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Kyusang LeeView profile

Associate Professor

• Optoelectronics
• Thin-Film and Flexible Devices
• Neuromorphic Computing
• +5 more

Kyusang Lee is an Associate Professor in the Electrical and Computer Engineering and Materials Science and Engineering departments at the University of Virginia. His research focuses on optoelectronic devices, neuromorphic computing, and smart sensors, emphasizing applications in solar energy conversion and flexible electronics. He holds a B.S. from Korea University (2005), M.S. from Johns Hopkins University (2009), and Ph.D. from the University of Michigan (2014). He conducted postdoctoral research at the University of Michigan and MIT. Education: B.S., Electrical Engineering, Korea University, 2005 M.S., Electrical and Computer Engineering, Johns Hopkins University, 2009 Ph.D., Electrical Engineering and Computer Science, University of Michigan, 2014 Postdoctoral Fellowships: University of Michigan (EECS), MIT (Mechanical Engineering) His research interests span thin-film and flexible optoelectronics, neuromorphic computing architectures, and AIoT-enabled smart sensors. Notable contributions include remote epitaxy techniques for semiconductor membrane integration and solar-tracking concentrator designs. His work bridges materials science and device engineering to advance energy-efficient optoelectronics and bioinspired systems. Key Research Themes: Organic/inorganic optoelectronic devices for solar energy Flexible and stretchable electronics Neuromorphic hardware for edge computing Gas sensing and bioinspired sensor systems Lee’s publications reflect interdisciplinary innovation, with recent work on ferroelectric transistors, neuromorphic vision systems, and high-efficiency photovoltaics. He received the NSF CAREER Award (2020) and AFOSR YIP Award (2023).

• Computational Mechanics
• Dynamics of Learning
• Evolutionary Dynamics
• +12 more

Jim Crutchfield is a Distinguished Professor of Physics at the University of California, Davis, where he also serves as Director of the Complexity Sciences Center. He holds additional affiliations as President and Scientific Director of the Art & Science Laboratory in Santa Fe, External Faculty at the Santa Fe Institute, General Member of the Telluride Science Research Center, and Visiting Scholar at the Redwood Center for Theoretical Neuroscience. His work bridges physics, computation, and complex systems. Education: B.A. summa cum laude in Physics and Mathematics, University of California, Santa Cruz (1979) Ph.D. in Physics, University of California, Santa Cruz (1983) Crutchfield's research centers on computational mechanics , a framework he pioneered to quantify how natural systems store, process, and transmit information. His interests span nonlinear dynamics, evolutionary dynamics, information engines, quantum computation, and pattern discovery. He explores how structure emerges in complex systems, from cellular automata to biological evolution and neural networks. His recent work focuses on thermodynamic computing, causal inference, and the physics of intelligence. His publications reveal a consistent focus on the interplay between information, energy, and computation in physical systems. Themes include the thermodynamics of information engines, causal architecture in time series, emergent organization, and intrinsic computation in quantum and classical domains. These works span disciplines such as physics, computer science, biology, and cognitive science. Scientific Recognition: Postdoctoral Fellow, Miller Institute for Basic Research in Science IBM Postdoctoral Fellow, Condensed Matter Physics Distinguished Visiting Research Professor, Beckman Institute Bernard Osher Fellow, San Francisco Exploratorium NSF Graduate Fellow UCB Chancellor’s Fellow Crutchfield has advised over two dozen PhD students in physics, computer science, and mathematics, contributing significantly to the next generation of complexity scientists. He has led major interdisciplinary initiatives, including NSF-funded museum exhibits and workshops on network dynamics, collective cognition, and evolutionary dynamics. He has also been active in public discourse through talks, films, and publications on the philosophy of complexity. He leads research groups exploring the dynamics of learning, pattern discovery, and distributed intelligence, often in collaboration with institutions like the Santa Fe Institute and Caltech. His work continues to shape the theoretical foundations of complex systems science.

