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Harald WehnesView profile

Professor

• Project Management
• IT Infrastructure
• Healthcare Project Management
• +3 more

Prof. Dr. Harald Wehnes serves as a Professor at the University of Würzburg within the Faculty of Mathematics and Computer Science, specifically affiliated with the Institute of Computer Science's Chair of Computer Science III (Communication Networks). His office is located in room A206 at the Hubland campus, with contact email wehnes@informatik.uni-wuerzburg.de. His research centers on project management methodologies, with particular emphasis on the Project Excellence Model and its practical applications. Prof. Wehnes has developed significant expertise in applying project management frameworks to complex IT infrastructure initiatives and healthcare systems. His work demonstrates how theoretical project management concepts translate into real-world implementation, especially in cross-organizational contexts. Analysis of his publication history reveals a consistent focus on practical project management applications, particularly the NIMBUS project case study which documented the consolidation of 12 data centers into a single state data center. His publications demonstrate evolving expertise from foundational programming work (evidenced by his 1981 book "Strukturierte Programmierung mit FORTRAN 77" which went through seven editions) to sophisticated project management frameworks. Prof. Wehnes has maintained active engagement with the German Project Management Association (GPM), presenting at numerous forums and events. His international reach is evident through presentations at institutions including the University of the United Arab Emirates and the University of Canterbury in New Zealand. From 2013-2020, he taught specialized courses on professional project management (Spezialvorlesung aus der Praxis: Professionelles Projektmanagement) at the University of Würzburg, sharing his extensive practical experience with students. His work environment within the Chair of Computer Science III connects his project management expertise with research areas including 5G & 6G network technologies, network and service management, and green communication networks.

Margaret MaakaView profile

Professor

• Educational policy
• Educational psychology
• Indigenous educational psychology
• +4 more

Margaret Maaka is a Professor in Curriculum Studies at the University of Hawaiʻi at Mānoa's College of Education, with a distinguished career spanning Indigenous educational psychology, leadership, and policy development. Her work centers on decolonizing educational frameworks and advancing Indigenous knowledge systems. Her academic credentials include a PhD in Educational Psychology (1992) from the University of Hawaiʻi at Mānoa, a Master of Education (Honors) with English Literature focus (1978) from the University of Waikato, New Zealand, a Diploma of Teaching in Elementary and Secondary Education (1978) from New Zealand's Department of Education, and a Bachelor of Education (Honors) with English Literature specialization (1977) from the University of Waikato. Maaka's research explores Indigenous leadership complexities, multiliteracies, and cognitive development within Māori and Hawaiian contexts. She investigates how language revitalization intersects with educational policy to foster Indigenous advancement, emphasizing place-based pedagogies and self-determination in schooling systems. Her scholarship consistently challenges Western epistemological dominance in education. Her publications since 2009 reveal a trajectory prioritizing Indigenous research sovereignty, with recurring themes of decolonization, community-led leadership models, and culturally grounded curriculum design. Key contributions examine contested spaces in Indigenous schooling and the politics of knowledge production. Her teaching excellence has been recognized through: Board of Regents’ Medal for Excellence in Teaching (1998) Presidential Citation for Meritorious Teaching (1996) Graduate Student Organization Outstanding Graduate Assistant Teaching Award (1991) While specific advising details are not documented, Maaka's mentorship is reflected in her collaborative publications with emerging Indigenous scholars. Her work demonstrates sustained commitment to transforming educational paradigms through Indigenous epistemologies.

