Electrical Engineering and Computer Science Professor Qinru Qiu Recognized as IEEE Fellow

Qinru Qiu

Electrical engineering and computer science professor, Qinru Qiu, has been recognized as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) for her impactful contributions to the modeling and optimization of energy-efficient computing systems.  

IEEE is a global organization that is committed to advancing technology for the betterment of humanity. With over 409,000 members in more than 160 countries, less than 0.1% of voting members are chosen for elevation to this member grade each year.  

Qiu’s research interest focuses on improving the energy efficiency of computing, from runtime power and thermal management of computer systems, and energy harvesting real-time embedded systems, to her recent works in brain-inspired hardware and software for neuromorphic computing.  The goal of her research is to provide machine intelligence to today’s computing platforms to achieve autonomous resource management with energy and thermal awareness and explore emerging computing paradigms.  

“Professor Qiu has been leading the research community to seek solutions for highly energy-efficient machine intelligence through adopting biologically inspired models and processing mechanisms,” says nominator Diana Marculescu. “Her ground-breaking research has enabled a completely new computing paradigm, which leverages the unique property of different types of spike coding to replace the numerical calculation with simple logic operations, resulting in significant energy reduction.” 

“I am excited and thankful for the recognition and truly grateful for all the support that I have received,” says Qiu. “I look forward to continuing my work in developing and promoting techniques to improve the energy efficiency of emerging computing systems.” 

Smart Speakers, Smarter Protection

Whether you’re looking to try a new recipe, dimming the lights in your living room, or curious about the species of bacteria living inside your mouth, Amazon Alexa has got you covered. With a simple voice command, Alexa’s ability to perform various tasks or answer questions has made it widely popular, with over 40 million users in the United States alone. Despite the convenience smart speakers like Alexa offer, these devices have also raised some privacy concerns. 

Amazon has been known to collect data on users which includes their shopping habits, preferences, and even their location for personalized marketing. But that’s not all. When using waking words such as “Hey Alexa” to activate smart speakers, the audio of your voice command is also recorded and stored, becoming Amazon’s property. This means that Amazon owns your voice audio and can do whatever they want with it. 

“Big tech companies are using our personal information. We’re less like customers and more like their product,” says graduate student Brian Testa ’24. “I’ve always been sensitive to that. I don’t use a lot of technology at home for that reason.” 

Using voice data, companies like Amazon and Google have now developed technology that poses even more threats to privacy: AI and machine learning that can determine people’s emotional state or mood from their voice. This patented technology can even pick up on feelings from emotionally neutral phrases like “What’s the weather?” Since there are no laws in place to prevent this, there’s no protection against it. 

“In the US for the last five to 10 years, lots of researchers have been working on how they can use voice to infer emotions, mood or even mental health,” says assistant professor in electrical engineering and computer science, Asif Salekin. “In my own lab, we have previous works on tech that can infer mental disorders like depression, social anxiety, manic disorder, and even suicidal tendencies from one’s voice.” 

While this technology can be useful in certain circumstances, most users, if not all, have not consented to having their emotions detected by smart speakers. These privacy concerns led Testa, Professor Salekin, graduate students Harshit Sharma ’26 and Yi Xiao 26, and undergraduate student Avery Gump ’24 to begin researching ways to protect users’ privacy from smart speakers. 

“Consent is key,” Salekin says. “We’d still like to use smart speakers since they’re quite useful – I have them in my own home. This project was about finding a way to use these devices without giving companies the power to exploit us.” 

Led by Testa, the group conducted extensive research and developed a device that can be attached to a smart speaker or downloaded as software onto a laptop. This device emits a mild noise that only the smart speaker can hear and masks the emotional tone in your voice, providing a new level of privacy protection for concerned users.

“Through the use of a speech emotion recognition (SER) classifier, a smart speaker can analyze how people are feeling based on how they sound. We created a microphone device that listens for the wake word ‘Hey Alexa’”, Testa says. “When the smart speaker activates, our device activates too and begins to emit a noise that disrupts the smart speaker from detecting your emotions. However, only the smart speaker hears this noise.”  

