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Deep JariwalaView profile

Associate Professor

• Nanoelectronics
• Nanophotonics
• 2D Materials
• +6 more

Deep Jariwala is an Associate Professor and the Peter and Susanne Armstrong Distinguished Scholar at the University of Pennsylvania, with primary appointment in the Department of Electrical and Systems Engineering and secondary affiliation in Materials Science and Engineering. He serves as the Undergraduate Chair for ESE and leads the Device Research and Engineering Laboratory. Dr. Jariwala's research focuses on nanometer and atomic scale devices, materials and interfaces for computing, sensing, information technology, and renewable energy applications. His interdisciplinary work bridges Electrical Engineering, Materials Science, Condensed Matter Physics, Optics, and Physical Chemistry. Key research areas include nanoelectronics, nanophotonics, 2D materials (particularly transition metal dichalcogenides), ferroelectric devices (especially AlScN-based systems), and advanced nanofabrication techniques. His laboratory investigates fundamental properties while developing practical device applications. Analysis of Dr. Jariwala's recent publications (2023-2025) reveals a strong emphasis on wafer-scale 2D materials growth, ferroelectric memory devices, and high-temperature electronics. His work spans from fundamental materials science to device applications, with particular focus on selector-free memory architectures, high-temperature operation (>500°C), and novel approaches to doping and contact engineering in 2D semiconductor devices. Recent trends show increasing emphasis on AI hardware and energy-efficient computing paradigms. Dr. Jariwala has been recognized with the Peter and Susanne Armstrong Distinguished Scholar award for his research contributions. As an academic advisor, Dr. Jariwala mentors undergraduate, graduate, and postdoctoral researchers in his laboratory. His team develops expertise in semiconductor processing, thin film deposition, nanofabrication, electrical characterization, and optical spectroscopy. He actively recruits students interested in nanoelectronics, nanophotonics, and atomic scale materials, providing hands-on experimental experience with close mentoring. The Device Research and Engineering Laboratory, located at 204 Singh Building (3205 Walnut Street), is equipped for advanced materials synthesis, device fabrication, and comprehensive electrical and optical characterization of nanoscale systems.

Jignesh M. PatelView profile

Professor

• Database Systems
• Query Optimization
• Data Analytics
• +3 more

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University, where he leads research on efficient data analysis methods. His work focuses on improving both system efficiency (e.g., high-performance data algorithms) and human efficiency (e.g., user productivity with data systems). Research Focus: Patel's group specializes in database systems, query optimization, hardware acceleration, and human-data interaction. Their interdisciplinary work spans: Transactional processing and real-time analytics Query optimization techniques Hardware-algorithm co-design Natural language interfaces for data systems Memory-efficient data processing Professional Activities: Co-founded four technology companies (Paradise, Locomatix, Quickstep, DataChat). Serves on program committees for premier conferences including SIGMOD and CIDR (as co-chair). Teaches database systems courses at CMU. Awards: Received Best Paper Award at DaMoN 2010 for work on cluster efficiency.

Dario GhersiView profile

Associate Professor

• Cancer Genomics
• Structural Bioinformatics
• Immunoinformatics
• +2 more

Dario Ghersi is Associate Professor and Graduate Program Chair for Biomedical Informatics, Cybersecurity, and IT Innovation at UNO's College of Information Science & Technology. He holds an MD from the University of Genoa and a PhD in Structural Bioinformatics from Mount Sinai School of Medicine. Research focuses on cancer genomics, structural bioinformatics, immunoinformatics, and biologically-inspired computing. His lab develops computational methods for understanding protein interactions, T-cell immunity, and therapeutic design. Recent publications demonstrate advancements in viral inhibition strategies, equitable algorithm design, and cancer biomarker discovery. Awards include a 2011 American-Italian Cancer Foundation fellowship. Service includes co-directing the BISB track in the IGPBS PhD program and multiple committee roles.

