Owen O'Donnell is a Full Professor of Applied Economics at the Erasmus School of Economics and Erasmus School of Health Policy & Management , Erasmus University Rotterdam. He is a Research Fellow at the Tinbergen Institute and an Editor of the Journal of Health Economics . Education: DPhil, University of York (UK) Research Interests: His work focuses on health economics, including health inequality, health insurance, financial protection, and labor economics, with applications in low-, middle-, and high-income countries. He employs cross-sectional analyses, equity-sensitive frameworks, and global health policy evaluation. Publications & Collaborations: He has published extensively in top journals like Lancet , PLOS Medicine , and Demography , addressing cardiovascular disease, hypertension screening, and healthcare access in India, Philippines, and Sub-Saharan Africa. His recent research emphasizes equity-sensitive welfare weights and treatment gain inequality. Professional Activities: He has served as an Editor for the Journal of Health Economics since 2007 and is actively involved in peer-review and editorial work until 2026.
Dr. Kaitlyn Zhou is an incoming Assistant Professor in the Department of Information Science at Cornell University's Bowers College of Computing and Information Science, commencing August 2026. Her research focuses on human-language model interaction dynamics, with recognition at premier NLP and HCI conferences. Education: PhD in Computer Science, Stanford University (Advised by Dan Jurafsky) B.Sc., B.Se., M.S. in Computer Science and Human Centered Design and Engineering, University of Washington Research Focus: Her work investigates how language models shape human decision-making through three pillars: (1) identifying model overconfidence risks, (2) developing context-aware evaluation frameworks, and (3) reimagining interactions for marginalized user groups. This spans NLP, HCI, and AI ethics with emphasis on trust calibration and inclusive design. Publication Trends: Recent work examines human reliance on unreliable language models (NAACL 2025 Best Paper Runner-Up), uncertainty expression failures (ACL 2024), and evaluation metric limitations. Collectively, these advance responsible AI through human-centered methodologies across 12 major publications from 2017-2025. Scientific Awards: NAACL Best Paper Runner-Up (2025) MIT EECS Rising Star (2024) Stanford Graduate Fellowship College of Engineering Dean's Medal School of Engineering Dean's Medal of Excellence (UW) Advising & Grants: Supported by Stanford Graduate Fellowship and research internships at Microsoft Research FATE (hosted by Olteanu/Blodgett) and Allen Institute for AI (hosted by Sap/Hwang/Ren). Will recruit NLP/HCI students at Cornell starting 2026. Appointed by Washington Governor to UW Board of Regents, advocating for educational equity. Research Ecosystem: Collaborates with Stanford's NLP group, Microsoft's FATE team, and Allen Institute researchers. Features in NYT/WSJ for methods impacting real-world AI deployment.
Steve Worthington is an Adjunct Professor at Swinburne University of Technology's School of Business, Law and Entrepreneurship in Melbourne, Australia. Previously, he held a Professorship in Marketing at Monash University until 2013. His research focuses on financial services distribution via electronic channels, payment systems regulation, and cross-cultural financial practices. He has contributed extensively to academic journals such as the International Journal of Bank Marketing and practitioner outlets like The Financial Times and ANZ's BlueNotes . His work includes analyses of payment card markets in China and Japan, regulatory interventions in Australia's payment system, and expert testimony on financial services marketing disputes. Worthington's research interests span payment systems innovation, consumer behavior in financial contexts, and the intersection of technology with financial services. Notable contributions include studies on cashless society debates, payment fraud dynamics, and the cultural dimensions influencing financial product adoption. He frequently engages with industry conferences and media, offering insights into emerging trends like digital payment adoption and regulatory challenges in the financial sector. His publications highlight interdisciplinary approaches, blending economic policy analysis with marketing strategy. Key themes include the societal implications of cash decline, fairness perceptions in financial services, and the ethical dimensions of payment technologies. Despite no explicit awards listed, his prolific media engagement and advisory roles reflect recognition of his expertise in payment systems and financial markets.