• Disaster Risk Reduction
• Sustainability
• Interdisciplinarity
• +5 more

Giuliano Di Baldassarre is a Professor of Hydrology and Environmental Analysis at the Department of Earth Sciences, Uppsala University , Sweden. He serves as Head of Division for LUVAL (Air, Water and Landscape Sciences) and directs the Centre of Natural Hazards and Disaster Science (CNDS) (2016–2025). His work bridges water, environment, and society through interdisciplinary methods , focusing on disaster risk reduction, climate adaptation, and sustainable development. Education : Details not explicitly provided in the text. His research examines feedbacks between human activities and hydrological processes , including floods, droughts, and reservoir management . Key themes include social-ecological systems , inequalities in water crises , and policy implications of hydrological extremes . He has pioneered sociohydrology and human-water system modeling . Recent articles highlight global drought-flood interactions , urban water inequality , climate service maladaptation , and sociohydrological modeling . His work spans Nature Sustainability , Science Advances , and Hydrological Sciences Journal . Scientific Awards : International Hydrology Prize (Volker Medal) Plinius Medal (EGU) Witherspoon Lecture Award (AGU) European Research Council Consolidator Grant He led Panta Rhei - Everything Flows (2013–2022), IAHS’s global initiative on water-society interactions. Current efforts include transdisciplinary praxis and climate risk reduction frameworks .

Monica P. ColaiacovoView profile

Professor

• Meiosis
• Chromosome dynamics
• DNA repair
• +6 more

Dr. Monica P. Colaiacovo is a Professor of Genetics at Harvard Medical School, where she leads research in the Department of Genetics within the Blavatnik Institute. Her laboratory is located in the New Research Building in Boston, Massachusetts. Dr. Colaiacovo's research focuses on the molecular mechanisms of meiosis, chromosome dynamics, and DNA repair in the Caenorhabditis elegans model system. Her work examines how environmental toxicants impact germline function and reproductive health, with particular emphasis on chromosome segregation, recombination, and the synaptonemal complex. Her publications reveal a consistent research trajectory examining critical aspects of meiotic chromosome behavior, including double-strand break formation and repair, crossover designation, and chromosome movement during prophase I. Her laboratory has made significant contributions to understanding how environmental exposures like bisphenol A and phthalates disrupt normal meiotic progression and lead to germline dysfunction. Dr. Colaiacovo's work demonstrates strong interdisciplinary connections between basic chromosome biology, environmental health sciences, and reproductive medicine. Her research group employs advanced genetic, molecular, and imaging techniques to dissect the complex mechanisms ensuring accurate chromosome segregation during gamete formation. Her laboratory actively collaborates with researchers studying aging, DNA repair pathways, and environmental toxicology, as evidenced by publications spanning multiple high-impact journals including Nature, PLoS Genetics, and Genetics.

Sharon RozovskyView profile

Professor

• Oxidative Stress
• Protein Quality Control
• ERAD Pathway
• +5 more

Sharon Rozovsky is a Professor in the Department of Chemistry and Biochemistry at the University of Delaware's College of Arts & Sciences, where she leads research on oxidative stress response mechanisms and protein quality control pathways. Her work bridges biochemistry, chemical biology, and structural biology with direct implications for understanding neurodegenerative diseases and viral pathogenesis. Her academic foundation includes a B.S. from Tel Aviv University (1994) and a Ph.D. from Columbia University (2000), establishing her expertise in protein dynamics and redox biochemistry. These credentials underpin her innovative approaches to studying cellular stress responses. Rozovsky's research program centers on selenoproteins—proteins containing the rare amino acid selenocysteine—and their critical roles in endoplasmic reticulum (ER) stress resolution. She investigates how membrane-bound selenoproteins like Selenoprotein S and K regulate the ER-associated degradation (ERAD) pathway, with recent work revealing their surprising autoproteolytic activity and involvement in SARS-CoV-2 replication. Her lab pioneers chemical tools including expressed protein ligation and advanced 77Se NMR spectroscopy to characterize these systems at molecular resolution. Analysis of her 2021-2025 publications shows dominant themes in selenoprotein structure-function relationships, ER stress mechanisms, and viral interactions, alongside methodological innovations in cryo-EM grid technology and NMR. This body of work demonstrates consistent focus on redox biochemistry with expanding applications in virology and structural biology. No major scientific awards or fellowships were explicitly documented in the available materials, though her research impact is evident through high-impact publications and methodological contributions. She directs the active Rozovsky Research Group, mentoring graduate students and postdoctoral researchers in biochemical and biophysical techniques. Her laboratory operations are supported by competitive funding including an NSF CAREER award (2011) focused on selenoprotein reactivity, reflecting sustained recognition of her innovative research program.

