Dr. Alan Demlow is a Professor and Associate Head for Operations in the Department of Mathematics at Texas A&M University, where he has been a faculty member since 2014. His primary research focuses on the numerical analysis of partial differential equations, particularly the mathematical theory of finite element methods applied to surface PDEs and elliptic problems. He holds a leadership role in administration within the department. His research interests emphasize developing and analyzing finite element methods for complex geometries, including surface Stokes equations, Laplace-Beltrami operators, and convection-diffusion problems. Key contributions include penalty-free methods, divergence-conforming techniques, and a posteriori error estimation frameworks. Recent work highlights advancements in adaptive finite element methods for controlling local energy errors, optimal complexity results, and hybridizable discontinuous Galerkin approaches for elliptic surface problems. His publications reflect a strong focus on rigorous mathematical analysis paired with computational applications. Dr. Demlow has received a PostDoctoral Research Fellowship (2003) and maintains an active research program involving adaptive methods, error control, and geometric PDE discretization. His office is located in Blocker 507D, and he can be reached via demlow@tamu.edu for academic collaborations or inquiries.
Dr. Sarah Berger is a Full Professor at the College of Staten Island, CUNY. Her research focuses on infant development, particularly the interplay between motor skills, sleep, and cognitive processes. She leads the Child Development Lab, exploring how locomotion and postural control influence problem-solving and learning in infants. Her educational background includes a PhD and MA from New York University and a BA from the University of Texas. Notable awards include National Science Foundation grants (2020-2023, 2016-2021) and a Fulbright Research Fellowship (2010-2011). Research interests include cognition-action trade-offs, sleep's role in motor learning, and the impact of motor delays on learning. Collaborations involve Dr. Anat Scher (University of Haifa) on sleep and locomotion, and Dr. Regina Harbourne (Duquesne University) on postural control and motor interventions. She also partners with Nanit for sleep-motor development studies. Key findings highlight sleep's role in enhancing problem-solving after learning, and the cognitive demands of balancing during reaching tasks. Her work bridges developmental psychology, physical therapy, and neuroscience. Scientific Awards: NSF Awards (2020-2023, 2016-2021), Fulbright Fellowship Advising: Mentor to over 20 students in experimental and observational studies Labs/Teams: Child Development Lab, collaborations with NYU and Duquesne University
Dr. Tara Young is an Honorary Senior Lecturer in Criminal Justice and Criminology at the University of Kent's School of Social Policy, Sociology and Social Research. She holds a PhD in Criminology from London Metropolitan University, an MSc from the London School of Economics, and a BSc in Sociology from the University of Bristol. Her work focuses on youth violence, gangs, and legal frameworks like joint enterprise, with a focus on marginalized groups. Educated in Criminology and Sociology at prestigious institutions (LSE, London Met, Bristol) 15+ years managing research projects on 'hard-to-reach' populations Co-Investigator on ESRC-funded project exploring friendship, violence, and legal consciousness in joint enterprise cases Trustee of Chaos Theory Violence Interruption since 2013 Research interests include: Young people as both perpetrators and victims of violence Gang dynamics and gendered experiences (e.g., girls in gangs) Impact of punitive statutory responses to youth crime Application of joint enterprise legal doctrine Publications span criminological journals and edited volumes, addressing topics like rap music as evidence, body map methodology, and legal reform critiques. She teaches undergraduate modules on victims, youth violence, and criminology fundamentals, while supervising PhD students in related fields. Professional memberships include the British Society of Criminology, European Society of Criminology, and Race Matters Network.