Currently, their device masks your emotional state by presenting it as a completely different emotion. When you speak, the smart speaker may detect from your voice that you’re sad, angry, or frustrated when you’re not feeling any of these emotions. This unpredictability makes it difficult for smart speakers to accurately determine your true emotions or mood and also prevents machine learning from picking up on any patterns and mood correlations. The group hopes to improve the device’s functionality by making it mask your emotions as neutral rather than presenting them as a different emotion. 

“To create the mild noise our device emits, we utilized genetic programming to identify a combination of specific frequencies that disrupt the smart speaker from determining a person’s mood,” Salekin says. “Only the speaker hears this noise, but it can hear your speech commands clearly, so the utility of the smart speaker remains intact.”  

Though the sound is only detected by the smart speaker, the group wanted to see how loud it would be when the device is used. Testa played the sound in the lab when Professor Salekin was having a meeting and Salekin didn’t even realize it was playing, which showed that the noise wasn’t disruptive. Additionally, they also conducted a survey with others to see if the noise was loud enough to be disruptive. 

Testa, Salekin, Sharma, Xiao, and Gump are currently working on patent submissions, form factors, and speaking with companies about commercializing their device. What sets their patent apart from similar concepts is that while past technology focused on determining people’s moods or emotions, their technology is all about protecting them. This unique approach makes their device the first of its kind.

“It was a fun project,” Testa says. “This paper was published by me and as the first listed author, I’m excited about it. I’ve been working towards my Ph.D., and this is another step towards that goal.”  

“Working with the students in real-world applications and research with real results was exciting,” Salekin says. “This research has many components and the collaboration between us was great. We’re excited to see what the future for this tech holds.” 

Electrical Engineering and Computer Science Professor Bryan S. Kim Receives Funding for Rack Scale Computing Research

Bryan Kim

Bryan S. Kim, assistant professor in electrical engineering and computer science, has received funding from semiconductor company FADU to explore how CXL, a new open standard for connecting computer components, would transform data center applications.

With CXL, an entire rack of computers can be connected through the peripheral component interconnect express (PCIe) bus with shared memory coherency, rethinking how computers access and share data.

“CXL is still in its infancy with only limited publicly available hardware. After all, its specification is only a few years old” Kim explained. “Furthermore, how CXL and its hardware would affect software system design is completely unexplored.”

Kim’s collaborative project will investigate the fundamental technologies for building a software system with CXL memory, the designs for resilient and reliable CXL fabric, and the transformation of data center applications due to CXL.

“While there is a large research community interest in CXL, there are only a handful of research groups who have published in this area,” Kim said. “I am grateful to be at the leading front and continuing the success of this project.”

Pankaj K. Jha

Degrees:

Ph. D., Physics, Texas A&M University

Masters of Science (5-Year Integrated), Physics, Indian Institute of Technology, Kanpur (IITK)

Areas of Expertise:

  • Quantum information science
  • Quantum sensing and metrology
  • Quantum nano- and meta-photonics
  • Bio-inspired materials
  • Bio-nano ​interfaces
  • Machine learning

Jha’s research focuses on developing quantum hardware using two-dimensional materials and heterostructures, III-V semiconductors, nanostructures, soft-materials, metamaterials, and hybrid combination of these materials. His research seeks to understand fundamental characteristics of these systems through combined experimental, theoretical, and computational studies and use those findings to gain control and induce novel optical, electrical, thermal, and mechanical responses in them. These responses, in turn, are leveraged to develop transformative devices and technologies for quantum information science, quantum sensing and metrology, nanophotonics, optoelectronics, and space exploration applications. Thus, his interdisciplinary research crosses the conventional scientific boundaries to merge applied physics with electrical engineering, materials science, and mechanical engineering.

