Kingsley Fong is an Associate Professor of Finance at the UNSW Business School , specifically within the School of Banking and Finance . He holds a PhD from the University of Sydney and a BCom (Hons) from UNSW. His research focuses on market microstructure , investment , household finance , and sustainable finance , and he co-founded the RISE Finance Lab to explore finance's role in societal well-being. He also developed the DATKIS framework for systemic coherence in financial practices. Research Interests : Market microstructure, household finance, sustainable finance, and empirical finance. Teaching : Courses such as WEALTH MANAGEMENT AND CLIENT ENGAGEMENT , SUSTAINABLE INVESTING , and SUSTAINABLE FINANCE . Key Trends in Research : His work spans liquidity proxies, algorithmic trading impacts, broker-client dynamics, and sustainable finance innovations. Notable collaborations include studies on market quality, tax-driven trading, and household investment behavior. Scientific Awards : 2017 Review of Finance Spängler IQAM Prize 2021 Aspen Institute Ideas Worth Teaching Award 2022 S&P Global Decarbonisation Hackathon Engagement : Co-Founder of UNSW RISE Finance Lab (2025) Australian Sustainable Finance Institute Reference Group (2024) Deputy Head of School Banking and Finance (2011–2019) Contact : k.fong@unsw.edu.au | Location : UNSW Business School, Ref E12, Level 3, Room 344B.
Dr. Leon Barron is a Reader in Analytical & Environmental Sciences at the School of Public Health, Imperial College London. His expertise spans chemical contaminant analysis, wastewater epidemiology, and environmental forensics. He leads the Emerging Chemical Contaminants team within the Environmental Research Group, focusing on pharmaceuticals, PFAS, and illicit drugs. Education: BSc(Hons) in Analytical Science (2001), PhD in Analytical Chemistry (2005), and a Postgraduate Certificate in Academic Practice (2011). Previously held roles at King's College London as Lecturer (2009–2015) and Senior Lecturer (2015–2020). Research interests include trace environmental analysis (LC, GC, MS), chemical risk assessment, and wastewater-based epidemiology. Key projects include global drug use monitoring via sewage analysis and PFAS removal from drinking water. His work has produced over 100 peer-reviewed articles, with recent focus on PFAS accumulation, biochar filtration, and urban pollution source apportionment. Awards include Fellowships from Royal Society of Chemistry and Chartered Society of Forensic Sciences. Supervises PhD students on topics like pesticide exposure, opioid monitoring, and machine learning in ecotoxicology. Collaborates internationally via initiatives like the Sewage Analysis CORE Group and NIHR HPRU in Environmental Exposures.
Professor Libby Porter is a leading academic and researcher in urban planning and human geography at RMIT University, Australia. As Director of the Centre for Urban Research (CUR) and Professor in the School of Global, Urban and Social Studies (GUSS), her work focuses on critical urban governance, decolonising planning practices, Indigenous rights, and urban displacement. She holds a prestigious position as a Fellow of the Higher Education Academy and has contributed extensively to international policy debates through her role as Assistant Editor for Planning Theory and Practice . Her research examines settler-colonial dynamics in urban development, public housing policy, and the intersection of planning with Indigenous sovereignty. Notable publications include Unlearning the Colonial Cultures of Planning (2010) and Planning in Indigenous Australia (2018). Libby has held academic roles in the UK and Australia and previously worked in urban planning policy for Victorian Government departments. She supervises students in Honours, Masters, and PhD programs, focusing on topics like Indigenous housing, urban displacement, and environmental policy. Her work has been featured in The Conversation , BBC News, and academic journals. Libby also leads the Critical Urban Governance Research Program and oversees the Bachelor of Urban and Regional Planning at RMIT. Awards: Fellow of the Higher Education Academy Key Roles: Director, Centre for Urban Research; Co-founder, Planners Network UK; Member, Expert Advisory Panel for Melbourne 2030 Research Themes: Property rights, urban sustainability, decolonisation, Indigenous planning, gentrification
Bertram Müller-Myhsok is a Research Professor and Research Group Leader at the Max Planck Institute of Psychiatry in Munich, Germany. His research focuses on statistical genetics and transcriptomic data analysis in psychiatric disorders, particularly major depression, PTSD, schizophrenia, and their treatment responses. He integrates machine learning with genetic and clinical data to develop predictive models and stratified treatment approaches. Professional activities include leadership roles in the International Max Planck Research School for Translational Psychiatry and collaborations with institutions like the Institut du Cerveau (Paris) and Bernhard Nocht Institute (Hamburg). His work spans genetic epidemiology, psychiatric genomics, and precision medicine, with over 400 publications in high-impact journals. Key research areas include identifying genetic risk factors for mental disorders, developing polygenic scores, and leveraging omics data to uncover disease mechanisms. He leads projects like Psych-STRATA, a Horizon Europe-funded initiative advancing personalized psychiatry through pharmacogenomics.