Zeb JamrozikView profile

Researcher

• Public health ethics
• Research ethics
• Human challenge studies
• +8 more

Zeb Jamrozik is a researcher affiliated with the Monash Bioethics Centre at Monash University, where he engages in critical work at the intersection of ethics and public health. His research is deeply embedded in global health policy, with a focus on ethical decision-making during public health emergencies and in the design of clinical research involving human participants. His research interests center on public health ethics, particularly in the context of infectious disease outbreaks. Key areas include the ethics of human challenge studies—especially for diseases like COVID-19—vaccination ethics, antimicrobial resistance, and health equity in global health research. He investigates how ethical principles can guide policy on infection control, resource allocation in intensive care, and research involving asymptomatic carriers of infection. The trends in his recent publications reveal a strong emphasis on pandemic preparedness and ethical governance. His work frequently addresses how scientific evidence, expert disagreement, and ethical frameworks interact in crisis settings. He contributes to high-impact journals in bioethics and infectious diseases, often collaborating with international bodies such as the World Health Organization. Zeb Jamrozik has served as a Chief Investigator or Primary Chief Investigator on multiple research projects funded by the Australian Research Council (ARC) and Safer Care Victoria. These projects explore ethical frameworks for ICU triage during the pandemic, equity in climate-sensitive infectious disease research, and guidelines for human challenge studies. His work has influenced clinical guidelines and been widely disseminated through news outlets, social media, and academic platforms. He is actively involved in research leadership, contributing to national and global conversations on bioethics. His projects often involve interdisciplinary teams and aim to produce actionable ethical guidance for policymakers and health institutions.

Thomas MussenbrockView profile

Professor

• Low-Temperature Plasmas
• Material Processing
• Nanoelectronics
• +3 more

Prof. Thomas Mussenbrock is a Full Professor (W3) of Applied Electrodynamics and Plasma Technology at Ruhr University Bochum, leading the Applied Electrodynamics and Plasma Technology department within the Faculty of Electrical Engineering and Information Technology. He holds a PhD from Ruhr University Bochum (2004) and habilitation (2009), previously serving as a professor at Brandenburg University of Technology (2016-2020). His research focuses on low-temperature plasmas, nanoelectronics, and plasma modeling, with applications in material processing and energy systems. Education: Bachelor's (1995) Bielefeld University of Applied Sciences Master's (1999) Ruhr University Bochum PhD (2004) Ruhr University Bochum Habilitation (2009) Ruhr University Bochum His research interests include plasma dynamics in atmospheric pressure jets, memristive devices for neuromorphic computing, and energy-efficient plasma processes. Recent work emphasizes CO₂ conversion via plasma jets, electron dynamics in capacitive discharges, and scalable plasma modeling techniques. He collaborates on projects like the TopING doctoral program and plasma catalysis initiatives. Publications span journals and conferences, with a focus on experimental and computational plasma physics. He advises on graduate studies and chairs faculty committees at Ruhr University Bochum.

Tommy LöfstedtView profile

Associate Professor

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +4 more

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.

• Eye-tracking
• Virtual Reality
• Extended Reality (XR)
• +7 more

Eakta Jain is an Associate Professor in the Department of Computer & Information Science & Engineering at the University of Florida's College of Engineering. Her research centers on human-computer interaction with a specialized focus on eye-tracking technologies, virtual reality, and privacy-preserving techniques in immersive environments. With over 15 years of sustained academic contributions, she has established herself as a leading researcher in gaze analysis and its applications across multiple domains. Dr. Jain's research interests span eye-tracking, virtual reality, extended reality (XR), privacy in immersive technologies, human-computer interaction, computer vision, and animation. Her work demonstrates a consistent trajectory from fundamental gaze analysis techniques to practical applications addressing critical privacy concerns in emerging technologies. She has made significant contributions to understanding how gaze data can be used to enhance user experience while simultaneously developing methods to protect user privacy in these systems. Analysis of her recent publications reveals a strong focus on privacy challenges in XR environments, with particular attention to gaze data protection, face-swapping technologies, and the psychological impacts of continuous monitoring. Her research bridges theoretical insights with practical implementations, often resulting in novel algorithms and frameworks that address real-world problems in immersive technologies. The interdisciplinary nature of her work connects computer science with cognitive psychology and human factors research. Dr. Jain has received recognition through publications in top-tier venues including IEEE Transactions on Visualization and Computer Graphics, ACM Transactions on Applied Perception, and the Symposium on Eye Tracking Research and Applications. Her work has been influential in shaping the discourse around privacy in immersive environments and has practical implications for the development of ethical XR systems. She actively mentors students and collaborators, with several junior researchers appearing as co-authors on her publications. Her research group appears to focus on the intersection of computer vision, graphics, and human-centered computing, with projects spanning from fundamental gaze analysis to applied privacy-preserving techniques in commercial VR systems. Current projects suggest strong industry connections and potential grant funding supporting her privacy-focused XR research.