Honors and Awards:

  • Tingye Li Innovation Prize for Early Career Professionals (Finalist): 2016.
  • American Physical Society, Travel Grant: 2011.
  • Herman F. Heep and Minnie Belle Heep Foundation Graduate Fellowship: 2010.
  • Robert A. Welch Foundation Graduate Fellowship: 2009-2012.

Selected Publications:

  • P. K. Jha*, H. Akbari*, Y. Kim*, S. Biswas, and H. A. Atwater, “Nanoscale axial position and orientation measurement of hexagonal boron nitride quantum emitters using a tunable nanophotonic environment,” Nanotechnology 33, 015001 (2022).
  • L. Kim*, S. Kim*, P. K. Jha, V. W. Brar, and H. A. Atwater, “Mid-Infrared radiative emission from bright hot plasmons in graphene,” Nat. Mater. 20, 805 (2021).
  • H. Ramezani, P. K. Jha, Y. Wang, and X. Zhang, “Nonreciprocal Localization of Photons,” Phys. Rev. Lett. 120, 043901(2018).
  • P. K. Jha, M. Mrejen, J. Kim, C. Wu, Y. Wang, Y. V. Rostovtsev, and X. Zhang, “Coherence-Driven Topological Transition in Quantum Metamaterials,” Phys. Rev. Lett. 116, 165502 (2016).
  • P. K. Jha*, X. Ni*, C. Wu, Y. Wang, and X. Zhang, “Metasurface-Enabled Remote Quantum Interference,” Phys. Rev. Lett. 115, 025501 (2015).
  • K. E. Dorfman, P. K. Jha, D. V. Voronine, P. Genevet, F. Capasso, and M. O. Scully, “Quantum-Coherence- Enhanced Surface Plasmon Amplification by Stimulated Emission of Radiation,” Phys. Rev. Lett. 111, 043601 (2013).

Yuzhe Tang

Degree:

  • Ph.D. Computer Science, Georgia Tech

Lab/ Center/ Institute affiliation:

Full Stack Security Lab (FSSL) at CST 4-294

Areas of Expertise:

  • Blockchain and cryptocurrencies.
  • Cyber-security, vulnerability discovery, attack detection and mitigation.
  • Distributed systems and performance optimization.
  • Software engineering.

Dr. Tang is broadly interested in cyber-security, systems, software engineering, and system measurement. His cyber-security research covers vulnerability discovery, attack detection, attack mitigation, and measurement of deployed systems.

His current research focuses on blockchain security, blockchain systems, blockchain applications, and blockchain education. He has worked on confidential computing, trusted execution environments, and cloud security.

Honors and Awards:

  • The Ethereum Foundation academic award
  • Two NSF SaTC grants and an NSF CNS grant
  • The Best Paper award in IEEE Cloud 2012
  • The Best Paper award in ACM/IEEE CCGrid 2015
  • The AFRL visiting faculty research fellowship, 2017

Selected Publications:

  • K. Li, Y. Wang, Yuzhe Tang. “DETER: Denial of Ethereum Txpool sERvices”, ACM CCS 2021, Acceptance rate=22%
  • K. Li, J. Chen, X. Liu, Yuzhe Tang, X. Wang, X. Luo. “As Strong As Its Weakest Link: How to Break (and Fix) Blockchain DApps at RPC Service”, ISOC NDSS 2021, Acceptance rate=15.2%
  • K. Li, Yuzhe Tang, J. Chen, Y. Wang, X. Liu. “TopoShot: Uncovering Ethereum’s Network Topology Leveraging Replacement Transactions”, ACM IMC 2021, Acceptance rate=28% 
  • Y. Wang, Q. Zhang, K. Li, Yuzhe Tang, J. Chen, X. Luo, T. Chen. “iBatch: Saving Ethereum Fees via Secure and Cost-Effective Batching of Smart-Contract Invocations” ESEC/FSE 2021, Acceptance rate=24.5%
  • C. Zhang, C. Xu, J. Xu, Yuzhe Tang, B. Choi. “GEM^2-Tree: A Gas-Efficient Structure for Authenticated Range Queries in Blockchain”, IEEE ICDE 2019, Full Paper, Acceptance rate=26.8%