Mohsen LesaniView profile

Associate Professor

• Reliability of software systems
• Security of software systems
• Concurrent systems
• +12 more

Mohsen Lesani is an Associate Professor in the Computer Science and Engineering Department at the University of California, Santa Cruz's Baskin School of Engineering. His research focuses on reliability and security of software systems, particularly concurrent and distributed systems, with recent emphasis on secure replicated systems and distributed machine learning. Dr. Lesani received his PhD from UCLA, MS in artificial intelligence from Sharif University of Technology, and BS in software engineering from University of Tehran. He was previously a postdoc at MIT. His educational background provides a strong foundation for his interdisciplinary research spanning programming languages, distributed systems, and security. His research interests center on creating reliable and secure distributed systems. Current projects include resilient and secure distributed systems, heterogeneous and reconfigurable secure distributed systems, automatic analysis and synthesis of replicated objects, verification of distributed systems, data analytics, secure exchange across blockchains, machine learning for performance models, domain-specific languages and type systems, and automatic fence insertion for concurrent systems. His work bridges theoretical foundations with practical implementations to address real-world challenges in distributed computing. Lesani's research has been recognized with several prestigious awards including the NSF CAREER award in 2020 and DARPA YFA award in 2022. His work has also received the SIGPLAN Research Highlight in 2019, a distinguished paper award at OOPSLA 2018, and a best paper award at ISSRE 2015. These accolades reflect the impact and quality of his contributions to the field. He actively mentors PhD students in the Safe and Secure Software (S3) lab, including Xiao Li, Eric Chan, Javad Saber-Latibari, and Tejas Mane. His research has been supported by multiple NSF grants, demonstrating sustained funding for his innovative work. Lesani serves on program committees for major conferences including POPL, PLDI, OOPSLA, and DISC, contributing to the academic community. Lesani leads the Safe and Secure Software (S3) lab at UC Santa Cruz, where his team works on cutting-edge research in distributed systems, programming languages, and security. The lab fosters a collaborative environment where theoretical insights are translated into practical systems that address real-world challenges in reliability and security of distributed applications.

Manfred DrosteView profile

Professor

• Theoretical Computer Science
• Automata Theory
• Logic
• +6 more

Manfred Droste is a Professor at the Institute of Computer Science of the University of Leipzig, where he leads the Research Group on Automata and Formal Languages. He serves as Director of the Graduate Centre Mathematics, Computer Science and Natural Sciences and is Vice-speaker of the DFG-Research Training Group Quantitative Logics and Automata. His academic career spans decades of research and leadership in theoretical computer science and algebra. Prof. Droste's research focuses on theoretical computer science, particularly automata theory, logic, algebraic models for concurrent systems, and domain theory. In algebra, his interests include model theory, automorphism groups, and ordered algebraic structures. His work bridges theoretical foundations with practical applications in formal language theory and quantitative systems. His extensive publication record demonstrates a consistent focus on weighted automata, formal languages, and their logical characterizations. Over the years, his research has evolved to address increasingly complex quantitative models, with recent work focusing on weighted complexity classes, weighted linear dynamic logic, and decidability boundaries for weighted automata. Prof. Droste has received significant recognition including election to Academia Europaea, an honorary doctorate from Immanuel Kant Baltic Federal University, and fellowship in the Asia-Pacific Artificial Intelligence Association. These honors reflect his substantial contributions to theoretical computer science. He has supervised numerous PhD students including Dietrich Kuske, Paolo Boldi, and Karin Quaas, many of whom have become prominent researchers. His extensive grant portfolio includes multiple DFG projects on weighted automata and international collaborations through DAAD funding. Prof. Droste leads a vibrant research team including Andrea Hesse, Karin Quaas, Erik Paul, and others. He has organized the international workshop series "Weighted Automata: Theory and Applications" since 2002, fostering global collaboration in this specialized field.

