Mary K. Firestone is Professor of the Graduate School in the Department of Environmental Science, Policy and Management at UC Berkeley. Her research revolutionized understanding of soil microbial processes, particularly oak regeneration techniques and microbial mediation of carbon/nitrogen cycling in terrestrial ecosystems. Firestone's work examines microbial responses to environmental change, including drought, rewetting, and warming scenarios. Her lab develops innovative methods like quantitative stable isotope probing (qSIP) to trace microbial activity in complex soil environments. Research spans grassland, rangeland, and agricultural ecosystems. Honors include election to the National Academy of Sciences (2017), Soil Ecology Society Career Achievement Award (2017), and American Geophysical Union Fellowship (2016). She holds the Betty and Isaac Barshad Chair in Soil Sciences. Current PhD candidates and postdoctoral researchers in the Firestone Lab investigate viral-bacterial interactions in soil, mycorrhizal carbon transfer, and drought impacts on soil carbon persistence. Field studies at California's Hopland Research Center examine grassland responses to climate variability.
Emil J. Straube is a Professor of Mathematics at Texas A&M University, where he has held the rank of Professor since 1996 and served as Department Head from 2011 to 2019. He earned his Ph.D. in Mathematics from the Swiss Federal Institute of Technology (ETH Zurich) in 1983 under Prof. K. Osterwalder. His research focuses on Several Complex Variables, with emphasis on the ∂-Neumann problem, Bergman kernel, and boundary regularity of solutions to Cauchy-Riemann equations. Education: Ph.D. in Mathematics, ETH Zurich, 1983 Diploma in Mathematics (dipl. math. ETH), ETH Zurich, 1977 Research Interests: His work bridges complex analysis, partial differential equations, and operator theory. Key topics include global regularity of the ∂-Neumann operator, compactness estimates, D’Angelo forms, and the Diederich-Fornaess index. He has contributed foundational results on Sobolev regularity and geometric conditions for subellipticity. Publications & Awards: With over 50 peer-reviewed articles, Straube has authored influential monographs such as Lectures on the L2-Sobolev Theory of the ∂-Neumann Problem . Notable accolades include the Stefan Bergman Prize (1995, jointly with H.P. Boas), AMS Fellow (2013), and Texas A&M’s Distinguished Achievement Award (1998). His work has been supported by NSF grants totaling over $3 million and international collaborations at institutions like the Erwin Schrödinger Institute (Vienna). Service & Leadership: Organized major conferences, including the 2015 Qatar Complex Analysis Conference Edited journals such as Journal of Mathematical Analysis and Applications and Complex Analysis and Its Synergies Guided 7 Ph.D. students and co-mentored numerous postdocs Current Activities: Active in teaching advanced graduate courses (e.g., Complex Variables I/II) and continues research in global regularity theory and CR geometry.
Supratik Chakraborty serves as the Bajaj Group Chair Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. He maintains dual affiliations with the Centre for Formal Design and Verification of Software and the Centre for Liberal Education at IIT Bombay, demonstrating his cross-disciplinary engagement. Professor Chakraborty's research spans formal methods with focus on formal verification, rigorous analysis of system models, and automated synthesis of systems from specifications. His work bridges theoretical foundations with practical applications, particularly in developing mathematically provable guarantees for increasingly complex hardware, software, and intelligent systems. Current research interests include constrained counting and sampling, scalable formal verification of software and hardware systems, automated synthesis of programs and circuits, and applications of automata, logic and finite model theory to practical verification challenges. His publication trajectory shows a significant evolution from traditional hardware and software verification toward addressing verification challenges in machine learning and AI systems. Recent work increasingly focuses on interpretability of black-box models, verification of neural networks, and synthesis techniques applicable to intelligent systems. The research demonstrates strong interdisciplinary connections between formal methods, programming languages, and artificial intelligence. IIT Bombay Excellence in Thesis (CSE) Award 2011 (for Bhargav Gulavani's thesis) IIT Bombay Excellence in Thesis (CSE) Award 2017 (for Abhisekh Sankaran's thesis) Best Paper in Algorithms and Architecture track at IEEE International Conference on Computer Design: VLSI in Computers and Processors, 1998 Professor Chakraborty has successfully supervised 11 doctoral students, with research spanning formal verification techniques, Boolean functional synthesis, constrained counting, and applications to hardware and software systems. His students have gone on to positions at major institutions including Microsoft Research, TCS Research, Georgia Tech, and BARC, reflecting the strong industry and academic impact of his mentorship. Current research directions show increasing emphasis on verification challenges posed by machine learning systems and AI. His research group at IIT Bombay, while not explicitly named in the materials, appears to focus on formal methods with strong connections to the Centre for Formal Design and Verification of Software. The group maintains active collaborations with international researchers including Moshe Y. Vardi at Rice University, and has made significant contributions to verification tools like VeriAbs that bridge theoretical advances with practical applications.
