Category: Awards
Yannis Paschalidis Named the 2020 Charles DeLisi Distinguished Lecturer
Professor Yannis Paschalidis is the 2020 recipient of the Charles DeLisi Award and Distinguished Lecture. This award celebrates high-impact research in engineering. This showcasing event allows all members of the Boston University community to meet a distinguished scholar selected from the College of Engineering faculty discussing a topic of recognized excellence.
Professor Paschalidis will present on “Data Science and Optimization Adventures in Computational Biology and Medicine.” In this lecture, he will present several seemingly disparate areas of his work in computational biology and medicine connected through the use of data science and optimization methods. Topics range from the molecular to the whole organism/disease level and models progress from predictive to prescriptive. View the abstract. The date and time of the presentation has been posted from April 27th, 4PM, to a date and time to be announced later.
Yannis Paschalidis is a Professor and Data Science Fellow in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences at Boston University. He is the Director of the Center for Information and Systems Engineering (CISE), a university-wide interdisciplinary research center at Boston University administered by the College of Engineering.
Professor Paschalidis has distinguished himself as a researcher, scholar, and educator whose work spans the fields of systems and control, optimization, networks, operations research, computational biology and medical informatics. Dr. Paschalidis’ research has impacted a breadth of applications, including predictive health analytics, protein docking, autonomous robots, sustainable energy, and smart cities, among others.
At BU since 1996, Dr. Paschalidis has authored more than 200 refereed publications with his students and collaborators from diverse disciplines. He has been a PI or Co-PI on numerous interdisciplinary grants totaling more than $43 million and has advised 24 Ph.D. theses.
Prof. Paschalidis’ work has been recognized with a CAREER award from the National Science Foundation, the second prize in the 1997 George E. Nicholson paper competition by INFORMS, the best student paper award at the 9th Intl. Symposium of Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt 2011) won by one of his Ph.D. students for a joint paper, an IBM/IEEE Smarter Planet Challenge Award, and a finalist best paper award at the IEEE International Conference on Robotics and Automation (ICRA). His work on protein docking (with his collaborators) has been recognized for best performance in modeling selected protein-protein complexes against 64 other predictor groups (2009 Protein Interaction Evaluation Meeting). His recent work on health informatics won an IEEE Computer Society Crowd Sourcing Prize and a best paper award by the International Medical Informatics Associations (IMIA). He was an invited participant at the Frontiers of Engineering Symposium organized by the National Academy of Engineering, and at the 2014 National Academies Keck Futures Initiative (NAFKI) Conference. Prof. Paschalidis is a Fellow of the IEEE and the founding Editor-in-Chief of the IEEE Transactions on Control of Network Systems.
Paschalidis received a Diploma (1991) from the National Technical University of Athens, Greece, and an M.S. (1993) and a Ph.D. (1996) from the Massachusetts Institute of Technology (MIT), all in Electrical Engineering and Computer Science.
By Maria Yaitanes, CISE Staff
BU-Harvard Team Wins $1.2M NSF Grant to Improve Women’s Reproductive Health using AI and Machine Learning
Researchers to advance distributed analytics to enhance fertility in families
A multidisciplinary team of researchers from Boston University and Harvard University is working to address women’s reproductive health challenges with the help of a $1.2M, four-year grant funded by the National Science Foundation (NSF) through its Smart and Connected Health (SCH) program. The BU-led project will leverage machine learning and artificial intelligence to develop an integrative approach to enable personalized reproductive/fertility predictions and individualized prescriptions to help address fertility problems. The researchers will also focus on improving the understanding of socioeconomic disparities in the use of infertility treatment services.
The demands of modern life, education and career choices, as well as the availability of assisted reproductive technologies, are leading many individuals and couples to delay childbearing. This has contributed to infertility and sub-fertility emerging as significant public health problems in the U.S., affecting about 15% of couples, and resulting in more than $5 billion spent annually in infertility services. Such costs are often not covered by health insurance and, consequently, generate access disparities.
