Besiki Stvilia is a Professor in the School of Information at Florida State University's College of Communication and Information. He holds a Ph.D. and M.S. in Library and Information Science from the University of Illinois at Urbana-Champaign, along with an M.S. in Applied Mathematics from Tbilisi State University. His research focuses on data quality assurance, digital curation, social informatics, and knowledge organization. He has led projects on research data management, metadata frameworks, and collaborative data practices in scientific communities. Stvilia has taught courses including LIS 6205 (Information Behavior), LIS 5263 (Information Retrieval Theory), and LIS 5787 (Metadata Practices). His funded research includes grants on data quality infrastructure for repositories, researcher identity curation, and mobile wellness application behavior studies. His work emphasizes bridging theoretical models with practical applications in digital libraries and academic systems. His research spans topics like GenAI credibility assessment, ontology development for data repositories, and user engagement with social Q&A platforms. He has advised multiple collaborative projects involving interdisciplinary teams and has published extensively in journals like Journal of Documentation and Information Processing & Management .
Patrick Skeba is a Teaching Assistant Professor at the University of Pittsburgh's Department of Computer Science within the School of Computing and Information. He holds a PhD in Computer Science from Lehigh University (2022) and bachelor's degrees in Cognitive Science and Computer Science from Johns Hopkins University (2017). His research focuses on internet privacy, AI ethics, and the responsible use of data. He teaches courses in machine learning and programming. Research Interests: Skeba's work bridges technology and societal impact, emphasizing privacy risks in data systems, algorithmic fairness, and user-centric privacy frameworks. His recent studies explore informational friction in data collection, community-based privacy strategies, and lay-expert disparities in understanding privacy-enhancing technologies (PETs). Publications: His articles analyze privacy dynamics in digital spaces, from pandemic-era discourse on r/privacy to methodological approaches for categorizing technology non-use. His earlier work includes breakthroughs in sleep disorder diagnostics, particularly periodic leg movement (PLM) analysis and telemedicine applications for neurological conditions. Awards: No scientific awards listed. Grants and advising details are currently unspecified. Labs/Teams: No specific lab affiliations mentioned in provided materials. His teaching and research emphasize collaboration across computational and social domains.
Christopher Piech is an Assistant Professor (Teaching) in the Department of Computer Science at Stanford University, with a courtesy appointment in the Graduate School of Education. He serves as a Faculty Affiliate at the Institute for Human-Centered Artificial Intelligence (HAI) and is affiliated with the Symbolic Systems Program. Current courses: AI for Social Good (CS 21SI), Introduction to Probability for Computer Scientists (CS 109), Researching Presenting and Publishing Work in AI & Education (CS 220/EDUC 481) Advises 11 Master's students and co-advises 3 Doctoral students His research focuses on computational education, leveraging artificial intelligence to enhance learning analytics, student collaboration detection, and knowledge tracing in programming education. Publications span ACM Technical Symposium on Computer Science Education (SIGCSE) and NeurIPS conferences. Key article trends include: (1) AI-driven educational tools for code analysis, (2) collaboration monitoring in large classes, and (3) probabilistic models for student learning trajectories.
Joséphine Gantois is an Assistant Professor in Human Dimensions of Biodiversity Conservation at the University of British Columbia, jointly appointed in the Institute for Resources, Environment and Sustainability (IRES) and the Food and Resource Economics Program within the Faculty of Land and Food Systems. Her work bridges economics, ecology, and data science to address ecological footprints in agricultural and natural landscapes. She holds a Ph.D. in Sustainable Development from Columbia University, an M.P.A. in International Development from the London School of Economics, and advanced degrees in economics and the sciences from École Polytechnique. Research Focus: Dr. Gantois investigates practical solutions for reconciling land use incentives with conservation goals, particularly in agricultural areas. Her research emphasizes causal inference methods, integrating remote sensing, machine learning, and qualitative tools like interviews. Key areas include biodiversity monitoring, policy impact assessment, and ecosystem function analysis. She has explored habitat restoration in Ontario grain farms during her postdoctoral work under Dr. Claire Kremen at UBC. Teaching & Engagement: She teaches in the Master of Food and Resource Economics (MFRE) program, focusing on interdisciplinary approaches to sustainability challenges. Her work highlights the intersection of human behavior, policy design, and ecological outcomes, aiming to inform actionable conservation strategies.
