Dr. Ting Hu is an Associate Professor in the School of Computing at Queen's University, affiliated with the Faculty of Arts and Science. She leads the Machine Intelligence & Biocomputing (MIB) Laboratory, focusing on bio-inspired AI and bioinformatics. Her research bridges evolutionary computing, machine learning, and biomedical data analysis. Dr. Hu holds a PhD in Computer Science from Memorial University and completed postdoctoral training at Dartmouth College. She teaches courses with strong student evaluations, winning the Howard Staveley Teaching Award (2019-2020) and recognition as a Mental Health Champion (2023). Education: B.Sc. in Computational Mathematics, Wuhan University M.Sc. in Computer Science, Wuhan University PhD in Computer Science, Memorial University Postdoctoral Fellowship, Geisel School of Medicine, Dartmouth College Research Interests: Evolutionary algorithms and genetic programming Interpretable and explainable AI Biomedical data mining (metabolomics, genomics) Complex network analysis Applications in precision medicine and disease prediction Awards & Recognition: Queen's AMS Undergraduate Mentorship Award (2025) IEEE CIBCB Best Student Award (2022) Howard Staveley Teaching Award (2019-2020) NSERC Discovery Grant Reviewer (2019) Memorial University's Best Professor Award (2016) Lab & Collaborations: MIB Lab develops tools like geneDRAGNN (graph neural networks for gene-disease prioritization) Active roles in IEEE Computational Intelligence Society and EuroGP Advances include vaccination strategies via graph-RL and interpretable clustering methods
Daniel M. Roy is a Professor at the University of Toronto with cross-appointments in the Departments of Computer Science and Electrical and Computer Engineering. He serves as Associate Chair, Statistics, and is a Research Director at the Vector Institute and a CIFAR Canada AI Chair. His research focuses on foundational principles of prediction, inference, and decision-making under uncertainty, spanning machine learning, statistics, mathematical logic, applied probability, and computer science. He has contributed to learning theory, statistical network analysis, probabilistic programming, and Bayesian nonparametric statistics. Education: Ph.D. in Computer Science from MIT (2011), advised by Leslie Kaelbling. Postdoctoral fellowships at the University of Cambridge (Newton International Fellow and Research Fellow). His research explores information theories of learning , online learning , and nonstandard foundations for decision theory . Recent work includes best paper awards at ICML 2024 and advancements in probabilistic programming systems like Church. His publications address problems in generalization bounds, causal bandits, neural network theory, and exchangeable random structures. Scientific Awards include the MIT/EECS George M. Sprowls Doctoral Dissertation Award and the ICML 2024 Best Paper Award. He advises students and postdocs across statistics, computer science, and machine learning, with alumni now holding positions at institutions like Princeton, Imperial College London, and the University of Chicago.
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.
Ke Wu is a Professor in the Department of Computer Science and Engineering at the University of Michigan. Their research focuses on the intersection of machine learning, biostatistics, and healthcare technology, with an emphasis on mobile health interventions, causal inference, and Bayesian methods. They lead a small, hands-on research group mentoring PhD students and postdocs. Key interests include developing predictive models for health outcomes, improving treatment effect estimation, and leveraging mobile technology for caregiver support. Their work has addressed critical challenges in clinical decision-making, public health surveillance, and healthcare innovation. Research projects span synthetic data generation for electronic health records, mHealth app development for care partners of traumatic brain injury patients, and algorithmic fairness in reinforcement learning. Ke Wu emphasizes interdisciplinary collaboration and has contributed to global health studies, including analyses of pneumonia etiology in low-resource settings and the PERCH study. Their group's methodologies often integrate wearable sensor data and machine learning to address real-world health challenges. Advising priorities include fostering student independence while maintaining close mentorship, with expectations for consistent research productivity and professional development. Students are encouraged to pursue teaching roles (e.g., GSI positions) and internships aligned with career goals. Funding support for conference participation is available through institutional and external grants. Ke Wu's contributions extend to statistical methodology, including Bayesian latent class models and dynamic risk prediction frameworks. They actively engage in translational research, bridging computational methods with clinical and public health applications, and prioritize open-source software development to advance reproducible research practices.
