . Recurring themes include object-centric learning , sound synthesis , and cross-modal consistency . Scientific Awards : Michael H. Granof Award (UT Austin’s Top 1 Doctoral Dissertation, 2021) Best Paper Award Runner-Up (BMVC 2021) Best Paper Award Finalist (CVPR 2019) Highlight Paper (CVPR 2023) He leads the UMD Multisensory Machine Intelligence Lab and collaborates with institutions like Stanford and The University of Texas at Austin . Contact: rhgao@umd.edu .

Ion StoicaView profile

Professor

• Distributed Systems
• Cloud Computing
• AI Systems
• +3 more

Ion Stoica is a Professor in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley, where he holds the Xu Bao Chancellor Chair. He serves as Director of the Sky Computing Lab and is Executive Chairman of both Databricks and Anyscale. His research spans distributed systems, cloud computing, and AI systems, with significant contributions to large-scale data processing frameworks. Stoica's research interests focus on the intersection of AI and systems, with emphasis on developing practical implementations that bridge theoretical foundations with real-world deployability. His work addresses fundamental challenges in distributed computing, resource management, and large-scale machine learning systems. Current projects include Ray (a distributed execution framework), vLLM (a high-throughput inference engine for LLMs), Chatbot Arena (an open platform for human preference evaluations), and SkyPilot (a framework for running AI workloads across clouds). His research output demonstrates a consistent trajectory toward more efficient, scalable systems for modern AI workloads, particularly focusing on optimizing inference performance, resource utilization, and cross-cloud deployment. Recent publications reflect growing interest in large language model serving, video generation optimization, and agent-based systems. ACM Fellow SIGOPS Hall of Fame Award (2015) SIGCOMM Test of Time Award (2011) ACM Doctoral Dissertation Award (2001) Member of National Academy of Engineering Honorary Member of the Romanian Academy Stoica has advised an extensive number of doctoral students who have gone on to prominent positions in academia and industry, including assistant professorships at Stanford, MIT, Carnegie Mellon, and other top institutions. He has received significant research funding through his lab activities and startup ventures. His research group has been particularly successful in translating academic research into widely adopted open-source technologies and commercial products. Stoica leads the Sky Computing Lab at UC Berkeley, which focuses on developing systems for AI workloads across multiple clouds. His research group has produced numerous influential open-source projects including Apache Spark, Apache Mesos, and Alluxio, which have become industry standards for large-scale data processing. The lab maintains strong industry partnerships while pursuing fundamental research in distributed systems and AI infrastructure.

• Mining Engineering
• Raw Materials Processing
• Underground Mining
• +7 more

Prof. Dr.-Ing. Elisabeth Clausen is a Professor and Director of the Chair and Institute for Advanced Mining Technologies at RWTH Aachen University. She holds key roles in the Specialist Group for Raw Materials and Disposal Technology, serves as a rectorate representative, and leads the Commission for EU Research Funding. Her research spans Underground mining automation Acoustic emission diagnostics Sustainable mining systems Space resource extraction Advanced sensor technologies Her recent publications focus on autonomous mining machinery, underground communication systems, and acoustic emission analysis across 15+ studies from 2013–2025, with particular emphasis on Ultra-wideband positioning Thermographic detection Crack monitoring in planetary gearboxes Explosive atmosphere safety Mineral processing diagnostics Digitalization trends Prof. Clausen contributes to mining education reform through initiatives like CDIO™ and has developed innovative learning spaces in underground mines. She coordinates international educational labs and integrates sustainability into mining engineering curricula, with publications on Adaptive ventilation systems Mining education frameworks Future-proof mineral extraction Entrepreneurial mindset in engineering

Jay PujaraView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Knowledge Graph Construction
• +5 more

