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Rebecca SternView profile

Associate Professor

• Victorian Literature and Culture
• Cultural Studies
• Gender Studies
• +3 more

Rebecca Stern is an Associate Professor in the Department of English Language and Literature at the University of South Carolina's McCausland College of Arts and Sciences. She holds a PhD from Rice University (1997) and specializes in Victorian Literature and Culture, Cultural Studies, and Gender Studies. Her research examines intersections between 19th-century scientific advancements, cultural practices, and literary forms, particularly focusing on temporalities and material culture. Her current book project, Conjugating Victorians , explores how Victorian authors and designers navigated evolving scientific understandings of time through narrative and material forms. Recent courses include Victorian Literature and The Victorian Novel. Stern has held prestigious fellowships, including a Mellon Foundation Fellowship and a National Endowment for the Humanities award. Education: PhD, Rice University, 1997 Key Awards: Yale Center for British Art Visiting Scholars Fellowship (2010), Research Opportunity Program Fellowship (2009-2010), NEH Summer Stipend Nominee (2008) Her publications include Home Economics: Domestic Fraud in Victorian Culture (2008) and articles on temporalities, sensation fiction, and Gothic literature. She has presented widely at conferences like the Dickens Universe and the Modern Language Association.

Paul MillerView profile

Professor

• Computational Neuroscience
• Neuroscience
• Dynamical Systems
• +5 more

Paul Miller is a Professor of Biology at Brandeis University and a member of the Volen National Center for Complex Systems. His research focuses on computational neuroscience, integrating dynamical systems theory with experimental data to understand neural mechanisms underlying decision-making, memory, and sensory processing. He holds a Ph.D. from the University of Bristol (UK) and a B.A. from Cambridge University. Before joining Brandeis in 2000, he was at Georgetown University and conducted postdoctoral work in theoretical physics at Oak Ridge National Laboratory. Education: B.A., Cambridge University (1991) Ph.D., University of Bristol (1994) Research Interests: Computational Neuroscience emphasizes modeling neural circuits for short-term/long-term memory, decision-making processes, and the dynamics of neural ensemble activity. His work bridges theoretical approaches (e.g., hidden Markov modeling, spiking neuron networks) with experimental data from cortical and hippocampal systems. He explores how quasistable attractor states enable robust computation and how homeostatic mechanisms stabilize neural activity. Notable Contributions: Developed models explaining how integral feedback control enables robust sequential decision-making, elucidated synaptic mechanisms for spatiotemporal discrimination, and analyzed the role of calcium/calmodulin-dependent protein kinase II (CaMKII) in long-term memory stability. His textbook An Introductory Course in Computational Neuroscience (MIT Press) is widely used in graduate programs. Labs/Teams: Leads the Computational Neuroscience Lab at Brandeis, collaborating with experimentalists to validate theoretical models. Active in interdisciplinary projects involving neurophysiology, systems biology, and mathematical neuroscience.

Rupak ShresthaView profile

Assistant Professor

• Political Geography
• Refugee Studies
• Indigeneity
• +6 more

Rupak Shrestha is an Assistant Professor in the School for International Studies and Global Asia Program at Simon Fraser University. He holds academic affiliations with the David Lam Centre and is part of the Aruna Global South collective. His research focuses on sovereignty, borders, indigeneity, and displacement in South Asia and diasporic contexts, employing visual ethnography. Shrestha earned a PhD in Geography from the University of Colorado Boulder, alongside MA and BA degrees in Geography from Miami University and St. Cloud State University respectively. Education: Ph.D. in Geography, University of Colorado Boulder M.A. in Geography, Miami University B.A. in Geography, Tourism, and GIS, St. Cloud State University Research interests include migration, refugee studies, political geography, and the intersection of security/surveillance with everyday life. His work examines how marginalized groups navigate state control through intimate partnerships and placemaking. Recent projects explore Tibetan refugee communities in Nepal, brick factory laborers' precarity, and US military veterans' experiences of homelessness. Publications highlight themes such as geopolitical humor, territorial futures, and affective borderings. His writings appear in journals like Political Geography , Environment and Planning C , and Dialogues in Human Geography . Teaching emphasizes radical acceptance of difference through critical social theory and reflexive pedagogy. He has taught at institutions including Macalester College and Eckerd College. Professional networks include memberships in the Association of American Geographers and collaborations with scholars like Jennifer Fluri. Research has been funded by the National Science Foundation.

