Eric Larson is an Associate Professor at Brown University's Department of Mathematics, specializing in algebraic geometry. His research focuses on moduli spaces, Brill-Noether theory, and algebraic curves. He collaborates with notable mathematicians such as Isabel Vogt and Izzet Coskun on topics like normal bundles, Chow rings, and stability conditions. Larson actively engages in academic outreach, organizing Putnam competition practices and undergraduate colloquia. He has developed computational tools for studying elliptic curves' Galois representations and contributed to expository works on interpolation problems and LaTeX accessibility.
Sanjiv Singh is a Research Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science. His academic journey at CMU spans from Systems Scientist (1995-2001), to Senior Research Scientist (2001-2003), Associate Research Professor (2003-2007), and finally Research Professor since 2007. He also holds an adjunct faculty position in Mechanical Engineering since 2009. Singh serves as Editor-in-Chief of the Journal of Field Robotics, demonstrating his leadership in the robotics community. His educational background includes a Ph.D. and M.S. in Robotics from Carnegie Mellon University (1995, 1992), an M.S. in Electrical Engineering from Lehigh University (1985), and a B.S. in Computer Science from the University of Denver (1983). Dr. Singh's research focuses on three primary themes: Autonomous Navigation (developing motion planning and control for ground and air vehicles with applications in agriculture, exploration, and low-flying aircraft), Coordinated Multi-Robots (examining team-based tasks like structure assembly and search/rescue operations), and Forceful Interaction with the world (using physical models to enable robots to handle complex, high-force interactions). His work spans aerial robotics, agricultural and forestry robotics, mining robotics, 3D vision, sensing and perception, visual servoing, motion planning, and field service robotics. Analysis of his recent publications (2016-2020) reveals a strong focus on collision avoidance algorithms, sensor fusion techniques, and real-time navigation systems. His research demonstrates consistent advancement in SLAM (Simultaneous Localization and Mapping) technologies, particularly in GPS-denied environments, with increasing sophistication in handling complex aerial maneuvers and multi-robot coordination. Editor-in-Chief of Journal of Field Robotics Dr. Singh has advised numerous graduate students throughout his career, with current advisees including Matt Aasted (Ph.D), Andrew Chambers (M.S), Hugh Cover (M.S), Michael Dille (Ph.D), and Justin Haines (M.S). His past students include prominent researchers like Sebastian Scherer, Joe Djugash, Fred Heger, Geoff Hollinger, and Ji Zhang who have gone on to make significant contributions in robotics. His research has been supported through various projects including CASC (agricultural applications), Riverine, Transformer, Trestle, and Ember. His laboratory work focuses on developing practical robotic systems capable of operating in challenging real-world environments, with particular emphasis on agricultural applications, search and rescue operations, and coordinated multi-robot teams that can work effectively alongside humans.
Prof. Kai S. Exner is a Professor of Theoretical Inorganic Chemistry at the University of Duisburg-Essen's Faculty of Chemistry and a member of the Zentrum für Medizinische Biotechnologie (ZMB). His research focuses on electrocatalysis , particularly optimizing electrode materials for energy applications such as batteries, electrolyzers, and fuel cells. He employs computational methods like ab initio calculations and microkinetic modeling to bridge theoretical insights with experimental and industrial applications, emphasizing sustainable catalyst design without relying on noble metals. His work integrates basic research with applied medical biotechnology through ZMB collaborations, though his primary affiliation lies in the Faculty of Chemistry. Exner has pioneered concepts like the electrochemical-step asymmetry index and free-energy span model, which are critical for screening electrocatalysts. His research group, 'Theoretical Inorganic Chemistry,' actively explores mechanisms behind oxygen and chlorine evolution reactions, emphasizing selectivity control and reaction pathway complexity. Exner received the Gottschalk-Diederich-Baedeker-Preis (2024) for advancing the energy transition through foundational electrocatalysis research. He leads Aktuelles Projekt funded by third-party grants and collaborates with facilities like the Analytics Core Facility Essen (ACE) and Imaging Center Campus Essen (ICCE). His advisory role focuses on guiding theoretical and computational approaches in electrochemistry, and he has established a reputation for interdisciplinary research connecting computational chemistry with practical energy solutions.
