جست‌وجوی استاد و پژوهشگر

Kirsten Ohm Kyvikمشاهده پروفایل

استاد

• Human Genetics
• Epidemiology
• Twin Studies
• +۱۲ مورد دیگر

Kirsten Ohm Kyvik is a Professor at the University of Southern Denmark, affiliated with the Department of Clinical Research, the Danish Twin Research Center, and the Research Unit of Clinical Genetics at KI, OUH in Odense. She holds an MD, PhD, and MPM, and is actively engaged in research and public discourse in human genetics and epidemiology. Institution: University of Southern Denmark School: Faculty of Health Sciences Department: Department of Clinical Research Additional Affiliations: Danish Twin Research Center, Research Unit of Clinical Genetics (Odense) Academic Rank: Professor Her research focuses on human genetics, epidemiology, twin studies, and ethical aspects of scientific research. She investigates heritability, genetic determinants of complex diseases, and the application of clinical registry data in understanding conditions like type 2 diabetes, Alzheimer’s disease, and asthma. Her work often involves large-scale cohort analyses and epigenetic profiling in twin populations. Recent publications highlight her work in diabetic retinopathy as a predictor of Alzheimer’s disease, DNA methylation in type 2 diabetes, and strategies to optimize laboratory test use in primary care. These studies reflect a strong trend toward translational research, public health applications, and research methodology improvement, particularly using registry-based and twin cohort data. Kirsten Kyvik has contributed to 398 research outputs and has been involved in numerous national and international collaborations. She has participated in key conferences such as the International Congress on Twin Studies and the World Congress on Twin Pregnancy. She was also a project participant in the 'Lægestereotyper' research project on physician stereotypes. She has been active in public engagement, contributing to 27 media appearances, including discussions on healthcare reform, anti-inflammatory diets, and thyroid disease. Her work emphasizes responsible research conduct and healthcare system efficiency. She supervises academic work and has delivered guest lectures and external teaching activities. Her lab and research team are deeply integrated into the Danish Twin Research Center and clinical genetics networks, supporting large-scale genetic and epidemiological studies.

Veit Schwämmleمشاهده پروفایل

دانشیار

• Computational Proteomics
• Bioinformatics
• Mass Spectrometry
• +۱۰ مورد دیگر

Veit Schwämmle is an Associate Professor in Computational Proteomics and Bioinformatics at the Department of Biochemistry and Molecular Biology, University of Southern Denmark (SDU), where he leads the Computational Proteomics Group. His research focuses on developing computational solutions for large-scale omics data analysis, particularly in proteomics and post-translational modifications. PhD in Physics, University of Stuttgart (2006) Postdoctoral Fellowships: Centro de Pesquisas Fisicas (Rio de Janeiro), ETH Zürich Postdoctoral Researcher and Assistant Professor, University of Southern Denmark Research interests include software and workflow development for protein mass spectrometry data analysis, chromatin biology through histone modifications, tools for omics data quantification and interpretation, and the application of deep learning methods to improve proteomics data processing. His work bridges physics-based modeling and biological data science, with a strong emphasis on open, reproducible research. His recent publications highlight trends in machine learning for proteomics, benchmarking of analysis workflows, and community-driven bioinformatics standards. He is actively involved in international initiatives like EuBIC-MS and bio.tools, promoting software interoperability and training. Member of the European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Contributor to the bio.tools registry for life sciences software Advocate for open science and reproducible workflows He supervises researchers and students in computational proteomics and has contributed to numerous collaborative projects in systems biology and biomedical mass spectrometry. His group develops tools such as VIQoR, CrossTalkMapper, and MetaboLink, supporting the broader scientific community. Visit the group’s webpage: http://computproteomics.bmb.sdu.dk

Edmond Chowمشاهده پروفایل

استاد

• High-Performance Computing
• Scientific Computing
• Numerical Linear Algebra
• +۷ مورد دیگر