Sharon Ihan HsiaoView profile

Assistant Professor

• Educational Technology
• Data-Driven Engineering
• Educational Data Mining
• +4 more

Dr. Sharon Ihan Hsiao is an Assistant Professor and David Packard endowed junior fellow in the Department of Computer Science & Engineering at Santa Clara University. She previously held positions at Arizona State University and Columbia University, where she established research projects and taught courses in computer science education. Her research focuses on educational technologies, computational modeling, and adaptive learning systems, with an emphasis on AI and HCI methodologies. She actively contributes to international conferences such as EDM, LAK, and AIED, and has held leadership roles including Program Chair of the 14th International Conference on Educational Data Mining. Education: Postdoctoral studies at Columbia University (2012-2014), PhD from the University of Pittsburgh (2006-2012), MS from Royal Holloway, University of London (2003-2004), and BA from National Central University (1999-2002). Research Interests: Dr. Hsiao's work centers on leveraging technology to enhance learning, including educational data mining, programming language learning analytics, and behavior modeling. Her projects include developing tools like WebPGA for programming assessment feedback, Ogmented for augmented reality learning, and Waste Genie for sustainability education. She explores distributed practice strategies, self-regulated learning behaviors, and immersive technologies to improve educational outcomes. Awards: Recognized with the FWA Outstanding Faculty Mentor Award (ASU) and the IEEE TCLT Young Researcher Award. Grants & Projects: Secured NSF funding for CatMapper (2-year grant) and led the Waste Genie project. Her research involves collaborations with industry partners and interdisciplinary teams, focusing on topics like sustainable waste management and computational thinking. Labs & Teams: Directs the Computer Science Innovation (CSI) Lab at Arizona State University and collaborates on projects like QuizIT and PiSA, which emphasize personalized learning and adaptive feedback systems.

• Heuristic search
• Program synthesis
• Deep learning for sound

Vadim Bulitko is a Professor in the Faculty of Science at the University of Alberta, affiliated with the Department of Computing Science. He holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. Research Interests: His work spans heuristic search, program synthesis, and deep learning applications for sound. Recent projects focus on AI-driven puzzle generation, pathfinding in dynamic game environments, and neural network classification. Publications: His 15 most recent articles explore AI advancements in puzzle design, video-game navigation, and bioacoustic classification, emphasizing heuristic optimization and explainable AI techniques.

• US Hispanic print culture
• US Antifascism
• Spanish Civil War Exile
• +4 more

Dr. M. Montserrat Feu Lopez (also known as Montse Feu) is an Associate Professor of Spanish in the World Languages & Cultures department at Sam Houston State University, where she teaches courses ranging from intermediate Spanish language instruction to specialized topics including Spanish for Criminal Justice and Contemporary Spanish American Literature at both undergraduate and graduate levels. Dr. Feu earned her MA in Humanities Studies at Hood College and her PhD in Hispanic Studies at the University of Houston. She has also obtained graduate certificates in Language Teaching Pedagogy, Women's Studies, and Fascism, Modernity, and Politics, reflecting the interdisciplinary nature of her scholarly work. Her research focuses on US Hispanic print culture , with particular expertise in antifascist movements , Spanish Civil War exile communities , and Hispanic anarchism in the United States . Dr. Feu examines how Spanish-language publications served as vehicles for political expression, cultural preservation, and community building among Hispanic populations across American history. She curates the digital project "Fighting Fascist Spain—The Exhibits," which showcases historical materials related to antifascist movements and exile communities. Her scholarly trajectory reveals consistent engagement with how Hispanic communities in America have used print media to navigate identity, politics, and cultural expression across different historical periods—from early 20th century Spanish-language newspapers to contemporary issues of refugee experiences—demonstrating her commitment to documenting marginalized voices and political resistance through meticulous archival research. Her notable recognition includes: Research Society for American Periodicals Article Prize (2016) for "The US Hispanic Flapper: Pelonas and Flapperismo in Spanish-language Newspapers 1920-1929" Dr. Feu has co-edited significant scholarly works including "Histories and Cultures of Latinas: Suffrage, Activism, and Women's Rights" (2023), "Writing Revolution: Hispanic Anarchism in the United States" (2019), and "Serving Refugee Children: Listening to Stories of Detention in the USA" (2021), demonstrating her commitment to both historical scholarship and contemporary social issues. Through her curation of "Fighting Fascist Spain—The Exhibits," Dr. Feu has established an important digital resource for scholars and students interested in transnational antifascist movements, Spanish Civil War exile communities, and the role of print culture in political resistance.

