Professor Jinyan Li is an Adjunct Professor at the University of Technology Sydney's Data Science Institute, where he leads the Bioinformatics Program. His research develops computational methods for genomic analysis, protein interaction prediction, and biomedical data mining. Educational background: PhD in Computer Science (University of Melbourne) M.Eng in Computer Engineering (Hebei University of Technology) B.Sc in Applied Mathematics (National University of Defense Technology) Research spans: Genomic error correction algorithms Protein binding prediction Single-cell multi-omics analysis CRISPR design optimization Machine learning in bioinformatics With 140+ journal publications and 100+ conference papers, his work appears in leading venues including Bioinformatics, Nucleic Acids Research, and IEEE TKDE.
Brian Jordan Jefferson is an Associate Professor in the Department of Geography & Geographic Information Science at the University of Illinois, Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences and the Unit for Criticism and Interpretive Theory. His research focuses on political geography, science & technology studies, and racial capitalism, particularly examining intersections between digital technologies, policing, and urban development. Jefferson holds a Ph.D. in Politics from the New School for Social Research and an MA in Democracy Studies from the University of Westminster. He teaches courses on global conflict geographies, geopolitics of technology, and geospatial ethics. His work critiques how digital systems encode racial and class hierarchies—evident in publications like Digitize and Punish: Racial Criminalization in the Digital Age (2020) and Cybernetic States (2024). Recent research explores algorithmic policing, smart city colonialism, and state surveillance mechanisms. His scholarship bridges critical geography with postcolonial theory, emphasizing how technology reinforces systemic inequalities. Jefferson’s research has been published across leading journals (Political Geography, Duke University Press) and interdisciplinary platforms. He actively engages with urban justice movements, critiquing carceral logics in urban planning and tech-driven governance systems.
Madhavi Ganapathiraju is an Associate Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine, where she conducts research at the intersection of computational biology and medicine. She also serves as Adjunct Faculty at the Language Technologies Institute (LTI) at Carnegie Mellon University, leveraging her expertise in natural language processing and machine learning for biomedical applications. Dr. Ganapathiraju received her Ph.D. in Language and Information Technologies from the School of Computer Science at Carnegie Mellon University in 2007. Prior to that, she earned an M.Eng. in Electrical Communications Engineering from the Indian Institute of Science (1993) and a B.Sc. in Electronics, Physics, and Mathematics from Delhi University (1990). Her research focuses on large-scale discovery of protein-protein interactions and their application to understanding disease mechanisms. She leads projects predicting mental health and inflammation (MHAIN) interactomes, funded by the NIMH BRAINS award. Using machine learning approaches including active learning and transfer learning, her lab develops methods to analyze interaction networks for biologically relevant insights. Her work bridges computational biology with clinical applications, particularly in schizophrenia, congenital heart disease, and inflammation-related disorders. Analysis of Dr. Ganapathiraju's publications reveals consistent focus on protein-protein interaction networks across multiple disease contexts. Her work spans computational method development for predicting interactions, application to specific disease areas (particularly schizophrenia and congenital heart conditions), and translation of network findings into potential therapeutic insights. The research demonstrates increasing sophistication in integrating multi-omics data with network analysis. Notable achievements include the NIMH BRAINS award supporting her work on mental health interactomes. Her publications in high-impact journals like Nature Genetics, Cell Stem Cell, and npj Schizophrenia demonstrate the significance of her contributions to understanding the genetic and molecular basis of complex diseases. Dr. Ganapathiraju mentors students through rotation projects focusing on genetic variant to function studies for complex diseases using interactome network analysis and prioritizing coding variants using protein structure, function and interaction studies. Her lab employs machine learning and bioinformatics approaches to address challenging problems in biomedical research.
Jennifer Bird-Pollan is an Associate Dean for Research and Faculty Development, and Professor of Law at Wayne State University, holding the Alan S. Schenk Chair in Taxation. She joined from the University of Kentucky Rosenberg College of Law in 2024. Her expertise spans tax law, distributive justice, wealth transfer taxation, and international tax, with a philosophical focus on fairness in tax systems. Prior to academia, she practiced tax law at Ropes & Gray in Boston and taught philosophy at Vanderbilt and Harvard. Education: J.D., Harvard Law School; Ph.D., Vanderbilt University; B.A., Penn State University. She is a Kentucky Colonel, Supreme Court bar member, and active in legal education. Research explores tax law-philosophy intersections, published in journals like Boston College Law Review and Pepperdine Law Review. Awards include the 2023 UK Great Teacher Award and 2017 Duncan Teaching Award. Leadership roles include UK Senate Chair and Associate Dean of Academic Affairs. She held a 2014-2015 Fulbright at Vienna University of Economics and taught at Indiana University, Fordham, and Cologne. Recent scholarship critiques university endowment taxation, tax reform history, and sovereignty in international treaties. She emphasizes tax systems' role in collective societal benefit, as seen in her op-eds advocating for public education funding.
