Deterministic Networks Professors and Supervisors | ApplySharif

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Sabita MaharjanView profile

Professor

University of OsloNorway

Sabita Maharjan is a Full Professor at the Department of Informatics, University of Oslo, Norway, and a Senior Research Scientist (adjunct) at Simula Metropolitan Centre for Digital Engineering. She holds a PhD in Networks and Distributed Systems from the University of Oslo (2013) and has been active in research and academic leadership since. Her roles include Associate Editor for IEEE Internet of Things Journal and Guest Editor for several high-impact journals. Education: Ph.D. in Networks and Distributed Systems (2013), University of Oslo; M.Sc. in Antenna and Propagation (2008), Tokyo Institute of Technology. Prior roles include Postdoctoral Fellow at Simula Research Lab (2014–2016) and Visiting Scholarships at Zhejiang University and UIUC. Research focuses on Green Networks, Energy Efficiency, Smart Grid, Vehicular Networks, and Machine Learning applications. Key interests include network security, privacy-preserving systems, and edge computing. Recent work emphasizes digital twins, federated learning, and resilience in smart grids. Publications span 5G/6G, blockchain for energy systems, and AI-driven network optimization. Notable contributions include privacy-preserving pricing schemes and secure vehicular edge computing frameworks. Awards include three Best Paper Awards (IEEE SCALCOM 2015, IEEE CS CLOUD 2016, IEEE ICCT 2019) and the 2020 IEEE TCGCC Outstanding Young Researcher Award. She is an IEEE Senior Member since 2019. Leadership roles: Vice-Chair of IEEE TCGCC SIG on Green AI (2020–present), Member of UiO’s AI/ML Study Programs Working Group. Supervises students in Energy Informatics and leads projects like CRISP (NFR-funded) and PriTEM. Research groups: Digital Infrastructure and Security (DIAS) at UiO and Simula’s Center for Resilient Networks. Active in organizing summer schools on Energy Informatics and Green Computing.

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Huiya YanView profile

Professor

University of Wisconsin-La CrosseUnited States

Huiya Yan is a Professor in the Department of Mathematics & Statistics at the University of Wisconsin-La Crosse. His research focuses on Graph Theory and its applications, including topics like supereulerian graphs, connectivity indices, and network topologies. He has held academic positions since 2005, progressing from a Graduate Teaching Assistant at West Virginia University (2005-2009) to Assistant (2009-2013), Associate (2013-2019), and Full Professor (2019-present) at UWL. Education: Ph.D., West Virginia University (2009) M.S., Beijing Institute of Technology (2005) B.S., Shandong University of Technology (2002) Professor Yan’s work bridges theoretical graph theory with practical applications in wireless networks and combinatorial optimization. His recent research explores topological indices (e.g., Zagreb index) and their implications for graph properties like Hamiltonicity and connectivity. He has published extensively in journals such as Discrete Applied Mathematics and IEEE Transactions on Mobile Computing , with a focus on advancing understanding of graph structures and network performance. His teaching spans undergraduate and graduate courses in mathematics, including Discrete Mathematics, Calculus, and Graph Theory. He advises students through initiatives like the Putnam Math Competition practice sessions and maintains virtual office hours for accessibility.

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Michael DrmotaView profile

Professor

Vienna University of TechnologyAustria

Prof. Michael Drmota is a distinguished academic at TU Wien's Department of Combinatorics and Algorithms, part of the Faculty of Mathematics and Geoinformation. He leads research in discrete mathematics, focusing on combinatorics, number theory, and random discrete structures. His work bridges pure mathematics with applications in computer science and statistical physics. Research Interests: Drmota's expertise spans analytic combinatorics, additive number theory, random graph theory, and the study of automatic sequences. He explores topics like prime number distributions, planar map structures, and stochastic processes in discrete systems. His methods often involve generating functions, singularity analysis, and probabilistic techniques. Key Projects: He coordinates major research initiatives including Arithmetic Randomness , Shape Characteristics of Planar Maps , and Infinite Singular Systems . These projects investigate foundational questions in combinatorics and number theory, with implications for algorithm design and mathematical physics. Advising & Collaboration: Drmota supervises graduate students and collaborates internationally. Notable advisees include Andreas Nessmann (discrete polyharmonic functions), Lucas Unterberger (Erdős-Ko-Rado theorems), and Guan-Ru Yu (pattern occurrences in planar maps). His work appears in top-tier journals and conference proceedings. Labs/Teams: Active in TU Wien's Network Lab , he fosters interdisciplinary research on complex networks and algorithmic combinatorics.