• Artificial Intelligence
• Chemical Engineering
• Process Optimization
• +3 more

Elena Niculina Dragoi is a Lecturer at the Faculty of Chemical Engineering and Environmental Protection 'Cristofor Simionescu' at Gheorghe Asachi Technical University in Iasi, Romania. Her academic work integrates Artificial Intelligence and Machine Learning tools for solving complex problems in Chemical Engineering and Environmental Protection . With over 30 published papers and six active research projects, her contributions span process optimization, nanomaterials, and sustainable technologies. Teaches Applied Informatics (Years 1 & 4) and Artificial Intelligence at the Faculty of Chemical Engineering Contributes to Programming Engineering at the Faculty of Computer Science, University 'Alexandru Ioan Cuza' Engaged in interdisciplinary courses at the Faculty of Automatic Control and Computer Engineering Research Interests : Elena's work focuses on modelling and optimization (90% emphasis) of chemical processes using AI methodologies, with cross-disciplinary applications in environmental engineering (70%) and chemical engineering (95%). Her recent publications highlight innovations in: 3D-printed nanocomposite adsorbents for pollutant removal Metaheuristic optimization algorithms for industrial processes Hydrogen generation via nanocatalysts Electrochemical biosensors for environmental and health monitoring AI-driven wastewater treatment systems Green chemistry applications in pharmaceutical and dye removal

Richard SegallView profile

Professor

• Data Mining
• Text Mining
• Web Mining
• +6 more

Dr. Richard Segall is a Professor in the Department of Information Systems and Business Analytics at Arkansas State University , affiliated with the Beck College of Sciences & Mathematics . He is also affiliated faculty in the Master of Engineering Management (MEM) Program , the Environmental Sciences Program , and serves on thesis committees at the University of Arkansas at Little Rock (UALR) . Education: Ph.D. in Operations Research, University of Massachusetts at Amherst (1984) M.S. in Operations Research and Statistics, Rensselaer Polytechnic Institute (1975) M.S. in Mathematics, Rensselaer Polytechnic Institute (1973) B.S. in Mathematics, Rensselaer Polytechnic Institute (1971) Dr. Segall's research spans data mining, text mining, web mining, big data analytics, bioinformatics, supercomputing applications, and mathematical modeling . His work bridges business analytics and computational biology , with a focus on transdisciplinary applications in agriculture, healthcare, and space systems. His recent publications emphasize genomic data analysis , plant disease diagnostics , AI-driven healthcare solutions , and space technology forecasting . The integration of machine learning , data visualization , and open-source tools is a recurring theme across domains. Scientific Awards & Grants: Three research awards from the National Research Council (NRC) Software grants from Oracle Corporation and SAS Institute, Inc. Dr. Segall has served on the editorial boards of the International Journal of Data Science , International Journal of Data Mining, Modelling and Management , and International Journal of Fog Computing . He previously contributed to the Arkansas Center for Plant-Powered Production (P3) and currently participates in the Center for No-Boundary Thinking (CNBT) .

• Power Electronics
• Building Automation
• Smart Grids
• +7 more

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.