Prof. Igors Gorbovickis is an Associate Professor of Mathematics at the Department of Mathematics, School of Computer Science and Engineering, Constructor University (formerly Jacobs University Bremen). His research focuses on complex dynamical systems, including topics such as renormalization theory, bifurcation analysis, Julia sets, and applications to mathematical physics. He also contributes to discrete geometry, particularly exploring conjectures like the Kneser-Poulsen problem. His work bridges pure mathematics with interdisciplinary applications, emphasizing rigorous analysis of nonlinear systems and geometric configurations. Key areas of investigation include critical point accumulations, Hausdorff dimension estimates, and equidistribution phenomena in parameter spaces. Recent publications highlight advancements in understanding chaotic systems, circle maps, and the interplay between algebraic structures and dynamical behavior. Prof. Gorbovickis collaborates internationally, with co-authored papers appearing in journals like Advances in Mathematics , Ergodic Theory and Dynamical Systems , and Nonlinearity . His office is located at Research I, Room 128 on the Constructor University campus in Bremen, Germany.
Assoc Prof Ng Teng Yong is an Associate Professor at the School of Mechanical & Aerospace Engineering (NTU), specializing in numerical modeling and simulation. With a background as Research Manager at A*STAR Institute of High Performance Computing, his work spans materials science, nanotechnology, and aerospace engineering. Current focus on graphene-based desalination membranes Expertise in molecular dynamics simulations Investigates nanoscale fluid mechanics and structural dynamics Recent publications highlight advancements in energy-efficient electrodialysis, smart robotics, and nonlinear vibration analysis. His interdisciplinary approach integrates computational methods with experimental validation in additive manufacturing and soft material mechanics.
Jason Eisner is a Professor in the Department of Computer Science at Johns Hopkins University's Whiting School of Engineering, with a secondary joint appointment in Cognitive Science. He is affiliated with the Center for Language and Speech Processing (CLSP), the Human Language Technology Center of Excellence, and leads JHU's cross-departmental machine learning group. His research focuses on developing probabilistic modeling, inference, and learning techniques for linguistic structure. Eisner has authored over 100 papers in computational linguistics, particularly in parsing, grammar induction, machine translation, computational phonology, computational morphology, and weighted finite-state methods. He is the lead designer of Dyna, a declarative programming language for AI research that allows concise programs backed by efficiency tricks. Eisner's work centers on novel methods in NLP and machine learning, with emphasis on probabilistic modeling and inference in complex, structured settings. His research program combines computer science with statistics and linguistics to create statistical models that capture linguistic structure and develop efficient algorithms for applying these models to data with minimal supervision or through large pre-trained models. As an ACL Fellow, Eisner has made significant contributions to the field. His recent work (2023-2025) shows a strong focus on large language models, semantic parsing, controlled text generation, model interpretability, and privacy-preserving techniques, continuing his long-standing interest in the intersection of probabilistic modeling and linguistic structure. ACL Fellow He teaches courses including Natural Language Processing (601.465/665), Machine Learning: Linguistic and Sequence Modeling (601.765), Declarative Methods (601.325/425/625), and Selected Topics in Natural Language Processing (601.865). His advising focuses on research students through the Argo research group, with emphasis on fundamental research questions in NLP rather than immediate applied engineering.
Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing
Zach Shahn is an Assistant Professor in the Department of Epidemiology and Biostatistics at the CUNY School of Public Health. He holds a PhD in Statistics from Columbia University and a BA in Mathematics from Stanford University. His research focuses on causal inference methods applied to healthcare data, particularly time-varying treatment effects and critical care applications. He has conducted postdoctoral work at Harvard School of Public Health and previously worked at IBM Research in Healthcare and Life Sciences. Education: PhD in Statistics and Probability, Columbia University BA in Mathematics, Stanford University Research interests include developing causal inference frameworks for evaluating treatment efficacy in dynamic healthcare settings, with a focus on critical care outcomes and methodological innovations. Recent work explores applications of causal diagrams, instrumental variables, and machine learning in healthcare decision-making. His studies often involve large-scale healthcare datasets to assess interventions' real-world impacts. Key trends in his articles include advancements in causal effect estimation under complex treatment regimes, bias analysis in observational studies, and methodological contributions to difference-in-differences and N-of-1 trial designs. His work bridges statistical theory with practical healthcare challenges, emphasizing reproducibility and policy relevance. No scientific awards are listed, though his contributions to causal inference methodologies are notable in academic circles. He has advised on healthcare data projects and published extensively without explicitly listed grants. His professional network includes collaborations with institutions like Harvard and IBM. He is affiliated with CUNY's public health programs and actively engages in academic discourse via Twitter and LinkedIn. His lab or team details are not explicitly mentioned in available materials.
Michael U. Gutmann is a Senior Lecturer in Machine Learning at the School of Informatics, University of Edinburgh, and a member of the Institute for Adaptive and Neural Computation. His research lies at the intersection of machine learning, statistics, and scientific applications, with a focus on developing inference methods for complex and implicit models. Education: PhD in Computational Neuroscience, University of Tokyo MSc in Engineering and Applied Mathematics, Swiss Federal Institute of Technology (ETH) Zurich MSc, Ecole Centrale Paris His primary research interests include Bayesian inference, likelihood-free inference, optimal experimental design, unsupervised learning, and applications in computational biology and neuroscience. He is best known for introducing Noise-Contrastive Estimation (NCE), a foundational technique for training unnormalized statistical models. His recent work spans variational inference, density ratio estimation, flow models for missing data, and AI-driven experimental design in behavioral and biological sciences. His publications, including in NeurIPS , ICML , JMLR , and eLife , demonstrate a strong emphasis on methodological innovation for scientific discovery. He has contributed to open-source tools such as ELFI (Engine for Likelihood-Free Inference) and developed practical implementations of robust inference algorithms. Scientific Awards: No specific awards listed in the provided texts. Michael Gutmann actively supervises students and collaborates with leading researchers in machine learning and computational biology. He has secured research funding from EPSRC and BBSRC for projects in generative modeling and infectious disease epidemiology. He teaches advanced courses such as Probabilistic Modelling and Reasoning and Data Mining, reflecting his deep engagement with both theoretical and applied aspects of machine learning. Labs and Research Groups: Institute for Adaptive and Neural Computation (ANC), University of Edinburgh Former affiliations with Department of Mathematics and Statistics and Department of Computer Science at the University of Helsinki and Aalto University
