David Rohrlich is a Professor of Mathematics and Statistics at Boston University, serving as Director of Graduate Studies. His primary affiliation is with the Department of Mathematics and Statistics. He specializes in Number Theory, focusing on topics such as Artin representations, arithmetic statistics, and Galois theory. His research explores areas including algebraic number theory, arithmetic geometry, and representation theory. Notable contributions include studies on self-dual Artin representations, quaternionic structures in arithmetic statistics, and the interplay between Galois representations and L-functions. Rohrlich has published extensively on topics such as Mordell-Weil groups, average multiplicities, and dihedral Artin representations. His work often involves intricate connections between algebraic structures and number-theoretic phenomena. He holds a PhD and has advised numerous graduate students (though specific names are not listed here). His office is located in CDS 433, with regular in-person and virtual office hours.
Kyle C. Hale is an Associate Professor at Oregon State University's School of Electrical Engineering and Computer Science (College of Engineering). He holds a Ph.D. and M.S. from Northwestern University (2016, 2013) and a B.S. in Computer Science from UT Austin (2010). Prior to joining Oregon State in 2024, he served as an Associate Professor at Illinois Tech in Chicago. His research spans operating systems, high-performance computing (HPC), virtualization, computer architecture, and system security. Current work focuses on specialized system software stacks for emerging computing paradigms like memory disaggregation and parallelism optimization. He leads the HExSA Lab and collaborates with the HiPCastor group. Scientific Awards: NSF CAREER Award (2023-2028) Illinois Tech College of Computing Excellence in Research (2023) Illinois Tech College of Computing Excellence in Teaching (2021) Illinois Tech Department of Computer Science Teacher of the Year (2020) EuroSys '22 Best Artifact Award Recent Research Trends: His publications emphasize compiler techniques for memory-disaggregated systems, optimizing parallel runtimes through hardware-software integration, virtualization at fine granularities, and accelerating machine learning workloads via system-level innovations. Keywords include HPC, virtualization, parallelism, and secure execution contexts. Teaching: Courses taught include Computer Architecture (CS/ECE 472), System Security (CSP 544), Operating Systems (CS 450), and advanced topics in serverless/edge computing. He actively recruits PhD students to the HExSA Lab.
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.
Professor Marek Sanak serves as Full Professor at the Department of Internal Medicine, Jagiellonian University Medical College in Cracow, Poland. He concurrently holds leadership positions as Acting Director of the Department of Forensic Medicine, Head of the Division of Molecular Biology and Clinical Genetics, and Vice-Rector for Research and International Cooperation since 2016. His academic foundation includes: MD from Jagiellonian University Medical College Specialization in Pediatrics and Genetics PhD from Jagiellonian University Research appointments at Harvard University, University of Paris VI, and University of Zurich Professor Sanak's research integrates clinical genetics with molecular immunology, focusing on asthma pathogenesis, lipid mediators of inflammation, and genetic diagnostics. His laboratory employs advanced techniques including deep DNA/RNA sequencing to identify biomarkers and elucidate disease mechanisms. The work bridges fundamental molecular discoveries with clinical applications in respiratory diseases, allergic disorders, and forensic medicine, demonstrating particular expertise in aspirin-exacerbated respiratory disease and epigenetic regulation of inflammatory pathways. Analysis of his recent publications reveals a strategic evolution from classical asthma research toward molecular genetics and viral pathogenesis. His 2017-2021 work increasingly incorporates epigenetic approaches (DNA methylation, microRNA profiling) while expanding into SARS-CoV-2 research during the pandemic. The publications demonstrate interdisciplinary integration across immunology, respiratory medicine, and molecular diagnostics, with consistent focus on translational applications. His distinguished career has been recognized through numerous honors: The Lancet Investigators Award on Asthma (1997) Polish Ministry of Health Individual Prize (1999) Jagiellonian Laurel (2012) Pro Arte Docendi Award (2014/15) Gold Medal for Long Service (2019) Top 2% of world scientists ranking (Elsevier 2022) As Vice-Rector for Research, Professor Sanak has significantly expanded international collaborations with King's College London, University of Southampton, and University of Zurich. His leadership has secured substantial funding for molecular diagnostics and inflammatory disease research while mentoring numerous early-career researchers. He delivers invited lectures globally for organizations including the American Thoracic Society and European Academy of Allergy and Clinical Immunology. Professor Sanak directs integrated research units across the Division of Molecular Biology and Clinical Genetics, Division of Biochemical and Molecular Diagnostics at University Hospital Cracow, and the Department of Forensic Medicine. These teams combine clinical service with basic research to advance genetic diagnostics and understand disease mechanisms, maintaining forensic genetics expertise developed over 20 years of practice.