Liza Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University's School of Engineering and Applied Science. She is also an associated faculty member of the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Her research focuses on randomized numerical linear algebra and the mathematics of data science, with connections to high-dimensional probability and stochastic optimization. She develops mathematically justified randomized algorithms for large-scale data, particularly interested in settings where data exhibits mathematical structure such as spectral decay, multi-modality, or non-negativity. Her work addresses challenges in data compression, recovery, and optimization under structured noise conditions. Dr. Rebrova's recent publications show a strong trend toward developing robust algorithms for linear systems, tensor data compression, and nonnegative matrix factorization. Her work bridges theoretical foundations with practical applications in machine learning and data science, with emphasis on memory efficiency and handling corrupted or incomplete data. NSF DMS-2309685 (Single PI, 2024-2026): "Outliers are not what they seem: data-aware, flexible, and robust randomized iterative methods" NSF DMS-2108479 (Collaborative, 2022-2024): "Fast, Low-Memory Embeddings for Tensor Data with Applications" (co-PIs Mark Iwen and Deanna Needell) She currently advises three PhD students (Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko) and has previously advised Abraar Chaudhry (PhD 2024) and Nicolo Grometto (Masters thesis 2023). At Princeton, she teaches graduate courses including ORF526 (Graduate Probability), ORF387 (Networks), and ORF523 (Convex and Conic Optimization).
Vitaly Shmatikov is a professor of computer science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science . Prior to his current position, he worked at the University of Texas at Austin and SRI International. His research focuses on digital privacy , computer security , and security and privacy issues in machine learning . Education: Ph.D. in Computer Science and M.S. in Engineering Economic Systems from Stanford University Teaching: Offers courses like CS 5435 Security & Privacy Concepts in the Wild Shmatikov's research has received multiple accolades, including the Caspar Bowden PET Award three times and Test-of-Time Awards from leading IEEE and ACM symposiums. His work addresses adversarial system design and practical applications of privacy and security. Scientific Awards: Caspar Bowden PET Award (3x) IEEE Test-of-Time Award ACM Test-of-Time Award ACM/IEEE Symposium on Logic in Computer Science Test-of-Time Award USENIX Security Outstanding Paper Award EMNLP Distinguished Paper Award He actively mentors students and collaborates on cutting-edge security research, with applications in cryptography, malware analysis, and behavioral economics in cybersecurity.
Robert Rohling is a Professor at the University of British Columbia's Faculty of Applied Science, affiliated with the Department of Mechanical Engineering and holding a joint appointment with the Department of Electrical and Computer Engineering. As Director of the Institute of Computing, Information and Cognitive Systems (ICICS), his research focuses on biomedical engineering, medical imaging, robotics, and computational methods. B.A.Sc. (UBC) M.Eng. (McGill) Ph.D. (Cambridge) Rohling's work spans three primary research areas: medical imaging (3D ultrasound, spatial compounding, elasticity reconstruction), medical information systems (radiologist navigation tools for large image datasets), and robotic calibration for surgical applications. His multidisciplinary approach integrates mechanical and electrical engineering principles with clinical needs. Rohling's publications (2020-2022) reveal trends in advanced ultrasound techniques (e.g., shear wave vibro-elastography), AI-driven image processing (cycleGAN translation), and computational optimization for diagnostic accuracy. Keywords across his work include Medical Imaging, Biomedical Engineering, Robotics, and Computational Modeling. As director of the Robotics and Control Laboratory , Rohling leads interdisciplinary collaborations with industry and clinical partners to address practical challenges in medical diagnostics and surgical robotics. His research emphasizes translating engineering innovations into clinical practice.