Jonny HuckView profile

Senior Lecturer

• Geographical Information Science
• GIS Applications in Global Health
• Environmental Restoration
• +4 more

Dr Jonny Huck is a Senior Lecturer in Geographical Information Science at the University of Manchester's Department of Geography. He holds additional roles as Honorary Professor at Gulu University, Uganda, and chairs several key groups including GISRUK National Steering Committee, Mapping & GIS Innovation Group, and Digital Humanities GIS Lead. His academic background includes a BSc (First Class) from Lancaster University, an MSc (Distinction) from the University of Leeds, and a PhD from Lancaster University. Research focuses on computational geography, particularly applying GIS to global health, environmental restoration, and urban segregation. Key interests include participatory GIS (PGIS), volunteered geographical information (VGI), and geographical simulation models. He developed the Map-Me PGIS platform and Shed Earth tool for geological analysis. Professional experience includes prior roles as Technical Manager in a UK wind farm developer and ongoing consultancy via Lune Geographic . Awards include three University of Manchester Making a Difference Awards for projects like Community Mapping Uganda and a Furness College Fellowship. His work aligns with UN SDGs related to health, environment, and innovation. Active in interdisciplinary projects such as the MEaSURE urban ecosystem initiative and Belfast Mobility Project analyzing sectarian spatial divides. Supervises research on prosthetics accessibility in Uganda and integrates AI with historical mapping data. Leads equipment management for the Small Uncrewed Aerial Systems facility.

Daniel HuttenlocherView profile

Professor

• Artificial Intelligence + Machine Learning
• AI and Society
• Graphics and Vision
• +3 more

Daniel Huttenlocher is the Dean of the MIT Stephen A. Schwarzman College of Computing and holds the Henry Ellis Warren (1894) Professorship in Computer Science and Artificial Intelligence + Decision-making (AI+D). He leads the interdisciplinary College of Computing while maintaining academic ties to the Electrical Engineering & Computer Science Department. His research focuses on AI ethics, societal impact of technology, machine learning applications, and vision systems. Key research areas include AI governance frameworks, adaptive public health strategies, and large-scale computer vision algorithms. He has pioneered work on decentralized collaboration systems, social media dynamics, and generative AI's societal implications. Huttenlocher’s recent publications address challenges in epidemic testing optimization and the evolving role of AI in knowledge ecosystems like Wikipedia. His leadership roles include steering MIT’s computing initiatives and fostering industry partnerships, such as the MIT Generative AI Impact Consortium. Despite no explicitly listed awards, his contributions to AI ethics and technical systems reflect significant academic influence. Advising and grant activities remain unspecified in available records. His work bridges technical innovation with societal challenges, emphasizing the ethical deployment of AI across healthcare, education, and digital economies.

Wenbin LiView profile

Senior Lecturer

• Robotics
• Computer Vision
• Machine Learning
• +4 more

Dr. Wenbin Li is a Senior Lecturer (Associate Professor) in Robotics at the University of Bath's Department of Computer Science. He leads the Pering Laboratory (Perceptual Intelligence Laboratory), affiliated with the AI & Machine Learning and Visual Computing groups. Previously, he held postdoctoral positions at Imperial College London (2016-2018) and UCL (2014-2016), and earned his PhD from the University of Bath in 2013, with earlier degrees from Imperial College London (MSc, 2009) and Xidian University (B.Eng, 2008). His research focuses on unified autonomous systems, including multi-sensory localization/mapping, dynamic motion capture, and uncontrolled scene understanding with applications in manufacturing and professional capture. Key areas include Robotics, Computer Vision, Graphics, and Machine Learning. He actively supervises doctoral students in these fields and has funded PhD openings. Dr. Li has been involved in major initiatives such as the My World - Strength in Places Fund (2021–2027), SLAM with Reinforcement Learning (2022–2023), and the CAMERA MC2 Award (2019–2023). His work aligns with UN Sustainable Development Goals, particularly in advancing technology for societal benefit. Recent publications emphasize aerial robotics, autonomous systems, and computer vision applications, including UAV package delivery reviews, Bayesian optimization for balloon station-keeping, and generative models for intrinsic image decomposition.