Radhakrishna (Suresh) Sureshkumar

Degrees:

  • Ph.D. in Chemical Engineering, University of Delaware, 1996
  • M.S. in Chemical Engineering, Syracuse University, 1992
  • B. Tech. in Chemical Engineering, Indian Institute of Technology, 1990

Experience:

  • Lecturer, Massachusetts Institute of Technology, 1996-97
  • Assistant Professor (1997-2002), Associate Professor (2002-2006), and Professor (2006-2009) of Chemical Engineering, Washington University in St. Louis
  • Visiting Professor, University of Michigan, Ann Arbor, 2008
  • Visiting Professor, University of Edinburgh, Scotland, 2008
  • Visiting Professor, University of Porto, Portugal, 2008

Lab/Center Affiliation(s):

  • Multiscale Modeling and Simulation Laboratory
  • Complex Fluids Laboratory

Research Interests:

  • Complex Fluids
  • Soft Condensed Matter
  • Nanotechnology
  • Smart Materials
  • Sustainable Energy
  • Multiscale Modeling and Simulation

Current Research:

Sureshkumar’s current research focuses on (i) understanding the structure, dynamics and rheology of complex fluids and soft matter, and (ii) nanoscale science and engineering of functional materials and interfaces. Multiscale modeling and simulations as well as experiments are used to probe the response of complex soft matter and interfaces to external stimuli such as mechanical deformation caused by flow, chemical/thermal gradients and optical fields. Major ongoing research efforts target investigations of self-assembly and self-organization routes to robust nanomanufacturing of optically tunable interfaces with applications to efficient light trapping in thin film photovoltaics, self-assembly of nanoparticles with surfactant micelles and polymers, interactions of nanoparticles with cell membranes to assess their cytotoxicity, rheology of viscoelastic polymer solutions/melts, coherent structures dynamics in turbulent flows in presence of drag reducing additives, bacterial biofilm mechanics as well as signaling between bacterial and mammalian cells.

Courses Taught:

  • Chemical engineering methods
  • Multiscale modeling and simulation
  • Structure and rheology of complex fluids

Honors:

  • Invited Speaker, University of Delaware Chemical Engineering Centennial Seminar Series, Newark, Delaware (2014)
  • Keynote Speaker, International Congress on Rheology, Lisbon, Portugal (2012)
  • Keynote Speaker, European Congress on Computational Methods in Applied Sciences and Engineering, Vienna, Austria (2012)
  • Keynote Speaker, Lorentz Center Workshop on Flow Instabilities and Turbulence, Leiden, Netherlands (2010)
  • University of Michigan Competitive Sabbatical Grant (2008)
  • Royal Scottish Society of Edinburgh International Exchange Award, University of Edinburgh, Edinburgh, Scotland (2008)
  • Distinguished Speaker, Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, Canada (2008)
  • Invited Speaker, American Physical Society Annual Meeting, Baltimore (2006)
  • Invited Speaker, Materials Research Society Annual Meeting, Boston (2006)
  • Invited Speaker, American Institute of Chemical Engineering, Salt Lake City (2007)
  • National Science Foundation CAREER Award (1999)
  • ACS/PRF New Faculty Grant (1998)
  • University of Delaware Allan P. Colburn Prize, Outstanding Doctoral Dissertation in Engineering and Mathematical Sciences (1996)
  • University of Delaware Competitive Fellowship (1995)

Student Awards:

  • Graduate Student Poster Award (Mr. Tao Cong), Society of Rheology Annual Meeting, Cleveland, (2011)
  • Graduate Student Poster Award (Dr. M. Vasudevan), Society of Rheology Annual Meeting, Salt Lake City, (2007)
  • Graduate Student Poster Award (Dr. R. Magan), Colloids & Surface Chemistry Division, ACS Annual Meeting, Philadelphia (2004)
  • Graduate Student Poster Award (Dr. R. Magan) Nanoscale S & E Forum, AIChE Annual Meeting, Austin (2004)