Dr. Jay Pujara is a Research Associate Professor of Computer Science at the University of Southern California (USC) and Director of the Center on Knowledge Graphs. He is also a Principal Scientist at the Information Sciences Institute (ISI) and leads research teams in data science and AI. Ph.D., University of Maryland, College Park (2016) M.S. and B.S. in Computer Science, Carnegie Mellon University Research Interests include artificial intelligence, probabilistic models, knowledge graph construction, statistical relational learning, NLP, and streaming inference. His work focuses on scalable algorithms for big data and uncertainty modeling in dynamic environments. Recent Publications highlight advancements in knowledge graphs, LLM reasoning, and table understanding. Notable topics include non-verbal abstract reasoning , faithful conversational datasets , and KGQA re-ranking . Scientific Awards : SWSA Ten-Year Award (2023), Outstanding Paper (IUI 2019), Top Reviewer (NeurIPS 2018), Best Paper (SRL Workshop 2016) Advising & Grants : Mentored 12+ graduate students, including Ph.D. advisees on topics like causal modeling and neuro-symbolic tasks. Secured NSF funding for table understanding in paleoclimate studies.

Sara HäggView profile

Senior Lecturer

• Epidemiology
• Gerontology
• Medical Genetics and Genomics
• +3 more

Sara Hägg is a Senior Lecturer at the Karolinska Institutet , affiliated with the Department of Medical Epidemiology and Biostatistics . She is also a Docent in molecular epidemiology. PhD in Computational Biology (Linköping University, 2009) MSc in Molecular Biology (Stockholm University, 2003) BSc in Computer Science (Stockholm University, 2003) Her research focuses on human biological aging , including measurement of aging markers (telomere length, epigenetic clocks, frailty index), causal pathway analysis, and identification of geroprotectors for age-related diseases. She utilizes longitudinal twin studies (SATSA, GENDER, HARMONY), UK Biobank, and Swedish cohorts with methods like Mendelian randomization and genome-wide analyses . Recent articles demonstrate trends in epidemiological aging research , with emphasis on cardiovascular aging , neurological disease interactions , metabolic profiling , and epigenetic clocks . Her work often involves multivariable modeling and cross-cohort validation . Leadership roles include Director of LifeGene Core Facility (2024-) and Founding Board Member of the Nordic Aging Society (2023-). She serves on expert groups for the Swedish Twin Registry and Strategic Research Area in Epidemiology and Biostatistics .

Anindita BanerjeeView profile

Associate Professor

• Science Fiction
• Environment and Sustainability
• Migration and Borders
• +6 more

Anindita Banerjee is an Associate Professor in the Department of Comparative Literature at Cornell University's College of Arts and Sciences, with cross-appointments in the Environment & Sustainability Program, History of Art and Visual Studies, Literatures in English, and Romance Studies. She serves as SAP Core Faculty for the South Asia Program and has been instrumental in establishing Cornell's Environmental and Sustainability Program, where she built and chaired the environmental humanities concentration for four years. Her research explores the relationship between imagining and making futures across diverse contexts, focusing on the evolving interfaces between science, technology, and culture; natural and built environments; media and information systems; and the migration of people, things, ideas, and images. Spanning Eastern Europe, Central Asia, the Indian subcontinent, and Latin and African Americas, her work examines networks of exchange, innovation, production, and consumption that emerge outside conventional coordinates of the modern world. Her scholarship has been featured in BBC Culture, Radio Free Europe, Medium, and Meduza. She has authored multiple books including the award-winning We Modern People: Science Fiction and the Making of Russian Modernity (2013), which won the Science Fiction and Technoculture Studies book prize, and is currently working on Reactionary Worldbuilding (MIT Press, 2026) and Border Environments (Cornell, 2026). Her research demonstrates consistent engagement with science fiction as a critical lens for examining global environmental, technological, and social transformations. Science Fiction and Technoculture Studies book prize Ivy+ Mellon Leadership Fellow (2024-25) Academic Venture Fund grant from Cornell Atkinson Center for Sustainability Social Sciences and Humanities Research Fellowship Banerjee serves as a faculty fellow of the Cornell Atkinson Center for Sustainability and is a participating member of cross-college programs in Biology and Society, Media Studies, and Visual Studies. She co-led a workshop series and engaged-learning course on border spaces and environmental justice supported by a grant from the Mario Einaudi Center for International Studies. As an Ivy+ Mellon Leadership Fellow, she serves as Special Assistant to the Dean of the Cornell School of Continuing Education, focusing on academic leadership and governance with an emphasis on diversity and inclusion.