Edmond Chow is a Professor and Associate Chair in the School of Computational Science and Engineering at Georgia Institute of Technology. His research focuses on numerical methods, high-performance computing, and scientific computing applications in quantum chemistry, molecular dynamics, and machine learning. He holds awards including the 2009 ACM Gordon Bell Prize and 2002 PECASE. He leads the Intel Parallel Computing Center, advancing computational chemistry algorithms for Intel architectures. Education: Ph.D., Computer Science (minor in Aerospace Engineering), University of Minnesota, 1997 Honors B.A.Sc., Systems Design Engineering, University of Waterloo, 1993 Research Interests: Parallel algorithms for sparse linear systems and preconditioning High-performance quantum chemistry simulations Asynchronous iterative methods for extreme-scale computing Kernel-based methods and hierarchical matrices for Gaussian processes Applications in biological macromolecule dynamics and machine learning Awards & Recognition: ACM Gordon Bell Prize (2009) PECASE (2002) SIAM Fellow (2021) SC23 Test of Time Award Advising & Grants: Advised 11+ PhD/MSc students in numerical methods and HPC Funded by NSF, DOE, DARPA, and Intel Developed software tools like H2Pack (hierarchical matrices), GTFock (quantum chemistry), and Simint (electron integrals) Labs & Teams: Leads the Intel Parallel Computing Center and collaborates with institutions like LLNL, Sandia, and Temple University on asynchronous computing and quantum chemistry projects.

Sebastian Probstمشاهده پروفایل

پژوهشگر ارشد

• Population Genetics
• Recombination Dynamics
• Mathematical Biology
• +۳ مورد دیگر

Sebastian Probst serves as a Research Fellow in the Biomathematics Research Group at Bielefeld University's Faculty of Technology, working under Prof. Ellen Baake. He holds dual roles as the group's computing officer and coordination support for the Priority Program 1590 ("Probabilistic Structures in Evolution"), managing its critical webservices infrastructure. His academic foundation includes a 2012 Diploma in Mathematics from Gottfried Wilhelm Leibniz Universität Hannover, completed in Marc Steinbach's Algorithmic Optimization group, followed by doctoral research culminating in October 2018 within Baake's Biomathematics workgroup at Bielefeld. Probst's research centers on population genetics with emphasis on recombination dynamics modeling. He integrates numerical mathematics and probability theory to estimate evolutionary model parameters, while developing frameworks to explain Richard Lenski's decades-long bacterial evolution experiment through advanced simulation methodologies. His work bridges theoretical biology with computational mathematics to decode complex evolutionary processes. Analysis of his publications reveals a cohesive research trajectory in theoretical population genetics, where stochastic modeling converges with computational biology. Both major works demonstrate rigorous mathematical treatment of evolutionary phenomena—from fundamental recombination mechanics in the Moran model to large-scale simulation of experimental evolution—highlighting consistent interdisciplinary innovation at the mathematics-biology interface. No scientific awards or honors were documented in the source material. While maintaining extensive teaching responsibilities since 2012 across probability theory, statistics, and mathematical biology courses, no formal graduate student advising or research grant activities were specified in the available records. Within the Biomathematics ecosystem, Probst anchors computational operations for the research group and provides essential coordination for SPP 1590. His technical leadership sustains collaborative frameworks for studying probabilistic evolutionary structures, directly enabling the program's research infrastructure through web service development and maintenance.

Koblar Jacksonمشاهده پروفایل

استاد

• Electronic Structure Theory
• Atomic Clusters
• Density Functional Theory
• +۵ مورد دیگر