Professor Niki Trigoni is a faculty member at the University of Oxford's Department of Computer Science and a Governing Body Fellow at Kellogg College. She holds the rank of Professor of Computing Science. Her research focuses on intelligent and autonomous sensor systems, with applications in positioning, healthcare, environmental monitoring, and smart cities. Trigoni leads the Cyber Physical Systems Group and directs the EPSRC Centre for Doctoral Training on Autonomous Intelligent Machines and Systems (AIMS), which integrates robotics, machine learning, verification/control, and sensor networks. Education: DPhil from the University of Cambridge (2001), followed by postdoctoral research at Cornell University (2002–2004) and a Lectureship at Birkbeck College (2004–2007). Current roles include leadership in AIMS and the Cyber Physical Systems Group. Research Interests: Her work spans sensor networks, inertial navigation, mmWave radar applications, and deep learning for localization and mapping. Recent projects include indoor positioning systems for emergency responders and wildlife monitoring. She has open positions for PhD and postdoc researchers in areas like sensor fusion, human-robot interaction, and SLAM. Publications: Over 50+ peer-reviewed articles, including work on mmPoint, P2-Net, and RandLA-Net. Her research emphasizes real-world applications in robotics and autonomous systems. Grants and Leadership: Received a 3-year NIST grant (2017) for indoor positioning systems and leads initiatives in cyber-physical systems. Active in conference organization, e.g., TPC chair for Sensys 2017 and IPSN 2016. Labs/Teams: Cyber Physical Systems Group focuses on sensor systems, robotics, and autonomous systems. Collaborations span academia and industry, addressing challenges in smart cities and healthcare.
Eleonora Vacca is a PhD student and Research Fellow in the Department of Automatic Control and Computer Science (DAUIN) at the Polytechnic University of Turin. She holds a B.S. in Electronic Engineering from the University of Palermo (2018) and an M.S. in Electronic Engineering-Embedded Systems from Politecnico di Torino (2021). Her research focuses on digital hardware design, reliability engineering, reconfigurable devices, and AI applications in aerospace and safety-critical systems. She is a member of the Aerospace and Safety Computing Lab and the CAD - Electronic CAD & Reliability Group (DAUIN). Her work addresses challenges such as radiation effects mitigation in space missions, fault-tolerant AI accelerators, and real-time anomaly detection in satellite telemetry. She has contributed to projects like the RAMSES CubeSat-1 Development (2025-2026), funded by commercial contracts. In 2024, she won the Best Student Paper Award at the NEWCAS Conference for her research on radiation effects in space missions. Vacca collaborates on teaching, including assisting in the course 'Electronic Calculators' for Computer Engineering students. Her recent publications explore AI resilience in RISC-V ecosystems, radiation environment analysis for space missions, and gesture recognition systems for smart cities. She actively contributes to conferences such as the ACM International Conference on Computing Frontiers and the IEEE International Smart Cities Conference.
Wesley Willett is an Associate Professor in the Department of Computer Science at the University of Calgary, holding the NSERC CRC II Chair in Visual Analytics. His primary research focuses on information visualization, human-computer interaction, and new media applications. He leads the Data Experience Lab and Interactions Lab, exploring innovative methods for data representation and interaction in augmented/virtual reality environments. Education includes a B.S. in Computer Science from the University of Colorado (2006) and a Ph.D. in Computer Science from UC Berkeley (2012). His work bridges technical innovation with user-centered design principles, emphasizing ethical considerations in data visualization and inclusive representation. Key research contributions include: spatial visualization techniques for large environments, gesture-based interfaces for AR/VR, and physical data representations through projects like Cetonia (swarm robotics visualization) and Data Embroidery. His work has been recognized with Best Paper awards at CHI 2015 and Pervasive 2010. Current research emphasizes immersive analytics, wearable visualization systems, and demographically diverse anthropographics. He collaborates with urban designers, neurologists, and environmental scientists to apply visualization in diverse domains like epilepsy surgery planning and air quality monitoring.