Victor Chestopal serves as Professor of Piano and Chairman of the Keyboard Section at the Royal Conservatory of Brussels since 2020, with concurrent professorship at Italy's Accademia Perosi. A globally recognized performer, he has collaborated with orchestras including the Moscow Philharmonic and Helsinki Philharmonic, and performed at venues like Brussels' Palais des Beaux-Arts and Moscow Conservatoire. His educational background includes: Early training at Moscow's Gnessin Music School and Central Music School of the Tchaikovsky Conservatoire Master's degree from Sibelius Academy (2001) Doctor of Music from Sibelius Academy (2010) with thesis "Temporal correlation in the Goldberg Variations" Studies at Imola's Accademia Pianistica (1992-1997) and Weimar's Hochschule für Musik (1995-1997) Chestopal's research integrates performance practice with musicological analysis, focusing on structural interpretation of Baroque and Romantic repertoire. His scholarly work examines temporal relationships in canonical piano works, while his artistic output spans Early Music, Contemporary Theatre, and Jazz Education. As a multilingual poet publishing in Russian, Italian, and French, he bridges musical and literary arts through collections like "Anno" (2014) and "Poeticheskie dnevniki" (2018). His honors include: First Prize at the “Carlo Soliva” International Competition (1990) As an educator since 2007, Chestopal conducts annual masterclasses in Liège and serves on international competition juries. He actively mentors students across European conservatories while leading charity initiatives for organizations including "Save the Children" and "Mary's Meals", often performing benefit concerts for humanitarian causes.
Wiebke von Carolsfeld serves as a Professor in Cinema at Concordia University, where she holds a primary academic appointment focused on film production and creative practice within the university's cinema program. Her research and creative practice spans directing, screenwriting, and editing across multiple cinematic formats including feature film, independent film, documentary, and shorts . She demonstrates particular expertise in narrative genres such as drama and comedy , with significant experience in international co-productions that facilitate cross-cultural collaborations. Her creative work extends beyond film into prose and novel writing , reflecting an interdisciplinary approach to storytelling. Professor von Carolsfeld maintains active engagement with the film community through her practice-based research and creative output, though specific advising relationships and grant activities are not detailed in available institutional documentation. She remains a current faculty member contributing to Concordia University's cinematic arts education mission.
Ole Winther is Professor in High dimensional biological data analysis/Machine learning at the Department of Biology, University of Copenhagen and Professor in Data science and complexity at DTU Compute, Technical University of Denmark. He serves as CRO and co-founder of raffle.ai, CTO and co-founder of FindZebra, Head of ELLIS Unit Copenhagen, and co-PI of the Machine Learning for Life Science Center. His research spans Bioinformatics , Machine Learning , and AI for Science , focusing on applying deep learning to biological sequence analysis, latent variable models, and medical NLP. Winther's work develops predictive and generative models for bioinformatics, with significant contributions to protein localization tools (SignalP, DeepLoc, DeepTMHMM), single-cell genomics, and novel deep learning architectures like variational autoencoders and diffusion models. Analysis of Winther's recent publications (2023-2025) reveals a strong trend toward integrating protein language models with traditional bioinformatics approaches and applying diffusion models to scientific problems. His work bridges theoretical machine learning advancements with practical applications in biology and medicine, particularly in protein sequence analysis, medical search engines, and scientific simulation acceleration. Winther currently supervises a diverse research group including Panagiotis Antoniadis, Rachael M. DeVries, Jun Wang, Beatrix M. G. Nielsen, Felix G. Teufel, Irene R. Rodriguez, Anders Christensen, and Christopher Heje Grønbech. His former students have established successful careers at institutions including Google, Apple, and various startups, with notable alumni like Casper Sønderby (Google Brain) and Søren Sønderby (Apple). He leads significant research initiatives including the ELLIS Unit Copenhagen and the Machine Learning for Life Science Center, while maintaining active industry partnerships through his co-founded companies raffle.ai (enterprise search using NLP) and FindZebra (search engine for rare diseases). His teaching includes Deep Learning courses at both DTU (02456) and University of Copenhagen (NDAK24002U).