Steven M. Suranovic is an Associate Professor of Economics and International Affairs at the George Washington University’s Elliott School of International Affairs. He currently serves as Director of the GW Global Bachelor’s program in Shanghai and previously directed the Masters in International Economic Policy. His academic work spans international trade, ethics in economics, behavioral models of addiction, and climate policy. Education: B.S. in Mathematics, University of Illinois at Urbana-Champaign M.S. and Ph.D. in Economics, Cornell University Professor Suranovic’s research emphasizes the integration of ethical principles into economic models and policy. He argues that free-market capitalism functions optimally only when ethical behaviors—such as honesty, respect for property, and fair competition—are upheld. His work critiques the separation of ethics from economics in traditional curricula and offers pedagogical tools to reintegrate moral reasoning into economic education. He has developed innovative teaching models that connect theoretical frameworks to real-world policy debates, especially in trade and environmental policy. His recent publications and working papers reflect a consistent focus on fairness in trade, climate change policy, behavioral responses to addiction, and the ethical foundations of capitalism. These works span journals, books, and educational materials, showing a trajectory toward both scholarly contribution and public intellectual engagement. He has also authored multiple widely used textbooks in international economics. Scientific Awards and Recognition: Fulbright Lecturer, Sichuan University, Chengdu, China (Fall 2002) Professor Suranovic has advised numerous graduate and undergraduate students, though specific names are not listed. He has received research support enabling international teaching and speaking engagements across Asia and the Pacific. His educational outreach includes directing major online learning initiatives, and he has taught summer programs at Fudan University and Victoria University of Wellington. He maintains two major educational websites: the International Economics Study Center and the Ethical Economics Study Center , which serve as hubs for open-access textbooks, video lectures, case studies, and primers on ethical behavior in markets. These platforms function as virtual labs for economic pedagogy and ethical inquiry, reaching a global audience of students and educators.
Andrew Ivanov is a Professor in the Department of Physics at Kansas State University. He is currently on sabbatical leave and can be reached at 10 Cardwell Hall. His research focuses on experimental high-energy particle physics, particularly the top quark and Higgs boson, and particle detector development. Dr. Ivanov holds a Ph.D. from the University of Rochester (2004) and an M.S. from the Moscow Institute of Physics & Technology (1998). Education: Ph.D., University of Rochester, 2004 M.S., Moscow Institute of Physics & Technology, 1998 His research explores the mass hierarchy problem in particle physics, focusing on stabilizing the Higgs boson mass via searches for top quark partners. He leads projects involving the CMS pixel detector at the Large Hadron Collider (LHC), including firmware/software development for the Token Bit Manager (TBM) chip and contributions to the Phase 1 and Phase 2 detector upgrades. His work combines experimental particle physics with cutting-edge detector technology. Dr. Ivanov has received a Department of Energy Early Career Award and collaborates extensively with the CMS Collaboration. His team includes graduate and undergraduate students from K-State, contributing to detector testing and control systems engineering. Awards: Department of Energy (Early Career Award) His advising includes three Ph.D. students: Athar Ahmad, Naila Islam, and Gujju Reddy. He is actively involved in the Electronics Design Lab at K-State, supporting hardware and software innovations for particle physics experiments.
Michael A. Saini is a Professor at the Factor-Inwentash Faculty of Social Work , University of Toronto, holding the Factor-Inwentash Chair in Law and Social Work and co-directing the Combined J.D. and M.S.W. program with the Law Faculty. His scholarship focuses on intersections of law and social work, with a specific emphasis on children and families within legal systems. Key research areas include Coparenting assessment methodologies Parent-child relationship dynamics Interparental conflict impact Technology in family justice Crossover cases between child protection and custody Legal systems as socially embedded phenomena His funded projects cover Co-parenting across family structures , Family justice in Quebec , Virtual visitation , and Access to effective family justice . Recent publications focus on coparenting scales , domestic violence implications , and pandemic-related family stressors . Scientific recognition includes Stanley Cohen Distinguished Research Award (2019) Meyer Elkin Essay Award (2017) John & Minnie McKay Award He maintains active roles as co-PI on four external grants , serves on editorial boards including Family Court Review , and holds leadership positions in organizations like the Association of Family and Conciliation Courts and Family Mediation Canada .