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Milad SiamiView profile

Associate Professor

Northeastern UniversityUnited States

Milad Siami is an Associate Professor in the Department of Electrical & Computer Engineering at Northeastern University. His research focuses on sparse sensing, distributed systems, and network control with applications in robotics and epidemic modeling. He holds a PhD from Lehigh University and has conducted postdoctoral research at MIT. Education: Postdoctoral Associate, MIT (2019) Ph.D., Mechanical Engineering (Control), Lehigh University (2017) M.Sc., Mechanical Engineering (Control), Lehigh University (2014) M.Sc., Electrical Engineering (Control), Sharif University of Technology (2011) B.Sc., Electrical Engineering (Control) and Pure Mathematics, Sharif University of Technology (2009) Research Interests: Sparse Sensing and Control in Cyber-Physical Systems Robotic Activity Recognition via WiFi Sensing Epidemic Control through Network Centrality Analysis Fundamental Limits in Large-Scale Dynamical Networks Grants & Awards: NSF Award ($1.1M) for Epidemic Control Modeling (2022) DoD Award ($7.5M) for Robust AI Systems (2021) Tenured Associate Professor at Northeastern (2025) Lab & Teams: The Siami Lab develops methods for efficient network control, including the RoboFiSense WiFi-based robotic activity recognition framework and Sparse Sensor Selection algorithms. Current projects include real-time sensor scheduling and decision-support systems for pandemic mitigation.

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Zaida Ann Luthey-SchultenView profile

Professor

University of Illinois Urbana-ChampaignUnited States

Zaida Ann Luthey-Schulten is a Professor in the Department of Chemistry at the University of Illinois, with joint appointments at the Beckman Institute for Advanced Science and Technology and the Carl R. Woese Institute for Genomic Biology. She holds the Murchison-Mallory Chair in Chemistry. Education: B.S. in Chemistry (University of Southern California, 1969), M.S. in Chemistry (Harvard University, 1972), Ph.D. in Applied Mathematics (Harvard University, 1975). Previous Positions: Research Fellow at Max-Planck Institute for Biophysical Chemistry (1975-1980) and Technical University of Munich's Department of Theoretical Physics (1980-1985). Her research focuses on computational biology, particularly whole-cell simulations of bacterial and eukaryotic organisms, aiming to determine the rules of life for minimal cells . Key areas include: GPU-accelerated modeling of stochastic and deterministic cellular processes Biomolecular energy landscapes for structure prediction and folding kinetics Protein folding thermodynamics and kinetics via statistical methods Structural genomics of metabolic pathways Physical bioinformatics tools like VMD/Multiple Alignment Evolution of translation and cellular networks Recent articles highlight whole-cell modeling , diffusive molecular motion , and 3D visualization via Minecraft . She has pioneered the Lattice Microbes software for hybrid stochastic-deterministic simulations. Scientific awards include: Fellow, Biophysical Society (2019) Murchison-Mallory Professor of Chemistry (2019) Fellow of the American Physical Society (2000) Fellowships at LMU Munich (2014) and Hebrew University (1998) She leads interdisciplinary collaborations in physical bioinformatics , molecular dynamics , and systems biology , with significant citations and media coverage of her work on cellular simulations.