Selected Publications:

Sambasivam, A.V. Sangwai & R. Sureshkumar, Dynamics and scission of rod-like cationic surfactant micelles in shear flow, Phys. Rev. Lett., 114, 158302 (2015)

Dhakal & R. Sureshkumar, Topology, Length Scales and Energetics of Surfactant Micelles, J. Chem. Phys., 143, 024905 (2015)

S.C. DeSalvo, Y. Liu, G.S. Choudhary, D. Ren, S. Nangia & R. Sureshkumar, Signaling Factor Interactions with Polysaccharide Aggregates of Bacterial Biofilms, Langmuir, 31, 1958-66 (2015)

Estime, D. Ren & R. Sureshkumar, Effects of plasmonic film filters on microalgal growth and biomass composition, Algal Research, 11, 85-89 (2015)

Israelowitz, J. Amey, T. Cong & R. Sureshkumar, Spin Coated Plasmonic Nanoparticle Interfaces for Photocurrent Enhancement in Thin Film Si Solar Cells, Journal of Nanomaterials, Article ID 639458 (2014)

Kim & R. Sureshkumar, Spatiotemporal evolution of hairpin eddies, Reynolds stress, and polymer torque in polymer drag-reduced turbulent channel flows, Phys. Rev. E., 87, 063002 (2013)

Nangia & R. Sureshkumar, Effects of nanoparticle charge and shape anisotropy on translocation through cell membranes, Langmuir, 28, 1766-1771 (2012). Cover Article

Sangwai & R. Sureshkumar, Binary interactions and salt-induced coalescence of spherical micelles of cationic surfactants from molecular dynamics simulations, Langmuir, 28 (2), 1127–1135 (2012)

Cong, S.N. Wani & R. Sureshkumar, Structure and optical properties of self-assembled multicomponent plasmonic nanogels, Applied Physics Letters, 99, 043112 (2011)

Sangwai & R. Sureshkumar, Coarse-Grained Molecular Dynamics Simulations of the Sphere to Rod Transition in Surfactant Micelles, Langmuir, 27 (11), 6628–6638 (2011)

Torkamani, S. Wani, Y. Tang & R. Sureshkumar, Plasmon-enhanced microalgal growth in mini-photobioreactors, Applied Physics Letters, 97, 043703 (2010); Highlighted in Nature, 466 799 (2010)

Vasudevan, E. Buse, D. Lu, H. Krishna, R. Kalyanaraman, A.Q. Shen, B. Khomami & R. Sureshkumar, Irreversible nanogel formation in surfactant solutions by microporous flow, Nature Materials, 9, 436-441 (2010). Commentary by M. Pasquali, Nature Materials, 9, 381-382 (2010)

D.G. Thomas, B. Khomami & R. Sureshkumar, Nonlinear Dynamics of Viscoelastic Taylor-Couette Flow: Effect of Elasticity on Pattern Selection, Molecular Conformation and Drag, J. Fluid Mech., 620, 353-382 (2009).

Trice, C. Favazza, D.G. Thomas, H.G. Garcia, R. Kalyanaraman, R. Sureshkumar, A novel self-organization mechanism in ultrathin liquid films: theory and experiment, Phys. Rev. Lett., 101, 017802 (2008)

Kim, R.J. Adrian, S. Balachandar & R. Sureshkumar, Dynamics of hairpin vortices and polymer-induced turbulent drag reduction, Phys. Rev. Lett., 100, 134504 (2008)

C M. Vasudevan, A.Q. Ashen, B. Khomami & R. Sureshkumar, Self-similar shear-thickening behavior in CTAB/NaSal surfactant solutions, J. Rheol., 52, 527-50 (2008)

Asif Salekin

Degree:

  • Ph.D. in Computer Science, University of Virginia
  • Master of Computer Science, University of Virginia
  • B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology

Lab Affiliation:

Ubiquitous and Intelligent Sensing (UIS Lab)

Areas of Expertise:

  • Pervasive and Ubiquitous Computing
  • Machine Learning
  • Internet of Things (IoT)
  • Human Centric Computing and Sensing
  • Wireless, Connected, and Mobile Health.

