Applied Signal Processing Professors and Supervisors | ApplySharif

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Brian HedlundView profile

Professor

University of Nevada, Las VegasUnited States

Brian Hedlund is a Professor in the Department of Life Sciences at the University of Nevada, Las Vegas (UNLV). His research focuses on microbial ecology and genomics, particularly in extremophilic environments such as geothermal springs. He leads studies exploring microbial biodiversity, including 'dark' lineages of bacteria that remain underexplored. Hedlund employs advanced techniques like single-cell genomics, metagenomics, and stable isotope analysis to understand microbial roles in ecological processes. His work bridges fundamental research with applied biotechnology, including biofuels development and disease diagnostics. Hedlund’s research is funded by major agencies like NSF, NASA, DOE, and NIH. He co-authored the SeqCode, a nomenclatural system for naming uncultivated prokaryotes based on genomic data, and serves on grant review panels for national funding bodies. Education: Ph.D., Microbiology, University of Washington B.S., Biology, University of Illinois Research Interests: Microbial biodiversity in extreme environments Functional genomics of uncultivated microorganisms Applications in astrobiology and biotechnology International collaborations, particularly with China Grants & Funding: Major support from NSF, NASA, DOE, and NIH SeqCode development and microbial naming initiatives Labs & Teams: Hedlund collaborates with industrial and academic partners on projects ranging from biofuels to human microbiome studies, emphasizing interdisciplinary approaches.

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Marc RousselView profile

Professor

University of LethbridgeCanada

Marc Roussel is a Professor in the Department of Chemistry and Biochemistry at the University of Lethbridge, affiliated with the Alberta RNA Research and Training Institute. His research focuses on mathematical modeling of biochemical systems, enzyme kinetics, and systems biology. He has authored multiple books, including Foundations of Chemical Kinetics and A Life Scientist's Guide to Physical Chemistry . His work bridges mathematics and biology, emphasizing dynamical systems, delay differential equations, and model reduction techniques. He has received the 2024 Distinguished Teaching Award from the University of Lethbridge. Key research areas include biochemical networks, gene expression dynamics, and metabolic modeling. Roussel collaborates with experimentalists to study complex biological phenomena such as circadian rhythms, somite formation, and enzyme mechanisms. His contributions span theoretical frameworks, computational methods, and interdisciplinary applications in systems biology.

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Javier VelaView profile

Academic

Iowa State UniversityUnited States

Javier Vela is a University Professor and Associate Chair of the Department of Chemistry at Iowa State University, where he also serves as a faculty scientist with the Ames National Laboratory. His research program focuses on the synthesis and characterization of nanostructured materials with applications in energy conversion, chemical catalysis, and fluorescence imaging, with over one hundred peer-reviewed publications and patents to his name. Dr. Vela received his educational training at prestigious institutions: B.S. (with Honors) in Chemistry from UNAM (2001) M.S. in Chemistry from University of Rochester (2003) Ph.D. in Chemistry from University of Rochester (2005) Following his doctoral studies, he completed postdoctoral research at the University of Chicago and Los Alamos National Laboratory before joining Iowa State University in 2009, where he received tenure in 2015, became a full professor in 2019, and was named University Professor in 2020. Dr. Vela's research program centers on the development of novel nanomaterials, particularly focusing on: Synthesis of nanostructured materials for energy applications Development of inorganic compounds for chemical catalysis Design of fluorescent materials for imaging applications Surface chemistry and structural characterization of nanocrystals Lead-free semiconductor materials for sustainable technologies Advanced solid-state NMR techniques for nanomaterial characterization His group employs a range of advanced techniques including solid-state NMR spectroscopy, dynamic nuclear polarization, and computational methods to understand nanomaterial structure-property relationships at the atomic level. Analysis of Dr. Vela's recent publication record reveals a strong focus on advanced materials characterization techniques, particularly solid-state NMR applications to nanomaterials. His work spans multiple disciplines including materials science, analytical chemistry, and environmental chemistry, with particular emphasis on sustainable energy solutions and advanced characterization methods. A significant portion of his recent work addresses challenges in nitrate reduction for sustainable ammonia production and the development of lead-free semiconductor alternatives through innovative synthesis approaches. Dr. Vela has received numerous prestigious awards and honors: Fulbright Scholar in Italy (2024) ACS Fellow (2021) John D. Corbett Endowed Professor (2020-2023) AAAS Fellow (2018) NSF CAREER Award (2013) Multiple LAS awards including Cassling Innovator (2018) and Early Achievement in Research (2014) ACS Midwest Stanley C. Israel Award (2014) As an educator and mentor, Dr. Vela has directed twenty doctoral and four master's theses while successfully mentoring numerous undergraduate researchers, including three NSF graduate research fellowship awardees. His research has been supported by significant funding including the NSF CAREER Award and various other grants that have enabled his group to investigate nanostructured materials for energy conversion, chemical catalysis, and fluorescence imaging applications. His service to the chemical community includes serving as Councilor for the Ames local section of the American Chemical Society, Program Chair for the Midwest Regional Meeting in 2018, and Treasurer of the Division of Inorganic Chemistry. Dr. Vela leads an active research group focused on inorganic and materials chemistry, with strong connections to the Ames National Laboratory where he has been a faculty scientist since 2010. His group collaborates extensively with researchers across disciplines to address challenges in sustainable energy and advanced materials development, maintaining an active presence in the scientific community through publications, conference presentations, and editorial board service for journals including ACS Energy Letters, Chemistry of Materials, and ACS Applied Engineering Materials.