Giulia GiordanoView profile

Professor

• Automatic control
• Complex networks
• Control systems
• +12 more

Giulia Giordano is a Full Professor in the Department of Industrial Engineering at the University of Trento, Italy, where she leads the Dynamical Networks and Systems Biology research group. She also holds a dual appointment as Visiting Professor and Delft Technology Fellow at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands. Her career includes previous positions as Assistant Professor at Delft University of Technology (2017-2019), Postdoctoral Research Fellow at Lund University, Sweden (2016-2017), and Research Fellow at the University of Udine, Italy (2016). Giulia earned her Ph.D. in Industrial and Information Engineering: Automation (Excellent) from the University of Udine with a thesis titled "Structural Analysis and Control of Dynamical Networks." She completed her M.Sc. and B.Sc. in Electrical Engineering (both Summa cum laude) at the same institution. She also undertook research visits at Caltech (2012) as a SURF Fellow and at the University of Stuttgart (2015) as a DAAD Research Scholar. Her primary research focuses on the analysis and control of dynamical networks with applications in systems biology, mathematical ecology, and mathematical epidemiology. She develops mathematical frameworks that bridge control theory, network theory, and dynamical systems to address complex problems in biological systems. Her recent work spans epidemic modeling, opinion dynamics, biochemical networks, and neurological disorders, with a particular emphasis on structural analysis of networked systems. She employs both theoretical and computational approaches to understand system behavior under uncertainty. Giulia's publications reveal a strong interdisciplinary focus, spanning from theoretical control systems to practical applications in epidemiology and biology. Her recent work shows increasing emphasis on epidemic modeling (particularly related to mpox and SARS-CoV-2), network synchronization, and the application of control theory to biological phenomena like fibromyalgia pathogenesis and opinion formation. Many of her papers appear in top-tier control journals including Automatica and IEEE Transactions on Automatic Control. 2024: Outstanding Service as Associate Editor of IEEE Control Systems Letters 2021: SIAM Activity Group on Control and Systems Theory Prize 2020: Outstanding Reviewer, Annals of Internal Medicine 2017: NAHS Best Paper Prize and EECI PhD Award 2016: Outstanding TAC Reviewer, IEEE Transactions on Automatic Control Giulia actively mentors students and postdoctoral researchers, currently supervising five postdoctoral researchers and two Ph.D. students at the University of Trento. She has advised numerous M.Sc. and B.Sc. students on topics ranging from bio-inspired modeling to optimal control of epidemic systems. Her research is supported by competitive grants including the ERC Starting Grant INSPIRE (Integrated Structural and Probabilistic Approaches for Biological and Epidemiological Systems). She serves as Associate Editor for IEEE Control Systems Letters and Automatica, and is a Senior Member of IEEE and the Control Systems Society. Giulia leads the Dynamical Networks and Systems Biology research group at the University of Trento, which maintains strong international collaborations across Europe and North America. The group's work combines theoretical advances in control theory with practical applications to pressing problems in public health and biological systems, demonstrating the power of mathematical approaches to understanding complex phenomena in the life sciences.

Laura ToniView profile

Associate Professor

• Large-scale signal processing for machine learning
• Graph signal processing
• Reinforcement learning
• +9 more

