Prasanna (Sonny) Tambe is a Professor at the Wharton School of the University of Pennsylvania, specializing in the economics of technology and labor markets. His research explores AI’s impact on workforce dynamics, HR algorithms, and the gender wage gap in tech industries. Education: Ph.D. in Managerial Science and Applied Economics (Wharton, UPenn); S.B. and M.Eng. in Electrical Engineering and Computer Science (MIT). His work leverages internet-scale data from job platforms and patent databases to analyze trends in skill acquisition, remote work diversity, and algorithmic bias in hiring. Recent studies examine AI’s role in HR decision-making, the economics of emerging technologies, and labor market responses to IT innovation. Scientific Awards: Best Undergraduate Professors (Poets & Quants, 2020) Best Paper Awards (Management Science, Information Systems Research) ISS Sandra A. Slaughter Early Career Award (2016) Tambe co-directs Wharton Human-AI Research, focusing on ethical AI integration in organizations. His teaching includes award-winning courses on AI’s societal implications and data-driven business strategies.
Shai Ben-David is a Professor and University Research Chair at the Department of Computer Science, University of Waterloo. He is affiliated with the Cheriton School of Computer Science and can be reached at shai@uwaterloo.ca . His office is located in DC 2643. Education: Ph.D., Hebrew University, Jerusalem, Israel (1987) M.Sc., Hebrew University Jerusalem, Israel (1979) B.Sc., Hebrew University Jerusalem, Israel (1978) Research interests focus on foundational aspects of machine learning theory, including unsupervised learning (clustering), domain adaptation, fairness, interpretability, and alternative approaches to worst-case computational complexity. He also explores logic applications in computer science theory. His research trends emphasize theoretical challenges in machine learning, particularly clustering, fairness in representations, and the interplay between computational feasibility and learnability. He investigates how unlabeled data and sample compression techniques impact learning robustness and efficiency. No scientific awards are listed. His advising record shows no formal advisees listed here. Grants and funding details are not provided in the text. He has contributed to organizing events like the Dagstuhl Seminar on Foundations of Unsupervised Learning (2017) and co-edited MFCS 2016 proceedings. His work addresses both theoretical questions and practical gaps in ML implementation.
Dr. Aditya Joshi is a Senior Lecturer in the School of Computer Science & Engineering at the University of New South Wales (UNSW). He specializes in Natural Language Processing (NLP), with a focus on sarcasm detection, dialectal NLP, and ethical AI applications in public health and cybersecurity. He joined UNSW in 2023 following industry roles at SEEK, Notiv, and Fractal Analytics, where he developed NLP systems for recommendation engines and meeting analytics. His research has garnered over 3,000 citations (h-index 26) and secured $3.1M in grants, including Defence Trailblazer and Google exploreCSR awards. Education: Joint PhD (2018) from IIT Bombay (India) and Monash University (Australia); MTech in CSE (2011) from IIT Bombay. Research Interests: Making NLP models robust for non-native English speakers and the LGBTI+ community, algorithmic enhancements to transformers, and applications in public health, cybersecurity, and societal issues. His work spans epidemic intelligence (collaborations with EPIWATCH and IFCYBER), cybersecurity tools like AuditNet, and inclusive AI initiatives such as queer-inclusive workshops funded by Google. He designed UNSW's new NLP course (COMP6713) and co-authored a Wiley textbook on NLP. Notable grants include the A$1.4M 'Comprehensive Defence Data Platform' (Lead CI) and A$92K Google exploreCSR grant for benchmarking dialectal sentiment. His awards include the Best PhD Thesis from IITB-Monash and Best Paper accolades at FAccT 2023 and MoMM 2020. He supervises projects on kernel-based attention reformulation, prompt-based sarcasm detection, and multilingual small-scale LLMs. His service roles include Executive Committee Member at ALTA and arXiv moderator for computational linguistics.
Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.
