John Serences is a Professor in the Department of Psychology at the University of California, San Diego (UCSD). He leads the Perception and Cognition Lab, which participates in the Neuroscience Graduate Program. His research focuses on how behavioral goals and attention influence perception, memory, and decision-making, employing techniques like psychophysics, computational modeling, EEG, and fMRI. Key projects explore serial dependence, neural adaptation in visual cortex, and the interplay between sensory processing and mnemonic storage. Recent work highlights mechanisms reconciling repulsive neuronal adaptation with attractive behavioral biases. Affiliations: Department of Psychology, UCSD; Neuroscience Graduate Program. Research Themes: Visual perception, working memory, decision-making, neuroimaging. His lab investigates neural dynamics underlying cognitive processes, with particular emphasis on how attentional modulations and stimulus history shape neural representations. Notable contributions include studies on adaptive sensory coding and the role of top-down signals in perceptual stability.
Dino Pedreschi is a Full Professor of Computer Science at the University of Pisa, affiliated with the Department of Computer Science (DI-UNIPI). He co-leads the Pisa KDD Lab, a joint research initiative between the University of Pisa and the Italian National Research Council’s Institute of Information Science and Technology, one of the earliest labs focused on data mining and knowledge discovery. His research spans Big Data Analytics , Social Network Analysis , Human Mobility Analysis , Privacy-by-Design , Explainable AI (XAI) , and ethical data mining . He is a pioneer in privacy-preserving data mining and has contributed significantly to understanding societal impacts of AI and big data. Recent publications highlight trends in explainable AI , fairness-aware data mining , urban mobility modeling , and socio-economic nowcasting , reflecting a strong interdisciplinary focus combining computer science, social science, and policy. Notable scientific awards include: Google Research Award on Privacy (2009) University of Pisa Ordine del Cherubino (2017) Pedreschi has played leadership roles in major conferences such as ECML/PKDD (Co-Chair 2004), ICDM (Vice-Chair 2005), and ICDE (Vice-Chair 2014). He founded the Business Informatics MSc program at the University of Pisa to train interdisciplinary data scientists. He has been a visiting scientist at the University of Texas at Austin, CWI Amsterdam, UCLA, and the Barabási Lab at Northeastern University. He actively contributes to European research initiatives including SoBigData++, FAIR, and TAILOR, and has advised on AI policy, including testimony before the Italian Parliament on AI and labor markets. He is a key member of the Pisa KDD Lab, a leading research group in data science and AI ethics, fostering collaboration between academia and public institutions.
Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University (CMU), where he has made significant contributions to cybersecurity, privacy, and AI research. He has co-founded and co-directed several groundbreaking graduate programs at CMU, including the Privacy Engineering Program (2012-present), the Ph.D. Program in Societal Computing (2003-2013), and the MBA track in Technology Strategy and Product Management (2005-2017). Carnegie Mellon University, School of Computer Science Software and Societal Systems Department CyLab Security and Privacy Institute Manufacturing Futures Institute Dr. Sadeh received his Ph.D. in Computer Science at CMU with a major in Artificial Intelligence and a minor in Operations Research. He holds an M.Sc. in computer science from the University of Southern California and a BS/MS degree in electrical engineering and applied physics from the Free University of Brussels (Belgium) as 'Ingénieur Civil Physicien.' Professor Sadeh's research spans cybersecurity, online privacy, Human-AI Interaction, AI governance, mobile computing, the Internet of Things, user-oriented machine learning, and language technologies. He is particularly known for his pioneering work on AI-based privacy enhancing technologies, including privacy assistants, automated privacy compliance tools, and NLP-based privacy solutions. His work has influenced the design of privacy features at major technology companies including Apple, Google, and Facebook/Meta, as well as privacy policies at regulatory agencies like the Federal Trade Commission and the California Office of the Attorney General. Analysis of his recent publications shows a strong focus on practical privacy solutions, particularly in mobile and IoT contexts, with an emphasis on making privacy more usable and understandable for end users. His work bridges technical innovation with policy implications, addressing both the technological and human aspects of privacy protection. 2018 Outstanding Entrepreneur of the Year award from the Pittsburgh Venture Capital Association Test of time award by the AAAI Conference on Web and Social Media (ICWSM) Gartner Group's Magic Quadrant leader in Security Awareness Computer-Based Training for 4 consecutive years Deloitte's Technology Fast 500 recognition for 3 consecutive years Professor Sadeh has advised numerous students, including PhD candidates like Aerin (Shikhun) Zhang, whose dissertation focused on understanding diverse privacy attitudes. His research has been funded through various grants, including NSF SaTC projects, and has resulted in technologies that protect tens of millions of users worldwide. He also founded Wombat Security Technologies, which was acquired by Proofpoint in 2018 and whose technologies are used by over 75% of Fortune 100 companies. Professor Sadeh leads several research initiatives including the Privacy Engineering Program, the Usable Privacy Policy Project, the Personalized Privacy Assistant Project, and CMU's Privacy Infrastructure for the Internet of Things. His Mobile Commerce Lab and E-Supply Chain Management Lab have produced influential research that has been commercialized by major organizations including IBM, Raytheon, Boeing, and the U.S. Army.