Laura Toni is an Associate Professor in the Department of Electronic and Electrical Engineering at University College London's Faculty of Engineering Sciences. She serves as the leader of a research team focused on advanced signal processing and machine learning applications, documented at https://lasp-ucl.github.io . Additionally, she holds prestigious affiliations as an ELLIS (European Laboratory for Learning and Intelligent Systems) Member and Turing Fellow Alumni. PhD in Electrical Engineering, University of Bologna (2009) MS in Electrical Engineering, University of Bologna (2005) Professor Toni's research spans theoretical and applied aspects of machine learning with particular emphasis on graph-based approaches. Her work integrates signal processing techniques with modern AI methodologies to address complex problems in communication systems, multimedia processing, and scientific discovery. She has made significant contributions to reinforcement learning theory, graph signal processing, and their applications across diverse domains including drug discovery and immersive technologies. Analysis of her recent publications reveals a strong focus on graph-based machine learning approaches, with increasing emphasis on reinforcement learning applications. Her work demonstrates a progression from theoretical foundations to practical implementations, particularly in multimedia processing, network science, and drug discovery applications. Many of her recent papers combine graph neural networks with diffusion models and reinforcement learning for complex prediction and generation tasks. Professor Toni has received notable recognition through her ELLIS membership and Turing Fellow Alumni status, which represent significant achievements in the European AI research community. ELLIS (European Laboratory for Learning and Intelligent Systems) Member Turing Fellow Alumni As an academic leader, Professor Toni supervises postgraduate students and leads a research team at UCL, focusing on cutting-edge projects at the intersection of signal processing and machine learning. Her team has secured research funding through various channels including European initiatives and industry partnerships, enabling them to pursue ambitious projects in graph learning, reinforcement learning, and multimedia processing. The team actively collaborates with institutions worldwide, including previous connections with UCSD and EPFL. Professor Toni leads the LASP research group at UCL (https://lasp-ucl.github.io), which focuses on Large-scale Adaptive Signal Processing for intelligent systems. The team comprises researchers working on graph signal processing, reinforcement learning, and multimedia applications, with strong connections to both theoretical foundations and practical implementations across various domains including healthcare, communications, and immersive technologies.

• Human Geography
• Science and Technology Studies
• Climate Change Adaptation
• +4 more

Dr. Sophie Adams serves as an ARC DECRA Fellow in the Department of Sociology at La Trobe University, where she conducts interdisciplinary research at the intersection of human geography and Science and Technology Studies. Her work critically examines the social dimensions of climate change mitigation and adaptation, with particular emphasis on renewable energy transitions and the societal implications of technological solutions to environmental crises. Adams' research program centers on the politics of environmental knowledge , investigating how climate science informs policy formation and the negotiation of social objectives in decarbonisation processes. She employs qualitative and participatory methodologies to analyze climate change adaptation in energy systems , social license for technological interventions , and the redistribution of expertise in public health and environmental governance. Her scholarship consistently addresses power dynamics in knowledge production and the democratic challenges of sustainability transitions. Analysis of her publication trajectory (2019-2025) reveals increasing focus on energy-social interfaces, with significant contributions to understanding virtual power plants, demand-side management, and the role of modeling in climate policy. Her work demonstrates methodological innovation through participatory approaches while maintaining critical engagement with resilience thinking and adaptation frameworks. Scientific recognition includes: Australian Research Council Discovery Early Career Researcher Award (ARC DECRA) Supported by her DECRA fellowship, Adams leads independent research projects examining the societal dimensions of energy transitions. Her collaborative work spans geography, public health, and energy engineering, as evidenced by interdisciplinary co-authorship patterns. Current projects investigate Australian householders' conditions for participating in virtual power plants and the social implications of climate modeling in adaptation planning, reflecting her commitment to socially relevant environmental research.