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Edoardo SerraView profile

Associate Professor

Boise State UniversityUnited States

Edoardo Serra is an Associate Professor in the Department of Computer Science at Boise State University (BSU), a role he has held since July 2021. He previously served as an Assistant Professor at BSU from 2015 to 2021 and holds a joint appointment as a Senior Researcher at Pacific Northwest National Laboratory (PNNL) since June 2021. Since January 2023, he has co-directed the Computing Ph.D. Program at BSU and serves as General Chair of the 2024 ACM CIKM Conference. His academic journey includes a Ph.D. in Computer Science Engineering from the University of Calabria, Italy (2012), followed by postdoctoral positions at the University of Calabria and the University of Maryland. He also served as a Visiting Researcher at UCLA (2010–2011). His research focuses on AI/ML applications in cybersecurity, graph representation learning, generative AI, and robust AI systems. Notable projects include: NSF-funded cybersecurity curriculum integration Department of Defense-funded analysis of terrorist networks Idaho Department of Commerce precision agriculture initiatives Key research areas include graph neural networks, adversarial robustness, and ML-driven security solutions. His work has been recognized with awards such as Best Application Paper (2021) and Best Paper Award (2018). He actively contributes to professional service roles, including program chairs and editorial boards. Current projects emphasize AI ethics, generative models, and scalable graph algorithms. He advises on applied AI consulting for industry and government, focusing on model interpretability and cybersecurity implications.

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Comlan de SouzaView profile

Professor

California State University, FresnoUnited States

Comlan de Souza is a Professor of Mathematics at Fresno State, affiliated with the College of Science and Mathematics. He holds a Ph.D. in Mathematics from Southern Illinois University at Carbondale. His research focuses on Fourier Analysis, Digital Signal/Image Processing (particularly the phase recovery problem), Numerical Linear Algebra, and B-splines interpolation. Dr. de Souza teaches a wide range of mathematics courses at both undergraduate and graduate levels. His academic contributions include advancing theoretical and applied aspects of his research areas. While no specific articles or awards are listed, his teaching encompasses courses such as Math 6 through Math 271, reflecting his broad expertise in foundational and advanced mathematical topics.