I am directing the Laboratory for Ubiquitous and Intelligent Sensing (UIS Lab) at Syracuse University. My research takes a multi-disciplinary approach to develop novel and practical human event sensing technologies that capture observable low-level physical signals from human bodies and surrounding environments and employ new machine learning, signal processing, and natural language processing techniques to rectify the existing sensing technologies. My research exquisition goes beyond the conventional learning or sensing approaches and addresses the research challenges, such as the uncertainties in physical world sensing, interpretability of ML inference, human factors such as the user-context and mobility, limitation of current technologies (i.e., IoT, CPS), and resource constraints of the sensing data and computation platform. A core focus of my research program is to integrate passive sensing and interpretable AI to advance human health assessment, identify latent markers, and automate health monitoring and interventions. Major ongoing funded research projects are (1) NSF SCH (Medium): Psychophysiological Sensing to Enhance Mindfulness-Based Interventions for Self-Regulation of Opioid Cravings, (2) NIH R021 and NIH R-01: Understanding speech, speech-motor-control, and emotional process in early childhood stuttering, (3) NSF CPS (Small): Developing a Socio-Psychological CPS for the Health and Wellness of Dairy Cows. 

Honors and Awards:

  • IAAI Deployed Application Award, The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21)
  • Graduate Student Award for Outstanding Research, Department of Computer Science, UVA, 2018
  • Nominated for the best paper award (AsthmaGuide), Wireless health 2016

Selected Publications:

  • Harshit Sharma, Yi Xiao, Victoria Tumanova, Asif Salekin, “Psychophysiological Arousal in Young Children Who Stutter: An Interpretable AI Approach”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2022. (and Ubicomp 2022)
  • Jingyu Xin, Vir V. Phoha, Asif Salekin, “Combating False Data Injection Attacks on Human-Centric Sensing Applications”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2022. (and Ubicomp 2022)
  • Cramer et al., “Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States”, The Proceedings of the National Academy of Sciences USA (PNAS), 2022.
  • Fatih Altay, Guillermo Ramón Sánchez, Yanli James, Stephen V. Faraone, Senem Velipasalar, Asif Salekin. Preclinical Stage Alzheimer’s Disease Detection Using Magnetic Resonance Image Scans, The Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21).
  • Tianjia He, Lin Zhang, Fanxin Kong, and Asif Salekin. Exploring Inherent Sensor Redundancy for Automotive Anomaly Detection, The 57th Design Automation Conference (DAC), 2020.
  • Salekin, Jeremy W. Eberle, Jeffrey J. Glenn, Bethany A. Teachman, and John A. Stankovic. 2018. A Weakly Supervised Learning Framework for Detecting Social Anxiety and Depression, ACM Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT), Vol. 2, No. 2, Article 81 (June 2018), 26 pages. (and Ubicomp 2018)
  • Salekin, Z. Chen, M. Ahmed, J. Lach, D. Metz, K. de la Haye, B. Bell, and J. Stankovic, Distance Emotion Recognition, ACM Interactive, Mobile, Wearable, and Ubiquitous Technologies (IMWUT), Vol. 1, Issue 3, Sept. 2017, 96:1-96:24 (Ubicomp 2017)

Qinru Qiu

Degree(s):

  • PhD

Lab/Center Affiliation(s):

  • AMPS (Advanced Microprocessor and Power-aware Systems)

Research Interests:

  • Dynamic power and thermal management for computer systems
  • Power and performance optimization of energy harvesting real-time embedded systems
  • Neuromorphic computing and high performance computing for cognitive applications

Current Research:

Excessive energy dissipation has become one of the limiting factors that prevents the sustained growth of computation power of IT facilities. High power consumption reduces system reliability, increases energy and cooling cost, and cuts the battery cycle time of mobile devices. Aiming at curbing the system energy dissipation, green computing has attracted substantial interests in recent years. Dr. Qiu’s primary research interest covers different areas in green computing, from runtime power and thermal management of computer systems to energy harvesting real-time embedded system. The goal of her research is to provide machine intelligence to today’s computing platforms to achieve autonomous resource management with energy and thermal awareness.