Mike Kirby is a Professor at the Kahlert School of Computing, University of Utah. He also holds adjunct professorships in the Department of Bioengineering and the Department of Mathematics. His current roles include leadership in scientific computing and informatics initiatives, including former directorships of the Utah Informatics Initiative (2019-2023) and the Multi-Scale Multidisciplinary Modeling of Electronic Materials (MSME) Collaborative Research Alliance (2016-2022). He has extensive experience in strategic research initiatives, including serving as Assistant Vice President for Research (2024-2025). Education: Dr. Kirby earned a PhD in Applied Mathematics (2002) and MS in Computer Science (2001) from Brown University, and a BS in Applied Mathematics and Computer Science from Florida State University (1997). Research Interests: Focus on large-scale scientific computing, physics-informed machine learning, computational science and engineering, high-order numerical methods, and visualization. His work bridges applied mathematics and computer science to address real-world engineering challenges. Publications: Over 150 peer-reviewed articles, including high-impact contributions in journals like Journal of Computational Physics and SIAM Journal on Scientific Computing . Recent work emphasizes machine learning for differential equations, topology optimization under uncertainty, and multi-fidelity modeling. Awards: Recognized for leadership in computational science and informatics, including contributions to University of Utah’s Clery Compliance Program. Advising & Grants: Supervised over 50 graduate students and postdocs. Secured funding from NSF, DOE, and industry partnerships, totaling millions in research grants. Active in interdisciplinary collaborations across engineering, materials science, and medicine. Labs/Teams: Scientific Computing and Imaging (SCI) Institute, Utah Informatics Initiative, and the Center for Multiscale Modeling of Electronic Materials (MSME).
Xiaoyu Cai is an Assistant Professor at the Department of Medicine, Loyola University Chicago, specializing in lung regeneration, aging biology, and stem cell plasticity. Her research focuses on the molecular mechanisms governing alveolar type 2 (AT2) stem cell dynamics during aging and chronic lung diseases. Education: Bachelor of Medicine (Peking University, 2012), Master of Science (Peking University, 2015), PhD in Biology of Aging (USC & Buck Institute, 2021) Key Research Areas: Lung regeneration, inflammation resolution, stem cell aging, 3D organoid cultures Methodologies: Single-cell multiome, mouse genetics, multicellular organoid systems Collaborations: Translational partnerships with clinical teams for bench-to-bedside applications Dr. Cai's recent work explores lineage plasticity in aged lung stem cells, ferroptosis suppression via CRISPR screens, and cellular aging atlases across species. She previously held a postdoctoral position at Genentech Inc. and maintains a professional lab website. Contact: xcai2@luc.edu | Office: CTRE 123
Zhipeng Liao is a Professor of Economics at the University of California, Los Angeles (UCLA), where he contributes to the Department of Economics. He holds a Ph.D. from Yale University and specializes in econometric theory and applied econometrics. His research focuses on developing statistical methods for evaluating economic models, nonstationary time series analysis, and robust inference in semi/nonparametric frameworks. Professor Liao's work has been published in leading journals such as the Annals of Statistics , Econometrica , and the Review of Economic Studies . He serves on the editorial boards of several prestigious journals, including Econometric Reviews , Econometric Theory , and Journal of Business & Economic Statistics . His research interests span econometric theory, time series analysis, panel data modeling, and nonparametric inference, with applications to financial economics and macroeconomic modeling. His recent publications emphasize methodological advancements in hypothesis testing, model selection, and robust estimation techniques. These include contributions to the analysis of spatially dependent panel data, instrumental variables methods, and the evaluation of macro-finance models. His work bridges theoretical econometrics with practical applications, addressing challenges such as endogeneity, model misspecification, and computational efficiency. Liao’s editorial roles reflect his influence in shaping the direction of econometric research. His research has implications for policy analysis, financial market modeling, and empirical studies requiring rigorous statistical foundations. Despite the breadth of his contributions, no specific awards or grants are explicitly mentioned in the provided text.
