Elisa Kreiss is an Assistant Professor in the Department of Communication at UCLA, affiliated with the College of Letters & Science. She leads the Coalas Lab (Computation and Language for Society Lab), focusing on advancing understanding of how communicative context shapes language use through natural language processing, psycholinguistics, and human-computer interaction. Her work addresses challenges in image accessibility for visually impaired users, supported by grants from Google, NSF, and Stanford initiatives. Education: Ph.D. in Linguistics, Stanford University (advised by Christopher Potts) Research Interests: Her research bridges AI ethics, image accessibility, and human-centered evaluation of machine learning systems. Key themes include: Generating context-aware image descriptions for accessibility Ethical implications of vision-language models Interpretable AI through causal reasoning Awards: Google Research Awards National Science Foundation Grant Stanford Human-Centered AI Initiative Support Stanford Community Impact Award (2022) Lab & Advocacy: Directs the Coalas Lab, emphasizing inclusive research environments. Advocates for diversity in STEM and accessible technology design.
Michael Gastpar is a full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the Laboratory for Information in Networked Systems (LINX). He previously held faculty positions at the University of California, Berkeley (2003-2011, earning tenure in 2008) and Delft University of Technology. His research spans information theory, signal processing, communications, and systems neuroscience. His research interests focus on network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. Recent work demonstrates a strong shift toward exploring the theoretical foundations of modern machine learning, particularly investigating transformer architectures from an information-theoretic perspective. His research group at EPFL explores how information theory principles can provide fundamental limits and novel approaches for contemporary machine learning problems. His recent publications reveal a clear trend toward bridging classical information theory with modern machine learning. The 15 most recent papers show increasing focus on theoretical analysis of transformers, rate-distortion frameworks for language models, universal prediction methods, and applications of information measures to machine learning theory. This represents a strategic evolution from his earlier work on sensor networks and physical-layer network coding toward foundational questions in artificial intelligence. Scientific Awards: IEEE Fellow 2013 Communications Society & Information Theory Society Joint Paper Award Information Theory Society Distinguished Lecturer (2009-2011) ERC Starting Grant (2010) Okawa Foundation Research Grant (2008) NSF CAREER award (2004) 2002 EPFL Best Thesis Award Professor Gastpar has advised over 20 PhD students who have gone on to successful careers in both academia and industry. His research has been generously supported by major grants including an ERC Starting Grant "ComCom" (2011-2016) and ongoing support from the Swiss National Science Foundation. He has served in significant editorial roles, including as Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11) and as Technical Program Committee Co-Chair for the IEEE International Symposium on Information Theory in 2010 and 2021. He leads the Laboratory for Information in Networked Systems (LINX) at EPFL, which brings together researchers working at the intersection of information theory, machine learning, and networked systems. The lab maintains strong connections with both theoretical research communities and practical applications in communications and neuroscience.
Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.
Giorgia Ramponi is an Assistant Professor (tenure-track) in Artificial Intelligence for Cyber-Physical Systems at the University of Zurich (UZH), leading the Autonomous Learning and Predictive Intelligence Lab. She holds affiliations with ETH Zurich’s AI Center and Chalmers University of Technology. Previously, she was a postdoctoral researcher at ETH AI Center, sponsored by Google Brain. Her research focuses on machine learning and mathematical modeling, particularly Reinforcement Learning (RL), Multi-Agent Learning, and Imitation Learning. Notable contributions include work on non-cooperative Markov Decision Processes, mean-field games, and batch IRL for multiple intentions. Publications highlight advancements in constrained MDPs, policy optimization, and applications of RL in cyber-physical systems. Awards include the IBM Best Student Award (2016) and a Hassler Research Grant (2024). She advises students on topics ranging from RL theory to social network analysis. Her academic journey includes a PhD (Politecnico di Milano, 2021) and MSc/BSc (La Sapienza, Rome) with honors. She has taught courses on AI, data science, and machine learning, and contributed to open-source projects like GAN_Time_Series.