“This project is an exciting illustration of the tremendous potential modern data science methods have to leverage the increasing availability of data and impact our health and wellbeing,” says Boston University College of Engineering Professor Ioannis (Yannis) Paschalidis, principal investigator (PI) of the project and director of the Center for Information and Systems Engineering. “With the emergence of personalized medicine, aided by data and algorithmic advances, we now have the ability to learn from available data and develop personalized predictions and intervention recommendations for each individual. The overall goal is to enable people to optimize health before conception, identify modifiable determinants of fertility, and reduce health risks during pregnancy and beyond.”
Innovative team of engineers and clinicians bring a synergistic range of expertise
The BU-Harvard University research team reflects the integrative nature of the project, bringing together algorithms, public health, and medical expertise to address clinical and socio-economic challenges contributing to infertility and women’s reproductive health.
The BU team of principal investigators include: Yannis Paschalidis, BU Professor of Electrical and Computer Engineering, Systems Engineering, and Biomedical Engineering, an expert in decision theory, networks, machine learning, optimization, and computational biology/medicine; Alex Olshevsky, BU Professor of Electrical and Computer Engineering and Systems Engineering, an expert in distributed optimization methods; and Lauren Wise, ScD, BU Professor of Epidemiology, BU School of Public Health, an expert in reproductive epidemiology and PI of the BU Pregnancy Study Online (PRESTO).
Harvard University is the subaward recipient on this grant, and brings the expertise of Co-PI Shruthi Mahalingaiah, Assistant Professor of Environmental Reproductive and Women’s Health. Professor Mahalingaiah is a reproductive endocrinologist, infertility physician scientist and PI of the Ovulation/Menstruation Health Study. Dr. Mahalingaiah holds an adjunct appointment at the BU School of Medicine in the Department of OB/GYN and directs the Program in Environment and Women’s Health, a multi-institutional collaborative effort which includes Boston Medical Center, BU School of Medicine, BU College of Engineering (Paschalidis), and Massachusetts General Hospital, and is housed at the Harvard T.H. Chan School of Public Health.
A shifting paradigm in health data collection
Electronic Health Record (EHR) data are distributed at many locations, hospitals, doctor offices, and other clinics. Medical devices are also becoming smaller with the ability to transmit and store information at home monitoring stations, a patient’s smartphone, and the cloud.
“While traditional learning methods require collecting all data at a central location, this is becoming increasingly harder, even undesirable for privacy reasons,” says Co-PI Alex Olshevsky. “Clearly, the more locations contain sensitive data, the greater is the likelihood of a data breach. In this context, it becomes important to develop fully distributed algorithms that can provide privacy guarantees.”
The researchers will develop an integrative approach to enable personalized reproductive/fertility predictions and prescriptions using distributed, privacy-preserving algorithms trained using multiple data sources. The algorithms will combine information from self-administered surveys, electronic health records, and personal health records to produce highly accurate personalized predictions and prescriptions or recommendations, enabling individuals and their physicians to make the most appropriate, individualized health care decisions.
“Collaboration for improving discovery and improving care for women across the lifecourse is critically important,” says Dr. Mahalingaiah. “Merged datasets including self reporting, lifestyle, and exposures, clinical-grade data, and data collected from wearable devices will provide personalized insights so that women can be empowered to understand information on the health of their bodies and make the best choices for their health and futures.”
Predictive fertility/family planning models
The research team will develop fertility/family planning models, including predicting pregnancy, the success of an IVF cycle, the presence of specific reproductive health issues affecting fertility, and making related health care recommendations. Predictive models will identify the most important factors associated with reduced fertility or Assisted Reproductive Technologies (ART) success rates, which could pinpoint specific lifestyle habits, environmental factors, and other key drivers of reduced positive outcomes that can inform health policy recommendations. A key focus will be on ovulation disorders, including Polycystic Ovary Syndrome, which are the leading cause of female infertility and are associated with an increased risk of chronic diseases, such as diabetes and cardiovascular disease.