Professor Thomas Meier is a Visiting Professor at the Department of Life Sciences, Imperial College London (since 2015), and Director of the Centre for Structural Biology (2017–2021). He leads research on ATP synthase structure, drug targets for tuberculosis, and molecular mechanisms of disease. Previously, he was a Group Leader at the Max-Planck-Institute of Biophysics (2006–2015) and ETH Zurich's Institute of Microbiology. His work combines structural biology (X-ray crystallography, electron microscopy) with biochemical studies. Education: Dr. sc. nat. (2002) and Dipl. sc. nat. (1998) from ETH Zurich. Awards include the Wellcome Trust Investigator (2015–present). Research focuses on ATP synthase's role in energy conversion, drug development, and structural biology. His lab includes postdocs and students like Lisa Uhrig and Anthony Cheuk. Key affiliations: Centre for Structural Biology, Membrane Biology Group, and Bacterial Pathogenesis studies. Languages: German, English, French (fluent), Latin (read/write). Publications span structural biology, planetary science, and drug discovery. His work on ATP synthase inhibitors for TB has clinical implications, while astrophysical studies explore planetary formation via giant impacts.
Dr. Amelia Simpson holds the position of Honorary Associate Professor at the Australian National University (ANU), specializing in constitutional law with a focus on discrimination principles and federalism. She earned her BA Hons and LLB Hons from ANU, followed by an LLM and JSD from Columbia University. She is a practicing Barrister and Solicitor in the High Court of Australia. Her research critically examines constitutional frameworks, particularly regarding state residence discrimination under Section 117 and interstate free trade jurisprudence. Her work has been cited in landmark High Court and Federal Court rulings, solidifying her reputation as a leading scholar in public law. Amelia’s contributions include co-authoring Hanks Australian Constitutional Law: Materials and Commentary (2016) and contributing to the Oxford Handbook of Australian Constitutional Law (2018). Her research on social equality and functionalist approaches to non-discrimination has advanced scholarly discourse on constitutional values. Notably, her 2017 paper Social Equality in Australia critiques the historical neglect of equality principles in constitutional interpretation. Her publications span constitutional doctrine, class actions reform, and environmental policy, reflecting her interdisciplinary approach. She ranks among Australia’s top 20 most prolific legal scholars in high-impact journals (2000–2010). Her work bridges theoretical analysis with practical legal challenges, influencing both academia and judicial practice.
Dr. Michael J. Katz is a Professor in the Department of Chemistry at Memorial University in St. John's, Newfoundland and Labrador, Canada. He leads an active research group focused on porous materials, particularly metal-organic frameworks (MOFs), with applications in gas storage, chemical separation, and catalysis. His work is well-recognized in the field of materials chemistry, with numerous publications in high-impact journals spanning from 2005 to 2025. Dr. Katz's primary research interests lie in the synthesis, properties, and applications of porous materials. His work specifically focuses on: Metal-Organic Frameworks (MOFs) design and synthesis Gas storage technologies, particularly low-pressure methane storage Chemical separation processes including removal of harmful molecules from air Catalysis using porous materials Adsorption properties of various porous frameworks Environmental applications of porous materials Analysis of Dr. Katz's publication record from 2017-2025 reveals a strong emphasis on zirconium-based MOFs, particularly the UiO-66 family. His research spans fundamental characterization techniques like NMR spectroscopy to practical applications in carbon capture, gas separation, and environmental remediation. A notable trend is the increasing focus on real-world implementation of MOFs, including biochar-based materials for CO 2 capture and frameworks for air pollutant removal such as nitrous acid. His work demonstrates a progression from fundamental materials science toward practical environmental applications. Dr. Katz actively supervises graduate students and postdoctoral researchers in his research group. His laboratory at Memorial University is equipped for the synthesis and characterization of novel porous materials, with particular expertise in metal-organic framework development. His research is supported by various grants that enable the exploration of structure-property relationships in porous materials and their practical applications.