Rehan Syed is a Professor at Queensland University of Technology's School of Information Systems within the Faculty of Science and Engineering. His research focuses on Business Process Management (BPM), Robotic Process Automation (RPA), and Process Mining, with significant contributions to understanding digital transformation challenges in public and healthcare sectors. He has authored/co-authored over 35 peer-reviewed publications in top journals and conferences like BPM, HICSS, and ECIS. Key areas of expertise include leadership in IT initiatives, healthcare data quality, and low-code adoption strategies. Recent work emphasizes RPA's impact on organizational processes and knowledge retention. His research bridges academic theory with practical implementation, often collaborating with institutions like UN agencies and healthcare organizations. Publications span case studies in developing countries, systematic reviews, and curriculum development frameworks for BPM education. His work is cited across disciplines, reflecting its relevance to both academia and industry.
Hemant K. Bhargava is a Distinguished Professor at the Graduate School of Management, University of California Davis, where he holds the Jerome and Elsie Suran Chair in Technology Management. He serves as Associate Dean for Academic Affairs and Director of the Center for Analytics and Technology in Society (CATS), and previously served as Associate Dean for Instructional Programs (2010–2013) and Faculty Chair. He co-founded and was the first Academic Director of the UC Davis Master of Science in Business Analytics (MSBA) program. His educational background includes: Ph.D. in Decision Sciences, The Wharton School, University of Pennsylvania (1989) MBA, Indian Institute of Management Bangalore (1986) B.S. in Mathematics, University of Delhi (1984) Bhargava is a leading scholar in technology management, information systems, and decision analytics. His research focuses on the economics of digital goods and platforms, pricing strategies, AI in business, healthcare IT, and platform ecosystems. He builds economic models to study operations, marketing, and competitive strategy in tech-driven markets, with applications in healthcare, media, electric vehicles, and generative AI. His recent publications reveal a strong trend toward platform business models, data sharing, AI’s impact on research and education, and healthcare cost transparency. He explores how digital platforms shape competition, how data can be leveraged across markets, and how real-time decision tools can reduce prescription drug costs. His work bridges theory and practice, often involving collaboration with industry and policy makers. His scientific awards include: INFORMS Journal on Computing Test of Time Award (2023) INFORMS CIST Best Paper Award (2021) Google Research Excellence Gift (2017–18) Distinguished Alumni Award, IIM Bangalore (2024) INFORMS ISS President's Service Award (2023) Global 100 Top Academic Data Leaders (2020) Bhargava has played a pivotal role in academic leadership and program development. He co-founded the Theory in Economics of Information Systems (TEIS) workshop and has served on editorial boards of top journals including Management Science (where he is Department Editor for Information Systems), Operations Research, and Marketing Science. He has advised numerous campus initiatives, including the UC Davis Data Lab and Religions of India Initiative. His grants and collaborations include work with Google and healthcare organizations on real-time price transparency tools. He leads the Center for Analytics and Technology in Society (CATS), which focuses on the societal implications of data and AI. He also co-founded the MSBA program and continues to shape the future of business education in the age of AI through strategic planning committees.