John F. Dolan is a Distinguished Professor of Law Emeritus at Wayne State University School of Law. He joined the faculty in 1975 after a judicial clerkship and private practice. His expertise lies in the Uniform Commercial Code (UCC), particularly Article 5 (Letters of Credit). He authored influential treatises and articles cited extensively by U.S. courts and academic literature. He served on editorial boards of journals like Banking Law Journal and testified as an expert in letter of credit litigation. He held visiting professorships in the Netherlands, China, and Ireland. His research focuses on letters of credit, international trade law, and commercial compliance. Retired in 2015, he remains active in legal scholarship and editorial work. Education: LL.B., University of Illinois College of Law (1965) Research Interests: Professor Dolan’s work centers on the legal and practical dimensions of letters of credit, including UCC Article 5 compliance, fraud in transactions, and the intersection of banking law with international trade. His scholarship emphasizes doctrinal clarity and real-world application, often addressing gaps in legal frameworks and their impact on commercial practices. His contributions have shaped judicial interpretations and industry standards globally. Awards: Wayne State University Academy of Scholars (2010) University Distinguished Professorship (2000) Multiple teaching awards, including the President’s Award for Excellence in Teaching (1994) Professional Contributions: He advised on UCC revisions, participated in international legal drafting committees, and authored seminal works like The Law of Letters of Credit . His Drafting History of UCC Article 5 (2016) remains a critical resource for legal and financial professionals.
Trevor Cickovski is an Assistant Professor at Florida International University's Knight Foundation School of Computing and Information Sciences, with a focus on bioinformatics and computational biology. Holding Graduate Faculty status, he specializes in microbiome analysis, GPU computing, network analysis, and Unix systems. His academic journey began with a Ph.D. in Computer Science and Engineering from the University of Notre Dame (2008). Current roles: Associate Director, Assistant Teaching Professor Research affiliations: Bioinformatics Research Group (BioRG), ACM, IEEE Dr. Cickovski's research spans microbiome analysis, software engineering, and GPU-accelerated computing. His work explores the microbiome's role in neuropsychiatric disorders (ADHD), genetic conditions (A1AD), respiratory diseases (COPD), and environmental phenomena (red tides) through multi-omics integration. His publications in Journal of Medical Microbiology , Bioinformatics , and IEEE/ACM TCBB reflect strengths in plugin-based software frameworks (PluMA), network analysis, and computational pipeline optimization. Awards include multiple teaching excellence honors and industry funding from NVIDIA. 2020 Graduate Faculty Status 2019 FIU CAT Fellow 2019 FIU Faculty Teaching Award 2017 Teaching Excellence Award 2014 Promotion to Associate Professor As lead developer of PluMA, he enables cross-language plugin development for bioinformatics workflows. Funded by NIJ and NSF, his projects apply machine learning to epigenetics and computational approaches to vaccine discovery.
Dr. Ruben Laukkonen is a Senior Lecturer (Associate Professor equivalent) in cognitive science and computational neuroscience at Southern Cross University, with honorary fellowships at Vrije Universiteit Amsterdam and The University of Queensland. His award-winning research integrates neural, psychological, and computational approaches to study meditation, insight, and consciousness. Research highlights: Bayesian models of advanced meditation states Mechanisms of true/false insight experiences Neural correlates of consciousness using EEG/Machine Learning Chief Investigator on Australia's largest psychedelic clinical trial ($4M funding) He publishes in leading journals, speaks internationally, and consults for the OECD on AI and education. His work explores rare states of consciousness across multiple explanatory levels, from neural dynamics to subjective phenomenology. Laukkonen received the 2024 Mid-Career Researcher Award for Research Excellence.
David L. Sheinberg is a Research Professor at Brown University specializing in cognitive neuroscience and visual perception. He graduated from Yale College with degrees in Computer Science and Psychology before completing graduate work in Cognitive Science at Brown. His postdoctoral research focused on neurophysiology under Nikos Logothetis at Baylor College of Medicine. Sheinberg's research examines the neural mechanisms underlying visual perception, object recognition, and attention processes. His recent work investigates how visual areas contribute to physics simulations and how shape information is processed independently across sensory modalities. He employs neurophysiological techniques and computational modeling to understand human-like visual processing. He serves in editorial capacities for multiple neuroscience journals, including as Associate Editor for Neuroscience and Psychology at Frontiers for Young Minds and Review Editor for Perception Science at Frontiers in Neuroscience.