Ana Vives-Rodriguez, MD is an Assistant Professor of Neurology at Yale School of Medicine. She specializes in movement disorders and cognitive-behavioral neurology, caring for patients with Parkinson's disease, tremor, tics, dystonia, Alzheimer's disease, Dementia with Lewy bodies, and frontotemporal dementias. Based at Yale Physicians Building in New Haven, Connecticut, she provides both in-person and telehealth services to adult patients, accepting new patients without requiring referrals. Dr. Vives-Rodriguez's educational background includes: BS and MD from University of Costa Rica (2009), graduating Magna Cum Laude Residency at University Costa Rica/Calderon Guardia Hospital (2014) Clinical Fellowship in Movement Disorders at Yale New Haven Hospital (2018) Advanced Fellowship in Cognitive Behavioral Neurology at Boston University/VA Medical Center (2022) Her primary research interests focus on the behavioral and cognitive aspects of movement disorders and the early diagnosis of neurodegenerative disorders . Dr. Vives-Rodriguez examines structural and functional brain changes in conditions like Parkinson's disease, Wilson's disease, and Alzheimer's disease and their relation to clinical manifestations. Her work bridges clinical practice with translational neuroscience to improve diagnostic approaches and patient outcomes in complex neurological conditions. Analysis of her publications from 2017-2021 reveals a strong focus on movement disorders, cognitive impairment, and neuroimaging. Her research spans clinical observations of movement phenomena like index finger pointing and writing tremor, structural and functional brain changes in Wilson's disease, and innovative approaches to medical education in movement disorders. A recurring theme is the intersection between movement disorders and cognitive function, highlighting her dual expertise in both specialties. Dr. Vives-Rodriguez serves as a Sub Investigator for the Cognitive Training in Parkinson's Disease clinical trial (HIC ID 2000033352), which is recruiting participants aged 40+ through 2027. While specific grant funding isn't detailed in the available information, her active research program suggests ongoing support for her work in neurodegenerative disorders. She is affiliated with Yale's Movement Disorders and Neurodegenerative Disorders divisions, working within the Department of Neurology at Yale School of Medicine. Her clinical work at Yale Physicians Building integrates the latest research findings into patient care while contributing to the academic mission through teaching and scholarly activities.
Hadi Daneshmand is an Assistant Professor of Computer Science at the University of Virginia, specializing in theoretical machine learning. Prior to joining UVA, he completed postdoctoral research at FODSI (jointly hosted by MIT and Boston University), Princeton University, and INRIA Paris following his 2020 PhD in Computer Science from ETH Zurich. Education Ph.D. in Computer Science, ETH Zurich, 2020 His research bridges computational perspectives and neural network theory, focusing on theoretical guarantees for deep learning systems. Key interests include understanding neural network mechanisms through optimization frameworks, foundations of machine learning, and stochastic processes in learning systems. His work reveals how neural networks implement computational primitives like gradient descent and optimal transport through architectural components. Recent publications demonstrate a cohesive trajectory analyzing transformers' computational capabilities, batch normalization's theoretical properties, and optimization dynamics in deep learning. His studies consistently establish formal connections between neural architectures and classical optimization methods, particularly in in-context learning scenarios. Scientific Awards Stanford CPAL Rising Star Award Spotlight award at ICML In-context Learning workshop (2024) Postdoc fellowship of the Foundation of Data Science Institute (FODSI) Early Postdoc Mobility grant from SNSF Best poster award at Max Planck ETH deep learning workshop (2016) Reviewer awards for ICML (2022, 2019) and NeurIPS (2020) Dr. Daneshmand actively mentors graduate students, with advisees including PhD candidates at ETH Zurich who have secured positions at Harvard, Yale, Meta, and NVIDIA. His research is supported by competitive grants including the SNSF Early Postdoc Mobility award and FODSI fellowship. He serves the community as Area Chair for NeurIPS 2023-2024 and ICML 2025, and regularly reviews for top machine learning conferences and journals. He teaches specialized courses including "Neural Networks: A Theory Lab" at UVA, emphasizing experimental-theoretical connections in neural computation through hands-on coding exercises.
