Serena Booth is an incoming Assistant Professor in Computer Science at Brown University. Previously, she served as an AAAS AI Policy Fellow in the U.S. Senate, advising the Senate Banking Committee on AI policy. She holds a PhD from MIT CSAIL (2023) and a BA from Harvard College (2016). Her research focuses on human-AI interaction, specification design for AI systems, and ethical AI practices. She also worked as an Associate Product Manager at Google, scaling ARCore to 100 million devices. Her research explores how humans specify AI behaviors, assess system success, and mitigate misalignment risks. Key contributions include Bayes-TrEx (model transparency via Bayesian sampling) and RoCUS (robot controller understanding). Her work has been supported by NSF GRFP and MIT Presidential Fellowships. She advocates for science policy equity through MIT's Science Policy Initiative and co-founded initiatives to support women in computing (e.g., GW6 at MIT). Education: PhD MIT CSAIL (2023), BA Harvard College (2016) Awards: Rising Star in EECS, HRI Pioneer, NSF GRFP Key Areas: Reward design pitfalls, human-robot trust, ethical AI curriculum development Her recent publications analyze reward function misdesign (AAAI 2023), human-AI teaching frameworks (HRI 2022), and feature attribution reliability (AAAI 2022). She currently seeks PhD students/postdocs focusing on human-AI alignment, reinforcement learning, and policy implications.
Shunyuan Zhang is an Assistant Professor at Harvard Business School with research focusing on AI algorithms, economic inequality, and computer vision applications in business contexts. His work examines how algorithmic systems impact economic outcomes, particularly in sharing economy platforms like Airbnb. His research interests include AI algorithms, economic inequality, pricing algorithms, machine learning, computer vision, and the sharing economy. Zhang's work often combines technical computer vision approaches with economic analysis to understand platform dynamics. Zhang's recent publications demonstrate a strong focus on the intersection of AI, fairness, and economic outcomes. His work analyzes how algorithmic pricing affects racial disparities on platforms like Airbnb, and how visual content impacts demand in the sharing economy. His research employs sophisticated methodologies including deep learning, structural modeling, and causal inference. He has published in top journals and working paper series, with notable work including 'Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb' and 'What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features.' Zhang collaborates extensively with leading researchers at Carnegie Mellon University and University of Toronto, particularly on topics related to algorithmic fairness and platform economics. His work has significant implications for both academic understanding and practical policy recommendations regarding algorithmic systems in marketplace contexts.
Prof. Helge Stein is a Professor in the Department of Chemistry at the Technical University of Munich (TUM), leading the Professorship for Digital Catalysis. He holds a doctorate in mechanical engineering from Ruhr University Bochum (summa cum laude) and conducted postdoctoral research at Caltech before joining TUM in 2023. His research focuses on accelerating materials discovery and optimization through digital tools like machine learning, robotics, and data management, with applications in catalysis and battery systems. Stein has pioneered the development of Materials Acceleration Platforms (MAPs) to streamline experimental and computational workflows globally. Education: Bachelor/Master in Physics, Georg August University of Göttingen (2008–2013) Doctorate in Mechanical Engineering, Ruhr University Bochum (2017, summa cum laude) Postdoc, California Institute of Technology (2017–2020) Tenure-track Professor, Karlsruhe Institute of Technology (2020–2023) Research Interests: Integration of robotics and AI in materials research High-throughput experimentation for battery and catalysis systems Data-driven approaches to nonlinear material-behavior analysis Development of decentralized Materials Acceleration Platforms (MAPs) Key Awards: ACS Engineering Au Rising Star (2023) Kit Innovation Award (2023) Masao Horiba Award (2021) Eickhoff Prize (2018) Teaching: Leads courses on high-throughput methods, data management in chemistry, and digital catalysis seminars. Collaborates with interdisciplinary teams across TUM's School of Natural Sciences and the Munich Institute of Robotics and Machine Intelligence. Labs/Teams: Directs research groups focused on automated electrochemistry, materials robotics, and AI-driven battery design, with partnerships spanning academia and industry for global MAP implementations.
Kyle Chan is a Postdoctoral Research Associate and Lecturer in Sociology at Princeton University, affiliated with the Paul and Marcia Wythes Center on Contemporary China and the M. S. Chadha Center for Global India. He is also an adjunct researcher at the RAND Corporation and a 2025 fellow with the Penn Project on the Future of US-China Relations. His research focuses on industrial policy, clean technology, and infrastructure in China and India. He is currently working on a book explaining China’s dominance in industries like electric vehicles, solar energy, and semiconductors. He publishes a popular newsletter High Capacity and has been featured in major media outlets including the Wall Street Journal and New Yorker . Education: Ph.D. in Sociology (Princeton University), M.Sc. in Political Sociology (London School of Economics), B.A. in Economics (University of Chicago). Research interests include Chinese industrial policy tools, state-owned enterprises, and comparative bureaucracy. He has conducted fieldwork in China and India on railway infrastructure development and testified before the U.S.-China Economic and Security Review Commission. Labs/teams: Collaborates with institutions like the RAND Corporation and maintains affiliations with global China-India research centers.