Aline NormoyleView profile

Assistant Professor

• Digital Humans
• Character Animation
• Video Games
• +4 more

Aline Normoyle is an Assistant Professor of Computer Science at Bryn Mawr College, where she leads the MOVR (Motion Capture and Virtual Reality) lab. Her research focuses on character animation, video game design, and analyzing human motion from video. She holds a B.Sc. from McGill University, and a M.Sc. and Ph.D. from the University of Pennsylvania. Prior to academia, she worked as a software engineer for 10 years, specializing in networking, graphics, and behavioral modeling. Her work bridges computational methods with experimental approaches to study non-player characters, avatars, and game mechanics. Normoyle’s contributions include open-source tools for sign language analysis and VR experiments, and she actively collaborates across disciplines including linguistics and psychology. She has been recognized with the Best Student Presentation Award at the 2013 Symposium on Applied Perception. Her teaching spans foundational computer science courses and specialized topics in game programming and animation. Education: B.Sc. in Computer Science, McGill University (1999) M.Sc./Ph.D. in Computer Science, University of Pennsylvania (2015) Research Interests: Procedural character animation and behavior Motion capture and analysis techniques Immersive VR/AR applications Game-based experimental platforms Accessibility in digital human modeling Recent Work Trends: Normoyle’s recent publications explore gesture-based user identification, controller effects in AR games, and the role of hand motions in virtual communication. Her work emphasizes interdisciplinary approaches, combining AI with traditional animation techniques to create more expressive and interactive virtual characters. Awards: Best Student Presentation Award, Symposium on Applied Perception (2013) Advising & Grants: Normoyle advises students in animation, VR, and game design, requiring foundational coursework in computer graphics and systems programming. She has received support for her research through collaborations with institutions like Clemson University and New York University. Labs & Teams: Director of the MOVR lab at Bryn Mawr, focusing on motion capture, VR experiments, and open-source tools for sign language analysis.

Polly DaltonView profile

Professor

• Human Attention
• Multisensory Perception
• Cognitive Psychology
• +3 more

Polly Dalton is a Professor of Psychology at Royal Holloway, University of London, leading the Attention Lab. She specialises in the psychological mechanisms of human attention, awareness, and multisensory experiences. As Head of Audience Futures at the CoSTAR National Lab, she conducts large-scale interdisciplinary studies on user engagement with technologies. She also co-directs the UKRI AI Centre for Doctoral Training in AI for Digital Media Inclusion. Her research explores attentional capture, sensory modalities (vision, hearing, touch), and applied contexts like aviation, remote vehicle operation, and VR. Projects include CoSTAR (Creative Industries R&D), StoryFutures (immersive storytelling), and HYPHEN (immersive production tools). Her work has been featured on BBC Breakfast TV, Sky News, The Guardian, and MSNBC. She collaborates internationally and contributes to UN Sustainable Development Goals through pro-environmental behavior research. Key grants include funding from AHRC, Innovate UK, and Daphne Jackson Trust. She mentors graduate students and oversees labs focused on advancing understanding of human experience in technological contexts.

Zhen XieView profile

Assistant Professor

• High-Performance Computing (HPC)
• Machine Learning
• Parallel Computing
• +3 more

Zhen Xie is an Assistant Professor in the Department of Computer Science at Binghamton University (SUNY), serving as Director of the Parallel Computing and Intelligent System (PCIS) Lab. He holds a PhD from the Chinese Academy of Sciences and a BA from Wuhan University of Technology. His research focuses on high-performance computing (HPC), machine learning, and their intersections, particularly optimizing performance for HPC and AI/DL applications across heterogeneous architectures. Research Highlights: Dr. Xie’s work emphasizes system-level performance optimization for ML and HPC, including GPU acceleration, memory optimization, and AI accelerator selection. His team has won the ACM Gordon Bell Special Prize (2022) for their GenSLMs project predicting SARS-CoV-2 evolution. Recent grants include a 2024 gift from OpenAI for AI testbed initiatives. Awards: ACM Gordon Bell Special Prize (2022), Impact Argonne Awards (2023) Lab: PCIS Lab explores middleware for parallel computing, targeting scientific simulations and big data analytics. Collaborations include Argonne National Lab and Lawrence Berkeley National Lab. Teaching: Teaches Distributed Systems (CS 457/557) and oversees independent studies. Previously trained researchers at Argonne’s ATPESC program. Grants & Collaborations: Subcontract with Lawrence Berkeley Lab (HEVI-LOAD), Argonne testbed expeditions, and OpenAI-funded projects. Active in DOE labs like Summit and Aurora supercomputers.