“Machine learning has the potential to identify novel determinants of subfertility in large prospective cohort studies like PRESTO,” says Dr. Wise. “Developing tools for individuals to quantify their probability of conception based on personal inputs is paradigm-shifting. We are delighted to be partnering with Dr. Paschalidis and his colleagues on this innovative study.”
Professor Paschalidis adds, “It’s one thing to be able to predict one thing will happen, but the important question is, what do you do? How do you improve the condition of the individual? By bringing together this truly multidisciplinary collaboration of clinicians and engineers, this project brings the deep, diverse expertise and requisite resources for advancing the health and wellbeing of women.”
By Maureen Stanton, CISE
Federated Learning from Electronic Health Records Paper Receives Best Paper Honors
Our paper on
``Federated learning of predictive models from federated Electronic Health Records'' (with Theodora S. Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, and Wei Shi), International Journal of Medical Informatics, Vol. 112, April, 2018, pages 59- 67,
has been selected as a Best Paper and is included in the 2019 Yearbook of the
International Medical Informatics Associations (IMIA), Section on Clinical
Research Informatics.
Researchers Win $900k NSF Grant to Predict Heart Disease, Diabetes Using Machine Learning
By Maureen Stanton, CISE
Researchers from the Boston University College of Engineering and Boston Medical Center (BMC) will use a three-year, $900,000 grant from the National Science Foundation to develop and pilot a health informatics system to predict patients at risk of heart disease or diabetes, and enable early intervention and personalized treatment.
“Our research vision is to deliver personalized healthcare, from prediction to diagnostics to population health management,” said Professor Ioannis Paschalidis (ECE, BME, SE), director of the Center for Information & Systems Engineering (CISE). “In earlier work, we demonstrated how machine learning could predict hospitalizations due to these two chronic diseases about a year in advance with an accuracy rate of as much as 82 percent, a significant improvement over existing risk models — such as the Framingham study for cardiovascular disease. Now, with our synergistic team of scientists and physicians, we are developing more robust predictive methods and capabilities for offering personalized recommendations that can guide physicians and patients as they make health-related decisions. This is a new frontier in medical informatics research and we expect it will impact medical practice.”
Diagnosing these chronic diseases requires complex sets of clinical and pathological data, which often are not comprehensive, consistent, nor up to date for treating physicians. The result is that patients at higher risk often don’t get needed treatment, while those at lower risk do, leading to poor patient outcomes and unnecessary costs. The new, interdisciplinary research collaboration will be led by Paschalidis, CISE member Professor Christos Cassandras (ECE, SE) and BMC Associate Chief Medical Information Officer and BU School of Medicine Assistant Professor of Medicine Rebecca Mishuris. They will develop a new generation of predictive methods based on supervised machine learning techniques that are interpretable, have higher predictive power, and can handle more data.
The researchers will develop an approach based on novel mathematical methods and the requisite algorithms where electronic health records and real-time health data — including wearable, implantable, and home-based, networked diagnostic devices – can be used to develop prediction analytics that anticipate future events such as hospitalizations, re-admissions, and transitioning to an acute stage of a disease. These predictions trigger personalized interventions, ranging from increased monitoring and doctor visits to optimized treatment policies adapted to each patient.
The researchers will also focus on enabling personalized treatment based on learning and optimizing treatment protocols for chronic diseases. “Protocols of this type are typically empirical, using a one-size-fits-all approach,” explained Cassandras, head of the Systems Engineering Division. “They assess the stage of the disease and adapt medication based on the stage but not the patient. By incorporating specific personalized interventions with recommendations, clinicians can, therefore, intervene before a patient’s condition reaches a critical phase.”
In addition to methodological and algorithmic development, the project will pilot the newly developed algorithms by integrating them into the electronic health record system at BMC and with 14 affiliated Community Health Centers.