Dr. Bharanidharan Shanmugam is an Associate Professor in Information Technology at Charles Darwin University's Faculty of Science and Technology. He specializes in cybersecurity, IoT security, and cyber risk management in microgrids. His research focuses on addressing real-world challenges in IoT, smart grids, and medical devices to enhance community impact. **Research Interests:** - IoT Security - Cyber Risk Assessment in Microgrids - Network and Information Security - Applied Cybersecurity Solutions - Blockchain and Privacy-Preserving Technologies **Key Projects (2019–2025):** - Cyber Territory Skills Hub (2023–2025) - Renewable Energy Microgrid Hub (2021–2024) - Blockchain-Based Digital Identity (2019–2020) **Publications:** Focuses on IoT security frameworks, intrusion detection systems, and AI-driven cybersecurity solutions. Recent works include studies on smart grid load forecasting, medical IoT threat detection, and water leakage detection using machine learning. **Grants & Supervision:** Principal Investigator on multiple ARC-funded projects. Supervises PhD students in DevSecOps and IoT security. Active in organizing workshops on digital awareness for indigenous communities.
Sagar Samtani is an Associate Professor and Weimer Faculty Fellow at the Kelley School of Business , Indiana University. He serves as Director of the Kelley’s Data Science and Artificial Intelligence Lab (DSAIL) . His research focuses on Artificial Intelligence for Cybersecurity , including cyber threat intelligence, deep learning, and dark web analytics. He holds a PhD from the University of Arizona (2018), and has received prestigious awards such as the Indiana University Outstanding Junior Faculty Award (2023) and IEEE Big Data Security Junior Research Award (2023). Education : PhD in Information Systems, University of Arizona, 2018 MSMIS, University of Arizona, 2014 BSBA, University of Arizona, 2013 Research Interests : Samtani’s work addresses cybersecurity challenges through AI, including proactive threat detection, vulnerability assessment, and healthcare analytics. He emphasizes explainable AI (XAI) for transparency in cybersecurity systems. Grants & Awards : NSF Grant: CyberCorps SFS Program ($2.3M, 2020–2025) NSF Grant: AI4Cyber Research Education ($300K, 2020–2022) Multiple teaching awards, including the Trustees Teaching Award (2023) and recognition as one of Top 50 Undergraduate Professors (2022) Labs & Teams : Leads the DSAIL lab, focusing on AI-driven solutions for business and cybersecurity. Collaborates with NSF-funded initiatives on cyber AI education and threat intelligence.
Elizabeth Mynatt is the Dean and Professor of the Khoury College of Computer Sciences at Northeastern University. Her research focuses on human-centered computing, personal health informatics, and assistive technologies, particularly for aging populations. She leads the National Science Foundation's AI Institute (AI-CARING) and co-directs Emory University's Cognitive Empowerment Program. Mynatt advocates for federal research funding, emphasizing its role in technological innovation and economic growth. She is a Fellow of the ACM and member of the American Academy of Arts and Sciences. Her leadership includes fostering inclusive computing education and developing the Oath for Computing Professionals . Mynatt has over 100 publications, with work spanning ubiquitous computing, health informatics, and AI ethics. She previously held roles at Georgia Tech, including Regents' Professor and director of the Institute for People and Technology. Key contributions include designing AI systems to support aging-in-place, technology-mediated caregiving, and addressing challenges in mental health medication management. Her collaborations with industry and global institutions highlight her commitment to sociotechnical systems that enhance human capabilities while respecting cultural contexts.