Koblar Jackson is a Professor in the Department of Physics within the College of Science & Engineering at Central Michigan University. His research lies at the intersection of computational physics and materials science, focusing on the electronic structure and properties of atomic clusters. Education: Ph.D. in Physics, University of Wisconsin, 1988 B.S. in Physics, Northwestern University, 1980 His research interests include electronic structure theory, atomic clusters, density functional theory, nanomaterials, and surface physics. He employs first-principles methods to explore how atomic bonding deviates from bulk behavior in clusters and surfaces, particularly examining size-dependent properties such as metallicity and dielectric response. The publication record shows a sustained focus on cluster science, electronic structure methods, and quantum simulations over three decades, with recent work emphasizing self-interaction corrections and metallic transitions in nanoscale systems. Scientific Awards: No awards listed in the provided text. Koblar Jackson leads a computational research group supported by grants from the National Science Foundation (NSF), the Department of Energy (DoE), and Central Michigan University. His team utilizes a high-performance cluster of workstations for large-scale simulations. While no students are listed, his collaborative publications suggest active engagement with researchers across institutions. His laboratory focuses on developing and applying advanced electronic structure techniques, particularly within density functional theory, to understand the fundamental behavior of materials at the nanoscale.

Giovanni Negliaمشاهده پروفایل

پژوهشگر

• Performance Evaluation of Distributed Systems
• Cache Networks
• Distributed Machine Learning
• +۷ مورد دیگر

Giovanni Neglia is a Research Director (DR2) at Inria, affiliated with the NEO team and the 3IA Côte d'Azur Chair, focusing on performance evaluation of distributed systems, cache networks, and large-scale machine learning. He is based in Sophia Antipolis, France. Inria, Sophia Antipolis NEO Team 3IA Côte d'Azur Chair His research interests center on distributed systems, leveraging mathematical tools such as Markov processes, control theory, fluid models, and game theory. Key areas include caching policies, distributed optimization for machine learning, and performance modeling of networks. He has previously worked on P2P networks, delay tolerant networks, smart grids, and complex networks. Recent publications demonstrate a strong focus on similarity caching, federated learning, and distributed optimization. Trends include developing online learning algorithms for caching, improving privacy and efficiency in federated systems, and optimizing content delivery in 5G and small cell networks. Best paper award at ITC-33 IEEE Infocom Distinguished TPC member (2017, 2018, 2022) Best paper award at ITC28 2016 Best paper award at IEEE Online Greencomm 2014 Best paper award at IEEE Infocom NetSciCom 2014 Best paper award at IEEE VTC2013-Spring Best student paper award at VALUETOOLS 2012 Best paper award at BIONETICS 2007 Giovanni Neglia leads the Fed-Malin Inria challenge on federated machine learning and has secured grants including a 3IA Côte d'Azur chair for PERUSALS (Pervasive Sustainable Learning Systems). He advises PhD students such as Othmane Marfoq and Chuan Xu, and collaborates with Accenture Labs, SAP, and Nokia Bell Labs. He has organized winter schools and tutorials on complex networks and similarity caching. He leads the NEO team and previously led the Maestro team. He is involved in the joint Brazilian-French research team Thanes on network science and has served on numerous technical program committees for major conferences including IEEE Infocom, INFOCOM, and SIGMETRICS.

Suresh Raja Neethirajanمشاهده پروفایل

دانشیار

• Digital Agriculture
• Artificial Intelligence
• Big Data Analytics
• +۱۱ مورد دیگر

Dr. Suresh Raja Neethirajan is an Associate Professor and Dalhousie University Research Chair in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. His research program focuses on digital livestock farming, integrating artificial intelligence, big data analytics, and advanced sensor technologies to improve animal health, welfare, and sustainable agricultural practices. His research interests span Digital Agriculture , Artificial Intelligence , Big Data Analytics , Animal-Computer Interaction , and Sensors & Bio-Instrumentation . He leads the Mooanalytica research lab, which develops innovative tools such as facial recognition systems for emotion detection in cows and pigs, rapid diagnostic sensors for avian influenza and food allergens, and wearable devices for non-invasive physiological monitoring. The recent publications reflect a strong trend in developing AI-driven, sensor-based solutions for precision livestock farming. These include emotion recognition in pigs, sex detection in chicken eggs, and circadian rhythm modeling in dairy cows, all leveraging computer vision, machine learning, and multimodal data fusion to advance animal welfare and farm efficiency. Dalhousie University Research Chair Dr. Neethirajan supervises BSc, MSc, and PhD students and is actively involved in teaching and guest lectures on sensor technologies. His lab has access to state-of-the-art research facilities in the Netherlands and collaborates across veterinary, agricultural, and biomedical domains. He has previously held academic and research positions at Wageningen University, the University of Guelph, Oak Ridge National Laboratory, and the National Agricultural Research Organization in Japan. His team has developed numerous industrial-ready solutions, including on-farm detection tests for ketosis and coronavirus, and is focused on transforming laboratory innovations into practical applications for the agri-food industry. Future work continues to emphasize resilience sensing, data management systems, and non-contact vital sign technologies.