Isuru Godage is an Assistant Professor in the Department of Engineering Technology & Industrial Distribution at Texas A&M University's College of Engineering. He holds affiliated faculty positions in Mechanical Engineering and Multidisciplinary Engineering. His work focuses on advanced robotics systems, particularly soft robots, continuum arms, and their applications in surgery and blockchain-based collaboration. He earned a B.Sc. (Hons) in Electronic and Telecommunication Engineering from the University of Moratuwa, Sri Lanka (2007), and a Ph.D. in Robotics, Cognition, and Interaction Technologies from the University of Genova – Italian Institute of Technology, Italy (2013). Research Interests: Soft robots and continuum robots Modular robotic systems MRI-compatible surgical robotics for intracerebral hemorrhage evacuation Motion planning and control of underactuated systems Blockchain-enabled trustless collaboration between humans and robots His publications emphasize dynamic control of soft robotic arms, kinematic modeling of continuum systems, and bio-inspired designs for medical and industrial applications. Recent work explores locomotion strategies for soft quadrupeds and snake-like robots, alongside innovations in decentralized robotic data frameworks. Dr. Godage has secured grants such as the NSF CAREER Award (2021) focused on transformable soft robots and collaborative projects with the National Robotics Initiative (NRI). His research bridges robotics mechanics, control theory, and emerging technologies like blockchain for swarm robotics.
Michał Pałasz, PhD, is an Associate Professor at Jagiellonian University's Faculty of Management and Social Communication, Institute of Culture. His research focuses on management in the Anthropocene, posthumanism, ecological humanities, and climate policy. He serves as a European Climate Pact Ambassador, advocating for socio-environmental change through academic and community engagement. His research interests include exploring non-human agency, degrowth, and critical management studies. He has published extensively on climate action, cultural management, and digital culture, with notable works like Culture Management Must Fuel Socioenvironmental Change (2024) and Language Affects Climate (2024). His work bridges theory and practice, emphasizing inclusive and planetary-bound solutions. Pałasz has collaborated on projects such as Tick-tock, the End of the World (2022), examining multidisciplinary responses to the climate crisis. His academic contributions span journal articles, edited books, and conference presentations, often addressing the intersection of management, culture, and environmental sustainability. He teaches courses on posthumanistic management and climate policy, integrating critical perspectives into education. His work highlights the urgency of rethinking management practices to address polycrisis challenges, advocating for more-than-human-centered approaches.
Santiago Segarra is the W. M. Rice Trustee Associate Professor in the Department of Electrical and Computer Engineering at Rice University, with courtesy appointments in Computer Science and Statistics. He joined Rice in 2018 and collaborates with Microsoft Research since 2022. His expertise spans network theory, machine learning, graph signal processing, and optimization. Segarra earned his B.Sc. in Industrial Engineering from ITBA (2011), and M.S. and Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2014-2016), followed by a postdoc at MIT (2016-2018). Research Focus: His work integrates algebraic topology, signal processing, and machine learning to analyze networked systems. Key areas include social/technological network clustering, graph-based data analysis, and applications in neuroscience and communication networks. Recent projects address fair graph learning, distributed GNN training, and network topology inference. Awards: Penn’s Wolf Award for Best Dissertation (2017), Argentine National Engineering Honors (2011), and ITBA’s Best Thesis Award (2011). Grants/Sponsors: Supported by NSF, ONR, and industry collaborations. Labs/Groups: Leads the Rice Wireless group and collaborates with Microsoft Research on applied network science. Advises students in interdisciplinary research combining theory and real-world applications.
M.Sc. Maximilian Mühlbauer is a researcher at the Chair of Sensor-Based Robot Systems and Intelligent Assistance Systems at Technische Universität München (TUM), part of the Faculty of Computer Science. His work focuses on robotics, artificial intelligence, and space robotics, particularly in areas like in-orbit manufacturing, virtual fixtures, and human-robot interaction. He contributes to projects such as the ACOR initiative and the AI-In-Orbit-Factory, exploring fault-tolerant processes and adaptive robotic systems for space applications. Research Interests: Maximilian’s research emphasizes AI-driven robotics , space robotics , and control systems . He develops methodologies for virtual fixtures , reconfigurable robotic systems , and teleoperation with shared control . His work integrates probabilistic models and machine learning for resilient systems in challenging environments like space. Publications: His recent work spans topics from in-orbit manufacturing and force-sensitive space manipulators to multi-modal haptic teleoperation , reflecting a focus on practical robotic applications in aerospace and industry. Grants/Advising: Maximilian oversees available theses on topics like mixture of experts fixture learning and virtual fixture adaptation , inviting collaboration on AI-driven robotics projects. He collaborates with Prof. Alin Albu-Schäffer and contributes to TUM’s research initiatives in autonomous systems. Labs: He is part of the Sensor-Based Robot Systems lab, advancing robotics for human-centric and space-oriented applications.