Yifan Sun is an Assistant Professor in the Department of Computer Science at Stony Brook University. She is also affiliated with the AI Institute and the Institute of Advanced Computational Science (IACS) at Stony Brook. Dr. Sun received her PhD in Electrical Engineering from UCLA in 2015, with research focusing on convex optimization and semidefinite programming. Prior to joining Stony Brook, she worked at Technicolor Research and Innovation on machine learning applications and completed postdoctoral research at the University of British Columbia in Vancouver and INRIA in Paris. Her research centers on the design and analysis of optimization algorithms, particularly those arising in large-scale machine learning and scientific computing. Dr. Sun studies how structural properties like sparsity, curvature, and decomposability can be exploited to design faster, more stable, and more interpretable optimization methods. Her work spans both theoretical foundations and practical applications, covering first-order methods, quasi-Newton techniques, and algorithms for convex programming to nonconvex deep models. She also investigates how optimization theory, particularly insights from linear algebra and geometry, can improve understanding of deep learning models, regularization techniques, and representation learning. Dr. Sun's recent publications demonstrate strong trends in large-scale graph learning, advanced optimization techniques (particularly Frank-Wolfe variants), and applications to natural language processing. Her work often bridges theoretical guarantees with practical implementations, as evidenced by her open-source code repositories. She leads the OptML Research lab at Stony Brook, focusing on optimization methods for machine learning challenges. Her publications appear in top venues including NeurIPS, ICML, CVPR, and various optimization journals, reflecting her interdisciplinary approach that connects computer science, applied mathematics, and engineering disciplines.
Stephen Wolgast is the Knight Chair in Audience and Community Engagement for News and Professor of the Practice at the A. Q. Miller School of Journalism and Mass Communications at the University of Kansas. With three decades of experience in journalism and academia, he previously served as director of Collegian Media Group at Kansas State University and taught reporting classes. His career spans roles at major publications including The New York Times , The Times-Picayune , and The Baltic Independent . Education: B.A. in Political Science (Kansas State University), M.S. in Journalism (Columbia University) His research focuses on audience engagement , media law , and journalism education . Notably, he explores the historical dimensions of First Amendment rights and labor journalism , as well as modern challenges like news deserts . Recent publications analyze journalism curriculum gaps , academic dress traditions , and community media dynamics . Scientific awards include contributing to a Pulitzer Prize-winning special section post-9/11 and mentoring students who won national honors. He has presented internationally on topics ranging from academic traditions to government press overreach .
Shannon D. Blunt is the Roy A. Roberts Distinguished Professor of Electrical Engineering & Computer Science (EECS) at the University of Kansas (KU), where he also serves as Director of the KU Radar Systems Lab (RSL) and Director of the Kansas Applied Research Lab (KARL). With a distinguished career spanning over two decades, Prof. Blunt has established himself as a leading expert in radar signal processing and waveform design. Dr. Blunt earned his B.S., M.S., and Ph.D. degrees in Electrical Engineering from the University of Missouri, completing his doctorate in 2002 under the supervision of K.C. Ho. After working at the U.S. Naval Research Laboratory from 2002-2005, he joined the University of Kansas faculty, where he has remained ever since, advancing to his current distinguished professorship. Prof. Blunt's research focuses on sensor signal processing and system design with particular emphasis on waveform diversity and spectrum sharing techniques. His work has made significant contributions to radar and sonar systems that have been deployed operationally. His research spans radar waveform design, spectrum coexistence, cognitive radar, and the integration of radar and communication systems. The 15 most recent publications demonstrate his continued leadership in random FM radar waveforms, spectrum sharing techniques, and experimental validation of novel radar concepts, with numerous papers appearing in top journals like IEEE Transactions on Radar Systems and presented at major radar conferences. His scientific achievements have been recognized with numerous prestigious awards including the IEEE/AESS Nathanson Memorial Radar Award (2012), IEEE Fellowship (2016), IET Radar, Sonar & Navigation Premium Award (2020), and the IEEE/AESS Warren D. White Award (2025). In 2019, he was appointed to the U.S. President's Council of Advisors on Science & Technology (PCAST), and in 2024 he was named Fellow of the MSS. Prof. Blunt has successfully mentored numerous graduate students, with many of his former PhD students now holding positions at prestigious institutions including MIT Lincoln Laboratory, Johns Hopkins University Applied Physics Lab, Naval Research Laboratory, and academic positions at the University of Kansas. His research has been supported by over $30M in funding from organizations including NRL, DARPA, AFRL, ARL, ARO, DoE, NAVSEA, and ONR. He has served in significant editorial roles including Founding Editor-in-Chief of IEEE Transactions on Radar Systems and editorial board member for IET Radar, Sonar & Navigation. As Director of both the KU Radar Systems Lab and the Kansas Applied Research Lab, Prof. Blunt leads research teams focused on advancing radar technology and applying it to real-world problems. His labs have developed numerous innovative radar techniques that address spectrum congestion challenges while maintaining radar performance.