Dr. Chee Kiat Seow is an Associate Professor at the University of Glasgow's School of Computing Science. He holds a PhD from Nanyang Technological University (NTU) and an MSc from the National University of Singapore (NUS). His research focuses on cyber-physical security, wireless communication localization, and IoT systems leveraging AI/ML. He has led projects valued in the millions, winning awards like the IEEE Best Student Paper and National Instruments Engineering Impact Awards. Education: PhD (NTU), MSc (NUS) Research: Specializes in UWB positioning, spoofing detection, and IoT integration with 5G/GNSS. Teaching: Courses include Big Data, Software Engineering, and Data Analytics. His recent work addresses NLOS mitigation in indoor localization and cyber-physical security threats. Over 63 publications span journals like IEEE Transactions and conferences such as IPIN and WF-IoT. Supervised 6+ PhD/MSc students on topics like autonomous robotics and AI-driven localization. Grants: Includes $853K for 5G-X Smart Building projects and $797K for GNSS signal authentication. Awards: IEEE PIERS Best Student Paper (2019), NI Engineering Impact Awards (2015-2016). He advises on IoT and cybersecurity for organizations like ARTC and National Instruments. Active in IEEE Signal Processing and Computer Society.
Daniel Sage is a Lecturer and Scientific Advisor at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Biomedical Imaging Laboratory (LIB) under the College of Engineering (STI) and School of Life Sciences (SV) . He specializes in bioimage informatics , structured-illumination microscopy , and deep learning applications for biomedical imaging. His work spans algorithm development for single-molecule localization microscopy (SMLM) , fluorescence imaging , and 3D reconstruction . His research group has developed open-source tools like FlexSIM for light inhomogeneity correction, DeepImageJ for integrating deep learning in ImageJ, and Steer'n'Detect for orientation-accurate template detection. His publications focus on correcting multiple-blinking artifacts in PALM, optimal transport metrics for SMLM evaluation, and contextual feature analysis for xenograft cell classification. He mentors PhD students and contributes to interdisciplinary education through courses such as Bioimage Informatics and Fundamentals of Image Analysis , emphasizing practical software solutions and Java programming for bioimage processing. His collaborations include institutions like Howard Hughes Medical Institute and Centre National de la Recherche Scientifique (CNRS) .
Katherine Hubbard is an Associate Professor of Art and Director of the MFA Program at Carnegie Mellon University School of Art. Her practice integrates photography, writing, and performance to explore the intersection of social politics, history, and narrative through a lens of bodily engagement. Hubbard's work emphasizes analog photography as a medium that mimics and interrogates the body's relationship to visual processes, often staging performances where participants' physical positioning becomes integral to the artwork's temporal experience. Education: MFA from the Milton Avery Graduate School of the Arts at Bard College (2010). Research focuses on the politics of looking, the malleability of vision, and bridging the imaginary with the familiar through interdisciplinary projects. Notable collaborations include the Poetry Parade series (2012–ongoing), a migratory feminist reading action performed in institutions like the Whitney Museum, and cyclops & slashes (2015), a participatory performance addressing social dynamics through photography and text. Awards: 2020 Guggenheim Fellowship in Photography. Hubbard has exhibited widely, including solo shows at Higher Pictures (New York) and Company Gallery (NY), with works featured in Frieze Magazine . Her projects often involve site-specific installations, such as Four shoulders and thirty five percent everything else (2014), which interrogates landscape and perception in Utah's desert.
Gianvito Lanzolla is a Professor of Strategy at Bayes Business School (formerly Cass), City, University of London, where he has been employed since April 2006 and served as Dean of the Faculty of Management from 2016-2020. He founded the Bayes Digital Leadership Research Centre and is a Fellow of the Royal Society of Arts. Education: MSc in Mechanical Engineering, PhD in Strategic Management Visiting Appointments: London Business School, Indian School of Business, ESMT Berlin His research focuses on digital transformation, technology strategy, institutional change, and competitive dynamics. He examines how CEOs' digital orientation and board characteristics affect firm value, with publications in Academy of Management Journal , Harvard Business Review , and California Management Review . His work has won multiple academic prizes and been featured in major media outlets. Recent publications analyze digital ecosystems, agile transformation, and AI applications in finance. He teaches 'Strategic Leadership' and 'Digital Transformation' in MBA programs, having won teaching excellence awards in 2007, 2008, 2009, 2012, and 2015. Scientific Awards: City University Teaching Excellence Awards (2015, 2012, 2009, 2008) Strategic Management Society Best Paper Award (2012) SSRN Top Ten Downloaded Paper (2008) Spanish Academy of Management Best Paper Award (2009)
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.