John RinzelView profile

Professor

• Computational Neuroscience
• Biophysical Mechanisms
• Neural Computations
• +3 more

John Rinzel is a Professor of Neural Science and Mathematics at New York University, affiliated with the Center for Neural Science and the Courant Institute. He holds academic positions within the College of Arts and Science and the Graduate School of Arts and Science. His research focuses on computational neuroscience, integrating biophysical mechanisms with mathematical modeling to understand neural computations at cellular and network levels. Education: Ph.D. in Mathematics (1973) and M.S. in Mathematics (1968) from NYU’s Courant Institute, and a B.S. in Engineering from the University of Florida (1967). Ph.D. in Mathematics, NYU Courant Institute (1973) M.S. in Mathematics, NYU Courant Institute (1968) B.S. in Engineering, University of Florida (1967) Research Interests: Rinzel studies biophysical mechanisms underlying neural computations, including dendritic computation, neuronal excitability, auditory pathway modeling, perceptual bistability, and rhythmic timing. His work combines theoretical models with experimental collaborations, emphasizing reduced biophysical models for cellular and network-level dynamics. Recent research trends include auditory streaming, perceptual dynamics, and rhythmic beat generation in music. His models explore gamma oscillations, thalamic spindle rhythms, and sleep-related neural excitability. Publications highlight applications in sensory processing, neural network oscillations, and cognitive functions like attention and memory coordination. No scientific awards explicitly listed. Rinzel leads a research group focusing on computational neuroscience, collaborating on projects involving auditory neuroscience, mathematical biology, and theoretical neurophysiology. His lab is part of NYU’s Center for Neural Science, fostering interdisciplinary approaches to brain function.

Kerstin BunteView profile

Professor

• Machine Learning
• Artificial Intelligence
• XAI (Explainable Artificial Intelligence)
• +7 more

Kerstin Bunte is a Professor of Machine Learning for interdisciplinary data analysis at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute's Intelligent Systems Group. She holds an Honorary Fellowship at the University of Birmingham and leads the Intelligent Systems Group. Her research focuses on interpretable machine learning, interdisciplinary applications (e.g., astrophysics and biomedical data), and visualization techniques. Research Interests: - Machine Learning - Artificial Intelligence - Explainable AI (XAI) - Interpretable Models - Dimensionality Reduction - Data Visualization - Astrophysical Data Analysis - Medical Imaging Awards & Grants: - DSSC XS funding (2023) - NWO VIDI grant (2020) - Rosalind Franklin Fellowship (2016–present) Advising & Students: Supervised PhD students include Elisa Oostwal, Janis Norden, Matteo Marcantoni, and Petra Awad. Research spans topics like tumor segmentation in medical imaging, astrophysical structure detection, and autonomous navigation systems. Labs & Collaborations: Leads the Intelligent Systems Group, collaborating with institutions like the University of Birmingham and the University of Warwick. Work involves interdisciplinary projects combining machine learning with astronomy, biomedical sciences, and robotics.

Sascha SchneiderView profile

Assistant Professor

• Educational Technology
• Multimedia Learning
• Cognitive Processes
• +3 more

Prof. Dr. Sascha Schneider is an Assistant Professor of Educational Technology at the University of Zurich's Institute of Educational Science, affiliated with the Digital Society Initiative (DSI). His research focuses on optimizing digital learning media design to enhance cognitive, emotional, and motivational processes in education. He holds a PhD in Media and Instructional Psychology and has held postdoctoral positions at TU Chemnitz. His work investigates topics like decorative elements' impact, motivational effects of choice, emotional design, and adaptive learning environments, targeting K-12 students, university learners, and adults. Education: Bachelor's: Educational Science & English, University of Erfurt Master's: Continuing Education & Educational Technology, University of Erfurt PhD: Media and Instructional Psychology, TU Chemnitz (2017) Habilitation: Educational Psychology, TU Chemnitz (2022) His research emphasizes experimental and meta-analytical methods to explore multimedia learning theory extensions, including social processes and instructional design principles. Key projects include the DEEP initiative for differentiated instruction and the LiT+ educational transfer program. Prof. Schneider's awards include the Erik De Corte Award (2021), a Dissertation Prize (2019), and the Commerzbank Prize (2018). He actively contributes to academic communities, organizing conferences and serving on editorial boards. Advising & Grants: Supervises doctoral research in educational technology Leads projects funded by BMBF and Swiss National Science Foundation His team at the Chair of Educational Technology collaborates on labs like NARRATID , focusing on narrative-driven instructional design.

