Benjamin Eysenbach is an Assistant Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science since 2023. His research focuses on developing principled reinforcement learning (RL) algorithms that improve simplicity, scalability, and robustness in state-of-the-art systems, particularly through probabilistic inference techniques. Ph.D., Machine Learning, Carnegie Mellon University (2023) B.S., Mathematics, Massachusetts Institute of Technology Research interests center on reinforcement learning with emphasis on long-horizon reasoning, exploration strategies, and robustness. He explores intersections with probabilistic inference and self-supervised learning to enhance RL capabilities. Recent publications highlight trends in contrastive learning for goal-conditioned RL, temporal distance modeling , and hierarchical control . Key themes include reward-free learning, scalable architectures, and uncertainty quantification in decision-making systems. 2025: Junior Faculty Award for Excellence in Research and Teaching, Princeton School of Engineering and Applied Science Eysenbach's work bridges theoretical foundations with practical implementations in AI training frameworks, emphasizing performance optimization and safety mechanisms.
Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich , leading the Interactive Visual Data Analysis (IVDA) Group . He is associated with the Digital Society Initiative (DSI) and holds a PhD in Computer Science from Technische Universität Darmstadt (2015) with a focus on time-oriented data analysis. His academic journey includes postdoctoral research at TU Darmstadt and the University of British Columbia. Education : Diploma in Computer Science (2009, TU Darmstadt) PhD in Computer Science (2015, TU Darmstadt) Research Interests : Dr. Bernard specializes in interactive visual data analysis , explainable machine learning , and human-centered AI . His work explores time series analysis , multivariate data exploration , and user-driven preference elicitation . He develops visual analytics systems for domains like healthcare , digital humanities , and industrial applications , with a particular focus on responsible AI and transparency in algorithmic systems . Research Trends : His publications emphasize interactive machine learning workflows , visual analytics for healthcare , and time-stamped event sequence analysis . Recent work includes LLM validation frameworks (Human-Data-Model Interaction Canvas) and personalized ranking systems funded by the Swiss National Science Foundation. He integrates temporal data with multivariate analysis across applications from medical manufacturing to chronic disease management . Scientific Recognition : EuroGraphics Young Researcher Award (2022) EuroVis Young Researcher Award (2021) Best Paper Awards at IEEE VIS (2021), EuroVA (2021, 2025) Dirk Bartz Prize (2017), Hugo-Geiger Preis (2016) Teaching & Grants : He teaches Interactive Visual Data Analysis (6 ECTS), Digital Health Seminars , and People-Oriented Computing . Currently leads a SNF Grant on Personalized Visual Analytics for multi-criteria decision support (2024-2028) with ETH Zurich's Prof. M. El-Assady.
Christopher G. Healey is the Goodnight Distinguished Professor of Analytics in the Institute for Advanced Analytics and a Professor in the Department of Computer Science at North Carolina State University. His research spans visualization, data analytics, text analytics, sentiment analysis, machine learning, cognitive psychology, computer graphics, and social media analytics. He has graduated 15 Ph.D. and 26 master's students and secured over $6 million in research funding from agencies including the National Science Foundation, Department of Defense, National Security Agency, Army Research Office, and various industry partners. He has published over 100 peer-reviewed articles and is a senior member of both IEEE and ACM, as well as a member of the NC State Academy of Outstanding Teachers. His research focuses on developing visualization techniques that leverage visual perception to support rapid, accurate, and effective analysis of large, complex datasets. More recently, he has been investigating machine learning for natural language processing and text analytics. His work includes projects on visualizing election results, sentiment estimation for social media, and wildfire narratives using large-scale social media data. His publications demonstrate a strong trend toward integrating machine learning with visualization, particularly for text analytics and social media analysis. He has made significant contributions to visualizing deep neural networks, cyber situation awareness, and pandemic response analytics, showing how visualization can enhance understanding of complex systems and large datasets across multiple domains. IBM Faculty Award (2007, 2008, 2010, 2011, 2012) Senior member, Association of Computing Machinery (ACM) (2007) Senior member, Institute of Electrical and Electronics Engineers (IEEE) (2007) NC State Academy of Outstanding Teachers inductee (2003) National Science Foundation Faculty Early CAREER Award (2001) He has successfully mentored numerous graduate students and secured significant research funding across multiple projects. His work with the Laboratory for Analytic Sciences, National Science Foundation, and Department of Defense demonstrates strong industry and government partnerships. His recent projects focus on visualizing social media narratives, deep neural networks for text understanding, and predictive analytics for large document collections. He leads research groups focused on visualization and analytics, working with teams to develop innovative approaches for data exploration and analysis. His current work continues to push the boundaries of how visualization can be used to enhance understanding of complex data across domains including public health, cybersecurity, and social media analysis.
Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.
Dominik Wermke serves as an Assistant Professor in the Department of Computer Science at North Carolina State University. He is affiliated with multiple research entities including the Secure Computing Institute (SCI), the Wolfpack Security and Privacy Research (WSPR) Lab, and the Secure Software Supply Chain Center (S3C2). His educational background includes: Ph.D. in Computer Science from Leibniz University Hannover (2023) M.Sc. from Saarland University (2016) B.Sc. from Saarland University (2015) Wermke's research focuses on computer security with emphasis on human-centered security, examining how security mechanisms align with software professionals' and end users' needs, practices, and limitations. His work employs mixed-methods approaches including interviews, user studies, surveys, and large-scale ecosystem analyses to identify behavioral patterns and systemic risks in secure software development. His expertise spans cybersecurity, human-computer interaction, and user experience with particular focus on software supply chain security and open source ecosystems. His publication record shows a clear trend toward addressing software supply chain security challenges through empirical studies of developer practices, trust mechanisms in open source communities, and reproducible builds. Recent work increasingly examines the human factors in security implementations, with publications appearing in top venues including IEEE S&P, USENIX Security, ACM CCS, and NDSS. His notable recognition includes: Distinguished Paper Award at the 43rd IEEE Symposium on Security and Privacy (2022) Wermke actively mentors students interested in security research, with his lab focusing on empirical security studies that bridge technical and human aspects of software security. His research has been supported through institutional affiliations with NC State's security research centers which provide infrastructure for large-scale security ecosystem analyses. He maintains active collaborations with researchers at CISPA Helmholtz Center for Information Security and other institutions, evidenced by multi-institutional publications. His current research agenda continues to explore security challenges in modern software development practices with particular attention to supply chain vulnerabilities and trust mechanisms in distributed development environments.
Roel C.G.M. Loonen is an Associate Professor at the Unit Building Physics and Services within the Department of the Built Environment at Eindhoven University of Technology (TU/e), Netherlands. He holds joint appointments with EAISI High Tech Systems and EIRES Research groups, focusing on building performance simulation and energy systems. His work bridges academic research with practical applications through collaborations with SMEs in the building industry. Loonen received his BSc and MSc (cum laude) in Building Services from Eindhoven University of Technology, followed by a PhD in 2018 with a dissertation on 'Approaches for computational performance optimization of innovative adaptive facade concepts.' His educational background has positioned him as a leading expert in building performance simulation and sustainable building technologies. His research interests center on developing and applying modeling and simulation strategies to support decision-making for designing buildings that combine high indoor quality with minimal environmental impact. Key areas include adaptive facades, building-integrated renewable energy systems, and energy-efficient building envelopes. He specializes in creating and validating new building performance simulation models to advance innovative building technologies. His recent publications demonstrate a strong focus on practical applications of building performance simulation, with emphasis on residential energy efficiency, photovoltaic systems, and occupant-centered approaches to building design. The work shows increasing integration of machine learning techniques with traditional building simulation methods, particularly for sensitivity analysis and optimization of building performance. REHVA Young Scientist Award (2021) Best PhD supervisor award from Department of the Built Environment, TU/e (2018) First prize - REHVA International student competition (2011) Smart daylight control for optimal building performance (NWO Take-off award, 2018) Best paper award (2021) Loonen actively supervises PhD and Master's students, evidenced by his Best PhD Supervisor Award in 2018. He manages multiple research projects including Sustainable Summer Comfort (2024-2027), Modeling Innovative Use Scenarios for Future Domestic Comfort (2023-2026), and Just Prepare (2022-2026), with funding from sources including the Dutch Research Council (NWO). His professional service includes being a board member of the Dutch-Flemish IBPSA affiliate and co-chair of IBPSA World's website committee, plus reviewing for 35 academic journals. He leads research within the Building Performance group, focusing on creating practical tools and methodologies that bridge the gap between theoretical building performance models and real-world implementation in the construction industry. His work particularly emphasizes the integration of occupant behavior and practices into building performance models, recognizing that human factors are critical to achieving sustainable building performance in practice.
Dr Ehsan Nabavi is a Senior Lecturer in Technology and Society at the College of Asia and the Pacific (CPAS), Australian National University (ANU). He leads ANU’s Responsible Innovation Lab, focusing on responsible computing, modeling, and AI ethics. His interdisciplinary work bridges technical and social sciences, particularly addressing wicked problems in sustainability and water governance. Current Affiliation: ANU (CPAS), since 2020 Former Roles: Research Fellow at ANU School of Cybernetics (2018–2020), Harvard Kennedy School (2016–2017) Visiting Positions: SOAS University of London, University of Bonn (ZEF) His research spans responsible AI , transdisciplinary modeling , and socio-technical systems , as reflected in his publications across journals like Nature Humanities and Social Sciences Communications , IEEE Transactions on Technology and Society , and Water Alternatives . His recent articles emphasize ethical AI deployment, human-water systems, and integrating social aspects into modeling. Labs: Responsible Innovation Lab (ANU) Future Work: Developing frameworks for responsible AI in sustainability and policy contexts
Leid Zejnilovic is an Assistant Professor at Nova School of Business and Economics (Nova SBE), where he co-founded the Data Science Knowledge Center and serves as Academic Director, and co-founded the Open and User Innovation Knowledge Center as Scientific Deputy Director. He also co-founded the Patient Innovation platform, enabling patients and caregivers to share self-made healthcare solutions. With a double PhD from Carnegie Mellon University and Católica-Lisbon School of Business and Economics, his career spans over 20 years of international consulting, academic entrepreneurship, and teaching at institutions like Imperial College Business School and Ludwig Boltzmann Institute. PhD in Strategy, Entrepreneurship and Technological Change (Carnegie Mellon University / Catholic University of Portugal, 2014) Master in Engineering and Public Policy (Carnegie Mellon University, 2012) Master in Information Technology (Dzemal Bijedic University, 2007) Bachelor in Telecommunications (University of Sarajevo, 2002) His research focuses on Technology and Innovation Management, Human-Computer Interaction, and data-driven solutions across healthcare, tourism, and education. He has published extensively in journals like California Management Review , PLoS ONE , and Marine Policy , with recent work analyzing big data in tourism, machine learning for oral health, and pandemic impacts on fisheries. As an Associate Editor for Data & Policy Journal , his contributions bridge academic research and real-world applications. Co-founding the Data Science for Social Good Foundation and leading over 100 talks in industry and academia, Zejnilovic's career emphasizes translating innovation into social and economic impact through platforms, policy, and education.
Michael Harrison is an Assistant Professor in the Department of Cell and Developmental Biology at Weill Cornell Medicine, where he leads the Regeneration and Development Lab within the Graduate School of Medical Sciences. His research focuses on vascular development and regeneration using zebrafish as a model organism, with emphasis on coronary and cerebral vasculature. Education: B.Sc. in Genetics, University of Edinburgh (2005) Ph.D. in Developmental Genetics, University of Sheffield (mentor: Vincent Cunliffe) Postdoctoral Fellowship, Saban Research Institute, Children’s Hospital Los Angeles (CIRM Fellow) Harrison's research centers on understanding how blood and lymphatic vessels form and regenerate, particularly in the heart and brain. His lab investigates coronary vessel development, the role of lymphatic systems in inflammation and regeneration, and revascularization after injury. By leveraging zebrafish genetics and advanced imaging, his work aims to uncover pathways that could be harnessed for regenerative therapies in humans. His recent publications reveal key signaling mechanisms such as Cxcr4-Cxcl12 in coronary development and the two-step formation of cardiac lymphatics. The 15 most recent articles demonstrate a strong, consistent research trajectory in vascular biology and regeneration, with increasing use of advanced techniques like single-nuclei multiomics, fluidic imaging devices, and CRISPR-based genome editing. His work spans developmental mechanisms, functional imaging, and translational applications in cardiac repair. Scientific Awards: No awards explicitly mentioned in the provided text. Harrison actively mentors a team of postdoctoral fellows, research assistants, and students, several of whom have progressed to medical school or research careers. His lab collaborates extensively, particularly with Ching-Ling Lien's group. He has secured research space and funding to support ongoing projects in cardiac and cerebral vasculature. The lab is actively recruiting rotation students from BCMB, PBSB, IMP, and Tri-Institutional programs, as well as postdoctoral researchers and research assistants, indicating an expanding research team and active grant support. Labs and Teams: Regeneration and Development Lab, Weill Cornell Medicine Collaborations with Ching-Ling Lien Lab Member of Tri-Institutional PhD Programs Active participation in BCMB, PBSB, and IMP training programs
Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.
Frank Chan is a Professor of Information Systems at ESSEC Business School in France, where he currently serves as Department Head of Information Systems, Decision Sciences and Statistics (2022-2025). He has been with ESSEC since 2013, progressing from Assistant Professor to Associate Professor and now Professor. His academic career focuses on the intersection of information systems, public administration, and organizational behavior. Dr. Chan earned his Ph.D. in Information Systems from Hong Kong University of Science and Technology (HKUST) in 2010 and completed his BBA in Information Systems and Finance from the same institution in 2003. His educational background provided the foundation for his research in technology implementation and electronic government. His research interests span electronic government, technology implementation, agile methodologies, and internet privacy. Dr. Chan's work examines how digital technologies transform public services, organizational processes, and citizen experiences. He investigates the human aspects of technology adoption, including leadership dynamics in agile teams, citizen satisfaction with e-government services, and privacy concerns in digital environments. His multidisciplinary approach combines insights from information systems, public administration, and organizational behavior. Analysis of Dr. Chan's publication record reveals a consistent focus on e-government systems and technology implementation, with increasing attention to agile development methodologies in recent years. His work demonstrates a progression from foundational technology adoption studies to more nuanced investigations of leadership dynamics, privacy concerns, and the societal impacts of digital initiatives. The interdisciplinary nature of his research bridges business, public administration, and technology domains. Pacific Asia Conference on Information Systems Best Associate Editor Award (2022) International Conference on Information Systems Outstanding Associate Editor Award (2019) MIS Quarterly Reviewer of the Year Award (2019) MIS Quarterly Reviewer of the Year Award (2018) Journal of Operations Management Ambassador Award (2017) Finalist for Journal of Operations Management Jack Meredith Best Paper Award (2012) As a Senior Editor for Information Systems Journal since 2021 (previously Associate Editor 2016-2020), Dr. Chan has significantly contributed to the academic community. He has served as Track Co-Chair for major conferences including International Conference on Information Systems and Pacific Asia Conference on Information Systems. His consulting work with United Nations ESCAP on digitalization of tax administrations in Asia demonstrates the real-world impact of his expertise. Dr. Chan teaches courses in Research Design, Quantitative Research Methods, and Digital Business at ESSEC.
Betsy Foxman serves as the Hunein F. and Hilda Maassab Professor of Epidemiology at the University of Michigan School of Public Health. She directs three major initiatives: the Center for Molecular and Clinical Epidemiology of Infectious Diseases, the Integrated Training in Microbial Systems program, and the Certificate in Healthcare Infection Prevention & Control. Her academic leadership spans decades with continuous research contributions. Dr. Foxman earned her PhD and MSPH from UCLA (1983, 1980) and BS from UC Berkeley (1977). Her research centers on infectious disease transmission, microbiome ecology, antibiotic resistance, and wastewater surveillance . Key projects include analyzing the oral microbiome in dental caries using genomic methods, studying nose/throat microbiome associations with respiratory infections in nursing facilities, and developing wastewater monitoring for antibiotic-resistant pathogens. Her work integrates next-generation sequencing with epidemiological analysis to identify novel interventions. Publication trends reveal consistent focus on microbiome-pathogen interactions across multiple body sites (oral, vaginal, gut, respiratory). Recent articles demonstrate methodological innovation in wastewater epidemiology (2024 Norovirus GII monitoring) and clinical applications like predicting vancomycin-resistant enterococci contamination (2023 Lancet study). Her research bridges molecular microbiology with population health, emphasizing translational potential for diagnostics and public health interventions. Fellow of the Infectious Disease Society of America Fellow of the American College of Epidemiology Fellow of the American Academy of Microbiology Dr. Foxman's advising portfolio includes numerous NIH-funded projects on microbiome dynamics and infection control. Her leadership in the Center for Molecular and Clinical Epidemiology drives collaborative research across departments. Current initiatives focus on wastewater surveillance standardization and microbiome-based diagnostics for infection prevention. She maintains active laboratories for genomic analysis of microbial communities and clinical sample processing.