Andrés Monroy-Hernández is an Assistant Professor at Princeton University's Department of Computer Science, leading the Human-Computer Interaction Lab. He holds a PhD from MIT's Media Lab and has held roles at Microsoft Research and Snap Inc. His research focuses on social computing, public-interest technology, and augmented reality, emphasizing technologies that foster collaboration and connection. He is affiliated with Princeton's Center for Information Technology and Policy, the Keller Center, and serves on Crisis Text Line's board. Education: PhD in Media Arts and Sciences, MIT (2012) Master's in Media Arts and Sciences, MIT (2007) Bachelor's in Electronics Engineering, Tecnológico de Monterrey (2001) Research Interests: Monroy-Hernández explores social computing systems, public-interest platforms (e.g., OpenDeli for food delivery), and social AR (e.g., Capybara). His work emphasizes ethics, marginalized communities, and democratizing technology. Notable projects include Scratch (children's programming), Microsoft's Calendar.help, and Significant Otter (biosignal sharing). Awards & Recognition: MIT Technology Review Innovator Under 35 (2013) CNET's Most Influential Latinos in Tech (2013) Best Paper Awards at CHI, CSCW, HCOMP, ICWSM Grants & Labs: Active in designing open-source protocols and advising tech leaders. His lab collaborates across disciplines, addressing societal challenges through technology. Recent efforts include ethical AI and decentralized social media. Teaching: Teaches social computing and mentors students in HCI and public-interest tech.
Kristian J. Hammond is the Bill and Cathy Osborn Professor of Computer Science at Northwestern University's McCormick School of Engineering. He directs both the Master of Science in Artificial Intelligence Program and the Center for Advancing Safety of Machine Intelligence (CASMI). His research focuses on artificial intelligence, natural language generation, narrative generation, conversational interfaces, and ethical AI applications across domains like law, education, and journalism. Hammond co-founded Narrative Science, leveraging AI for automated journalism from data. Education: PhD, MS, and BA in Philosophy (all from Yale University). His work spans technical innovation and societal impact, with notable contributions to AI transparency, bias mitigation, and machine learning ethics. Hammond has authored influential articles on AI governance, conversational systems, and the future of work in an automated economy. His leadership roles emphasize interdisciplinary collaboration between computer science, business, and humanities. Research highlights include developing AI systems that enhance human capabilities, exploring ethical frameworks for machine intelligence, and advancing AI safety through initiatives like CASMI. He frequently engages in public discourse via TEDx talks and commentaries on AI's societal implications, emphasizing the need for human-centered technology design.
Ken Holstein is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII) , where he directs the CoALA Lab and contributes to the CMU-NIST Cooperative Research Center on AI Measurement Science & Engineering (AIMSEC) . His work bridges human-computer interaction, AI, cognitive science, and design to create systems that respect and elevate human expertise in AI development. Research focuses on participatory and expertise-driven approaches to AI design, development, and evaluation Emphasizes AI's impacts on human workers and integrating diverse expertise (domain, lived, technical) across the AI lifecycle His research is supported by grants from the National Science Foundation (NSF) , NIST , The Block Center for Technology and Society , and industry partners like Microsoft, Google, and Amazon. He collaborates with labs including CASMI , UL Research Institutes , and MetaGov . Visit his personal website or Google Scholar page for publications.
Dr. Victoria C. P. Chen is a Professor in the Industrial, Manufacturing, and Systems Engineering (IMSE) department at The University of Texas at Arlington (UTA), where she has served since 2002. She previously held positions at the Georgia Institute of Technology from 1993-2001. Dr. Chen has held several leadership roles at UTA, including Interim Department Chair (2012-2014), Director of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) (2008-2012, and again from 2017-present), and Director of Doctoral Studies (2019-present). She was also the George & Elizabeth Pickett Professor from 2015-2017 and was inducted into the UT Arlington Academy of Distinguished Teachers in 2019. Dr. Chen is actively involved with INFORMS (Institute for Operations Research and the Management Science), where she currently serves as Secretary on the Executive Board. Dr. Chen earned her B.S. in Mathematical Sciences from The Johns Hopkins University, and her M.S. and Ph.D. in Operations Research and Industrial Engineering from Cornell University. Her academic journey includes visiting professorships at the University of Genoa, Italy, and Iowa State University. Dr. Chen's research utilizes statistical perspectives to create new methodologies for operations research problems appearing in engineering