Her second research area is architecture design of neuromorphic computing. Neuromorphic computing refers to the emerging computation concept inspired by the principles of information processing in human neural system. It is widely accepted that human beings are much superior to machines in some areas such as image recognition. With the increase of our knowledge on brain function and our capability in realizing massive parallel computation and communication, it is time to investigate new algorithm and hardware architecture for signal processing and perception. Dr. Qiu’s research focuses on the software and hardware development for such computing systems.

Courses Taught:

  • VLSI Design
  • Computer architecture

Honors:

  • ACM SIGDA Distinguished Service Award (2011)
  • NSF Career Award (2009)
  • American Society for Engineering Education (ASEE) Summer Research Faculty Fellowship (2007)

Selected Publications:

Shen, Y. Tan, J. Lu, Q. Wu and Qinru Qiu, “Achieving Autonomous Power Management Using Reinforcement Learning,”ACM Transactions on Design Automation of Electronic Systems, Vol. 18, Iss. 2, pp. 24032, March 2013.

Ge, Qinru Qiu, and Q. Wu, “A Multi-Agent Framework for Thermal Aware Task Migration in Many-Core Systems,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Volume: 20 , Issue: 10, pp. 1758 – 1771, 2012.

Liu, J. Liu, Q. Wu and Qinru Qiu, “Harvesting-Aware Power Management for Real-Time Systems with Renewable Energy,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Volume: 20 , Issue: 8, pp. 1473 – 1486, 2012.

Qinru Qiu, Q. Wu, M. Bishop, R. Pino, and R. W. Linderman, “A Parallel Neuromorphic Text Recognition System and Its Implementation on a Heterogeneous High Performance Computing Cluster,” IEEE Transactions on Computers, Digital Object Identifier: 10.1109/TC.2012.50.

H. Lu, Qinru Qiu, A. R. Butt and K. W. Cameron, “End-to-End Energy Management,” Computer, 44 (11), November 2011.

Kristopher Micinski

Degree:

  • Doctorate of Philosophy, Computer Science, University of Maryland at College Park
  • Bachelor of Science, Computer Engineering, Michigan State University

Areas of Expertise:

  • Programming Languages
  • Static Analysis
  • Formal Methods
  • Foundations of Computer Security and Privacy

My research lies at the intersection of the theory and application of program analyses. Program analyses are tools that examine programs and determine (prove) facts about them. For example, a program analysis might prove that a program can never crash due to a type error. In general, however, program analyses can be arbitrarily complex and infer subtle program invariants relating to myriad applications (such as computer security).

Because program analyses must always approximate program behavior (otherwise they could solve the halting problem), there is an inherent tradeoff between analysis precision and analysis performance. Currently, program analyses are often applied only in limited contexts, as gaining acceptable performance requires too many compromises in terms of analysis precision. My current work focuses on three concurrent threads: tackling fundamental issues relating to scaling static analysis (specifically, scaling analyses to run on supercomputers rather than a single machine as all current analyses do); engineering those analyses (to allow analysis reuse); and applying those analyses to computer security (e.g., to check properties such as information flow and to support complex reverse engineering tasks).

Recent Publications:

  • Symbolic Path Tracing to Find Android Permission-Use Triggers. NDSS Workshop on Binary Analysis Research (BAR 2019).
  • User Comfort with Android Background Resource Accesses in Different Contexts Symposium on Usable Privacy and Security (SOUPS 2018).
  • User Interactions and Permission Use on Android (CHI 2017).