Marta-Marika Urbanik is an Associate Professor in the Sociology Department at the University of Alberta, specializing in urban ethnography with a focus on gangs, policing, and drug policy. She holds a Ph.D. in Sociology (Criminology specialization) from the University of Alberta and an M.A. from the University of Toronto’s Centre for Criminology and Sociolegal Studies. Her research explores gang violence, policing practices, neighbourhood redevelopment, and the intersection of social media with street culture. She currently serves as Acting Director of the BA Criminology Program and Book Review Editor of The Canadian Journal of Sociology. Education: Ph.D., Sociology (Criminology), University of Alberta (awarded SSHRC Bombardier, Killam Memorial, and President’s Doctoral Prizes) M.A., Centre for Criminology and Sociolegal Studies, University of Toronto Her research interests include gang dynamics, policing strategies, drug policy, and the impact of urban redevelopment on criminal networks. Recent projects focus on invasive policing in Toronto’s inner-city, supervised consumption sites in Calgary/Edmonton, and prison gangs in Western Canada. She combines ethnography with digital methods to study emerging phenomena like ‘cuckooing’ and social media’s role in perpetuating gang culture. Her work has been showcased at major conferences (e.g., American Society of Criminology, Eurogang Network) and published in top journals like British Journal of Criminology and Qualitative Sociology . She frequently engages with media to discuss drug policy, policing, and urban crime. Key Awards: Social Science and Humanities Research Council (SSHRC) Joseph-Armand Bombardier Award Izaak Walton Killam Memorial Scholarship University of Alberta President’s Doctoral Prize of Distinction Teaching includes courses on criminology, deviance, and gangs, alongside supervising undergraduate/graduate research assistants and field placements. She emphasizes bridging academic research with community engagement and policy advocacy.
Terence Broad is a Senior Lecturer at the Creative Computing Institute, University of the Arts London, and Acting Course Leader of the MSc Applied Machine Learning for Creatives. He is currently completing a PhD at Goldsmiths, University of London. His work bridges art and technology, focusing on generative AI, computational creativity, and the use of machine learning as artistic materials. His research has been exhibited globally at venues like The Whitney Museum of American Art and SIGGRAPH conferences. Education: MSci Creative Computing from Goldsmiths, University of London (2012–2016). Research interests include expressive manipulation of deep generative models, exploring the latent possibilities of 'black-box' systems, and ethical considerations in AI art. He has pioneered methods like 'Network Bending' for creative model customization. Key achievements include winning the ICCV Computer Vision Art Gallery Grand Prize (2019) and serving on the SIGGRAPH jury (2021). His work is part of Geneva's contemporary art collection and has been featured in media like The Independent and Vox. Professional activities include organizing conferences like IGGI 2020 and reviewing for journals like Leonardo and ACM Transactions on Image Processing. He actively participates in exhibitions, artist talks, and academic workshops.
Matthieu Chapman is an Associate Professor in the Department of Theatre Arts at SUNY New Paltz, specializing in critical race theory, Renaissance drama, and Shakespearean studies. Their research interrogates racial representation in early modern English drama, focusing on transracial casting, the black body as theatrical prop, and the intersection of race, gender, and cultural identity in performance contexts. Key research trends in Chapman's publications include deconstructing racialized corporeality in Shakespearean adaptations, analyzing dog-whistle semiotics in early modern texts, and challenging dominant paradigms of racial representation. Their work frequently examines the historical construction of racial antagonisms in theatrical contexts and contemporary implications for performance practice. Chapman's scholarship appears in journals like Theatre History Studies , with articles exploring topics such as: Latinidad in Shakespearean contexts, stained glass metaphors in staging, and fragmented Black identity narratives. They have contributed to edited collections and special sections addressing race in Renaissance performance.
Gerui Wang is a Lecturer at Stanford University’s Center for East Asian Studies and the University of California, Santa Cruz. She specializes in AI ethics, digital humanities, and the intersection of art, technology, and environment. Her research includes a focus on governance and ecology in Chinese visual culture, supported by grants from the Chiang Ching-kuo Foundation and Mellon Foundation. She leads the digital humanities project Storytelling with AI , archived by Stanford Libraries, and has published in Journal of Chinese History and Newsletter for International China Studies . Awards include the 2024 Stanford Teaching Advancement Award for her AI-focused courses. Gerui also contributes to AI policy discussions via platforms like the Alan Turing Institute and Forbes, addressing topics like AI’s societal impact and ethical challenges. Her interdisciplinary work spans academia, media, and policy, with affiliations including the NEH-funded Teaching Art History with AI group at the University of Pittsburgh.