• Human-Computer Interaction
• User Interface Design
• Soft Robotics
• +10 more

Jan Borchers is a full professor of computer science and head of the Media Computing Group, an endowed Chair in the Computer Science Department at RWTH Aachen University. He serves as deputy member of the Faculty Council for Mathematics, Computer Science and Natural Sciences (2024-2026) and previously headed the Computer Science Department's Examination Board from 2015 until February 2025. Borchers established his research group in 2003, pioneering modern HCI academic research and teaching in Germany, and opened Germany's first Fab Lab in 2009. Borchers received his PhD 'summa cum laude' in computer science from Darmstadt University of Technology in 2000. Before joining RWTH Aachen, he held faculty positions at Stanford University and ETH Zurich. His PhD thesis, 'A Pattern Approach to Interaction Design,' became the first book to bring design patterns to HCI. His research focuses on Human-Computer Interaction with particular interest in new user interfaces for soft robotics, textile user interfaces, 3D printing and personal fabrication, augmented reality, wearable and tangible computing, interfaces for software development, deceptive patterns, and interactive guides and exhibits. He is a member of ACM, SIGCHI, SIGCHI Germany, and GI, and introduced the Interactivity format to the CHI conference in 2005. Recent publications reveal a strong emphasis on deceptive patterns/dark patterns in UI design, textile interfaces, and the impact of generative AI on creative teamwork. His work spans from fundamental research in interaction techniques to practical applications in smart homes, accessibility, and children's interfaces, with a consistent focus on usability and user-centered design. IDC 2025 Best Work in Progress: 'If They Have No Choice, They'll Accept!' CHI'25 Student Games Competition Winner: 'The Deceptive Dungeon' RWTH-Wissenschaftsnacht 2024 Best Science Slam: 'Usability: 4 Prinzipien guter User Interfaces' Multiple CHI/UIST Honorable Mentions and Best Paper awards spanning two decades Author of influential books including 'A Pattern Approach to Interaction Design' and 'Arduino In A Nutshell' Borchers has provided extensive consulting, training, and user interface design services to major clients including AirBus, Apple, ARD, Bayer, Children's Museum Boston, Daimler, Handelsblatt, OTIS, Scout24, TEDx, and Deutsche Telekom. He actively organizes the Computer Science Department's weekly Faculty Lunch since 2003 and coordinates the department's public relations and web presence since 2010. He leads the Media Computing Group, which has become a leading German lab in terms of archival publications at CHI, the top international conference in HCI. Since March 2025, he serves as faculty patron for TechLabs Aachen, a student initiative providing practical digital skills training.

• Computational Materials Science
• Condensed Matter Physics
• Nanomaterials
• +6 more