and science. Her expertise includes the design of experiments, statistical modeling, and data mining, particularly for computer experiments and stochastic optimization. Through her statistics-based approach, she has developed computationally-tractable decision-making methods for many high-dimensional complex systems. Her work spans multiple domains including sustainability, energy, water management, healthcare, and law enforcement. Specific application areas include inventory forecasting, airline optimization, water reservoir networks, wastewater treatment, air quality monitoring, green building design, nurse assignment systems, and pain management programs. Her recent publications demonstrate continued innovation in mixed integer programming for electric vehicle charging stations, vacuum ultraviolet spectroscopy prediction, and sustainable building education. Senior Member, Institute for Operations Research and the Management Sciences (INFORMS) (2024) Data Mining Prize (Lifetime Achievement Award), INFORMS Society on Data Mining (2023) College of Engineering Teaching Award, UT Arlington (2021) Third Place Award, C3.ai COVID-19 Grand Challenge (2020) Academy of Distinguished Teachers, University of Texas at Arlington (2019) George & Elizabeth Pickett Professorship (2015-2017) As an educator and mentor, Dr. Chen has advised over 25 doctoral students across diverse research topics in operations research and systems engineering. She has secured substantial research funding from multiple sources including the National Science Foundation (over $1.5 million in active projects), Environmental Protection Agency, National Institute of Justice, and industry partners like Luminant and Dallas-Fort Worth International Airport. Her current research projects focus on decision analytics for sustainable urban environments, optimization for Texas water management, and statistical methods for pain management programs. She has served as Principal Investigator or Co-PI on more than 20 externally funded research projects totaling over $3 million in funding. Dr. Chen co-founded the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at UTA with Dr. H. W. Corley. This research center brings together faculty and students from multiple disciplines to address complex problems through advanced statistical and optimization methods. She also leads interdisciplinary research teams working on projects related to sustainable infrastructure, energy systems, and healthcare optimization, frequently collaborating with researchers from civil engineering, environmental science, and medical fields.
Dr. Hai Phan is an Associate Professor in Data Science at New Jersey Institute of Technology's Ying Wu College of Computing. He holds a Ph.D. in Computer Science and Engineering from CNRS, University Montpellier 2 (2013), an M.S. from Konkuk University (2010), and a B.S. from HCM City University of Technology (2008). His research explores privacy-preserving machine learning and computational health analytics: Federated learning systems and optimization Privacy-enhancing technologies (differential privacy) Health informatics and social media analysis Cybersecurity defenses and adversarial learning Fair and ethical AI systems Dr. Phan's publications demonstrate strong emphasis on federated learning architectures with privacy guarantees, defenses against emerging security threats, and analysis of health-related behaviors through social media. Recent work focuses on IoT applications, large language model security, and mobile federated learning ecosystems. His research integrates techniques from distributed systems, cryptography, and machine learning. No scientific awards are mentioned in available sources. Information regarding student advising, research grants, or laboratory affiliations is not provided in available documentation.
Johanna Pirker serves as an Associate Professor at the Institute of Human-Centred Computing, Graz University of Technology, where she holds teaching authorization in Applied Computer Science. Her work bridges academic research with practical applications in interactive technologies, maintaining active consultation hours for students every Monday morning. Her research centers on human-centered computing with emphases on virtual/augmented reality systems, serious game design, and AI-driven interactive experiences. She investigates player behavior, user experience optimization, and therapeutic/educational applications of immersive technologies across diverse contexts including rehabilitation, engineering education, and social platforms. Recent 2025 publications reveal strong trends in AI integration for gaming ecosystems (toxicity detection, dialogue systems), VR-based educational tools across disciplines, and cross-cultural analyses of gaming communities. Her work consistently combines experimental user studies with novel system development to address real-world challenges. While specific grant details and student advising records aren't documented in source materials, her extensive publication output across venues like FDG and iLRN indicates active leadership in interdisciplinary collaborations focused on advancing immersive technologies for societal benefit.