Robert Ian Whitfieldمشاهده پروفایل

عضو هیئت علمی

• Systems Engineering
• Design Coordination
• Collaborative Design
• +۱۱ مورد دیگر

Dr Robert Ian Whitfield is a Reader in the Department of Design, Manufacturing and Engineering Management within the Faculty of Engineering at the University of Strathclyde. His work spans research, teaching, and leadership in systems engineering, with a focus on collaborative design, process optimization, product lifecycle management, and engineering risk. He has extensive experience in large-scale, multi-partner EU projects in shipbuilding and defense industries, and his research tools have been adopted in aerospace, defense, and new product development sectors. His research interests include: design coordination, modular design, integration, resource management, process modeling and optimization, product data and lifecycle management, and engineering risk management—all under the umbrella of Systems Engineering. He has developed practical tools such as a modular design tool for hierarchical product structuring, a design process optimization tool for reducing rework, a collaborative design environment for distributed teams, and decision-support systems for complex engineering programs. The recent articles highlight trends in ontological harmonisation, complexity in New Product Development, sustainability-oriented lightweighting, digital thread integration, and innovative pedagogical approaches in engineering education. These reflect a strong interdisciplinary focus bridging engineering systems, digital transformation, and sustainable design practices. CeeD (Centre for Engineering, Education and Development) Industry Award (2025) Top three reviewer - Journal of Engineering Design (2009) Member, Design Society (2007) Member, Institution of Engineering Designers (2003) Dr Whitfield has supervised PhD students, including Ross Brisco, and has been Principal or Co-investigator on numerous research grants, such as the RAEng Visiting Professorship in Applied Systems Engineering Management, KTP projects with Spirit Aerostructures, and initiatives with NHS on systems engineering for healthcare. He is actively involved in professional service, serving on editorial boards (e.g., Design Science ), peer-reviewing for journals, and organizing major conferences like the 25th International Conference on Engineering Design. His work contributes to UN Sustainable Development Goals through engineering education and sustainable design innovation. He leads key teaching modules including DM103 (Technology Concepts), DM200 (Design 2), DM206 (Sports Engineering), and DM933 (Engineering Risk Management), all noted for high student satisfaction and pedagogical excellence. His teaching strategies are used as departmental best practice.

Kim-Chuan Tohمشاهده پروفایل

استاد

• Optimization
• Mathematical Programming
• Convex Programming
• +۳ مورد دیگر

Kim-Chuan Toh is a Professor of Mathematics at the National University of Singapore (NUS), specializing in algorithmic design for convex and matrix optimization problems. His work bridges theoretical mathematics with practical applications in data science and computational engineering. Educational background: BSc in Mathematics, National University of Singapore (1990) PhD in Applied Mathematics, Cornell University (1996) under Nick Trefethen Research focuses on: Efficient algorithms for convex programming and large-scale optimization Semidefinite programming (SDP) and quadratic SDP Matrix optimization techniques for data science applications Software development for high-performance computational mathematics Scalable solutions to industrial-scale optimization challenges Numerical methods in mathematical programming He serves as Area Editor for Mathematical Programming Computation and Associate Editor for SIAM Journal on Optimization , contributing significantly to the optimization research community through editorial leadership and peer review. Professional engagement includes: Secretary of SIAM Activity Group on Optimization Invited speaker at SIAM Annual Meeting (2010) and International Symposium on Mathematical Programming (2006) Program committee roles for ICCOPT (2007) and SIAM Conference on Optimization (2011) Cluster chair for ISMP 2009, 2012 and ICCOPT 2016 Visiting Scientist at Simons Institute's Fall 2017 program on Bridging Continuous and Discrete Optimization

• Agroecology
• Farming Systems
• Cropping Systems
• +۱۲ مورد دیگر

Walter Rossing is a senior researcher and academic affiliated with the Farming Systems Ecology (FSE) group at Wageningen University & Research (WUR), the Netherlands. He holds a 'dr.ir.' degree and serves as a project leader, promotor (PhD supervisor), and principal investigator in multiple national and international research initiatives. His work is embedded in the PE&RC research school, focusing on sustainable and agroecological farming systems. His research interests include agroecology, cropping systems, strip cropping, biodiversity, climate change adaptation, and systemic transitions in agriculture. He employs case studies, modeling, and interdisciplinary methods to analyze and redesign farming systems for sustainability. His work spans diverse regions including the Netherlands, Denmark, Mexico, Romania, Uruguay, and Benin. The recent publications highlight a strong focus on agroecological transitions, biodiversity enhancement through strip cropping, climate adaptation strategies, and sustainable intensification of mixed farms. These works reflect a consistent trend toward integrating ecological, economic, and social dimensions in agricultural research. Walter Rossing has supervised numerous PhD and postdoctoral researchers, including Cuperus, F., Ruggia, A., and Paresys, L., and leads major projects such as Agroecology-TRANSECT. He is active in transdisciplinary research, public engagement, and science communication, as evidenced by media appearances and lectures on systemic transitions. Labs and Research Teams: He is a core member of the Farming Systems Ecology (FSE) group at WUR, contributing to the PE&RC (Postgraduate School for Production Ecology & Resource Conservation). His team engages in field studies, modeling, and participatory research to support ecological intensification and sustainable farming transitions.

David Hung-Chang Duمشاهده پروفایل

استاد

• Intelligent Storage Systems
• Sensor/Vehicular Networks
• Cyber Physical Systems
• +۵ مورد دیگر

David Hung-Chang Du is a Professor and Qwest Chair Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities. He served as the Center Director of the NSF multi-university I/UCRC Center of Research in Intelligent Storage (CRIS) from 2009-2021, which was sponsored by 18 companies with $3.35M industrial membership funding. Prior to that, he organized an Intelligent Storage Consortium at Digital Technology Center with industrial funding from 2002-2009. He was also a Program Director at the National Science Foundation CISE/CNS Division from March 2006 to August 2008. Dr. Du received his B.S. degree in Mathematics from National Tsing-Hua University (Taiwan) in 1974 and M.S. and Ph.D. degrees from University of Washington (Seattle) in 1980 and 1981 respectively. He joined University of Minnesota as a faculty member in 1981 and has been there for over 40 years. He has also been a visiting professor in Germany, Korea, Singapore, Hong Kong and Taiwan. Dr. Du's research focuses on intelligent storage systems, sensor/vehicular networks, and cyber physical systems. His early career work included parallel processing and database design, followed by Computer-Aided Design for VLSI circuits in the 1980s and 1990s. From the late 1980s to present, he has worked on computer networking and its related applications including multimedia computing. Starting from 2000, his research has focused on new memory and storage technologies for handling extremely large volumes of available data and long-term data preservation. His current research focuses on hyper-converging infrastructure, recognizing that the Internet has become the largest existing computer system where data collection, storage, and networking must be integrated. His recent publications (2019-2022) demonstrate continued innovation across multiple domains including DNA storage technologies, hybrid storage systems, key-value store optimization, and edge computing. These works reflect his ability to adapt to emerging technological landscapes while maintaining focus on fundamental storage and systems challenges. The publications show strong industry collaboration, particularly with major technology companies like Facebook, where his team has characterized and optimized key database workloads. IEEE Fellow (since 1998) Fellow of the Minnesota Supercomputer Institute ACM Recognition of Service Award (2013) IEEE Certificate of Appreciation (2012, 2007) NSF Director's Award for Collaborative Integration (2008) Best Paper Award, International Conference on Internet of Vehicles (2015) Best Paper Award, International Conference on Computer Design (1998) Dr. Du has been highly active in professional service, serving as Editor of IEEE Transactions on Computers (1993-1998), member of several editorial boards, and holding leadership positions in numerous conferences including General Chair for IEEE Security and Privacy Symposium (2009), Program Committee Co-Chair for International Conference on Parallel Processing (2009), and General Chair for IEEE International Conference on Distributed Computing Systems (2010-2011). He has graduated 67 Ph.D. students (58 as single adviser and 9 jointly supervised) and 109 Master's students over his 40+ year career. His research has been supported by substantial grants from NSF, industry partners, and other funding sources totaling millions of dollars.

• Theoretical Computer Science
• Property Testing
• Approximation Algorithms
• +۸ مورد دیگر

Yuichi Yoshida is a Professor at the National Institute of Informatics (NII), affiliated with the Principles of Informatics Research Division. He also holds a concurrent position as Senior Researcher at Preferred Networks. His academic career at NII spans from Assistant Professor (2012–2015), Associate Professor (2015–2022), to full Professor since 2022. He serves as Vice Director of the Global Research Center for Big Data Mathematics at NII and has held advisory and research roles at Preferred Infrastructure and the Ministry of Education, Culture, Sports, Science and Technology (MEXT). Ph.D. in Informatics, Kyoto University, 2012 Master of Informatics, Kyoto University, 2009 Bachelor of Engineering, Kyoto University, 2007 His research focuses on theoretical computer science, particularly property testing , approximation algorithms , sublinear-time algorithms , and constraint satisfaction problems . He investigates how theoretical insights can be applied to real-world graphs, with recent emphasis on average sensitivity of algorithms and Lipschitz continuity in combinatorial optimization . His work bridges theory and practical scalability in large network analysis and machine learning. The most recent 15 publications (2022–2025) demonstrate a strong trend in algorithmic stability, spectral methods for hypergraphs, influence propagation in networks, and learning-augmented algorithms. Key venues include FOCS, SODA, ICALP, and NeurIPS, reflecting high impact in theoretical computer science and machine learning. Topics such as Lipschitz continuity, average sensitivity, spectral sparsification, and sublinear algorithms recur, indicating a cohesive research program on robust and efficient computation. Best Paper Award, AISTATS (2018) MEXT Commendation for Science and Technology – Young Scientists’ Prize (2017) Inoue Research Award for Young Scientists (2014) KDDI Foundation Award (2024) Funai Information Technology Award (2024) Yuichi Yoshida advises PhD students and hosts numerous postdocs, RAs, and international interns. He leads major research projects funded by JSPS and JST, including Grants-in-Aid for Scientific Research (S) and PRESTO programs. His service includes program committee roles for ICML, NeurIPS, SODA, and ICALP. He has co-authored a book on Property Testing and contributed to encyclopedias on big data technologies. He leads research teams on algorithm desensitization and large-scale graph algorithms, and has been program co-chair for GRADES-NDA'23. His lab fosters international collaboration, hosting interns from top universities worldwide.