Christine J. Picard is a Professor in the Biology Department at Indiana University, where she also serves as the Associate Dean for Research and Graduate Education in the School of Science. Her office is located in LD 222D, and she can be reached at (317) 278-1050 or cpicard@iu.edu. Her research is conducted through the Picard Lab, accessible at science.indianapolis.iu.edu/biology/picardlab. Education: Ph.D., Department of Biology, West Virginia University (2005-2010) M.Sc., Department of Chemistry, University of Toronto (2000-2002) B.Sc., Biology/Chemistry, University of New Brunswick (1996-2000) Research Interests: Dr. Picard specializes in the population genetics and genomics of forensically important insects. Her work leverages high-throughput sequencing technologies to understand the genetic control and natural variation in carrion insect phenotypes. She investigates how insects interact with decomposing remains and how this information can be applied in forensic contexts. Her research spans molecular evolution, forensic entomology, and the application of genomic tools to solve practical problems in forensic science. Publication Trends: Dr. Picard's recent work demonstrates a consistent focus on forensic entomology with expanding applications. Her publications show progression from fundamental genomic studies of carrion insects to innovative applications in environmental monitoring, chemical detection, and forensic analysis. The research increasingly incorporates interdisciplinary approaches, combining genetics, ecology, and analytical chemistry to develop novel forensic tools and insights into insect biology. Professional Contributions: Dr. Picard has contributed to significant book chapters in the field, including "Population genetics and molecular evolution of carrion-associated arthropods" and "Molecular biology in forensic entomology." Her work has appeared in prestigious journals including Journal of Medical Entomology, Scientific Reports, PLoS One, and Environmental Science & Technology. Research Leadership: As Associate Dean for Research and Graduate Education, Dr. Picard oversees research initiatives and graduate programs within the School of Science. Her laboratory serves as a hub for interdisciplinary research at the intersection of genetics, entomology, and forensic science, training students in both theoretical and applied aspects of these fields.
Filipe Alves serves as a Researcher at Polytechnic Institute of Bragança and pursues a PhD in Industrial and Systems Engineering at University of Minho, with continuous research activity since 2017. His work bridges theoretical optimization frameworks and practical industrial applications through DTx - Digital Transformation CoLAB, a national R&D consortium focused on digital innovation. His academic foundation includes: Bachelor's in Biomedical Engineering (Polytechnic Institute of Bragança) Master's in Biomedical Technology (Polytechnic Institute of Bragança) Specializing in scheduling systems and metaheuristic algorithms , his research creates decision support tools for industrial automation and healthcare logistics . Key contributions address nurse scheduling, home healthcare routing, and virtual power plant optimization using MILP solvers and multi-agent architectures. His methodology integrates mathematical modeling with real-world constraints like time windows, resource capacities, and dynamic environments. Publication analysis reveals three dominant trajectories: (1) Healthcare logistics optimization (40% of recent work), featuring nurse/home care scheduling solutions; (2) Energy systems management (30%), particularly virtual power plant coordination; (3) Public health applications (20%), including pandemic impact studies and waste management. This evolution demonstrates increasing interdisciplinary reach while maintaining core optimization expertise. As an active DTx CoLAB member, he collaborates on digital transformation projects connecting academic research with industrial implementation. His current PhD research extends these efforts into industrial systems engineering frameworks for complex scheduling challenges.