“Personalized, predictive healthcare is the future of medicine,” said Dr. Mishuris. “Our research is geared toward providing clinicians with powerful, interpretable data to achieve that goal. Physicians are drowning in data and administrative processes. Our research approach will help physicians manage the deluge of clinical and patient data to make decisions in a more systematic fashion.”
Wuyang Dai receives the College of Engineering 2015 Societal Impact Dissertation Award
Wuyang Dai, a 2015 graduate of NOC, received the College of Engineering 2015 Societal Impact Dissertation Award for his thesis "Detection and Prediction Problems with Applications in Personalized Health Care."
The thesis developed algorithmic methods to support personalized preventive medicine. One of the problems addressed is the prediction of hospitalizations due to heart disease based on a patient's Electronic Medical Record. Analyzing records of more than 45,000 Boston Medical Center patients, the new algorithms predict about 80% of hospitalizations with few false positives. Prediction enables prevention which can improve outcomes and reduce the more than $9B spent annually in the US on preventable heart-related hospitalizations.
Three ECE Profs Named as IEEE Fellows
Professors W. Clem Karl (ECE, BME, SE), Theodore Moustakas (ECE, MSE) and Yannis Paschalidis (ECE, SE) have been named as 2014 IEEE Fellows, the highest grade of membership in the world’s leading professional association for advancing technology for the benefit of society.
The IEEE confers the grade of Fellow upon individuals with outstanding records of accomplishment in any of the organization’s fields of interest, which range from aerospace systems, computers and telecommunications to biomedical engineering, electric power and consumer electronics. Less than 0.1 percent of voting members—the IEEE currently has 400,000 members in 160 countries—are selected annually for this member grade elevation, considered a major career achievement and prestigious honor across the technical community.
W. Clem Karl
Karl was recognized for his contributions to “statistical signal processing and image reconstruction.” He hasdeveloped several statistical models for the extraction of information from diverse data sources in the presence of uncertainty, and applied them in projects that include automatic target detection and recognition for synthetic aperture radar; locating oil deposits and analyzing the earth’s atmosphere; and monitoring medical conditions using tomography and MRI.
“This is a great honor, and I’m humbled that my peers would confer it on me,” said Karl.
A member of the BU faculty since 1995, Karl has assumed many leadership roles for the IEEE. Currently editor-in-chief of IEEE Transactions on Image Processing, he is a member of the Board of Governors and Conference Board of the Signal Processing Society; the Transactions on Medical Imaging Steering Committee; the Biomedical Image and Signal Processing Technical Committee; and the Technical Committee Review Board. He has co-organized IEEE workshops on statistical signal processing and bioinformatics, and was general chair of the 2009 IEEE International Symposium on Biomedical Imaging.
Among other things, Karl is developing methods to improve the detection of explosives in luggage. The technology could increase passenger safety while reducing delays and other inconveniences for air travelers, such as having to remove laptops and other electronic devices from bags.
Theodore Moustakas
Moustakas was recognized for his contributions to “the epitaxial growth of nitride semiconductors.” He is a trailblazer in molecular beam epitaxy, a versatile and advanced thin-film growth technique used to make high-precision, nitride (nitrogen compound-based) semiconductor materials used in fiber-optic, cellular, satellite and other applications.
His most notable achievements include pioneering the nucleation steps for the growth of gallium nitride on sapphire and other substrates, an essential process for the manufacture of blue LEDs, which are widely used in solid state lighting applications; and developing highly-efficient, deep ultraviolet (UV) LEDs, which are expected to provide environmentally friendly water and air purification.
“I am delighted to receive this prestigious award and I am very grateful to many of my collaborators at BU and other institutions, as well the outstanding past and current students that I have had the fortune of mentoring,” said Moustakas.
A member of the ENG faculty for more than 30 years and ENG Distinguished Scholar who helped shape the Materials Science & Engineering Division, Moustakas has had a broad impact on his field, through 25 patents, hundreds of invited talks and journal papers and 10,000 citations in research literature. Recently selected as the recipient of the Molecular Beam Epitaxy (MBE) Innovator Award, he is also a Fellow of the American Physical Society and Electrochemical Society, and Charter Fellow of the National Academy of Inventors. In 2013 he was named the Boston University Innovator of the Year.