• Energy Systems
• Cogeneration
• Decarbonisation
• +۷ مورد دیگر

Alberto Poggio is a Tenured Assistant Professor at the Department of Energy (DENERG) at the Polytechnic University of Turin. He serves as University Energy Manager and co-coordinator of the "Energy and Climate Change" unit of the Green Team at the university. His academic roles include membership in the College of Electrical and Energy Engineering and serving as a course instructor for Cogeneration Plants and Computational Heat Transfer Laboratory and Machine Fundamentals in the Energy Engineering program. Dr. Poggio's research focuses on energy and environmental planning, with particular expertise in the decarbonization of urban and industrial energy systems, cogeneration plants, district heating systems, and the energy use of biomass and waste. His work addresses critical challenges in sustainable energy transition, particularly in the context of urban infrastructure and climate change mitigation. His research integrates technical, economic, and environmental perspectives to develop practical solutions for energy systems transformation. His recent publications show a clear trend toward district heating decarbonization, with increasing focus on machine learning applications for energy demand modeling, CO2 emission factor calculations, and the integration of renewable energy sources into existing thermal networks. His research spans both theoretical modeling and practical implementation, with strong connections to regional and European sustainability initiatives. Dr. Poggio has served as scientific director for numerous research projects including PROSPECT2030, ITEM2+AGAPE2, and QUADRANTE, among others. His leadership extends to coordinating the Energy working group in the Network of Universities for Sustainable Development (RUS) from 2016-2023 and serving as Curriculum Coordinator for the National Doctorate in Sustainable Development and Climate Change at IUSS Pavia from 2021-2023. He currently supervises PhD student Chiara Monzani, whose research focuses on the decarbonization of district heating systems. His work bridges academic research with practical applications through numerous consulting contracts with local authorities, energy operators, and industrial companies, particularly in the areas of district heating planning and renewable energy integration.

Krzysztof Wojtasمشاهده پروفایل

استادیار

• Chemical Engineering
• Process Intensification
• Separation Processes
• +۷ مورد دیگر

Krzysztof Wojtas is an Assistant Professor at the Department of Separation Processes , Faculty of Chemical and Process Engineering, Warsaw University of Technology. His work bridges computational modeling and experimental validation in chemical engineering systems. Research Interests: Process intensification, turbulent mixing, precipitation dynamics, and LES/CFD applications Collaborations: Engaged in multidisciplinary projects with biologists, physicists, and industry partners Scientific Contributions: Focused on jet reactor dynamics and microwave catalytic reactors for hydrogen production. His publications demonstrate expertise in: Mathematical modeling of dispersed systems Population balance equation applications Experimental validation techniques Industrial process optimization Infrastructure: Utilizes advanced equipment like ANSYS Fluent for CFD simulations, high-shear homogenizers, and sol-gel synthesis facilities.

Mher Safaryanمشاهده پروفایل

پژوهشگر

• Optimization theory and algorithms
• Machine learning
• Federated learning
• +۷ مورد دیگر

Dr. Mher Safaryan is a postdoctoral researcher at the Institute of Science and Technology Austria (ISTA), affiliated with Prof. Dan Alistarh's research group since 2022. Previously, he held postdoctoral positions at King Abdullah University of Science and Technology (KAUST) from 2019-2022 and served as a research technician there from 2016-2019. He earned his Ph.D. in Mathematics from Yerevan State University in 2018 under Prof. Grigori Karagulyan's supervision. Education: Ph.D. in Mathematics (2018, YSU) Past Affiliations: KAUST (2016-2022), Neural Magic/Red Hat (industrial secondment) His research focuses on optimization theory and algorithms for machine learning , particularly in developing communication/computation/memory-efficient methods for large-scale training and federated learning. Key contributions include LDAdam (low-dimensional gradient statistics optimization), GradSkip (accelerated local gradient methods), and Unified Scaling Laws for compressed representations. He has published in top venues like NeurIPS, ICML, ICLR, TMLR, and The Journal of Geometric Analysis. Scientific Awards: Marie Skłodowska-Curie Fellowship (MSCA COFUND IST-BRIDGE) His work bridges machine learning optimization with mathematical foundations from his earlier research in real harmonic analysis. Current collaborations include Prof. Dan Alistarh (ISTA), Dr. Alexandre Marques (Neural Magic), and Prof. Peter Richtárik (KAUST).