Luigi De Russis is an Associate Professor at the Department of Control and Computer Engineering (DAUIN) within Politecnico di Torino . He serves as Deputy Director of DAUIN and is a member of the PIC4SeR (PoliTO Interdepartmental Centre for Service Robotics). His academic roles focus on Human-Computer Interaction , Digital Wellbeing , and Artificial Intelligence applications. Research interests: Accessibility, Conversational agents, Developers tools, Digital wellbeing, Intelligent user interfaces, Internet of Things Teaching: Courses in Human-AI Interaction, Web Applications, and Computer Vision at undergraduate and graduate levels Leadership: Vice-President of ACM SIGCHI (2024-), Executive Committee member (2021-2024) His research explores: Digital wellbeing education for teens through gamified systems AI-assisted UI design tools Smart home interaction via multimodal commands End-user development for self-control technologies Integration of accessibility guidelines in AI systems Recent article trends show a focus on generative AI for interface design, attention-capturing heuristics, and educational systems for step-by-step learning. He has received the Most Influential Paper Award (2024) and Best Late Breaking Results Award (2025) from ACM SIGCHI. Scientific awards: Award of Scientific Excellence, University of Salamanca (2011) Most Influential Paper Award, International Conference on Intelligent Environments (2024) Best Late Breaking Results Paper Award, ACM SIGCHI Symposium (2025) As advisor, he supervises PhD students in Artificial Intelligence and Computer Engineering , focusing on topics like user-centered AI, generative models, and digital self-control interfaces. He leads the ELITE research group and contributes to commercial projects including TEIA (AI tourism) and MAPP (interactive museums).
Sainyam Galhotra is an Assistant Professor in the Department of Computer Science at Cornell University. His research focuses on developing data science tools for effective and responsible analytics, leveraging techniques from causal inference, data management, theoretical computer science, machine learning, and human-computer interaction to address challenges in trustworthy system design including robustness, explainability, and fairness. Education: Postdoc: University of Chicago PhD: University of Massachusetts Amherst (supervised by Barna Saha) BTech: Indian Institute of Technology Delhi (IIT Delhi) (supervised by Prof. Amitabha Bagchi) Research Interests: Dr. Galhotra's research spans several interconnected areas in data science and artificial intelligence. His work primarily focuses on Responsible Data Science , where he develops methods to ensure that data-driven systems operate fairly and transparently. Within this broad area, his specific interests include: Causal Inference techniques for understanding cause-effect relationships in complex data Algorithmic Fairness approaches to mitigate bias in machine learning systems Explainable AI methods that make black-box models more interpretable Data Management systems for efficient and reliable data processing Entity Resolution techniques for integrating data from multiple sources Trustworthy System Design that addresses robustness, explainability, and fairness His recent publications demonstrate a clear trend toward developing frameworks that combine causal reasoning with practical data management systems, particularly focusing on how to make data-driven decisions more transparent and equitable. The intersection of database systems with fairness considerations appears to be a particularly active area of his research. Scientific Awards: Rising Star in Data Science at the Data Science Institute, UChicago (Oct 2021) Computing Innovation Fellowship Award Recipient (by CRA, CCC and NSF) (Apr 2021) DAAD AInet Fellow (Feb 2021) ACM SIGMOD Entity Resolution Programming Contest – Top 5 finalist (May 2020) Most reproducible paper award in SIGMOD 2018 and 2019 (Jun 2019) First recipient of Krithi Ramamritham Computer Science Scholarship (Jun 2019) Best paper award in SIGSOFT FSE 2017 (May 2017) Dr. Galhotra is actively seeking students to collaborate with on his research projects. His work has been supported by various fellowships and awards, including the prestigious Computing Innovation Fellowship. He has mentored several students through his research projects, with a focus on developing the next generation of data scientists who can build responsible and trustworthy systems. His research group appears to focus on the intersection of database systems and responsible AI, developing tools like HypeR for causal reasoning, Ver for view discovery, and Nexus for correlation discovery in spatio-temporal data. This work suggests a cohesive research agenda centered around making data systems more transparent, fair, and user-friendly.
Hasti Seifi is an Affiliated Associate Professor at the Department of Computer Science (DIKU), University of Copenhagen, specializing in Human-Centred Computing. Her research focuses on haptics, augmented reality, and human-robot interaction. Institution: University of Copenhagen Department: Department of Computer Science Section: Human-Centred Computing Research interests include: Designing innovative haptic feedback systems Exploring tactile experiences in augmented reality Developing human-robot interaction frameworks Creating generative models for haptic design Investigating social touch technologies Advancing mid-air and ultrasound haptic interfaces Recent publications demonstrate a strong focus on: Generative haptic modeling for AR/XR systems Human-robot interaction dynamics Text input in extended reality environments Visual-haptic multisensory integration Ultrasound mid-air haptic design tools Social touch technologies