• Decision making
• Knowledge engineering
• Knowledge graphs
• +3 more

Marie-Hélène Abel is a Professor and Director of the Computer Science Department at the University of Technology of Compiègne (UTC), France. She leads the Heudiasyc laboratory (UMR CNRS 7253) and specializes in decision making, knowledge engineering, knowledge graphs, and multi-agent systems. Her work focuses on collaborative systems, organizational learning, and the integration of social data into decision support frameworks. She has been recognized with prestigious awards, including Senior IEEE membership (2017) and the 2017 K2 Trophy for participatory computing innovations. Her research emphasizes the development of systems like the MEMORAe platform, which supports learning ecosystems through collaborative environments and organizational memory. She collaborates internationally with institutions such as the Pontifical Catholic University of Paraná (Brazil) and industry partners including Thales, Alstom Transport, and PSA. Key projects include crisis simulation systems and ontology-based frameworks for Industry 4.0 collaboration. Abel’s academic contributions span over 50 peer-reviewed articles, with recent works addressing context-aware recommender systems, crisis management simulations, and teacher efficacy in digital education. Her leadership roles include directing research projects, overseeing the Computer Engineering Department, and advancing ethical AI practices through initiatives like the Sorbonne.AI network. Education: HDR (Habilitation à Diriger des Recherches) on 'Contribution of Organizational Memories in a Learning Context' (2007) Administrative Roles: Equality Officer, University Antiracism/Anti-Semitism Officer International Collaborations: Brazil, Egypt, and Tunisia

Simon LiView profile

Senior Lecturer

• Healthcare Human Factors
• Cognitive Psychology
• Medical Technology Safety
• +3 more

Dr. Simon Li is a Senior Lecturer at the School of Psychological Science, University of Western Australia (UWA), and an affiliate of the UWA Medical School and the Institute for Paediatric Perioperative Excellence. He holds a PhD in Psychology from University College London (2006) and previously served as an Assistant Professor in Hong Kong (2011–2020). His research focuses on healthcare human factors, aiming to enhance the safety and effectiveness of healthcare technologies and practices through cognitive psychology and clinical simulation studies. Education: PhD in Psychology, University College London (2002–2006) MSc in Ergonomics (Human-Computer Interaction), University College London (1999–2000) BSc in Psychology, University College London (1996–1999) Research Interests: Auditory displays in healthcare to address alarm fatigue and multitasking challenges Explainable AI (XAI) in clinical decision-making Cognitive psychology applied to healthcare technology design Grants & Projects: Lead investigator on the Hong Kong Research Grants Council-funded project "Auditory displays for anaesthetists" (2020–2021) Recruiting PhD students in auditory displays and XAI Labs/Teams: Active in the Institute for Paediatric Perioperative Excellence and collaborates on human factors research in healthcare.

• Event Cognition
• Human-Robot Interaction
• Visual Working Memory
• +4 more

Frank Papenmeier is a Professor in the Department of Psychology at the University of Tübingen, within the Faculty of Science. His research focuses on event cognition, human-robot interaction, visual working memory, and visual attention. He coordinates the 'Coordination Cognitive Psychology and Research Methods' research group. His work explores how people perceive and interact with dynamic environments, including studies on event segmentation, cognitive offloading, and aesthetic judgments. He has contributed to over 100 peer-reviewed articles, with recent work addressing topics like the impact of framing on art perception and the role of AI in education. Papenmeier's research integrates experimental methods with interdisciplinary approaches, including collaborations on teleoperation systems and AI-based tutoring. He has presented at major conferences such as the European Society for Cognitive Psychology and the Psychonomic Society. His lab emphasizes methodological rigor, evidenced by contributions to replication databases and open science initiatives. Education: Not explicitly stated in the text, but his titles include Dr. rer. nat. (Doctor of Natural Sciences) and Diplom-Psychologe (Psychology Diploma). Research Interests: His primary areas include event cognition, human-robot interaction (e.g., helping behavior toward robots), visual working memory (e.g., spatial configuration processing), and cognitive offloading (e.g., impact on memory and performance). He also investigates aesthetic judgments and narrative comprehension through eye-tracking and experimental paradigms. Articles Trends: Recent work addresses applied topics like cookie consent interfaces, AI in education (e.g., R programming tutors), and perceptual effects in 3D cinema. His studies often bridge cognitive theory with real-world applications, such as usability design and social robotics. Labs/Teams: Leads the research group 'Coordination Cognitive Psychology and Research Methods' at the University of Tübingen. Collaborates with interdisciplinary teams on projects involving robotics, AI, and human-computer interaction.