Song Ma is a Professor of Finance and Entrepreneurship at Yale School of Management and a Faculty Research Fellow at the National Bureau of Economic Research (NBER). He is also an affiliated faculty member at Yale Law School Center for the Study of Corporate Law and Yale SOM Program on Entrepreneurship, having joined Yale SOM Faculty in 2016. His educational background includes: PhD in Finance from Duke University's Fuqua School of Business (2016) BA in Economics from Zhejiang University (2010) Professor Ma's research primarily focuses on innovation economics, entrepreneurship, financial economics, AI, and big data. His work extends to corporate strategy, industrial organization, antitrust, labor, and business law. He has made significant contributions to understanding how innovation interacts with financial markets, corporate strategy, and competition policy, particularly through his influential 'Killer Acquisitions' paper which has been cited in Congressional antitrust reports and lawsuits against major tech companies. His recent publications demonstrate an interdisciplinary approach combining finance, economics, and data science methodologies. Many papers examine the intersection of innovation and corporate finance, with increasing incorporation of AI and big data techniques as seen in his video analysis research. His work shows evolution from traditional finance topics toward more policy-relevant research with real-world impact on antitrust regulation and innovation policy. Professor Ma has received numerous prestigious awards: 2023 Best Paper Award, China International Conference in Finance 2022 Best Paper on Competition Economics, Association of Competition Economics 2022 Jerry S. Cohen Award for Antitrust Scholarship 2021 GARP Best Paper in Risk Management Award 40 Under 40 Best Business School Professors by Poets & Quants (2021) Robert F. Lanzillotti Prize for Antitrust Economics (2020) Jensen Prize for Best Paper on Corporate Finance (2019) In teaching, Professor Ma delivers popular courses including 'Entrepreneurial Finance,' 'Venture Capital and Private Equity,' and 'Finance and the Society.' He co-organizes WEFI (Workshop on Entrepreneurial Finance and Innovation), a bi-weekly virtual research forum. His research has been referenced by major regulatory bodies worldwide including the FTC, EU Competition Commission, and UK Competition and Markets Authority, and featured in leading media outlets like Wall Street Journal and New York Times. Professor Ma actively incorporates new data science technologies into his empirical economic research, focusing on unstructured data analysis and machine learning applications.
Prof. Matt Pritchard is a Professor in Earth & Atmospheric Sciences at Cornell University, based at Snee Hall. His research focuses on volcanology, geodesy, and remote sensing, with a particular emphasis on using satellite data to monitor volcanic activity, deformation, and glacial interactions. He leads studies on global volcanic systems, including Indonesia's Semeru and Raung volcanoes, Chile's Cordón Caulle, and Bolivia's Uturuncu, applying techniques like InSAR, SAR, and thermal imaging. Key research interests include volcanic eruption dynamics, magma-hydrothermal systems, and the integration of multi-sensor datasets. He has contributed to developing tools like Hotspotter for automated volcanic thermal feature detection and has explored planetary volcanism (e.g., Venus). His work bridges geophysics, glaciology, and computational methods, addressing both Earth and extraterrestrial systems. Prof. Pritchard has received recognition such as the William Bowie Lecture (2022, 2023). His projects often involve international collaborations, including the CEOS Volcano Demonstrator initiative and the EarthDEM/ArcitcDEM projects. He also engages in geothermal energy research and seismic monitoring in Ithaca, NY.
Elisabeth Prince is an Assistant Professor at the University of Waterloo, specializing in polymer chemistry and biomaterials. Her research focuses on developing advanced materials for biomedical applications, sustainable materials, and microfluidic technologies. Key areas include conductive hydrogels, cleavable polymers for recyclability, and biomimetic systems for drug delivery and cancer therapy. Her work bridges disciplines such as materials science, nanotechnology, and biomedical engineering. Recent studies involve applications in strain-stiffening hydrogels, filamentous aerogels for electromagnetic shielding, and microfluidic platforms for organoid production. These innovations aim to address challenges in regenerative medicine, environmental sustainability, and personalized cancer treatments. No scientific awards are listed in the provided materials. Research activities include collaborations on 3D-printed microfluidic devices and nanofibrillar hydrogels mimicking biological systems. No advising relationships or grants are explicitly mentioned in the text.
Kenneth Ross is a Professor in the Computer Science Department at Columbia University in New York City. His primary appointment is within the Department of Computer Science, with affiliations including the Foundations of Data Science Committee. His work bridges theoretical database research and practical system implementation. His research focuses on database systems with particular expertise in query processing, query language design, data warehousing, and architecture-sensitive database system design. Additional research spans computational biology, especially analysis of large genomic data sets. Current projects include Linear Algebra Operators in Databases for machine learning workloads and Repeats and Somatic Mutation analysis in genomics. His work consistently addresses the intersection of hardware capabilities and database system design. Ross leads the Database Research Lab at Columbia, which has produced significant work on query optimization, GPU database processing, and hardware-conscious database systems. His recent publications demonstrate strong focus on adapting database systems to modern hardware including GPUs, SIMD processors, and persistent memory. His scientific recognition includes: Packard Foundation Fellowship Sloan Foundation Fellowship NSF Young Investigator Award Distinguished Faculty Teaching Award (2008) Ross actively advises undergraduate engineering students (juniors with last names P-Z) and has taught foundational courses including Introduction to Databases and Programming and Problem Solving for over two decades. His teaching portfolio shows consistent engagement with both theoretical concepts and practical implementation challenges in computer science education.
Academic Profile: Damir Filipovic is a Full Professor and the Swissquote Chair in Quantitative Finance at the College of Management of Technology (CDM) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He previously held academic positions at the University of Vienna, University of Munich, and Princeton University, and served as Head of the Vienna Institute of Finance. Research Focus: Quantitative finance, risk management, stochastic processes, term structure modeling, volatility risk, and machine learning applications in financial markets. Industry Collaboration: Co-developed the Swiss Solvency Test for insurance capital requirements while consulting for the Swiss Federal Office of Private Insurance. Publications: Contributed extensively to journals like Journal of Financial Economics, Mathematical Finance, and Annals of Applied Probability, with a textbook on Term-Structure Models. Academic Service: Editorial board member of multiple journals and organizer of advanced workshops on systemic risk and financial technology. Recent Research: His work emphasizes machine learning for portfolio risk management, kernel-based yield curve estimation, and robust stochastic modeling. Keynote speaker at international conferences on finance and insurance mathematics, with over 15 recent publications in 2023-2025 addressing high-dimensional financial problems, neural control systems, and causal inference in market data. Education: Ph.D. in Mathematics from ETH Zurich (2000). Graduate of ETH Zurich and University of Vienna. Teaching & Mentorship: Supervises current and former EPFL Ph.D. students in quantitative finance, including Nicolas Camenzind, Joshua Hayes, Andrea Ruglioni, and ten others. Former students like Damien Ackerer and Lotfi Boudabsa now lead research in risk management. Labs & Programs: Directs EPFL's Finance and Technology Programme, leads the Computational Finance Group (CSF) at EPFL, and contributes to Swiss Finance Institute initiatives. Scientific Leadership: Served on EPFL Committee of Academic Evaluation and Doctoral Program Finance committee.