Sean Cao serves as Associate Professor (with tenure) at the Robert H. Smith School of Business, University of Maryland, where he is Director and Co-founder of the AI Initiative for Capital Market Research. He also holds an affiliation as professor at Harvard Business School's D 3 Institute. His academic journey began with a Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Cao's research focuses on the intersection of artificial intelligence and capital markets, with particular expertise in how machine learning transforms financial analysis, corporate disclosure practices, and investment decision-making. His work examines the evolving relationship between human analysts and AI systems, blockchain applications in financial reporting, and the strategic adaptation of corporate communications for machine readership. He has pioneered research on the "AI divide" among investor groups and developed frameworks for human-AI collaborative stock analysis. His publication portfolio spans top journals including Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, and Management Science. The research demonstrates consistent thematic progression toward increasingly sophisticated AI applications in finance, with recent work exploring distributed ledger technologies for auditing, machine learning for extracting private information from disclosures, and the economics of greenwashing in ESG funds. His studies frequently combine textual analysis with traditional financial metrics to uncover novel market insights. Fama-DFA Prize from Journal of Financial Economics for best paper in capital markets and asset pricing Michael J. Brennan Award from Review of Financial Studies Deloitte Initiative for AI and Learning award for developing trustworthy AI for social equity PanAgora Asset Management's Dr. Richard A. Crowell Memorial Prize Multiple best paper awards from Midwest Finance Association, Global AI Finance Conference, and Asian Finance Association Dr. Cao has delivered over 200 invited research talks at major institutions including the Central Bank of Japan, Central Bank of Thailand, and U.S. Securities and Exchange Commission. He serves as Guest Associate Editor for Management Science and has co-chaired Review of Financial Studies conferences on FinTech and Machine Learning. His educational initiatives include a widely adopted free AI textbook for finance and accounting that has been implemented at universities worldwide including Indiana University, UT Dallas, and University of Minnesota. As Director of the AI Initiative for Capital Market Research, Dr. Cao leads a multidisciplinary team exploring practical AI applications in finance. The initiative has secured significant funding including a $150,000 grant from GRF CPAs & Advisors. His research group maintains strong industry connections through partnerships with regulatory bodies, financial institutions, and technology companies, facilitating the translation of academic research into practical financial applications.
Daniel Krutz is an Associate Professor in the Department of Software Engineering at Rochester Institute of Technology (RIT) , with a secondary appointment as Courtesy Associate Professor at the University of Florida's Department of Computer & Information Science & Engineering. He directs the AWARE Lab , focusing on Autonomous Systems, Warfare Applications, and Software Engineering , supported by $6M+ in federal grants from NSF, NSA, DOD, and AFRL. His research spans AI/ML, Self-Adaptive Systems, Decision Support Systems, and Accessibility in Computing Education . He holds a PhD from Nova Southeastern University and has taught courses including Software Engineering Freshman Seminar and Capstone Research Project . He is an NSF CAREER Award recipient (2022) and former AFRL Fellow (2018). Educations: BS, St. John Fisher College MS, Rochester Institute of Technology PhD, Nova Southeastern University Labs: AWARE Lab (Autonomy, Warfare, and Engineering) Awards: NSF CAREER Award (2022) AFRL Research Faculty Fellowship (2018) Teaching: Undergraduate/Graduate courses in software engineering, accessibility, and AI/ML Research Interests: Focus on experiential learning in computing education, neuroevolutionary algorithms, and AI ethics. Recent projects address empathy-building interventions for inclusive software development and context-aware decision support systems . Grants & Funding: Over $6M secured since 2018 from NSF, DOD, and NSA for projects in AI/ML, cybersecurity, and adaptive systems. Current work explores AI-driven stock trading frameworks and accessibility education modules.
Darryl Jones is Professor at Griffith University's Centre for Planetary Health and Food Security, specializing in behavioral ecology and human-wildlife interactions. His research examines urban ecosystems, wildlife conflict resolution, and conservation strategies, with particular emphasis on bird ecology and road infrastructure impacts. Professor Jones has authored influential books including 'Feeding the Birds at Your Table' and 'A Clouded Leopard in the Middle of the Road' that bridge scientific research and public engagement. His work explores: Wildlife responses to urbanization Bird feeding practices and consequences Road ecology and mitigation design Human dimensions of conservation Koala-vehicle collision patterns Recent publications document COVID-19's impact on global bird feeding behaviors, analyze 20-year trends in koala road mortality, and evaluate LED streetlight effects on urban wildlife. He leads research on dingo population viability in Fraser Island and develops innovative wildlife monitoring technologies like the open-source WildWID RFID system. Professor Jones collaborates extensively with government agencies on conservation policy and has secured funding for projects examining wildlife crossing infrastructure effectiveness. His research informs evidence-based approaches to human-wildlife coexistence in rapidly changing landscapes.