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Federico PancieraView profile

Researcher

Paris-Saclay UniversityFrance

Federico Panciera is a CNRS Research Scientist (CRCN) at the Center for Nanoscience and Nanotechnology (C2N) in Palaiseau, France. His research focuses on fundamental and applied aspects of semiconductor physics and materials science at the nanoscale, particularly growth mechanisms of III-V nanowires for applications in optoelectronics, renewable energy, and quantum computing. Research employs advanced in situ transmission electron microscopy techniques to study nanowire growth dynamics, phase selection, and nanostructure synthesis. Recent investigations include electric field modulation of growth, crystal phase switching, and van der Waals superlattice fabrication. As local contact for the NanoMAX microscope through METSA network, he facilitates nanoscale characterization. Publications demonstrate expertise in real-time nanomaterial observation, with themes including vapor-liquid-solid growth mechanisms, carbon nanotube synthesis under electric fields, and defect engineering in semiconductor nanowires. Research collaborations include University of Cambridge, National University of Singapore, and IBM T.J. Watson Research Center.

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Ran MeiView profile

Assistant Professor

University of Illinois Urbana-ChampaignUnited States

Ran Mei is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign. His research investigates microbial ecology at the microbiome-environment interface in natural and engineered water systems, developing microbiology-informed engineering strategies for sustainable water systems. The Mei Research Group integrates genomics, evolutionary biology, and engineering design to decode complex microbiomes and develop solutions for wastewater treatment and environmental protection. Research themes include: 1) Microbial evolution in engineered ecosystems to discover novel metabolic capabilities for pollutant degradation; 2) Microbial biomass degraders for efficient waste decomposition; and 3) Bridging biological and physical dynamics for optimized bioreactor performance. Professor Mei teaches Water Quality Control II (CEE 538) and Water Quality Engineering (CEE 437). He has received several awards including the DOE JGI CSP New Investigator Award (2023) and AEESP Outstanding Doctoral Dissertation Award (2021). Current research grants include projects on microplastic-degrading algae and novel aeration strategies for wastewater treatment.

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Lavanya MarlaView profile

Associate Professor

University of Illinois Urbana-ChampaignUnited States

Lavanya Marla is an Associate Professor with tenure in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign (UIUC). She holds a PhD in Transportation Systems from MIT (2010) and dual M.S. degrees in Transportation and Operations Research from MIT (2007). Her academic roles include visiting appointments at NYU (2023-2024), Indian School of Business (2015), and a prior position as Assistant Professor at UIUC (2013–2022). She also holds zero-time appointments in Civil and Environmental Engineering, Information Trust Institute, and the Center for Autonomy. Her research focuses on applying large-scale optimization, machine learning, and data-driven methods to enhance logistics and transportation systems' efficiency, resilience, and sustainability. Key areas include air transportation networks, emergency medical services (EMS), and cyber-physical systems. She bridges Operations Research and AI, emphasizing decision-making under uncertainty and robust network design. Recent work addresses climate change impacts on airline networks, cybersecurity in shipping ports, and algorithmic solutions for healthcare logistics. Her articles span journals like Transportation Science , IEEE Transactions , and Operations Research Forum . Notable awards include the Alfred P. Sloan Fellowship (2008–2009) and the 2021 Center for Advanced Study Fellowship. Her grants include funding from NSF, DHS, and collaborations with industries like Flyr Labs and Deepair Solutions. She mentors over 20 students, emphasizing diversity and equity in STEM. Current projects include drone-bystander-ambulance coordination and pandemic healthcare management through the Jump-ARCHES initiative.