Andrew Miller is an Associate Professor in the Electrical and Computer Engineering department at the University of Illinois, specializing in Programming Languages, Formal Methods, Software Engineering, Security and Privacy, and Systems and Networking. His research focuses on blockchain technologies, cryptography, and secure systems. Professor Miller's research spans multiple critical areas in modern computer security. His work primarily focuses on blockchain technologies , where he has made significant contributions to understanding and improving the security, privacy, and performance of systems like Bitcoin and Ethereum. He has conducted empirical analyses of privacy in the Lightning Network and developed protocols for confidential smart contracts. His work in cryptography includes research on multiparty computation, zero-knowledge proofs, and formal methods for cryptographic protocol design. Miller also investigates security vulnerabilities in proof-of-stake systems and resource exhaustion attacks, contributing to the robustness of decentralized systems. His applied security research extends to privacy-preserving health applications, as evidenced by his work on the Safer Illinois platform for COVID-19 contact tracing. Miller's publication record demonstrates consistent contributions to top security and systems venues including IEEE Security & Privacy, ACM CCS, Financial Cryptography, and USENIX Security. His research shows a clear trajectory from foundational work in blockchain security to more applied systems addressing real-world privacy and security challenges. Recent work focuses on making multiparty computation services publicly auditable and developing decentralized identity solutions that maintain compatibility with existing systems. Distinguished Reviewer Award, IEEE Security & Privacy 2018 Professor Miller has advised students including Vivek Nair, who joined the prestigious Hertz Fellows program in 2022. He has taught various courses including Introduction to Algorithms & Models of Computation, Advanced Computer Security, Cryptography, Applied Cryptography, and Ideal Functionality in Cryptography. His research has received support through grants including the SaTC: CORE: Medium project on "Automated Support for Writing High-Assurance Smart Contracts" in 2018. Miller is actively involved in research groups focusing on blockchain security, cryptographic protocols, and privacy-preserving systems. His lab appears to collaborate extensively with researchers across multiple institutions, as evidenced by the diverse author lists on his publications. Current work seems to be focused on making decentralized systems more secure, privacy-preserving, and accessible for real-world applications.
Bryan H. Choi is an Associate Professor of Law at the University of Colorado Law School , where he bridges law and computer science to address software and AI safety. His work on software liability has influenced national cybersecurity strategy discussions. As an Adviser for the ALI Principles Project on Civil Liability for Artificial Intelligence , he shapes legal frameworks for emerging technologies. Education : JD and AB in Computer Science from Harvard University; clerkships with U.S. Court of Appeals judges Leonard I. Garth and William C. Bryson. Roles : Former joint appointment at Ohio State University Law School and Computer Science Department; Faculty Fellow at UPenn's CTIC; Director of Law and Media at Yale's ISP. Research Focus : Choi's scholarship examines software liability , AI accountability , and privacy law through interdisciplinary lenses. He critiques institutional approaches to software safety and advocates for empirical legal frameworks over participation-based models. Recent Articles address AI malpractice , NIST software standards , and forensic tool validation , reflecting trends in AI regulation and cyber-physical system liability . His 2021 NSF grant funded technical-legal methods for safety-critical systems. Awards & Grants : National Science Foundation (NSF) Grant (2021) Adviser, ALI Principles Project on Civil Liability for Artificial Intelligence Community Engagement : Active in Law and Computer Science communities , serving on committees for the ACM Symposium , Cybersecurity Law and Policy Scholars Conference , and co-organizing the AAAI Bridge Program on AI and Law .
Dylan Hadfield-Menell is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, holding the Bonnie and Marty (1964) Tenenbaum Career Development Professorship. His research focuses on AI alignment and human-AI interaction within MIT's School of Engineering. His research interests center on agent alignment problems in AI systems, particularly examining uncertainty in objective optimization for human-robot teams and societal oversight of machine learning systems. Key areas include the principal-agent alignment problem , assistance games frameworks , and robust preference learning that accounts for hidden contextual factors in reinforcement learning from human feedback. His recent publications reveal strong trends toward multi-agent cooperation , formal contract mechanisms for resolving social dilemmas, and advanced evaluation methodologies for AI safety. The research spans theoretical frameworks like open-universe assistance games while addressing practical challenges in language model alignment and cultural bias assessment. Scientific awards include: AI2050 Early Career Fellowship from Schmidt Futures Berkeley Fellowship NSF Graduate Research Fellowship C.V. Ramamoorthy Distinguished Research Award His work bridges theoretical computer science with real-world AI governance challenges, as demonstrated through MIT's participation in AI policy white papers. Current research directions include developing frameworks for transparent AI systems and addressing fundamental limitations in aligning recommender systems with human values through interdisciplinary synthesis.