Chris H. Wiggins serves as Associate Professor of Applied Mathematics at Columbia Engineering and Chief Data Scientist at The New York Times. He is a founding member of Columbia's Data Science Institute executive committee and Department of Systems Biology, with additional affiliations in Statistics, Foundations of Data Science, Health Analytics, Computational Social Science, and Education. His educational background includes: PhD in Theoretical Physics from Princeton University (1993-1998) Courant Instructor position at NYU (1998-2001) Wiggins' research bridges data science ethics , systems biology , and applied mathematics , focusing on societal implications of technology. His work examines disinformation ecosystems, ethical product design frameworks, and algorithmic accountability through interdisciplinary lenses combining computational methods with social science perspectives. His publications reveal a consistent emphasis on ethical infrastructure in data science, particularly how social media platforms can implement Tukey-inspired principles through measurable objectives. This reflects his broader mission to operationalize ethics in technology development. Award highlights: Fellow of the American Physical Society Columbia’s Avanessians Diversity Award Wiggins co-founded hackNY (2010), establishing semesterly student hackathons and the Fellows Program connecting students with NYC startups. His advisory work focuses on creating structured pathways for academic-industry collaboration in data science education. He leads Columbia's Computational Social Science initiatives and hackNY's educational infrastructure, fostering communities where students develop real-world data science solutions while engaging with ethical challenges.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Garrett Warnell is a Visiting Researcher in the Department of Computer Science at The University of Texas at Austin, specializing in artificial intelligence, computer vision, and robotics with applications in autonomous navigation systems. Education: PhD in Electrical Engineering, University of Maryland Master's in Electrical Engineering, University of Maryland B.S. in Computer Engineering, Michigan State University Research Interests: Dr. Warnell's work focuses on machine learning for robotic control , computer vision for scene understanding , and autonomous navigation in challenging environments . His contributions span imitation learning with limited demonstrations, preference-aware path planning, and off-road mobility. Recent research integrates vision-language models and transformer architectures for social navigation and terrain adaptation, emphasizing human-robot collaboration and robustness in constrained spaces. Publication Trends: Analysis of Dr. Warnell's 2023-2025 publications reveals dominant themes in off-road navigation robustness, with emphasis on particle filtering, diffusion models, and transformer networks for geo-localization and terrain adaptation. A significant trend involves human preference alignment through extrapolation techniques and open-vocabulary models for costmap generation, reflecting growing integration of natural language understanding in robotic systems. Scientific Awards: No awards specified in available documentation. Advising and Grants: Public records indicate no listed advisees or grant funding details. Labs and Teams: Affiliated with UT Austin's Computer Science Department, though specific research group affiliations remain undocumented in provided materials.
Elena Karahanna is a Professor at the Terry College of Business , University of Georgia. Her research spans Artificial Intelligence , Health Information Technology , and the social and algorithmic implications of digital platforms . PhD in MIS, University of Minnesota (1993) MBA in Business Administration, Lehigh University (1988) BS in Computer Science, Lehigh University (1986) Her work examines how conversational agents and social bots reshape e-commerce and social media, the algorithmic coordination in organizations, and the integration of IT in healthcare systems . She has contributed to foundational theories like the Needs-Affordances-Features (NAF) framework for social media analysis. Recent publications highlight her focus on digital governance (e.g., firm-sponsored online communities), chatbot applications in public health (e.g., pandemic response), and privacy concerns in online social networks. Her research often bridges information systems and marketing (e.g., hyper-privacy and dark web insights). Scientific Awards Terry College of Business Service Award, 2024 INFORMS Information Systems Society President's Service Award, 2023 MIS Quarterly Best Paper Award, 2022 AIS Leo Award for Exceptional Lifetime Contributions, 2020 AIS Fellow, 2012 Karahanna has served as Senior Editor for MIS Quarterly , Information Systems Research , and Journal of AIS . She co-founded the Doctoral Student Corner (2014) and the Senior Scholar Consortium (2004), emphasizing her leadership in academic mentorship. She has taught courses on theory development and business intelligence , and her international teaching includes stints in Hong Kong, Cyprus, Singapore, and Australia .