Luca ManneschiView profile

Lecturer

• Machine Learning
• Neuromorphic Computing
• Biological Networks
• +3 more

Dr Luca Manneschi is a Lecturer in Machine Learning at the School of Computer Science, University of Sheffield, with an IBM Liaison role. He holds a PhD in Physics from the University of Sheffield (2021) and completed a PostDoc there before his current position since March 2022. His research focuses on designing learning algorithms inspired by biological networks for physically defined systems, emphasizing neuromorphic computing, reservoir computing, and stochastic environments. Education: Bachelor's in Physics: University of Padua (Italy) Master's in Physics: Sapienza University of Rome (Italy) PhD in Physics: University of Sheffield (2021) Research Interests: Dr. Manneschi explores algorithms for physically defined networks, leveraging biological network principles to enhance computation in dynamic environments. His work bridges machine learning with neuromorphic hardware, emphasizing adaptability and energy efficiency. Key areas include reservoir computing, magnetic metamaterials, and multi-timescale learning strategies. Grants & Funding: "Real-time Reservoir Computing on Prosthetic Devices" (2023–2025, Royal Society, PI) "MARCH: Magnetic Architectures for Reservoir Computing Hardware" (2021–2025, EPSRC, Co-PI) "CausalXRL: Causal Explanations in Reinforcement Learning" (2021–2024, EPSRC, Co-PI) "ActiveAI" (2019–2024, EPSRC, Co-PI) Lab/Team Affiliation: Machine Learning Research Group, School of Computer Science.

Michael BurkeView profile

Senior Lecturer

• Robotics
• Computer Vision
• Machine Learning
• +3 more

Dr. Michael Burke is a Senior Lecturer and Deputy Graduate School Coordinator at the Department of Electrical and Computer Systems Engineering, Monash University. He specializes in robotics, focusing on probabilistic machine learning and computer vision for autonomous systems. His roles include research supervision, curriculum development, and fostering graduate culture. Education: PhD in Statistical Signal Processing, University of Cambridge (2012–2016) MSc in Electronic Engineering, Stellenbosch University (2009–2011) BEng in Electronic Engineering, University of Pretoria (2005–2008) Research Interests: Robot learning and control Probabilistic machine learning for robotics Interpretable robotics systems and hybrid control architectures Robot perception and visuomotor control His work bridges robotics with machine learning to develop safe, adaptive autonomous systems for applications like surgical robotics and field robotics. Awards and Recognition: Best Paper Award at AMDO 2014 Runner-Up for Best Paper at CoRL 2019 Advising & Grants: Supervising over 20 PhD and Master’s students in robotics and related fields Principal Investigator for ARC-funded projects on robotic pharmaceutical formulation and human-robot interaction (2024–2027) Labs and Teams: Past leadership of the Mobile Intelligent Autonomous Systems group at CSIR, South Africa (2009–2018) Current collaborations with researchers at the University of Edinburgh and University of Melbourne

Hassan KarimiView profile

Professor

• Geoinformatics
• Machine Learning
• Location-Based Services
• +5 more

Hassan Karimi is a Professor at the University of Pittsburgh's School of Computing and Information, Department of Informatics and Networked Systems. His research focuses on Geoinformatics, Machine Learning, Location-Based Services, and Navigation Applications, with expertise in Mobile Computing and Distributed/Parallel Computing. He holds a Ph.D. in Geomatics Engineering from the University of Calgary, along with an MS in Computer Science from the University of Calgary and a BS in Computer Science from the University of New Brunswick. His research interests encompass computational geometry, geospatial data science, and smart city technologies. Notable contributions include methodologies for obstacle detection for visually impaired navigation, collaborative wayfinding systems, and spatiotemporal activity prediction. He leads the Geoinformatics Laboratory, which explores geospatial data science, mobile computing, and navigation systems. Key publications include works on geospatial data science techniques, navigation systems, and machine learning applications. His lab develops technologies for autonomous vehicles, environmental monitoring, and precision agriculture. He is the author of several books, including *Geospatial Data Science Techniques and Applications* (Taylor & Francis, 2018) and *Big Data: Techniques and Technologies in Geoinformatics* (Taylor & Francis, 2014). His work bridges theoretical research and practical applications, addressing challenges in smart cities, environmental sustainability, and health informatics. The Geoinformatics Lab collaborates on projects such as sensor networks, spatial data mining, and geovisualization.