Dr. Shuo Zhang is an Assistant Professor in the Department of Physics & Astronomy at Michigan State University's College of Natural Science. Her research focuses on observational high-energy astrophysics and particle astrophysics, with particular emphasis on supermassive black holes, Galactic cosmic-ray origins, and large dataset analysis. As a member of the Event Horizon Telescope collaboration, she leads X-ray observation campaigns of the Galactic center supermassive black hole and its vicinity. Dr. Zhang received her educational training at prestigious institutions: Ph.D. in Physics, Columbia University, 2016 B.S. in Engineering Physics, Tsinghua University, 2010 Her research interests span observational high-energy astrophysics and particle astrophysics, focusing on supermassive black holes including Sgr A* flaring activities, outburst history, and radiation in quiescence. She investigates Galactic cosmic-ray origins and exotic physics, particularly TeV electrons and PeV protons pointing to Galactic PeVatrons. Her work constrains MeV-GeV proton/electron populations in the central 1 kpc of the Galaxy and examines supernova remnant and molecular cloud interaction sites. Dr. Zhang's recent publications reveal a strong emphasis on multi-messenger astronomy, combining neutrino, X-ray, and radio observations to understand cosmic particle acceleration. Her work spans from Galactic center studies of Sgr A* to extragalactic investigations of active galactic nuclei like M87. The research demonstrates increasing sophistication in analyzing complex datasets from multiple observatories including IceCube, ALMA, NuSTAR, and Chandra. Her notable scientific achievements include: NASA Hubble/Einstein Fellowship at Boston University (2019-2020) Heising-Simons Fellowship at MIT (2016-2019) NASA Earth and Space Science Fellowship for research on Galactic center supermassive black hole Dr. Zhang's career path demonstrates a steady progression from her doctoral work at Columbia University through prestigious postdoctoral fellowships to her current faculty position. She has developed significant expertise in X-ray observations using the NuSTAR space telescope and has been instrumental in Galactic plane survey campaigns. Her research group combines high-energy photon and neutrino signals from PeVatron candidates to address fundamental questions about cosmic-ray origins and particle acceleration mechanisms. As a member of the Event Horizon Telescope collaboration, Dr. Zhang contributes to cutting-edge research on black hole physics, utilizing multi-wavelength observations to understand accretion, feedback, and particle acceleration mechanisms around supermassive black holes. Her work bridges observational astronomy with theoretical astrophysics to address some of the most fundamental questions in modern astrophysics.
Xavier Brusset is a Professor in Supply Chain at SKEMA Business School since 2016, where he also serves as Director of the PRISM Research Center since 2017. Previously, he held professorial positions at Toulouse Business School (2015-2016) and ESSCA School of Management (2009-2015), where he was responsible for the Master 2 in Purchasing and Supply Chain Management program. His academic journey includes a PhD in Management Sciences from Université Catholique de Louvain (2010) and a Habilitation à Diriger des Recherches from Université Paris Ouest Nanterre La Défense (2016). His research spans multiple critical areas in supply chain management, with particular focus on supply chain resilience, blockchain applications, weather risk management, and pandemic impacts on supply chains. Brusset has developed innovative approaches to understanding how supply chain partners interact, how information affects their behavior, and how external disruptions like weather anomalies and pandemics impact operational efficiency. His work bridges theoretical models with practical applications, often developing decision-support tools for managers facing complex supply chain challenges. Brusset's publication record shows a clear evolution of research interests, beginning with foundational work on supply chain contracts and information sharing, then expanding to weather risk management, and most recently focusing on pandemic disruptions and blockchain applications. His 15 most recent publications (2018-2025) demonstrate increasing sophistication in modeling complex supply chain phenomena, with particular emphasis on network effects, ripple effects, and multi-echelon optimization under disruption scenarios. Editorial board member of Logistics Research Editor of International Journal of Retail and Distribution Management (2022-2023) Recognized EU expert for CINEA research projects evaluation Organizer of the Colloquium on European Research in Retailing (CERR) Reviewer for multiple top journals including International Journal of Production Economics As an advisor, Brusset has supervised doctoral students including R. Alkhudary (co-director, Université Paris 2 Panthéon-Assas) and V. Capocasale (rapporteur). His professional experience extends beyond academia to include industry roles in financial markets and logistics technology, having co-founded WebLogistix, a platform for sharing logistics information in Argentina. His research has practical applications across multiple sectors, particularly in retail, food supply chains, and manufacturing, where he develops tools to help managers mitigate risks and optimize operations under uncertainty.