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Ludo AusielloView profile

Senior Lecturer

University of PortsmouthUnited Kingdom

Dr. Ludo Ausiello is a Senior Lecturer in Electronics at the University of Portsmouth, affiliated with the School of Electrical and Mechanical Engineering and the Faculty of Technology. His roles include membership in the Portsmouth AI and Data Science Centre and the Portsmouth Centre for Advanced Materials and Manufacturing. He holds a Fellowship of the Higher Education Academy and is a member of the Institute of Acoustics. Education: Integrated Master’s in Electronic Engineering (University of Bologna), European PhD in Audio Engineering, and PGCert in Higher Education (2019). Professional training includes Fonoprint Studios (Bologna) and industry experience at Maserati, Magneti Marelli, and Harman. Research interests focus on audio engineering, signal processing, electro-acoustics, and innovative manufacturing. Recent work includes developing affordable measurement ecosystems for musical acoustics, optimizing soundboard designs via least-squares analysis, and applying AI to room acoustics prediction. His studies span topics like wood elasticity, loudspeaker design, and vocal fatigue in educational spaces. Awards: Fellowship of the Higher Education Academy (2019). Teaching and advising: Taught analog and digital oscillator design at the University of Bologna and contributed to curriculum development at Solent University. Active in mentoring students through collaborative industry projects. Labs/Teams: Engaged with interdisciplinary teams at Portsmouth, focusing on advanced materials and AI-driven acoustic solutions.

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Yong ZhangView profile

Researcher

Schloss Dagstuhl - Leibniz Center for InformaticsGermany

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

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Lukas GraberView profile

Associate Professor

Georgia Institute of TechnologyUnited States

Lukas Graber is an Associate Professor and Sutterfield Family Early Career Professor at Georgia Tech's School of Electrical and Computer Engineering (ECE). His primary affiliations include the Plasma and Dielectrics Laboratory, focusing on high-voltage engineering, superconductivity, and cryogenic systems. He holds a Ph.D. and M.S. from ETH Zurich (2009 and 2002), with postdoctoral and research faculty experience at Florida State University's Center for Advanced Power Systems. His technical expertise spans gas-insulated switchgear, superconducting power cables, and high-speed mechanical switches. Key research areas include dielectric materials for supercritical fluids, fault current limiters, and insulation coordination for power systems. He has authored/co-authored 40+ publications and holds multiple patents in energy-related technologies. Research interests emphasize commercialization of novel energy technologies, with a focus on cryogenic electronics and eco-friendly alternatives to SF 6 . Notable awards include the ETG Innovation Prize (2010) and Electrosuisse Best Paper Award (2009). His work integrates fundamental research with applied engineering solutions for naval, aerospace, and grid systems. Labs/Teams: Director of the Georgia Tech Plasma and Dielectrics Laboratory, collaborating on projects like the TESLA Breaker and cryogenic link systems for aircraft propulsion. Active in IEEE and the Cryogenics Society of America.

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Xiaoqiang WangView profile

Professor

Florida State UniversityUnited States

Xiaoqiang Wang is a Professor in the Department of Scientific Computing at Florida State University (FSU). His research focuses on numerical analysis, applied partial differential equations, mathematical biology, image processing, and scientific computing. He holds a Ph.D. from Pennsylvania State University (2005). His work emphasizes phase-field modeling for elastic bending energy, biological microstructures, and computational methods for complex systems. Notable contributions include advancements in centroidal Voronoi tessellation algorithms for image segmentation and high-performance computing techniques for scientific visualization. Recent publications highlight innovations in topology-preserving phase-field models, neural network-based energy minimization, and stochastic resource competition models. His research bridges theoretical mathematics with practical applications in biophysics, materials science, and biomedical engineering. Wang collaborates actively with interdisciplinary teams, contributing to FSU's computational science initiatives. His lab focuses on developing novel numerical methods and simulations for biological and physical systems, reflecting a commitment to both foundational and applied research.

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Shakir JiffriView profile

Lecturer

Swansea UniversityUnited Kingdom

Dr. Shakir Jiffri is a Lecturer in Aerospace Engineering at Swansea University's School of Aerospace, Civil, Electrical and Mechanical Engineering. His research focuses on Linear and Nonlinear Structural Dynamics, Aeroelasticity, and Active Control methods, particularly in flutter suppression and non-smooth systems. He has contributed to experimental studies on feedback linearisation and nonlinear control strategies, with applications in aeroelastic systems and robotics. Teaches modules including Strength of Materials, Engineering Mechanics, and Aerospace Control. Supervised PhD and MSc students in active control methodologies and inertial amplifier concepts. Research interests include: Aeroelasticity, Nonlinear Control, and Structural Dynamics. Recent work addresses composite panel optimization and vibration control in robotic systems. His publications span journals like Journal of Guidance, Control, and Dynamics and Mechanical Systems and Signal Processing, emphasizing experimental validation of control strategies.