Moustakas is currently working to create visible and UV LEDs and lasers for solid-state white lighting, water and air sterilization, and identification of biological and chemical agents; investigating indium gallium nitride “quantum dots” that boost solar cell efficiency; and, in collaboration with Associate Professor Roberto Paiella (ECE, MSE), studying the use of nitride semiconductor structures for green LED applications and for emitters and detectors operating in the far infrared.
Yannis Paschalidis
Paschalidis was recognized for his contributions to “the control and optimization of communication and sensor networks, manufacturing systems and biological systems.” Since joining the College of Engineering faculty in 1996, he has developed sophisticated algorithms for everything from a homeland security early warning sensor network to a next-generation electronic healthcare management system.
“I am elated to have been named an IEEE Fellow,” said Paschalidis. “Much credit is due to all my students and postdoctoral associates, past and present, who have contributed to the work being recognized, and all my collaborators, many of them here at Boston University.”
Co-director of the College’s Center for Information and Systems Engineering (CISE), an ENG Distinguished Faculty Fellow and affiliate of the BioMolecular Engineering Research Center, Paschalidis has a diverse research portfolio that spans the fields of systems and control, networking, applied probability, optimization, operations research, computational biology and bioinformatics. His work has resulted in new applications in communication and sensor networks, protein docking, logistics, cyber-security, robotics, the smart grid and finance.
Paschalidis has received several honors, including a CAREER award from the National Science Foundation, an invitation to participate in the National Academy of Engineering Frontiers of Engineering Symposium, two best paper awards, and best performance at a computational biology competition. He is the editor-in-chief of the IEEE Transactions on Control of Network Systems and a member of the Board of Governors of the IEEE Control Systems Society.
Visiting Professor Vivek Goyal (ECE), who will be an assistant professor in the ECE Department starting in January, was also named an IEEE Fellow.
Dedicated to the advancement of technology, the IEEE publishes 30 percent of the world’s literature in the electrical and electronics engineering and computer science fields, and has developed more than 900 active industry standards. The association also sponsors or co-sponsors nearly 400 international technical conferences each year.
Paschalidis Named 2011 Distinguished Faculty Fellew
By Mark Dwortzen
Professor Yannis Paschalidis (ECE, SE)
Since joining the College of Engineering faculty in 1996, Professor Ioannis Paschalidis (ECE, SE) has developed sophisticated algorithms for everything from a homeland security early warning sensor network to a next-generation electronic healthcare management system. Based on his impressive body of work, Paschalidis was named the College’s 2011 Distinguished Faculty Fellow, an award that recognizes mid-career faculty members for significant contributions to their field.
Paschalidis will receive $20,000 per year for the next five years to support his research.
“I am elated and deeply honored to receive this award,” he said. “The funds will be extremely useful in seeding new directions for my research, especially in the area of medical informatics.”
Co-director of the Center for Information and Systems Engineering (CISE), academic director of the College of Engineering’s Sensor Network Consortium and affiliate of the Biomolecular Engineering Research Center, Paschalidis has a diverse research portfolio that spans the fields of systems and control, networking, applied probability, optimization, operations research, computational biology and bioinformatics. His work could lead to new applications in communication and sensor networks, protein docking, logistics, cyber-security, robotics, the smart grid and finance.
Since earning his PhD degree in Electrical Engineering and Computer Science in 1996 at MIT, Paschalidis has received several honors, including a CAREER award from the National Science Foundation, an invitation to participate in the National Academy of Engineering Frontiers of Engineering Symposium, two best paper awards, best performance at a computational biology competition, and a College of Engineering Dean’s Catalyst Award. He is a senior member of the IEEE and an associate editor of ACM Transactions on Sensor Networks and SIAM Journal on Control and Optimization.
In receiving this year’s Distinguished Faculty Fellow award, Paschalidis joins the 2010 recipient, Kamil Ekinci (ME), and the 2009 winners, Professor Mark Grinstaff (BME, MSE), Associate Professor Joyce Wong (BME, MSE) and Associate Professor Xin Zhang (ME, MSE).
Dean’s Catalyst Awards Energize Early-Stage Projects
By Mark Dwortzan
Assistant Professor Ajay Joshi (ECE) is one of seven College of Engineering faculty members to receive a 2011 Dean's Catalyst Award.
College of Engineering research projects in nanofluidics, integrated circuit design, data-driven healthcare management and Terahertz radiation generation are set to take off, thanks to the Dean’s Catalyst Awards (DCA) grant program. This year four research teams will each receive up to $50,000 in DCA funding to develop novel techniques to investigate these topics.
Established by Dean Kenneth R. Lutchen in 2007 and organized by a faculty committee, the annual Dean’s Catalyst Awards program encourages early-stage, innovative, interdisciplinary projects that could spark new advances in a variety of engineering fields. By providing each project with seed funding, the awards give full-time faculty the opportunity to generate initial proof-of-concept results that could help secure external funding.
This year’s DCA-winning projects promise to improve the nation’s healthcare, defense, information and communications systems.
Leveraging their respective skills in computational modeling of nanomaterials and fabrication/testing of nano-electromechanical systems, Assistant Professor Harold Park and Associate Professor Kamil Ekinci (both ME, MSE) will study the ability of graphene, the world’s only known 2D material, to increase fluid flow through nanoscale channels. By utilizing the hydrophobic properties of the world’s thinnest known material (graphene is composed of a single layer of carbon atoms) to significantly enhance the fluid flow rate in confined, nanoscale channels, the researchers aim to improve the performance of nanofluidic devices and lab-on-a-chip technologies. Potential application areas include biological and chemical sensing, homeland security and healthcare.
Combining low-power circuit and micro-architecture design techniques with ideas from coding and information theory, ECE Assistant Professors Ajay Joshi and Bobak Nazer plan to develop a new approach to digital VLSI (very large scale integrated circuit) design that tolerates errors at the device level while maintaining global reliability at the architectural level. Rather than paying the ever-increasing energy and area costs required to keep all devices well-behaved, the researchers intend to allow individual transistors to be “noisy.” The goal is to develop an architecture that exploits feedback and redundancy to maintain nearly the same end-to-end reliability and performance as today's designs while consuming much less energy.
Professor Ioannis Paschalidis (ECE, SE), Daniel Newman, BUMC assistant professor of medicine and Boston Medical Center (BMC) chief medical information officer, and Shiby Thomas, BMC director of enterprise analytics, will pursue a comprehensive and systematic approach to intelligently processing Electronic Health Records (EHRs) and directing physician attention to preventing serious medical conditions. Using algorithms that assess patients for disease risk and trigger physician actions based on their risk classification, and wireless body sensors that dispatch medical information to the clinic in near real-time, the researchers’ proposed system could significantly reduce costs and improve efficiencies in the U.S. health care system.
Despite major application opportunities in medical and chemical imaging and security screening, there are few sources that emit electromagnetic radiation in the Terahertz (THz) range (0.3-10 trillion Hz). Combining their respective expertise in graphene fabrication and THz radiation studies, Professors Anna Swan and Roberto Paiella (both ECE, MSE) propose an entirely novel way of generating THz radiation that, unlike current state-of-the-art methods, is not limited by intrinsic material properties. They plan to develop a “tabletop cyclotron source” of THz light that exploits the excellent electron transport properties of graphene to produce radiation in the Terahertz range.
“The Dean’s Catalyst Award is an ideal platform for exploring novel ideas that are promising, but need more probing to understand their true potential,” said Joshi. “We are grateful for this opportunity to refine our ideas and develop preliminary results before applying for external funding.”