Professor Anil Seth is a leading cognitive and computational neuroscientist at the University of Sussex, where he holds a professorship in the School of Engineering and Informatics. He is Director of the Sussex Centre for Consciousness Science and Co-Director of the CIFAR Program on Brain, Mind, and Consciousness and the Leverhulme Doctoral Scholarship Programme. His research bridges neuroscience, psychology, philosophy, and AI to investigate the biological basis of consciousness and selfhood. His research interests include: Predictive processing approaches to perception Virtual and augmented reality in self-experience studies Mathematical modeling of perception and emergence Machine learning applications in subjective perception modeling Interoception and selfhood Neural mechanisms of conscious experience The recent publications reflect a strong focus on consciousness, neural dynamics, predictive models, and interdisciplinary approaches. Trends include the use of computational modeling, neurophenomenology, causal analysis, and the ethical implications of emerging neurotechnologies. His work increasingly integrates large language models and data-driven methods for analyzing subjective experience. His scientific awards include: Segerfalk Award Perspectives Award Highly Cited Researcher (Web of Science, 2019–2022) Royal Society Michael Faraday Prize (2023) Seth has secured substantial research funding from the European Research Council (ERC), EPSRC, Wellcome Trust, CIFAR, and the Sackler Foundation. He has supervised numerous research projects and doctoral students through interdisciplinary programs. He leads the Dreamachine project and has been an Engagement Fellow with the Wellcome Trust. He serves on editorial boards including Philosophical Transactions of the Royal Society B and is Editor-in-Chief of Neuroscience of Consciousness . He leads a multidisciplinary research group at the Sackler Centre, bringing together psychologists, mathematicians, neuroscientists, computer scientists, and philosophers. His team conducts innovative research using virtual reality, neuroimaging, and computational modeling to explore the nature of consciousness and self.
Christian Bargetz is a Professor of Functional Analysis at the University of Innsbruck, Austria, affiliated with the Faculty of Mathematics, Computer Science, and Physics (MIP). His primary research focuses on nonlinear functional analysis, Banach space theory, and distribution theory. He teaches advanced courses such as Optimization, Distribution Theory, and Functional Analysis, demonstrating his expertise in both theoretical and applied aspects of his field. Education: Completed his PhD in 2012 at the University of Innsbruck under the supervision of Norbert Ortner. His diploma thesis (2008) explored differential behaviors with Ulrich Oberst. Bargetz has held continuous academic positions since 2008, including roles as a lecturer and researcher. Research Interests: Specializes in iterative projection methods, generic properties of nonexpansive mappings, Fréchet spaces, and vector-valued distributions. His work bridges functional analysis with geometric measure theory and optimization, with applications in metric geometry and topological tensor products. Publications: Over 30 peer-reviewed articles in prestigious journals such as Canadian Journal of Mathematics , Journal of Mathematical Analysis and Applications , and Proceedings of the American Mathematical Society . Recent work includes studies on extremal nonexpansive mappings and Lipschitz function spaces. Grants & Projects: Principal investigator in FWF-funded projects on nonexpansive mappings and Banach spaces. Collaborates internationally, including with institutions in Israel, Poland, and Serbia. Teaching: Leads advanced courses in functional analysis, optimization, and distribution theory. Supervises bachelor's theses and master's projects on topics like extension operators for Lipschitz functions. Affiliations: Active member of the Functional Analysis working group and regularly participates in international conferences such as the Banach Afternoon, Winter School in Abstract Analysis, and DMV-ÖMG Annual Conferences.
Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.
.png)