Andrew O. ArnoldView profile

Adjunct Professor

• Machine Learning
• Natural Language Processing
• Quantitative Trading
• +5 more

Andrew O. Arnold is a Principal Applied Machine Learning Engineer at Shopify and an Adjunct Professor at New York University's Tandon School of Engineering, Department of Finance and Risk Engineering. He earned his Ph.D. in Machine Learning from Carnegie Mellon University and a BA in Computer Science and Artificial Intelligence from Columbia University. Education Ph.D., Machine Learning, Carnegie Mellon University BA, Computer Science and Artificial Intelligence, Columbia University His research focuses on robust machine learning , developing models that perform well in low signal-to-noise regimes, handle distributional shifts (transfer learning), and extract features from unstructured data. Key applications include time series analysis and natural language processing in financial and other domains. Recent publications highlight work on large language models (LLMs) for code generation, including multitask pretraining, contrastive learning, and quantization techniques for efficiency. He has contributed to understanding model robustness and adapting NLP methods to dynamic market conditions. Arnold teaches NYU FRE GY 7871: News Analytics and Machine Learning , covering NLP and ML techniques for quantitative trading strategies. The course emphasizes practical applications of sentiment analysis, text relevance, and novelty detection in financial contexts. He has led teams at Amazon Web Services (AI Labs), served as Chief Scientist at Oracle Alpha, and worked at Microsoft Research, IBM Research, and other institutions. His technical expertise spans code generation , anomaly detection , and NLP for commerce , with patents in these areas.

• Atmospheric Dynamics
• Climate Science
• Arctic Climate
• +4 more

Lukas Papritz is a Lecturer at the Department of Environmental Systems Science at ETH Zürich , Switzerland. He specializes in atmospheric dynamics, focusing on large-scale weather systems, Arctic climate processes, and air-sea interactions. Research Interests : Dynamics of extratropical cyclones and atmospheric blocking Arctic climate system, including air mass transformations Atmospheric and oceanic energy exchanges Physics of temperature extremes (cold/warm) Development of dynamical frameworks for weather system analysis His recent publications examine baroclinic wave energetics, heatwave thermodynamics, cold-air outbreak dynamics, and synoptic-scale moisture transport. These works integrate Lagrangian methods, climatological analysis, and regional climate modeling. Current Affiliation : Professorship for Atmospheric Dynamics (Professur für Atmosphärendynamik), ETH Zürich

• Surface Catalysis
• Photocatalysis
• Energy-Related Catalytic Reactions
• +7 more

Dr. Qing Guo is an Associate Professor in the Department of Chemistry at South University of Science and Technology (SUSTech), where he joined in April 2019 as a research group leader and doctoral supervisor. His research focuses on fundamental processes of energy-related catalytic reactions, with particular emphasis on surface photocatalysis and reaction mechanisms. Bachelor of Science: University of Science and Technology of China (2007) PhD: University of Chinese Academy of Sciences (2013) Research Assistant/Associate Fellow: Dalian Institute of Chemical Physics, Chinese Academy of Sciences (2013-2019) Associate Professor: SUSTech Department of Chemistry (2019-present) Dr. Guo's research interests center on surface catalysis mechanisms, particularly in energy-related photocatalytic reactions. He develops novel scientific instruments to investigate photocatalytic C-H bond activation of small alkane molecules, carbon chain growth, and the interaction mechanism between metal clusters and substrates. His work spans photocatalytic hydrogen production, alkane activation and conversion, biomass conversion, and single-atom catalysis. His innovative approach combines instrument development with fundamental surface science to address critical challenges in energy conversion. Analysis of Dr. Guo's recent publications reveals a strong focus on titanium dioxide-based photocatalysis, with particular attention to reaction mechanisms at the molecular level. His work systematically investigates the roles of surface oxygen species, photon energy effects, and reaction intermediates in hydrocarbon conversion processes. The research shows an increasing sophistication in both experimental techniques and theoretical understanding, with recent work extending to ammonia synthesis, electrocatalysis, and excitonic effects at molecule/metal oxide interfaces. Dr. Guo leads an active research group that has produced over 80 publications in high-impact journals including J. Am. Chem. Soc., JACS Au, Chem. Sci., and Adv. Mater. His work demonstrates significant contributions to understanding fundamental photocatalytic processes, with potential applications in green energy and sustainable chemical production. Dr. Guo serves as a doctoral supervisor and has mentored numerous graduate students, many of whom appear as co-authors on his publications. His research group has developed several large-scale research instruments, including a surface photochemical kinetics research device, an in situ catalytic mechanism research device, a surface scattering kinetics research device, and a device for preparing controllable size clusters. These instruments have enabled systematic investigation of important energy-related catalytic reactions. Dr. Guo's laboratory focuses on the development and application of advanced surface science techniques to study energy-related catalytic reactions. His team combines experimental surface science approaches with theoretical modeling to gain molecular-level understanding of photocatalytic processes. The group actively recruits postdoctoral researchers and research assistants with backgrounds in vacuum technology, optics, semiconductors, and surface chemistry.