Xia Ben HuView profile

Professor

• Machine Learning
• Automated Machine Learning
• Deep Learning
• +7 more

Xia Ben Hu is a Professor in the Department of Computer Science at Rice University's Brown School of Engineering. He leads research in automated and interpretable machine learning algorithms with applications across social informatics, health informatics, and information security. His work has resulted in widely adopted systems including AutoKeras, TODS, and RLCard. Education: PhD from Arizona State University (supervised by Dr. Huan Liu) Master and Bachelor degrees from Beihang University Prof. Hu's research focuses on developing automated and interpretable machine learning algorithms for large-scale, networked, dynamic and sparse data. His work spans automated machine learning (AutoML), deep learning, fairness in AI, time series analysis, and interpretable AI. He has made significant contributions to neural architecture search, collaborative filtering, anomaly detection, and reinforcement learning in imperfect information games. His publication record shows a clear progression from foundational work in network embedding and collaborative filtering (2017) to more recent work on LLM optimization, quantization, and extending context windows (2024). A consistent thread throughout his research is the focus on making complex machine learning systems more accessible, efficient, and interpretable. Selected Awards: ACM SIGKDD Rising Star Award (2021) NSF CAREER Award (2018) Teaching + Research Excellence Award, Rice University (2023) Multiple Best Paper Awards at top venues including ICML, CIKM, and AMIA Prof. Hu has successfully mentored numerous graduate students, with recent graduates securing tenure-track positions at major universities. His research is generously supported by federal agencies including DARPA (XAI, D3M, NGS2), NSF (CAREER, III, SaTC), NIH, and industrial sponsors such as Adobe, Apple, Google, LinkedIn, and JP Morgan. He has served as General Co-Chair for WSDM 2020 and ICHI 2023, and Program Chair for AIHC 2024. He leads the DATA Lab at Rice University, which develops open-source systems for automated machine learning and reinforcement learning. The lab's AutoKeras system has over 8,000 GitHub stars and 1,000 forks, and their work has been integrated into TensorFlow, Apple production systems, and Bing production systems.

Parviz MoinView profile

Professor

• Computational Physics
• Turbulent Boundary Layers
• Hypersonic Flows
• +4 more

Parviz Moin holds the Franklin P. and Caroline M. Johnson Professorship in Stanford University's School of Engineering. As founding director of the Center for Turbulence Research (CTR)—a NASA-Stanford consortium established in 1987—he has pioneered computational methods for turbulence physics, including direct numerical simulation and Large Eddy Simulation (LES) techniques. CTR serves as an international hub for turbulence studies across engineering, mathematics, and physics disciplines. Moin's research encompasses computational physics of turbulent flows, with emphasis on boundary layer control, hypersonic aerodynamics, propulsion systems, and aircraft icing. His recent work advances high-fidelity simulations for aerospace applications, particularly developing wall models for LES that accurately capture separation phenomena under complex pressure gradients and Reynolds number effects. Recent publications demonstrate extensive applications of LES to aircraft design challenges, including transonic buffet prediction, high-lift configuration analysis, and icing aerodynamics. Investigations consistently address fundamental turbulence physics while developing practical computational tools for aerospace engineering, with particular focus on hypersonic boundary layers, flow separation mechanisms, and conjugate heat transfer in iced environments.

Themistoklis SapsisView profile

Professor

• Probabilistic Prediction
• Nonlinear Systems Analysis
• Machine Learning in Fluid Dynamics
• +3 more

Themistoklis Sapsis is a Professor in the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where he also holds an affiliation with the MIT Institute for Data, Systems, and Society. He earned his Ph.D. in Mechanical Engineering from MIT in 2011 and previously served as an Assistant Research Scientist at NYU’s Courant Institute of Mathematical Sciences. His research focuses on developing analytical, computational, and data-driven methods to predict and quantify extreme events in high-dimensional nonlinear systems, such as turbulent fluid flows and mechanical systems. Key areas include probabilistic modeling of climate extremes, machine learning for climate simulation corrections, and uncertainty quantification in complex dynamical systems. Recent work emphasizes applications in ocean engineering (e.g., vortex-induced vibrations, wave energy systems) and environmental science (e.g., spatially resolved climate extremes, bias correction in Earth system models). His methodologies combine stochastic emulators, Bayesian experimental design, and neural networks to address challenges in data sparsity and model fidelity. Notable contributions include frameworks for correcting coarse-scale climate simulations using machine learning, real-time ocean temperature reconstruction from satellite data, and data-driven modeling of hydrodynamic interactions in marine risers. His research bridges theoretical developments with practical applications in energy systems, structural monitoring, and autonomous systems. Prof. Sapsis collaborates with interdisciplinary teams and has contributed to initiatives such as FIRSTLING-DIGIMAR (a marine riser digital twin) and multi-fidelity frameworks for autonomous seakeeping. His work is supported by grants focused on advancing machine learning in scientific modeling and extreme event prediction.