Qimin Liu is an Assistant Professor at Boston University, serving as Lab Director of the Quantitative Psychopathology Laboratory. He holds a PhD in Psychological Sciences from Vanderbilt University with specializations in Clinical Science and Quantitative Methods. His research focuses on emotional disturbances across development, statistical methodology development, and health equity with an emphasis on intersectional marginalization. He has expertise in analyzing intensive longitudinal data and has published extensively on topics like irritability, suicidality, and mental health disparities among sexual and gender minority populations. Dr. Liu’s work frequently integrates advanced statistical techniques such as latent variable modeling, network analysis, and machine learning. His recent studies explore the temporal dynamics of affect, the impact of stigma on mental health, and the role of childhood adversity in psychiatric outcomes. Notable contributions include developing methods for analyzing zero-inflated longitudinal data and creating algorithms for digital phenotyping of mood disorders through mobile device usage patterns. His scholarship emphasizes bridging clinical phenomena with rigorous quantitative approaches, addressing gaps in understanding how social determinants and individual differences shape mental health trajectories. He has collaborated on large-scale datasets like the Collaborative Psychiatric Epidemiological Surveys and contributed to interdisciplinary research on public health outcomes among aging sexual minority men. Dr. Liu’s methodological innovations include the DACF framework for ceiling/floor effect data and the lamme package for log-analytic multiplicative effects modeling. He actively publishes in high-impact journals such as Psychological Methods and Journal of Abnormal Psychology , focusing on both empirical findings and statistical best practices.
Huiyan Sang is a Professor and Director of the Undergraduate Program in the Department of Statistics at Texas A&M University (College of Arts & Sciences). She earned her Ph.D. in Statistics from Duke University and a B.Sc. in Mathematics and Applied Mathematics from Peking University. Her research focuses on spatial statistics, Bayesian nonparametric methods, machine learning, computational statistics, and applications in environmental sciences, geosciences, urban planning, and biomedical research. Her interdisciplinary work integrates statistical methodologies with real-world challenges, such as analyzing extreme environmental events, optimizing urban infrastructure, and modeling complex systems like human mobility during pandemics. She has contributed to advancing spatio-temporal modeling, Gaussian processes, and Bayesian hierarchical frameworks for large datasets. Recent publications highlight innovations in nonparametric regression, spatial functional data analysis, and stochastic frontier analysis, often leveraging computational efficiency and scalability. Her work addresses critical societal issues, including the impact of community design on public health and environmental monitoring through remote-sensing data. No scientific awards are explicitly listed in the provided texts. She advises no students or grants in the current dataset but collaborates widely on interdisciplinary projects. Her research lab focuses on developing cutting-edge statistical tools with applications in engineering, public health, and environmental science.
Stefano Fusi is an Associate Professor of Neuroscience at Columbia University's Vagelos College of Physicians and Surgeons, with joint affiliations at the Mortimer B. Zuckerman Mind Brain Behavior Institute and Kavli Institute. His laboratory focuses on computational modeling of neural circuits and neuromorphic engineering. Education PhD in Physics, Hebrew University of Jerusalem (1999) BS in Physics, Sapienza University of Rome (1992) Research Focus Fusi investigates how biological complexity supports neural computation through three primary domains: theoretical analysis of neural circuit dynamics, representational geometry in learning systems, and hardware implementations of brain-inspired algorithms. His work bridges machine learning, neurophysiology, and theoretical physics, emphasizing high-dimensional representations and memory optimization. Recent publications demonstrate consistent focus on neural coding principles across hippocampus, prefrontal cortex, and sensory systems, with innovations in modeling working memory, stress responses, and cross-species computational paradigms. Collaborations & Labs Leads an interdisciplinary laboratory collaborating with Columbia experimental neuroscientists, MIT engineers, and Stanford computational researchers to validate theoretical models. Current projects include neuromorphic hardware development and neural decoding of emotional states.
Jelle Hellings is an Assistant Professor in the Department of Computing and Software at McMaster University , Canada. His research focuses on high-performance large-scale data management systems with a strong theoretical and algorithmic component, including resilient systems (blockchains) , graph databases , and external-memory algorithms . He previously worked as a Postdoc Scholar at the University of California, Davis and earned his PhD from Hasselt University in Belgium. Education: Doctor of Sciences in Computer Science (2018), Hasselt University Master of Science in Computer Science and Engineering (2011), Eindhoven University of Technology His research interests include scalable resilient systems with Byzantine fault tolerance, database theory, graph query languages, constraints on graph data, and external-memory algorithms for large graph datasets. He has authored numerous high-impact publications on blockchain-based resilient systems, query optimization in graph databases, and theoretical advancements in relation algebra expressiveness. Hellings actively contributes to academic service through program committee memberships and tutorial organization, and he currently teaches courses on future resilient databases and foundational computer science topics.