• Television Studies
• Software Studies
• Critical Data Studies
• +5 more

Dr. Karin van Es is an Associate Professor in the Department of Media and Culture Studies at the Faculty of Humanities, Utrecht University. She serves as project lead for the Humanities at Data School and is an affiliate and impact liaison at the Centre for Digital Humanities. Her work bridges academic research and practical applications in the digital society, with a focus on collaborating with external parties on interdisciplinary projects. She is also part of the GenAI in Education Humanities taskforce at Utrecht University. Her research is situated at the intersection of television studies, software studies, and critical data and algorithm studies, with a particular focus on streaming video culture and industries. Her notable publications include the book The Future of Live (Polity Press, 2016) and co-edited volumes such as The Datafied Society (AUP, 2017), Situating Data (AUP, 2023), Collaborative Research in a Datafied Society (AUP, 2024), and Governing the Digital Society (AUP, 2025). She has published extensively in journals including Television & New Media , Media, Culture and Society , Critical Studies in Television , Social Media + Society , Big Data and Society , and First Monday . Dr. van Es's recent publications reveal a strong focus on the governance of digital platforms, AI ethics, and the impact of streaming services on media consumption. Her work increasingly examines the societal implications of datafication, with particular attention to educational contexts, public values, and methodological innovations for studying digital phenomena. She has pioneered approaches like "data walking" and "data donations" as research methods for understanding how people interact with digital platforms in everyday contexts. Her scholarship demonstrates a consistent commitment to critical technical practice that bridges theoretical insights with practical interventions in the datafied society. Editor of special issue "Critical Technical Practice(s)" for Convergence Organizer of "Innovative Methods for Video-on-Demand Research" workshop (2024) Speaker at ECREA 2024 conference on "Netflix Uncovered: Insights from Data Donations" Member of editorial board for Convergence journal Her media contributions include appearances on WORT 89.9 FM discussing "The Meaning of Live" (2022), participation in the "De Maatschappelijk Betrokken Docent" program (2022), and earlier contributions on topics like "Wat is beeldradio?" (2016) and "Live liveness in realtime" (2015). She leads the Media and Performance Studies research group and is deeply involved with the Data School at Utrecht University. Her work with the GenAI in Education Humanities taskforce focuses on understanding and shaping the integration of generative AI in educational contexts. Through her role at the Centre for Digital Humanities, she helps bridge academic research with practical applications in society, particularly around issues of data governance, digital literacy, and the societal impact of emerging technologies.

• Speech Processing
• Automatic Speech Recognition
• Speaker Recognition
• +3 more

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.

Brooke Foucault WellesView profile

Professor

• Network Science
• Social Networks
• Computational Social Science
• +7 more

Brooke Foucault Welles is a Professor in the College of Arts, Media and Design at Northeastern University, where she also serves as Interim Dean and Director of the Network Science PhD program. Her research focuses on how social networks and communication technologies shape power dynamics, particularly in contexts of marginalization and social justice. PhD in Media, Technology and Society from Northwestern University MS and BS in Communication from Cornell University Her research spans multiple domains including: Network science of AI and social systems Digital activism and social movement dynamics Health information and (mis)information flows Open source community structures Race/ethnicity in digital contexts Computational social science methodologies Recent publications focus on attention dynamics in social networks, hate speech protection mechanisms, and open-source software sustainability. Her work has been supported by grants from the NSF, NULab, and Chan Zuckerberg Initiative. Awards include: McGannon Book Award (2021) for #HashtagActivism Best Paper Honorable Mention at CSCW 2019 She leads the Communication Media and Marginalization Lab (CoMM Lab) which includes PhD students and postdocs from diverse disciplines. Her advising approach emphasizes interdisciplinary collaboration and methodological training in both quantitative and qualitative approaches.