Mariana Resener is an Assistant Professor in the School of Sustainable Energy Engineering at Simon Fraser University (SFU). She holds a Ph.D. in Electrical Engineering (2016) from the Federal University of Rio Grande do Sul, Brazil, alongside M.Sc. (2011) and B.Sc. (2008) degrees in the same field. Her research focuses on optimizing power systems, particularly in distributed energy resources, energy storage, and volt/var control. She teaches courses like Power Electronics and Power Systems Analysis & Design. Her work emphasizes sustainable development in grid planning and energy infrastructure. As a Senior Member of IEEE and an Associate Editor for the Energy Systems Journal (Springer), she contributes to advancing smart grid technologies and renewable integration. Her research spans metaheuristic optimization, stochastic modeling, and grid resilience strategies for distributed systems. Recent projects include hybrid renewable energy systems for substations, EV charging station optimization, and fault analysis in unbalanced grids. She collaborates with industry on practical solutions for grid modernization and reliability enhancement.
Keval Vora is an Associate Professor at the School of Computing Science, Simon Fraser University. His research focuses on scalable solutions for modern data analytics systems, particularly in graph processing and distributed computing. He leads the Parallel Data and Computing Lab (PDCL), developing systems like Peregrine , GraphBolt , and GraphBolt . Contact: TASC1 9419, keval@sfu.ca. Education: PhD in Computer Science from the University of California, Riverside (2017). Previously worked at Morgan Stanley on low-latency trading software. Teaching: Courses include Distributed Systems (CMPT 431) and Special Topics in Networks and Systems (CMPT 982). Advises graduate and undergraduate students on projects involving distributed systems and graph analytics. Research Interests: Parallel/Distributed Computing, Irregular Big Data Processing, High-Performance Computing. His work emphasizes efficient techniques with provable guarantees for large-scale systems. Software Contributions: Peregrine (pattern-based analytics), GraphBolt (dynamic graph processing), and Lumos (disk-based graph processing). These systems address challenges in scalability, efficiency, and real-time data handling.
Prof. Nicolas Perkowski is a Professor in the Department of Mathematics at Freie Universität Berlin, specializing in Stochastic Analysis and Probability Theory. He holds roles such as Vice Spokesperson of DFG CRC/TRR 388 (since 2024) and Chair of the Master of Mathematics Examination Board. His research focuses on stochastic partial differential equations (SPDEs), rough paths, and applications in mathematical physics. Notable contributions include work on singular SPDEs, fractional processes, and the KPZ equation. Perkowski has authored/co-authored numerous publications in top journals like the Annals of Probability and Communications in Mathematical Physics, and he serves as an associate editor for several journals. His institutional responsibilities include leadership in research collaborations and academic governance. Education: PhD and diploma in stochastic population models (details not explicitly provided in text). Research Interests: Stochastic Analysis, Probability Theory, SPDEs, Mathematical Physics, Nonlinear Filtering. Recent Articles: Focus on fractional processes, SPDEs with singular terminal conditions, and stochastic sewing lemmas. His work bridges theoretical probability with applications in physics and engineering, with a strong emphasis on rigorous mathematical frameworks for complex stochastic systems.
Adam Finkelstein is a Professor in the Department of Computer Science at Princeton University, where he has been a faculty member since 1997. He holds a PhD and Master's in Computer Science from the University of Washington and a dual degree in Physics and Computer Science from Swarthmore College. Finkelstein is renowned for his interdisciplinary work at the intersection of computer graphics, audio processing, and machine learning, and he co-organized the Art of Science exhibition at Princeton. Education: PhD, Computer Science, University of Washington MS, Computer Science, University of Washington BA, Physics and Computer Science, Swarthmore College His research spans audio processing (e.g., speech enhancement, voice conversion, audio metrics), computer graphics (e.g., line drawing algorithms, stylized rendering, image manipulation), and machine learning (e.g., self-supervised learning, differentiable programming). His work often bridges technical and creative domains, exemplified by collaborations at Pixar and Adobe Creative Technologies Lab. Recent publications highlight advancements in audio super-resolution and voice conversion using deep learning frameworks, as well as stylized line rendering for animated 3D models. His contributions to perceptual audio metrics and shader optimization further underscore his impact on human-centric computational systems. Scientific Awards: NSF CAREER Award Alfred P. Sloan Fellowship Fellow of the Association for Computing Machinery (ACM) Finkelstein has secured foundational grants for his research and actively mentors students, though no specific advisees are listed. He also explores collaborative tools for internet music performance, reflecting his broader interest in distributed systems and user interfaces.