Paul Erhart is a Professor in Condensed Matter and Materials Theory at the Department of Physics, Chalmers University. He received his PhD from Technische Universität Darmstadt in 2006, followed by postdoctoral and staff positions at Lawrence Livermore National Laboratory from 2007, before joining Chalmers in 2011. His research bridges computational physics, materials science, and machine learning to tackle fundamental problems in materials design and characterization. Dr. Erhart's research focuses on computational materials science with particular emphasis on condensed matter physics, nanomaterials, and quantum materials. His work spans from developing computational methods like machine-learned potentials (GPUMD, neuroevolution potentials) to studying fundamental phenomena in perovskites, 2D materials, thermal transport, and plasmonics. He has pioneered approaches connecting simulation with experimental techniques through correlation functions and has made significant contributions to understanding phase transitions, defect physics, and electronic structure in complex materials systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional computational physics methods. His work increasingly focuses on developing and applying neuroevolution potentials to study thermal properties, phase transitions, and optical phenomena in materials. There's also a clear emphasis on connecting computational results with experimental observations, particularly in neutron scattering, Raman spectroscopy, and plasmonic sensing applications. His research spans fundamental materials physics to applied areas like hydrogen sensing and sustainable materials development. Dr. Erhart has contributed to numerous software packages essential to the computational materials science community, including WulffPack for Wulff constructions, Dynasor for extracting dynamical structure factors, calorine for neuroevolution potential models, and ICET for alloy cluster expansions. His collaborative work spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern materials research. His contributions to understanding perovskite materials, thermal transport phenomena, and plasmonic systems have established him as a leading researcher in computational materials science.

Michael FeigView profile

Professor

• Molecular Dynamics
• Computational Biophysics
• Protein Structure and Dynamics
• +3 more

Michael Feig serves as Professor in the Department of Biochemistry & Molecular Biology at Michigan State University, leading the Feig Lab within the BioMolecular Science Gateway initiative. His research bridges computational modeling and molecular biology to investigate protein behavior in cellular contexts, with particular emphasis on molecular dynamics simulations and machine learning applications. His academic background includes: Ph.D. (1999) from the University of Houston M.S. (1994) from Technical University of Berlin Feig's research program focuses on computational biophysics of protein systems, specializing in molecular dynamics simulations of crowded cellular environments, bacterial microcompartments, and intrinsically disordered proteins. His lab develops advanced modeling techniques including coarse-grained approaches (COCOMO2) and machine learning frameworks to predict protein properties and conformational landscapes. Current work explores temperature-dependent structural ensembles, enzyme cargo loading mechanisms in engineered microcompartments, and biomolecular condensate physics under shear flow. Analysis of his 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) integration of deep learning with molecular dynamics for protein structure prediction, (2) engineering of bacterial microcompartments for synthetic biology applications, and (3) fundamental studies of macromolecular crowding effects on diffusion and phase separation. His work consistently emphasizes methodological innovation with biological relevance, notably through enhancements to the CHARMM simulation platform. His scientific recognition includes: Alfred P. Sloan Fellowship (2005) As principal investigator of the Feig Lab, he directs research teams in computational biophysics projects supported by active funding mechanisms. While specific grant details aren't provided, his continuous publication pipeline and lab infrastructure indicate sustained research support. His mentorship spans graduate students in the Cell & Molecular Biology Program, with recent work involving multi-institutional collaborations on bacterial microcompartment engineering and protein phase separation. The Feig Lab operates at the intersection of high-performance computing and molecular biology, maintaining strong connections with experimental groups for method validation. Current initiatives include developing generative models for temperature-dependent protein conformations and investigating cytoplasmic protein capture mechanisms in microcompartments, with potential applications in metabolic engineering and nanobiotechnology.

• Artificial Intelligence in Education
• Science Education
• Self-Regulated Learning
• +5 more

Yael Feldman Maggor is a Postdoctoral Fellow at KTH Royal Institute of Technology, affiliated with the Media Technology & Interaction Design Division and the Digital Futures research center. Her work bridges educational technologies, artificial intelligence, and science education, with a focus on enhancing pedagogy through innovative tools. Research Themes: Generative AI in education, self-regulated learning, learning analytics, chemistry education, and ethical considerations in AI integration. Key Projects: Contributions to the International Journal of Science Education, development of AI-driven evaluation frameworks, and pandemic-era online teaching analysis. Methodologies: Expertise in quantitative and qualitative research, educational data mining, and design of interactive learning platforms. Recent publications emphasize cross-cultural trust in AI, generative AI applications in chemistry education, and explainable AI for teacher professional development. She co-authored studies on nanotechnology courses for educators and self-regulation strategies in online learning environments.