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Maya PrzybylskiView profile

Associate Professor

University of WaterlooCanada

Maya Przybylski is O’Donovan Director and Associate Professor at the School of Architecture, University of Waterloo , where she co-founded the DATAlab research group. She holds an M.Arch from the University of Toronto’s Faculty of Architecture, Landscape and Design—earning the Royal Architectural Institute of Canada Medal—and a BSc in Computer Science from the University of Toronto. Research Focus Maya’s scholarship integrates architecture with software engineering to interrogate how data abundance and computational design transform architectural theory, methods, tools, and outcomes. Her SSHRC-funded “Soft Materials” project (2018-) develops architecture-specific approaches that weave sociocultural agency into the technical capacities of data-driven design. Her design work fuses software, data, hardware and physical materiality to envision citizen-oriented, software/data-enabled cities. Awards & Recognition Sandford Fleming Foundation Award for Excellence in Undergraduate Teaching (2018) University of Waterloo Faculty Award (2019) Teaching & Advising Maya has taught core and advanced design studios and research seminars including: ARCH 193 – Design Studio ARCH 484 – Architectural Research ARCH 684 – Special Topics in Architecture ARCH 693 – Thesis Research & Design Studio II She is currently accepting applications from graduate students interested in computational design, data ethics, and experimental material systems. Editorial Leadership Maya is a founding editor of the bracket book series, co-editing bracket [On Farming] (2010) and bracket [At Extremes] (2015), both supported by the Graham Foundation for the Arts.

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Michal BajcsyView profile

Associate Professor

University of WaterlooCanada

Michal Bajcsy is an Associate Professor at the University of Waterloo, affiliated with the Institute for Quantum Computing (IQC). His research focuses on quantum optics, photonics, and atomic physics, with a particular emphasis on quantum state engineering, photon manipulation, and nanotechnology applications. He has contributed to advancements in non-classical light generation, deterministic photon subtraction techniques, and the integration of quantum emitters with atomic ensembles. His work spans theoretical and experimental domains, including the development of novel devices such as hollow-core photonic-bandgap fibers for cold atom confinement and high-efficiency quantum dot-based photon sources. He has also explored applications in biosensing using graphene-based field-effect transistors and microwave-to-optical quantum transducers. Bajcsy’s publications highlight innovations in quantum communication, such as frequency conversion for quantum repeaters and strategies to enhance quantum state control. His research often intersects with material science, as seen in studies on hydrogen-doped titanium dioxide and co-doped graphene oxide gels for electronic applications. While his profile does not explicitly mention awards, grants, or formal advisees, his extensive publication record reflects a sustained focus on experimental quantum systems and their practical implementations.

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Weronika Potok-SzybinskaView profile

Lecturer

ETH ZurichSwitzerland

Dr. Weronika Potok-Szybinska is a Lecturer at the Department of Health Sciences and Technology at ETH Zürich. Her work focuses on neurophysiological mechanisms of sensory and motor systems, with particular emphasis on the effects of non-invasive brain stimulation techniques such as transcranial random noise stimulation (tRNS) and transcranial alternating current stimulation (tACS). She investigates how these interventions modulate visual contrast sensitivity, motor cortex responsiveness, and neural plasticity across various brain regions. Her research integrates neuroimaging (e.g., fMRI), electrophysiological recordings, and behavioral assessments to explore topics like handedness-related brain lateralization, praxis-language network interactions, and pandemic-era guidelines for clinical neurostimulation safety. Key areas of expertise include transcranial stimulation protocols, sensory-motor integration, and neuroplasticity mechanisms. Research Themes: Transcranial Stimulation Effects, Neuroplasticity, Visual-Motor Systems, Cerebral Lateralization Methodologies: fMRI, TMS/tES, Electrophysiology, Behavioral Testing Recent publications highlight advancements in understanding how tRNS acutely lowers motor circuit thresholds, enhances visual contrast detection, and interacts with brain regions such as the primary visual cortex and supramarginal gyrus. Collaborative projects include exploring the neural basis of manual praxis and language production networks in left-handed individuals.