Haryadi S. Gunawi is a Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). His work focuses on improving the dependability of storage and cloud computing systems, with a particular emphasis on addressing performance stability, reliability, and scalability challenges in modern computing environments. Dr. Gunawi received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. Following his doctoral studies, he was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012 before joining the University of Chicago faculty. His research focuses on three main areas: (1) performance stability, where he builds storage and distributed systems robust to latency tails and "limping" hardware; (2) reliability and scalability, where he addresses concurrency and scalability bugs in cloud-scale distributed systems; and (3) the intersection of machine learning and systems, exploring how machine learning techniques can solve operating and storage system problems. His work often combines theoretical insights with practical system implementations that address real-world challenges in cloud and storage infrastructure. Dr. Gunawi's publication record shows a consistent focus on storage and cloud system reliability, with recent work increasingly incorporating machine learning techniques to address traditional systems challenges. His research spans the full stack from hardware interfaces to distributed system design, with a strong emphasis on practical solutions that can be deployed in production environments. His work often involves close collaboration with industry partners to ensure real-world relevance and impact. Dr. Gunawi has received numerous prestigious awards including the NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, multiple NetApp Faculty Fellowships, and an Honorable Mention for the 2009 ACM Doctoral Dissertation Award. He has also received the Provost's Global Faculty Award and Facebook Faculty Research Award, highlighting the broad recognition of his contributions to the field. As an advisor, Dr. Gunawi has mentored several PhD students including Ruidan Li, Ray Andrew, Rani Ayu Putri, and William Nixon. His research has been supported by major grants from NSF, Google, Facebook, and NetApp, enabling his team to pursue ambitious research projects at the intersection of systems, storage, and machine learning. Dr. Gunawi leads the UCARE research group at UChicago, which focuses on improving the dependability of storage and cloud-scale distributed systems. He is also involved with the Chameleon cloud research infrastructure project and the broader Systems Group at UChicago, contributing to a vibrant research community focused on systems, programming languages, and software engineering.
James King is an Associate Professor in the Department of Geography at the University of Montreal, specializing in geomorphology and aeolian processes. His research focuses on wind erosion, mineral dust dynamics, and their impacts on climate and ecosystems, particularly in high-latitude regions like the Yukon and Namibia. He holds a BSc in Earth surface sciences from the University of Guelph and a PhD in Physical Geography from an unmentioned institution, with postdoctoral training in climatology. Key research areas include dust emission climatology, glacial retreat impacts, and aerosol-climate interactions. Over 20 ongoing and completed projects, including dust dynamics in proglacial valleys and high-latitude dust sources, are funded by CRSNG, FCI, and international collaborations. King supervises graduate students on topics like dust deposition effects on ecosystems and remote sensing applications. His work integrates field measurements, remote sensing, and climate modeling to advance understanding of dust processes in arid and semi-arid environments. He collaborates with global teams, such as the Hominin Dispersals Research Group, and contributes to initiatives like the Changing Atmospheric Chemistry, Transport, and Emissions (ACTE) project.
Benjamin C. Lee is a Professor at the University of Pennsylvania, affiliated with both the Department of Electrical and Systems Engineering and the Department of Computer and Information Science. He also serves as Associate Department Chair and Co-Director of the NSF Expedition in Computing: Carbon Connect. His research spans computer architecture, energy efficiency, and environmental sustainability, with interdisciplinary links to machine learning and algorithmic economics. Education: Ph.D. and S.M. from Harvard University, B.S. from UC Berkeley, postdoctoral work at Stanford University. His research integrates computer architecture with datacenter-scale systems, focusing on energy-efficient designs, statistical learning for performance analysis, and sustainable computing. Past projects include the Hound framework for straggler diagnosis in datacenters and the CORE library for regression modeling in microarchitecture. The 15 most recent articles reflect expertise in datacenter architecture, mobile computing, and regression modeling for hardware. Awards include IEEE Fellow (2024), ACM Distinguished Scientist (2019), and multiple best paper/prize recognitions from SIGMETRICS, ASPLOS, and HPCA. Doctoral and Masters alumni have pursued roles at institutions like Meta, Microsoft, and University of Waterloo. Current affiliations include the Distributed Systems Laboratory (DSL) and PRECISE center, with industry collaborations at Google, Meta, and Intel.