Prof. Bryan Ford leads the Decentralized/Distributed Systems (DEDIS) lab at EPFL. He focuses on secure decentralized systems, including blockchain technology, privacy, and systems security. He earned his Ph.D. from MIT and held faculty positions at Yale University and EPFL. His work spans distributed consensus protocols, peer-to-peer networking, and privacy-preserving systems. Key projects include QuePaxa (timeout-free consensus), UIA (global connectivity for mobile devices), and MedCo (secure healthcare data sharing). He advises numerous PhD students and contributes to open-source projects like Bitcoin collective signing and privacy networks like Riffle. Education: Ph.D., MIT; Postdoctoral work at Yale Research interests include blockchain scalability, consensus algorithms, and cryptographic privacy. His lab develops systems like TRIP for coercion-resistant voting and F3B to mitigate blockchain front-running. His work on NAT traversal and peer-to-peer protocols (e.g., STUN/ICE) remains foundational in network architecture. He emphasizes practical, auditable security solutions such as CertiKOS and atomic cross-chain transactions (Atom). Notable contributions: CoSi (collective signing), OmniLedger (sharded blockchain), and privacy-preserving protocols like PURBs (Protected Unsealable Recursive Boxes). His lab collaborates with Swiss Post to audit e-voting systems and designs democratic cryptocurrencies like PoPCoin.
Motahhare Eslami is an Assistant Professor at Carnegie Mellon University’s School of Computer Science, Human-Computer Interaction Institute. Her research bridges human-computer interaction, social computing, and AI ethics. Education : PhD in Computer Science from University of Illinois at Urbana-Champaign, advised by Karrie Karahalios Research Focus : Dr. Eslami investigates algorithmic opacity and user behavior in socio-technical systems, developing frameworks to enhance transparency and stakeholder participation in AI governance. Her work addresses: Algorithmic bias mitigation through participatory audits Ethical implications of generative AI and smart assistants Inclusion of marginalized communities in AI design Transparency mechanisms for opaque algorithms Civic technology and public sector AI Recent Article Trends : Her publications analyze algorithmic harms through lenses of: Medical imaging and data generation Labor market equity and low-wage employment Youth perspectives on AI ethics Content creator experiences with demonetization Explainability in black-box AI systems Scientific Recognition : Best Paper at AAAI HCOMP (2025) Google Academic Research Award (2024) Microsoft AI & Society Fellowship (2024) 100 Brilliant Women in AI Ethics (2023) Teaching Innovation Award at CMU (2023) Advising & Collaborations : Mentors PhD students Shixian Xie, Wesley Deng, Seyun Kim, and former post-doc Jaemarie Solyst. Collaborates with NSF AI Institute for Collaborative Assistance (2022–2027), Amazon, Google, and Microsoft on responsible AI initiatives.
Juan Zhai is an Assistant Professor in the Manning College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. She co-directs the Laboratory for Advanced Software Engineering Research (LASER) and is a member of the UMass NLP group. Her research advances software engineering through automated techniques for building high-quality systems with emphasis on behavioral specifications, AI safety, and trustworthy AI. Her work addresses the fundamental challenge of aligning software behavior with intended specifications through two main directions: automated specification synthesis (translating natural language comments to formal specifications via tools like C2S and LLMCup) and defect detection/repair (developing frameworks for AI system testing, bias mitigation, and training diagnostics). Her vision integrates these into end-to-end assurance systems that continuously validate, repair, and audit evolving software in dynamic environments. Recent publications (2024-2025) reveal dominant trends at the software engineering/AI intersection: formal specification synthesis for IoT and code generation, comment maintenance using LLMs, deep learning framework testing (DevMuT, Citadel), bias detection in LLMs, and automated training repair (AutoTrainer, DREAM). These contributions appear in top venues including ICSE, FSE, ASE, ISSTA, and ACL. Professor Zhai currently advises PhD student Gehao Zhang (focusing on Software Engineering and AI Safety) and actively recruits new PhD/Master's students. Her LASER lab develops practical tools for specification inference, LLM-driven synthesis, and trustworthy AI, while collaborating with the UMass NLP group on language-centric software analysis. The LASER lab, co-directed by Zhai, pioneers techniques for behavioral specification enforcement across traditional and AI-powered systems. Key projects include CPC for bidirectional code-comment analysis, ModelMeta for deep learning framework testing, and frameworks for bias mitigation across the ML lifecycle. The lab emphasizes practical, scalable tools that enhance correctness, robustness, and fairness in critical AI applications.