• Antibiotic Resistance
• Micropollutants
• Pyrolysis
• +7 more

Dr. Patrick J. McNamara is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at Marquette University's College of Engineering. He directs the McNamara Research Group, which focuses on understanding how chemicals from consumer products impact public health and the environment once they pass through water treatment systems. His research bridges environmental engineering and microbiology to address critical water quality challenges facing modern infrastructure. Dr. McNamara's educational background includes: Ph.D., 2012, Civil Engineering, University of Minnesota, Twin Cities M.S., 2008, Environmental and Water Resources Engineering, University of Texas at Austin B.S., 2006, Civil Engineering (Minor - Spanish for the Business Professions), Marquette University His research program investigates how consumer product chemicals impact engineering treatment processes that rely on healthy bacteria to treat water. The McNamara Research Group develops non-traditional treatment processes to remove these chemicals from water and mitigate their environmental effects. His work spans antibiotic resistance in water systems, micropollutant removal technologies, pyrolysis of biosolids, PFAS contamination, and electrochemical treatment processes. Specific areas include the impact of corrosion inhibitors on antibiotic resistance, removal of chemicals via drinking water treatment, environmental antibiotic resistant bacteria, beneficial biosolids reuse, and pyrolysis applications. Dr. McNamara's publication record demonstrates a strong focus on emerging water quality challenges, particularly the intersection of chemical contaminants and antibiotic resistance. His recent work examines corrosion inhibitors' impact on antibiotic resistance in drinking water, PFAS mitigation through advanced treatment processes, and environmental drivers of antibiotic resistance in stormwater systems. His research combines fundamental microbiology with practical engineering solutions to address complex water quality issues. Dr. McNamara has received numerous honors and awards: 2022 OCOE Outstanding Researcher Award from Marquette University Marquette University's Campus 2020 KEEN Rising Star Faculty Scholar Award from Provost Office (2019) Central States Water Environment Association Bill Boyle Outstanding Educator Award (2018) Way Klingler Young Scholar Award (Marquette University, 2018) Excellence in Review Award – Environmental Science & Technology (2017) Dr. McNamara has secured significant research funding as Principal Investigator on multiple projects, including NSF grants focused on mitigating antibiotic resistance in drinking water and studying the environmental impacts of quaternary ammonium compounds. His current research portfolio includes projects on PFAS removal through novel electrocoagulation-peroxidation processes, designing green stormwater infrastructure to combat antibiotic resistance, and removing contaminants from greywater using electrocoagulation technology in collaboration with industry partners like Kohler Company. The McNamara Research Group at Marquette University maintains strong collaborations with researchers across multiple institutions and works closely with water utilities and industry partners to translate research findings into practical solutions for water treatment challenges. Their work addresses critical infrastructure needs while protecting environmental and public health through innovative engineering approaches.

Max AlekseyevView profile

Associate Professor

• Computational Graph Theory
• Enumerative Combinatorics
• Computational/Algorithmic Biology
• +1 more

Max Alekseyev is an Associate Professor in the Mathematics Department and Computational Biology Institute. His research spans computational graph theory, enumerative combinatorics, computational/algorithmic biology, and comparative genomics. He focuses on interdisciplinary problems, blending mathematics with biological applications, particularly in genome assembly and analysis. His work includes advancements in genome scaffolding algorithms, combinatorial sequence analysis, and mathematical biology. Notable contributions involve genome assembly tools like CAMSA and studies on ancestral genome reconstruction. He also explores theoretical topics such as Bernoulli series generalizations and modular data classification. His research trends highlight a blend of pure mathematics (e.g., number theory, graph theory) and applied computational methods, addressing challenges in genomics and evolutionary biology. He secured an NSF Student Travel Grant in 2018 for computational molecular biology.