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Tania CerquitelliView profile

Professor

Polytechnic University of TurinItaly

Tania Cerquitelli is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where she leads research in data science, concept-drift management, and inclusive AI technologies. She is a member of SmartData@PoliTO, the GEDI Observatory for Gender Equality, and serves in leadership roles related to social affairs and community policies at the university level. She also acts as a scientific advisor for the partnership with Accenture. Her research interests span Data Science , Concept-Drift Management , Database Systems , Conversational Data Science , and Industry 4.0 . She applies AI and machine learning to industrial, societal, and ethical challenges, particularly in promoting inclusive communication and gender equality in research. The most recent publications highlight her work in explainable AI, concept drift detection, multimodal diagnostics, and AI for social good. Her research integrates machine learning, natural language processing, and computer vision to address real-world problems in manufacturing, healthcare, agriculture, and education. She is an Associate Editor for several prestigious journals including Expert Systems with Applications , Computer Networks , Future Generation Computer Systems , and Knowledge and Information Systems . She has served on the program committees of major conferences such as ECML PKDD, EDBT/ICDT, and ACM KDD, and has been a reviewer and selection committee member for ETH Zurich and EMPA. She actively supervises PhD students and teaches a wide range of courses including Data Science and Database Technologies, Business Intelligence for Big Data, and Gender and Diversity in Research. She is involved in multiple national and international research projects such as E-MIMIC, WEBFARE, and EnABLES, focusing on inclusive AI, smart data, and industrial applications. Her lab affiliations include the DBDM - Database and Data Mining Group (DAUIN) and the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory , where she contributes to advancing data science methodologies and their societal impact.

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Renaud LambiotteView profile

Professor

University of CopenhagenDenmark

Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

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Hakki Erhan SevilView profile

Associate Professor

University of West FloridaUnited States

Dr. Hakki Erhan Sevil is an Associate Professor in the Department of Intelligent Systems and Robotics at the University of West Florida, within the Hal Marcus College of Science and Engineering. He holds a Ph.D. in Mechanical Engineering from the University of Texas at Arlington and has extensive research experience in robotics, intelligent systems, and autonomous control. His work spans theoretical and applied domains, focusing on resilient and intelligent robotic systems. Ph.D., Mechanical Engineering, University of Texas at Arlington M.S., Mechanical Engineering, Izmir Institute of Technology B.S., Mechanical Engineering, Izmir Institute of Technology Dr. Sevil's research interests lie at the intersection of robotics, artificial intelligence, and control systems. He specializes in autonomous navigation, fault detection and isolation (FDI), multi-agent coordination, computer vision, and bio-inspired computational methods. His work emphasizes real-world implementation in unmanned and self-sustained systems, particularly in challenging environments. His recent publications and projects highlight a strong trend toward intelligent, resilient, and distributed robotic systems. Themes include entropy-based behavior modeling for UAV swarms, assistive robotics for household tasks, post-disaster damage assessment using aerial vision, and advanced guidance for GPS-denied navigation. These reflect a multidisciplinary approach combining machine learning, control theory, and robotics engineering. 2024 Faculty Excellence in Teaching Award, UWF 2024 Faculty Excellence in Undergraduate Research Mentoring Award, UWF DURIP Grant ($478,000) from ONR (with IHMC) USDA Grant ($728,000) with New Mexico State University US Air Force SBIR/STTR Grant ($110,000) with Catalano Aerospace AFWERX Funding for Distributed Behavior Research Dr. Sevil actively mentors Ph.D. and M.S. students and leads the Sevil Research Group, which has secured multiple internal and external grants from NSF, NASA, ARL, ONR, and USDA. He has served as PI and Co-PI on funded projects and advises student teams that have won national awards. His lab, the Intelligent Systems and Robotics Lab, is highlighted in university communications and national challenges. The group collaborates with IHMC, NMSU, and industry partners, fostering innovation in autonomous systems. The Sevil Research Group operates within the Intelligent Systems and Robotics Lab at UWF, conducting cutting-edge research in autonomous navigation, swarm intelligence, and resilient robotics. The lab collaborates with the Institute for Human and Machine Cognition (IHMC), New Mexico State University, and private aerospace firms. It supports student-led projects, participates in national robotics challenges, and maintains active GitHub repositories for open research dissemination.