Amir Gilad is a Scharf-Ullman endowed Assistant Professor (Senior Lecturer) at the Hebrew University of Jerusalem’s School of Computer Science and Engineering. His research focuses on responsible data science, including causal inference, differential privacy, fairness in data, and tools for data analysis. He holds a Ph.D. in Computer Science from Tel Aviv University, where he was advised by Prof. Daniel Deutch. Prior to this, he was a postdoctoral researcher at Duke University, mentored by Prof. Sudeepa Roy, Prof. Ashwin Machanavajjhala, and Prof. Jun Yang. Education: Ph.D. in Computer Science, Tel Aviv University (Advisor: Daniel Deutch) MSc in Computer Science, Tel Aviv University BSc in Mathematics and Computer Science, Tel Aviv University His research interests span data quality assessment, private and fair data generation, and causal inference applications . He has received notable awards, including the 2024 Alon Scholarship and the 2019 Google Ph.D. Fellowship. Recent Projects: Developing algorithms for data quality repair and assessing bias in datasets Generating differentially private data that satisfies fairness constraints Applying causal inference to enhance data analysis tools Awards and Honors: 2024 Alon Scholarship for Outstanding Faculty Integration 2019 Google Ph.D. Fellowship in Structured Data 2018 SIGMOD Research Highlight Award 2017 VLDB Best Paper Award Teaching: Courses include “Topics in Responsible Data Science” and “Seminar on Causal Inference in Data Analysis” at Hebrew University, and “Extended Introduction to Computer Science” at Tel Aviv University. He has also led workshops on Google Technologies. Labs & Teams: His work is centered around the School of Computer Science and Engineering’s database group, focusing on foundational and applied aspects of privacy-aware data systems.
Kourosh Davoudi is an Associate Professor of Computer Science at Ontario Tech University's Faculty of Science. He holds a PhD in Computer Science from York University with a focus on Machine Learning and Data Mining. Prior to joining Ontario Tech in 2019, he was a postdoctoral research fellow at the University of Waterloo's Department of Management Sciences. His research interests span Natural Language Processing, Deep Learning, Reinforcement Learning, Graph Mining, and Machine Learning. He actively supervises graduate students in these areas and teaches courses such as Data Mining and Artificial Intelligence. His research emphasizes practical applications of AI techniques in areas like outbreak detection, sentiment analysis, and automated grading systems. Recent work includes innovations in neural document segmentation, vision-language models, and hybrid outbreak detection using social media data. His publications consistently address challenges in algorithm design, explainable AI, and domain-specific NLP applications. Dr. Davoudi has contributed to conferences such as COLING, EMNLP, and IEEE transactions, focusing on interdisciplinary applications of machine learning. His work bridges theoretical advancements with real-world problems in healthcare, education, and social media analysis.
Maarten de Rijke is a Professor at the University of Amsterdam's Informatics Institute, leading the Information Retrieval Lab (IRLab). He specializes in information retrieval, machine learning, and recommendation systems, focusing on neural ranking models, fairness, and conversational search. His work bridges theory and practice, addressing challenges in reproducibility, robustness, and ethical AI. He supervises numerous PhD students and postdocs, including recent defenses by Barrie Kersbergen, Antonis Krasakis, and Vera Provatorova. His lab collaborates internationally, organizing events like SIGIR workshops and the Search Engines Amsterdam (SEA) meetup. Key awards include the Best Reproducibility Paper Award (2025) and Best Paper at WSDM 2021. Research interests span generative retrieval, adversarial robustness, and fairness in ranking. Notable projects include the FULTR dataset, FairDiverse toolkit, and studies on empathetic conversational systems. He actively promotes open science through reproducible methodologies and community-driven benchmarks.
Karim ZKIK is an Associate Professor of Cyber Security and Information Systems at ESAIP Graduate School of Engineering, Angers, France. Previously, he served as an Assistant Professor at the International University of Rabat (UIR), Morocco. His roles include Educational Manager of the Cyber Security track, Head of the Cybersecurity Innovation Hub, and committee member for ABET certification and curriculum design. He actively contributes to academic service, organizing conferences such as the International Conference on Cryptology, Coding Theory, and Cyber Security (I4CS 2022), and serves as a Guest Editor for Computers and Industrial Engineering . His research focuses on cybersecurity for connected systems, blockchain technologies, AI-driven security solutions, and cyber resilience in industrial control systems. Recent work explores integrating blockchain and machine learning for threat detection, secure IoT networks, and supply chain resilience. Key contributions include frameworks for cyber resilience in retail and airlines, blockchain-based crowdfunding security, and SDN-based attack mitigation. ZKIK holds a Habilitation (2024) and PhD in Cyber Security from Université d’Angers and Mohamed V University, Rabat. He holds over 20 certifications from EC-Council, IBM, and Cisco. His work bridges theoretical research and industry applications, addressing challenges in smart environments, industrial systems, and sustainable supply chains.