Stefano MartinianiView profile

Assistant Professor

• Computational and Statistical Physics
• Materials Science
• Artificial Intelligence for Science
• +5 more

Stefano Martiniani is an Assistant Professor of Physics, Chemistry, Mathematics, and Neuroscience at New York University, affiliated with the Center for Soft Matter Research and the Simons Center for Computational Physical Chemistry. His interdisciplinary research explores computational physics of complex systems, including neural circuit theories, non-equilibrium statistical mechanics, and AI-driven materials discovery. He has pioneered methods for analyzing high-dimensional energy landscapes and received prestigious awards like the NSF CAREER Award (2024) and IUPAP Early Career Prize (2023). Education: PhD in Physics (2017), University of Cambridge MPhil in Physics (2013), University of Cambridge BSc in Physics (2012), Imperial College London Research Interests: His work bridges statistical physics and artificial intelligence, focusing on: Engineering disordered materials with tailored spectral properties Quantifying entropy production in active matter Developing open science frameworks like ColabFit for machine learning interatomic potentials Neural circuit models for cortical communication Grants & Collaborations: Funded by NSF, NIH, Chan Zuckerberg Initiative, and Simons Foundation. Leads interdisciplinary teams in computational physics, AI, and materials science. Labs/Initiatives: Core member of NYU's Center for Soft Matter Research; develops software tools like FReSCo and KLIFF-Torch for computational materials science.

Tommaso CalarcoView profile

Professor

• Quantum Optimal Control
• Quantum Computing
• Quantum Technologies
• +3 more

Tommaso Calarco serves as Director of the Institute for Quantum Control (PGI-8) at Jülich Research Centre, leading cutting-edge research in quantum optimal control methodologies for next-generation quantum technologies. His work focuses on developing transformative computational frameworks applicable to natural sciences, logistics, and high-performance computing through advanced quantum device engineering. His research spans quantum optimal control for computation and many-body systems, emphasizing physical model development, model reduction techniques, and machine learning integration for scalable quantum hardware. Key focus areas include spin-qubit optimization, diamond quantum register engineering, and error suppression in gate operations, with significant contributions to ultracold atom systems and semiconductor-based quantum platforms. Analysis of his 2024-2025 publications reveals concentrated efforts on hardware-specific challenges across multiple quantum modalities: spin shuttling fidelity in semiconductor systems, gate optimization for nitrogen-vacancy centers, and photon-spin interface engineering. This work demonstrates a unifying thread of optimal control solutions tailored to platform-specific decoherence mechanisms and scalability constraints. As Director of PGI-8 within the Peter Grünberg Institut, Calarco oversees a dedicated research team advancing quantum control theory and applications, contributing substantially to European quantum technology roadmaps including the Quantum Flagship initiative and strategic European Commission reports.

• Machine Learning
• Generative Modeling
• Scientific Computing
• +3 more

Tanya Marwah is a Research Fellow at the Simons Foundation , collaborating with Polymathic AI . She earned her PhD from Carnegie Mellon University's Machine Learning Department, co-advised by Prof. Andrej Risteski and Prof. Zachary Lipton, and holds a master's degree from CMU's Robotics Institute. Her research bridges Machine Learning and Scientific Computing , focusing on generative modeling , inverse problems , and building scientific agents . Her work explores theoretical and empirical foundations for applying ML to differential equations, with key contributions in neural operators , memory mechanisms , and edge embeddings in GNNs . Recent publications highlight trends in PDE solvers via LLMs , cross-modal adaptation , and implicit regularization in SGD . She has received the prestigious Siebel Scholar award and actively contributes to top ML venues (NeurIPS, ICML, ICLR, TMLR). Her collaborations span institutions including Carnegie Mellon University, Polymathic AI, and CMU's Robotics Institute.