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Samik BasuView profile

Professor

Iowa State UniversityUnited States

Samik Basu is a Professor and Director of Graduate Education in the Department of Computer Science at Iowa State University. His research focuses on formal methods, software engineering, and computer security. He holds a Ph.D. from Stony Brook University (2003), an M.S. from Stony Brook (2001), and a B.E. from Jadavpur University (1998). Research Interests: Formal Verification and Model Checking Submodular Optimization and Algorithm Design Qualitative Preference Reasoning Web Services Composition and Security Autonomous Systems and Control Theory His recent work emphasizes submodular maximization algorithms, preference-based decision systems, and formal methods for safety-critical systems. Key grants include NSF awards IIS 225823, CCF 1555780, and others. He advises a diverse group of graduate students in the Formal Methods Group, focusing on projects like intrusion response systems, neural network verification, and multi-stakeholder preference analysis. Students advised include PhD candidates like Erik Rauer and Yanhui Zhu, along with numerous MS/BS researchers. His lab has published extensively in top venues like AAAI, IJCAI, and IEEE conferences.

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Richard PengView profile

Associate Professor

Carnegie Mellon UniversityQatar

Richard Peng is an Associate Professor in the Computer Science Department at Carnegie Mellon University, part of the School of Computer Science. He specializes in designing efficient algorithms for fundamental computational problems, particularly in graph algorithms, dynamic algorithms, and linear algebraic computations. Prior to joining CMU in 2023, he earned his BMath from the University of Waterloo, a PhD from CMU under Gary L. Miller, and completed a postdoc at MIT's Applied Math department. His research focuses on advancing algorithmic efficiency, including work on sparse linear systems, graph convolutions, and flow optimization. He advises PhD students Hoai-An Nguyen, Alicia Stepin, and Junzhao Yang. His teaching includes courses such as 15495 and 15151, reflecting his engagement in both research and education. Peng’s articles span topics like approximate spanning tree counting, dynamic graph algorithms, and Laplacian solvers, emphasizing practical scalability and theoretical guarantees. His contributions bridge theoretical computer science with applied challenges in network analysis and numerical computation.

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David D. GamarnikView profile

Professor

Massachusetts Institute of TechnologyUnited States

David D. Gamarnik is a Professor of Operations Research at the MIT Sloan School of Management, affiliated with the Operations Research and Statistics Group. He holds a BA in Mathematics from New York University (1993) and a PhD in Operations Research from MIT (1998). Prior to joining MIT in 2005, he was a Research Staff Member at IBM T.J. Watson Research Center. His research focuses on discrete probability, optimization, quantum computing, machine learning, and stochastic processes. He serves as an Area Editor for Mathematics of Operations Research and has held editorial roles in journals such as Operations Research and Annals of Applied Probability . Education: Bachelor of Arts in Mathematics, New York University (1993) PhD in Operations Research, MIT (1998) His honors include the Erlang Prize, Best Publication Award from INFORMS’ Applied Probability Society, and recognition as a finalist for the Franz Edelman Prize. He is a Fellow of the Institute for Mathematical Statistics, Institute for Operations Research and Management Science, and the American Mathematical Society. His academic contributions span theoretical computing, algorithm design, and applications in healthcare (e.g., kidney exchange models). Recent work explores algorithmic barriers in optimization, quantum computing, and high-dimensional statistics.

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Matthias KatzfussView profile

Professor

University of Wisconsin-MadisonUnited States

Matthias Katzfuss is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications in environmental science and satellite remote sensing. He has received funding from NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. His scientific achievements include awards such as the NSF Career Award, a Fulbright Scholarship, and the Early Investigator Award from the American Statistical Association’s Section on Statistics and the Environment. His work bridges theoretical advancements and real-world applications, particularly in environmental monitoring and large-scale data fusion. Recent research trends include scalable methods for high-dimensional spatial data, nonstationary covariance modeling, and Bayesian transport maps. His publications address challenges in remote sensing, climate science, and machine learning, emphasizing computational efficiency and methodological innovation. Matthias collaborates extensively with institutions like JPL and NASA, contributing to global-scale environmental studies. His lab focuses on developing open-source software tools, such as the GPgp package, to enable reproducible research in spatial statistics.

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