Ryan Kelly is an Associate Professor in the School of Computing Technologies at RMIT University, specializing in Human-Computer Interaction (HCI), digital health, and mixed reality. His research focuses on technology-enabled social connection and wellbeing, including designing communication tools for close relationships, digital health initiatives like the Music-Attuned Care via eHealth (MATCH) project, and VR applications for mental health. He collaborates with industry partners such as Google, Netflix, and the Worldwide Fund for Nature, and has developed technologies for older adults, including simplified video calling systems for aged care during the pandemic. His work has been featured globally in media, including Al-Jazeera's documentary All Hail the Algorithm . He actively contributes to the HCI community as a reviewer and committee member for conferences like ACM CHI and CSCW, holding roles such as Editor for CSCW 2025 and Awards Co-Chair for CSCW 2023. Ryan supervises PhD candidates exploring human-centered AI for social connection and online safety for older adults. His research aligns with UN Sustainable Development Goals 3 (Good Health), 9 (Industry Innovation), and 11 (Sustainable Cities). Key projects include the VR mindfulness application used by over 200,000 Australians and the Painpad device for pain logging in UK hospitals. He advocates for participatory design frameworks, emphasizing ethical AI and technology’s role in fostering meaningful human experiences.
Sreedhari Desai is an award-winning, tenured Associate Professor of Organizational Behavior and Crist W. Blackwell Scholar at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. Her research focuses on negotiations, ethical decision-making, and gender dynamics in organizations. She holds a PhD in Organizational Behavior from the University of Utah, an MS in Finance, and a BS in Metallurgical Engineering from Punjab Engineering College. Dr. Desai’s academic career includes roles as a visiting assistant professor at Duke University’s Fuqua School of Business and research fellowships at Harvard University’s Edmond J. Safra Center for Ethics and Harvard Kennedy School’s Women and Public Policy Program. She has been honored with prestigious awards such as the Mariner S. Eccles Graduate Fellowship and finalist recognition for the ASPEN Dissertation Proposal Award. Her research explores topics like unethical behavior mitigation, gender and racial disparities in negotiations, and the impact of organizational income inequality. Notable findings include the role of virtuous quotes in curbing unethical requests and the link between marital structure and workplace gender attitudes. Her work has been featured in outlets like Harvard Business Review, Wall Street Journal, and BBC. As an educator, Desai teaches MBA, Master of Accounting, and PhD courses on leadership, negotiations, and ethics. She is a sought-after keynote speaker and consultant for global organizations, military leaders, and Fortune 500 companies, advising on strategic decision-making and ethical practices. Her consulting clients include Boeing, IBM, ExxonMobil, and the U.S. Army. Dr. Desai’s grants and fellowships include funding from the National Stock Exchange of India and UNC’s University Research Council. She has also received the MBA Teaching All-Star award multiple times for her engaging pedagogy.
Omar Rifki is an Associate Professor (Maître de Conférences) specializing in combinatorial optimization and artificial intelligence applications. His research bridges theoretical computer science with practical logistics challenges, focusing on routing problems, process mining, and machine learning integration for complex decision systems. His core research interests include phase transitions in NP-hard problems, vehicle routing optimization under time constraints, and healthcare process modeling. Rifki's work demonstrates a consistent pattern of integrating reinforcement learning with traditional optimization techniques to solve large-scale real-world problems in transportation and logistics, with particular emphasis on spatio-temporal data effects and collaborative systems. Analysis of his 15 publications (2019-2025) reveals three dominant research thrusts: (1) Fundamental studies of combinatorial problem hardness using phase transition frameworks, (2) Practical applications of deep reinforcement learning in vehicle routing and taxi assignment, and (3) Healthcare process optimization through advanced process mining techniques. His work consistently addresses scalability challenges in real-world implementations while maintaining theoretical rigor. No scientific awards were documented in the provided materials. His collaborative work with researchers like Christine Solnon and Thierry Garaix indicates active participation in European operations research communities, though specific grant details remain unreported. Rifki's research shows increasing integration of graph theory and machine learning in transportation applications, particularly evident in his Lyon City case studies on autonomous ride-sharing systems.
Vassilios Tzerpos is an Associate Professor at the Lassonde School of Engineering, York University, where he has been since 2001. He holds a Ph.D. in Computer Science from the University of Toronto (2001). His research focuses on audio processing for musical applications, deep learning, digital signal processing, machine listening, and software engineering education. He directs the APTLY lab exploring music-technology intersections and leads the LaSSoftE lab developing socially-oriented software solutions. Education: Ph.D. in Computer Science, University of Toronto, 2001 Research Highlights: Dr. Tzerpos' work spans music information retrieval (e.g., automatic music classification), synthetic speech detection using neural networks, and software engineering pedagogy. His recent projects include Music-STAR for audio re-instrumentation and OER-based learning path creation systems. He has pioneered methods in design pattern detection and software clustering evaluation. Grants & Labs: Leads two research groups: APTLY (music-tech) and LaSSoftE (social impact software). Active in developing adaptive cybersecurity solutions against DoS attacks and refining software architecture recovery techniques. Key Themes in Publications: Recent work emphasizes machine learning applications in music technology and cybersecurity, with foundational contributions to software clustering methodologies and design pattern detection algorithms. His work bridges theoretical computer science with practical applications in education and creative industries.
Chitra Raghavan is a tenured full professor and clinical psychologist at John Jay College of Criminal Justice, CUNY. She serves as Professor of Psychology, Director of the Forensic Mental Health Counseling Program, and Coordinator of Victimology Studies in Forensic Psychology. Her expertise spans trauma and abuse, human trafficking, intimate partner violence, and forensic evaluations. She holds a Ph.D. in Community and Clinical Psychology from the University of Illinois (1998) and a B.A. from Smith College (1992). Education: 2000: Postdoctoral Fellowship, Yale University School of Medicine 1998: Ph.D., University of Illinois at Urbana-Champaign 1995: M.A., University of Illinois at Urbana-Champaign 1992: B.A., Smith College (Magna Cum Laude) 1990–1991: Université Paris V (Smith Junior Year Abroad) Research Interests: Coercive control, sex trafficking, trauma bonding, multicultural assessment, and integrating Eastern psychological principles into forensic practice. She has authored over 50 articles and two books on gender justice, domestic violence, and self-determination in Muslim communities. Media and Awards: Named “New York’s New Abolitionists” (2014) by the New York State Anti-Trafficking Coalition Featured in The New York Times , MSNBC, and podcasts like Prevention is Now and Schein On . Teaching and Outreach: Teaches courses across BA, MA, and PhD programs in forensic psychology, trauma, and gender studies. Leads study abroad programs in Bali (cultural self-study) and Morocco (gender and feminism in non-Western contexts). Collaborates with Dr. Jennifer Pipitone on pedagogy and global psychology education. Labs/Teams: Active in research partnerships and case law contributions, including shaping New York State rulings through expert testimony (e.g., 2018 case law).
Dr. Carol Tan is a Senior Lecturer and Program Manager of the Master of Fashion Entrepreneurship at RMIT University's School of Fashion and Textiles. Her research focuses on fast-growth small-to-medium enterprises (SMEs), sustainable fashion practices, consumer behavior, and luxury fashion. She has been actively researching since 2003 and has contributed to peer-reviewed journals, media commentary, and industry reports. Dr. Tan holds six teaching awards, including the RMIT University Program Award for Innovation in Curricula (2013) and the 2011 Three-Minute Presentation Award. She currently supervises five PhD students. Research Highlights: Circular economy in fashion, virtual fashion consumption, blockchain in supply chains, and SME resilience. Media Engagement: Featured in ABC National Radio, The Age, Sydney Morning Herald, and industry reports on Australian fashion industry challenges. Committees: Member of RMIT's Learning and Teaching Committee (2024).
Patrick Wu is a Professor in the Department of Computer Science at American University, with additional affiliations as Faculty Fellow at the Center for Data Science and Faculty Affiliate at the Center for Security, Innovation, and New Technology. He holds a PhD in Political Science and Scientific Computing from the University of Michigan, an MA in Statistics from Michigan, and a BA in Political Science and Statistics from the University of Chicago. His research develops AI/ML and natural language processing approaches for computational social science, focusing on: Political elite and non-elite ideology measurement Affective polarization on social media platforms Detection of hateful/abusive speech and memes Application of large language models to political science research Recent work explores innovative methods for political attitude measurement using LLMs, in-context learning techniques for social media analysis, and frameworks for multimodal representation learning. His publications demonstrate consistent innovation in applying NLP and machine learning to political discourse analysis, with emerging focus on generative AI's impact on political science education and methodology. Wu teaches courses including Object-Oriented Programming and topics in Natural Language Processing/Text as Data.