Danqi Chen is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. Their research focuses on advancing large language models (LLMs), with emphasis on model alignment, safety, and long-context reasoning capabilities. Key research areas: LLMs, AI safety, retrieval systems, and model optimization Recent work explores theorem proving, context encoding, and ethical content generation Their 2025 publications highlight innovations in formal verification scaffolding, attention mechanism efficiency, and copyright-aware generation. 2024 studies investigate continual memorization, rule-based chatbot representations, and scientific literature retrieval benchmarks. Current projects demonstrate commitment to improving model robustness, interpretability, and security compliance in multimodal systems.
Peter Druschel is a Professor and founding Director of the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany. He holds adjunct professorships at Saarland University and the University of Maryland. His research focuses on distributed systems, operating systems, and privacy-preserving technologies. He earned his Ph.D. from the University of Arizona in 1994 and has held roles at Rice University since 1994, including Professor of Computer Science (2002–2005). Education: Ph.D. in Computer Science, University of Arizona (1994) Research Interests: Distributed systems, operating systems, network security, accountable computing, and privacy technologies. Current projects include privacy compliance in data systems (Thoth), secure communication (EbN), and privacy-aware image capture (I-Pic). Awards: SIGOPS Mark Weiser Award (2008) NSF CAREER Award (1995) Member of Academia Europaea and German Academy of Sciences Leopoldina Grants & Leadership: Leads the ERC Synergy Project imPACT, chairs the Max Planck Society’s Chemistry, Physics, and Technology Section, and collaborates with institutions like Cornell and Google. Advises on policy issues related to technology and privacy. Labs/Teams: Distributed Systems Group at MPI-SWS, collaborations with Microsoft Research and MIT. Current team includes students and postdocs working on privacy, security, and distributed systems.
Dalibor Radovanović is a researcher at Singidunum University , affiliated with the Faculty of Business Informatics . His work spans cybersecurity, blockchain technologies, and their applications in business and IoT systems. Education: Doctoral Dissertation (2016, Singidunum University) Master's & Basic Studies (Faculty of Business Informatics) Secondary Education: ETŠ Nikola Tesla Research Interests include: Security frameworks for IoT and blockchain integration Smart card and wireless network vulnerabilities E-governance and corporate IT audit methodologies Machine learning applications in cybersecurity Environmental performance optimization in agribusiness Publication Trends reveal a focus on blockchain (2022), cybersecurity (2009-2022), and IT governance (2010-2017). His work bridges theoretical analysis with practical implementations in Serbia's digital economy. Collaborations with scholars like Marko Šarac and Saša Adamović highlight interdisciplinary approaches to securing financial systems, educational institutions, and industrial IoT applications.
Wenpeng Yin is an Assistant Professor in Computer Science and Engineering, specializing in Natural Language Processing and Machine Learning. His research focuses on advancing Large Language Models (LLMs) and their applications in scientific, societal, and interdisciplinary domains. Research Interests : LLMs, medical QA, financial AI, model consistency, and instruction-following frameworks. Recent Work : Investigates low-resource NLP tasks, bias evaluation (Gptbias), and adaptive trading systems using LLMs. Current trends in his publications highlight innovations in multimodal learning, symbolic reasoning, and ethical AI, with a strong emphasis on practical implementations across diverse fields.
Tatsunori Hashimoto is an Assistant Professor of Computer Science at Stanford University, specializing in artificial intelligence, machine learning, and natural language processing. His research focuses on developing robust and ethical language models, addressing challenges in bias mitigation, fairness, and transparency. He leads projects like the Stanford Alpaca, exploring instruction-following models and their societal impacts. Key research interests include generative models, AI ethics, and privacy-preserving techniques. His recent work examines language model behaviors, security risks, and the societal implications of AI systems. Notable contributions include frameworks for auditing language models, improving factual accuracy, and reducing disparities in speech recognition. His publications highlight advancements in long-context processing, few-shot learning, and automated benchmarking. He emphasizes practical applications of AI while addressing dual-use concerns and ensuring alignment with human values.