Claudio Canizares is a University Professor and Hydro One Endowed Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. He also serves as Executive Director of the Waterloo Institute for Sustainable Energy (WISE). With a career spanning over 30 years, his research focuses on power systems stability, smart grids, microgrids, and renewable energy integration. He has secured nearly $118 million in grants and supervised 180+ researchers/students. Education: PhD (1991) and MSc (1988) in Electrical Engineering from University of Wisconsin-Madison; Electrical Engineering Diploma (1984) from Escuela Politécnica Nacional, Ecuador. Research Interests : Nonlinear systems theory, FACTS/HVDC applications, energy storage systems, microgrid stability/control, renewable integration in remote communities, and smart grid analytics. His work emphasizes bridging academic research with industrial applications through collaborations with utilities and tech firms. Key Achievements : IEEE Transactions on Smart Grid Editor-In-Chief; multiple IEEE Fellowships (IEEE, Royal Society of Canada, Canadian Academy of Engineering); 2017 IEEE PES Outstanding Educator Award; 2016 IEEE Canada Electric Power Medal. His publications (370+) include landmark papers on microgrid stability definitions and control frameworks, cited over 29,000 times. Teaching: Recently taught ECE 140 (Linear Circuits), ECE 467 (Power Systems Analysis), and graduate courses ECE 6601PD/ECE 6613PD on power systems modeling and analysis.
Atreyi Kankanhalli is a Professor at the National University of Singapore, specializing in Information Systems with a focus on knowledge management, healthcare IT, and digital innovation. Their research spans over three decades, with prolific contributions in top journals like MIS Quarterly, Journal of AIS, and Information & Management. They have co-authored over 150 papers addressing topics such as crowdsourcing, online communities, and the impact of AI on scholarly practices. Notable work includes studies on user adherence to health apps, innovation in public sector data utilization, and the ethical challenges of generative AI in peer review. Kankanhalli has also led research on global virtual teams and digital technologies' role in social justice, reflecting a commitment to both technical and societal dimensions of information systems. Education & Background: While specific degree details are not provided, their extensive publication history and academic roles imply advanced qualifications in Information Systems or related fields. They have collaborated with global researchers across institutions like NUS, University of Illinois, and Singapore Management University. Research Themes: Core areas include digital health interventions (e.g., fitness app adherence), organizational innovation via open data and crowdsourcing, and the socio-technical challenges of AI in academia. Their work often bridges theoretical frameworks with practical applications, such as healthcare decision support systems and policy-driven technology adoption. Impact & Influence: As an editorial board member and frequent conference contributor (e.g., ICIS, PACIS), Kankanhalli shapes the field's research agenda. Their recent focus on generative AI's implications highlights proactive engagement with emerging technologies' ethical and methodological challenges.
Sibel Pamukcu is a Professor in the Department of Civil and Environmental Engineering at Lehigh University, affiliated with the P.C. Rossin College of Engineering and Applied Science. Her research focuses on electroremediation of soils and groundwater, advanced geo-materials, and sensor systems for subsurface monitoring. She has held leadership roles including interim Chair of Civil and Environmental Engineering and co-director of Lehigh’s ADVANCE grant. Pamukcu teaches courses in geotechnical and environmental engineering at both undergraduate and graduate levels. Education: Ph.D. in Civil Engineering, Louisiana State University M.S. in Civil Engineering, Louisiana State University B.S. in Civil Engineering, Bogazici University, Turkey Research Interests: Her work spans electrochemical methods for environmental detoxification, development of polymer-enhanced sands, and distributed sensor networks for real-time subsurface monitoring. Key focus areas include: In-situ destruction of contaminants in clay-rich soils Enhanced oil recovery via electric fields Wireless and fiber-optic sensor systems for hazard mitigation Grants & Awards: Principal investigator on over 60 grants from NSF, DOE, DOD, and others. Notable awards include the Alfred Noble Robinson Award (Lehigh University) and the ASCE Civil Engineering Foundation Grant Award. Her lab holds multiple patents on soil remediation techniques and polymer-coated sands. Advising & Service: Supervised 9 Ph.D. and 25+ M.S. students Appointed to Martindale Center faculty and Sigma Xi leadership Contributed to 50+ peer-reviewed articles and 100+ technical publications Labs & Teams: Leads interdisciplinary initiatives in electrokinetic remediation and underground sensing, collaborating with industry partners like Electric Power Research Institute and the Pennsylvania Department of Transportation.