Prof. Dr. M. Bjørn von Rimscha is a full-time Professor of Media Economics at the Department of Social Sciences, Media and Sport, Johannes Gutenberg University Mainz. He serves as Prodekan for Studium und Lehre since 2023 and previously held leadership roles including Geschäftsführender Leiter of the Institute for Journalism. His research focuses on media economics, digital transformation, advertising markets, and cross-border media management. Positions: Professor (2015–present), Prodekan (2023–present) Education: Freie Universität Berlin, University of Ulster Key Collaborations: Co-edited De Gruyter Handbook of Media Economics (2024), Handbuch Medienökonomie (2021) His recent work examines digital disruption in media industries, payment models for journalism, and the economic value of creativity. He advises PhD students like Robin Riemann and holds editorial roles at the Journal of Media Business Studies . Contact: b.vonrimscha@uni-mainz.de
Dr. Muhammad Rashed is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, within the College of Engineering. He holds a Ph.D. in Computer Engineering from the University of Central Florida (2024) and a B.S. in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology (2015). Ph.D. : Computer Engineering, University of Central Florida, 2024 B.S. : Electrical and Electronics Engineering, Bangladesh University of Engineering and Technology, 2015 His research focuses on electronic design automation (EDA), in-memory computing, AI acceleration, and sustainable computing. He explores novel computing paradigms to overcome the limitations of traditional architectures, particularly in data-intensive applications such as AI and scientific computing. His work emphasizes hardware-software co-design and leveraging emerging non-volatile memories for energy-efficient processing. The 15 most recent publications highlight a consistent focus on in-memory computing, particularly in path-based and flow-based architectures, logic synthesis, and AI acceleration. Key themes include optimization, fault tolerance, verification, and the use of advanced data structures like sentential decision diagrams. His work is published in top-tier venues such as DAC, ICCAD, ASP-DAC, and IEEE/ACM journals. Scientific Awards: UTA CARES Grant for OER Creation Research Experiences for Undergraduates (REU) Grant Alireza Seyedi Doctoral Research Innovation Endowed Scholarship David T. & Jane M. Donaldson Memorial Scholarship IEEE/ACM William J. McCalla ICCAD Best Paper Award Nomination Best Research Video Award, Design Automation Conference (DAC) Dr. Rashed advises several graduate and undergraduate students in the NextGen Computing Lab and is involved in research grants including the UTA CARES Grant and REU funding. He actively contributes to academic service through roles such as conference TPC member, journal reviewer (e.g., IEEE TCAD, ACM TODAES), and committee participation in the department and college. His lab, the NextGen Computing Lab, is dedicated to building scalable, energy-efficient computing systems for next-generation AI and scientific workloads, aligning with national initiatives in advanced computing.
Kimberly A. Whitler serves as the Frank M. Sands Sr. Associate Professor of Business Administration at the University of Virginia's Darden School of Business. With nearly 20 years of industry experience in general management, strategy, and marketing roles within CPG and retailing sectors, she brings practical expertise to her academic work. Her career includes significant positions at Procter & Gamble, Aurora Foods, David's Bridal, and PetSmart, where she helped build $1B+ brands including Tide, Bounce, Downy and Zest. Dr. Whitler's educational background includes: B.A. in psychology and business administration from Eureka College MBA from the University of Arizona, Eller School of Business M.S. and Ph.D. from Indiana University, Kelley School of Business Whitler is an authority on marketing strategy, brand management, and marketing performance, with particular expertise in understanding how a firm's marketing performance is affected by its C-suite and board. Her research focuses on C-level marketing management challenges, digital marketing transformation, employer branding, and the emerging field of athlete branding through NIL (Name, Image & Likeness). She has authored over 350 articles as a Forbes senior contributor and published in top journals including Harvard Business Review, Journal of Marketing, and Journal of the Academy of Marketing Science. Her award-winning publications demonstrate a consistent progression from traditional brand management topics toward more strategic considerations of marketing's role at the executive and board levels. Recent work examines digital transformation, brand purpose, and the evolving CMO role, reflecting her deep understanding of how marketing strategy intersects with organizational leadership. Whitler has received numerous professional accolades: Ranked as a Top Influencer of CMOs Named Favorite Professor of Top MBA Students in Poets and Quants Winner of Darden's Morton Award in 2018 and 2021 Finalist for Journal of Marketing's 2018 MSI/Paul H. Root Award Winner of 2017 Robert D. Buzzell Best Paper award Winner of 2020 Sheth Foundation Award As an active consultant and speaker, Whitler has worked with numerous C-level organizations including Coca-Cola, McDonald's, U.S. Department of Defense, and Gartner. She has been cited over 3,600 times in major media outlets including Wall Street Journal, New York Times, and Bloomberg, establishing herself as a leading voice in marketing strategy. Her industry experience informs her teaching and research, creating a valuable bridge between academic theory and practical application. Whitler leads research initiatives focused on marketing leadership and brand strategy, often collaborating with industry partners to ensure practical relevance of her work. Her recent projects include examining the impact of marketers on corporate boards and developing frameworks for effective marketing technology implementation, positioning her at the forefront of strategic marketing research.