Yasmin B. Kafai is the Lori and Michael Milken President's Distinguished Professor at the University of Pennsylvania's Graduate School of Education. Her work focuses on empowering students through computational tools such as Scratch, electronic textiles, and biomaking, aiming to broaden participation in computing and promote equity in STEM education. Affiliations: Editor of the Journal of the Learning Sciences Former President of the International Society of the Learning Sciences Recipient of NSF Early Career Award and Rosenfield Community Prize Her research explores youth engagement with digital media, including gaming, coding, and creative technologies. She co-developed Scratch, a foundational platform for youth programming, and pioneered curricula integrating electronic textiles and biodesign in K-12 education. Key contributions include analyzing the societal impacts of virtual worlds, algorithmic literacy, and culturally responsive pedagogy. Publications & Awards: Author of award-winning books like Connected Gaming and Makeology Contributed to national policy reports, including the 2010 U.S. Department of Education's National Educational Technology Plan Recognized as a Fellow of the American Educational Research Association Her outreach initiatives include mentoring partnerships with the City of Los Angeles and collaborations with community tech centers like the Computer Clubhouse network. Current projects emphasize algorithmic justice, AI literacy, and equity-focused maker education.
Mirco Musolesi is a Full Professor of Computer Science at both University College London (UCL) and the University of Bologna. He leads the Machine Intelligence Lab at UCL, part of the UCL Centre for Artificial Intelligence. His research focuses on Machine Learning, Generative AI, and computational models of human behavior, with applications in ubiquitous systems and societal impacts of AI. Education: PhD in Computer Science from UCL (2007) and Laurea in Electronic Engineering from the University of Bologna (2002). Previous roles include positions at the University of Birmingham, Dartmouth College, and the Alan Turing Institute. Research spans multi-agent systems, reinforcement learning, and AI ethics. Notable awards include ACM UbiComp 10-Year Impact Award (2020/2024) and the NetExplorateur/UNESCO Top 100 Innovations (2011). His work on EmotionSense and CenceMe applications has been recognized with Test-of-Time awards. Recent publications (2024-2025) address moral alignment in AI agents, multi-agent environmental policy simulations, and creativity in LLMs. His labs explore AI-driven solutions for urban systems and ethical decision-making frameworks.
Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.
Bart Somers is an Associate Professor at Eindhoven University of Technology , affiliated with the Department of Mechanical Engineering . His primary affiliations include the Power & Flow Group and his own research group, Group Somers , alongside cross-cutting roles in EAISI (Eindhoven Artificial Intelligence Systems Institute) and EIRES (Eindhoven Research on Innovation and Sustainability in Energy Systems). He focuses on advancing combustion science , sustainable fuels , and engine efficiency , leveraging computational fluid dynamics (CFD) and experimental methods. His research interests span alternative fuels (hydrogen, bio-oils, biofuels), high-pressure spray combustion , and low-emission engine design . He investigates combustion optimization through CFD tools like large-eddy simulation (LES) and flamelet-generated manifolds (FGM), emphasizing fuel stratification , ignition dynamics , and emission control . His work bridges experimental diagnostics (e.g., spray visualization, OH* chemiluminescence) and numerical modeling. Academically, he teaches courses such as Thermodynamics , Clean Engines and Future Fuels , and Sustainable Vehicles , integrating practical projects into curricula. His educational activities emphasize interdisciplinary sustainability and innovation, including honors programs focused on professional development. Recent publications highlight his contributions to hydrogen injection strategies, biofuel applications in genset engines, and optimization of diesel-biofuel blends. His work aligns with global sustainability goals, addressing energy transition challenges through advanced combustion technologies.
Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.