Bilal Farooq is an Associate Professor and Program Director for the Master of Engineering in Interdisciplinary Engineering (MEIE) at Toronto Metropolitan University, holding the Canada Research Chair in Disruptive Transportation Technologies and Services within the Department of Civil Engineering. His educational background includes a PhD from the University of Toronto (2011), MASc from Lahore University of Management Sciences (2004), and BSc from the University of Engineering and Technology (2001). Dr. Farooq's research pioneers disruptive transportation solutions through cyber-physical systems, AI/machine learning applications, behavioral modeling, and optimization techniques. His work specifically targets on-demand multimodal systems, sustainable urban transportation, urban air mobility, automated vehicles, and extended reality applications, addressing critical urban mobility challenges with human-centered approaches. Analysis of his recent publications reveals a strong trend toward quantum-enhanced computational methods, privacy-preserving federated learning frameworks, and sustainability-focused decarbonization strategies across transportation domains, with increasing emphasis on human factors and real-world implementation. Notable scientific awards include: Ontario Early Researcher Award (2018) Canada Research Chair (2017) MassMotion Academic Pedestrian Modelling Project of the Year (2016) Québec Early Researcher Award (2014) Dr. Farooq actively supervises graduate students and secures significant research funding through his Canada Research Chair position and Early Researcher Awards. He directs the Laboratory of Innovations in Transportation (LiTrans), which develops interdisciplinary solutions integrating mathematics, engineering, computer science, and economics to address emerging transportation challenges. LiTrans focuses on disruptive transportation technologies, complete streets design, cyber-physical systems, pedestrian dynamics, resilience, and climate change impacts, collaborating with industry and government partners to translate research into practical urban mobility innovations for smart cities worldwide.
Kevin A. Shinpaugh is Collegiate Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech’s College of Engineering. Since 2019 he has led instruction and research in spacecraft design and propulsion, leveraging decades of experience in high-performance computing and space-systems engineering. Education Ph.D., Aerospace Engineering, Virginia Tech (1994) M.S., Aerospace Engineering, Virginia Tech (1989) B.S., Aerospace Engineering, Virginia Tech (1986) Research Focus Dr. Shinpaugh’s scholarship centers on the intersection of high-performance computing (HPC) and space systems engineering . He develops and applies advanced computational techniques to spacecraft design, propulsion analysis, and mission planning. His work spans numerical simulation of complex aerospace systems, optimization of propulsion architectures, and creation of scalable HPC frameworks that enable rapid design iteration for spacecraft and launch vehicles. Publication Trends Across more than thirty refereed papers and design-competition reports, a clear trajectory emerges: early contributions in experimental fluid-mechanics instrumentation (laser-Doppler velocimetry, fiber-optic sensors) evolved into large-scale computational studies of space systems, and most recently into student-led mission-concept designs for CubeSats, lunar exploration, and interplanetary missions. Keywords consistently include spacecraft design, propulsion, deployable structures, and mission architecture. Service & Committees Chair, Virginia Tech HPC User Committee (2004–2011) Member, VT HPC Advisory Board (2007–present) NSF TeraGrid/XSEDE Campus Champion for Virginia Tech (2006–2013) IBM HPC/AI Customer Advisory Council DC (2019–present) Member, VT AOE Seminar Committee (2019–present) Laboratory & Computing Resources Dr. Shinpaugh has long stewarded Virginia Tech’s high-performance computing ecosystem. He directs students and collaborators in leveraging the university’s Advanced Research Computing (ARC) clusters, as well as national facilities through XSEDE and DoD HPCMP, to execute spacecraft-design simulations and propulsion analyses at scale.