State University of New York at BuffaloUnited States
Prashant Sankaran is an Assistant Professor in the Department of Industrial and Systems Engineering at the University at Buffalo (School of Engineering and Applied Sciences). He holds a PhD in Mechanical & Industrial Engineering from Rochester Institute of Technology (2023), an MS in Industrial & Systems Engineering from RIT (2020), and a BTech in Mechanical Engineering from Sharda University (2014). His research focuses on artificial intelligence for reasoning under uncertainty, explainable AI, and applications in healthcare, energy management, transportation, and space exploration. He integrates operations research and AI techniques to address complex optimization challenges. Research interests include computational design of bioelectronic materials, kidney exchange optimization via graph machine learning, and solving NP-hard combinatorial problems with hybrid learning-optimization frameworks. His work bridges theoretical advancements (e.g., genetic algorithms) with real-world applications like autonomous logistics systems and renewable energy management. No scientific awards have been mentioned. While no advisees are listed, his publications reflect collaborations across AI, robotics, and healthcare sectors. His research spans topics from deep reinforcement learning in warehouse automation to synthetic data generation for transplant systems.
Caroline Bassett is Professor of Digital Humanities at the University of Cambridge and Director of Cambridge Digital Humanities. She previously held positions at the University of Sussex as Lecturer (2000-2008), Reader (2008-2016), and Professor of Digital Media and Communications (2016-2019), where she co-founded the Sussex Humanities Lab. Her career began in technology journalism before transitioning to academia. Her research critically examines computational technologies' societal and cultural impacts. Key interests include digital media theory, AI's transformation of knowledge cultures, feminist analyses of technology, media archaeologies, automation studies, and the intersection of science fiction with technological utopianism. Recent work explores sound studies, mobile media, gender politics in tech, and everyday digital practices. Bassett's publications consistently engage with digital culture through critical, feminist, and historical lenses. Major thematic clusters include: technological feminism and gender politics (35%), automation histories and anxieties (25%), media archaeology and theory (20%), digital humanities methodologies (15%), and sound/silence studies (5%). Her work increasingly addresses AI ethics and explainability in recent years. Honors and Awards: Helsingin Sanomat Foundation Fellow, Helsinki Collegium for Advanced Studies (2015-2017) IGSF Visiting Fellow, McGill University (Jan-Sept 2010) Leverhulme-funded Associate Visiting Faculty, George Mason University (2002-2003) She directs Cambridge Digital Humanities, a major interdisciplinary research initiative. Previously, she established the Sussex Humanities Lab, fostering digital research collaborations across arts, humanities, and social sciences.
University of California, Los AngelesUnited States
Andrew D. Selbst is a Professor of Law at UCLA School of Law and currently serves as the William J. Friedman and Alicia Townsend Friedman Visiting Professor of Law at Harvard Law School. He has been on the UCLA faculty since 2020 and previously held positions as a Postdoctoral Scholar at the Data & Society Research Institute and a Visiting Fellow at Yale Law School's Information Society Project. He has also taught as an Adjunct Professor at Fordham Law School. Professor Selbst received his educational training from prestigious institutions: S.B. in Physics and Electrical Science and Engineering from MIT (2004) M.Eng. in Electrical Engineering and Computer Science from MIT (2005) J.D. from the University of Michigan Law School (2011) Before entering academia, Professor Selbst worked as a design engineer at Cirrus Logic and Analog Devices. Following law school, he served as a Privacy Research Fellow at NYU School of Law's Information Law Institute, an Alan Morrison Supreme Court Assistance Fellow at Public Citizen Litigation Group, a Senior Associate in Hogan Lovells US LLP's Communications group, and clerked for federal judges including the Honorable Dolly M. Gee and the Honorable Jane R. Roth. Professor Selbst's research examines the complex relationship between law, technology, and society. Drawing on resources from computer science, sociology, and science and technology studies, he seeks to understand how technologies interfere with existing legal regimes and how legal actors can respond to the social effects of new technology. His recent work has focused specifically on the effects of machine learning and artificial intelligence on various legal domains, including discrimination law, policing practices, credit regulation, data protection frameworks, and tort law. His interdisciplinary approach combines technical understanding of AI systems with deep legal analysis to address emerging challenges in the digital age. Professor Selbst teaches courses in torts, information privacy and data protection, a seminar on law, technology and society, and beginning in 2025, a dedicated course on Artificial Intelligence Law. His publications have appeared in leading law journals including Boston University Law Review, California Law Review, Harvard Journal of Law and Technology, and University of Pennsylvania Law Review, as well as in the ACM Conference on Fairness, Accountability and Transparency. He is also the coauthor of a forthcoming casebook on Artificial Intelligence Law. His scholarly contributions demonstrate a consistent focus on the intersection of emerging technologies and legal frameworks, with particular attention to how AI systems create novel challenges for established legal doctrines. Professor Selbst's work has been influential in shaping academic and policy discussions around AI regulation, algorithmic accountability, and the adaptation of legal systems to technological change.
University of Maryland, Baltimore CountyUnited States
Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building