Melanie Weber is an Assistant Professor of Applied Mathematics and Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS), leading the Geometric Machine Learning Group. Her research focuses on leveraging geometric structures in data for designing efficient machine learning and optimization algorithms with theoretical guarantees. She holds a PhD from Princeton University (2021) and has held fellowships at the Mathematical Institute of Oxford, Brasenose College, and the Simons Institute. Her work bridges geometry, optimization, and machine learning, with funding from NSF, Sloan Foundation, and Harvard initiatives. Education : PhD in Applied Mathematics, Princeton University (2021) BSc/MSc in Mathematics and Physics, University of Leipzig (2016) Research Interests : Dr. Weber's research integrates geometric principles into machine learning and optimization, focusing on non-Euclidean spaces, graph structures, and manifold-based methods. Key areas include optimization on Riemannian manifolds, curvature-based analysis (e.g., Ricci curvature), and developing algorithms resilient to data geometry challenges like over-smoothing in graph neural networks. Her work emphasizes theoretical foundations while addressing practical scalability in high-dimensional data. Awards & Recognition : 2024 Sloan Research Fellowship 2023 Leslie Fox Prize in Numerical Analysis 2023 NSF Grant for Geometric Optimization Grants & Funding : Supported by National Science Foundation (NSF), Alfred P. Sloan Foundation, Aramont Foundation, Harvard Dean’s Fund, and Harvard Data Science Initiative. Labs & Collaborations : Leads the Geometric Machine Learning Group at SEAS, collaborating with institutions like MIT, Max Planck Institute, and industry labs (Facebook, Google, Microsoft). Active in organizing workshops on geometric methods and curvature analysis.
Noah A. Smith is an Adjunct Professor of Computer Science and Engineering at the University of Washington. His work focuses on computational linguistics, machine learning, and natural language processing. He holds a Ph.D. in Computer Science from Johns Hopkins University (2006). His research explores ethical AI applications, multimodal systems, and foundational aspects of language models. Key research areas include: Ethical considerations in NLP, such as detecting rights abuses through text analysis Efficient decoding and alignment strategies for large language models Large-scale evaluation frameworks for multitask and multimodal generation Understanding pretraining dynamics and data composition effects Recent work emphasizes transparency in language models (e.g., tracing outputs to training data) and improving alignment through human feedback. He has contributed to open-source projects like OLMo and Dolma, advancing reproducibility in NLP research. No awards explicitly listed in provided texts. No specific advising or grant details available, though extensive publication output indicates active research involvement.
Clyde Kruskal is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park. His research focuses on parallel architectures, models, and algorithms. He earned a Ph.D. from New York University in 1981 and a bachelor’s degree from Brandeis University in 1976. His work includes foundational contributions to parallel computing, such as the read–modify–write concept in distributed systems. Kruskal’s research spans topics like interconnection networks, synchronization mechanisms, and algorithm design for parallel systems. Education: Bachelor’s Degree: Brandeis University, 1976 Master’s Degree: New York University (Courant Institute), 1978 Ph.D.: New York University (Courant Institute), 1981 Research Interests: Parallel computing architectures, parallel algorithms design, multiprocessor synchronization, interconnection networks, and computational geometry problems like graph coloring and visibility analysis. His work emphasizes theoretical foundations and practical implementations in parallel systems. Notable Contributions: Kruskal co-authored the book Problems With A Point: Exploring Math And Computer Science (2019), and his research includes foundational papers on parallel prefix operations, sparse matrix algorithms, and synchronization protocols. His publications span over three decades, reflecting sustained contributions to parallel computing theory and practice. Advising & Outreach: He has mentored students through programs like the Summer Combinatorial Algorithms REU at UMD, fostering undergraduate research in algorithm design and parallel computing.