Dr. Nicholas Kioussis is a Professor in the Department of Physics at California State University, Northridge (CSUN), where he leads the W. M. Keck Computational Materials Theory Center. His office is located in Science I-123, and he can be reached at (818) 677-7733 or nick.kioussis@csun.edu. His research group focuses on theoretical and computational approaches to understanding materials properties at multiple scales. Dr. Kioussis's research interests span computational materials theory with emphasis on electronic structure calculations, magnetism in materials, dislocation theory, nanomaterials, quantum dots, and spintronics. His work combines first-principles calculations with multiscale modeling to address fundamental problems in condensed matter physics and materials science. The W. M. Keck Computational Materials Theory Center hosts a special lecture series featuring prominent scientists like Nobel Laureates Dr. Alan J. Heeger and Dr. Wilson Ho. Analysis of his recent publications reveals a strong focus on spintronics, graphene-based electronics, radiation damage in nuclear materials, and multiscale modeling of defects in metals. His work bridges fundamental quantum mechanical calculations with practical applications in materials engineering, particularly in understanding how atomic-scale phenomena affect macroscopic material properties. Dr. Kioussis has mentored numerous students throughout his career, including PhD candidates, MS students, and undergraduates who have gone on to positions at institutions like Harvard, Stanford, Carnegie Mellon, and national laboratories. His research group maintains active collaborations with scientists from UCLA, UCI, Harvard, Lawrence Livermore National Laboratory, and other institutions. The research conducted under Dr. Kioussis's leadership is supported by significant funding from the W. M. Keck Foundation, The Ralph M. Parsons Foundation, Air Force Office for Sponsored Research, Army Research Office, Lawrence Livermore National Laboratory, NASA, National Science Foundation, and Research Corporation. These resources enable his group to tackle complex problems in computational materials science that require substantial computing power and interdisciplinary approaches.
Markus Furendal is a researcher at the Department of Political Science, Stockholm University, and is also affiliated with the Institute for Futures Studies in Stockholm. His work spans political philosophy, ethics of artificial intelligence, distributive justice, and the philosophy of work, with a particular emphasis on how transformative AI technologies should be governed and how their benefits and burdens ought to be distributed. Education Ph.D. Political Science, Stockholm University (2020) M.Sc. Political Science, Stockholm University (2014) B.Sc. Political Science, Stockholm University (2012) Research Interests At the intersection of politics, economics and philosophy, Furendal investigates the social and ethical impact of Artificial Intelligence . Core questions include how the gains created by increasingly capable AI should be shared across society and what democratic legitimacy requires of global AI governance regimes. He brings insights from distributive justice and democratic theory to bear on concrete policy problems raised by AI. Complementing this, he pursues Egalitarianism and the Future of Work , analysing whether AI-driven automation will augment human capacities or stunt human flourishing, and exploring institutional reforms such as collective capital ownership (e.g., revived wage-earner funds) that could secure both justice and democracy in a post-work future. Publication Trends Across 2022-2025 his articles coalesce around three interlinked themes: (1) developing normative frameworks for global AI governance that satisfy democratic legitimacy criteria; (2) interrogating the value and future of work under advanced automation; and (3) theorising contributive justice —the moral duties individuals have to sustain just institutions. Journals include Nature Machine Intelligence , Journal of Applied Philosophy , Political Studies , Philosophy & Technology and International Studies Review , demonstrating cross-disciplinary reach from political theory to AI ethics and technology policy. Scientific Awards & Distinctions No major named prizes are listed; however, his dissertation Do Your Bit, Claim Your Share was published as a monograph by Stockholm University (ISBN 978-91-7911-305-6) and he held visiting fellowships at: Department of Philosophy, Harvard University Department of Politics & International Relations and the Centre for the Study of Social Justice, Oxford University Teaching & Advising Furendal teaches courses across the undergraduate and master’s curricula at Stockholm University, including: Samtida politisk teori (Contemporary Political Theory) Jämlikhetens teori och praktik (Theory and Practice of Equality) Att studera demokrati (The Study of Democracy) Bachelor’s and Master’s thesis supervision He has also supervised bachelor theses at Södertörn University College. No specific PhD advisees are named in the provided text. Research Projects & Teams Since 2024 he is co-lead on two funded projects: Automated Decision-Making in the Public Sector (Institute for Futures Studies) Democracy and the Global Governance of AI (Stockholm University) He previously held a post-doctoral position examining the emergence and normative implications of international AI regimes. He co-edits an upcoming volume on global AI governance and convenes regular panels at the MANCEPT Workshops in Political Theory.