Prof. Ivan Martinovic is a Professor of Computer Science at the University of Oxford's Department of Computer Science, holding a Fellowship at the institution. He specializes in cyber-physical systems security, wireless network security, and behavioral biometrics. His research focuses on authentication mechanisms, intrusion detection, and trade-offs between security and performance in systems like satellite communications, EV charging infrastructure, and aviation networks. Education: PhD (TU Kaiserslautern); MSc (TU Darmstadt, Germany). Prior roles include postdoctoral research at UC Berkeley (2011) and UC Irvine (2009–2010), supported by a Carl-Zeiss Foundation Fellowship (2009–2011). He has also been an associate lecturer at TU Kaiserslautern. Research Interests: Cyber-physical systems security, wireless networks, behavioral biometrics (e.g., continuous authentication via physiological signals), robust communication protocols, and analysis of security vs. performance trade-offs in distributed systems. Technologies include software-defined radios, NFC/RFIDs, and EEG/eye-tracking devices. Scientific Awards: Carl-Zeiss Foundation Fellowship (2009–2011). Advising & Grants: No explicit student listings; however, his work involves interdisciplinary collaborations and has been funded by government and industrial grants. His research spans labs and teams focused on satellite cybersecurity, smart infrastructure security, and biometric systems. Labs/Teams: Engages with groups exploring satellite communication security, EV charging infrastructure vulnerabilities, and behavioral authentication systems, though specific lab names are not disclosed in the text.
Ada M. Fenick, MD, is a Professor of Pediatrics (General Pediatrics) at Yale School of Medicine. She holds multiple leadership roles including Associate Director for Pediatrics in the Biopsychosocial Approach to Health clerkship, Medical Director of School-Based Health Centers, and Medical Director of the Medical-Legal Partnership Project. Her clinical practice focuses on economically disadvantaged pediatric populations, emphasizing developmental aspects of child health and addressing toxic stress impacts. Education: BS in Biomedical Sciences (University of Michigan, 1990), MD (University of Michigan, 1993), Pediatric Residency (Weill-Cornell/NY Medical Center, 1996) Research: Centers on pediatric primary care innovations such as group well-child care models, medical-legal partnerships, and obesity prevention. Key themes include health equity, social determinants of health, and curriculum development. Her recent work emphasizes leveraging medical student experiences to improve curricula addressing social drivers of health. Over 20 peer-reviewed publications since 2020 focus on topics like electronic phenotyping algorithms for obesity screening and frameworks for group pediatric care implementation. Awards: Includes Howard A Pearson MD Teaching Award (2013) and multiple institutional recognitions for clinical excellence and innovation. She leads initiatives like the Yale Primary Care Pediatrics Curriculum and collaborates with community organizations to enhance access to early intervention services for vulnerable children. Her work integrates clinical care, education, and policy advocacy to promote holistic child health outcomes.
Kenneth Ross is a Professor in the Computer Science Department at Columbia University in New York City. His primary appointment is within the Department of Computer Science, with affiliations including the Foundations of Data Science Committee. His work bridges theoretical database research and practical system implementation. His research focuses on database systems with particular expertise in query processing, query language design, data warehousing, and architecture-sensitive database system design. Additional research spans computational biology, especially analysis of large genomic data sets. Current projects include Linear Algebra Operators in Databases for machine learning workloads and Repeats and Somatic Mutation analysis in genomics. His work consistently addresses the intersection of hardware capabilities and database system design. Ross leads the Database Research Lab at Columbia, which has produced significant work on query optimization, GPU database processing, and hardware-conscious database systems. His recent publications demonstrate strong focus on adapting database systems to modern hardware including GPUs, SIMD processors, and persistent memory. His scientific recognition includes: Packard Foundation Fellowship Sloan Foundation Fellowship NSF Young Investigator Award Distinguished Faculty Teaching Award (2008) Ross actively advises undergraduate engineering students (juniors with last names P-Z) and has taught foundational courses including Introduction to Databases and Programming and Problem Solving for over two decades. His teaching portfolio shows consistent engagement with both theoretical concepts and practical implementation challenges in computer science education.