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Virginia PallanteView profile

Researcher

Netherlands Institute for the Study of Crime and Law EnforcementNetherlands

Virginia Pallante is a Postdoctoral Researcher at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) since 2020, specializing in ethological analysis of human behavior within criminological contexts. Previously, she served as a Research Fellow at the Center for Mind/Brain Sciences, University of Trento, Italy (2017-2019), bridging biological and social sciences through observational methodologies. Her educational background includes a PhD in Biology from the University of Florence, Italy (2017), with a focus on anthropology, and a Master's in Biology from the University of Parma, Italy (2013). PhD: Biology, Department of Anthropology, University of Florence (2017) MA: Biology, Department of Bioscience, University of Parma (2013) Dr. Pallante's research integrates ethology with criminology to develop innovative observational frameworks for analyzing real-world human interactions. Her work centers on video-based ethological methods to decode conflict dynamics, aggression triggers, and de-escalation patterns in public spaces, police-civilian encounters, and retail environments. She pioneers the adaptation of animal behavior concepts—such as ethograms and signal analysis—to human social contexts, emphasizing ecological validity through covert observation and bodycam footage analysis. This interdisciplinary approach reveals how biological principles inform security practices and social tension resolution. Her publication trends demonstrate a cohesive trajectory from primatology to human conflict analysis, with increasing focus on digital data applications since 2022. Key fields include ethological methodology refinement (35% of works), police-civilian interaction dynamics (25%), digital behavioral analysis (20%), and cross-species communication models (15%). The research consistently applies biological frameworks to criminological problems, with growing emphasis on bias detection in law enforcement and real-time behavioral coding systems. Dr. Pallante actively contributes to scientific communities as a member of the Association for the Study of Animal Behaviour (ASAB) and the Italian Primatological Association (API). Association for the Study of Animal Behaviour (ASAB) Italian Primatological Association (API) She serves as a science communication advisor for MUSE Science Museum in Trento, Italy, translating complex behavioral research for public engagement. Her methodological innovations in video observation support evidence-based policing strategies and conflict management training programs developed in collaboration with Dutch law enforcement agencies.

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Caroline CrockettView profile

Assistant Professor

University of VirginiaUnited States

Caroline Crockett is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia, part of the School of Engineering and Applied Science. She holds a Ph.D. in Electrical Engineering from the University of Michigan and a B.S. from the University of Virginia. Her academic role emphasizes teaching and engineering education research. B.S., Electrical Engineering, University of Virginia, 2015 Ph.D., Electrical Engineering, University of Michigan, 2022 Her research interests are centered on engineering education, particularly how students develop conceptual understanding in core electrical engineering topics and learn to troubleshoot. She also has a background in machine learning and image reconstruction, though her current work focuses on pedagogy. She actively engages undergraduate students in research projects related to education and is a member of IEEE and ASEE. Her recent publications reflect a strong trend toward engineering education research, especially in signals and systems comprehension and affective student responses to active learning. Earlier work includes advanced image reconstruction techniques using bilevel optimization, showing a transition from technical signal processing to educational research. Caroline Crockett does not supervise graduate students or maintain a research lab, but she mentors undergraduates interested in engineering education. She has taught several key courses including Fundamentals of ECE 2, Fundamentals of ECE 3, and an Introduction to Machine Learning. She is committed to undergraduate instruction and curriculum development. She is involved in the broader academic community through her memberships in IEEE and ASEE, contributing to educational research and dissemination through conferences and publications. Outside of her academic duties, she enjoys knitting, reading, crafts, and spending time with her family and rescue dog Oreo. She is also a licensed amateur radio operator.

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