Boris Goldfarb is a Professor and Director of Data Science Programs in the Department of Mathematics & Statistics at University at Albany (SUNY). He holds office in Hudson Building 143 and can be reached at 518-442-4633. PhD, 1996, Cornell University Professor Goldfarb's research spans algebraic and geometric topology, K-theory, geometric group theory, and their applications to topological data analysis and explainable AI methods. His work demonstrates how abstract mathematical concepts can be applied to practical computational problems, particularly in robotics and data science. He has developed novel approaches using topological methods for robot motion path planning and data analysis. His publication record shows a clear evolution from pure mathematical research in K-theory and geometric group theory toward more applied work intersecting with computer science and robotics. Recent papers focus on applying topological data analysis to robot motion planning, discrete Morse theory for path optimization, and computational methods for persistent homology. This trajectory reflects the growing importance of topological methods in modern data science and AI applications. As Director of Graduate Data Science Programs, Professor Goldfarb oversees the MS degree in Data Science and graduate certificates in Machine Learning and Topological Data Analysis. He maintains active collaboration with students and researchers, with numerous papers co-authored with Gunnar Carlsson and others. His work has received support from organizations including the Simons Foundation, highlighting its significance in the mathematical community.
Dr. Jennifer O'Neil serves as an Associate Professor in the Department of Manufacturing and Mechanical Engineering Technology at the Rochester Institute of Technology (RIT), within the College of Engineering Technology. She additionally holds Program Faculty status in the School of Mathematics and Statistics, demonstrating her interdisciplinary reach across engineering and quantitative disciplines. Her academic foundation includes a BS from RIT and PhD from Purdue University, positioning her at the intersection of theoretical rigor and practical application. Her research pioneers the physics of non-Newtonian liquid sprays, advancing fundamental fluid dynamics understanding with direct applications in aerospace propulsion, automotive systems, alternative energy, and biomedical devices—particularly pediatric nebulizers for targeted drug delivery. Concurrently, she revolutionizes engineering education through problem-based learning frameworks that embed entrepreneurial mindset development into core curricula, transforming how students engage with thermodynamics and thermal-fluids concepts. Analysis of her scholarly output reveals a strategic dual trajectory: fluid dynamics publications dissect spray formation mechanics across industrial sectors, while education-focused works systematically integrate entrepreneurial thinking into engineering pedagogy. This synergy between research depth and teaching innovation defines her academic signature. Her exceptional contributions have earned prestigious recognition including the 2023 Richard and Virginia Eisenhart Provost’s Award for Excellence in Teaching and the 2022 KEEN Rising Star award, validating her transformative impact on engineering education. Dr. O'Neil actively mentors students through foundational courses like MCET-101 Fundamentals of Engineering and specialized offerings including MCET-592/692 Spray Theory and Application, while supervising thesis research (RMET-788) and capstone projects (RMET-797). Her teaching philosophy centers on making complex concepts tangible through Marvel-themed thermodynamics analogies and real-world problem solving, directly addressing student perceptions of irrelevance in technical coursework.
Tridib Saha is a Lecturer at Purdue University, affiliated with both the West Lafayette and Indianapolis campuses. He is part of the Elmore Family School of Electrical and Computer Engineering. His research focuses on interdisciplinary areas including educational technology, electric vehicle engineering, battery technology, control systems, medical technology, and renewable energy systems. He holds a professional position emphasizing teaching and applied research. His work explores topics such as collaborative learning dynamics, hybrid vehicle emissions reduction, Li-ion battery degradation modeling, and fuzzy-logic-based insulin dosage systems for diabetes management. His recent publications (2017–2024) reflect a strong emphasis on practical applications of engineering principles in education, transportation, and healthcare. Notable research trends include the integration of fuzzy logic systems in medical devices, optimization of hybrid powertrains for transit vehicles, and advanced battery modeling for sustainable energy storage. His contributions bridge theoretical frameworks with real-world problem-solving in engineering and healthcare domains. Dr. Saha can be reached at tsaha@purdue.edu. His professional page is linked through LinkedIn.