Dr. Anett Hoppe is a research staff member at the Leibniz Information Centre for Science and Technology (TIB) in Hannover, Germany, where she works in the Visual Analytics research group. Her research focuses on the intersection of artificial intelligence, education technology, and information science, with particular emphasis on how people learn through search processes and educational video consumption. Dr. Hoppe completed her academic journey with: Ph.D. in Semantic Web technologies for online user profiles from the University of Burgundy, Dijon, France Her primary research interests span Search as Learning, software-based support for scientific reproducibility, and ethical considerations in computer-based decision making. She investigates how visual elements, reading sequences, and AI technologies impact knowledge acquisition during web search and educational video consumption. Her work bridges human-computer interaction, educational psychology, and information retrieval to create more effective learning experiences, with recent publications examining the role of large language models, vision-language models, and visual complexity in educational contexts. Analysis of her recent publications (2024-2025) reveals a strong interdisciplinary focus combining computer science, educational psychology, and information science. Her research examines video-based learning effectiveness, knowledge gain prediction, educational resource discovery, and the impact of visual elements on learning outcomes. She consistently explores how AI technologies can be leveraged to enhance educational experiences while maintaining attention to ethical considerations and scientific reproducibility. Dr. Hoppe maintains active collaborations with researchers across multiple institutions, with frequent co-authorship patterns indicating strong research partnerships, particularly with Ralph Ewerth and other members of the Visual Analytics group at TIB. Her work supports TIB's mission to advance knowledge infrastructure and scholarly communication through innovative technological solutions while directly addressing practical challenges in educational technology and information retrieval.
Li Tang is an Associate Professor with tenure at École polytechnique fédérale de Lausanne (EPFL), affiliated with the Institute of Bioengineering (IBI) and the Institute of Materials Science and Engineering (IMX) within the School of Engineering (STI). She leads the Laboratory of Biomaterials for Immunoengineering, focusing on developing innovative strategies at the intersection of immunology, materials science, and cancer therapy. Her work bridges fundamental research and clinical translation, with multiple ongoing clinical trials based on CAR-T cell therapies developed in her lab. B.S. in Chemistry, Peking University (2003–2007) Ph.D. in Materials Science and Engineering, University of Illinois at Urbana-Champaign (2007–2012) Postdoctoral Fellow, MIT (2013–2016) Her research lies at the forefront of immunoengineering, integrating chemical, metabolic, and mechanical approaches to modulate immune responses. Key areas include cancer immunotherapy, immune metabolism, mechano-immunology, and biomaterials. She investigates how physical and biochemical cues can reprogram T cells, overcome exhaustion, and enhance tumor targeting. Her work emphasizes multidimensional immunity-disease interactions, aiming to develop safer and more effective therapies for cancer and autoimmune diseases. The recent publications highlight a strong trend in engineering immune cells (especially CAR-T) for enhanced durability and function, using advanced biomaterials and metabolic reprogramming. There is a clear focus on overcoming challenges in solid tumors, modulating the tumor microenvironment, and translating findings into clinical applications. The use of nanoparticle delivery, single-cell analysis, and biomechanical cues are recurring themes across her work. Notable scientific awards include: Friedrich Miescher Award (2025) ERC Starting Grant (2018) MIT TR35 Innovators Under 35 (China Region, 2020) Nano Research Young Innovator Award (2018) Biomaterials Science Emerging Investigator (2019) Materials Horizons Emerging Investigator (2020) Li Tang actively mentors PhD students across multiple doctoral programs (EDBB, EDMS, EDMX) and has advised numerous graduates who have gone on to prestigious postdoctoral and faculty positions. She is involved in significant research grants, including an Innosuisse project with Novochizol SA, and her lab is supported by competitive funding. She teaches core courses such as Immunoengineering and Next-Generation Biomaterials, shaping the next generation of scientists. Her lab fosters interdisciplinary collaboration and innovation, with active projects in chemical, metabolic, and mechanical immunoengineering, as well as CAR-T cell development. She is the Principal Investigator of the Tang Lab, which includes postdoctoral fellows, PhD students, and technical staff. The lab is actively recruiting and has a strong publication and clinical translation record. Tang Lab is also involved in multiple MA/BA training projects and promotes student engagement in cutting-edge research. The lab’s discoveries are being translated into clinical trials, reflecting a strong commitment to translational science.
Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.