Mustafa Bilgic is a Professor and Chair of the Computer Science Department at Illinois Institute of Technology, where he also directs the Master of Artificial Intelligence program and the Machine Learning Laboratory. His research focuses on machine learning, active learning, explainable AI, and probabilistic graphical models, with applications in healthcare, social media analysis, and biomedical engineering. He has received funding from NSF, NIH, and Samsung, among others. Education: PhD in Computer Science, University of Maryland at College Park (2010) M.S. in Computer Science, University of Maryland at College Park (2006) B.S. in Computer Science, University of Texas at Austin (2004, with High Honors and Special Honors) Research Highlights: Dr. Bilgic's work emphasizes AI ethics, algorithm transparency, and interactive machine learning systems. Notable projects include analyzing political news engagement dynamics and developing frameworks for eliminating explanation noise in AI models. His lab explores tools like OrganoID for tracking organoid growth and IDGI for improving model interpretability. Awards: NSF CAREER Award (2014) ACM SIGKDD Best Student Paper Award (2008) Illinois Tech College of Computing Teaching Excellence Award (2021) Teaching and Leadership: Bilgic teaches advanced courses in AI, machine learning, and data mining. He leads initiatives to bridge AI theory and practical applications, emphasizing interdisciplinary collaboration. His administrative roles include overseeing the AI master’s program and fostering innovation in computing education.
Dr. Babak Taheri is a Full Professor at Texas A&M University's Department of Hospitality, Hotel Management & Tourism, affiliated with the College of Agriculture & Life Sciences. He holds an Honorary Professorship in Marketing at the University of Aberdeen and has held visiting professor roles at institutions like the University of Sassari and the University of Central Florida. His expertise spans consumer engagement, sustainability, and mixed-method research in tourism and hospitality. Education: BSc Industrial Engineering (Azad University), MSc Information Systems (Glasgow Caledonian University), PhD in Marketing (University of Strathclyde) with a focus on tourism. Additional credentials include a PhDip in Research Methods and MRes in Management Science. Research focuses on consumer behavior, co-creation value, CSR, and climate change impacts. He has published over 150 papers and serves as Associate Editor for The Service Industries Journal and International Journal of Contemporary Hospitality Management . Awards include Fellow of The Higher Education Academy and multiple Best Reviewer accolades. Leadership includes roles at Durham University, Strathclyde, and others. He secured grants exceeding $3M from Horizon 2020 and Innovate UK. Teaching spans BSc to PhD levels, emphasizing experiential learning. Media contributions include articles in The Conversation and The Irish Times. Grants & Leadership: Academic leadership in research and teaching, with grants focused on innovation and resilience in hospitality sectors.
Professor Roy Pea is the David Jacks Professor of Education & Learning Sciences at Stanford University, with a courtesy appointment in Computer Science. He served as Director of the H-STAR Institute (2007-2021) and founded Stanford’s PhD program in Learning Sciences and Technology Design. His research focuses on technology-enhanced learning, social foundations of human learning, and interdisciplinary applications of digital tools. Stanford University, School of Education Graduate School of Education Department Courtesy appointment in Computer Science His work spans complex domains like concussion education, climate change learning, and AI-driven mental health interventions. He co-authored the 2010 National Education Technology Plan and co-edited key texts including Video Research in the Learning Sciences and AI in Education . His NSF-funded LIFE Center (2004-2014) advanced learning science theories. Recent publications address: (1) linguistic framing of concussions and reporting behavior, (2) AI chatbots for mental health, (3) "engineering fiction" to reduce climate change abstractness, and (4) immersive AR/LLM learning experiences. His research integrates data science, psychology, and educational technology. Fellow, American Academy of Arts and Sciences (2019) Inaugural Fellow, International Society of the Learning Sciences (2018) Honorary Doctorate, The Open University (2018) Best Bridging Paper, EDM 2014 LAK13 Best Paper Award (2013) Roy mentors doctoral and master’s students in learning sciences, advising on topics related to technology, cognition, and equity. He contributes to digital education policy through roles on advisory boards for organizations like NSF, NIH, and the Joan Ganz Cooney Center. His patents include methods for digital video analysis and collaborative learning systems.