Pieter d'Hoine serves as Professor and Vice Dean of Education at KU Leuven's Higher Institute for Philosophy. He is affiliated with the Wulf Mansion Centre for Ancient, Medieval and Renaissance Philosophy and LECTIO – KU Leuven Institute for the Study of Textual Transmission. Additional institutional roles include membership on the Board of the Institute for Living Languages, HIW Faculty Council, and Philosophy Program Oversight Committees. His research centers on Ancient Philosophy with emphasis on Neoplatonism, particularly Proclean metaphysics, logical structures in Platonic/Aristotelian commentaries, and mechanisms of philosophical canon formation. He investigates knowledge transmission from Greek-Roman antiquity through medieval and Renaissance scholarship, integrating digital humanities methodologies to analyze textual evolution and conceptual continuity across historical periods. Recent publications (2024) exhibit concentrated focus on Proclus' metaphysical frameworks, including predication theory, modes of being, and theological elements within Platonic exegesis. His work bridges textual criticism with systematic philosophy, examining how ancient commentators shaped enduring philosophical paradigms through citation practices and logical innovation in works like the Elements of Theology . No major scientific awards are documented in the provided materials. He actively supervises doctoral research, including G.J. Ruz Troncoso's analysis of Proclus' argumentative structures and S-H. Yang's study of Aristotelian metaphysics. Current funding supports eight major projects: "Een nieuw model voor de editie van antieke filosofische fragmenten" (2025-2031) as Co-promotor "De filosofische canon en de citaties van Plato en Aristoteles" (2024-2028) as Promotor "Rechtvaardigheid voor dieren" (Justice for Animals, 2024-2028) as Promotor "De mechanica van Canonvorming" (Canon Formation Mechanics, 2024-2028) as Promotor His research ecosystem operates through the Wulf Mansion Centre and LECTIO institute, which coordinate international collaborations on premodern textual transmission, utilizing digital tools to map philosophical concept evolution across millennia.
Dr. Kaya de Barbaro is an Associate Professor in the Department of Psychology at the University of Texas at Austin (College of Liberal Arts). She holds a Ph.D. from the University of California San Diego. Her research focuses on bridging computer science and developmental/clinical psychology, particularly maternal mental health and infant social-emotional development. She directs the Daily Activity Lab, which uses mobile/wearable sensors and machine learning to analyze real-world interactions, aiming to develop just-in-time interventions for new mothers. Key research areas include maternal-infant dynamics, physiological synchronization, and the impact of environmental chaos on development. She has pioneered methods for analyzing high-density data, such as Granger causality and machine learning algorithms to detect behaviors like crying and holding. Recent work emphasizes leveraging ecological momentary assessment surveys and 24-hour LENA audio recordings to understand proximal mechanisms of development. Dr. de Barbaro teaches Psychology 333D (Introduction to Developmental Psychology) and has developed curricula for both in-person and online formats. She is actively involved in training students through the Eureka program at UT Austin. Her lab collaborates on tools like chatbots for postpartum mental health and has published extensively on sensor-based methodologies in developmental science.
Bobak Mortazavi is an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University. His research focuses on medical analytics, machine learning, wearable sensors, and cyber-physical systems. He leads interdisciplinary projects in healthcare technology, including AI-driven diagnostics and predictive modeling for cardiovascular diseases. He has received notable awards such as the Best Demonstration Award at IEEE EMBS 2012 and the Best Paper Award at the Fourth International Conference on Data Analytics 2015. His work bridges machine learning with clinical applications, emphasizing practical solutions for healthcare challenges. Recent research includes developing AI tools for aortic stenosis detection, electrolyte estimation via ECG, and real-time patient monitoring systems. He collaborates with industry and academic partners to advance telemedicine and wearable health technologies. Key contributions include the SMART-LV project for smartphone-based cardiac diagnostics and the ArterialNet framework for blood pressure reconstruction using wearable sensors. His work is published in top journals like IEEE Journal of Biomedical and Health Informatics and Elsevier's Pervasive and Mobile Computing.