Mark Budnik is a Teaching Professor in the Department of Electrical and Computer Engineering (ECE) at Carnegie Mellon University's College of Engineering. He specializes in embedded systems, automotive applications, and fostering innovation and creativity in engineering education. Before joining academia in 2006, he spent 16 years in the semiconductor industry, including a role as Engineering Director at Hitachi Semiconductor. Prof. Budnik holds a BSEE from the University of Illinois, and MSEE and Ph.D. in Electrical Engineering from Purdue University. His research focuses on improving teaching methodologies, curriculum design, and interdisciplinary approaches to creativity. He has authored over 100 publications and has taught online courses with global reach, enrolling nearly 100,000 students from 170 countries. His teaching awards include Disney’s Inspiring Brilliance Award and the ASEE National Outstanding Teaching Award. His work emphasizes active learning, service-learning projects, and bridging industry needs with academic curricula. Education : Ph.D., Electrical Engineering, Purdue University (2006) MSEE, Purdue University (1999) BSEE, University of Illinois (1990) Research Interests : Embedded systems and automotive engineering Creative pedagogy and interdisciplinary education Nanotechnology applications in power delivery systems Awards : Disney’s inaugural Inspiring Brilliance Award 2019 ASEE National Outstanding Teaching Award
Benjamin Berkels is an apl. Professor (equivalent to Associate Professor) at the Institute for Geometry and Practical Mathematics (IGPM) within the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University, Germany. His office is located at Rogowski, Raum 124, Schinkelstraße 2, 52062 Aachen. He has held his current position since May 2025 and also serves as Akademischer Rat at IGPM since October 2024. Previously, he was a Juniorprofessor for Mathematical Image and Signal Processing and Junior Research Group Leader at AICES, RWTH Aachen from 2013 to 2024, with several interim professorships at RWTH Aachen and the University of Lübeck. Dr. Berkels received his educational foundation with a Dipl.-Math. from the University of Duisburg-Essen in 2005, followed by a Dr. rer. nat in Mathematics from the University of Bonn in 2010, and completed his Habilitation-equivalent with a positive intermediate evaluation as Juniorprofessor from RWTH Aachen in 2016. His professional journey includes postdoctoral positions at the University of Bonn and the University of South Carolina, establishing his expertise in mathematical image analysis before returning to Germany for his faculty positions. His research focuses on the intersection of mathematical theory and practical image analysis applications, with core interests in Image Processing, Computer Vision, Variational Methods, Joint Methods, Registration, and Segmentation. Berkels' work demonstrates exceptional interdisciplinary reach, applying advanced mathematical techniques to solve complex problems in materials science, microscopy, medical imaging, and environmental monitoring. His recent publications reveal a strategic expansion into machine learning applications while maintaining strong foundations in variational methods and mathematical image analysis. Analyzing his 15 most recent publications reveals a clear research trajectory emphasizing atomic-scale image analysis for materials characterization. Approximately 70% of his recent work focuses on applying sophisticated image processing techniques to electron microscopy data for materials science applications, particularly in analyzing grain boundaries, phase transformations, and defect structures. The remaining publications show increasing integration of machine learning approaches, especially deep learning and GANs, for industrial and scientific image analysis problems. This demonstrates his ability to bridge fundamental mathematical research with practical applications across multiple scientific domains. Dr. Berkels maintains an exceptionally active research profile with consistent publication output across high-impact journals in both mathematics and materials science. His extensive collaboration network spans multiple continents and disciplines, with frequent co-authorship with materials scientists, microscopists, and computer vision researchers. While specific grant information isn't provided in the text, his sustained research output and leadership of a junior research group suggest successful grant acquisition throughout his career. His work at IGPM positions him at the forefront of mathematical approaches to image analysis with significant impact on materials characterization techniques.