Ignacio CofoneView profile

Professor

• AI Regulation
• Data Protection Law
• Privacy Law
• +3 more

Ignacio Cofone is a Professor of Law and Regulation of AI at the University of Oxford's Faculty of Law and a Fellow of Reuben College. He is affiliated with the Institute for Ethics in AI, Yale Law School's Information Society Project, and the Quebec AI Institute. Previously, he held the Canada Research Chair in AI Law and Data Governance at McGill University. His research focuses on legal adaptation to AI and data-driven technologies, particularly privacy frameworks, immaterial harms, and human-centered regulatory design. Education: Joint PhD (rerum politicarum) from Hamburg University and Erasmus University Rotterdam, JSD from Yale Law School, and degrees in common/civil law. Visiting appointments include NYU, University of St Gallen, and Tilburg University. He advises governments and organizations on AI regulation, including work with Canada's Privacy Commissioner on PIPEDA reform. Research Interests: AI regulation, data protection law, privacy law, algorithmic decision-making, and the intersection of law with technological change. His book The Privacy Fallacy (2023) critiques privacy frameworks and proposes duty-based reforms. Recent articles address AI fairness, vaccine passport ethics, and privacy class actions. Labs/Teams: Leads the Computers and Law Research Group. Actively supervises doctoral/postdoctoral researchers with interdisciplinary or comparative law backgrounds.

Joshua SunshineView profile

Assistant Professor

• Software Engineering
• Programming Languages
• Human-Computer Interaction (HCI)
• +5 more

Joshua Sunshine is an Assistant Professor in the S3D Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science. His research focuses on the intersection of programming languages, software engineering, and human-computer interaction (HCI), emphasizing usability-driven language design and tools for developers. He leads the REU Site in Software Engineering (REUSE), a National Science Foundation-funded program fostering undergraduate research and equity in computing. Key research areas include: (1) Formal methods for software testing and verification (e.g., TerzoN, Gradual C0), (2) Domain-specific languages for mathematical visualization (Penrose), (3) Secure blockchain programming (Obsidian), and (4) Automated test generation tools (Nanofuzz). His work bridges theory and practice through user-centered methodologies, evident in projects like PLATEAU workshops and Glacier's transitive immutability. Awards include the NSF CAREER Award (2024) and sustained leadership in REU programs. His grants emphasize reproducible scientific programming, framework debugging, and interdisciplinary tool development. Notable contributions include frameworks like Rose for web-based autodiff and Edgeworth for scalable visual activity authoring. Labs/Teams: Core contributor to the S3D group, collaborating with HCI and PL communities. Active in organizing PLATEAU workshops (2018–2023), fostering evaluation and usability research in programming languages.

Sheldon AndrewsView profile

Associate Professor

• Real-time physics simulation
• Articulated mechanism simulation
• 3D character animation
• +5 more

Sheldon Andrews is an Associate Professor of Software Engineering and IT at École de technologie supérieure (ETS) in Montreal, Canada, with an adjunct appointment in Computer Science at McGill University. He is a member of the Multimedia Research Laboratory and has established himself as a leading researcher in physics-based computer animation and simulation. Andrews earned his Ph.D. in Computer Science from McGill University (2015), MASc in Electrical and Computer Engineering from the University of Ottawa (2007), and B.Eng. in Computer Engineering from Memorial University (2004). His academic journey reflects a strong foundation in both theoretical and applied aspects of computer engineering and graphics. His research focuses on real-time physics simulation, articulated mechanism simulation, 3D character animation, motion capture, computational contact mechanics, and virtual environment modeling. Andrews' work bridges the gap between theoretical physics and practical applications in computer graphics, with particular emphasis on creating physically plausible animations that can run in real-time. His research has significant implications for video games, virtual reality, and robotics applications. Analysis of his recent publications (2022-2025) reveals a strong trend toward increasingly sophisticated physics-based character animation techniques, with growing integration of machine learning approaches. His work spans multiple subfields including collision detection, deformable object simulation, vehicle physics, and reinforcement learning for character control, demonstrating both breadth and depth in his research program. VRIPHYS 2012 best paper award for 'Policies for goal directed multi-finger manipulation' Andrews has advised numerous graduate students through their PhD and Master's degrees, with many going on to positions at major companies like DNEG, CM Labs Simulations, and AMD. His professional service is extensive, having served as Program Chair for SCA 2025 and MIG 2024, Conference Chair for I3D 2019, and on program committees for major conferences including SIGGRAPH, SCA, and MIG for multiple years. He has also been active in the Montreal SIGGRAPH Chapter as Secretary from 2018-2021. As a core member of the Multimedia Research Laboratory, Andrews collaborates with researchers across multiple disciplines to advance the state of the art in physics-based simulation. His lab maintains strong industry connections, including a visiting researcher position at Roblox Research, ensuring that theoretical advances translate to practical applications in gaming and virtual environments.