Bryan S. Kim

Degree:

  • Ph.D. in Computer Science and Engineering, Seoul National University
  • M.S. in Electrical Engineering and Computer Science, Seoul National University
  • B.S. in Electrical Engineering and Computer Science, University of California, Berkeley

Areas of Expertise:

  • Flash and non-volatile memory-based systems
  • Data storage systems
  • File systems and key-value stores
  • Next-generation storage architecture and hardware

I am broadly interested in computer systems and particularly focused on data storage systems. Current research directions include, but are not limited to, capacity-variant storage systems, self-learning systems, and next-generation key-value storage.

Recent Publications:

  • Hyeongyu Lee, Juwon Lee, Minwook Kim, Donghwa Shin, Sungjin Lee, Bryan S. Kim, Eunji Lee, and Sang Lyul Min. “SpartanSSD: a Reliable SSD under Capacitance Constraints,” in ACM/IEEE International Symposium on Low Power Electronics and Design, 2021
  • Jinhyung Koo, Junsu Im, Jooyoung Song, Juhyung Park, Eunji Lee, Bryan S. Kim, and Sungjin Lee. “Modernizing File System through In-Storage Indexing,” in USENIX Symposium on Operating Systems Design and Implementation, 2021
  • Jeseong Yeon, Leeju Kim, Youil Han, Hyeon Gyu Lee, Eunji Lee, and Bryan S. Kim. “JellyFish: A Fast Skip List with MVCC,” in ACM/IFIP International Middleware Conference, 2020
  • Youil Han, Bryan S. Kim, Jeseong Yeon, Sungjin Lee, and Eunji Lee. “TeksDB: Weaving Data Structures for a High-Performance Key-Value Stores,” in International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), 2019
  • Bryan S. Kim, Eunji Lee, Sungjin Lee, Sang Lyul Min. “CPR for SSDs,” in ACM SIGOPS Workshop on Hot Topics in Operating Systems (HotOS), 2019
  • Bryan S. Kim, Jongmoo Choi, and Sang Lyul Min. “Design Tradeoffs for SSD Reliability,” in USENIX Conference on File and Storage Technologies (FAST), 2019
  • Geonhee Lee, Hyeon Gyu Lee, Juwon Lee, Bryan S. Kim and Sang Lyul Min. “An Empirical Study on NVM-based Block I/O Caches,” in ACM SIGOPS Asia-Pacific Workshop on Systems (APSys), 2018
  • Bryan S. Kim, Hyun Suk Yang, and Sang Lyul Min. “AutoSSD: an Autonomic SSD Architecture,” in USENIX Annual Technical Conference (ATC), 2018
  • Bryan S. Kim. “Utilitarian Performance Isolation in Shared SSDs,” in USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage), 2018
  • Bryan S. Kim, Yonggun Lee, and Sang Lyul Min. “Framework for Efficient and Flexible Scheduling of Flash Memory Operations,” in IEEE Non-Volatile Memory Systems and Applications (NVMSA), 2017
  • Bryan S. Kim and Sang Lyul Min. “QoS-aware Flash Memory Controller,” in IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2017

Venkata S.S. Gandikota

Degrees:

  • Ph.D. Computer Science – Purdue University
  • MS Computer Science – Purdue University
  • MSc Mathematics – Birla Institute of Technology and Science, Goa, India
  • B.E. Computer Science – Birla Institute of Technology and Science, Goa, India

Areas of Expertise:

  • Coding Theory & Lattices
  • Sparse Recovery
  • Sublinear algorithms
  • Foundations of Machine Learning

Venkata’s research focuses on the algorithmic aspects of computing with structured data and their applications in various domains such as communication, data storage, health care, and collaborative learning. His research mainly aims to identify an underlying structure in the data that can be leveraged to design efficient algorithms for certain computational tasks. When the data is devoid of any favorable structure, we aim to embed one to facilitate computations. In the process, we use tools and develop techniques from several areas of mathematics and computer science like optimization, coding theory, high-dimensional geometry, the geometry of numbers, sublinear-time algorithms, and differential privacy.

Honors and Awards:

  • SOURCE 2022 — Research Assistant Grant to support undergraduate student involvement in research.
  • CUSE Seed Grant 2021.

Selected Publications: